agent-service-toolkit vs full-stack-ai-agent-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
full-stack-ai-agent-template
Project generator for FastAPI and Next.js apps with agents and RAG
| What we compare | agent-service-toolkit | full-stack-ai-agent-template |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 75, popular | 52, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 63, fair | 96, healthy |
| Easy to run | 50, easy | 0, hard |
| Agent-ready | 55, partly | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 4.5k | 1.9k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | None published | Oct 2026 |
| Language | Python | Python |
| Docker | Yes | No |
| 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
full-stack-ai-agent-template
CLI (pip install fastapi-fullstack) scaffolding a FastAPI backend and Next.js frontend via a wizard or presets, with Pydantic AI, Pydantic Deep Agents, LangChain, LangGraph or DeepAgents, RAG on Milvus, Qdrant, pgvector or Chroma, WebSocket chat, JWT, OAuth, admin, teams, Stripe billing, Celery, Docker and K8s. make bootstrap starts Postgres, migrations and a seeded admin; projects can pull template upgrades later.
Who it is for: Python teams scaffolding a full-stack agent product
Strengths
- Wizard and presets pick framework, vector store, auth and billing per project
- Template upgrades merge into generated projects on a branch
- Chat UI renders tool calls, plans, subagents, charts and Python execution
- 100% coverage badge and CI on the generator
Weaknesses
- Generator, not a repo to fork; output size depends on options chosen
- Seeds admin@example.com with a fixed password; rotate before exposing
- Optional services (Milvus, Redis, Celery) raise the local footprint
- Windows needs GNU Make or WSL2
- Python, Pydantic AI, Pydantic Deep Agents, LangChain, LangGraph
- Needs PostgreSQL, Redis (optional), Milvus, Qdrant, pgvector or ChromaDB (RAG), Stripe (billing), LLM provider API key
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