diff --git a/complex-agents/exa-deep-researcher/.dockerignore b/complex-agents/exa-deep-researcher/.dockerignore new file mode 100644 index 0000000..828308c --- /dev/null +++ b/complex-agents/exa-deep-researcher/.dockerignore @@ -0,0 +1,48 @@ +# Python bytecode and artifacts +**/__pycache__/ +**/*.py[cod] +**/*.pyo +**/*.pyd +**/*.egg-info/ +**/dist/ +**/build/ + +# Virtual environments +**/.venv/ +**/venv/ + +# Caches and test output +**/.cache/ +**/.pytest_cache/ +**/.ruff_cache/ +**/coverage/ + +# Logs and temp files +**/*.log +**/*.gz +**/*.tgz +**/.tmp +**/.cache + +# Environment variables +**/.env +**/.env.* + +# VCS, editor, OS +.git +.gitignore +.gitattributes +.github/ +.idea/ +.vscode/ +.DS_Store + +# Project docs and misc +README.md +LICENSE + +# Project tests +test/ +tests/ +eval/ +evals/ diff --git a/complex-agents/exa-deep-researcher/.gitignore b/complex-agents/exa-deep-researcher/.gitignore new file mode 100644 index 0000000..ed01686 --- /dev/null +++ b/complex-agents/exa-deep-researcher/.gitignore @@ -0,0 +1,45 @@ +# Generated reports +reports/ + +# Python cache +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python + +# Virtual environments +.venv/ +venv/ +ENV/ + +# Testing +.pytest_cache/ +.coverage +htmlcov/ + +# Logs +KMS/logs/ +*.log + +# IDE +.vscode/ +.idea/ +*.swp +*.swo +*~ + +# OS +.DS_Store +Thumbs.db + +# Environment variables +.env +.env.local + +# Frontend +frontend/node_modules/ +frontend/.next/ +frontend/out/ +frontend/build/ +frontend/dist/ \ No newline at end of file diff --git a/complex-agents/exa-deep-researcher/Dockerfile b/complex-agents/exa-deep-researcher/Dockerfile new file mode 100644 index 0000000..d8d1fa2 --- /dev/null +++ b/complex-agents/exa-deep-researcher/Dockerfile @@ -0,0 +1,72 @@ +# This is an example Dockerfile that builds a minimal container for running LK Agents +# For more information on the build process, see https://docs.livekit.io/agents/ops/deployment/builds/ +# syntax=docker/dockerfile:1 + +# Use the official UV Python base image with Python 3.13 on Debian Bookworm +# UV is a fast Python package manager that provides better performance than pip +# We use the slim variant to keep the image size smaller while still having essential tools +ARG PYTHON_VERSION=3.13 +FROM ghcr.io/astral-sh/uv:python${PYTHON_VERSION}-bookworm-slim AS base + +# Keeps Python from buffering stdout and stderr to avoid situations where +# the application crashes without emitting any logs due to buffering. +ENV PYTHONUNBUFFERED=1 + +# Create a non-privileged user that the app will run under. +# See https://docs.docker.com/develop/develop-images/dockerfile_best-practices/#user +ARG UID=10001 +RUN adduser \ + --disabled-password \ + --gecos "" \ + --home "/app" \ + --shell "/sbin/nologin" \ + --uid "${UID}" \ + appuser + +# Install build dependencies required for Python packages with native extensions +# gcc: C compiler needed for building Python packages with C extensions +# g++: C++ compiler needed for building Python packages with C++ extensions +# python3-dev: Python development headers needed for compilation +# We clean up the apt cache after installation to keep the image size down +RUN apt-get update && apt-get install -y \ + gcc \ + g++ \ + python3-dev \ + && rm -rf /var/lib/apt/lists/* + +# Create a new directory for our application code +# And set it as the working directory +WORKDIR /app + +# Copy just the dependency files first, for more efficient layer caching +COPY pyproject.toml uv.lock ./ +RUN mkdir -p src + +# Install Python dependencies using UV's lock file +# --locked ensures we use exact versions from uv.lock for reproducible builds +# This creates a virtual environment and installs all dependencies +# Ensure your uv.lock file is checked in for consistency across environments +RUN uv sync --locked + +# Copy all remaining pplication files into the container +# This includes source code, configuration files, and dependency specifications +# (Excludes files specified in .dockerignore) +COPY . . + +# Change ownership of all app files to the non-privileged user +# This ensures the application can read/write files as needed +RUN chown -R appuser:appuser /app + +# Switch to the non-privileged user for all subsequent operations +# This improves security by not running as root +USER appuser + +# Pre-download any ML models or files the agent needs +# This ensures the container is ready to run immediately without downloading +# dependencies at runtime, which improves startup time and reliability +RUN uv run "agent.py" download-files + +# Run the application using UV +# UV will activate the virtual environment and run the agent. +# The "start" command tells the worker to connect to LiveKit and begin waiting for jobs. +CMD ["uv", "run", "agent.py", "start"] diff --git a/complex-agents/exa-deep-researcher/README.md b/complex-agents/exa-deep-researcher/README.md new file mode 100644 index 0000000..eaf0655 --- /dev/null +++ b/complex-agents/exa-deep-researcher/README.md @@ -0,0 +1,321 @@ +# EXA Deep Researcher + +A voice-controlled deep research agent that uses [EXA](https://exa.ai) for web intelligence. The agent runs comprehensive research jobs in the background while remaining conversationally available, streaming progress to a UI and saving cited reports to disk. + +## Overview + +**ExaResearchAgent** - An AI-powered voice agent that conducts deep research on any topic using EXA's search capabilities. Simply tell it what to research using natural voice commands, and it will gather information from across the web, synthesize findings, and generate a comprehensive markdown report with inline citations. + +## Features + +- **Voice-Only Control**: Start and cancel research using natural voice commands +- **Single Background Job**: One active research job at a time with full state management +- **EXA Integration**: Leverages EXA's search, contents, and similarity APIs with full parameter support +- **Real-time Streaming**: Progress, notes, and sources streamed to UI via RPC +- **Smart Compression**: Token-aware compression to handle large research contexts +- **Cited Reports**: Comprehensive markdown reports with inline citations saved to disk +- **Concise Voice Updates**: Rate-limited spoken status updates that don't overwhelm the conversation +- **Built using LiveKit's AI Gateway** for seamless inference: + - Speech-to-Text (STT) + - Large Language Model (LLM) + - Text-to-Speech (TTS) + - Voice Activity Detection (VAD) + +## How It Works + +1. User gives a voice command to research a topic (e.g., "Research the impact of CRISPR on gene therapy") +2. The agent starts a background research job while remaining available for conversation +3. The research workflow: + - Creates a research brief and title + - Decomposes the topic into subtopics using a supervisor LLM + - For each subtopic: + - Searches EXA for relevant sources + - Fetches and analyzes content + - Extracts citations and synthesizes notes + - Applies smart compression to manage token limits + - Generates a final report with citations +4. Progress and findings stream to the UI in real-time via RPC +5. The agent provides periodic voice updates (rate-limited to avoid overwhelming the conversation) +6. Final report is saved to disk and a summary is spoken to the user + +### Research Flow Diagram + +```mermaid +flowchart TD + Start([User Voice Command]) --> Tool[start_research_job Tool] + Tool --> QuickSearch[Quick Search Phase
3-5 results from EXA] + + QuickSearch --> Clarify{Autonomous
Clarification
Needed?} + + Clarify -->|Yes| AskUser[Ask User for Clarification
State: CLARIFYING] + AskUser --> WaitUser[Wait for User Response] + WaitUser --> Briefing + + Clarify -->|No| Briefing[Briefing Phase
State: BRIEFING] + + Briefing --> GenBrief[Generate Research Brief
Create Title
Generate Subtopics] + + GenBrief --> ResearchLoop[Iterative Research Loop
State: RESEARCHING] + + ResearchLoop --> SupervisorEval[Supervisor LLM
Evaluates Findings] + + SupervisorEval --> Decision{Research
Complete?} + + Decision -->|No, Need More| ChooseTopic[Choose Next Topic
Prefer Planned Subtopics] + + ChooseTopic --> SubtopicResearch[Subtopic Research] + + SubtopicResearch --> EXASearch[EXA Search for Topic] + EXASearch --> FetchContent[Fetch Content
Parallel/Batched] + FetchContent --> Citations[Extract Citations] + Citations --> Synthesize[Synthesize Notes with LLM] + + Synthesize --> CheckNotes{Notes >= 2?} + CheckNotes -->|Yes| Compress[Compress Notes
State: COMPRESSING
Token Management] + CheckNotes -->|No| IterCheck + + Compress --> IterCheck{Max Iterations
Reached?} + IterCheck -->|No| SupervisorEval + IterCheck -->|Yes| Decision + + Decision -->|Yes| ReportGen[Report Generation
State: REPORTING] + + ReportGen --> FinalReport[Generate Final Report
with Citations] + + FinalReport --> SaveDisk[Save to Disk
reports/YYYY-MM-DD_HHMMSS_title.md] + + SaveDisk --> Done[State: DONE] + Done --> Speak[Speak Summary
2-3 Key Findings
Max 600 chars] + + Speak --> End([Complete]) + + %% Parallel Systems + ResearchLoop -.->|Real-time| RPC[RPC Streaming to UI
Status, Notes, Citations] + SupervisorEval -.->|Rate-limited| Voice[Voice Updates
Every ~20 seconds] + SubtopicResearch -.->|Real-time| RPC + + %% Styling + classDef stateClass fill:#e1f5ff,stroke:#0066cc,stroke-width:2px + classDef decisionClass fill:#fff4e6,stroke:#ff9800,stroke-width:2px + classDef processClass fill:#f0f4ff,stroke:#4a5fdc,stroke-width:2px + classDef parallelClass fill:#e8f5e9,stroke:#4caf50,stroke-width:2px,stroke-dasharray: 5 5 + + class Briefing,ResearchLoop,ReportGen,Done,AskUser stateClass + class Clarify,Decision,CheckNotes,IterCheck decisionClass + class QuickSearch,GenBrief,SupervisorEval,ChooseTopic,SubtopicResearch,EXASearch,FetchContent,Citations,Synthesize,Compress,FinalReport,SaveDisk,Speak processClass + class RPC,Voice parallelClass +``` + +**State Transitions:** IDLE → CLARIFYING (optional) → BRIEFING → RESEARCHING → COMPRESSING (periodic) → REPORTING → DONE + +**Key Features:** + +- **Autonomous Clarification**: LLM evaluates if quick search results match user intent before expensive deep research +- **Supervisor Pattern**: Iterative loop where an LLM continuously evaluates findings and decides what to research next +- **Parallel Processing**: EXA content fetching happens in batches (configurable concurrency) +- **Smart Compression**: After 4+ notes, compress to manage token limits (optional for 2-3 notes) +- **Real-time Streaming**: All progress streams to UI via RPC while voice updates are rate-limited + +## Prerequisites + +- Python 3.10+ +- `livekit-agents`>=1.0 +- LiveKit account and credentials +- EXA API key (get one at [exa.ai](https://exa.ai)) + +## Installation + +1. Clone the repository + +2. Install dependencies: + + ```bash + pip install -r requirements.txt + ``` + + Or using uv: + + ```bash + uv sync + ``` + +3. Create a `.env` file in the project root: + + ```bash + # Required + EXA_API_KEY=your_exa_api_key + LIVEKIT_URL=wss://your-project.livekit.cloud + LIVEKIT_API_KEY=your_livekit_api_key + LIVEKIT_API_SECRET=your_livekit_api_secret + + # Optional - Environment (for dev/prod separation) + DEV=1 # Set DEV to any value to use dev agent name (exa-deep-researcher-dev) + + # Optional - Research Configuration + EXA_MAX_ITERATIONS=3 # Max research subtopics (default: 3) + EXA_DEFAULT_MAX_RESULTS=10 # Results per search (default: 10) + EXA_MAX_CONCURRENT_FETCHES=3 # Parallel content fetches (default: 3) + ``` + +> [!NOTE] +> **Dev/Prod Separation**: Set `DEV` to any value in your `.env` file to use the dev agent name (`exa-deep-researcher-dev`). This allows you to run dev and prod agents simultaneously without conflicts. The same variable should be set in both Python agent and frontend `.env.local` files. + +## Running the Agent + +Start the agent: + +```bash +python complex-agents/exa-deep-researcher/agent.py dev +``` + +Then start the frontend (in a separate terminal): + +```bash +cd complex-agents/exa-deep-researcher/frontend +pnpm install +pnpm dev +``` + +Open http://localhost:3000 in your browser and click "Start a conversation" to begin. + +## Deploy to LiveKit Cloud + +Deploy your agent to LiveKit Cloud for production use with automatic scaling and global availability. + +### First-Time Setup + +1. **Install LiveKit CLI**: + + ```bash + curl -sSL https://get.livekit.io/cli | bash + ``` + +2. **Authenticate with LiveKit Cloud**: + + ```bash + lk cloud auth + ``` + +3. **Create and deploy your agent**: + ```bash + lk agent create + ``` + This generates `Dockerfile`, `.dockerignore`, and `livekit.toml` configuration files, then deploys your agent. + +### Monitoring + +After deployment: + +- View logs: `lk agent logs` +- Check status: `lk agent status` +- Manage in dashboard: [cloud.livekit.io/projects/p\_/agents](https://cloud.livekit.io/projects/p_/agents) + +For CI/CD deployment with GitHub Actions, see the [LiveKit Cloud Deployment Guide](https://docs.livekit.io/agents/ops/deployment/). + +## Usage + +### Example Interaction + +``` +User: "Research the latest developments in fusion energy" + +Agent: "Starting deep research on: the latest developments in fusion energy. + I'll keep you updated as I make progress!" + +[Background research begins] + +Agent: "Found 12 sources from Nature and MIT Technology Review" + [20 seconds later] + "Analyzing breakthrough from National Ignition Facility" + +[Research completes] + +Agent: "Research complete! Key findings: The National Ignition Facility + achieved net energy gain in December 2022, marking a historic milestone. + Multiple private companies are now racing toward commercial fusion with + projected timelines of 2030-2035. The full report is on your screen." +``` + +## Architecture Details + +### Components + +1. **ExaResearchAgent**: Main voice agent with two function tools + + - `start_research_job(query, max_results?, include_domains?, exclude_domains?)` + - `cancel_research_job()` + +2. **JobManager**: Single-job state machine enforcing one active research at a time + + - States: idle → briefing → researching → compressing → reporting → done/canceled/error + +3. **ResearchOrchestrator**: Background workflow coordinator + + - Clarify (optional) → Brief → Supervise → Research subtopics → Compress → Final report + +4. **EXAClient**: REST API wrapper with retry logic and exponential backoff + - Search with rich filters (type, category, country, dates, domains, text) + - Contents with livecrawl options + - Find similar pages + +### Voice Policy + +**Spoken Output:** + +- Max length: 600 characters (~15-20 seconds) +- Status updates rate-limited to one every 20 seconds +- Completion summary: 2-3 key findings only +- No URLs in speech (refer to domains plainly) +- Natural language only (no markdown or technical jargon) + +**UI Output:** + +- Full reports with complete markdown and citations +- Real-time progress timeline +- Source content with on-demand fetching +- Clickable citations with quotes + +## Customization + +You can customize the agent by: + +1. Modifying the agent instructions in `agent.py` +2. Adjusting research parameters in `.env` (max iterations, results per search, etc.) +3. Changing the LLM model or voice settings in the agent configuration +4. Adding custom EXA search filters (domains, date ranges, content types) + +## Troubleshooting + +**"No results found"** + +- Check EXA API key is valid +- Try broader search terms +- Remove restrictive domain filters + +**"Rate limited"** + +- The agent automatically retries with exponential backoff +- Reduce `EXA_MAX_CONCURRENT_FETCHES` if hitting limits frequently + +**Voice not working** + +- Check LiveKit credentials are correct +- Verify microphone permissions in browser + +**Reports not saving** + +- Check write permissions on `reports/` directory +- Ensure disk space is available + +## Related Examples + +- [Note Taking Assistant](../note-taking-assistant/) - Real-time transcription and notes +- [Shopify Voice Shopper](../shopify-voice-shopper/) - Voice-controlled shopping with RPC +- [Medical Office Triage](../medical_office_triage/) - Multi-agent voice system + +## Resources + +- [EXA API Documentation](https://docs.exa.ai) +- [LiveKit Agents Documentation](https://docs.livekit.io/agents) +- [Function Tools Guide](https://docs.livekit.io/agents/build/tools) +- [RPC Documentation](https://docs.livekit.io/home/client/data/rpc) diff --git a/complex-agents/exa-deep-researcher/__init__.py b/complex-agents/exa-deep-researcher/__init__.py new file mode 100644 index 0000000..23e1c1b --- /dev/null +++ b/complex-agents/exa-deep-researcher/__init__.py @@ -0,0 +1,5 @@ +"""EXA Deep Researcher - Voice-controlled research agent""" + + + + diff --git a/complex-agents/exa-deep-researcher/agent.py b/complex-agents/exa-deep-researcher/agent.py new file mode 100644 index 0000000..969281d --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent.py @@ -0,0 +1,332 @@ +""" +--- +title: EXA Deep Researcher +category: complex-agents +tags: [exa, research, voice_controlled, background_jobs, rpc_streaming] +difficulty: advanced +description: Voice-controlled deep research agent using EXA for web intelligence +demonstrates: + - Voice-only control + - Single background research job with state management + - EXA API integration for search and content fetching + - RPC streaming to UI for status, notes, and reports + - Token-aware compression and concurrency control + - Cited final reports saved to disk +--- +""" +import asyncio +import logging +from pathlib import Path +from datetime import datetime +from typing import Optional, Annotated +from dataclasses import dataclass, field +import os + +from dotenv import load_dotenv +from pydantic import Field +from livekit.agents import ( + Agent, AgentSession, JobContext, WorkerOptions, cli, function_tool, RoomInputOptions +) +from livekit.agents.voice import RunContext +from livekit.plugins import silero +from livekit.plugins.turn_detector.multilingual import MultilingualModel +from livekit.plugins import noise_cancellation + +from agent.schemas import JobState +from agent.job_manager import JobManager + +# Agent modules +from agent.handlers.status import StatusHandler +from agent.handlers.rpc import send_rpc_to_ui_safe +from agent.job_runner import run_research_job + +# Research components +from agent.exa_client import EXAClient +from orchestrator import ResearchOrchestrator +from agent.prompts import supervisor_prompt + +logger = logging.getLogger("exa-deep-researcher") +logger.setLevel(logging.INFO) + +load_dotenv() + + +def get_agent_name() -> str: + """Get agent name based on environment (dev or prod)""" + if os.getenv("DEV"): + return "exa-deep-researcher-dev" + return "exa-deep-researcher" + + +@dataclass +class ExaUserData: + """User data for EXA research agent""" + ctx: JobContext + session: Optional[AgentSession] = None + job_manager: Optional[JobManager] = None + exa_client: Optional[EXAClient] = None + orchestrator: Optional[ResearchOrchestrator] = None + status_handler: Optional[StatusHandler] = None + status_queue: list = field(default_factory=list) + status_task: Optional[asyncio.Task] = None + + +class ExaResearchAgent(Agent): + """Voice-controlled deep research agent""" + + def __init__(self): + super().__init__( + instructions=supervisor_prompt(), + ) + + async def on_enter(self): + """Called when agent enters the session""" + self.session.say("Hey! I'm your research assistant. I can help you do deep research on any topic using EXA. Just tell me what you'd like to learn about!") + + @function_tool() + async def start_research_job( + self, + context: RunContext[ExaUserData], + query: Optional[str] = None, + confirmed: Annotated[bool, Field(default=False)] = False, + ) -> str: + """ + Start a deep research job on a given topic. Only one job can run at a time. + + The system will first do a quick search to understand what you're looking for. + If clarification is needed, it will ask you before starting the full research. + + Args: + query: The research question or topic to investigate (optional if confirming) + confirmed: Set to True if you're confirming after clarification (internal use) + + Returns: + Confirmation message or clarification question + """ + userdata = context.userdata + job_manager = userdata.job_manager + + if not job_manager: + job_manager = JobManager() + userdata.job_manager = job_manager + + if job_manager.is_active(): + if job_manager.state == JobState.CLARIFYING and (confirmed or (query and query != job_manager.original_query)): + pass + else: + logger.info(f"Blocked call - research already active (state: {job_manager.state.value})") + return "I'm already working on research. Please wait for it to complete or ask me to cancel it first." + + if not userdata.orchestrator: + status_handler = StatusHandler(ctx=userdata.ctx, session=self.session) + status_handler.userdata = userdata + userdata.status_handler = status_handler + + userdata.orchestrator = ResearchOrchestrator( + ctx=userdata.ctx, + exa_client=userdata.exa_client, + llm=self.session.llm, + status_callback=status_handler.handle_status_update, + notes_callback=status_handler.handle_notes_update, + report_callback=status_handler.handle_report_ready, + ) + + if job_manager.state == JobState.CLARIFYING: + if not confirmed and (query is None or query == job_manager.original_query): + return "I'm waiting for your answer to the clarification question above. Please confirm or provide more details." + + if confirmed: + final_query = query if query and query != job_manager.original_query else job_manager.original_query + else: + final_query = query + + if not final_query: + return "I don't have a research query to work with. Please provide a research question." + + job_manager.clarified_query = final_query + request_id = job_manager.request_id or f"research_{int(datetime.now().timestamp())}" + job_manager.request_id = request_id + job_manager.state = JobState.BRIEFING + + job_manager.task = asyncio.create_task( + run_research_job(self.session, userdata, userdata.orchestrator, request_id, final_query) + ) + + return f"Perfect! Starting deep research on: {final_query}. I'll keep you updated as I make progress!" + + if not query: + return "Please provide a research question or topic to investigate." + + job_manager.reset() + request_id = f"research_{int(datetime.now().timestamp())}" + job_manager.request_id = request_id + job_manager.original_query = query + job_manager.state = JobState.CLARIFYING + + try: + quick_results = await userdata.orchestrator.quick_naive_search(request_id, query, num_results=5) + job_manager.clarification_results = quick_results + + needs_clarification, clarification_message = await userdata.orchestrator.autonomous_clarification( + request_id, + query, + quick_results, + conversation_history=job_manager.conversation_history + ) + + if needs_clarification and clarification_message: + await send_rpc_to_ui_safe(userdata.ctx, "exa.research/status", { + "requestId": request_id, + "phase": "clarifying", + "title": "Clarification needed", + "message": clarification_message, + "stats": {}, + "ts": datetime.now().timestamp(), + "clarification": { + "question": clarification_message, + "originalQuery": query + } + }) + + return clarification_message + else: + job_manager.clarified_query = query + job_manager.state = JobState.BRIEFING + + job_manager.task = asyncio.create_task( + run_research_job(self.session, userdata, userdata.orchestrator, request_id, query) + ) + + return f"Perfect! I found relevant results. Starting deep research on: {query}. I'll keep you updated as I make progress!" + + except Exception as e: + logger.error(f"Error in quick search or clarification: {e}", exc_info=True) + job_manager.clarified_query = query + job_manager.state = JobState.BRIEFING + + job_manager.task = asyncio.create_task( + run_research_job(self.session, userdata, userdata.orchestrator, request_id, query) + ) + + return f"Starting deep research on: {query}. I'll keep you updated as I make progress!" + + @function_tool() + async def cancel_research_job(self, context: RunContext[ExaUserData]) -> str: + """ + Cancel the currently running research job. + + Returns: + Confirmation message + """ + userdata = context.userdata + job_manager = userdata.job_manager + + if not job_manager or not job_manager.is_active(): + return "There's no research job currently running." + + job_manager.cancel() + + return f"Canceling the research job. Give me a moment to wrap things up." + + @function_tool() + async def check_research_status(self, context: RunContext[ExaUserData]) -> str: + """ + Check the status of the current research job. + + Call this when the user asks about research progress or status. + Examples: "how's it going?", "what's the status?", "are you done?" + + Returns: + Current status and progress information + """ + userdata = context.userdata + job_manager = userdata.job_manager + + if not job_manager or job_manager.state == JobState.IDLE: + return "No research job is currently running. You can start one by asking me to research a topic!" + + state = job_manager.state.value + progress = job_manager.progress + + if job_manager.state == JobState.DONE: + notes_count = len(job_manager.notes) + return f"Research completed! I found {notes_count} key insights. The full report is ready on your screen." + + if job_manager.state == JobState.CANCELED: + return "The research job was canceled." + + if job_manager.state == JobState.ERROR: + return "The research job encountered an error and couldn't complete." + + status_parts = [f"Research is currently {state}"] + + if progress: + if progress.current_subtopic: + status_parts.append(f"Working on: {progress.current_subtopic}") + + if progress.subtopics_completed > 0: + total = progress.total_subtopics or "unknown" + status_parts.append(f"Progress: {progress.subtopics_completed}/{total} subtopics completed") + + if progress.sources_found > 0: + status_parts.append(f"Found {progress.sources_found} sources so far") + + return ". ".join(status_parts) + "." + + @function_tool() + async def get_last_report(self, context: RunContext[ExaUserData]) -> str: + """ + Get the full content of the last completed research report. + + Call this when the user asks questions about the research findings, + wants details from the report, or asks you to explain something from the research. + + Examples: "what did you find?", "tell me more about X", "explain the findings" + + Returns: + The full research report content, or a message if no report exists + """ + userdata = context.userdata + job_manager = userdata.job_manager + + if not job_manager or not job_manager.final_report: + return "No research report is available yet. I haven't completed any research jobs." + + report = job_manager.final_report + title = job_manager.report_title or "Research Report" + + return f"# {title}\n\n{report}" + + +async def entrypoint(ctx: JobContext): + """Main entrypoint for the EXA research agent""" + + exa_client = EXAClient() + + userdata = ExaUserData( + ctx=ctx, + exa_client=exa_client, + ) + + session = AgentSession[ExaUserData]( + userdata=userdata, + vad=silero.VAD.load(), + stt="assemblyai/universal-streaming", + llm="deepseek-ai/deepseek-v3", + tts="inworld/inworld-tts-1:Ashley", + turn_detection=MultilingualModel(), + ) + + userdata.session = session + + agent = ExaResearchAgent() + + await session.start(agent=agent, room=ctx.room, room_input_options=RoomInputOptions( + noise_cancellation=noise_cancellation.BVC(), + )) + + +if __name__ == "__main__": + agent_name = get_agent_name() + logger.info(f"Starting agent with name: {agent_name}") + cli.run_app(WorkerOptions(agent_name=agent_name, entrypoint_fnc=entrypoint)) diff --git a/complex-agents/exa-deep-researcher/agent/__init__.py b/complex-agents/exa-deep-researcher/agent/__init__.py new file mode 100644 index 0000000..dc502ec --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/__init__.py @@ -0,0 +1,10 @@ +""" +Agent components for EXA Deep Researcher + +This package contains agent-specific functionality: +- handlers: RPC and status handling +- voice: Voice updates and status queue +- storage: Report storage to disk +- execution: Job execution coordination +""" + diff --git a/complex-agents/exa-deep-researcher/agent/exa_client.py b/complex-agents/exa-deep-researcher/agent/exa_client.py new file mode 100644 index 0000000..d98e3d5 --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/exa_client.py @@ -0,0 +1,200 @@ +""" +EXA API client for deep research using official exa_py SDK +""" +import os +import asyncio +import logging +from typing import List, Optional +from exa_py import Exa + +try: + from agent.schemas import EXASearchParams, EXAContentOptions, EXAResult, EXAContent +except ImportError: + from schemas import EXASearchParams, EXAContentOptions, EXAResult, EXAContent + +logger = logging.getLogger("exa-client") +logger.setLevel(logging.INFO) + + +class EXAClient: + """Client for EXA API using official SDK""" + + def __init__(self, api_key: Optional[str] = None): + self.api_key = api_key or os.environ.get("EXA_API_KEY") + if not self.api_key: + raise ValueError("EXA_API_KEY environment variable or api_key parameter is required") + + self.client = Exa(self.api_key) + + async def search( + self, + params: EXASearchParams, + retry_count: int = 3, + backoff_factor: float = 2.0 + ) -> List[EXAResult]: + """ + Search using EXA API + + Args: + params: Search parameters + retry_count: Number of retries on failure + backoff_factor: Exponential backoff multiplier + + Returns: + List of search results + """ + # Build kwargs for search + kwargs = { + "num_results": params.num_results or 10, + } + + # Add use_autoprompt only if explicitly set (not all SDK versions support it) + if params.use_autoprompt is not None: + kwargs["use_autoprompt"] = params.use_autoprompt + + # Add optional parameters + if params.search_type: + kwargs["type"] = params.search_type + if params.result_category: + kwargs["category"] = params.result_category + if params.start_published_date: + kwargs["start_published_date"] = params.start_published_date + if params.end_published_date: + kwargs["end_published_date"] = params.end_published_date + if params.start_crawl_date: + kwargs["start_crawl_date"] = params.start_crawl_date + if params.end_crawl_date: + kwargs["end_crawl_date"] = params.end_crawl_date + if params.include_domains: + kwargs["include_domains"] = params.include_domains + if params.exclude_domains: + kwargs["exclude_domains"] = params.exclude_domains + if params.include_text: + kwargs["include_text"] = [params.include_text] if isinstance(params.include_text, str) else params.include_text + if params.exclude_text: + kwargs["exclude_text"] = [params.exclude_text] if isinstance(params.exclude_text, str) else params.exclude_text + + # Retry logic with exponential backoff + for attempt in range(retry_count): + try: + # Run in executor to avoid blocking + loop = asyncio.get_event_loop() + response = await loop.run_in_executor( + None, + lambda: self.client.search(params.query, **kwargs) + ) + + # Parse results + results = [] + for item in response.results: + result = EXAResult( + id=item.id, + url=item.url, + title=item.title or "", + score=getattr(item, 'score', None), + published_date=getattr(item, 'published_date', None), + author=getattr(item, 'author', None) + ) + results.append(result) + + return results + + except Exception as e: + logger.error(f"EXA search error (attempt {attempt + 1}/{retry_count}): {e}") + if attempt == retry_count - 1: + raise + await asyncio.sleep(backoff_factor ** attempt) + + return [] + + async def get_contents( + self, + urls: List[str], + options: EXAContentOptions, + retry_count: int = 3, + backoff_factor: float = 2.0 + ) -> List[EXAContent]: + """ + Get contents for a list of URLs + + Args: + urls: List of URLs to fetch content for + options: Content fetch options + retry_count: Number of retries on failure + backoff_factor: Exponential backoff multiplier + + Returns: + List of content objects + """ + # Build kwargs for get_contents based on EXA SDK signature + kwargs = {} + + # Add text options - can be bool or dict with maxCharacters + if options.text: + if options.max_characters: + kwargs["text"] = {"max_characters": options.max_characters} + else: + kwargs["text"] = True + + # Add highlights options + if options.highlights: + if isinstance(options.highlights, dict): + kwargs["highlights"] = options.highlights + else: + kwargs["highlights"] = True + + # Add summary options + if options.summary: + if isinstance(options.summary, dict): + kwargs["summary"] = options.summary + else: + kwargs["summary"] = True + + # Add livecrawl options + if options.livecrawl: + kwargs["livecrawl"] = options.livecrawl + if options.livecrawl_timeout: + kwargs["livecrawl_timeout"] = options.livecrawl_timeout + + # Add subpages + if options.subpages: + kwargs["subpages"] = options.subpages + if options.subpage_target: + kwargs["subpage_target"] = options.subpage_target + + # Retry logic with exponential backoff + for attempt in range(retry_count): + try: + # Run in executor to avoid blocking + loop = asyncio.get_event_loop() + response = await loop.run_in_executor( + None, + lambda: self.client.get_contents(urls, **kwargs) + ) + + # Parse results + contents = [] + for item in response.results: + raw_text = getattr(item, 'text', None) + raw_highlights = getattr(item, 'highlights', None) + raw_summary = getattr(item, 'summary', None) + + contents.append(EXAContent( + id=item.id, + url=item.url, + title=item.title or "", + text=raw_text, + highlights=raw_highlights, + summary=raw_summary + )) + + return contents + + except Exception as e: + logger.error(f"EXA get_contents error (attempt {attempt + 1}/{retry_count}): {e}") + if attempt == retry_count - 1: + raise + await asyncio.sleep(backoff_factor ** attempt) + + return [] + diff --git a/complex-agents/exa-deep-researcher/agent/execution/job_runner.py b/complex-agents/exa-deep-researcher/agent/execution/job_runner.py new file mode 100644 index 0000000..1400170 --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/execution/job_runner.py @@ -0,0 +1,103 @@ +""" +Research job execution coordination + +This module coordinates the execution of research jobs, managing the +workflow from briefing through research to final report generation. +""" +import asyncio +import logging +from typing import TYPE_CHECKING + +from schemas import JobState +from agent.storage.reports import save_report +from agent.voice.speaker import speak_final_report +from agent.voice.queue import StatusQueue + +if TYPE_CHECKING: + from livekit.agents import AgentSession + +logger = logging.getLogger("job-runner") + + +async def run_research_job( + session: 'AgentSession', + userdata, + orchestrator, + request_id: str, + query: str +): + """ + Run the complete research workflow in background + + This function coordinates all phases of research: + 1. Write brief (plan research) + 2. Iterative research (supervisor pattern) + 3. Generate final report + 4. Save to disk + 5. Speak final report + + Args: + session: AgentSession for voice updates + userdata: ExaUserData with job manager + orchestrator: ResearchOrchestrator for research execution + request_id: Request identifier + query: Research query + """ + job_manager = userdata.job_manager + + try: + brief, title, subtopics = await orchestrator.write_brief( + request_id, + query, + conversation_history=job_manager.conversation_history + ) + job_manager.research_brief = brief + job_manager.report_title = title + job_manager.state = JobState.RESEARCHING + + original_query = job_manager.original_query or query + notes = await orchestrator.iterative_supervise( + request_id, + brief, + original_query, + planned_subtopics=subtopics, + conversation_history=job_manager.conversation_history + ) + + for note in notes: + job_manager.notes.append(note) + + job_manager.state = JobState.REPORTING + final_report = await orchestrator.generate_final_report( + request_id, title, brief, notes + ) + job_manager.final_report = final_report + + await save_report(title, final_report) + + job_manager.state = JobState.DONE + + if userdata.status_task and not userdata.status_task.done(): + userdata.status_task.cancel() + try: + await userdata.status_task + except asyncio.CancelledError: + pass + userdata.status_queue.clear() + + await speak_final_report(session, title, final_report) + + except asyncio.CancelledError: + job_manager.state = JobState.CANCELED + try: + session.say("Research was canceled.") + except (RuntimeError, AttributeError): + pass + except Exception as e: + logger.error(f"Research job error: {e}", exc_info=True) + job_manager.state = JobState.ERROR + try: + session.say(f"Sorry, I encountered an error during research: {str(e)[:100]}") + except (RuntimeError, AttributeError): + pass + diff --git a/complex-agents/exa-deep-researcher/agent/handlers/__init__.py b/complex-agents/exa-deep-researcher/agent/handlers/__init__.py new file mode 100644 index 0000000..129a122 --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/handlers/__init__.py @@ -0,0 +1,19 @@ +""" +Event handlers for agent + +This package contains: +- RPC communication with UI +- Status, notes, and report update handling +""" + +from .rpc import send_rpc_to_ui_safe, format_status_payload, format_notes_payload, format_report_payload +from .status import StatusHandler + +__all__ = [ + "send_rpc_to_ui_safe", + "format_status_payload", + "format_notes_payload", + "format_report_payload", + "StatusHandler", +] + diff --git a/complex-agents/exa-deep-researcher/agent/handlers/rpc.py b/complex-agents/exa-deep-researcher/agent/handlers/rpc.py new file mode 100644 index 0000000..13ae629 --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/handlers/rpc.py @@ -0,0 +1,226 @@ +""" +RPC communication with UI + +This module handles sending RPC messages to the frontend UI, +formatting payloads for status updates, notes, and reports. +""" +import json +import logging +from datetime import datetime +from typing import Dict, Any +from livekit.agents import JobContext + +from agent.schemas import ResearchNote + +logger = logging.getLogger("rpc-handler") + + +async def send_rpc_to_ui_safe(ctx: JobContext, method: str, payload: dict) -> None: + """ + Send RPC message to UI - safe version that works even if session is closed + + Args: + ctx: JobContext with room access + method: RPC method name (e.g., "exa.research/status") + payload: Data payload to send + """ + try: + room = ctx.room + remote_participants = list(room.remote_participants.values()) + + if not remote_participants: + logger.debug(f"No remote participants, skipping RPC {method}") + return + + client_participant = remote_participants[0] + + phase = payload.get("phase", "unknown") + title = payload.get("title", "unknown") + request_id = payload.get("requestId", "unknown") + logger.info(f"Sending RPC [{phase}] {title} (requestId={request_id})") + + await room.local_participant.perform_rpc( + destination_identity=client_participant.identity, + method=method, + payload=json.dumps(payload) + ) + + except Exception as e: + logger.error(f"Error sending RPC {method}: {e}", exc_info=True) + + +async def send_report_via_byte_stream( + ctx: JobContext, + request_id: str, + title: str, + report: str, + num_sources: int +) -> None: + """ + Send report via byte stream instead of RPC (for large payloads) + + Args: + ctx: JobContext with room access + request_id: Request identifier + title: Report title + report: Full report content + num_sources: Number of unique sources cited + """ + try: + room = ctx.room + remote_participants = list(room.remote_participants.values()) + + if not remote_participants: + logger.debug(f"No remote participants, skipping report byte stream") + return + + client_participant = remote_participants[0] + + report_data = { + "requestId": request_id, + "phase": "reporting", + "title": "Report complete", + "message": f"Final report ready: {title}", + "stats": {"numSources": num_sources}, + "ts": datetime.now().timestamp(), + "report": { + "title": title, + "content": report, + "sizeBytes": len(report.encode('utf-8')), + "numSources": num_sources + } + } + + report_json = json.dumps(report_data) + report_bytes = report_json.encode('utf-8') + + logger.info(f"Streaming report via byte stream: {title}, {len(report_bytes)} bytes, {num_sources} sources") + + writer = await room.local_participant.stream_bytes( + name="report.json", + total_size=len(report_bytes), + mime_type="application/json", + topic="exa_research_report", + destination_identities=[client_participant.identity], + attributes={ + "requestId": request_id, + "title": title, + "numSources": str(num_sources), + "type": "research_report" + }, + ) + + await writer.write(report_bytes) + await writer.aclose() + + logger.info(f"Report byte stream sent successfully: {title}") + + except Exception as e: + logger.error(f"Error sending report via byte stream: {e}", exc_info=True) + + +def format_status_payload( + request_id: str, + phase: str, + title: str, + message: str, + stats: Dict[str, Any] +) -> dict: + """ + Format status update payload for RPC + + Args: + request_id: Request identifier + phase: Current phase (e.g., "briefing", "researching") + title: Status title + message: Status message + stats: Additional statistics dictionary + + Returns: + Formatted payload dict + """ + return { + "requestId": request_id, + "phase": phase, + "title": title, + "message": message, + "stats": stats, + "ts": datetime.now().timestamp() + } + + +def format_notes_payload(request_id: str, note: ResearchNote) -> dict: + """ + Format research note payload for RPC + + Args: + request_id: Request identifier + note: ResearchNote to format + + Returns: + Formatted payload dict + """ + citations_data = [ + { + "id": c.id, + "url": c.url, + "title": c.title, + "quote": c.quote, + "publishedAt": c.published_at + } + for c in note.citations + ] + + return { + "requestId": request_id, + "phase": "researching", + "title": "Research note complete", + "message": f"Completed research on: {note.subtopic}", + "stats": { + "subtopic": note.subtopic, + "numCitations": len(citations_data) + }, + "ts": datetime.now().timestamp(), + "note": { + "subtopic": note.subtopic, + "summaryMarkdown": note.summary_markdown, + "citations": citations_data + } + } + + +def format_report_payload( + request_id: str, + title: str, + report: str, + num_sources: int +) -> dict: + """ + Format final report payload for RPC + + Args: + request_id: Request identifier + title: Report title + report: Full report content + num_sources: Number of unique sources cited + + Returns: + Formatted payload dict + """ + report_size_bytes = len(report.encode('utf-8')) + + return { + "requestId": request_id, + "phase": "reporting", + "title": "Report complete", + "message": f"Final report ready: {title}", + "stats": {"numSources": num_sources}, + "ts": datetime.now().timestamp(), + "report": { + "title": title, + "content": report, + "sizeBytes": report_size_bytes, + "numSources": num_sources + } + } + diff --git a/complex-agents/exa-deep-researcher/agent/handlers/status.py b/complex-agents/exa-deep-researcher/agent/handlers/status.py new file mode 100644 index 0000000..bbf5db2 --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/handlers/status.py @@ -0,0 +1,162 @@ +""" +Status, notes, and report update handling + +This module provides a StatusHandler class that processes callbacks +from the orchestrator and formats them for RPC communication and voice updates. +""" +import logging +from typing import Optional +from agent.schemas import ResearchNote +from .rpc import send_rpc_to_ui_safe, format_status_payload, format_notes_payload, format_report_payload, send_report_via_byte_stream +from agent.voice.queue import StatusQueue +from livekit.agents import AgentSession + +logger = logging.getLogger("status-handler") + + +class StatusHandler: + """ + Handles status updates, notes, and report callbacks from orchestrator + + This class serves as the bridge between the orchestrator's research workflow + and the agent's UI/voice systems. It formats and dispatches updates appropriately. + """ + + def __init__(self, ctx, session: Optional['AgentSession'] = None): + """ + Initialize status handler + + Args: + ctx: JobContext for RPC communication + session: Optional AgentSession for voice updates + """ + self.ctx = ctx + self.session = session + self.userdata = None + + async def handle_status_update( + self, + request_id: str, + phase: str, + title: str, + message: str, + stats: dict + ): + """ + Handle status updates from orchestrator + + Sends status to UI via RPC and queues voice update if session is available. + + Args: + request_id: Request identifier + phase: Current phase (e.g., "briefing", "researching") + title: Status title + message: Status message + stats: Additional statistics + """ + try: + payload = format_status_payload(request_id, phase, title, message, stats) + await send_rpc_to_ui_safe(self.ctx, "exa.research/status", payload) + + if self.session and self.userdata: + try: + await self._speak_status_update(phase, message, title) + except (RuntimeError, AttributeError): + logger.debug("Session closed, skipping voice update") + except Exception as e: + logger.error(f"Error handling status update: {e}") + + async def handle_notes_update(self, request_id: str, note: ResearchNote): + """ + Handle research notes from orchestrator + + Formats note with citations and sends to UI. + + Args: + request_id: Request identifier + note: ResearchNote with findings and citations + """ + try: + payload = format_notes_payload(request_id, note) + await send_rpc_to_ui_safe(self.ctx, "exa.research/status", payload) + except Exception as e: + logger.error(f"Error handling notes update: {e}") + + async def handle_report_ready( + self, + request_id: str, + title: str, + report: str, + num_sources: int + ): + """ + Handle report ready notification + + Sends report to UI via byte stream (for large payloads). + Also sends a lightweight status update via RPC to notify the UI. + + Args: + request_id: Request identifier + title: Report title + report: Full report content + num_sources: Number of unique sources cited + """ + try: + report_size_mb = len(report.encode('utf-8')) / (1024 * 1024) + logger.info(f"Preparing report byte stream: {title}, {len(report)} chars, {report_size_mb:.2f}MB, {num_sources} sources") + + status_payload = format_status_payload( + request_id, + "reporting", + "Report complete", + f"Final report ready: {title}", + {"numSources": num_sources} + ) + await send_rpc_to_ui_safe(self.ctx, "exa.research/status", status_payload) + + await send_report_via_byte_stream(self.ctx, request_id, title, report, num_sources) + + logger.info(f"Report byte stream sent successfully: {title}") + except Exception as e: + logger.error(f"Failed to send report byte stream: {e}", exc_info=True) + + async def _speak_status_update(self, phase: str, message: str, title: str = None): + """ + Queue a status update for speaking + + This is called internally when a status update should be voiced. + The actual queuing logic is handled by the voice module. + + Args: + phase: Current phase + message: Status message + title: Optional status title + """ + try: + if not self.userdata: + return + + title_to_check = title or phase + should_speak = False + + if phase == "researching" and "Starting research" in title_to_check: + should_speak = True + elif phase == "reporting": + should_speak = True + + if not should_speak: + logger.debug(f"Skipping voice update for phase={phase}, title={title_to_check}") + return + + self.userdata.status_queue.append({ + "phase": phase, + "message": message, + "title": title or phase + }) + if not self.userdata.status_task or self.userdata.status_task.done(): + status_queue = StatusQueue(self.session, self.userdata) + self.userdata.status_task = status_queue.start_processing() + + except Exception as e: + logger.error(f"Error queuing status update: {e}") + diff --git a/complex-agents/exa-deep-researcher/agent/job_manager.py b/complex-agents/exa-deep-researcher/agent/job_manager.py new file mode 100644 index 0000000..1e1c69c --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/job_manager.py @@ -0,0 +1,67 @@ +""" +Job Manager for managing research job state and lifecycle + +The JobManager maintains the state of a single active research job, +including progress tracking, notes collection, and lifecycle management. +""" +import asyncio +from typing import Optional, List, Dict, Callable +from dataclasses import dataclass, field + +from agent.schemas import JobState, JobProgress, ResearchNote, EXAResult + + +@dataclass +class JobManager: + """Manages a single active research job""" + request_id: Optional[str] = None + state: JobState = JobState.IDLE + progress: Optional[JobProgress] = None + notes: List[ResearchNote] = field(default_factory=list) + research_brief: str = "" + report_title: str = "" + final_report: str = "" + cancel_requested: bool = False + task: Optional[asyncio.Task] = None + + # Source content cache + source_cache: Dict[str, str] = field(default_factory=dict) + + # Clarification support + original_query: str = "" + clarification_results: List[EXAResult] = field(default_factory=list) + clarified_query: Optional[str] = None + clarification_event: Optional[asyncio.Event] = None + clarification_callback: Optional[Callable] = None + + # Conversation history for context + conversation_history: List = field(default_factory=list) + + def is_active(self) -> bool: + """Check if a job is currently active (including initialization)""" + return self.state not in [JobState.IDLE, JobState.DONE, JobState.CANCELED, JobState.ERROR] + + def cancel(self): + """Request cancellation of the current job""" + self.cancel_requested = True + if self.task and not self.task.done(): + self.task.cancel() + + def reset(self): + """Reset to idle state""" + self.request_id = None + self.state = JobState.IDLE + self.progress = None + self.notes = [] + self.research_brief = "" + self.report_title = "" + self.final_report = "" + self.cancel_requested = False + self.task = None + self.source_cache = {} + self.original_query = "" + self.clarification_results = [] + self.clarified_query = None + self.clarification_event = None + self.clarification_callback = None + diff --git a/complex-agents/exa-deep-researcher/agent/job_runner.py b/complex-agents/exa-deep-researcher/agent/job_runner.py new file mode 100644 index 0000000..b77c57f --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/job_runner.py @@ -0,0 +1,102 @@ +""" +Research job execution coordination + +This module coordinates the execution of research jobs, managing the +workflow from briefing through research to final report generation. +""" +import asyncio +import logging +from typing import TYPE_CHECKING + +from agent.schemas import JobState +from agent.storage.reports import save_report +from agent.voice.speaker import speak_final_report + +if TYPE_CHECKING: + from livekit.agents import AgentSession + +logger = logging.getLogger("job-runner") + + +async def run_research_job( + session: 'AgentSession', + userdata, + orchestrator, + request_id: str, + query: str +): + """ + Run the complete research workflow in background + + This function coordinates all phases of research: + 1. Write brief (plan research) + 2. Iterative research (supervisor pattern) + 3. Generate final report + 4. Save to disk + 5. Speak final report + + Args: + session: AgentSession for voice updates + userdata: ExaUserData with job manager + orchestrator: ResearchOrchestrator for research execution + request_id: Request identifier + query: Research query + """ + job_manager = userdata.job_manager + + try: + brief, title, subtopics = await orchestrator.write_brief( + request_id, + query, + conversation_history=job_manager.conversation_history + ) + job_manager.research_brief = brief + job_manager.report_title = title + job_manager.state = JobState.RESEARCHING + + original_query = job_manager.original_query or query + notes = await orchestrator.iterative_supervise( + request_id, + brief, + original_query, + planned_subtopics=subtopics, + conversation_history=job_manager.conversation_history + ) + + for note in notes: + job_manager.notes.append(note) + + job_manager.state = JobState.REPORTING + final_report = await orchestrator.generate_final_report( + request_id, title, brief, notes + ) + job_manager.final_report = final_report + + await save_report(title, final_report) + + job_manager.state = JobState.DONE + + if userdata.status_task and not userdata.status_task.done(): + userdata.status_task.cancel() + try: + await userdata.status_task + except asyncio.CancelledError: + pass + userdata.status_queue.clear() + + await speak_final_report(session, title, final_report) + + except asyncio.CancelledError: + job_manager.state = JobState.CANCELED + try: + session.say("Research was canceled.") + except (RuntimeError, AttributeError): + pass + except Exception as e: + logger.error(f"Research job error: {e}", exc_info=True) + job_manager.state = JobState.ERROR + try: + session.say(f"Sorry, I encountered an error during research: {str(e)[:100]}") + except (RuntimeError, AttributeError): + pass + diff --git a/complex-agents/exa-deep-researcher/agent/prompts.py b/complex-agents/exa-deep-researcher/agent/prompts.py new file mode 100644 index 0000000..df4e2a2 --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/prompts.py @@ -0,0 +1,694 @@ +""" +Prompts for EXA Deep Researcher agent +""" +from datetime import datetime + + +def get_today_str() -> str: + """Get today's date as a string""" + return datetime.now().strftime("%Y-%m-%d") + + +def supervisor_prompt() -> str: + """System prompt for the supervisor that coordinates research""" + return f"""You are a lead research coordinator conducting deep research using EXA search. + +Today's date: {get_today_str()} + +Your role: +- Break down complex research questions into focused subtopics +- Coordinate research across multiple angles and perspectives +- Ensure comprehensive coverage without redundancy +- Synthesize findings into coherent insights + +CRITICAL RULES: +- **ONLY ONE RESEARCH JOB CAN RUN AT A TIME** - Never start multiple research jobs simultaneously +- **NEVER call start_research_job() more than once in a single response** - Call it exactly once or not at all +- **NEVER call start_research_job() with different variations of the same query** - Call it with ONE query only + +IMPORTANT: When the user asks you to research something, follow this process: + +1. **Start Research**: Use the start_research_job(query, confirmed) tool EXACTLY ONCE with the user's research question. + - Call the tool with ONE query: start_research_job(query="user's question", confirmed=False) + - **CRITICAL**: Always include `confirmed=False` when starting a new research job - this parameter is REQUIRED + - Do NOT call it multiple times with different phrasings of the same question + - The system will do a quick search and analyze if clarification is needed + - If clarification is needed, the tool will return a clarification question + - **CRITICAL: You MUST speak this clarification question aloud to the user** - don't just return it silently + - After speaking the question, wait for the user's response + +2. **Clarification Phase** (if clarification question was returned): + - The tool returned a clarification question - you must speak it to the user immediately + - After speaking the question, wait for the user's response + - When user responds: + * If user confirms (says "yes", "correct", "that's right", "proceed", etc.): + → Call start_research_job(confirmed=True) - provide confirmed=True (query can be omitted) + * If user provides new details or corrected query: + → Call start_research_job(query="new clarified query", confirmed=False) - ALWAYS include confirmed=False + * If user asks a different question: + → Call start_research_job(query="new question", confirmed=False) - ALWAYS include confirmed=False + - **CRITICAL**: The `confirmed` parameter is REQUIRED. Always include `confirmed=False` for new research queries, or `confirmed=True` when confirming. + - If no clarification was needed, the tool will automatically start research + +3. **Research Phase**: + - Once research starts, it runs in the background + - **DO NOT call start_research_job() again while research is running** + - You'll receive periodic updates which you can share with the user + - You remain conversational and can answer other questions while research runs + - **IMPORTANT**: If the user asks about progress/status, CALL check_research_status() tool immediately - don't just say you'll check + +4. **Completion**: + - When research completes, you'll be notified + - Present a concise summary to the user + - The full report is available on screen and saved to disk + +Available tools: +- start_research_job(query?, confirmed): Start research or confirm after clarification + - **query** (optional): The research question or topic to investigate + - **confirmed** (REQUIRED): Always include this parameter. Use `confirmed=False` when starting a new research job, and `confirmed=True` when confirming after clarification + - **When to call:** + * First time: start_research_job(query="user's question", confirmed=False) - ALWAYS include confirmed=False + * After clarification, if user confirms: start_research_job(confirmed=True) - can omit query + * After clarification, if user provides new query: start_research_job(query="new query", confirmed=False) - include confirmed=False + - **CRITICAL**: The `confirmed` parameter is REQUIRED in every call. Always include `confirmed=False` for new research jobs. + - If clarification question is returned: SPEAK IT ALOUD and wait for user response +- cancel_research_job(): Cancel current research +- check_research_status(): Check progress of current research + - **IMPORTANT**: Call this tool whenever user asks about status, progress, or "how's it going" + - The tool will return status information - always call it, don't just say you will +- get_last_report(): Get the most recent completed research report + - **IMPORTANT**: Call this tool whenever user asks for report, results, or findings + - The tool will return the report or inform if none exists - always call it, don't just say you will + +Remember: +- ALWAYS speak clarification questions aloud - don't just return them silently +- Wait for user response after asking clarification +- Keep spoken responses conversational and under 600 characters +- Don't read URLs aloud; refer to domains naturally (e.g., "from Nature") +- The full report will be available on screen and saved to disk +- You can be interrupted and respond to other questions while research runs +- **CRITICAL**: When user asks about status or for report, CALL THE TOOL (check_research_status or get_last_report) - don't just say you will check + +IMPORTANT: Clarification Flow +- If start_research_job() returns a clarification question, the next user input MUST be processed through start_research_job() again: + * If user confirms (says "yes", "correct", "that's right", "proceed", etc.): call start_research_job(confirmed=True) + * If user provides new details or corrected query: call start_research_job(query="new clarified query", confirmed=False) + * If user asks a different question: call start_research_job(query="new question", confirmed=False) +- Research will NOT start until you receive the user's response to the clarification question +- Do NOT call start_research_job() with the same query again if a clarification question was returned - wait for user's answer first +- **CRITICAL Parameter usage**: The `confirmed` parameter is REQUIRED in every call. Always include `confirmed=False` when starting new research or providing a new query. Only use `confirmed=True` when explicitly confirming after clarification. + +CRITICAL REMINDERS: +- When user asks you to research something, call start_research_job() EXACTLY ONE TIME +- NEVER call it twice with different phrasings like "history of X" and "History of X" +- ONE call per user request, that's it +- NEVER call start_research_job() while research is already running +- When user asks about status/progress: CALL check_research_status() tool immediately +- When user asks for report/results: CALL get_last_report() tool immediately +- Don't say "I'll check" without actually calling the tool +""" + + +def clarify_with_user_instructions(messages: str, date: str) -> str: + """Instructions for clarification check""" + return f"""These are the messages that have been exchanged so far from the user asking for the report: + + + +{messages} + + + +Today's date is {date}. + +Assess whether you need to ask a clarifying question, or if the user has already provided enough information for you to start research. + +IMPORTANT: If you can see in the messages history that you have already asked a clarifying question, you almost always do not need to ask another one. Only ask another question if ABSOLUTELY NECESSARY. + +If there are acronyms, abbreviations, or unknown terms, ask the user to clarify. + +If you need to ask a question, follow these guidelines: + +- Be concise while gathering all necessary information + +- Make sure to gather all the information needed to carry out the research task in a concise, well-structured manner. + +- Use bullet points or numbered lists if appropriate for clarity. Make sure that this uses markdown formatting and will be rendered correctly if the string output is passed to a markdown renderer. + +- Don't ask for unnecessary information, or information that the user has already provided. If you can see that the user has already provided the information, do not ask for it again. + +Respond in valid JSON format with these exact keys: + +"need_clarification": boolean, +"question": "", +"verification": "" + +If you need to ask a clarifying question, return: + +"need_clarification": true, +"question": "", +"verification": "" + +If you do not need to ask a clarifying question, return: + +"need_clarification": false, +"question": "", +"verification": "" + +For the verification message when no clarification is needed: + +- Acknowledge that you have sufficient information to proceed + +- Briefly summarize the key aspects of what you understand from their request + +- Confirm that you will now begin the research process + +- Keep the message concise and professional""" + + +def transform_messages_into_research_topic_prompt(messages: str, date: str) -> str: + """Transform conversation messages into research brief""" + return f"""You will be given a set of messages that have been exchanged so far between yourself and the user. + +Your job is to translate these messages into a more detailed and concrete research question that will be used to guide the research. + +The messages that have been exchanged so far between yourself and the user are: + + + +{messages} + + + +Today's date is {date}. + +You will return a single research question that will be used to guide the research. + +Guidelines: + +0. Length & Style + +- Default to concise: keep it to 1-2 short paragraphs (about 120-200 words) focused on the core question and explicit constraints. + +- Only expand beyond this if the user explicitly requested a comprehensive brief or if multiple distinct constraints would be lost. If you expand, keep it under ~400 words and avoid filler. + +- Do not include sections, enumerated outlines, or step-by-step plans. Do not include meta commentary. Return a single brief, readable block. + +1. Maximize Specificity and Detail + +- Include all known user preferences and explicitly list key attributes or dimensions to consider. + +- It is important that all details from the user are included in the instructions. + +2. Fill in Unstated But Necessary Dimensions as Open-Ended + +- If certain attributes are essential for a meaningful output but the user has not provided them, explicitly state that they are open-ended or default to no specific constraint. + +3. Avoid Unwarranted Assumptions + +- If the user has not provided a particular detail, do not invent one. + +- Instead, state the lack of specification and guide the researcher to treat it as flexible or accept all possible options. + +4. Use the First Person + +- Phrase the request from the perspective of the user. + +5. Sources + +- If specific sources should be prioritized, specify them in the research question. + +- For product and travel research, prefer linking directly to official or primary websites (e.g., official brand sites, manufacturer pages, or reputable e-commerce platforms like Amazon for user reviews) rather than aggregator sites or SEO-heavy blogs. + +- For academic or scientific queries, prefer linking directly to the original paper or official journal publication rather than survey papers or secondary summaries. + +- For people, try linking directly to their LinkedIn profile, or their personal website if they have one. + +- If the query is in a specific language, prioritize sources published in that language.""" + + +def lead_researcher_prompt(date: str, max_concurrent_research_units: int) -> str: + """Prompt for iterative supervisor that coordinates research""" + return f"""You are a research supervisor evaluating research progress and deciding what to research next. Today's date is {date}. + + +You will be given: +1. A research brief/question +2. A list of topics already researched +3. Findings from previous research iterations + +Your job is to evaluate the current state and decide: +- Should we research a NEW topic (because there are important gaps)? +- Is research COMPLETE (because we have comprehensive coverage)? + +You respond with JSON indicating your decision. The system will automatically execute the research you specify. + + + +1. You will receive the research brief and current findings +2. Evaluate: What do we know? What's missing? What needs deeper investigation? +3. **CRITICAL**: Respond ONLY with valid JSON, no other text. Start with {{ and end with }}. + Example format: + {{"action": "research_topic", "topic": "specific topic to research", "reason": "why this topic"}} + OR + {{"action": "research_complete", "reason": "why research is comprehensive"}} +4. The system will automatically research the topic you specify +5. After research completes, you'll be asked to evaluate again +6. Continue iterating until you decide research is complete +7. Maximum {max_concurrent_research_units} topics can be researched in parallel + + + +**Focus on information gaps, not formatting** +- Don't worry about how findings are formatted - that's handled separately +- Focus only on whether you have ENOUGH information to answer the research question +- Don't ask to research topics you've already covered (check the researched topics list carefully) + +**Be strategic about what to research next** +- Research is expensive (time and cost) +- As more research accumulates, be more selective about asking for additional topics +- Only ask for topics that are ABSOLUTELY necessary to answer the question comprehensively +- Make sure topics are substantially different from what's already been researched +- Consider dependencies - some topics may need to be researched in sequence + +**Parallel research consideration** +- The system can research up to {max_concurrent_research_units} topics in parallel +- Use parallel research when topics are independent (e.g., comparing X vs Y, researching different entities) +- Consider cost vs time - parallel research saves time but increases cost + +**Research depth varies by question** +- Broad questions: shallower research is fine +- Terms like "detailed" or "comprehensive": need deeper, more thorough research +- Adjust your expectations based on the original question + + + +- You respond with JSON, not tool calls: {{"action": "research_topic" | "research_complete", "topic": "...", "reason": "..."}} +- When specifying a topic, make it clear, specific, and self-contained +- **CRITICAL: Topic Format for Search** + - The topic you specify MUST be formatted as a ready-to-use search query for EXA (a semantic search engine) + - Include the main subject/entity from the research brief in EVERY topic + - If the entity name is ambiguous, add disambiguating terms (e.g., "Tesla Inc." not "Tesla") + - Make topics specific enough to avoid generic results (3-10 words) + - Examples: "Tesla Inc. battery technology innovations", "Apple Inc. international market expansion" +- Don't use acronyms or abbreviations unless they're clearly defined in the context +- Only ask for topics needed to answer the overall research question +- If findings comprehensively cover all key aspects, use "research_complete" +""" + + +def iterative_supervisor_prompt(max_iterations: int = 5, max_concurrent: int = 3) -> str: + """Prompt for iterative supervisor that evaluates and decides next research actions""" + return lead_researcher_prompt(get_today_str(), max_concurrent) + + +def research_system_prompt(date: str, max_search_queries: int) -> str: + """Prompt for the researcher conducting EXA searches""" + return f"""You are a research assistant conducting deep research on the user's input topic. Use the tools and search methods provided to research the user's input topic. For context, today's date is {date}. + + + +Your job is to use tools and search methods to find information that can answer the question that a user asks. + +You can use any of the tools provided to you to find resources that can help answer the research question. You can call these tools in series or in parallel, your research is conducted in a tool-calling loop. + + + + + +- Make sure you review all of the tools you have available to you, match the tools to the user's request, and select the tool that is most likely to be the best fit. + +- In each iteration, select the BEST tool for the job, this may or may not be general websearch. + +- When selecting the next tool to call, make sure that you are calling tools with arguments that you have not already tried. + +- Tool calling is costly, so be sure to be very intentional about what you look up. Some of the tools may have implicit limitations. As you call tools, feel out what these limitations are, and adjust your tool calls accordingly. + +- You have a maximum of {max_search_queries} web search queries available, so use them strategically and efficiently. + +- This could mean that you need to call a different tool, or that you should call "ResearchComplete", e.g. it's okay to recognize that a tool has limitations and cannot do what you need it to. + +- Don't mention any tool limitations in your output, but adjust your tool calls accordingly. + + + + + +- In addition to tools for research, you will also be given a special "ResearchComplete" tool. This tool is used to indicate that you are done with your research. + +- The user will give you a sense of how much effort you should put into the research. This does not translate ~directly~ to the number of tool calls you should make, but it does give you a sense of the depth of the research you should conduct. + +- DO NOT call "ResearchComplete" unless you are satisfied with your research. + +- One case where it's recommended to call this tool is if you see that your previous tool calls have stopped yielding useful information. + + + + + +1. If you haven't conducted any searches yet, start with broad searches to get necessary context and background information. Once you have some background, you can start to narrow down your searches to get more specific information. + +2. Different topics require different levels of research depth. If the question is broad, your research can be more shallow, and you may not need to iterate and call tools as many times. + +3. If the question is detailed, you may need to be more stingy about the depth of your findings, and you may need to iterate and call tools more times to get a fully detailed answer. + + + + + +- You MUST conduct research using web search or a different tool before you are allowed to call "ResearchComplete"! You cannot call "ResearchComplete" without conducting research first! + +- Do not repeat or summarize your research findings unless the user explicitly asks you to do so. Your main job is to call tools. You should call tools until you are satisfied with the research findings, and then call "ResearchComplete". + +""" + + +def researcher_prompt(max_search_queries: int = 5, original_query: str = "", research_brief: str = "") -> str: + """Prompt for the researcher conducting EXA searches - uses research_system_prompt""" + base_prompt = research_system_prompt(get_today_str(), max_search_queries) + + context_section = "" + if original_query or research_brief: + context_section = "\n\nOVERALL RESEARCH CONTEXT:\n" + if original_query: + context_section += f"Original Research Goal: {original_query}\n" + if research_brief: + context_section += f"Research Brief: {research_brief}\n" + context_section += "\nIMPORTANT: You are researching a specific subtopic as part of a larger research effort. Always keep the overall goal in mind and ensure your findings are relevant to answering the main research question." + base_prompt += context_section + + return base_prompt + + +def compress_research_system_prompt(date: str) -> str: + """Prompt for compressing research notes""" + return f"""You are a research assistant that has conducted research on a topic by calling several tools and web searches. Your job is now to clean up the findings, but preserve all of the relevant statements and information that the researcher has gathered. For context, today's date is {date}. + + + +You need to clean up information gathered from tool calls and web searches in the existing messages. + +All relevant information should be repeated and rewritten verbatim, but in a cleaner format. + +The purpose of this step is just to remove any obviously irrelevant or duplicative information. + +For example, if three sources all say "X", you could say "These three sources all stated X". + +Only these fully comprehensive cleaned findings are going to be returned to the user, so it's crucial that you don't lose any information from the raw messages. + + + + + +1. Your output findings should be fully comprehensive and include ALL of the information and sources that the researcher has gathered from tool calls and web searches. It is expected that you repeat key information verbatim. + +2. This report can be as long as necessary to return ALL of the information that the researcher has gathered. + +3. In your report, you should return inline citations for each source that the researcher found. + +4. You should include a "Sources" section at the end of the report that lists all of the sources the researcher found with corresponding citations, cited against statements in the report. + +5. Make sure