# fastapi-langgraph-agent-production-ready-template: what the template actually wires up > A FastAPI and LangGraph backend skeleton with checkpointing, mem0 memory, JWT auth and Langfuse tracing already connected. The parts it leaves to you are the parts that decide whether it fits. **wassim249/fastapi-langgraph-agent-production-ready-template** — A production-ready FastAPI template for building AI agent applications with LangGraph integration. This template provides a robust foundation for building scalable, secure, and maintainable AI agent services. - Repository: https://github.com/wassim249/fastapi-langgraph-agent-production-ready-template - Stars: 2,686 · Forks: 635 - Language: Python - License: MIT - Published: 2026-09-09 · Updated: 2026-09-09 · Language: en - Canonical page: https://hysenlabs.com/projects/wassim249-fastapi-langgraph-agent-production-ready-template ## The gap this template fills for LangGraph agent backends LangGraph gives you a graph, a checkpointer interface and a state object. It does not give you a user table, a JWT flow, a per-user memory store, a rate limiter or a trace of which model answered which request. Those are the pieces most agent demos skip and most agent products need, and the README frames the project exactly that way: it handles stateful conversations, long-term memory, tool calling, observability, rate limiting and auth so you can focus on agent logic. The target reader is stated plainly in the README: AI engineers who want a solid foundation rather than a tutorial project. That is a real distinction. A tutorial shows one happy path through a chat endpoint. This repository ships alembic migrations, a Prometheus and Grafana directory, an evals package, a Dockerfile with a non-root user and a pyproject.toml that pins langgraph and langchain at major version 1. The value is in the assembled edges, not in the graph itself. ## Request flow: middleware, LLM service, memory and checkpointing The repository layout in the README tells most of the story. Routes live under app/api/v1, settings under app/core/config.py, the agent graph and tools under app/core/langgraph, and the LLM, database and memory services under app/services. Middleware in app/core/middleware.py handles metrics, logging context and profiling, and app/core/limiter.py holds rate limiting. Two design choices stand out. First, the LLM service is described as having circular model fallback, exponential backoff retries and a total timeout budget. A timeout budget is the more interesting of the three: it caps the whole call chain rather than each attempt, which is what you want when a fallback model must still answer inside the client's patience. Second, long-term memory is mem0 over pgvector, described as semantic search per user with a cache layer. The cache is optional and sits behind app/core/cache.py, which the README says uses Valkey or Redis with an in-memory fallback. That fallback matters for local work and is a liability in a multi-process deployment, since each worker keeps its own copy. Checkpointing comes from langgraph-checkpoint-postgres, which is a declared dependency, so conversation state persists in the same PostgreSQL instance as the application data. docker-compose.yml uses the pgvector/pgvector:pg16 image, which covers both the vector store and the ordinary relational tables. ## Installing it and making a first authenticated call The README gives a four-command quickstart. Clone the repository, copy the environment example, install dependencies and start the compose stack, which brings up the API plus PostgreSQL. ```bash git clone my-agent && cd my-agent cp .env.example .env.development # fill in your keys make install make docker-up # starts API + PostgreSQL ``` After that, the README says to open http://localhost:8000/docs for the interactive API. Before you run anything, edit .env.development. The compose file requires JWT_SECRET_KEY and fails with an explicit message if it is missing, and OPENAI_API_KEY is a placeholder string in the example file. If you would rather point the template at an OpenAI-compatible endpoint instead of OpenAI itself, the README shows the three variables to change. This is how you would set them in .env.development. ```env OPENAI_API_KEY= OPENAI_BASE_URL=https://api.atlascloud.ai/v1 DEFAULT_LLM_MODEL=deepseek-ai/deepseek-v4-pro ``` The README states that the LLMRegistry uses langchain_openai.ChatOpenAI, so any wire-compatible endpoint works without touching the graph. Note the compose file's own comment: the .env files set POSTGRES_HOST to localhost, which is right for a local run but unreachable from inside a container, so compose overrides it to db. If you run the app outside Docker against a containerised database, keep localhost. If you run both in compose, leave the override alone. ## Where the template stops helping The README does not document rollback for its Alembic migrations, and it does not describe what happens to in-flight LangGraph runs when the API restarts mid-stream. Those are the two questions I would want answered before putting this behind real traffic, and the documentation is silent on both. The in-memory cache fallback is the sharper limitation. It is a convenience for development, but a deployment that scales the app service to more than one replica without setting VALKEY_HOST loses cache coherence silently. Nothing in the README suggests the application detects that condition or warns about it. Authentication is JWT with session management, and the default token