PydanticAI Research Agent
https://github.com/coleam00/pydanticai-research-agent- Category
- AI Agents
- Rank
- No. 1536Tools index
Previous survey · No. 1547 ·
- Listed in
- #5 Research with an agent
- Pricing
- Open Source
- Type
- AGENT
- Builder
- coleam00
- GitHub
- 144 stars
- Date
About
Production-ready research agent built with Pydantic AI and the PRP framework template by Cole Medin.
What it does
This Python agent researches current topics through Brave Search, turns results into structured summaries, and can delegate Gmail draft creation to a specialized email agent. Users can interact through a streaming terminal conversation or call the research agent from Python. Configuration comes from environment variables, while Gmail access uses a guided OAuth2 flow.
Why it's ranked here
The project demonstrates a useful end-to-end agent pattern: external research, validated data, delegated email drafting, streaming output, and mocked testing. Its practical scope is stronger than a bare agent example. However, duplicated settings and CLI structures, plus conflicting Python requirements and dependency specifications, weaken confidence that every documented route works consistently.
What's good
The code validates search queries and email addresses with Pydantic models. Brave requests include timeouts, connection limits, rate limiting, retries, and specific handling for authentication and quota failures. Gmail uses OAuth2 with compose-oriented access in the setup wizard, and the wizard validates credentials, saves a token, and tests the connection. Tests can replace the language model and external services with predictable substitutes.
Tradeoffs
Running the full workflow requires language-model and Brave credentials, plus a Google Cloud project and Gmail OAuth credentials for drafting. Brave usage has quotas, and Gmail tokens may require renewed authentication. Packaging metadata requires Python 3.9 or newer, while the README says Python 3.11 or newer. The repository also contains parallel settings, model, tool, and CLI structures with differing defaults, which raises maintenance and configuration risk.
How to use it well
Use it as a reference or starting point for a Python workflow that researches a topic, preserves structured results, and prepares a Gmail draft for human review. It suits engineers learning agent delegation, dependency injection, streamed terminal output, and mock-based testing. It does not replace a general email client, send messages automatically, or provide a hosted web interface. Budget setup time for three external service configurations.
Technical notes+
pyproject.toml uses Hatchling, declares the pydantic-ai-research-email package, includes agents, cli, and tests in wheels, and exposes a research-email console script. Its Python floor and dependency set differ from README.md and requirements.txt. research_email_cli.py and cli/chat.py both implement Rich-based streaming loops around PydanticAI iteration, while config/settings.py and agents/settings.py define separate environment models with different Gmail paths and fallback behavior. agents/tools.py adds Brave rate limiting, exponential backoff, request validation, and structured result conversion. gmail_setup.py performs desktop OAuth, pickles credentials, and tests Gmail access. agents/models.py defines validated search, summary, and email structures.
Observed
- License
- MIT, declared in pyproject.toml
- Primary language
- Python
- Packaging
- Hatchling build backend with pip-installable project dependencies
- Interfaces
- Streaming CLI and programmatic Python agent usage
- External services
- Brave Search API, Gmail API through OAuth2, and OpenAI or Anthropic model providers
- Test surface
- Pytest configuration and a tests package are included in the wheel target
- Python support
- Packaging requires Python 3.9 or newer; README prerequisites specify Python 3.11 or newer
Read from README.md, pyproject.toml, requirements.txt, gmail_setup.py, research_email_cli.py, cli/chat.py, cli/__init__.py, agents/tools.py, agents/models.py, tools/__init__.py, agents/__init__.py, models/__init__.py, agents/settings.py, config/settings.py, agents/providers.py.
What it can do
Conduct structured research on topics
Research query or topic → Comprehensive research report with findings and sources
Validate and structure research data
Raw research information → Validated, properly formatted data models
Generate research insights and analysis
Research parameters and data sources → Analytical insights and conclusions
Process research requests with type safety
Research parameters and constraints → Type-validated research results
Execute multi-step research workflows
Research objectives and methodology → Completed research workflow with documented steps
Tags
Tech Stack
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