to include ALL of the sources that the researcher gathered in the report, and how they were used to answer the question! + +6. It's really important not to lose any sources. A later LLM will be used to merge this report with others, so having all of the sources is critical. + + + + + +The report should be structured like this: + +**List of Queries and Tool Calls Made** + +**Fully Comprehensive Findings** + +**List of All Relevant Sources (with citations in the report)** + + + + + +- Assign each unique URL a single citation number in your text + +- End with ### Sources that lists each source with corresponding numbers + +- IMPORTANT: Number sources sequentially without gaps (1,2,3,4...) in the final list regardless of which sources you choose + +- Example format: + + [1] Source Title: URL + + [2] Source Title: URL + + + +Critical Reminder: It is extremely important that any information that is even remotely relevant to the user's research topic is preserved verbatim (e.g. don't rewrite it, don't summarize it, don't paraphrase it).""" + + +compress_research_simple_human_message = """All above messages are about research conducted by an AI Researcher. Please clean up these findings. + +DO NOT summarize the information. I want the raw information returned, just in a cleaner format. Make sure all relevant information is preserved - you can rewrite findings verbatim.""" + + +def final_report_generation_prompt(research_brief: str, date: str, findings: str) -> str: + """Prompt for generating the final research report""" + return f"""Based on all the research conducted, create a comprehensive, well-structured answer to the overall research brief: + + + +{research_brief} + + + +Today's date is {date}. + +Here are the findings from the research that you conducted: + + + +{findings} + + + +Please create a detailed answer to the overall research brief that: + +1. Is well-organized with proper headings (# for title, ## for sections, ### for subsections) + +2. Includes specific facts and insights from the research + +3. References relevant sources using [Title](URL) format + +4. Provides a balanced, thorough analysis. Be as comprehensive as possible, and include all information that is relevant to the overall research question. People are using you for deep research and will expect detailed, comprehensive answers. + +5. Includes a "Sources" section at the end with all referenced links + +You can structure your report in a number of different ways. Here are some examples: + +To answer a question that asks you to compare two things, you might structure your report like this: + +1/ intro + +2/ overview of topic A + +3/ overview of topic B + +4/ comparison between A and B + +5/ conclusion + +To answer a question that asks you to return a list of things, you might only need a single section which is the entire list. + +1/ list of things or table of things + +Or, you could choose to make each item in the list a separate section in the report. When asked for lists, you don't need an introduction or conclusion. + +1/ item 1 + +2/ item 2 + +3/ item 3 + +To answer a question that asks you to summarize a topic, give a report, or give an overview, you might structure your report like this: + +1/ overview of topic + +2/ concept 1 + +3/ concept 2 + +4/ concept 3 + +5/ conclusion + +If you think you can answer the question with a single section, you can do that too! + +1/ answer + +REMEMBER: Section is a VERY fluid and loose concept. You can structure your report however you think is best, including in ways that are not listed above! + +Make sure that your sections are cohesive, and make sense for the reader. + +For each section of the report, do the following: + +- Use simple, clear language + +- Use ## for section title (Markdown format) for each section of the report + +- Do NOT ever refer to yourself as the writer of the report. This should be a professional report without any self-referential language. + +- Do not say what you are doing in the report. Just write the report without any commentary from yourself. + +Format the report in clear markdown with proper structure and include source references where appropriate. + + + +**IMPORTANT**: The findings already contain inline citations [1], [2], [3] within each section. + +Your job: +1. Synthesize information from all sections into a cohesive, well-structured report +2. Use the inline citations from the findings to support your statements +3. Write ONLY the report content - DO NOT create a "### Sources" section +4. The sources list will be automatically appended after you finish + +Focus on creating a comprehensive narrative that synthesizes all the research findings. + +""" + + +def final_report_prompt(research_brief: str) -> str: + """Alias for final_report_generation_prompt - maintains backward compatibility""" + return final_report_generation_prompt(research_brief, get_today_str(), "") + + +def status_update_prompt(action_type: str, messages_content: str, context_info: str) -> str: + """Prompt for generating status updates""" + return f"""Generate a status update based on the specific action that just completed. + +Action type: {action_type} + +Current state: + +{messages_content}{context_info} + +Focus on the CURRENT ACTION, not the overall research topic. Create titles and messages that reflect what specifically just happened: + +TITLE REQUIREMENTS: + +- Use "-ing" conjugation consistently (e.g., "Finding 12 relevant sources", "Completing market analysis", "Identifying 3 key trends") + +- Be specific to the action type and vary the language + +- Include concrete details when available (numbers, specific findings, etc.) + +- Keep under 50 characters + +MESSAGE REQUIREMENTS: + +- Focus on what is being accomplished in this specific step + +- Use "-ing" tense consistently to match the title + +- Include concrete details, findings, or next steps when relevant + +- Vary sentence structure and avoid repetitive phrases + +- Be specific rather than generic + +- Keep under 200 characters + +Examples by action type: + +- Web search: Title: "Finding 8 sources on AI ethics", Message: "Locating research papers, industry reports, and expert opinions from MIT, Stanford, and leading tech companies." + +- Analysis: Title: "Identifying 3 key market trends", Message: "Discovering cloud adoption accelerating 40% YoY, edge computing gaining traction, and regulatory changes impacting data storage." + +- Research task: Title: "Breaking down into 4 focus areas", Message: "Splitting analysis into market leaders, emerging players, regulatory landscape, and future projections." + +- Final report: Title: "Synthesizing findings", Message: "Combining insights from 15 sources into actionable investment recommendations with risk assessments.\"""" + + +def brief_generation_prompt(user_query: str) -> str: + """Prompt for generating a research brief from user query - uses transform_messages_into_research_topic_prompt""" + # For backward compatibility, but ideally should use transform_messages_into_research_topic_prompt + return f"""Based on the user's query, create a focused research brief. + +User Query: +{user_query} + +Your task: +- Identify the core research question +- List 3-5 key subtopics that need investigation +- Specify any important constraints (time period, geography, etc.) + +Respond with valid JSON in this exact format: +{{ + "title": "Clear, descriptive title (50 chars max)", + "brief": "Focused research brief describing what to investigate", + "subtopics": ["Subtopic 1", "Subtopic 2", "Subtopic 3", ...] +}} + +Guidelines: +- Include 3-5 subtopics that are specific and focused +- Each subtopic should represent a distinct research angle +- Keep subtopics concise but descriptive (5-10 words each) + +**CRITICAL: Subtopic Format for Search** +- Each subtopic MUST be formatted as a ready-to-use search query for EXA (a semantic search engine) +- Include the main subject/entity from the original question in EVERY subtopic +- If the entity name is ambiguous (e.g., "Tesla" could mean the company OR Nikola Tesla the inventor), add disambiguating terms like "Inc.", "company", "corporation", specific products, or time context +- Make subtopics specific enough to avoid generic results and prevent confusion with similarly-named entities +- Focus on finding authoritative sources (news, academic, official sites) +- Examples: + * Good: "Tesla Inc. battery technology innovations", "Tesla company autopilot development history" + * Bad: "Tesla patents" (ambiguous - could be Nikola Tesla inventor or Tesla company) +- Subtopics will be used directly as search queries, so format them accordingly""" + + +def generate_search_query_prompt(subtopic: str, original_query: str, research_brief: str) -> str: + """Prompt for generating contextualized search query""" + return f"""You need to create a search query for EXA (a semantic search engine) to find relevant sources. + +Original Research Question: {original_query} + +Research Brief: {research_brief} + +Current Subtopic to Research: {subtopic} + +Your task: Generate 1-3 targeted search queries that will find high-quality, relevant sources about this subtopic in the context of the original research question. + +Requirements: +- Include the main subject/entity from the original question in EVERY query +- **CRITICAL**: If the entity name is ambiguous (e.g., "Tesla" could mean the company OR Nikola Tesla the inventor), add disambiguating terms like "Inc.", "company", "corporation", specific products, or time context (e.g., "Tesla Inc. patents", "Tesla company technology", "Tesla electric vehicles") +- Make queries specific enough to avoid generic results and prevent confusion with similarly-named entities +- Focus on finding authoritative sources (news, academic, official sites) +- Queries should be 3-10 words each + +Examples: +- If researching "Technology Innovations" for "Tesla History" → "Tesla Inc. battery technology innovations", "Tesla company autopilot development history" +- If researching "Market Expansion" for "Apple Inc" → "Apple Inc. international market expansion strategy", "Apple corporation global manufacturing footprint" +- **BAD**: "Tesla patents" (ambiguous - could be Nikola Tesla inventor or Tesla company) +- **GOOD**: "Tesla Inc. patents electric vehicle technology", "Tesla company patent portfolio automotive" + +Return ONLY a JSON array of query strings, nothing else: +["query 1", "query 2", "query 3"]""" + + +def compact_summary_prompt(full_report: str, max_chars: int = 600) -> str: + """Prompt for generating a compact spoken summary""" + return f"""Create a concise spoken summary of this research report. + +Full Report: +{full_report} + +Requirements: +- Maximum {max_chars} characters +- Highlight 3 most important findings +- Sound natural when spoken aloud +- Don't mention URLs or technical citations +- End with "The full report is on your screen" + +Focus on: +- What was discovered +- Why it matters +- Key takeaways +""" diff --git a/complex-agents/exa-deep-researcher/agent/research/__init__.py b/complex-agents/exa-deep-researcher/agent/research/__init__.py new file mode 100644 index 0000000..aaf4b51 --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/research/__init__.py @@ -0,0 +1,10 @@ +""" +Research logic components + +This package contains research workflow components: +- clarification: Query clarification logic +- engine: Core research execution +- compression: Note compression for token management +- reporting: Final report generation +""" + diff --git a/complex-agents/exa-deep-researcher/agent/research/clarification/__init__.py b/complex-agents/exa-deep-researcher/agent/research/clarification/__init__.py new file mode 100644 index 0000000..4e7286f --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/research/clarification/__init__.py @@ -0,0 +1,13 @@ +""" +Clarification logic + +This package handles: +- Autonomous clarification analysis +- Quick search for clarification +""" + +from .analyzer import AutonomousClarification +from .search import quick_search + +__all__ = ["AutonomousClarification", "quick_search"] + diff --git a/complex-agents/exa-deep-researcher/agent/research/clarification/analyzer.py b/complex-agents/exa-deep-researcher/agent/research/clarification/analyzer.py new file mode 100644 index 0000000..c62e22d --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/research/clarification/analyzer.py @@ -0,0 +1,120 @@ +""" +Autonomous clarification analysis + +This module uses an LLM to analyze search results and determine +if clarification is needed before starting deep research. +""" +import logging +from typing import List, Optional, Tuple + +from agent.schemas import EXAResult +from agent.utils import execute_llm_chat + +logger = logging.getLogger("clarification-analyzer") + + +class AutonomousClarification: + """ + Analyzes search results to determine if user clarification is needed + + This class uses an LLM to evaluate whether search results match the user's + intent. If the query is ambiguous or results are unclear, it requests + clarification before proceeding with expensive deep research. + """ + + def __init__(self, llm): + """ + Initialize clarification analyzer + + Args: + llm: LLM instance for analysis + """ + self.llm = llm + + async def analyze( + self, + query: str, + results: List[EXAResult], + conversation_history: Optional[list] = None + ) -> Tuple[bool, Optional[str]]: + """ + Autonomously determine if clarification is needed based on search results + + After doing an initial search, this function uses an LLM to analyze whether the + results match what the user is looking for. If the query is ambiguous or results + are unclear, it asks for clarification before proceeding with deep research. + + This prevents wasting time researching the wrong thing. + + Args: + query: User's search query + results: List of search results to analyze + conversation_history: Optional conversation context + + Returns: + Tuple of (needs_clarification: bool, clarification_message: Optional[str]) + - If needs_clarification is False, proceed with research + - If True, clarification_message contains what to ask the user + """ + if not results: + return True, f"I couldn't find any results for '{query}'. Could you clarify what you're looking for?" + + results_summary = "\n".join([ + f"{i+1}. {r.title} - {r.url[:60]}..." + for i, r in enumerate(results[:5]) # Show top 5 results + ]) + + clarification_prompt = f"""Analyze whether these search results match what the user is looking for. + +User Query: {query} + +Search Results Found: +{results_summary} + +Based on the query and results: +1. Do these results clearly match what the user is asking for? +2. Is the query specific enough that we can proceed with research? +3. Are there any ambiguities that need clarification? + +Respond with JSON: +{{ + "needs_clarification": true/false, + "confidence": "high/medium/low", + "reason": "brief explanation", + "clarification_message": "what to ask user if needed, or null" +}} + +If confidence is "high" and results clearly match, set needs_clarification to false. +If query is ambiguous or results are unclear, set needs_clarification to true.""" + + try: + response = await execute_llm_chat( + llm=self.llm, + system_prompt="You are an expert at determining if search results match user intent. Be decisive - only ask for clarification if truly needed.", + user_prompt=clarification_prompt, + parse_json=True, + extract_json=True, + conversation_history=conversation_history + ) + + if isinstance(response, dict): + needs_clarification = response.get("needs_clarification", False) + clarification_message = response.get("clarification_message") + confidence = response.get("confidence", "medium") + + logger.info(f"Clarification analysis: needs={needs_clarification}, confidence={confidence}") + + if needs_clarification and clarification_message: + return True, clarification_message + elif needs_clarification: + return True, f"I found some results for '{query}', but I want to make sure I'm researching the right thing. Could you clarify?" + else: + return False, None + else: + logger.warning(f"Clarification analysis returned non-dict: {type(response)}") + return True, f"I found some results for '{query}'. Is this what you're looking for?" + + except Exception as e: + logger.error(f"Error in autonomous clarification: {e}") + return True, f"I found some results for '{query}'. Could you confirm this is what you're looking for?" + diff --git a/complex-agents/exa-deep-researcher/agent/research/clarification/search.py b/complex-agents/exa-deep-researcher/agent/research/clarification/search.py new file mode 100644 index 0000000..7623aff --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/research/clarification/search.py @@ -0,0 +1,60 @@ +""" +Quick search for clarification + +This module performs quick searches to gather initial results +that help determine if clarification is needed. +""" +import logging +from typing import List, Callable, Optional + +from agent.schemas import EXASearchParams, EXAResult +from agent.exa_client import EXAClient + +logger = logging.getLogger("clarification-search") + + +async def quick_search( + exa_client: EXAClient, + request_id: str, + query: str, + num_results: int = 5, + status_callback: Optional[Callable] = None +) -> List[EXAResult]: + """ + Perform a quick naive search to get initial results for clarification + + This search is fast and limited in scope - just enough to understand + if the query is clear and results are relevant. + + Args: + exa_client: EXA API client + request_id: Request identifier + query: Search query + num_results: Number of results to fetch (default: 5) + status_callback: Optional callback for status updates + + Returns: + List of EXA search results (typically 3-5 results) + """ + if status_callback: + logger.info(f"🔍 [CLARIFYING] Quick search starting for: {query} (requestId={request_id})") + await status_callback( + request_id, + "clarifying", + "Quick search", + f"Searching for: {query}", + {} + ) + + search_params = EXASearchParams( + query=query, + num_results=num_results, + use_autoprompt=None + ) + + results = await exa_client.search(search_params) + + logger.info(f"Quick search found {len(results)} results for: {query}") + + return results + diff --git a/complex-agents/exa-deep-researcher/agent/research/compression/__init__.py b/complex-agents/exa-deep-researcher/agent/research/compression/__init__.py new file mode 100644 index 0000000..0428f7a --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/research/compression/__init__.py @@ -0,0 +1,11 @@ +""" +Note compression + +This package handles: +- Compressing research notes to manage token usage +""" + +from .compressor import compress_notes + +__all__ = ["compress_notes"] + diff --git a/complex-agents/exa-deep-researcher/agent/research/compression/compressor.py b/complex-agents/exa-deep-researcher/agent/research/compression/compressor.py new file mode 100644 index 0000000..c560276 --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/research/compression/compressor.py @@ -0,0 +1,78 @@ +""" +Note compression for token management + +This module handles compressing accumulated research notes to manage +token usage in subsequent LLM calls during long research sessions. +""" +import logging +from typing import List, Optional, Callable + +from livekit.agents.llm import ChatMessage +from agent.schemas import ResearchNote +from agent.utils import stream_llm_chat, format_note_with_citations +from agent.prompts import compress_research_system_prompt, compress_research_simple_human_message, get_today_str + +logger = logging.getLogger("compressor") + + +async def compress_notes( + llm, + request_id: str, + notes: List[ResearchNote], + status_callback: Optional[Callable] = None +) -> str: + """ + Compress accumulated notes to manage token usage + + As we accumulate research notes, the context grows. This function uses an LLM + to compress/consolidate multiple notes into a more concise format while preserving + key information. This helps manage token limits in subsequent LLM calls. + + Called periodically during research (when 4+ notes are collected). + + Args: + llm: LLM instance for compression + request_id: Request identifier + notes: List of research notes to compress + status_callback: Optional callback for status updates + + Returns: + Compressed notes as markdown string (streamed from LLM) + """ + if not notes: + return "" + + if status_callback: + await status_callback( + request_id, + "compressing", + "Organizing findings", + f"Consolidating {len(notes)} research topics to keep everything organized", + {"notes_count": len(notes)} + ) + + messages = [ + ChatMessage(type="message", role="system", content=[compress_research_system_prompt(get_today_str())]) + ] + + for note in notes: + messages.append( + ChatMessage( + type="message", + role="assistant", + content=[format_note_with_citations(note.subtopic, note.summary_markdown, note.citations)] + ) + ) + + messages.append( + ChatMessage(type="message", role="user", content=[compress_research_simple_human_message]) + ) + + logger.info(f"Compressing {len(notes)} notes") + + compressed = await stream_llm_chat(llm, messages) + + logger.info(f"Compressed {len(notes)} notes into {len(compressed)} chars") + + return compressed + diff --git a/complex-agents/exa-deep-researcher/agent/research/engine/__init__.py b/complex-agents/exa-deep-researcher/agent/research/engine/__init__.py new file mode 100644 index 0000000..92552d2 --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/research/engine/__init__.py @@ -0,0 +1,13 @@ +""" +Research engine + +This package handles: +- Subtopic research execution +- Source synthesis and analysis +""" + +from .subtopic import ResearchSubtopic +from .synthesizer import synthesize_findings + +__all__ = ["ResearchSubtopic", "synthesize_findings"] + diff --git a/complex-agents/exa-deep-researcher/agent/research/engine/subtopic.py b/complex-agents/exa-deep-researcher/agent/research/engine/subtopic.py new file mode 100644 index 0000000..886bd06 --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/research/engine/subtopic.py @@ -0,0 +1,156 @@ +""" +Subtopic research execution + +This module handles researching a single subtopic, including +searching EXA, fetching content, and synthesizing findings. +""" +import logging +from typing import Optional, Callable +from datetime import datetime + +from agent.schemas import EXASearchParams, EXAContentOptions, ResearchNote +from agent.exa_client import EXAClient +from agent.utils import fetch_contents_in_batches +from .synthesizer import synthesize_findings + +logger = logging.getLogger("subtopic-research") + + +class ResearchSubtopic: + """ + Handles researching a single subtopic using EXA + + This class coordinates the research workflow for one subtopic: + 1. Search EXA for relevant sources + 2. Fetch content from those sources + 3. Synthesize findings into a note + """ + + def __init__( + self, + exa_client: EXAClient, + llm, + max_results_per_search: int = 10, + max_concurrent_fetches: int = 3, + status_callback: Optional[Callable] = None, + notes_callback: Optional[Callable] = None + ): + """ + Initialize subtopic researcher + + Args: + exa_client: EXA API client + llm: LLM instance for synthesis + max_results_per_search: Maximum results per search + max_concurrent_fetches: Max parallel content fetches + status_callback: Optional callback for status updates + notes_callback: Optional callback for completed notes + """ + self.exa_client = exa_client + self.llm = llm + self.max_results_per_search = max_results_per_search + self.max_concurrent_fetches = max_concurrent_fetches + self.status_callback = status_callback + self.notes_callback = notes_callback + + async def research( + self, + request_id: str, + subtopic: str, + iteration: int, + original_query: str = "", + research_brief: str = "", + conversation_history: Optional[list] = None + ) -> ResearchNote: + """ + Research a single subtopic using EXA + + This is the core research function that: + 1. Searches EXA for relevant sources about the subtopic + 2. Fetches the actual content from those sources + 3. Uses an LLM to synthesize findings from the sources + 4. Extracts citations and returns a ResearchNote + + Args: + request_id: Request identifier + subtopic: The specific subtopic to research + iteration: Iteration index + original_query: The original user query/goal + research_brief: The full research brief with context + conversation_history: Optional conversation context + + Returns: + ResearchNote with findings and citations + """ + if self.status_callback: + logger.info(f"[RESEARCHING] Starting research for: {subtopic} (requestId={request_id})") + await self.status_callback( + request_id, + "researching", + "Starting research", + f"Investigating: {subtopic}", + {"subtopic": subtopic} + ) + + # Use subtopic directly as search query (subtopics are generated as search-ready queries) + search_params = EXASearchParams( + query=subtopic, + num_results=self.max_results_per_search, + use_autoprompt=None + ) + + results = await self.exa_client.search(search_params) + + logger.info(f"Found {len(results)} results for subtopic: {subtopic}") + + if not results: + return ResearchNote( + subtopic=subtopic, + summary_markdown=f"No results found for: {subtopic}", + citations=[], + timestamp=datetime.now() + ) + + if self.status_callback: + await self.status_callback( + request_id, + "researching", + "Gathering sources", + f"Found {len(results)} relevant sources about {subtopic}", + {"subtopic": subtopic, "results": len(results)} + ) + + content_options = EXAContentOptions( + text=True, + max_characters=5000, + livecrawl="fallback" + ) + + result_urls = [r.url for r in results] + all_contents = await fetch_contents_in_batches( + self.exa_client, result_urls, content_options, self.max_concurrent_fetches + ) + + if self.status_callback: + await self.status_callback( + request_id, + "researching", + "Synthesizing findings", + f"Analyzing {len(all_contents)} sources about {subtopic}", + {"subtopic": subtopic, "sources_fetched": len(all_contents)} + ) + + note = await synthesize_findings( + llm=self.llm, + subtopic=subtopic, + sources=all_contents, + original_query=original_query, + research_brief=research_brief, + conversation_history=conversation_history + ) + + if self.notes_callback: + await self.notes_callback(request_id, note) + + return note + diff --git a/complex-agents/exa-deep-researcher/agent/research/engine/synthesizer.py b/complex-agents/exa-deep-researcher/agent/research/engine/synthesizer.py new file mode 100644 index 0000000..ca32c60 --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/research/engine/synthesizer.py @@ -0,0 +1,124 @@ +""" +Source synthesis and findings generation + +This module handles synthesizing findings from multiple sources +into coherent research notes with citations. +""" +import logging +from typing import List, Optional +from datetime import datetime + +from agent.schemas import ResearchNote, Citation, EXAContent +from agent.utils import execute_llm_chat, build_research_context +from agent.prompts import researcher_prompt + +logger = logging.getLogger("synthesizer") + + +async def synthesize_findings( + llm, + subtopic: str, + sources: List[EXAContent], + original_query: str = "", + research_brief: str = "", + conversation_history: Optional[list] = None +) -> ResearchNote: + """ + Synthesize findings from sources into a research note + + Args: + llm: LLM instance for synthesis + subtopic: Current subtopic being researched + sources: List of content results from EXA + original_query: Original user query for context + research_brief: Full research brief for context + conversation_history: Optional conversation context + + Returns: + ResearchNote with summary and citations + """ + if not sources: + logger.warning(f"No sources provided for subtopic: {subtopic}") + return ResearchNote( + subtopic=subtopic, + summary_markdown=f"No sources found for: {subtopic}", + citations=[], + timestamp=datetime.now() + ) + + sources_with_numbers = "" + for idx, source in enumerate(sources, 1): + content = source.text or source.summary or "(No content available)" + sources_with_numbers += f"\n\n---\n\n**[{idx}] {source.title}**\nURL: {source.url}\n\n{content}" + + if not sources_with_numbers.strip(): + logger.warning(f"No content extracted from {len(sources)} sources") + return ResearchNote( + subtopic=subtopic, + summary_markdown=f"No content could be extracted from {len(sources)} sources", + citations=[], + timestamp=datetime.now() + ) + + context_info = build_research_context(original_query, research_brief) + + response = await execute_llm_chat( + llm=llm, + system_prompt=researcher_prompt(original_query=original_query, research_brief=research_brief), + user_prompt=f"""Current Subtopic to Research: {subtopic} +{context_info} + +Sources Found (use these numbers for citations): +{sources_with_numbers} + +**IMPORTANT INSTRUCTIONS**: +1. **FIRST: Validate each source** - Check if the source is about the CORRECT entity/subject from the research goal: + - If researching "Tesla Inc." (the car company founded 2003), REJECT sources about "Nikola Tesla" (the inventor from 1800s-1900s) + - If researching a company, REJECT sources about people with the same name + - If researching a person, REJECT sources about companies/products with the same name + - Check dates, context, and content to ensure sources match the research subject +2. Write a comprehensive markdown summary about "{subtopic}" using ONLY relevant, validated sources +3. If most/all sources are about the WRONG entity, acknowledge this: "Sources found were primarily about [wrong entity] rather than [correct entity]. Limited/no relevant information available." +4. Reference sources using inline citations: [1], [2], [3], etc. +5. You can reference multiple sources like [1][2] or [1,3,5] +6. Do NOT invent information not in the sources +7. Do NOT return a JSON object - just return the markdown summary text +8. Include specific facts, quotes, and data from validated sources +9. Make the summary relevant to both this subtopic AND the overall research goal + +Example format: +Tesla was founded in 2003 by Martin Eberhard and Marc Tarpenning [1]. Elon Musk joined as chairman and investor in 2004 [2]. The company's first vehicle, the Roadster, was released in 2008 [1][3]. + +Now write your summary:""", + parse_json=False, + extract_json=False, + conversation_history=conversation_history + ) + + summary = str(response).strip() + + if not summary: + summary = f"Could not generate summary for: {subtopic}" + + citations = [ + Citation( + id=source.id, + url=source.url, + title=source.title, + quote=(source.text or source.summary or "")[:600], + published_at=None + ) + for source in sources + ] + + note = ResearchNote( + subtopic=subtopic, + summary_markdown=summary, + citations=citations, + timestamp=datetime.now() + ) + + logger.info(f"Synthesized note for '{subtopic}' with {len(citations)} citations") + + return note + diff --git a/complex-agents/exa-deep-researcher/agent/research/reporting/__init__.py b/complex-agents/exa-deep-researcher/agent/research/reporting/__init__.py new file mode 100644 index 0000000..eb75db4 --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/research/reporting/__init__.py @@ -0,0 +1,11 @@ +""" +Report generation + +This package handles: +- Final report generation from research notes +""" + +from .generator import generate_report + +__all__ = ["generate_report"] + diff --git a/complex-agents/exa-deep-researcher/agent/research/reporting/generator.py b/complex-agents/exa-deep-researcher/agent/research/reporting/generator.py new file mode 100644 index 0000000..3267719 --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/research/reporting/generator.py @@ -0,0 +1,91 @@ +""" +Final report generation + +This module generates comprehensive research reports from +accumulated research notes and findings. +""" +import logging +from typing import List, Optional, Callable + +from agent.schemas import ResearchNote +from agent.utils import execute_llm_chat, format_findings_for_report +from agent.prompts import final_report_generation_prompt, get_today_str + +logger = logging.getLogger("report-generator") + + +async def generate_report( + llm, + request_id: str, + title: str, + brief: str, + notes: List[ResearchNote], + status_callback: Optional[Callable] = None, + report_callback: Optional[Callable] = None +) -> str: + """ + Generate final research report + + Args: + llm: LLM instance for report generation + request_id: Request identifier + title: Report title + brief: Research brief with context + notes: List of research notes to synthesize + status_callback: Optional callback for status updates + report_callback: Optional callback when report is complete + + Returns: + Final report as markdown string + """ + if status_callback: + await status_callback( + request_id, + "reporting", + "Writing final report", + f"Compiling all findings into a comprehensive report on: {title}", + {"title": title} + ) + + # Collect global sources first (must match the order in format_findings_for_report) + global_sources = [] + seen_urls = set() + for note in notes: + for citation in note.citations: + if citation.url and citation.url not in seen_urls: + seen_urls.add(citation.url) + global_sources.append(citation) + + findings = format_findings_for_report(notes) + + report_prompt = final_report_generation_prompt( + research_brief=brief, + date=get_today_str(), + findings=findings + ) + + logger.info(f"Generating final report: {title}") + + report = await execute_llm_chat( + llm=llm, + system_prompt="", + user_prompt=report_prompt, + conversation_history=None + ) + + report = str(report).strip() + + if global_sources: + if "### Sources" not in report and "## Sources" not in report: + report += "\n\n### Sources\n\n" + for idx, citation in enumerate(global_sources, 1): + report += f"[{idx}] {citation.title}: {citation.url}\n" + + num_unique_sources = len(global_sources) + logger.info(f"Generated report: {title} ({len(report)} chars, {num_unique_sources} sources)") + + if report_callback: + await report_callback(request_id, title, report, num_unique_sources) + + return report + diff --git a/complex-agents/exa-deep-researcher/agent/schemas.py b/complex-agents/exa-deep-researcher/agent/schemas.py new file mode 100644 index 0000000..99839f4 --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/schemas.py @@ -0,0 +1,116 @@ +""" +Data schemas for EXA Deep Researcher agent +""" +from typing import Optional, List, Literal +from pydantic import BaseModel, Field +from datetime import datetime +from enum import Enum + + +# Job State Machine +class JobState(str, Enum): + IDLE = "idle" + CLARIFYING = "clarifying" + BRIEFING = "briefing" + RESEARCHING = "researching" + COMPRESSING = "compressing" + REPORTING = "reporting" + DONE = "done" + CANCELED = "canceled" + ERROR = "error" + + +# EXA API Parameters +class EXASearchParams(BaseModel): + """Parameters for EXA search API""" + query: str + search_type: Optional[Literal["auto", "keyword", "neural"]] = "auto" + result_category: Optional[str] = None + country: Optional[str] = None + num_results: Optional[int] = 10 + + # Date filters + start_published_date: Optional[str] = None # ISO format + end_published_date: Optional[str] = None + start_crawl_date: Optional[str] = None + end_crawl_date: Optional[str] = None + + # Domain filters + include_domains: Optional[List[str]] = None + exclude_domains: Optional[List[str]] = None + + # Text filters + include_text: Optional[str] = None # exact phrase, 5 words max + exclude_text: Optional[str] = None + + # Advanced options + use_autoprompt: Optional[bool] = None # Not supported in all EXA SDK versions + + +class EXAContentOptions(BaseModel): + """Options for EXA contents API""" + text: Optional[bool] = True # Get full text + highlights: Optional[bool] = False + summary: Optional[bool] = False + + # Livecrawl options + livecrawl: Optional[Literal["always", "fallback", "never"]] = "fallback" + livecrawl_timeout: Optional[int] = 5000 # milliseconds + + # Content format + max_characters: Optional[int] = None + include_html_tags: Optional[bool] = False + subpages: Optional[int] = None # Include subpages + subpage_target: Optional[str] = None + + +# EXA API Response Models +class EXAResult(BaseModel): + """Single search result from EXA""" + id: str + url: str + title: str + score: Optional[float] = None + published_date: Optional[str] = None + author: Optional[str] = None + + +class EXAContent(BaseModel): + """Content fetched from EXA""" + id: str + url: str + title: str + text: Optional[str] = None + highlights: Optional[List[str]] = None + summary: Optional[str] = None + + +# Research Job Models +class Citation(BaseModel): + """A citation with quote and metadata""" + id: str + url: str + title: str + quote: str + published_at: Optional[str] = None + + +class ResearchNote(BaseModel): + """Notes for a research subtopic""" + subtopic: str + summary_markdown: str + citations: List[Citation] + timestamp: datetime + + +class JobProgress(BaseModel): + """Progress tracking for a research job""" + request_id: str + state: JobState + phase: str + title: str + message: str + progress_pct: Optional[float] = None + stats: dict = Field(default_factory=dict) + timestamp: datetime + diff --git a/complex-agents/exa-deep-researcher/agent/storage/__init__.py b/complex-agents/exa-deep-researcher/agent/storage/__init__.py new file mode 100644 index 0000000..caed02d --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/storage/__init__.py @@ -0,0 +1,12 @@ +""" +Storage management for research artifacts + +This package handles: +- Saving reports to disk +- File naming and organization +""" + +from .reports import save_report, generate_filename + +__all__ = ["save_report", "generate_filename"] + diff --git a/complex-agents/exa-deep-researcher/agent/storage/reports.py b/complex-agents/exa-deep-researcher/agent/storage/reports.py new file mode 100644 index 0000000..668008c --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/storage/reports.py @@ -0,0 +1,72 @@ +""" +Report storage to disk + +This module handles saving research reports to the local filesystem +with proper naming and formatting. +""" +import logging +from pathlib import Path +from datetime import datetime + +logger = logging.getLogger("report-storage") + + +def generate_filename(title: str) -> str: + """ + Generate a safe filename for a report + + Creates a timestamp-prefixed filename with sanitized title. + + Args: + title: Report title + + Returns: + Safe filename string (e.g., "2025-01-15_123456_Research_Topic.md") + """ + timestamp = datetime.now().strftime("%Y-%m-%d_%H%M%S") + safe_title = "".join(c for c in title if c.isalnum() or c in (' ', '-', '_')).strip() + safe_title = safe_title[:50] # Limit length + return f"{timestamp}_{safe_title}.md" + + +async def save_report(title: str, report: str, base_dir: Path = None) -> Path: + """ + Save research report to disk + + Creates a reports directory if it doesn't exist and saves the report + as a markdown file with metadata. + + Args: + title: Report title + report: Full report content + base_dir: Base directory for reports (defaults to script directory) + + Returns: + Path to saved report file + + Raises: + Exception: If report cannot be saved + """ + try: + if base_dir is None: + base_dir = Path(__file__).parent.parent.parent + + reports_dir = base_dir / "reports" + reports_dir.mkdir(exist_ok=True) + + filename = generate_filename(title) + filepath = reports_dir / filename + + with open(filepath, 'w', encoding='utf-8') as f: + f.write(f"# {title}\n\n") + f.write(f"*Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}*\n\n") + f.write("---\n\n") + f.write(report) + + logger.info(f"Report saved to: {filepath}") + return filepath + + except Exception as e: + logger.error(f"Error saving report: {e}") + raise + diff --git a/complex-agents/exa-deep-researcher/agent/utils.py b/complex-agents/exa-deep-researcher/agent/utils.py new file mode 100644 index 0000000..28795d0 --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/utils.py @@ -0,0 +1,217 @@ +""" +Utility functions for EXA Deep Researcher +""" +import json +import asyncio +import logging +import re +from typing import Optional, Any, Union, List +from livekit.agents.llm import ChatContext, ChatMessage + +logger = logging.getLogger("exa-utils") + + +async def execute_llm_chat( + llm, + system_prompt: str, + user_prompt: str, + parse_json: bool = False, + extract_json: bool = False, + conversation_history: Optional[list] = None, +) -> Union[str, dict, list]: + """ + Centralized LLM chat execution with response parsing + + Args: + llm: The LLM instance to use + system_prompt: System message defining behavior + user_prompt: User message with the task + parse_json: If True, parse response as JSON + extract_json: If True and parse fails, try to extract JSON from response + + Returns: + String response, or parsed JSON if parse_json=True + """ + messages = [ChatMessage(type="message", role="system", content=[system_prompt])] + + if conversation_history: + recent_history = conversation_history[-10:] + for msg in recent_history: + messages.append(msg) + + messages.append(ChatMessage(type="message", role="user", content=[user_prompt])) + + chat_ctx = ChatContext(messages) + + response = "" + chunk_count = 0 + non_empty_chunks = 0 + + try: + async with llm.chat(chat_ctx=chat_ctx) as stream: + async for chunk in stream: + chunk_count += 1 + if chunk is None: + continue + + content = getattr(chunk.delta, 'content', None) if hasattr(chunk, 'delta') else str(chunk) + + if content is not None: + if content.strip(): + non_empty_chunks += 1 + response += content + + except Exception as e: + logger.error(f"Error streaming LLM response: {e}", exc_info=True) + raise + + response = response.strip() + + if not response: + logger.warning( + f"LLM returned empty response. " + f"Total chunks: {chunk_count}, Non-empty chunks: {non_empty_chunks}, " + f"parse_json={parse_json}, extract_json={extract_json}" + ) + if parse_json: + logger.error("Empty response but JSON parsing was requested - this will fail") + + if not parse_json: + return response + + try: + return json.loads(response) + except json.JSONDecodeError: + if extract_json: + import re + code_block_pattern = r'```(?:json)?