Fastagentic

The Deployment Layer for AI Agents

Your agent works locally. Now make it production-ready in minutes, not weeks.

FastAgentic wraps any agent framework—PydanticAI, LangGraph, CrewAI, LangChain—and adds everything you need for production:

Your Agent (any framework)  →  FastAgentic  →  Production-Ready API

Install

pip install fastagentic

30-Second Example

Wrap any existing agent with zero code changes:

from your_agent import research_agent  # Any agent, any framework
from fastagentic import run_opaque

# One line. That's it.
result = await run_opaque(
    research_agent,
    query="AI trends 2025",
    run_id="research-001",  # Enables resume on crash
)

Re-run with the same run_id? Instant cached result. No re-execution.

Full Application

from fastagentic import App, tool, agent_endpoint
from fastagentic.adapters import LangGraphAdapter

app = App(title="My Agent API")

@tool
async def search(query: str) -> list[str]:
    """Search the knowledge base."""
    return await kb.search(query)

@agent_endpoint("/chat", runnable=LangGraphAdapter(my_graph))
async def chat(message: str) -> str:
    ...

fastagentic run              # HTTP API server
fastagentic mcp serve        # MCP stdio server
fastagentic agent chat       # Interactive testing

Framework Adapters

Why FastAgentic?

The problem: You've built an agent. It works. But shipping it means adding:

• Crash recovery for long-running tasks
• Monitoring and cost tracking
• Authentication and authorization
• Rate limiting and retries
• API endpoints and protocols

The solution: FastAgentic handles all of this. You keep your agent code unchanged.

Documentation

docs.neullabs.com/fastagentic

• Getting Started — Up and running in 5 minutes
• Adapters — Connect any framework
• Checkpointing — Resume long-running agents
• MCP Protocol — Tool sharing standard
• Deployment — Docker, Kubernetes, cloud

Contributing

git clone https://github.com/neullabs/fastagentic
cd fastagentic
uv sync --extra dev
uv run pytest tests/ -v

License

MIT