Research Agent

An AI-powered research agent that autonomously researches any topic and produces comprehensive reports with citations.

What It Does

1. Plans - Generates targeted research questions
2. Searches - Queries the web for information (Tavily API)
3. Analyzes - Identifies gaps and searches again
4. Synthesizes - Produces a structured report with citations

Example Output

Input: "quantum computing applications in finance"

Output: 
- 44 findings extracted
- 33 unique sources cited
- 3 research iterations
- Comprehensive report with executive summary, 
  detailed sections, and key takeaways

Architecture

User Query
    │
    ▼
┌─────────────────┐
│  Plan Research  │ → Generate 4-6 targeted questions
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  Research Loop  │ → Search → Extract → Analyze Gaps
└────────┬────────┘   (repeats up to 3 iterations)
         │
         ▼
┌─────────────────┐
│   Synthesize    │ → Combine findings into report
└────────┬────────┘
         │
         ▼
  Markdown Report
  (with citations)

Tech Stack

Component	Choice	Why
LLM	Claude (Anthropic)	Planning, extraction, synthesis
Search	Tavily API	Web search optimized for AI agents
Compute	AWS Lambda	Serverless, pay-per-use
UI	Gradio	Local web interface
Framework	Pure Python	No LangChain/LlamaIndex dependencies

Quick Start

Local Development

# Clone
git clone https://github.com/woodstocksoftware/research-agent.git
cd research-agent

# Environment
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

# API Keys
export ANTHROPIC_API_KEY="your-key"
export TAVILY_API_KEY="your-key"

# Run with Gradio UI
python app.py
# Open http://localhost:7860

AWS Deployment

# Build
sam build --template infrastructure/template.yaml

# Deploy
sam deploy --resolve-s3 \
  --stack-name research-agent \
  --capabilities CAPABILITY_IAM CAPABILITY_NAMED_IAM \
  --parameter-overrides \
    AnthropicApiKey=$ANTHROPIC_API_KEY \
    TavilyApiKey=$TAVILY_API_KEY

Usage

Local (Gradio UI):

python app.py
# Open http://localhost:7860

AWS (CLI helper):

./research.sh "your research topic"

AWS (Direct Lambda):

aws lambda invoke \
  --function-name research-agent-dev \
  --cli-read-timeout 300 \
  --cli-binary-format raw-in-base64-out \
  --payload file://payload.json \
  response.json

Note: Research takes 2-3 minutes. API Gateway has a 29-second timeout, so use direct Lambda invocation or the CLI helper for best results.

Project Structure

research-agent/
├── app.py                      # Gradio UI
├── research.sh                 # CLI helper for AWS
├── infrastructure/
│   └── template.yaml           # SAM/CloudFormation template
├── src/
│   ├── agent/
│   │   └── researcher.py       # Core agent logic
│   ├── tools/
│   │   └── search.py           # Tavily search wrapper
│   └── lambda/
│       └── handler.py          # AWS Lambda handler
└── requirements.txt

How It Works

1. Planning Phase

The agent generates 4-6 specific research questions covering different aspects of the topic (definition, current state, key players, challenges, trends).

2. Research Loop (up to 3 iterations)

For each question:

• Search the web using Tavily
• Extract key findings with source attribution
• Analyze gaps in coverage
• Generate new queries if gaps exist

3. Synthesis Phase

Combines all findings into a structured report:

• Executive summary
• Organized sections with headers
• Inline citations with links
• Key takeaways
• Source list

Why No Framework?

This agent is built with pure Python to demonstrate understanding of:

• Agent loops and control flow
• Tool integration patterns
• Prompt engineering for structured outputs
• Multi-step reasoning

No magic. Just code you can read, understand, and modify.

Cost Estimate

Service	Cost
Claude API	~$0.10-0.20 per research
Tavily API	Free tier: 1000 searches/month
AWS Lambda	~$0.01 per research

License

MIT

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Built by Jim Williams | GitHub