\s*\n?(.*?)```' + matches = re.findall(code_block_pattern, response, re.DOTALL | re.IGNORECASE) + for match in matches: + cleaned = match.strip() + if cleaned.startswith('{') or cleaned.startswith('['): + try: + return json.loads(cleaned) + except json.JSONDecodeError: + pass + + logger.warning( + f"Failed to parse JSON from LLM response. " + f"Response length: {len(response)}, " + f"Preview: {response[:500]}..." + ) + return response + + +async def stream_llm_chat(llm, messages: List[ChatMessage]) -> str: + """Stream LLM chat from ChatMessage list""" + chat_ctx = ChatContext(messages) + response = "" + async with llm.chat(chat_ctx=chat_ctx) as stream: + async for chunk in stream: + if not chunk: + continue + content = getattr(chunk.delta, 'content', None) if hasattr(chunk, 'delta') else str(chunk) + if content: + response += content + return response.strip() + + +def format_note_with_citations(subtopic: str, summary: str, citations) -> str: + """Format a research note with citations""" + note_text = f"## {subtopic}\n\n{summary}\n\n" + if citations: + note_text += "**Citations:**\n" + for cit in citations: + note_text += f"- [{cit.title}]({cit.url})\n" + return note_text + + +def build_research_context(original_query: str = "", research_brief: str = "") -> str: + """Build context info string for research subtopics""" + if not (original_query or research_brief): + return "" + + context = "\n\nOVERALL RESEARCH CONTEXT:\n" + if original_query: + context += f"Original Research Goal: {original_query}\n" + if research_brief: + context += f"Research Brief: {research_brief}\n" + context += "\nRemember: This subtopic is part of a larger research effort. Make sure your findings are relevant to the overall goal.\n" + return context + + +def extract_content_from_sources(contents) -> str: + """Extract text content from EXA contents""" + return "\n\n---\n\n".join([ + f"Source: {c.title}\nURL: {c.url}\n\n{c.text or c.summary or '(No content)'}" + for c in contents if c.text or c.summary + ]) + + +def parse_citations_from_response(citation_data: List[dict]) -> List: + """Parse citations from LLM response data""" + from agent.schemas import Citation + + return [ + Citation( + id=str(cit.get("id", "")), + url=cit.get("url", ""), + title=cit.get("title", ""), + quote=cit.get("quote", ""), + published_at=cit.get("published_at") + ) + for cit in citation_data + ] + + +def parse_json_response_with_fallback(response, fallback_value): + """Parse JSON response with fallback if not dict""" + if not isinstance(response, dict): + return fallback_value + return response + + +async def fetch_contents_in_batches(exa_client, urls, content_options, max_concurrent: int = 3): + """Fetch EXA contents in batches to respect concurrency limits""" + all_contents = [] + for i in range(0, len(urls), max_concurrent): + batch_urls = urls[i:i + max_concurrent] + contents = await exa_client.get_contents(batch_urls, content_options) + all_contents.extend(contents) + if i + max_concurrent < len(urls): + await asyncio.sleep(0.5) + return all_contents + + +def format_findings_for_report(notes) -> str: + """ + Format research notes with global citation numbering + + This remaps local [1], [2], [3] in each section to global [1]-[N] + so the LLM sees consistent citation numbers across all sections. + """ + global_sources = [] + url_to_global_idx = {} + + for note in notes: + for citation in note.citations: + if citation.url not in url_to_global_idx: + url_to_global_idx[citation.url] = len(global_sources) + 1 + global_sources.append(citation) + + formatted_sections = [] + + for note in notes: + local_to_global = {} + for local_idx, citation in enumerate(note.citations, 1): + global_idx = url_to_global_idx.get(citation.url) + if global_idx: + local_to_global[local_idx] = global_idx + + summary = note.summary_markdown + for local_idx, global_idx in sorted(local_to_global.items(), reverse=True): + summary = re.sub(rf'\[{local_idx}\]', f'[{global_idx}]', summary) + + formatted_sections.append(f"### {note.subtopic}\n\n{summary}\n\n") + + return "\n\n".join(formatted_sections) diff --git a/complex-agents/exa-deep-researcher/agent/voice/__init__.py b/complex-agents/exa-deep-researcher/agent/voice/__init__.py new file mode 100644 index 0000000..621a2c0 --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/voice/__init__.py @@ -0,0 +1,13 @@ +""" +Voice update management + +This package handles: +- Status queue for voice updates +- Voice synthesis of reports and status +""" + +from .queue import StatusQueue +from .speaker import speak_final_report, truncate_for_speech + +__all__ = ["StatusQueue", "speak_final_report", "truncate_for_speech"] + diff --git a/complex-agents/exa-deep-researcher/agent/voice/queue.py b/complex-agents/exa-deep-researcher/agent/voice/queue.py new file mode 100644 index 0000000..a5aaa14 --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/voice/queue.py @@ -0,0 +1,114 @@ +""" +Status queue management for voice updates + +This module manages a queue of status updates that need to be spoken to the user, +processing them sequentially with natural language generation. +""" +import asyncio +import logging +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from livekit.agents import AgentSession + +logger = logging.getLogger("status-queue") + + +class StatusQueue: + """ + Manages queued status updates for voice synthesis + + This class processes status updates one by one, using the LLM to generate + natural-sounding voice updates from the raw status messages. + """ + + def __init__(self, session: 'AgentSession', userdata): + """ + Initialize status queue + + Args: + session: AgentSession for generating voice replies + userdata: ExaUserData containing the queue + """ + self.session = session + self.userdata = userdata + + def start_processing(self) -> asyncio.Task: + """ + Start processing the status queue + + Returns: + Asyncio task for the processing loop + """ + return asyncio.create_task(self.process_queue()) + + async def process_queue(self): + """ + Process queued status updates one by one + + This method runs in the background, pulling status updates from the queue + and generating natural voice updates using the LLM. + """ + try: + while self.userdata.status_queue: + try: + _ = self.session.userdata + except (RuntimeError, AttributeError): + logger.debug("Session closed, clearing status queue") + self.userdata.status_queue.clear() + break + + status = self.userdata.status_queue.pop(0) + phase = status["phase"] + title = status.get("title", "") + message = status["message"] + + # Generate natural-sounding status update + # The message already contains context like subtopic names, so use it naturally + user_input = f"""Generate a brief, natural spoken status update (one sentence, max 10 seconds when spoken). + +Current activity: {title} +Details: {message} + +Make it conversational and friendly. Don't be robotic. Examples: +- Good: "I'm investigating the company's funding history and found 8 relevant articles." +- Good: "I'm analyzing sources about the founder's background." +- Bad: "Synthesizing findings for subtopic 1" +- Bad: "Currently researching subtopic 2" + +Generate the update:""" + + try: + await self.session.generate_reply(user_input=user_input, allow_interruptions=True) + except (RuntimeError, AttributeError): + logger.debug("Session closed during status queue processing") + self.userdata.status_queue.clear() + break + + await asyncio.sleep(1.0) + + except asyncio.CancelledError: + raise + except Exception as e: + logger.error(f"Error processing status queue: {e}") + finally: + self.userdata.status_task = None + + async def clear(self): + """ + Clear the status queue and cancel any ongoing processing + """ + try: + if self.userdata.status_task and not self.userdata.status_task.done(): + self.userdata.status_task.cancel() + try: + await self.userdata.status_task + except asyncio.CancelledError: + pass + + self.userdata.status_queue.clear() + self.userdata.status_task = None + + except Exception as e: + logger.error(f"Error clearing status queue: {e}") + diff --git a/complex-agents/exa-deep-researcher/agent/voice/speaker.py b/complex-agents/exa-deep-researcher/agent/voice/speaker.py new file mode 100644 index 0000000..ea8b178 --- /dev/null +++ b/complex-agents/exa-deep-researcher/agent/voice/speaker.py @@ -0,0 +1,86 @@ +""" +Voice synthesis for reports + +This module handles speaking the final research report to the user, +including truncation and natural delivery. +""" +import logging +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from livekit.agents import AgentSession + +logger = logging.getLogger("voice-speaker") + + +def truncate_for_speech(report: str, max_chars: int = 4500) -> str: + """ + Truncate report if too long for voice synthesis + + Estimates ~150 words per minute, so ~750 words for 5 minutes = ~4500 chars. + Tries to find a natural breaking point (sentence or paragraph end). + + Args: + report: Full report text + max_chars: Maximum characters to include (default: 4500) + + Returns: + Truncated report text with continuation notice if truncated + """ + if len(report) <= max_chars: + return report + + truncated = report[:max_chars] + last_period = truncated.rfind('.') + last_newline = truncated.rfind('\n') + cut_point = max(last_period, last_newline) + + if cut_point > max_chars * 0.8: + truncated = truncated[:cut_point + 1] + + return truncated + "\n\n[The full report continues in your on-screen document...]" + + +async def speak_final_report(session: 'AgentSession', title: str, report: str): + """ + Read the final report immediately after research completes + + This function creates a natural-sounding voice presentation of the research + findings, truncating if necessary to keep it to a reasonable length. + + Args: + session: AgentSession for voice generation + title: Report title + report: Full report content + """ + try: + _ = session.userdata + + report_text = truncate_for_speech(report) + + user_input = f"""Read this research report to the user naturally and conversationally. + +Report Title: {title} + +Report Content: +{report_text} + +Instructions: +- Read it in a clear, engaging way +- Pause briefly between major sections +- If the report was truncated, mention that the full version is available on screen +- Sound like you're presenting findings, not just reading text +- Keep a natural pace""" + + await session.generate_reply(user_input=user_input, allow_interruptions=True) + + except (RuntimeError, AttributeError) as e: + logger.debug(f"Session closed, skipping final report voice reading: {e}") + except Exception as e: + logger.error(f"Error reading final report: {e}", exc_info=True) + try: + if hasattr(session, '_closed') and not session._closed: + session.say(f"Research complete! The full report on {title} is available on your screen.") + except: + pass + diff --git a/complex-agents/exa-deep-researcher/frontend/.gitignore b/complex-agents/exa-deep-researcher/frontend/.gitignore new file mode 100644 index 0000000..5ef6a52 --- /dev/null +++ b/complex-agents/exa-deep-researcher/frontend/.gitignore @@ -0,0 +1,41 @@ +# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. + +# dependencies +/node_modules +/.pnp +.pnp.* +.yarn/* +!.yarn/patches +!.yarn/plugins +!.yarn/releases +!.yarn/versions + +# testing +/coverage + +# next.js +/.next/ +/out/ + +# production +/build + +# misc +.DS_Store +*.pem + +# debug +npm-debug.log* +yarn-debug.log* +yarn-error.log* +.pnpm-debug.log* + +# env files (can opt-in for committing if needed) +.env* + +# vercel +.vercel + +# typescript +*.tsbuildinfo +next-env.d.ts diff --git a/complex-agents/exa-deep-researcher/frontend/README.md b/complex-agents/exa-deep-researcher/frontend/README.md new file mode 100644 index 0000000..0f220df --- /dev/null +++ b/complex-agents/exa-deep-researcher/frontend/README.md @@ -0,0 +1,65 @@ +# Exa Deep Researcher Frontend + +This is a web interface for the [Exa Deep Researcher](../) agent built with [LiveKit Agents](https://docs.livekit.io/agents). It provides a real-time voice interface with visual research progress tracking using the [LiveKit JavaScript SDK](https://github.com/livekit/client-sdk-js). + +This template is built with Next.js and is free for you to use or modify as you see fit. + +## Features + +- Real-time voice interaction with the research agent +- Visual research progress timeline +- Live streaming of research notes and citations +- Daily statistics tracking (results, sources, domains) +- Responsive split-view interface +- Light/dark theme support + +## Getting started + +> [!TIP] +> Make sure you have the Python agent running first before starting the frontend. See the [main README](../) for agent setup instructions. + +1. Install dependencies: + + ```bash + pnpm install + ``` + +2. Configure environment variables in `.env.local` (copy from `.env.example` if you don't have one): + + ```bash + LIVEKIT_URL=wss://your-project.livekit.cloud + LIVEKIT_API_KEY=your_api_key + LIVEKIT_API_SECRET=your_api_secret + + # Optional - Environment (for dev/prod separation) + DEV=1 # Set DEV to any value to use dev agent name (exa-deep-researcher-dev) + ``` + + > [!NOTE] + > **Dev/Prod Separation**: Set `DEV` to any value to match your Python agent's environment. This ensures both frontend and agent use the same agent name (`exa-deep-researcher-dev` for dev, `exa-deep-researcher` for prod). + +3. Run the development server: + + ```bash + pnpm dev + ``` + +4. Open http://localhost:3000 in your browser + +> [!NOTE] +> The frontend expects the Python agent to be running and will connect to it automatically when you click "Connect to Room". + +## How It Works + +1. User clicks "Connect to Room" to connect to the agent +2. The agent greets the user and is ready to accept research requests +3. User gives a voice command like "Research quantum computing breakthroughs" +4. The frontend receives real-time updates via RPC: + - Status updates showing research phase and progress + - Research notes as the agent discovers information + - Citations with clickable links +5. When research completes, the full report is available on disk + +## Contributing + +This template is open source and we welcome contributions! Please open a PR or issue through GitHub, and don't forget to join us in the [LiveKit Community Slack](https://livekit.io/join-slack)! diff --git a/complex-agents/exa-deep-researcher/frontend/components.json b/complex-agents/exa-deep-researcher/frontend/components.json new file mode 100644 index 0000000..edcaef2 --- /dev/null +++ b/complex-agents/exa-deep-researcher/frontend/components.json @@ -0,0 +1,22 @@ +{ + "$schema": "https://ui.shadcn.com/schema.json", + "style": "new-york", + "rsc": true, + "tsx": true, + "tailwind": { + "config": "", + "css": "src/app/globals.css", + "baseColor": "neutral", + "cssVariables": true, + "prefix": "" + }, + "iconLibrary": "lucide", + "aliases": { + "components": "@/components", + "utils": "@/lib/utils", + "ui": "@/components/ui", + "lib": "@/lib", + "hooks": "@/hooks" + }, + "registries": {} +} diff --git a/complex-agents/exa-deep-researcher/frontend/eslint.config.mjs b/complex-agents/exa-deep-researcher/frontend/eslint.config.mjs new file mode 100644 index 0000000..05e726d --- /dev/null +++ b/complex-agents/exa-deep-researcher/frontend/eslint.config.mjs @@ -0,0 +1,18 @@ +import { defineConfig, globalIgnores } from "eslint/config"; +import nextVitals from "eslint-config-next/core-web-vitals"; +import nextTs from "eslint-config-next/typescript"; + +const eslintConfig = defineConfig([ + ...nextVitals, + ...nextTs, + // Override default ignores of eslint-config-next. + globalIgnores([ + // Default ignores of eslint-config-next: + ".next/**", + "out/**", + "build/**", + "next-env.d.ts", + ]), +]); + +export default eslintConfig; diff --git a/complex-agents/exa-deep-researcher/frontend/next.config.ts b/complex-agents/exa-deep-researcher/frontend/next.config.ts new file mode 100644 index 0000000..e9ffa30 --- /dev/null +++ b/complex-agents/exa-deep-researcher/frontend/next.config.ts @@ -0,0 +1,7 @@ +import type { NextConfig } from "next"; + +const nextConfig: NextConfig = { + /* config options here */ +}; + +export default nextConfig; diff --git a/complex-agents/exa-deep-researcher/frontend/package-lock.json b/complex-agents/exa-deep-researcher/frontend/package-lock.json new file mode 100644 index 0000000..ad053b7 --- /dev/null +++ b/complex-agents/exa-deep-researcher/frontend/package-lock.json @@ -0,0 +1,10253 @@ +{ + "name": "frontend", + "version": "0.1.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "frontend", + "version": "0.1.0", + "dependencies": { + "@livekit/components-react": "^2.9.15", + "@livekit/components-styles": "^1.1.6", + "@livekit/protocol": "^1.42.2", + "class-variance-authority": "^0.7.1", + "clsx": "^2.1.1", + "livekit-client": "^2.15.14", + "livekit-server-sdk": "^2.14.0", + "lucide-react": "^0.552.0", + "next": "16.0.1", + "react": "19.2.0", + "react-dom": "19.2.0", + "streamdown": "^1.4.0", + "tailwind-merge": "^3.3.1" + }, + "devDependencies": { + "@tailwindcss/postcss": "^4", + "@types/node": "^20", + "@types/react": "^19", + 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"^1.42.2", + "clsx": "^2.1.1", + "livekit-client": "^2.15.14", + "livekit-server-sdk": "^2.14.0", + "lucide-react": "^0.552.0", + "next": "16.0.1", + "react": "19.2.0", + "react-dom": "19.2.0", + "streamdown": "^1.4.0", + "tailwind-merge": "^3.3.1" + }, + "devDependencies": { + "@tailwindcss/postcss": "^4", + "@types/node": "^20", + "@types/react": "^19", + "@types/react-dom": "^19", + "eslint": "^9", + "eslint-config-next": "16.0.1", + "tailwindcss": "^4", + "tw-animate-css": "^1.4.0", + "typescript": "^5" + } +} diff --git a/complex-agents/exa-deep-researcher/frontend/postcss.config.mjs b/complex-agents/exa-deep-researcher/frontend/postcss.config.mjs new file mode 100644 index 0000000..61e3684 --- /dev/null +++ b/complex-agents/exa-deep-researcher/frontend/postcss.config.mjs @@ -0,0 +1,7 @@ +const config = { + plugins: { + "@tailwindcss/postcss": {}, + }, +}; + +export default config; diff --git a/complex-agents/exa-deep-researcher/frontend/src/app/api/token/route.ts b/complex-agents/exa-deep-researcher/frontend/src/app/api/token/route.ts new file mode 100644 index 0000000..c69ee49 --- /dev/null +++ b/complex-agents/exa-deep-researcher/frontend/src/app/api/token/route.ts @@ -0,0 +1,80 @@ +import { NextRequest, NextResponse } from "next/server"; +import { AccessToken } from "livekit-server-sdk"; +import { RoomConfiguration, RoomAgentDispatch } from "@livekit/protocol"; + +function generateRoomName(): string { + return `exa-research-${Math.random().toString(36).substring(2, 11)}`; +} + +function getAgentName(): string { + return process.env.DEV ? "exa-deep-researcher-dev" : "exa-deep-researcher"; +} + +export async function POST(request: NextRequest) { + try { + const { identity } = await request.json(); + + const livekitUrl = process.env.LIVEKIT_URL; + const livekitApiKey = process.env.LIVEKIT_API_KEY; + const livekitApiSecret = process.env.LIVEKIT_API_SECRET; + + if (!livekitUrl || !livekitApiKey || !livekitApiSecret) { + return NextResponse.json( + { + error: + "LiveKit credentials not configured. Please set LIVEKIT_URL, LIVEKIT_API_KEY, and LIVEKIT_API_SECRET in your environment variables.", + }, + { status: 500 } + ); + } + + const roomName = generateRoomName(); + const agentName = getAgentName(); + + console.log( + `[Token API] Using agent name: ${agentName} (DEV=${ + process.env.DEV || "not set" + })` + ); + + const at = new AccessToken(livekitApiKey, livekitApiSecret, { + identity: identity || "user", + }); + + const roomConfig = new RoomConfiguration({ + agents: [ + new RoomAgentDispatch({ + agentName: agentName, + }), + ], + }); + + at.addGrant({ + room: roomName, + roomJoin: true, + canPublish: true, + canSubscribe: true, + }); + + // Set room configuration for agent dispatch + at.roomConfig = roomConfig; + + const token = await at.toJwt(); + + return NextResponse.json({ + token, + serverUrl: livekitUrl, + room: roomName, + }); + } catch (error) { + console.error("Token generation error:", error); + return NextResponse.json( + { + error: + "Failed to generate token: " + + (error instanceof Error ? error.message : String(error)), + }, + { status: 500 } + ); + } +} diff --git a/complex-agents/exa-deep-researcher/frontend/src/app/favicon.ico b/complex-agents/exa-deep-researcher/frontend/src/app/favicon.ico new file mode 100644 index 0000000..718d6fe Binary files /dev/null and b/complex-agents/exa-deep-researcher/frontend/src/app/favicon.ico differ diff --git a/complex-agents/exa-deep-researcher/frontend/src/app/globals.css b/complex-agents/exa-deep-researcher/frontend/src/app/globals.css new file mode 100644 index 0000000..713c397 --- /dev/null +++ b/complex-agents/exa-deep-researcher/frontend/src/app/globals.css @@ -0,0 +1,317 @@ +@import "tailwindcss"; +@import "tw-animate-css"; + +@custom-variant dark (&:is(.dark *)); + +@theme inline { + --color-background: var(--background); + --color-foreground: var(--foreground); + --color-bg0: var(--bg0); + --color-bg1: var(--bg1); + --color-bg2: var(--bg2); + --color-bg3: var(--bg3); + --color-bgSerious1: var(--bgSerious1); + --color-bgSerious2: var(--bgSerious2); + --color-bgAccent1: var(--bgAccent1); + --color-bgAccent2: var(--bgAccent2); + --color-bgSuccess1: var(--bgSuccess1); + --color-bgSuccess2: var(--bgSuccess2); + --color-bgModerate1: var(--bgModerate1); + --color-bgModerate2: var(--bgModerate2); + --color-fg0: var(--fg0); + --color-fg1: var(--fg1); + --color-fg2: var(--fg2); + --color-fg3: var(--fg3); + --color-fg4: var(--fg4); + --color-fg5: var(--fg5); + --color-fgAccent1: var(--fgAccent1); + --color-fgAccent2: var(--fgAccent2); + --color-fgSuccess: var(--fgSuccess); + --color-fgModerate: var(--fgModerate); + --color-fgSerious1: var(--fgSerious1); + --color-fgSerious2: var(--fgSerious2); + --color-separator1: var(--separator1); + --color-separator2: var(--separator2); + --color-separatorAccent: var(--separatorAccent); + --color-separatorSuccess: var(--separatorSuccess); + --color-separatorModerate: var(--separatorModerate); + --color-separatorSerious1: var(--separatorSerious1); + --color-separatorSerious2: var(--separatorSerious2); + --color-ring: var(--ring); + --font-sans: var(--font-geist-sans); + --font-mono: var(--font-geist-mono); + --color-sidebar-ring: var(--sidebar-ring); + --color-sidebar-border: var(--sidebar-border); + --color-sidebar-accent-foreground: var(--sidebar-accent-foreground); + --color-sidebar-accent: var(--sidebar-accent); + --color-sidebar-primary-foreground: var(--sidebar-primary-foreground); + --color-sidebar-primary: var(--sidebar-primary); + --color-sidebar-foreground: var(--sidebar-foreground); + --color-sidebar: var(--sidebar); + --color-chart-5: var(--chart-5); + --color-chart-4: var(--chart-4); + --color-chart-3: var(--chart-3); + --color-chart-2: var(--chart-2); + --color-chart-1: var(--chart-1); + --color-input: var(--input); + --color-border: var(--border); + --color-destructive: var(--destructive); + --color-accent-foreground: var(--accent-foreground); + --color-accent: var(--accent); + --color-muted-foreground: var(--muted-foreground); + --color-muted: var(--muted); + --color-secondary-foreground: var(--secondary-foreground); + --color-secondary: var(--secondary); + --color-primary-foreground: var(--primary-foreground); + --color-primary: var(--primary); + --color-popover-foreground: var(--popover-foreground); + --color-popover: var(--popover); + --color-card-foreground: var(--card-foreground); + --color-card: var(--card); + --radius-sm: calc(var(--radius) - 4px); + --radius-md: calc(var(--radius) - 2px); + --radius-lg: var(--radius); + --radius-xl: calc(var(--radius) + 4px); +} + +:root { + --radius: 0.625rem; + --bg0: #000000; + --bg1: #070707; + --bg2: #131313; + --bg3: #1f1f1f; + --bgSerious1: #1f0e0b; + --bgSerious2: #421510; + --bgAccent1: #051518; + --bgAccent2: #012a32; + --bgSuccess1: #001905; + --bgSuccess2: #003213; + --bgModerate1: #1a0e04; + --bgModerate2: #3f2208; + --fg0: #ffffff; + --fg1: #cccccc; + --fg2: #b2b2b2; + --fg3: #999999; + --fg4: #666666; + --fg5: #555555; + --fgAccent1: #1fd5f9; + --fgAccent2: #15889f; + --fgSuccess: #23de6b; + --fgModerate: #ffb752; + --fgSerious1: #ff7566; + --fgSerious2: #791b11; + --separator1: #202020; + --separator2: #30302f; + --separatorAccent: #012a32; + --separatorSuccess: #003213; + --separatorModerate: #3f2208; + --separatorSerious1: #421510; + --separatorSerious2: #791b11; + --ring: oklch(0.708 0 0); + --background: oklch(1 0 0); + --foreground: oklch(0.145 0 0); + --card: oklch(1 0 0); + --card-foreground: oklch(0.145 0 0); + --popover: oklch(1 0 0); + --popover-foreground: oklch(0.145 0 0); + --primary: oklch(0.205 0 0); + --primary-foreground: oklch(0.985 0 0); + --secondary: oklch(0.97 0 0); + --secondary-foreground: oklch(0.205 0 0); + --muted: oklch(0.97 0 0); + --muted-foreground: oklch(0.556 0 0); + --accent: oklch(0.97 0 0); + --accent-foreground: oklch(0.205 0 0); + --destructive: oklch(0.577 0.245 27.325); + --border: oklch(0.922 0 0); + --input: oklch(0.922 0 0); + --chart-1: oklch(0.646 0.222 41.116); + --chart-2: oklch(0.6 0.118 184.704); + --chart-3: oklch(0.398 0.07 227.392); + --chart-4: oklch(0.828 0.189 84.429); + --chart-5: oklch(0.769 0.188 70.08); + --sidebar: oklch(0.985 0 0); + --sidebar-foreground: oklch(0.145 0 0); + --sidebar-primary: oklch(0.205 0 0); + --sidebar-primary-foreground: oklch(0.985 0 0); + --sidebar-accent: oklch(0.97 0 0); + --sidebar-accent-foreground: oklch(0.205 0 0); + --sidebar-border: oklch(0.922 0 0); + --sidebar-ring: oklch(0.708 0 0); +} + +body { + background: var(--bg0); + color: var(--fg0); + font-family: var(--font-geist-sans), system-ui, -apple-system, sans-serif; +} + +/* Ensure dark mode for LiveKit components */ +[data-lk-theme="default"] { + color-scheme: dark; +} + +/* Smooth transitions */ +@layer base { + * { + @apply border-separator1; + } + + :root { + transition: background-color 0.3s cubic-bezier(0.4, 0, 0.2, 1), + color 0.3s cubic-bezier(0.4, 0, 0.2, 1); + } + + @media (prefers-reduced-motion: reduce) { + :root { + transition: none; + } + } +} + +/* Make Chat component fill container */ +.lk-chat, +.lk-chat-wrapper, +[class*="lk-chat"] { + width: 100% !important; + max-width: 100% !important; +} + +.lk-chat * { + max-width: 100%; +} + +/* Cyber activity panel animations */ +@keyframes scroll-up { + from { + transform: translateY(100%); + opacity: 0; + } + to { + transform: translateY(0); + opacity: 1; + } +} + +@keyframes scan-line { + 0% { + transform: translateY(-100%); + opacity: 0.3; + } + 50% { + opacity: 0.6; + } + 100% { + transform: translateY(100%); + opacity: 0.3; + } +} + +@keyframes glow-pulse { + 0%, + 100% { + box-shadow: 0 0 5px var(--fgAccent1), 0 0 10px var(--fgAccent1); + } + 50% { + box-shadow: 0 0 10px var(--fgAccent1), 0 0 20px var(--fgAccent1), + 0 0 30px var(--fgAccent1); + } +} + +.cyber-scan-line { + animation: scan-line 3s linear infinite; +} + +.cyber-glow { + animation: glow-pulse 2s ease-in-out infinite; +} + +.dark { + --background: oklch(0.145 0 0); + --foreground: oklch(0.985 0 0); + --card: oklch(0.205 0 0); + --card-foreground: oklch(0.985 0 0); + --popover: oklch(0.205 0 0); + --popover-foreground: oklch(0.985 0 0); + --primary: oklch(0.922 0 0); + --primary-foreground: oklch(0.205 0 0); + --secondary: oklch(0.269 0 0); + --secondary-foreground: oklch(0.985 0 0); + --muted: oklch(0.269 0 0); + --muted-foreground: oklch(0.708 0 0); + --accent: oklch(0.269 0 0); + --accent-foreground: oklch(0.985 0 0); + --destructive: oklch(0.704 0.191 22.216); + --border: oklch(1 0 0 / 10%); + --input: oklch(1 0 0 / 15%); + --ring: oklch(0.556 0 0); + --chart-1: oklch(0.488 0.243 264.376); + --chart-2: oklch(0.696 0.17 162.48); + --chart-3: oklch(0.769 0.188 70.08); + --chart-4: oklch(0.627 0.265 303.9); + --chart-5: oklch(0.645 0.246 16.439); + --sidebar: oklch(0.205 0 0); + --sidebar-foreground: oklch(0.985 0 0); + --sidebar-primary: oklch(0.488 0.243 264.376); + --sidebar-primary-foreground: oklch(0.985 0 0); + --sidebar-accent: oklch(0.269 0 0); + --sidebar-accent-foreground: oklch(0.985 0 0); + --sidebar-border: oklch(1 0 0 / 10%); + --sidebar-ring: oklch(0.556 0 0); +} + +@layer base { + * { + @apply border-border outline-ring/50; + } + body { + @apply bg-background text-foreground; + } + + /* BarVisualizer styling */ + .lk-audio-bar { + min-height: 4px; + background-color: var(--color-fg3); + opacity: 0.3; + } + + .lk-audio-bar.lk-highlighted { + background-color: var(--color-fgAccent1); + opacity: 1; + } + + /* Agent visualizer styling */ + .agent-visualizer { + border-radius: 8px; + padding: 16px; + display: flex; + justify-content: center; + align-items: center; + gap: 8px; + height: 100%; + width: 100%; + background: transparent !important; + } + + /* Remove background from BarVisualizer component itself */ + .agent-visualizer, + .agent-visualizer > *, + [class*="lk-bar-visualizer"], + [class*="lk-audio-visualizer"] { + background: transparent !important; + } + + .agent-visualizer > .lk-audio-bar { + width: 40px; + min-height: 16px; + background-color: #1fd5f9 !important; + border-radius: 4px; + transition: height 0.1s ease; + opacity: 0.6; + } + + .agent-visualizer > .lk-audio-bar[data-lk-highlighted="true"] { + opacity: 1 !important; + background-color: #1fd5f9 !important; + } +} diff --git a/complex-agents/exa-deep-researcher/frontend/src/app/layout.tsx b/complex-agents/exa-deep-researcher/frontend/src/app/layout.tsx new file mode 100644 index 0000000..e0e03c4 --- /dev/null +++ b/complex-agents/exa-deep-researcher/frontend/src/app/layout.tsx @@ -0,0 +1,41 @@ +import type { Metadata } from "next"; +import { Geist, Geist_Mono } from "next/font/google"; +import "./globals.css"; + +const geistSans = Geist({ + variable: "--font-geist-sans", + subsets: ["latin"], +}); + +const geistMono = Geist_Mono({ + variable: "--font-geist-mono", + subsets: ["latin"], +}); + +export const metadata: Metadata = { + title: "Exa + LiveKit Deep Researcher - Voice AI Research Assistant", + description: "Autonomous deep research agent with iterative planning, real-time UI updates via RPC streaming, and intelligent query clarification. Demonstrates how voice agents can conduct comprehensive multi-step research workflows.", + keywords: ["voice AI", "research assistant", "Exa", "LiveKit", "deep research", "AI agent"], + authors: [{ name: "LiveKit" }], + openGraph: { + title: "Exa + LiveKit Deep Researcher", + description: "Voice AI Research Assistant - Autonomous deep research with real-time updates", + type: "website", + }, +}; + +export default function RootLayout({ + children, +}: Readonly<{ + children: React.ReactNode; +}>) { + return ( + + + {children} + + + ); +} diff --git a/complex-agents/exa-deep-researcher/frontend/src/app/page.tsx b/complex-agents/exa-deep-researcher/frontend/src/app/page.tsx new file mode 100644 index 0000000..a2df639 --- /dev/null +++ b/complex-agents/exa-deep-researcher/frontend/src/app/page.tsx @@ -0,0 +1,228 @@ +"use client"; + +import { + LiveKitRoom, + RoomAudioRenderer, + TrackToggle, + useRoomContext, + useVoiceAssistant, +} from "@livekit/components-react"; +import { Track } from "livekit-client"; +import "@livekit/components-styles"; +import { useState } from "react"; +import { ResearchPanel } from "@/components/ResearchPanel"; +import { Search, Volume2, VolumeX } from "lucide-react"; + +export default function Home() { + const [token, setToken] = useState(""); + const [serverUrl, setServerUrl] = useState(""); + const [connected, setConnected] = useState(false); + const [error, setError] = useState(""); + const [connecting, setConnecting] = useState(false); + + const connectToRoom = async () => { + setConnecting(true); + setError(""); + try { + const response = await fetch("/api/token", { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ + identity: "user-" + Date.now(), + }), + }); + const data = await response.json(); + if (data.error) { + setError(data.error); + setConnecting(false); + return; + } + setToken(data.token); + setServerUrl(data.serverUrl); + setConnected(true); + setError(""); + } catch (err) { + setError("Failed to connect. Please check your server configuration."); + console.error(err); + setConnecting(false); + } + }; + + if (!connected) { + return ( +
+
+
+ {/* Icon */} +
+ +
+ + {/* Title */} +
+

