deepagents vs agent-service-toolkit
Python frameworks for building and serving LangGraph agents
deepagents provides a harness with planning, delegation, file operations, and context management built in. agent-service-toolkit connects LangGraph agents to a FastAPI service, reusable client, and Streamlit chat interface, so it focuses more on the application path around an agent.
deepagents: Build Agents with Planning and Tooling Built In
Deep Agents is a Python agent harness for building LLM agents with planning, delegation, file access, and context management built in. It suits developers who need a more capable starting point than a minimal agent loop, while retaining control over models and tools.
agent-service-toolkit: Build and Serve LangGraph Agents
A Python starter toolkit that connects LangGraph agents to a FastAPI service, reusable client, and Streamlit chat interface. It suits developers who want an end-to-end foundation for building, testing, and customizing agent applications.
| deepagents | agent-service-toolkit | |
|---|---|---|
| Language | Python | Python |
| License | MIT | MIT |
| Stars | 29.9k | 4.5k |
| Forks | 4.2k | 789 |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- deepagents bundles agent capabilities such as sub-agent delegation, file tools, memory, and context management; agent-service-toolkit bundles service, client, and chat UI components.
- deepagents supports customizable tools and sandboxed shell commands; agent-service-toolkit includes examples for APIs, multiple agent routes, voice chat, and a ChromaDB-backed RAG agent.
- deepagents is suited to building multi-step agent workflows; agent-service-toolkit is suited to serving and testing agents through an API and web interface.
- Both projects use Python and have MIT licenses. deepagents is built on LangGraph, while agent-service-toolkit specifically combines LangGraph with FastAPI and Streamlit.
- deepagents has 29.9k stars and 4.2k forks; agent-service-toolkit has 4.5k stars and 789 forks.
Choose deepagents if you…
- need built-in planning, delegation, file access, or context management for multi-step agents.
- want to customize an agent harness and its tools or sandbox boundaries.
- need workflows that can persist useful state across sessions.
Choose agent-service-toolkit if you…
- want a LangGraph agent starter with a FastAPI service, reusable client, and Streamlit interface.
- need streaming endpoints, multiple agent routes, or a sample human-in-the-loop flow.
- want to adapt an included ChromaDB-backed RAG example.
Compare the numbers side by side, or add more projects
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.