The deployment layer
for agentic applications.
Build agents with anything. Ship them with FastAgentic. Wrap agents built in PydanticAI, LangGraph, CrewAI, or LangChain and expose them as REST + MCP + A2A with one decorator.
pip install fastagentic Works with the agent framework you already use
Built for the way agents ship to production.
FastAgentic sits between your agent framework and your users — handling protocols, durability, governance, and telemetry so your team can focus on reasoning, not plumbing.
One decorator, three protocols
Define an agent endpoint once and expose it simultaneously as REST, MCP (Model Context Protocol), and A2A (Agent-to-Agent). Schemas stay in lock-step automatically.
Framework-agnostic adapters
Bring PydanticAI, LangGraph, CrewAI, LangChain — or a custom Runnable. Swap frameworks without rewriting deployment, auth, or observability.
Durable checkpoints
StepTracker and run_opaque cache workflow progress to Redis, Postgres, or S3. Resume after crashes without external orchestrators.
Policy & cost control
Budget caps, per-tenant rate limits, RBAC, and PII masking baked in. Stop runaway LLM bills before they start.
Streaming-first
SSE, WebSocket, and MCP events out of the box. Token streaming, tool calls, and intermediate steps — all with zero boilerplate.
Production observability
OpenTelemetry, Langfuse, Portkey, and Datadog integrations. Structured logs, per-run cost tracking, and audit trails ready on day one.
One file. Every protocol.
A PydanticAI agent, exposed over REST, MCP, and A2A — with auth, durability, and cost tracking baked in. That's it. That's the app.
from fastagentic import App, agent_endpoint
from fastagentic.adapters import PydanticAIAdapter
from pydantic_ai import Agent
agent = Agent("openai:gpt-4o", system_prompt="You are a helpful research assistant.")
app = App(title="Research Service")
@agent_endpoint("/research", adapter=PydanticAIAdapter(agent))
async def research(query: str) -> str:
"""Answer research questions with cited sources."""
...
# One decorator gives you:
# POST /research (REST + streaming SSE)
# MCP tool research(query) (Model Context Protocol)
# A2A skill research (Agent-to-Agent)
# + auth, cost tracking, checkpoints, OpenTelemetryStart here
Hand-picked guides for teams shipping agents to production.
Deploying LangGraph to production with FastAgentic
A practical, opinionated guide to taking a LangGraph pipeline from notebook to production: durable checkpoints, streaming, cost caps, and resumption after crashes.
Build an MCP server from your FastAPI app in 10 minutes
Expose your internal APIs and agents as Model Context Protocol tools that Claude, Cursor, Zed, and other MCP clients can call — without writing a separate server.
Agent cost control: patterns that actually work
Concrete patterns for keeping LLM spend predictable in a multi-tenant agent platform — budgets, per-step caps, model routing, and the hard cut-off that saves the weekend.
From the blog
Thoughts on agentic infra, FastAPI, LangGraph, and MCP.
What is 'agentic deployment', actually?
A precise definition of agentic deployment — why it's different from classic API deployment and classic ML model serving, and why existing toolchains don't fit.
One decorator should feed all three protocols
Why we think REST, MCP, and A2A schema parity is non-negotiable for the next generation of agent platforms — and what breaks when you try to maintain three separate definitions.
Hiring FastAPI and LangGraph experts: what to actually look for
A field guide for engineering leaders trying to hire or contract senior Python agent-platform engineers — the skills, the red flags, and where to find them.
Need FastAPI, LangGraph, or agent platform expertise?
Neul Labs — the team behind FastAgentic — takes on a limited number of consulting engagements each quarter. We help teams ship agents to production, fix broken LangGraph pipelines, and design governance for multi-tenant LLM platforms.