lifetime in .env.example is 30 days. That is a long window for a template aimed at production, and the example secret is a literal placeholder string. The pyproject.toml also requires Python 3.13 or newer, which rules out teams pinned to 3.11 or 3.12 for other dependencies. Finally, this is a backend template: there is no frontend, no admin UI, and no user registration flow described in the README beyond the auth endpoints in docs/authentication.md. ## How it compares to a plain LangGraph server LangGraph's own server deployment path gives you a graph, a runtime and a persistence layer, and it is the right choice when the graph is the whole product and you want the platform to own execution. This template takes the opposite assumption: you own the FastAPI process, the routes, the middleware and the database schema, and LangGraph is one component inside app/core/langgraph. That difference shows up in what you get and what you give up. You get JWT auth, slowapi rate limits, structured logging with request, session and user context on every line, Langfuse tracing, Prometheus metrics and Grafana dashboards in the same repository. You give up the platform's managed execution and streaming semantics, and you take on the operational surface of a PostgreSQL instance, an optional Valkey instance and the monitoring stack. If your agent is a single stateless endpoint, the plain LangGraph path is less to run. If you need per-user memory and an auth boundary, this template has already made those choices. ## Maintenance, licence and upgrade surface The licence is MIT, which permits commercial use and modification, and the README points to LICENSE rather than restating terms. The practical licence question is not the template itself but its dependency chain: mem0, Langfuse, slowapi, supabase and the LangChain packages each carry their own terms, and the README does not survey them. Check those separately if you redistribute. The last push to the default branch was on 2026-08-16, so the project has recent activity. There are no retrieved releases, which means versioning is currently by commit rather than by published tag, and pinning a commit hash is safer than tracking master if you depend on it. Upgrade cost concentrates in three places. The Dockerfile installs from uv.lock with uv sync --frozen, so dependency bumps require regenerating the lock file and rebuilding. langfuse is pinned exactly at 3.9.1 in pyproject.toml, so that one moves only when you change it deliberately. And the LangChain and LangGraph entries are major-version floors, which means a new major release from either project can land in your environment on the next lock refresh. The evals package and the alembic directory are the two areas where a schema or graph change will require work on your side rather than a version bump. ## Conclusion Adopt it if you are building a Python agent backend that needs sessions, per-user memory and tracing, and you would rather edit an existing wiring than write one. Skip it if your agent is a single endpoint with no per-user state, or if your stack is Node or Go: the template's value is in the Python service layer, not the API shape. Before committing, run make docker-up and confirm the app container reports healthy, because the compose file overrides POSTGRES_HOST to db while .env files set it to localhost, and that mismatch is the first thing that breaks a local setup. ## FAQ ### What is fastapi-langgraph-agent-production-ready-template? It is an MIT-licensed FastAPI template for AI agent backends built on LangGraph. The README describes it as bundling stateful conversations, long-term memory, tool calling, observability, rate limiting and JWT auth so you can focus on agent logic. ### How do I install fastapi-langgraph-agent-production-ready-template? The README quickstart clones the repository, copies .env.example to .env.development, fills in keys, then runs make install and make docker-up, which starts the API and PostgreSQL. The interactive API is then at http://localhost:8000/docs. ### Can fastapi-langgraph-agent-production-ready-template use a model other than OpenAI? Yes, if the provider is OpenAI-compatible. The README states that setting OPENAI_BASE_URL, OPENAI_API_KEY and DEFAULT_LLM_MODEL is enough, because the LLMRegistry uses langchain_openai.ChatOpenAI and needs no change to the LangGraph logic. ### What Python version does fastapi-langgraph-agent-production-ready-template require? The pyproject.toml sets requires-python to >=3.13, and the Dockerfile builds from python:3.13.2-slim. Teams pinned to an earlier Python release would need to change both before adopting it. ### Does fastapi-langgraph-agent-production-ready-template include a frontend? No. The repository contains the FastAPI backend, migrations, evals, Docker configuration and monitoring directories. The README documents API endpoints and authentication but no user interface. ## Sources - [Issues](https://github.com/wassim249/fastapi-langgraph-agent-production-ready-template/issues) - [License: MIT](https://github.com/wassim249/fastapi-langgraph-agent-production-ready-template/blob/master/LICENSE) - [README](https://github.com/wassim249/fastapi-langgraph-agent-production-ready-template/blob/master/README.md) - [wassim249/fastapi-langgraph-agent-production-ready-template on GitHub](https://github.com/wassim249/fastapi-langgraph-agent-production-ready-template) --- Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/wassim249-fastapi-langgraph-agent-production-ready-template