+ Exa + LiveKit Deep Researcher +

+

Voice AI Research Assistant

+

+ Demonstrates autonomous deep research with iterative planning, + real-time UI updates via RPC streaming, and intelligent query + clarification—showcasing how voice agents can conduct + comprehensive multi-step research workflows. +

+
+ + {/* Local Development Notice */} +
+

+ + Local development: + {" "} + Make sure to run the Python agent first with{" "} + + python agent.py dev + +

+
+ + {/* Documentation Links */} + + + {/* Connect Button */} + + + {/* Error Message */} + {error && ( +
+ {error} +
+ )} +
+
+
+ ); + } + + return ( +
+ + + {/* Research Panel */} +
+ +
+ + {/* Audio Renderer */} + + +
+ ); +} + +function HeaderControls() { + const room = useRoomContext(); + const { audioTrack } = useVoiceAssistant(); + + // Derive mute state from publication properties + const agentMuted = audioTrack?.publication + ? !(audioTrack.publication.isSubscribed && audioTrack.publication.isEnabled) + : false; + + const toggleAgentMute = async () => { + if (!room || !audioTrack?.publication) return; + + // Get the remote participant and their audio track publication + const remoteParticipants = Array.from(room.remoteParticipants.values()); + const agentParticipant = remoteParticipants.find((p) => p.isAgent); + + if (agentParticipant) { + // Find the audio track publication + const audioPublications = Array.from( + agentParticipant.audioTrackPublications.values() + ); + const audioPublication = audioPublications.find( + (pub) => pub.trackSid === audioTrack.publication.trackSid + ); + + if (audioPublication) { + // Use setEnabled() for subscriber-side mute (proper LiveKit API) + // Toggle: if currently enabled, disable it; if disabled, enable it + audioPublication.setEnabled(agentMuted); + } + } + }; + + return ( +
+
+

Exa + LiveKit Deep Researcher

+
+ Voice AI Research Assistant +
+
+ + + +
+
+ ); +} diff --git a/complex-agents/exa-deep-researcher/frontend/src/components/ActivityTerminal.tsx b/complex-agents/exa-deep-researcher/frontend/src/components/ActivityTerminal.tsx new file mode 100644 index 0000000..26e1a5a --- /dev/null +++ b/complex-agents/exa-deep-researcher/frontend/src/components/ActivityTerminal.tsx @@ -0,0 +1,144 @@ +interface ActivityTerminalProps { + statusHistory: Array<{ + phase: string; + title: string; + message: string; + stats?: Record; + ts: number; + }>; + rollingTextIndex: number; +} + +function truncate(text: string, maxLength: number): string { + if (text.length <= maxLength) return text; + return text.slice(0, maxLength - 3) + "..."; +} + +function formatStatusMessage( + status: ActivityTerminalProps["statusHistory"][0] +): string { + const { phase, title, message, stats } = status; + + // Show more informative messages based on phase and stats + // Check specific actions first, then fall back to general topic display + if (phase === "researching") { + // Check specific actions first (these are more informative) + if (title.includes("Synthesizing")) { + return `Analyzing ${ + stats?.sources_fetched || stats?.sources || "" + } sources`; + } + if (title.includes("Gathering")) { + return `Found ${stats?.results || stats?.sources || ""} sources`; + } + if (title.includes("Starting")) { + const topic = stats?.subtopic || stats?.topic; + return topic ? `Starting: ${truncate(String(topic), 35)}` : message; + } + // Fallback to general topic display + if (stats?.subtopic) { + return `Researching: ${truncate(String(stats.subtopic), 40)}`; + } + if (stats?.topic) { + return `Researching: ${truncate(String(stats.topic), 40)}`; + } + if (message && message.length > 0) { + return truncate(message, 50); + } + } + + if (phase === "compressing") { + const notesCount = stats?.notes_count || ""; + return notesCount ? `Consolidating ${notesCount} topics` : message; + } + + if (phase === "reporting") { + return message || title; + } + + if (phase === "briefing") { + return message || title; + } + + return message || title; +} + +export function ActivityTerminal({ + statusHistory, + rollingTextIndex, +}: ActivityTerminalProps) { + if (statusHistory.length <= 1) return null; + + return ( +
+
+
+
+ +
+
+
+
+
+
+
+
+
+ ▶ + + {(() => { + const recent = statusHistory.slice(-5).reverse(); + const active = recent[rollingTextIndex] || recent[0]; + if (!active) return "STANDBY"; + const message = formatStatusMessage(active); + return `[${active.phase.toUpperCase()}] ${message}`; + })()} + +
+
+
+
+ +
+ {statusHistory + .slice(-10) + .reverse() + .map((status, idx) => { + const message = formatStatusMessage(status); + return ( +
+ + {new Date(status.ts * 1000).toLocaleTimeString()} + + + + [{status.phase.toUpperCase()}] + {" "} + {message} + +
+ ); + })} +
+
+ ); +} diff --git a/complex-agents/exa-deep-researcher/frontend/src/components/ClarificationCard.tsx b/complex-agents/exa-deep-researcher/frontend/src/components/ClarificationCard.tsx new file mode 100644 index 0000000..a369402 --- /dev/null +++ b/complex-agents/exa-deep-researcher/frontend/src/components/ClarificationCard.tsx @@ -0,0 +1,41 @@ +import { HelpCircle } from "lucide-react"; + +interface Clarification { + question: string; + originalQuery: string; +} + +interface ClarificationCardProps { + clarification: Clarification; +} + +export function ClarificationCard({ clarification }: ClarificationCardProps) { + return ( +
+
+
+
+ +
+
+
+

+ Clarification Needed +

+
+

+ {clarification.question} +

+
+ Original query:{" "} + + {clarification.originalQuery} + +
+
+
+
+
+ ); +} + diff --git a/complex-agents/exa-deep-researcher/frontend/src/components/EmptyState.tsx b/complex-agents/exa-deep-researcher/frontend/src/components/EmptyState.tsx new file mode 100644 index 0000000..457f786 --- /dev/null +++ b/complex-agents/exa-deep-researcher/frontend/src/components/EmptyState.tsx @@ -0,0 +1,204 @@ +import { BarVisualizer, useVoiceAssistant } from "@livekit/components-react"; +import { + Search, + Sparkles, + TrendingUp, + Brain, + Rocket, + Code, + Lightbulb, +} from "lucide-react"; +import { useState } from "react"; + +const RESEARCH_SUGGESTIONS = [ + { + icon: Brain, + title: "AI & Machine Learning", + topic: "Latest advances in large language models and transformers", + color: "fgAccent1", + bgColor: "bgAccent1", + }, + { + icon: Rocket, + title: "Space Technology", + topic: "Recent breakthroughs in commercial space exploration", + color: "fgModerate", + bgColor: "bgModerate1", + }, + { + icon: Code, + title: "Web Development", + topic: "Modern frontend frameworks and their performance comparisons", + color: "fgSuccess", + bgColor: "bgSuccess1", + }, + { + icon: TrendingUp, + title: "Quantum Computing", + topic: "Current state of quantum computing applications", + color: "fgAccent2", + bgColor: "bgAccent2", + }, + { + icon: Lightbulb, + title: "Clean Energy", + topic: "Innovations in renewable energy and battery technology", + color: "fgCaution1", + bgColor: "bgCaution1", + }, + { + icon: Sparkles, + title: "Biotechnology", + topic: "CRISPR and gene editing recent developments", + color: "fgAccent1", + bgColor: "bgAccent1", + }, +]; + +export function EmptyState() { + // Use useVoiceAssistant to get agent audio track + const { state: agentState, audioTrack } = useVoiceAssistant(); + const [hoveredIndex, setHoveredIndex] = useState(null); + + return ( +
+
+ {/* Icon */} +
+ +
+ + {/* Audio Visualizer */} +
+ {audioTrack ? ( + + ) : ( +
+
+ Waiting for agent to join... +
+
+ )} +
+ + {/* Text Content */} +
+

