You didn't pick a Python agent framework to write protocol adapters.
Agents that stay in a notebook are cheap. Agents that ship to production are expensive — not because the reasoning is hard, but because every team ends up rebuilding the same infrastructure: streaming, auth, MCP/A2A surfacing, checkpoints, cost caps, observability.
FastAgentic is the deployment layer that makes all of that disappear behind one decorator, for every major framework, without locking you in.
The problem with the agent-to-production gap
A modern agent service has to do at least seven things beyond reasoning: expose REST endpoints with streaming, publish itself as an MCP tool so IDEs and assistants can call it, advertise A2A skills so other agents can collaborate, authenticate callers, enforce per-tenant cost and rate limits, persist partial progress so crashes don't incinerate hour-long runs, and emit structured telemetry your SRE team can trust.
None of that is research-level work. All of it is hard to get right. Most teams discover this six weeks after their demo landed, usually at 2am during an incident, and end up writing it anyway — badly, under pressure, and inconsistently across projects.
What FastAgentic actually is
FastAgentic is a thin, opinionated layer on top of FastAPI that turns your agent into a multi-protocol service. It provides:
- A single decorator,
@agent_endpoint, that emits a REST route, an MCP tool, and an A2A skill from one definition with unified schemas. - Framework adapters for PydanticAI, LangGraph, CrewAI, LangChain — and a
Runnableinterface for anything custom. - Zero-code wrapping via
run_opaque()so you can lift an existing agent without touching its code and immediately get resumability. - Durable checkpoints through
StepTracker, backed by Redis, Postgres, or S3 — resumption happens at the step level, not run level. - Built-in governance: OAuth2/OIDC, scope-based RBAC, per-tenant cost budgets with hard cut-offs, PII masking, and audit logs.
- Observability by default: OpenTelemetry spans, structured logging, and native Langfuse / Portkey / Datadog integrations.
Who it's for
Teams shipping agents to production — especially ones supporting multiple frameworks, multiple tenants, or multiple protocols. If you're a lone developer who just wants a REST endpoint and nothing else, raw FastAPI is fine. The moment you add a second agent, a second framework, or a second tenant, FastAgentic starts paying for itself.
What it replaces
In production deployments we've seen FastAgentic replace: a hand-rolled FastAPI + Celery + Redis stack for resumable runs, a custom MCP wrapper for exposing internal tools to Claude/Cursor, a bespoke cost-tracking middleware, and in several cases an entire LangServe deployment that couldn't support non-LangChain frameworks.
Frequently asked
- Why not just use FastAPI directly?
- You can — and FastAgentic is built on top of it. But an agent endpoint needs streaming, checkpoints, cost tracking, policy enforcement, MCP/A2A schema mirroring, and resumable runs. Writing that for every endpoint costs you roughly 500 lines of boilerplate per project. FastAgentic gives you all of it through one decorator and keeps the FastAPI ergonomics you already know.
- Do I have to rewrite my existing agents?
- No. run_opaque wraps existing agent objects unchanged — it snapshots inputs and outputs and makes the whole run resumable. You keep your current PydanticAI, LangGraph, CrewAI, or LangChain code and gain deployment features for free.
- How is this different from LangServe?
- LangServe is tightly coupled to LangChain. FastAgentic supports every major framework, adds MCP and A2A protocol surfaces, ships durable step-level checkpoints, and includes policy, cost, and audit governance that LangServe does not.
- Is it production-ready?
- Yes. FastAgentic is MIT-licensed, 1.x stable, and runs in production at companies shipping multi-tenant agent platforms. It supports Redis, Postgres, and S3 backends and integrates with OpenTelemetry, Langfuse, Portkey, and Datadog.
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.