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.
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README.md
FastAPI LangGraph Agent Template
A production-ready template for building AI agent backends with FastAPI and LangGraph. Handles the hard parts — stateful conversations, long-term memory, tool calling, observability, rate limiting, auth — so you can focus on your agent logic.
Built for AI engineers who want a solid foundation, not a tutorial project.
Powered by Atlas Cloud — Drop-in LLM Backend for LangGraph Agents
Atlas Cloud provides an OpenAI-compatible LLM API that integrates seamlessly into this FastAPI + LangGraph template — no code changes to your agent graph needed. Just swap OPENAI_BASE_URL and OPENAI_API_KEY to access DeepSeek, Qwen, GLM, Kimi, MiniMax, Gemini, Claude, GPT and more through a single unified endpoint.
The LLMRegistry in this template uses langchain_openai.ChatOpenAI — Atlas Cloud is wire-compatible, so you get instant access to 59+ curated reasoning models without touching any LangGraph logic.
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(
model="deepseek-ai/deepseek-v4-pro",
openai_api_base="https://api.atlascloud.ai/v1",
openai_api_key="<your-atlascloud-key>",
max_tokens=512, # reasoning model requires max_tokens >= 512
)
<details>
<summary>📋 Full model catalog (59 LLMs available)</summary>
</details>
This works as a drop-in replacement anywhere ChatOpenAI is used in your LangGraph agent — including the LLMRegistry, the circular fallback service, and mem0 long-term memory.
Structured logging with request/session/user context on every line
Quickstart
git clone <repo-url> my-agent && cd my-agent
cp .env.example .env.development # fill in your keys
make install
make docker-up # starts API + PostgreSQL
What is this template?
A production-ready foundation for AI agent backends built on FastAPI + LangGraph. It bundles the components you'd otherwise wire up by hand: stateful conversations, long-term memory, tool calling, observability, rate limiting, and JWT auth.
How does this differ from a basic LangGraph setup?
The base LangGraph quickstart stops at "agent runs locally". This template adds Alembic migrations, mem0 + pgvector long-term memory, Langfuse tracing, Prometheus + Grafana dashboards, JWT sessions, slowapi rate limiting, structured logging with per-request context, and a circular-fallback LLM service — production concerns you'd otherwise build separately.
Setup & Configuration
Do I need Docker?
Recommended but not required. make docker-up starts the API + PostgreSQL together. For local-only setup see docs/getting-started.md.
Which LLM providers are supported?
Today: OpenAI only via the LLMRegistry in app/services/llm/registry.py. Multi-provider support (Anthropic, Google, OpenRouter) via LangChain's init_chat_model is planned — see #51. Configure your model via DEFAULT_LLM_MODEL in .env.development.
How do I configure long-term memory?
Long-term memory is self-hosted: mem0 runs in-process and persists into your existing PostgreSQL via pgvector — there is no separate mem0 cloud account or API key. You only need a working OPENAI_API_KEY (used for fact extraction + embeddings) and the pgvector extension enabled. See docs/memory.md for details.
Development
How do I add a custom tool?
Drop a LangChain @tool-decorated function in app/core/langgraph/tools/ and register it in the tools list exported from that package. The agent picks it up on next start; no graph changes needed.
How does the LLM service handle failures?
Two layers: (1) per-call exponential-backoff retry via tenacity, (2) circular fallback — if the active model exhausts its retries, the service rotates to the next model in LLMRegistry and continues. A total timeout budget caps the whole call so latency stays bounded. See docs/llm-service.md.
Can I use this without Langfuse?
Yes. Set LANGFUSE_TRACING_ENABLED=false (or omit the Langfuse keys). The agent runs unchanged; structured logs still capture request/session/user context.
Troubleshooting
The API won't start
Ensure PostgreSQL is running (make docker-up brings it up alongside the API)
Confirm .env.development exists — copy from .env.example and fill in required keys
Apply migrations: make migrate
Memory / semantic search returns nothing
Verify the pgvector extension is enabled in your PostgreSQL instance
Confirm OPENAI_API_KEY is valid (mem0 calls OpenAI for fact extraction + embeddings)
Check LONG_TERM_MEMORY_MODEL and LONG_TERM_MEMORY_EMBEDDER_MODEL are set in .env.development
Rate limiting is too aggressive
Limits are defined in app/core/limiter.py (slowapi). Adjust per-route decorators or the default rate in that file. See docs/configuration.md for the related env vars.
Ecosystem Role
Standard MoltPulse indexed agent.
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