+ Ready to research +

+

+ Start a research job using voice commands, or try one of these + topics: +

+
+ + {/* Research Suggestions */} +
+ {RESEARCH_SUGGESTIONS.map((suggestion, index) => { + const Icon = suggestion.icon; + const isHovered = hoveredIndex === index; + + return ( + + ); + })} +
+ + {/* Additional Hint */} +
+
+ Click any suggestion to see the voice command +
+ + {/* Documentation Links */} + +
+
+ ); +} diff --git a/complex-agents/exa-deep-researcher/frontend/src/components/ReportCard.tsx b/complex-agents/exa-deep-researcher/frontend/src/components/ReportCard.tsx new file mode 100644 index 0000000..9f54ebe --- /dev/null +++ b/complex-agents/exa-deep-researcher/frontend/src/components/ReportCard.tsx @@ -0,0 +1,141 @@ +import { Streamdown } from "streamdown"; +import { useEffect, useRef } from "react"; + +interface Report { + reportTitle: string; + reportContent: string; +} + +interface ReportCardProps { + report: Report; +} + +/** + * Converts plain URLs in the report content to clickable markdown links. + * Also makes inline citation numbers clickable, linking to their sources. + * + * Handles multiple source formats: + * 1. Inline citations: [1] -> clickable anchor link to #source-1 + * 2. Source with title: [1] Title: https://url.com -> [1] [Title](url) + * 3. Source no title: [1]: https://url.com or [1] : https://url.com -> [1] [url](url) + * 4. Source direct URL: [1] https://url.com -> [1] [url](url) + */ +function makeSourcesClickable(content: string): string { + let processedContent = content; + + // First pass: Handle sources with empty/no title: [1]: https://url.com or [1] : https://url.com + const sourceNoTitlePattern = /(\[\d+\])\s*:\s*(https?:\/\/[^\s]+)/g; + processedContent = processedContent.replace( + sourceNoTitlePattern, + (match, number, url) => { + const sourceNum = number.match(/\d+/)?.[0]; + return `${number} [${url}](${url})`; + } + ); + + // Second pass: Handle sources with title: [1] Title: https://url.com + const sourceWithTitlePattern = + /(\[\d+\])\s+([^:\n]+?):\s*(https?:\/\/[^\s]+)/g; + processedContent = processedContent.replace( + sourceWithTitlePattern, + (match, number, title, url) => { + const sourceNum = number.match(/\d+/)?.[0]; + const cleanTitle = title.trim(); + + // If title is empty or just whitespace, use the URL + if (!cleanTitle) { + return `${number} [${url}](${url})`; + } + + return `${number} [${cleanTitle}](${url})`; + } + ); + + // Third pass: Handle direct URLs without colon: [1] https://url.com (that weren't caught above) + const sourceDirectUrlPattern = /(\[\d+\])\s+(https?:\/\/[^\s<]+)/g; + processedContent = processedContent.replace( + sourceDirectUrlPattern, + (match, number, url) => { + // Skip if this was already processed (contains ${number} [${url}](${url})`; + } + ); + + // Make inline citations clickable (but not in the Sources section) + const sourcesIndex = processedContent.indexOf("\nSources\n"); + + if (sourcesIndex !== -1) { + const mainContent = processedContent.substring(0, sourcesIndex); + const sourcesSection = processedContent.substring(sourcesIndex); + + // Pattern to match inline citations like [1], [2], etc. + const inlineCitationPattern = /(? { + const sourceNum = match.match(/\d+/)?.[0]; + return `[${match}](#source-${sourceNum})`; + } + ); + + processedContent = processedMain + sourcesSection; + } + + return processedContent; +} + +export function ReportCard({ report }: ReportCardProps) { + const contentRef = useRef(null); + + // Process the report content to make sources clickable + const processedContent = makeSourcesClickable(report.reportContent); + + // Add target="_blank" and rel="noopener noreferrer" to all links for security + useEffect(() => { + if (contentRef.current) { + const links = contentRef.current.querySelectorAll("a"); + links.forEach((link) => { + link.setAttribute("target", "_blank"); + link.setAttribute("rel", "noopener noreferrer"); + }); + } + }, [processedContent]); + + return ( +
+
+

{report.reportTitle}

+
+ +
+ {report.reportContent ? ( +
+
+ {processedContent} +
+
+ ) : ( +
+

+ Report content not available. Check console for details. +

+
+ )} +
+
+ ); +} diff --git a/complex-agents/exa-deep-researcher/frontend/src/components/ResearchNotesList.tsx b/complex-agents/exa-deep-researcher/frontend/src/components/ResearchNotesList.tsx new file mode 100644 index 0000000..8cf1819 --- /dev/null +++ b/complex-agents/exa-deep-researcher/frontend/src/components/ResearchNotesList.tsx @@ -0,0 +1,143 @@ +import { useState } from "react"; + +interface ResearchNote { + subtopic: string; + summaryMarkdown: string; + citations: Array<{ + id: string; + url: string; + title: string; + quote: string; + }>; + ts: number; +} + +interface ResearchNotesListProps { + notes: ResearchNote[]; +} + +function truncate(text: string, maxLength: number): string { + if (text.length <= maxLength) return text; + return text.slice(0, maxLength - 3) + "..."; +} + +export function ResearchNotesList({ notes }: ResearchNotesListProps) { + const [expandedNotes, setExpandedNotes] = useState>(new Set()); + + if (notes.length === 0) return null; + + const toggleExpanded = (noteKey: string) => { + setExpandedNotes((prev) => { + const newSet = new Set(prev); + if (newSet.has(noteKey)) { + newSet.delete(noteKey); + } else { + newSet.add(noteKey); + } + return newSet; + }); + }; + + return ( +
+
+

Research Findings

+
+ {notes.length} {notes.length === 1 ? "finding" : "findings"} +
+
+
+ {notes + .filter((note) => { + const title = note.subtopic?.toLowerCase().trim() || ""; + return ( + title && title !== "untitled" && !title.startsWith("untitled") + ); + }) + .map((note, idx) => { + const noteKey = `${note.subtopic}-${note.ts}-${idx}`; + const isExpanded = expandedNotes.has(noteKey); + const validCitations = note.citations.filter((c) => { + const title = c.title?.toLowerCase().trim() || ""; + return ( + title && title !== "untitled" && !title.startsWith("untitled") + ); + }); + + const citationsToShow = isExpanded + ? validCitations + : validCitations.slice(0, 3); + + return ( +
+
+
+
+

+ {note.subtopic} +

+ +
+
+ {validCitations.length > 0 && ( +
+ {citationsToShow.map((cit) => ( + + {truncate(cit.title, 30)} + + ))} + {validCitations.length > 3 && ( + + )} +
+ )} +
+
+
+ ); + })} +
+
+ ); +} diff --git a/complex-agents/exa-deep-researcher/frontend/src/components/ResearchPanel.tsx b/complex-agents/exa-deep-researcher/frontend/src/components/ResearchPanel.tsx new file mode 100644 index 0000000..f05a2d8 --- /dev/null +++ b/complex-agents/exa-deep-researcher/frontend/src/components/ResearchPanel.tsx @@ -0,0 +1,147 @@ +import { ResearchPlanSteps } from "./ResearchPlanSteps"; +import { ResearchStatsHeader } from "./ResearchStatsHeader"; +import { ClarificationCard } from "./ClarificationCard"; +import { ReportCard } from "./ReportCard"; +import { StatusCard } from "./StatusCard"; +import { ActivityTerminal } from "./ActivityTerminal"; +import { ResearchNotesList } from "./ResearchNotesList"; +import { EmptyState } from "./EmptyState"; +import { useResearchStatus } from "../hooks/useResearchStatus"; +import { useResearchPlan } from "../hooks/useResearchPlan"; +import { useResearchStats } from "../hooks/useResearchStats"; +import { useRollingText } from "../hooks/useRollingText"; + +const PHASE_CONFIG = { + briefing: { + label: "Briefing", + icon: "file-text", + bg: "bg-bgAccent1/30", + border: "border-separatorAccent", + text: "text-fgAccent1", + progressBg: "bg-fgAccent1", + dot: "bg-fgAccent1", + }, + researching: { + label: "Researching", + icon: "search", + bg: "bg-bgAccent1/30", + border: "border-separatorAccent", + text: "text-fgAccent1", + progressBg: "bg-fgAccent1", + dot: "bg-fgAccent1", + }, + compressing: { + label: "Compressing", + icon: "package", + bg: "bg-bgModerate1/30", + border: "border-separatorModerate", + text: "text-fgModerate", + progressBg: "bg-fgModerate", + dot: "bg-fgModerate", + }, + reporting: { + label: "Reporting", + icon: "file-text", + bg: "bg-bgSuccess1/30", + border: "border-separatorSuccess", + text: "text-fgSuccess", + progressBg: "bg-fgSuccess", + dot: "bg-fgSuccess", + }, + clarifying: { + label: "Clarifying", + icon: "help-circle", + bg: "bg-bgAccent1/30", + border: "border-separatorAccent", + text: "text-fgAccent1", + progressBg: "bg-fgAccent1", + dot: "bg-fgAccent1", + }, +}; + +export function ResearchPanel() { + const { + currentStatus, + statusHistory, + notes, + report, + researchTitle, + clarification, + reset, + } = useResearchStatus(); + + const planSteps = useResearchPlan( + currentStatus, + statusHistory, + researchTitle, + notes, + report, + clarification + ); + + const stats = useResearchStats(notes, report); + const rollingTextIndex = useRollingText(statusHistory); + + const phaseConfig = currentStatus ? PHASE_CONFIG[currentStatus.phase] : null; + + return ( +
+ 0 || !!report || statusHistory.length > 0} + /> + +
+ {clarification && currentStatus?.phase === "clarifying" && ( + + )} + + {statusHistory.length > 0 && planSteps.length > 0 && ( + + )} + + {report && ( +
+ +
+ )} + +
+ {currentStatus && phaseConfig && ( +
+ + +
+ )} + + {notes.length > 0 && } + + {!currentStatus && notes.length === 0 && !report && } +
+
+
+ ); +} diff --git a/complex-agents/exa-deep-researcher/frontend/src/components/ResearchPlanSteps.tsx b/complex-agents/exa-deep-researcher/frontend/src/components/ResearchPlanSteps.tsx new file mode 100644 index 0000000..b015cf3 --- /dev/null +++ b/complex-agents/exa-deep-researcher/frontend/src/components/ResearchPlanSteps.tsx @@ -0,0 +1,125 @@ +import { HelpCircle, FileText, Search, Package, CheckCircle2, Loader2 } from "lucide-react"; + +interface PlanStep { + id: string; + label: string; + icon: string; + status: "pending" | "active" | "completed"; + description?: string; + timestamp?: number; +} + +interface ResearchPlanStepsProps { + steps: PlanStep[]; + title?: string | null; +} + +// Map icon names to Lucide React components +const iconMap: Record = { + clarification: HelpCircle, + planning: FileText, + topic: Search, + organizing: Package, + report: FileText, +}; + +function truncate(text: string, maxLength: number): string { + if (text.length <= maxLength) return text; + return text.slice(0, maxLength - 3) + "..."; +} + +export function ResearchPlanSteps({ steps, title }: ResearchPlanStepsProps) { + if (steps.length === 0) return null; + + return ( +
+
+
+ {title && ( +

{title}

+ )} +
+ {steps.map((step, idx) => { + const isLast = idx === steps.length - 1; + const isActive = step.status === "active"; + const isCompleted = step.status === "completed"; + const prevStep = idx > 0 ? steps[idx - 1] : null; + const prevCompleted = prevStep?.status === "completed"; + + const IconComponent = iconMap[step.icon] || FileText; + + return ( +
+
+ {/* Icon container */} +
+
+ {isActive ? ( + + ) : ( + + )} +
+ {/* Active indicator (small pulse dot) */} + {isActive && ( +
+ )} + {/* Completed checkmark */} + {isCompleted && ( +
+ +
+ )} +
+ + {/* Text container with fixed height */} +
+

+ {truncate(step.label, 35)} +

+ {step.description && ( +

+ {step.description} +

+ )} +
+
+ + {/* Connector */} + {!isLast && ( +
+ )} +
+ ); + })} +
+
+
+
+ ); +} diff --git a/complex-agents/exa-deep-researcher/frontend/src/components/ResearchStatsHeader.tsx b/complex-agents/exa-deep-researcher/frontend/src/components/ResearchStatsHeader.tsx new file mode 100644 index 0000000..921e0c1 --- /dev/null +++ b/complex-agents/exa-deep-researcher/frontend/src/components/ResearchStatsHeader.tsx @@ -0,0 +1,69 @@ +import { Trash2 } from "lucide-react"; + +interface ResearchStatsHeaderProps { + totalNotes: number; + uniqueSources: number; + totalSources: number; + onReset?: () => void; + hasData?: boolean; +} + +export function ResearchStatsHeader({ + totalNotes, + uniqueSources, + totalSources, + onReset, + hasData, +}: ResearchStatsHeaderProps) { + if (totalNotes === 0 && !hasData) return null; + + return ( +
+
+
+ {totalNotes > 0 && ( + <> +
+ {totalNotes}{" "} + + topics + +
+
+ {uniqueSources}{" "} + + sources + +
+
+ {totalSources}{" "} + + citations + +
+ + )} +
+ {onReset && hasData && ( + + )} +
+
+ ); +} diff --git a/complex-agents/exa-deep-researcher/frontend/src/components/StatusCard.tsx b/complex-agents/exa-deep-researcher/frontend/src/components/StatusCard.tsx new file mode 100644 index 0000000..457527b --- /dev/null +++ b/complex-agents/exa-deep-researcher/frontend/src/components/StatusCard.tsx @@ -0,0 +1,118 @@ +import { FileText, Search, Package, HelpCircle } from "lucide-react"; + +interface StatusCardProps { + phase: string; + title: string; + message: string; + progressPct?: number; + stats: Record; + phaseConfig: { + label: string; + icon: string; + bg: string; + border: string; + text: string; + progressBg: string; + dot: string; + }; +} + +const iconMap: Record = { + "file-text": FileText, + "search": Search, + "package": Package, + "help-circle": HelpCircle, +}; + +export function StatusCard({ + phase, + title, + message, + progressPct, + stats, + phaseConfig, +}: StatusCardProps) { + const IconComponent = iconMap[phaseConfig.icon] || FileText; + + return ( +
+
+
+
+
+
+ + + {phaseConfig.label} + +
+
+ {progressPct !== undefined && ( +
+ {Math.round(progressPct)}% +
+ )} +
+
+ +
+
+

