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
What is FastAgentic?
FastAgentic is the deployment layer for agentic applications. It's an open-source (MIT) Python runtime, built on FastAPI, that wraps agents written in PydanticAI, LangGraph, CrewAI, or LangChain and exposes each one simultaneously as a REST endpoint, an MCP tool, and an A2A skill — from a single decorator. Auth, durable checkpoints, cost tracking, and observability come built in, so agents that work in a notebook can ship to production without a bespoke platform team behind them.
Bring the agent framework you already use. Swap it later without rewriting deployment, auth, or telemetry.
One definition emits REST, MCP, and A2A surfaces with schemas kept in lock-step automatically.
Durable checkpoints, cost budgets, RBAC, PII masking, and OpenTelemetry are defaults, not add-ons.
The problems FastAgentic solves
None of these are reasoning problems. They are the same deployment problems every team rebuilds — usually at 2am, during an incident. FastAgentic solves them once.
The agent-to-production gap
The problem
Your agent reasons fine in a notebook. Shipping it means streaming, auth, MCP/A2A surfaces, checkpoints, cost caps, and telemetry — roughly 500 lines of boilerplate no one wants to own.
With FastAgentic
One @agent_endpoint decorator emits all of it. You write the agent; FastAgentic writes the platform.
Framework lock-in
The problem
LangServe only speaks LangChain. Framework SDKs assume you never switch. Two years in, half your agents are on a stack you would not choose today.
With FastAgentic
Adapters for PydanticAI, LangGraph, CrewAI, LangChain — and a Runnable interface for anything custom. Deployment stays identical across all of them.
Runs that die at hour one
The problem
A long agent run crashes at minute 55 and you lose the whole thing. External orchestrators add a Celery + Redis stack you now have to operate.
With FastAgentic
StepTracker checkpoints every workflow step to Redis, Postgres, or S3. run_opaque wraps existing agents unchanged and makes them resumable at the step level.
Runaway LLM bills
The problem
A loop misfires, a tenant hammers an endpoint, and the invoice arrives before your alert does. Per-run cost is invisible until accounting flags it.
With FastAgentic
Per-run, per-user, per-tenant cost tracking with hard budget cut-offs, RBAC, and PII masking — enforced before the spend happens.
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, OpenTelemetryHow FastAgentic works
A single decorator threads every request through protocol handling, governance, and durability before it ever reaches your agent — and back out again.
The same pipeline runs in reverse for the response — including token streaming, cost accounting, and checkpoint commits.
Start 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.
LangServe is deprecated — meet the framework-agnostic, open-source alternative
Wrap any agent and serve it as REST + MCP + A2A at once, with no cloud lock-in. See the LangServe alternative →
Explore FastAgentic
Everything you need to evaluate and ship — how it works, what it does, and how it fits your stack.
How it works
One decorator emits REST, MCP, and A2A surfaces — durability, governance, and observability included.
Quickstart
Install, wrap an agent in @agent_endpoint, and serve it as REST + MCP + A2A from a single file in minutes.
Features
Protocols, adapters, durable checkpoints, cost governance, streaming, and observability in one runtime.
Use cases
From adding agents to a FastAPI app to running a cost-governed, multi-tenant internal agent platform.
Comparisons
Honest comparisons vs LangServe, raw FastAPI, standalone PydanticAI, and BentoML for agents.
FAQ
Frameworks, protocols, durability, cost control, and production readiness — answered plainly.
Glossary
REST, MCP, A2A, adapters, checkpoints, StepTracker, RBAC, and the rest of the agentic-deployment vocabulary.
For FastAPI developers
Extend FastAPI into an agent platform without leaving the FastAPI mental model.
For LangGraph teams
A production runtime for LangGraph pipelines: durability, cost control, MCP, and telemetry.
For PydanticAI users
A first-class deployment layer for PydanticAI agents — no rewrite required.
For platform engineers
One runtime, every framework, unified governance across all your teams’ agents.
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.