Where FastAgentic fits
FastAgentic is the deployment layer for agentic apps — you bring the agent (PydanticAI, LangGraph, CrewAI, or LangChain), it ships as REST + MCP + A2A with auth, checkpoints, cost tracking, and observability. Here is where teams reach for it.
By who you are
Add agents to a FastAPI app
Keep your decorators, Pydantic models, dependency injection, and tests. Add streaming, MCP/A2A surfaces, durable checkpoints, and cost tracking without leaving the FastAPI mental model.
Read more →Deploy a LangGraph graph to production
Wrap an existing LangGraph graph and serve it as REST + MCP + A2A with auth, durable checkpoints, and observability — without rewriting your graph.
Read more →Serve PydanticAI agents as endpoints
Turn PydanticAI agents into typed, streaming HTTP endpoints that are simultaneously MCP tools and A2A skills, with policy-based rate limits and budgets.
Read more →Standardize how agents ship
Give every team one deployment layer: consistent auth, cost attribution per tenant, checkpoints, and observability across whatever framework each team chose.
Read more →By what you are building
Internal agent platform
A platform team offers a paved road so product teams can ship agents built in different frameworks with the same auth, budgets, and telemetry.
Expose an agent over MCP
A single agent function becomes an MCP tool and an A2A skill from one schema, so other agents and IDEs can call it without a second implementation.
Crash-safe long-running agents
run_opaque() and durable checkpoints let a long agent run resume after a crash or redeploy instead of restarting from scratch.
Cost-governed multi-tenant agents
Per-request token accounting attributed to user/tenant, with declarative budget caps and rate limits that compose like FastAPI dependencies.
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.