agent-service-toolkit vs generative-ai-project-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
generative-ai-project-template
uv workspace with FastAPI, NiceGUI, LiteLLM and Promptfoo evals
| What we compare | agent-service-toolkit | generative-ai-project-template |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 75, popular | 13, niche |
| Freshness | 100, active | 100, active |
| Maintenance | 63, fair | 60, fair |
| Easy to run | 50, easy | 50, easy |
| Agent-ready | 55, partly | 0, none |
| Facts from GitHub and the README | ||
| Stars | 4.5k | 118 |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Sep 2026 |
| Last release | None published | Sep 2026 |
| Language | Python | Python |
| Docker | Yes | Yes |
| GPU | Not needed | Optional |
| 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
generative-ai-project-template
Python 3.12 uv workspace with a FastAPI backend (port 8000) and NiceGUI frontend (port 8080) for chat, information extraction and RAG over documents, with models served locally by Ollama or through any LiteLLM provider. Ships Makefiles for install, run, test, Docker (CPU and CUDA compose), pre-commit with ruff and detect-secrets, pytest, Promptfoo and Ragas evals, GitHub Actions, Renovate and an mkdocs site.
Who it is for: Python teams starting an LLM app with evals built in
Strengths
- LiteLLM naming lets you switch between Ollama and cloud models by env
- Promptfoo and Ragas evaluation wired into the template
- CPU and CUDA docker compose variants
- CI tests the app against local Ollama models
Weaknesses
- NiceGUI frontend is unusual for product UIs
- No auth, database or persistence described
- Ubuntu 22.04 or macOS only per prerequisites
- CUDA path installs PyTorch; heavier install
- Python, LiteLLM (any provider), Ollama
- Needs Ollama (local models) or an LLM provider key via LiteLLM
- GitHub template
- Docker
- env example file
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- fastapi-langgraph-agent-production-ready-template vs full-stack-ai-agent-template
- fastapi-langgraph-agent-production-ready-template vs generative-ai-project-template
- full-stack-ai-agent-template vs generative-ai-project-template
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