+ {title} +

+

+ {message} +

+
+ + {progressPct !== undefined && ( +
+
+
+
+
+ )} + + {Object.keys(stats).length > 0 && ( +
+
+ {Object.entries(stats) + .slice(0, 4) + .map(([key, value]) => ( +
+ + {String(value)} + + {key !== "title" && ( + {key} + )} +
+ ))} +
+
+ )} +
+
+ ); +} + diff --git a/complex-agents/exa-deep-researcher/frontend/src/hooks/useResearchPlan.ts b/complex-agents/exa-deep-researcher/frontend/src/hooks/useResearchPlan.ts new file mode 100644 index 0000000..385a896 --- /dev/null +++ b/complex-agents/exa-deep-researcher/frontend/src/hooks/useResearchPlan.ts @@ -0,0 +1,269 @@ +import { useMemo } from "react"; +import type { + ResearchStatus, + ResearchNote, + Report, + Clarification, +} from "./useResearchStatus"; + +interface PlanStep { + id: string; + label: string; + icon: string; + status: "pending" | "active" | "completed"; + description?: string; + timestamp?: number; +} + +// Normalize topic strings for matching (trim, lowercase) +function normalizeTopic(topic: string): string { + return topic.trim().toLowerCase(); +} + +export function useResearchPlan( + currentStatus: ResearchStatus | null, + statusHistory: ResearchStatus[], + researchTitle: string | null, + notes: ResearchNote[], + report: Report | null, + clarification: Clarification | null +): PlanStep[] { + return useMemo(() => { + const steps: PlanStep[] = []; + + // Determine if research has started + const hasResearchStarted = + !!currentStatus?.requestId || statusHistory.length > 0; + + if (!hasResearchStarted) { + return steps; // Return empty if research hasn't started + } + + // Filter status history to only include the active request (if report exists, use its requestId) + const activeRequestId = report?.requestId || currentStatus?.requestId; + const filteredStatusHistory = activeRequestId + ? statusHistory.filter((s) => s.requestId === activeRequestId) + : statusHistory; + + // 1. Clarification step (optional, but show if it happened) + // Always at position 0 if present + // Check if clarification happened in this request by looking at statusHistory + const activeClarification = + clarification && clarification.requestId === activeRequestId + ? clarification + : null; + + // Check if there was ANY clarification phase in the filtered history + const hadClarificationPhase = filteredStatusHistory.some( + (s) => s.phase === "clarifying" + ); + + const isClarificationActive = currentStatus?.phase === "clarifying"; + + // Show clarification step if: + // 1. Currently clarifying, OR + // 2. Had clarification phase in history (completed), OR + // 3. Have clarification data + const showClarificationStep = + isClarificationActive || hadClarificationPhase || !!activeClarification; + + if (showClarificationStep) { + // Determine status: + // - active: currently clarifying + // - completed: had clarification phase and moved past it + const isClarificationCompleted = + (hadClarificationPhase || !!activeClarification) && + !isClarificationActive; + + steps.push({ + id: "clarification", + label: "Clarification", + icon: "clarification", + status: isClarificationActive + ? "active" + : isClarificationCompleted + ? "completed" + : "pending", + description: isClarificationCompleted ? "Question answered" : undefined, + // Force order=0 (always first) + timestamp: 0, + }); + } + + // 2. Planning step (always visible, always at position 1 after clarification) + // Find the FIRST briefing status (from the current research session) + const briefingStatuses = filteredStatusHistory.filter( + (s) => s.phase === "briefing" + ); + const firstBriefingStatus = + briefingStatuses.length > 0 + ? briefingStatuses[0] // Use first briefing (earliest timestamp) + : null; + + // Planning is completed if: + // 1. Report exists (research completed), OR + // 2. We have topics (means we moved past planning), OR + // 3. Current phase is beyond briefing (researching, compressing, reporting) + const hasMovedPastPlanning = + currentStatus && + (currentStatus.phase === "researching" || + currentStatus.phase === "compressing" || + currentStatus.phase === "reporting"); + + const isPlanningCompleted = + !!report || hasMovedPastPlanning || !!firstBriefingStatus; + + steps.push({ + id: "briefing", + label: "Planning", + icon: "planning", + status: + currentStatus?.phase === "briefing" + ? "active" + : isPlanningCompleted + ? "completed" + : "pending", + description: researchTitle || firstBriefingStatus?.title || undefined, + // Force order=1 (after clarification which is 0) + timestamp: 1, + }); + + // 3. Collect all topics from statusHistory (when they appear) + const allTopics = new Map< + string, + { + topic: string; + normalizedTopic: string; + firstSeen: number; + completedAt?: number; + } + >(); + + // From statusHistory - when subtopic appears in stats + filteredStatusHistory.forEach((status) => { + if (status.phase === "researching" && status.stats?.subtopic) { + const topic = String(status.stats.subtopic); + const normalized = normalizeTopic(topic); + if (!allTopics.has(normalized)) { + allTopics.set(normalized, { + topic, + normalizedTopic: normalized, + firstSeen: status.ts, + }); + } + } + }); + + // From notes - mark as completed (with normalized matching) + // Only include notes from the same requestId + const filteredNotes = activeRequestId + ? notes.filter((n) => n.requestId === activeRequestId) + : notes; + filteredNotes.forEach((note) => { + const normalizedNoteTopic = normalizeTopic(note.subtopic); + const topicInfo = allTopics.get(normalizedNoteTopic); + if (topicInfo) { + topicInfo.completedAt = note.ts; + } + }); + + // Create topic steps (sorted by firstSeen timestamp) + // Use stable ordering: topics get order 100 + index + const topicSteps: PlanStep[] = Array.from(allTopics.values()) + .sort((a, b) => a.firstSeen - b.firstSeen) + .map((topicInfo, index) => { + // Check currentStatus first, then check last status in filteredStatusHistory + const currentTopicFromStatus = + typeof currentStatus?.stats?.subtopic === "string" + ? normalizeTopic(currentStatus.stats.subtopic) + : null; + + // Also check the last status in history (in case currentStatus is slightly delayed) + const lastResearchingStatus = filteredStatusHistory + .filter((s) => s.phase === "researching" && s.stats?.subtopic) + .slice(-1)[0]; + const lastTopicFromHistory = lastResearchingStatus?.stats?.subtopic + ? normalizeTopic(String(lastResearchingStatus.stats.subtopic)) + : null; + + // Use currentStatus topic if available, otherwise use last from history + const currentTopic = currentTopicFromStatus || lastTopicFromHistory; + + const isCompleted = !!topicInfo.completedAt; + const isActive = + currentTopic === topicInfo.normalizedTopic && + (currentStatus?.phase === "researching" || + lastResearchingStatus?.phase === "researching"); + + return { + id: `topic-${topicInfo.topic}`, + label: topicInfo.topic, + icon: "topic", + status: (isCompleted + ? "completed" + : isActive + ? "active" + : "pending") as "pending" | "active" | "completed", + description: isCompleted + ? "Completed" + : isActive + ? "Researching..." + : undefined, + // Use fixed order: 100 + index to ensure topics stay between Planning and Report + timestamp: 100 + index, + }; + }); + + // Add topic steps + steps.push(...topicSteps); + + // 4. Final Report step (always visible, always at the end) + // Note: Compressing/Organizing is an internal process that happens between topics, + // not a separate step in the timeline + const reportingStatus = filteredStatusHistory.find( + (s) => s.phase === "reporting" + ); + const isReportingActive = currentStatus?.phase === "reporting"; + // Report is completed only if we have the actual report data + const isReportingCompleted = !!report; + + steps.push({ + id: "reporting", + label: "Final Report", + icon: "report", + status: isReportingActive + ? "active" + : isReportingCompleted + ? "completed" + : "pending", + description: report + ? "Report ready" + : reportingStatus + ? "Writing..." + : undefined, + // Force order=10000 (always last) + timestamp: 10000, + }); + + // Sort by timestamp (simple numeric sort) + // We use fixed timestamps to ensure stable ordering: + // - Clarification: 0 + // - Planning: 1 + // - Topics: 100 + index (order of appearance) + // - Final Report: 10000 (always last) + steps.sort((a, b) => { + const aTime = a.timestamp ?? 9999; + const bTime = b.timestamp ?? 9999; + return aTime - bTime; + }); + + return steps; + }, [ + currentStatus, + statusHistory, + researchTitle, + notes, + report, + clarification, + ]); +} diff --git a/complex-agents/exa-deep-researcher/frontend/src/hooks/useResearchStats.ts b/complex-agents/exa-deep-researcher/frontend/src/hooks/useResearchStats.ts new file mode 100644 index 0000000..188f2e3 --- /dev/null +++ b/complex-agents/exa-deep-researcher/frontend/src/hooks/useResearchStats.ts @@ -0,0 +1,31 @@ +import { useMemo } from "react"; +import type { ResearchNote, Report } from "./useResearchStatus"; + +export interface ResearchStats { + totalSources: number; + totalNotes: number; + uniqueSources: number; +} + +export function useResearchStats( + notes: ResearchNote[], + report: Report | null +): ResearchStats { + return useMemo(() => { + const validCitations = notes.flatMap((note) => + note.citations.filter((c) => { + const title = c.title?.toLowerCase().trim() || ""; + return title && title !== "untitled" && !title.startsWith("untitled"); + }) + ); + + // If report exists, use numSources from report (actual citations in final report) + // Otherwise, count all citations from notes + return { + totalSources: report?.numSources ?? validCitations.length, + totalNotes: notes.length, + uniqueSources: new Set(validCitations.map((c) => c.url)).size, + }; + }, [notes, report]); +} + diff --git a/complex-agents/exa-deep-researcher/frontend/src/hooks/useResearchStatus.ts b/complex-agents/exa-deep-researcher/frontend/src/hooks/useResearchStatus.ts new file mode 100644 index 0000000..d53b8f2 --- /dev/null +++ b/complex-agents/exa-deep-researcher/frontend/src/hooks/useResearchStatus.ts @@ -0,0 +1,315 @@ +import { useRoomContext } from "@livekit/components-react"; +import { useState, useEffect, useRef } from "react"; +import { RpcInvocationData } from "livekit-client"; + +export interface ResearchStatus { + requestId: string; + phase: + | "briefing" + | "researching" + | "compressing" + | "reporting" + | "clarifying"; + title: string; + message: string; + progressPct?: number; + stats: Record; + ts: number; + clarification?: { + question: string; + originalQuery: string; + }; + note?: { + subtopic: string; + summaryMarkdown: string; + citations: Array<{ + id: string; + url: string; + title: string; + quote: string; + }>; + }; + report?: { + title: string; + content: string; + sizeBytes: number; + numSources: number; + }; +} + +export interface ResearchNote { + requestId: string; + subtopic: string; + summaryMarkdown: string; + citations: Array<{ + id: string; + url: string; + title: string; + quote: string; + publishedAt?: string; + }>; + ts: number; +} + +export interface Report { + requestId: string; + reportTitle: string; + reportContent: string; + sizeBytes: number; + numSources: number; +} + +export interface Clarification { + requestId: string; + question: string; + originalQuery: string; + ts: number; +} + +const STORAGE_KEY = "exa-research-state"; + +interface PersistedState { + currentStatus: ResearchStatus | null; + statusHistory: ResearchStatus[]; + notes: ResearchNote[]; + report: Report | null; + researchTitle: string | null; + clarification: Clarification | null; +} + +function loadStateFromStorage(): PersistedState | null { + try { + const stored = localStorage.getItem(STORAGE_KEY); + if (!stored) return null; + return JSON.parse(stored) as PersistedState; + } catch (error) { + console.error("Failed to load state from localStorage:", error); + return null; + } +} + +function saveStateToStorage(state: PersistedState): void { + try { + localStorage.setItem(STORAGE_KEY, JSON.stringify(state)); + } catch (error) { + console.error("Failed to save state to localStorage:", error); + } +} + +function clearStateFromStorage(): void { + try { + localStorage.removeItem(STORAGE_KEY); + } catch (error) { + console.error("Failed to clear state from localStorage:", error); + } +} + +export function useResearchStatus() { + const room = useRoomContext(); + + const initialState = loadStateFromStorage(); + + const [currentStatus, setCurrentStatus] = useState( + initialState?.currentStatus ?? null + ); + const [statusHistory, setStatusHistory] = useState( + initialState?.statusHistory ?? [] + ); + const [notes, setNotes] = useState(initialState?.notes ?? []); + const [report, setReport] = useState( + initialState?.report ?? null + ); + const [researchTitle, setResearchTitle] = useState( + initialState?.researchTitle ?? null + ); + const [clarification, setClarification] = useState( + initialState?.clarification ?? null + ); + const seenStatusIds = useRef>(new Set()); + + useEffect(() => { + const state: PersistedState = { + currentStatus, + statusHistory, + notes, + report, + researchTitle, + clarification, + }; + saveStateToStorage(state); + }, [ + currentStatus, + statusHistory, + notes, + report, + researchTitle, + clarification, + ]); + + useEffect(() => { + if (!room) return; + + const handleStatusRpc = async ( + rpcInvocation: RpcInvocationData + ): Promise => { + try { + const payload = JSON.parse(rpcInvocation.payload) as ResearchStatus; + + const statusId = `${payload.requestId}:${payload.phase}:${payload.title}:${payload.message}:${payload.ts}`; + + if (seenStatusIds.current.has(statusId)) { + return JSON.stringify({ success: true, duplicate: true }); + } + + seenStatusIds.current.add(statusId); + + setCurrentStatus(payload); + setStatusHistory((prev) => [...prev, payload].slice(-50)); + + if (payload.clarification) { + setClarification({ + requestId: payload.requestId, + question: payload.clarification.question, + originalQuery: payload.clarification.originalQuery, + ts: payload.ts, + }); + } + + if (payload.note) { + const note = payload.note; + setNotes((prev) => [ + ...prev, + { + requestId: payload.requestId, + subtopic: note.subtopic, + summaryMarkdown: note.summaryMarkdown, + citations: note.citations, + ts: payload.ts, + }, + ]); + } + + if (payload.report) { + const reportData = payload.report; + setReport({ + requestId: payload.requestId, + reportTitle: reportData.title, + reportContent: reportData.content, + sizeBytes: reportData.sizeBytes, + numSources: reportData.numSources, + }); + setResearchTitle((prev) => prev || reportData.title); + } + + if (payload.title && payload.title !== "Planning research") { + setResearchTitle((prev) => { + if (!prev || prev === "Planning research") { + return payload.title; + } + return prev; + }); + } + + return JSON.stringify({ success: true }); + } catch (error) { + console.error("Error handling status RPC:", error); + return JSON.stringify({ success: false, error: String(error) }); + } + }; + + const setupRpcHandlers = async () => { + if (room.state !== "connected") { + await new Promise((resolve) => { + const checkConnection = () => { + if (room.state === "connected") { + resolve(); + } else { + setTimeout(checkConnection, 100); + } + }; + checkConnection(); + }); + } + + try { + try { + room.unregisterRpcMethod("exa.research/status"); + } catch { + // Ignore errors + } + room.registerRpcMethod("exa.research/status", handleStatusRpc); + } catch (error) { + console.error("Error registering RPC methods:", error); + } + }; + + setupRpcHandlers(); + + const handleReportByteStream = async (reader: { + info: { attributes?: { requestId?: string }; mimeType?: string }; + readAll: () => Promise; + }) => { + try { + const requestId = reader.info.attributes?.requestId || ""; + const chunks = await reader.readAll(); + const blob = new Blob(chunks as BlobPart[], { + type: reader.info.mimeType || "application/json", + }); + const text = await blob.text(); + const reportData = JSON.parse(text) as ResearchStatus; + + if (reportData.report) { + setReport({ + requestId: reportData.requestId || requestId, + reportTitle: reportData.report.title, + reportContent: reportData.report.content, + sizeBytes: reportData.report.sizeBytes, + numSources: reportData.report.numSources, + }); + setResearchTitle((prev) => prev || reportData.report!.title); + setCurrentStatus(reportData); + setStatusHistory((prev) => [...prev, reportData].slice(-50)); + } + } catch (error) { + console.error("Failed to process report byte stream:", error); + } + }; + + if (room) { + room.registerByteStreamHandler( + "exa_research_report", + handleReportByteStream + ); + } + + return () => { + if (room) { + try { + room.unregisterRpcMethod("exa.research/status"); + room.unregisterByteStreamHandler("exa_research_report"); + } catch (e) { + console.error("Error unregistering RPC/byte stream handlers:", e); + } + } + }; + }, [room]); + + return { + currentStatus, + statusHistory, + notes, + report, + researchTitle, + clarification, + reset: () => { + setCurrentStatus(null); + setStatusHistory([]); + setNotes([]); + setReport(null); + setResearchTitle(null); + setClarification(null); + seenStatusIds.current.clear(); + clearStateFromStorage(); + }, + }; +} diff --git a/complex-agents/exa-deep-researcher/frontend/src/hooks/useRollingText.ts b/complex-agents/exa-deep-researcher/frontend/src/hooks/useRollingText.ts new file mode 100644 index 0000000..e5c2b5d --- /dev/null +++ b/complex-agents/exa-deep-researcher/frontend/src/hooks/useRollingText.ts @@ -0,0 +1,20 @@ +import { useState, useEffect } from "react"; +import type { ResearchStatus } from "./useResearchStatus"; + +export function useRollingText(statusHistory: ResearchStatus[]): number { + const [rollingTextIndex, setRollingTextIndex] = useState(0); + + useEffect(() => { + if (statusHistory.length === 0) return; + const interval = setInterval(() => { + setRollingTextIndex((prev) => { + const recent = statusHistory.slice(-5).reverse(); + return (prev + 1) % recent.length; + }); + }, 2000); + return () => clearInterval(interval); + }, [statusHistory]); + + return rollingTextIndex; +} + diff --git a/complex-agents/exa-deep-researcher/frontend/src/lib/utils.ts b/complex-agents/exa-deep-researcher/frontend/src/lib/utils.ts new file mode 100644 index 0000000..bd0c391 --- /dev/null +++ b/complex-agents/exa-deep-researcher/frontend/src/lib/utils.ts @@ -0,0 +1,6 @@ +import { clsx, type ClassValue } from "clsx" +import { twMerge } from "tailwind-merge" + +export function cn(...inputs: ClassValue[]) { + return twMerge(clsx(inputs)) +} diff --git a/complex-agents/exa-deep-researcher/frontend/tsconfig.json b/complex-agents/exa-deep-researcher/frontend/tsconfig.json new file mode 100644 index 0000000..cf9c65d --- /dev/null +++ b/complex-agents/exa-deep-researcher/frontend/tsconfig.json @@ -0,0 +1,34 @@ +{ + "compilerOptions": { + "target": "ES2017", + "lib": ["dom", "dom.iterable", "esnext"], + "allowJs": true, + "skipLibCheck": true, + "strict": true, + "noEmit": true, + "esModuleInterop": true, + "module": "esnext", + "moduleResolution": "bundler", + "resolveJsonModule": true, + "isolatedModules": true, + "jsx": "react-jsx", + "incremental": true, + "plugins": [ + { + "name": "next" + } + ], + "paths": { + "@/*": ["./src/*"] + } + }, + "include": [ + "next-env.d.ts", + "**/*.ts", + "**/*.tsx", + ".next/types/**/*.ts", + ".next/dev/types/**/*.ts", + "**/*.mts" + ], + "exclude": ["node_modules"] +} diff --git a/complex-agents/exa-deep-researcher/orchestrator.py b/complex-agents/exa-deep-researcher/orchestrator.py new file mode 100644 index 0000000..b27a44a --- /dev/null +++ b/complex-agents/exa-deep-researcher/orchestrator.py @@ -0,0 +1,353 @@ +""" +Research orchestrator for managing background research jobs + +This module coordinates the deep research workflow using components +from the research/ package. +""" +import os +import asyncio +import logging +from typing import Optional, List, Dict, Callable + +from livekit.agents import JobContext +from livekit.agents.llm import LLM + +from agent.schemas import JobState, ResearchNote, EXAResult +from agent.exa_client import EXAClient +from agent.prompts import brief_generation_prompt, lead_researcher_prompt, get_today_str +from agent.utils import execute_llm_chat, parse_json_response_with_fallback + +# Research components +from agent.research.clarification.search import quick_search +from agent.research.clarification.analyzer import AutonomousClarification +from agent.research.engine.subtopic import ResearchSubtopic +from agent.research.compression.compressor import compress_notes +from agent.research.reporting.generator import generate_report + +logger = logging.getLogger("research-orchestrator") +logger.setLevel(logging.INFO) + + +class ResearchOrchestrator: + """ + Orchestrates the deep research workflow + + This class manages the entire research process by coordinating + components from the research/ package: + 1. Clarification (optional) - asks user to clarify ambiguous queries + 2. Briefing - creates a research plan from the query + 3. Research Loop - iteratively researches topics until comprehensive + 4. Compression - periodically compresses notes to manage tokens (when 4+ notes) + 5. Reporting - generates final comprehensive report + + The research uses an "iterative supervisor" pattern where an LLM supervisor + evaluates findings after each iteration and decides what to research next. + """ + + def __init__( + self, + ctx: JobContext, + exa_client: EXAClient, + llm: LLM, + status_callback: Optional[Callable] = None, + notes_callback: Optional[Callable] = None, + report_callback: Optional[Callable] = None, + ): + self.ctx = ctx + self.exa_client = exa_client + self.llm = llm + self.status_callback = status_callback + self.notes_callback = notes_callback + self.report_callback = report_callback + + self.max_iterations = int(os.environ.get("EXA_MAX_ITERATIONS", "4")) + self.max_results_per_search = int(os.environ.get("EXA_DEFAULT_MAX_RESULTS", "10")) + self.max_concurrent_fetches = int(os.environ.get("EXA_MAX_CONCURRENT_FETCHES", "3")) + self.max_concurrent_research_units = int(os.environ.get("EXA_MAX_CONCURRENT_RESEARCH_UNITS", "3")) + + # Initialize research components + self.clarification_analyzer = AutonomousClarification(llm=self.llm) + self.subtopic_researcher = ResearchSubtopic( + exa_client=self.exa_client, + llm=self.llm, + max_results_per_search=self.max_results_per_search, + max_concurrent_fetches=self.max_concurrent_fetches, + status_callback=self.status_callback, + notes_callback=self.notes_callback + ) + + async def _send_status(self, request_id: str, phase: str, title: str, message: str, stats: Dict = None): + """Send status update""" + if self.status_callback: + await self.status_callback(request_id, phase, title, message, stats or {}) + if phase in ["briefing", "researching", "compressing", "reporting"]: + logger.info(f"[{phase.upper()}] {title}") + + async def quick_naive_search(self, request_id: str, query: str, num_results: int = 5) -> List[EXAResult]: + """ + Perform a quick naive search to get initial results for clarification + + Returns: + List of search results (typically 3-5 results) + """ + return await quick_search( + exa_client=self.exa_client, + request_id=request_id, + query=query, + num_results=num_results, + status_callback=self.status_callback + ) + + async def autonomous_clarification( + self, + request_id: str, + query: str, + results: List[EXAResult], + conversation_history: Optional[list] = None + ) -> tuple[bool, Optional[str]]: + """ + Autonomously determine if clarification is needed based on search results + + Returns: + (needs_clarification: bool, clarification_message: Optional[str]) + """ + return await self.clarification_analyzer.analyze( + query=query, + results=results, + conversation_history=conversation_history + ) + + async def write_brief(self, request_id: str, query: str, conversation_history: Optional[list] = None) -> tuple[str, str, List[str]]: + """ + Generate research brief, title, and subtopics + + Takes the user's query and creates a focused research brief that will guide + the entire research process. + + Returns: + (brief, title, subtopics) tuple + """ + logger.info(f"[BRIEFING] Creating research plan for: {query} (requestId={request_id})") + await self._send_status(request_id, "briefing", "Planning research", f"Creating research plan for: {query}", {}) + + response = await execute_llm_chat( + llm=self.llm, + system_prompt=brief_generation_prompt(query), + user_prompt=f"Create a focused research brief and title for: {query}", + parse_json=True, + extract_json=True, + conversation_history=conversation_history + ) + + if isinstance(response, dict): + brief = response.get("brief", query) + title = response.get("title", f"Research: {query[:50]}") + subtopics = response.get("subtopics", []) + if not isinstance(subtopics, list): + subtopics = [] + else: + brief = str(response) or query + title = f"Research: {query[:50]}" + subtopics = [] + + logger.info(f"Generated brief: {title} with {len(subtopics)} subtopics") + + await self._send_status(request_id, "briefing", title, f"Research plan ready: {title}", {}) + + return brief, title, subtopics + + async def iterative_supervise( + self, + request_id: str, + brief: str, + original_query: str, + planned_subtopics: Optional[List[str]] = None, + conversation_history: Optional[list] = None + ) -> List[ResearchNote]: + """ + Iterative supervisor pattern: evaluates findings and dynamically decides what to research next. + + If planned_subtopics are provided, supervisor will see them as recommended topics to prefer. + + This is the main research loop that: + 1. Asks an LLM supervisor to evaluate current findings + 2. Decides what topic to research next (preferring planned topics if provided) + 3. Researches the chosen topic + 4. Compresses notes periodically (when 4+ notes) to manage token usage + 5. Repeats until supervisor says "complete" or max iterations reached + + Returns: + List of ResearchNote objects from all research iterations + """ + notes = [] + compressed_context = "" + researched_topics = set() + iteration = 0 + + while iteration < self.max_iterations: + iteration += 1 + logger.info(f"Supervisor iteration {iteration}/{self.max_iterations}") + + await self._send_status( + request_id, + "researching", + "Evaluating research", + f"Assessing findings and deciding next steps (iteration {iteration})", + {"iteration": iteration} + ) + + findings_summary = "" + if compressed_context: + findings_summary = f"\n\n## Current Research Findings (Compressed):\n\n{compressed_context}\n" + elif notes: + findings_summary = "\n\n## Current Research Findings:\n\n" + for i, note in enumerate(notes, 1): + findings_summary += f"### Research {i}: {note.subtopic}\n" + findings_summary += f"{note.summary_markdown[:500]}...\n\n" + + researched_list = "\n".join([f"- {topic}" for topic in sorted(researched_topics)]) + + planned_list = "" + if planned_subtopics: + remaining_planned = [ + topic for topic in planned_subtopics + if not any( + topic.lower().strip() in researched.lower().strip() or + researched.lower().strip() in topic.lower().strip() + for researched in researched_topics + ) + ] + if remaining_planned: + planned_list = f"\n\nPlanned Topics (prefer these):\n" + "\n".join([f"- {topic}" for topic in remaining_planned]) + + supervisor_prompt = f"""You are evaluating research progress. + +Research Brief: {brief} +{planned_list} + +Researched Topics: +{researched_list if researched_topics else "None yet"} + +{findings_summary} + +Based on the research findings so far: +1. What do we know? +2. What's missing or needs deeper investigation? +3. Should we research a new topic, go deeper on an existing one, or is research complete? + +Respond with JSON: +{{ + "action": "research_topic" | "research_complete", + "topic": "specific topic to research (if action is research_topic)", + "reason": "why you're making this decision" +}} + +Guidelines: +- **PREFER topics from the "Planned Topics" list** - use the EXACT wording from planned topics when possible +- If research is comprehensive and covers all key aspects, use "research_complete" +- If there are important gaps, use "research_topic" with a specific, focused topic +- **CRITICAL: Topic Format for Search** + - The topic you specify MUST be formatted as a ready-to-use search query for EXA + - Include the main subject/entity from the research brief in EVERY topic + - If the entity name is ambiguous, add disambiguating terms (e.g., "Tesla Inc." not "Tesla") + - Make topics specific enough to avoid generic results (3-10 words) +- Don't repeat topics we've already researched +- Maximum {self.max_concurrent_research_units} topics can be researched in parallel""" + + response = await execute_llm_chat( + llm=self.llm, + system_prompt=lead_researcher_prompt(get_today_str(), self.max_concurrent_research_units), + user_prompt=supervisor_prompt, + parse_json=True, + extract_json=True, + conversation_history=conversation_history + ) + + fallback_response = {"action": "research_topic", "topic": brief, "reason": "Need to start research"} + response = parse_json_response_with_fallback(response, fallback_response) + + action = response.get("action", "research_complete") + + if action == "research_complete": + reason = response.get("reason", "Research is comprehensive") + logger.info(f"Research complete: {reason}") + await self._send_status( + request_id, + "researching", + "Research complete", + f"Supervisor determined research is comprehensive: {reason}", + {"iterations": iteration} + ) + break + + topic = response.get("topic", "") + if not topic: + logger.warning("Supervisor requested research_topic but didn't provide topic, completing") + break + + topic_lower = topic.lower().strip() + if any(existing.lower().strip() in topic_lower or topic_lower in existing.lower().strip() + for existing in researched_topics): + if iteration >= self.max_iterations - 1: + logger.warning("Reached near max iterations with similar topics, completing research") + break + + researched_topics.add(topic) + + note = await self.subtopic_researcher.research( + request_id=request_id, + subtopic=topic, + iteration=iteration - 1, + original_query=original_query, + research_brief=brief, + conversation_history=conversation_history + ) + notes.append(note) + + # Compress notes when we have 4+ to manage token limits + # For 2-3 notes, compression is optional and may not be worth the LLM call + if len(notes) >= 4: + compressed_context = await compress_notes( + llm=self.llm, + request_id=request_id, + notes=notes, + status_callback=self.status_callback + ) + + await asyncio.sleep(0.5) + + if iteration >= self.max_iterations: + logger.warning(f"Reached max iterations ({self.max_iterations}), completing research") + await self._send_status( + request_id, + "researching", + "Max iterations reached", + f"Completed {iteration} research iterations", + {"iterations": iteration} + ) + + logger.info(f"Completed {iteration} iterations → {len(notes)} research topics") + return notes + + async def generate_final_report( + self, + request_id: str, + title: str, + brief: str, + notes: List[ResearchNote] + ) -> str: + """ + Generate final research report + + Returns: + Final report as markdown string + """ + return await generate_report( + llm=self.llm, + request_id=request_id, + title=title, + brief=brief, + notes=notes, + status_callback=self.status_callback, + report_callback=self.report_callback + ) diff --git a/complex-agents/exa-deep-researcher/pyproject.toml b/complex-agents/exa-deep-researcher/pyproject.toml new file mode 100644 index 0000000..2154c20 --- /dev/null +++ b/complex-agents/exa-deep-researcher/pyproject.toml @@ -0,0 +1,20 @@ +[project] +name = "exa-deep-researcher" +version = "0.1.0" +description = "EXA Deep Researcher Agent" +requires-python = ">=3.10" +dependencies = [ + "livekit-agents[turn-detector]~=1.2", + "livekit-plugins-turn-detector>=0.1.0", + "livekit-plugins-silero>=0.6.8", + "python-dotenv>=1.0.0", + "exa-py>=1.0.0", + "livekit-plugins-noise-cancellation~=0.2", +] + +[dependency-groups] +dev = [ + "pytest>=8.4.2", + "pytest-asyncio>=1.2.0", +] + diff --git a/complex-agents/exa-deep-researcher/test_orchestrator.py b/complex-agents/exa-deep-researcher/test_orchestrator.py new file mode 100644 index 0000000..589450d --- /dev/null +++ b/complex-agents/exa-deep-researcher/test_orchestrator.py @@ -0,0 +1,272 @@ +""" +Standalone test for EXA Deep Researcher orchestrator +Tests the research workflow without LiveKit dependencies +""" +import os +import asyncio +import logging +from pathlib import Path +from dotenv import load_dotenv + +# Set up logging +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) +logger = logging.getLogger("test-orchestrator") + +# Load environment - try current directory first, then parent directories +load_dotenv(dotenv_path=Path(__file__).parent / '.env') +if not os.environ.get("EXA_API_KEY"): + load_dotenv(dotenv_path=Path(__file__).parent.parent.parent / '.env') + +# Mock JobContext for testing +class MockJobContext: + """Mock JobContext for testing""" + def __init__(self): + self.room = None + +# Import after env is loaded +from agent.exa_client import EXAClient +from orchestrator import ResearchOrchestrator +from livekit.plugins import openai + +async def test_exa_connection(): + """Test EXA API connection""" + logger.info("=" * 60) + logger.info("TEST 1: EXA API Connection") + logger.info("=" * 60) + + try: + client = EXAClient(api_key=os.environ.get("EXA_API_KEY")) + logger.info("EXA client initialized") + + # Test search + from schemas import EXASearchParams + params = EXASearchParams( + query="artificial intelligence", + num_results=3 + ) + + results = await client.search(params) + logger.info(f"Search returned {len(results)} results") + + if results: + logger.info(f" First result: {results[0].title[:60]}...") + logger.info(f" URL: {results[0].url}") + + return True + + except Exception as e: + logger.error(f"EXA connection test failed: {e}") + return False + + +async def test_brief_generation(): + """Test research brief generation""" + logger.info("\n" + "=" * 60) + logger.info("TEST 2: Brief Generation") + logger.info("=" * 60) + + try: + # Create mock context + ctx = MockJobContext() + + # Create clients + exa_client = EXAClient(api_key=os.environ.get("EXA_API_KEY")) + llm = openai.LLM(model="gpt-4o-mini") + + # Create orchestrator + orchestrator = ResearchOrchestrator( + ctx=ctx, + exa_client=exa_client, + llm=llm, + ) + + # Test brief generation + query = "What are the latest developments in quantum computing?" + brief, title = await orchestrator.write_brief("test-123", query) + + logger.info(f"Brief generated") + logger.info(f" Title: {title}") + logger.info(f" Brief: {brief[:200]}...") + + return True + + except Exception as e: + logger.error(f"Brief generation test failed: {e}") + import traceback + traceback.print_exc() + return False + + +async def test_subtopic_decomposition(): + """Test research decomposition into subtopics""" + logger.info("\n" + "=" * 60) + logger.info("TEST 3: Subtopic Decomposition") + logger.info("=" * 60) + + try: + # Create mock context + ctx = MockJobContext() + + # Create clients + exa_client = EXAClient(api_key=os.environ.get("EXA_API_KEY")) + llm = openai.LLM(model="gpt-4o-mini") + + # Create orchestrator + orchestrator = ResearchOrchestrator( + ctx=ctx, + exa_client=exa_client, + llm=llm, + ) + + # Test decomposition + brief = "Research the impact of artificial intelligence on healthcare, focusing on diagnostic accuracy and patient outcomes" + subtopics = await orchestrator.supervise("test-123", brief) + + logger.info(f"Decomposed into {len(subtopics)} subtopics:") + for i, topic in enumerate(subtopics, 1): + logger.info(f" {i}. {topic}") + + return True + + except Exception as e: + logger.error(f"Subtopic decomposition test failed: {e}") + import traceback + traceback.print_exc() + return False + + +async def test_full_research_workflow(): + """Test complete research workflow""" + logger.info("\n" + "=" * 60) + logger.info("TEST 4: Full Research Workflow (Mini)") + logger.info("=" * 60) + + try: + # Create mock context + ctx = MockJobContext() + + # Create clients + exa_client = EXAClient(api_key=os.environ.get("EXA_API_KEY")) + llm = openai.LLM(model="gpt-4o-mini") + + # Status callback + async def status_cb(request_id, phase, title, message, stats): + logger.info(f" [{phase}] {title}: {message}") + + # Notes callback + async def notes_cb(request_id, note): + logger.info(f" [NOTE] {note.subtopic}: {len(note.citations)} citations") + + # Report callback + async def report_cb(request_id, title, report, num_sources): + logger.info(f" [REPORT] {title} - {len(report)} chars, {num_sources} sources") + + # Create orchestrator + orchestrator = ResearchOrchestrator( + ctx=ctx, + exa_client=exa_client, + llm=llm, + status_callback=status_cb, + notes_callback=notes_cb, + report_callback=report_cb, + ) + + # Override max iterations for quick test + orchestrator.max_iterations = 2 + orchestrator.max_results_per_search = 5 + + # Run workflow + query = "CRISPR gene editing applications" + + logger.info(f"Query: {query}") + + # Step 1: Brief + brief, title = await orchestrator.write_brief("test-123", query) + logger.info(f"Brief: {title}") + + # Step 2: Decompose + subtopics = await orchestrator.supervise("test-123", brief) + logger.info(f"Subtopics: {len(subtopics)}") + + # Step 3: Research first subtopic only (for speed) + if subtopics: + note = await orchestrator.research_subtopic("test-123", subtopics[0], 0) + logger.info(f"Research note: {len(note.summary_markdown)} chars") + logger.info(f" Citations: {len(note.citations)}") + + # Step 4: Generate mini report + notes = [note] + report = await orchestrator.generate_final_report( + "test-123", + title, + brief, + notes + ) + logger.info(f"Final report: {len(report)} chars") + logger.info(f"\nReport preview:\n{report[:500]}...