agent-service-toolkit vs fastapi-langgraph-agent-production-ready-template
Two of the top ai api backends, side by side: score, setup, license, activity and what each review found.
agent-service-toolkit
LangGraph agents served by FastAPI with a Streamlit chat client
fastapi-langgraph-agent-production-ready-template
FastAPI service for a LangGraph agent with auth, memory and tracing
| What we compare | agent-service-toolkit | fastapi-langgraph-agent-production-ready-template |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 75, popular | 63, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 63, fair | 46, patchy |
| Easy to run | 50, easy | 17, hard |
| Agent-ready | 55, partly | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 4.5k | 2.7k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Sep 2026 |
| Last release | None published | None published |
| Language | Python | Python |
| Docker | Yes | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Not stated | Not stated |
agent-service-toolkit
Python service where LangGraph v1 agents (interrupt, Command, Store) are served by FastAPI with streaming and non-streaming endpoints, AG-UI support, per-agent URL paths, /threads history and a Postgres checkpointer via docker compose. Includes an AgentClient, a Streamlit chat UI with voice, LangSmith feedback, Groq moderation, a ChromaDB RAG agent, unit, integration and smoke tests. Needs at least one LLM API key.
Who it is for: Python teams serving LangGraph agents over an API
Strengths
- AG-UI endpoint for CopilotKit-style frontends alongside the REST API
- docker compose watch runs Postgres, API and Streamlit with live reload
- Unit and integration tests plus smoke tests for Postgres, Mongo, AG-UI and Langfuse
- Hosted demo on Streamlit Cloud
Weaknesses
- Solo maintainer; issues triaged roughly biweekly
- Streamlit client is a demo UI, not a product frontend
- Content moderation needs a Groq API key
- Tests only run outside Docker
- Python, LangChain providers: OpenAI, Anthropic, Google, Ollama, VertexAI, vLLM/SGLang, AG-UI protocol
- Needs LLM API key (OpenAI, Anthropic, Google, Groq, Ollama or others), PostgreSQL (compose), LangSmith (optional), ChromaDB (RAG agent)
- GitHub template
- Docker
- env example file
fastapi-langgraph-agent-production-ready-template
FastAPI backend with a stateful LangGraph agent (Postgres checkpointing, tool calling, human-in-the-loop), mem0 long-term memory on pgvector, JWT auth and sessions, slowapi rate limiting, Alembic migrations, optional Valkey/Redis cache, Langfuse tracing, Prometheus and Grafana, and evals. make docker-up starts the API on port 8000 with PostgreSQL. OpenAI only via ChatOpenAI; any OpenAI-compatible base URL works.
Who it is for: Python teams productionizing a single LangGraph agent
Strengths
- JWT sessions, rate limiting and structured per-request logging included
- mem0 long-term memory runs in-process on pgvector; no mem0 cloud
- Circular model fallback with retries and a total timeout budget
- Langfuse, Prometheus and Grafana wired; Langfuse can be disabled
Weaknesses
- OpenAI (or OpenAI-compatible) only; multi-provider is an open issue
- README leads with a sponsor pitch for Atlas Cloud
- Needs the pgvector extension and an OpenAI key for memory
- Not a GitHub template; clone and strip
- Python, OpenAI via langchain_openai.ChatOpenAI (any OpenAI-compatible base URL)
- Needs PostgreSQL with pgvector, OpenAI API key, Valkey/Redis (optional), Langfuse (optional)
- Docker
- env example file
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