\n") + + return True + + except Exception as e: + logger.error(f"Full workflow test failed: {e}") + import traceback + traceback.print_exc() + return False + + +async def main(): + """Run all tests""" + logger.info("Starting EXA Deep Researcher Tests") + logger.info(f"EXA_API_KEY: {'Set' if os.environ.get('EXA_API_KEY') else 'Missing'}") + logger.info(f"OPENAI_API_KEY: {'Set' if os.environ.get('OPENAI_API_KEY') else 'Missing'}") + + if not os.environ.get("EXA_API_KEY"): + logger.error("EXA_API_KEY not set! Please set it in .env file") + return 1 + + if not os.environ.get("OPENAI_API_KEY"): + logger.warning("OPENAI_API_KEY not set! Skipping LLM-dependent tests") + logger.info("Set OPENAI_API_KEY in .env to run full tests") + + results = [] + + # Run tests + results.append(("EXA Connection", await test_exa_connection())) + + # Only run LLM tests if OPENAI_API_KEY is set + if os.environ.get("OPENAI_API_KEY"): + results.append(("Brief Generation", await test_brief_generation())) + results.append(("Subtopic Decomposition", await test_subtopic_decomposition())) + results.append(("Full Workflow", await test_full_research_workflow())) + else: + logger.warning("Skipping LLM-dependent tests (OPENAI_API_KEY not set)") + + # Summary + logger.info("\n" + "=" * 60) + logger.info("TEST SUMMARY") + logger.info("=" * 60) + + passed = sum(1 for _, result in results if result) + total = len(results) + + for test_name, result in results: + status = "PASS" if result else "FAIL" + logger.info(f"{status}: {test_name}") + + logger.info(f"\nTotal: {passed}/{total} tests passed") + + if passed == total: + logger.info("All tests passed!") + return 0 + else: + logger.error("Some tests failed") + return 1 + + +if __name__ == "__main__": + exit_code = asyncio.run(main()) + exit(exit_code) + diff --git a/complex-agents/exa-deep-researcher/tests/test_agent.py b/complex-agents/exa-deep-researcher/tests/test_agent.py new file mode 100644 index 0000000..a3332b2 --- /dev/null +++ b/complex-agents/exa-deep-researcher/tests/test_agent.py @@ -0,0 +1,553 @@ +""" +--- +title: EXA Deep Researcher Agent Test Suite +category: exa-deep-researcher +tags: [pytest, agent_testing, run_result, judge_llm, mock_tools, clarification] +difficulty: advanced +description: Test suite for EXA Deep Researcher agent with clarification flow testing +demonstrates: + - Agent testing with pytest + - RunResult expectations and assertions + - LLM judge for intent verification + - Tool mocking for EXA client + - Clarification flow testing + - Multiple conversation turns +--- +""" + +from __future__ import annotations + +import pytest +from unittest.mock import AsyncMock, MagicMock +from pathlib import Path +from dotenv import load_dotenv + +from livekit.agents import AgentSession, JobContext, ChatContext, inference +from livekit.agents.voice.run_result import mock_tools + +import sys +import importlib.util + +# Load agent.py file directly (not the agent/ package) +_agent_path = Path(__file__).parent.parent / "agent.py" +_spec = importlib.util.spec_from_file_location("agent_module", _agent_path) +agent_module = importlib.util.module_from_spec(_spec) +sys.modules['agent_module'] = agent_module +_spec.loader.exec_module(agent_module) + +# Import classes from agent.py +ExaResearchAgent = agent_module.ExaResearchAgent +ExaUserData = agent_module.ExaUserData + +# Now add to path for other imports +sys.path.insert(0, str(Path(__file__).parent.parent)) + +# Import from agent package +from agent.schemas import EXAResult +from agent.exa_client import EXAClient + +# Load environment variables +load_dotenv(dotenv_path=Path(__file__).parent.parent / '.env') + + +def _llm_string(): + """LLM string for AgentSession""" + return "qwen/qwen3-235b-a22b-instruct" + +def _llm_judge(): + """Create LLM instance for judge() calls in tests""" + return inference.LLM(model="qwen/qwen3-235b-a22b-instruct") + + +def _create_mock_exa_results(num_results: int = 3) -> list[EXAResult]: + """Create mock EXA search results""" + return [ + EXAResult( + id=f"result_{i}", + url=f"https://example.com/article_{i}", + title=f"Article {i} about the topic", + score=0.9 - i * 0.1, + published_date="2024-01-01", + ) + for i in range(num_results) + ] + + +@pytest.mark.asyncio +async def test_basic_research_request_with_results() -> None: + """Test basic research request when EXA returns results - agent should show results and ask for confirmation""" + mock_results = _create_mock_exa_results(5) + + # Mock EXA client's search method + async def mock_search(params): + return mock_results + + llm_string = _llm_string() + # Create mock job context + mock_ctx = MagicMock(spec=JobContext) + mock_ctx.room = MagicMock() + mock_ctx.room.local_participant = MagicMock() + mock_ctx.room.remote_participants = {} + + # Create userdata with mocked EXA client + userdata = ExaUserData(ctx=mock_ctx) + userdata.exa_client = MagicMock(spec=EXAClient) + userdata.exa_client.search = AsyncMock(side_effect=mock_search) + + # Create session with LLM string (gateway pattern) + async with ( + _llm_judge() as llm_judge, + AgentSession(llm=llm_string, userdata=userdata) as session, + ): + await session.start(ExaResearchAgent()) + + # User requests research + result = await session.run(user_input="Research Tesla company") + + # Agent may emit an initial message, then call start_research_job + # Skip initial messages and get the function call + result.expect.skip_next_event_if(type="message", role="assistant") + result.expect.next_event().is_function_call(name="start_research_job") + + # Get the function call output (the clarification message) + fnc_output = result.expect.next_event().is_function_call_output() + output_text = fnc_output.event().item.output + + # Verify the output asks for clarification (since mock results are generic) + assert ("looking for" in output_text.lower() or "specify" in output_text.lower() or + "clarify" in output_text.lower() or "confirm" in output_text.lower()) + + # Agent should respond with clarification message + await result.expect.next_event().is_message(role="assistant").judge( + llm_judge, + intent="Asks for clarification to disambiguate which Tesla entity or topic the user is referring to" + ) + + +@pytest.mark.asyncio +async def test_clarification_when_no_results() -> None: + """Test that agent asks for clarification when EXA returns no results""" + # Mock EXA client to return empty results + async def mock_search_empty(params): + return [] + + llm_string = _llm_string() + mock_ctx = MagicMock(spec=JobContext) + mock_ctx.room = MagicMock() + mock_ctx.room.local_participant = MagicMock() + mock_ctx.room.remote_participants = {} + + userdata = ExaUserData(ctx=mock_ctx) + userdata.exa_client = MagicMock(spec=EXAClient) + userdata.exa_client.search = AsyncMock(side_effect=mock_search_empty) + + async with ( + _llm_judge() as llm_judge, + AgentSession(llm=llm_string, userdata=userdata) as session, + ): + await session.start(ExaResearchAgent()) + + result = await session.run(user_input="Research nonexistent company XYZ") + + # Agent should call start_research_job (skip initial message if present) + result.expect.skip_next_event_if(type="message", role="assistant") + result.expect.next_event().is_function_call(name="start_research_job") + + # Function output should indicate no results found + fnc_output = result.expect.next_event().is_function_call_output() + output_text = fnc_output.event().item.output + + # Verify it asks for clarification + assert "couldn't find" in output_text.lower() or "no results" in output_text.lower() or "clarify" in output_text.lower() or "looking for" in output_text.lower() + + # Agent message should ask for clarification + await result.expect.next_event().is_message(role="assistant").judge( + llm_judge, + intent="Asks the user to clarify what they're looking for because no search results were found" + ) + + +@pytest.mark.asyncio +async def test_research_starts_immediately_with_good_results() -> None: + """Test that agent starts research immediately when results are good (no clarification needed per README flow)""" + # Create high-quality, specific mock results that look legitimate + mock_results = [ + EXAResult( + id="result_1", + url="https://openai.com/about", + title="OpenAI: About Us - Mission, Team, and AI Safety", + score=0.95, + published_date="2024-01-15", + ), + EXAResult( + id="result_2", + url="https://techcrunch.com/openai-company-history", + title="The Complete History of OpenAI: From Founding to ChatGPT", + score=0.93, + published_date="2024-01-10", + ), + EXAResult( + id="result_3", + url="https://www.theverge.com/openai-products-chatgpt-gpt4", + title="OpenAI's Product Line: ChatGPT, GPT-4, and Beyond", + score=0.91, + published_date="2024-01-05", + ), + EXAResult( + id="result_4", + url="https://en.wikipedia.org/wiki/OpenAI", + title="OpenAI - Wikipedia: Company Overview and Research", + score=0.89, + published_date="2023-12-20", + ), + EXAResult( + id="result_5", + url="https://fortune.com/openai-business-model-revenue", + title="Inside OpenAI's Business Model and Revenue Strategy", + score=0.87, + published_date="2023-12-15", + ), + ] + + async def mock_search(params): + return mock_results + + llm_string = _llm_string() + mock_ctx = MagicMock(spec=JobContext) + mock_ctx.room = MagicMock() + mock_ctx.room.local_participant = MagicMock() + mock_ctx.room.remote_participants = {} + + userdata = ExaUserData(ctx=mock_ctx) + userdata.exa_client = MagicMock(spec=EXAClient) + userdata.exa_client.search = AsyncMock(side_effect=mock_search) + + async with ( + _llm_judge() as llm_judge, + AgentSession(llm=llm_string, userdata=userdata) as session, + ): + await session.start(ExaResearchAgent()) + + # User requests research + result = await session.run(user_input="Research OpenAI company") + result.expect.skip_next_event_if(type="message", role="assistant") + result.expect.next_event().is_function_call(name="start_research_job") + + # Get function output + fnc_output = result.expect.next_event().is_function_call_output() + output_text = fnc_output.event().item.output + + # According to README flow: good results → no clarification → direct to research + # Output should indicate research has started + assert ("starting" in output_text.lower() or "started" in output_text.lower() or + "progress" in output_text.lower()) + + # Agent confirms research has started + await result.expect.next_event().is_message(role="assistant").judge( + llm_judge, + intent="Confirms that research has started on OpenAI" + ) + + +@pytest.mark.asyncio +async def test_new_query_during_clarification() -> None: + """Test that agent handles new/corrected query when user provides it during clarification""" + mock_results = _create_mock_exa_results(3) + + async def mock_search(params): + return mock_results + + llm_string = _llm_string() + mock_ctx = MagicMock(spec=JobContext) + mock_ctx.room = MagicMock() + mock_ctx.room.local_participant = MagicMock() + mock_ctx.room.remote_participants = {} + + userdata = ExaUserData(ctx=mock_ctx) + userdata.exa_client = MagicMock(spec=EXAClient) + userdata.exa_client.search = AsyncMock(side_effect=mock_search) + + async with ( + _llm_judge() as llm_judge, + AgentSession(llm=llm_string, userdata=userdata) as session, + ): + await session.start(ExaResearchAgent()) + + # First turn: user requests research + result1 = await session.run(user_input="Research Tesla") + result1.expect.skip_next_event_if(type="message", role="assistant") + result1.expect.next_event().is_function_call(name="start_research_job") + result1.expect.next_event().is_function_call_output() + result1.expect.next_event().is_message(role="assistant") + result1.expect.no_more_events() + + # Second turn: user provides corrected query + result2 = await session.run(user_input="Actually, I meant Tesla Motors, the car company") + + # Agent should call start_research_job with the new query + result2.expect.contains_function_call(name="start_research_job") + + # Verify agent handled the corrected query - get last assistant message if available + try: + if result2.expect.contains_message(role="assistant"): + last_msg = result2.expect[-1] + if last_msg.event().item.type == "message" and last_msg.event().item.role == "assistant": + await last_msg.is_message(role="assistant").judge( + llm_judge, + intent="Handles the corrected query appropriately" + ) + except AssertionError: + # No message found - agent may have hit state issues, but function was called + # This verifies the agent attempted to handle the corrected query + pass + + +@pytest.mark.asyncio +async def test_cancel_research_job() -> None: + """Test that agent can cancel an active research job""" + mock_results = _create_mock_exa_results(5) + + async def mock_search(params): + return mock_results + + llm_string = _llm_string() + mock_ctx = MagicMock(spec=JobContext) + mock_ctx.room = MagicMock() + mock_ctx.room.local_participant = MagicMock() + mock_ctx.room.remote_participants = {} + + userdata = ExaUserData(ctx=mock_ctx) + userdata.exa_client = MagicMock(spec=EXAClient) + userdata.exa_client.search = AsyncMock(side_effect=mock_search) + + async with ( + _llm_judge() as llm_judge, + AgentSession(llm=llm_string, userdata=userdata) as session, + ): + await session.start(ExaResearchAgent()) + + # Start a research job + result1 = await session.run(user_input="Research OpenAI") + result1.expect.skip_next_event_if(type="message", role="assistant") + result1.expect.next_event().is_function_call(name="start_research_job") + result1.expect.next_event().is_function_call_output() + result1.expect.next_event().is_message(role="assistant") + result1.expect.no_more_events() + + # Confirm to start research + result2 = await session.run(user_input="Yes, start the research") + + # Agent might call start_research_job with confirmed=True, or handle state + # Find the last assistant message + last_message = None + while True: + try: + next_event = result2.expect.next_event() + if next_event.event().item.type == "message" and next_event.event().item.role == "assistant": + last_message = next_event + # Check if no more events + try: + result2.expect.no_more_events() + break + except AssertionError: + continue + except AssertionError: + break + + if last_message: + # Just verify agent responded appropriately + pass # We'll verify cancel works instead + else: + # No message - might be in progress + pass + + # Cancel the research + result3 = await session.run(user_input="Cancel the research") + + # Agent should call cancel_research_job + result3.expect.contains_function_call(name="cancel_research_job") + + # Verify agent responded about cancellation - get last assistant message + if result3.expect.contains_message(role="assistant"): + try: + last_msg = result3.expect[-1] + if last_msg.event().item.type == "message" and last_msg.event().item.role == "assistant": + await last_msg.is_message(role="assistant").judge( + llm_judge, + intent="Confirms that the research job is being canceled or handles the cancellation request" + ) + except (IndexError, AssertionError): + # Try to find any assistant message + pass + + +@pytest.mark.asyncio +async def test_good_results_skip_clarification() -> None: + """Test that good, specific results skip clarification per README flow diagram""" + # Create highly relevant Tesla results + mock_results = [ + EXAResult( + id="result_1", + url="https://tesla.com/about", + title="Tesla Inc - About Us - Electric Vehicles and Clean Energy", + score=0.95, + published_date="2024-01-01", + ), + EXAResult( + id="result_2", + url="https://investor.tesla.com", + title="Tesla Investor Relations - Financial Reports", + score=0.90, + published_date="2024-01-01", + ), + EXAResult( + id="result_3", + url="https://tesla.com/products", + title="Tesla Products - Electric Vehicles and Energy Solutions", + score=0.85, + published_date="2024-01-01", + ), + ] + + async def mock_search(params): + return mock_results + + llm_string = _llm_string() + mock_ctx = MagicMock(spec=JobContext) + mock_ctx.room = MagicMock() + mock_ctx.room.local_participant = MagicMock() + mock_ctx.room.remote_participants = {} + + userdata = ExaUserData(ctx=mock_ctx) + userdata.exa_client = MagicMock(spec=EXAClient) + userdata.exa_client.search = AsyncMock(side_effect=mock_search) + + async with ( + _llm_judge() as llm_judge, + AgentSession(llm=llm_string, userdata=userdata) as session, + ): + await session.start(ExaResearchAgent()) + + result = await session.run(user_input="Research Tesla Inc electric vehicle company") + + # Skip initial message if present + result.expect.skip_next_event_if(type="message", role="assistant") + + # Agent should call start_research_job + result.expect.next_event().is_function_call(name="start_research_job") + + # Get function output + fnc_output = result.expect.next_event().is_function_call_output() + output_text = fnc_output.event().item.output + + # Per README: Good results → No clarification → Direct to briefing/research + # Should indicate research is starting or has started + assert ("starting" in output_text.lower() or "started" in output_text.lower() or + "progress" in output_text.lower() or "research" in output_text.lower()) + + # Agent message should confirm research started + await result.expect.next_event().is_message(role="assistant").judge( + llm_judge, + intent="Confirms that research has begun on Tesla" + ) + + +@pytest.mark.asyncio +async def test_check_status_when_no_job() -> None: + """Test that agent appropriately responds when asked about status with no active research""" + llm_string = _llm_string() + mock_ctx = MagicMock(spec=JobContext) + mock_ctx.room = MagicMock() + mock_ctx.room.local_participant = MagicMock() + mock_ctx.room.remote_participants = {} + + userdata = ExaUserData(ctx=mock_ctx) + userdata.exa_client = MagicMock(spec=EXAClient) + + async with ( + _llm_judge() as llm_judge, + AgentSession(llm=llm_string, userdata=userdata) as session, + ): + await session.start(ExaResearchAgent()) + + # User asks about status with no active job + result = await session.run(user_input="What's the status of my research?") + + # Agent may respond directly or call check_research_status tool + # Check if tool was called + has_function_call = False + try: + result.expect.contains_function_call(name="check_research_status") + has_function_call = True + except AssertionError: + pass + + if has_function_call: + # Agent called the tool - verify the output + result.expect.skip_next_event_if(type="message", role="assistant") + result.expect.next_event().is_function_call(name="check_research_status") + fnc_output = result.expect.next_event().is_function_call_output() + output_text = fnc_output.event().item.output + assert "no research" in output_text.lower() or "not running" in output_text.lower() + # Get final message after tool call + await result.expect.next_event().is_message(role="assistant").judge( + llm_judge, + intent="Informs the user that no research is currently active" + ) + else: + # Agent responded directly without calling tool + await result.expect.next_event().is_message(role="assistant").judge( + llm_judge, + intent="Responds to the user's status query, acknowledging the request" + ) + + +@pytest.mark.asyncio +async def test_get_last_report_when_no_report() -> None: + """Test that agent appropriately responds when asked for report with no completed research""" + llm_string = _llm_string() + mock_ctx = MagicMock(spec=JobContext) + mock_ctx.room = MagicMock() + mock_ctx.room.local_participant = MagicMock() + mock_ctx.room.remote_participants = {} + + userdata = ExaUserData(ctx=mock_ctx) + userdata.exa_client = MagicMock(spec=EXAClient) + + async with ( + _llm_judge() as llm_judge, + AgentSession(llm=llm_string, userdata=userdata) as session, + ): + await session.start(ExaResearchAgent()) + + # User asks for findings with no completed research + result = await session.run(user_input="What did you find in your research?") + + # Agent may respond directly or call get_last_report tool + # Check if tool was called + has_function_call = False + try: + result.expect.contains_function_call(name="get_last_report") + has_function_call = True + except AssertionError: + pass + + if has_function_call: + # Agent called the tool - verify the output + result.expect.skip_next_event_if(type="message", role="assistant") + result.expect.next_event().is_function_call(name="get_last_report") + fnc_output = result.expect.next_event().is_function_call_output() + output_text = fnc_output.event().item.output + assert "no research" in output_text.lower() or "not available" in output_text.lower() + # Get final message after tool call + await result.expect.next_event().is_message(role="assistant").judge( + llm_judge, + intent="Informs the user that no research report is available" + ) + else: + # Agent responded directly without calling tool + await result.expect.next_event().is_message(role="assistant").judge( + llm_judge, + intent="Informs the user that no research has been completed yet and no report is available" + ) + diff --git a/complex-agents/exa-deep-researcher/tests/test_orchestrator.py b/complex-agents/exa-deep-researcher/tests/test_orchestrator.py new file mode 100644 index 0000000..b66dfe8 --- /dev/null +++ b/complex-agents/exa-deep-researcher/tests/test_orchestrator.py @@ -0,0 +1,275 @@ +""" +Standalone test for EXA Deep Researcher orchestrator +Tests the research workflow without LiveKit dependencies +""" +import os +import asyncio +import logging +import pytest +from pathlib import Path +from dotenv import load_dotenv + +# Set up logging +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) +logger = logging.getLogger("test-orchestrator") + +# Load environment - try current directory first, then parent directories +load_dotenv(dotenv_path=Path(__file__).parent.parent / '.env') + +# Mock JobContext for testing +class MockJobContext: + """Mock JobContext for testing""" + def __init__(self): + self.room = None + +# Import after env is loaded +from agent.exa_client import EXAClient +from orchestrator import ResearchOrchestrator +from livekit.agents import llm as llm_module + +@pytest.mark.asyncio +async def test_exa_connection(): + """Test EXA API connection""" + logger.info("=" * 60) + logger.info("TEST 1: EXA API Connection") + logger.info("=" * 60) + + try: + client = EXAClient(api_key=os.environ.get("EXA_API_KEY")) + logger.info("EXA client initialized") + + # Test search + from agent.schemas import EXASearchParams + params = EXASearchParams( + query="artificial intelligence", + num_results=3 + ) + + results = await client.search(params) + logger.info(f"Search returned {len(results)} results") + + if results: + logger.info(f" First result: {results[0].title[:60]}...") + logger.info(f" URL: {results[0].url}") + + return True + + except Exception as e: + logger.error(f"EXA connection test failed: {e}") + return False + + +@pytest.mark.asyncio +async def test_brief_generation(): + """Test research brief generation""" + logger.info("\n" + "=" * 60) + logger.info("TEST 2: Brief Generation") + logger.info("=" * 60) + + try: + # Create mock context + ctx = MockJobContext() + + # Create clients + exa_client = EXAClient(api_key=os.environ.get("EXA_API_KEY")) + llm = "qwen/qwen3-235b-a22b-instruct" + + # Create orchestrator + orchestrator = ResearchOrchestrator( + ctx=ctx, + exa_client=exa_client, + llm=llm, + ) + + # Test brief generation + query = "What are the latest developments in quantum computing?" + brief, title = await orchestrator.write_brief("test-123", query) + + logger.info(f"Brief generated") + logger.info(f" Title: {title}") + logger.info(f" Brief: {brief[:200]}...") + + return True + + except Exception as e: + logger.error(f"Brief generation test failed: {e}") + import traceback + traceback.print_exc() + return False + + +@pytest.mark.asyncio +async def test_subtopic_decomposition(): + """Test research decomposition into subtopics""" + logger.info("\n" + "=" * 60) + logger.info("TEST 3: Subtopic Decomposition") + logger.info("=" * 60) + + try: + # Create mock context + ctx = MockJobContext() + + # Create clients + exa_client = EXAClient(api_key=os.environ.get("EXA_API_KEY")) + llm = "qwen/qwen3-235b-a22b-instruct" + + # Create orchestrator + orchestrator = ResearchOrchestrator( + ctx=ctx, + exa_client=exa_client, + llm=llm, + ) + + # Test decomposition + brief = "Research the impact of artificial intelligence on healthcare, focusing on diagnostic accuracy and patient outcomes" + subtopics = await orchestrator.supervise("test-123", brief) + + logger.info(f"Decomposed into {len(subtopics)} subtopics:") + for i, topic in enumerate(subtopics, 1): + logger.info(f" {i}. {topic}") + + return True + + except Exception as e: + logger.error(f"Subtopic decomposition test failed: {e}") + import traceback + traceback.print_exc() + return False + + +@pytest.mark.asyncio +async def test_full_research_workflow(): + """Test complete research workflow""" + logger.info("\n" + "=" * 60) + logger.info("TEST 4: Full Research Workflow (Mini)") + logger.info("=" * 60) + + try: + # Create mock context + ctx = MockJobContext() + + # Create clients + exa_client = EXAClient(api_key=os.environ.get("EXA_API_KEY")) + llm = "qwen/qwen3-235b-a22b-instruct" + + # Status callback + async def status_cb(request_id, phase, title, message, stats): + logger.info(f" [{phase}] {title}: {message}") + + # Notes callback + async def notes_cb(request_id, note): + logger.info(f" [NOTE] {note.subtopic}: {len(note.citations)} citations") + + # Report callback + async def report_cb(request_id, title, report, num_sources): + logger.info(f" [REPORT] {title} - {len(report)} chars, {num_sources} sources") + + # Create orchestrator + orchestrator = ResearchOrchestrator( + ctx=ctx, + exa_client=exa_client, + llm=llm, + status_callback=status_cb, + notes_callback=notes_cb, + report_callback=report_cb, + ) + + # Override max iterations for quick test + orchestrator.max_iterations = 2 + orchestrator.max_results_per_search = 5 + + # Run workflow + query = "CRISPR gene editing applications" + + logger.info(f"Query: {query}") + + # Step 1: Brief + brief, title = await orchestrator.write_brief("test-123", query) + logger.info(f"Brief: {title}") + + # Step 2: Decompose + subtopics = await orchestrator.supervise("test-123", brief) + logger.info(f"Subtopics: {len(subtopics)}") + + # Step 3: Research first subtopic only (for speed) + if subtopics: + note = await orchestrator.research_subtopic("test-123", subtopics[0], 0) + logger.info(f"Research note: {len(note.summary_markdown)} chars") + logger.info(f" Citations: {len(note.citations)}") + + # Step 4: Generate mini report + notes = [note] + report = await orchestrator.generate_final_report( + "test-123", + title, + brief, + notes + ) + logger.info(f"Final report: {len(report)} chars") + logger.info(f"\nReport preview:\n{report[:500]}...\n") + + return True + + except Exception as e: + logger.error(f"Full workflow test failed: {e}") + import traceback + traceback.print_exc() + return False + + +async def main(): + """Run all tests""" + logger.info("Starting EXA Deep Researcher Tests") + logger.info(f"EXA_API_KEY: {'Set' if os.environ.get('EXA_API_KEY') else 'Missing'}") + logger.info(f"OPENAI_API_KEY: {'Set' if os.environ.get('OPENAI_API_KEY') else 'Missing'}") + + if not os.environ.get("EXA_API_KEY"): + logger.error("EXA_API_KEY not set! Please set it in .env file") + return 1 + + if not os.environ.get("OPENAI_API_KEY"): + logger.warning("OPENAI_API_KEY not set! Skipping LLM-dependent tests") + logger.info("Set OPENAI_API_KEY in .env to run full tests") + + results = [] + + # Run tests + results.append(("EXA Connection", await test_exa_connection())) + + # Only run LLM tests if OPENAI_API_KEY is set + if os.environ.get("OPENAI_API_KEY"): + results.append(("Brief Generation", await test_brief_generation())) + results.append(("Subtopic Decomposition", await test_subtopic_decomposition())) + results.append(("Full Workflow", await test_full_research_workflow())) + else: + logger.warning("Skipping LLM-dependent tests (OPENAI_API_KEY not set)") + + # Summary + logger.info("\n" + "=" * 60) + logger.info("TEST SUMMARY") + logger.info("=" * 60) + + passed = sum(1 for _, result in results if result) + total = len(results) + + for test_name, result in results: + status = "PASS" if result else "FAIL" + logger.info(f"{status}: {test_name}") + + logger.info(f"\nTotal: {passed}/{total} tests passed") + + if passed == total: + logger.info("All tests passed!") 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calling in an agent. demonstrates: - Using the most basic form of tool calling in an agent to print to the console -- file_path: basics/uninterruptable/uninterruptable.py +- file_path: basics/uninterruptable.py title: Uninterruptable Agent category: basics tags: @@ -233,19 +233,6 @@ examples: - Setting allow_interruptions=False in agent configuration - Testing interruption handling behavior - Agent-initiated conversation with on_enter -- file_path: complex-agents/jeopardy/benchmark_agent.py - title: Jeopardy Agent - category: benchmarking - tags: - - benchmarking - - openai - - deepgram - difficulty: beginner - description: A simple Jeopardy contestant agent that answers questions. - demonstrates: - - Basic voice agent that listens and responds - - Jeopardy-style answer formatting - - Using the metrics_collected event to track performance - file_path: complex-agents/role-playing/agents/base_agent.py title: Base Game Agent category: complex-agents @@ -321,6 +308,24 @@ examples: - Background audio playback during session - RPC handler registration for external control - Structured userdata for session state +- file_path: complex-agents/exa-deep-researcher/agent.py + title: EXA Deep Researcher + category: complex-agents + tags: + - exa + - research + - voice_controlled + - background_jobs + - rpc_streaming + difficulty: advanced + description: Voice-controlled deep research agent using EXA for web intelligence + demonstrates: + - Voice-only control + - Single background research job with state management + - EXA API integration for search and content fetching + - RPC streaming to UI for status, notes, and reports + - Token-aware compression and concurrency control + - Cited final reports saved to disk - file_path: complex-agents/role-playing/core/game_state.py title: Game State Management category: complex-agents @@ -644,8 +649,6 @@ examples: category: egress tags: - recording - - openai - - deepgram difficulty: intermediate description: Shows how to create an agent that can record the input to a room and save it to a file. demonstrates: @@ -656,7 +659,7 @@ examples: tags: - events - openai - - deepgram + - assemblyai difficulty: beginner description: Shows how to use events in an agent to trigger actions. demonstrates: @@ -670,19 +673,38 @@ examples: tags: - events - openai - - deepgram + - assemblyai difficulty: beginner description: Shows how to use event emitters in an agent to trigger actions. demonstrates: - Using event emitters in an agent to trigger actions like welcome and farewell messages for the sake of example (even though there are already events for this) +- file_path: complex-agents/exa-deep-researcher/tests/test_agent.py + title: EXA Deep Researcher Agent Test Suite + category: exa-deep-researcher + tags: + - pytest + - agent_testing + - run_result + - judge_llm + - mock_tools + - clarification + difficulty: advanced + description: Test suite for EXA Deep Researcher agent with clarification flow testing + demonstrates: + - Agent testing with pytest + - RunResult expectations and assertions + - LLM judge for intent verification + - Tool mocking for EXA client + - Clarification flow testing + - Multiple conversation turns - file_path: flows/declarative_flow.py title: Declarative Flow category: flows tags: - flows - openai - - deepgram + - assemblyai difficulty: intermediate description: Shows how to create a declarative flow using a dictionary of mutliple agents and their transitions. demonstrates: @@ -694,7 +716,7 @@ examples: tags: - flows - openai - - deepgram + - assemblyai difficulty: intermediate description: Shows how to create a multi-stage flow using a series of agents. demonstrates: @@ -705,7 +727,7 @@ examples: tags: - flows - openai - - deepgram + - assemblyai difficulty: beginner description: Shows how to create a simple one-way flow using a series of agents. demonstrates: @@ -760,19 +782,45 @@ examples: tags: - home-automation - openai - - deepgram + - assemblyai difficulty: intermediate description: Shows how to create an agent that can control home automation devices. demonstrates: - Using function tools to control home automation devices. - Using a wake word to trigger the agent. +- file_path: mcp/http_client.py + title: MCP Agent + category: mcp + tags: + - mcp + - openai + - assemblyai + difficulty: beginner + description: Shows how to use a LiveKit Agent as an MCP client. + demonstrates: + - Connecting to a local MCP server as a client. + - Connecting to a remote MCP server as a client. + - Using a function tool to retrieve data from the MCP server. +- file_path: mcp/stdio_client.py + title: MCP Agent + category: mcp + tags: + - mcp + - openai + - assemblyai + difficulty: beginner + description: Shows how to use a LiveKit Agent as an MCP client. + demonstrates: + - Connecting to a local MCP server as a client. + - Connecting to a remote MCP server as a client. + - Using a function tool to retrieve data from the MCP server. - file_path: mcp/agent.py title: MCP Agent category: mcp tags: - mcp - openai - - deepgram + - assemblyai difficulty: beginner description: Shows how to use a LiveKit Agent as an MCP client. demonstrates: @@ -808,7 +856,7 @@ examples: tags: - metrics - openai - - deepgram + - assemblyai difficulty: beginner description: Shows how to use the STT metrics to log metrics to the console. demonstrates: @@ -845,7 +893,7 @@ examples: tags: - metrics - openai - - deepgram + - assemblyai difficulty: beginner description: Shows how to use the VAD metrics to log metrics to the console. demonstrates: @@ -862,7 +910,7 @@ examples: tags: - multi-agent - openai - - deepgram + - assemblyai difficulty: intermediate description: Shows how to create a multi-agent that can switch between a long and short agent using a function tool. demonstrates: @@ -1172,6 +1220,17 @@ examples: demonstrates: - Using the `tts_node` method to override the default TTS node and add custom logic to do replacements on the output, like replacing "lol" with "". +- file_path: pipeline-tts/sarvam.py + title: Sarvam TTS and STT + category: pipeline-tts, pipeline-stt + tags: + - pipeline-tts + - pipeline-stt + - sarvam + difficulty: beginner + description: Shows how to use the Sarvam TTS and STT models. + demonstrates: + - Using the Sarvam TTS and STT models. - file_path: rag/rag_db_builder.py title: RAG Database Builder category: rag @@ -1207,6 +1266,19 @@ examples: - Function tool registration - Flexible thinking phase handling - Random message selection +- file_path: realtime/gemini_realtime_api.py + title: Basic Gemini Realtime Agent + category: realtime + tags: + - gemini_realtime + - minimal_setup + difficulty: beginner + description: Minimal Gemini Realtime model agent setup + demonstrates: + - Gemini Realtime model basic usage + - Minimal agent configuration + - Session-based generation + - VAD with Silero - file_path: realtime/openai-realtime.py title: Basic OpenAI Realtime Agent category: realtime @@ -1240,6 +1312,19 @@ examples: - Discount code application - Order summary and finalization - Real-time order state printing +- file_path: realtime/gemini_live_vision.py + title: Gemini Realtime Agent with Live Vision + category: realtime + tags: + - gemini_realtime + - live_vision + difficulty: beginner + description: Minimal Gemini Realtime model agent setup with live vision capabilities + demonstrates: + - Gemini Realtime model basic usage + - Live vision capabilities + - Session-based generation + - VAD with Silero - file_path: realtime/openai-realtime-tools.py title: OpenAI Realtime with 50+ Tools category: realtime @@ -1552,3 +1637,13 @@ examples: - Custom STT configuration with translation capabilities - Event-driven transcription and speech synthesis - Advanced multilingual processing pipeline +- file_path: vision/moondream_vision.py + title: Moondream Vision Agent + category: vision + tags: + - moondream + - vision + difficulty: intermediate + description: Moondream Vision Agent + demonstrates: + - Adding vision capabilities to an agent when the LLM does not have vision capabilities