langchain-ai/deepagents
The batteries-included agent harness. observed · 2026-08-28
Health v2 · maintenance only
81/100
- Activity 99
- Release rhythm 87
- Longevity 28
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 0
- age_days: 402
- days_rel: 7
- days_push: 7
- n_releases_24m: 246
Adoption not part of the score
28559 stars · 4000 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
Deep Agents is an open-source, batteries-included agent harness from LangChain built on LangGraph, providing an opinionated agent with filesystem tools, sub-agent delegation, context management, persistent memory, and human-in-the-loop approval out of the box. It is model-agnostic (works with any tool-calling LLM), available in Python and TypeScript, and every component can be extended, overridden, or replaced.
Use cases
- build an AI agent that can plan and execute multi-step tasks
- delegate subtasks to subagents with isolated context windows
- give an LLM agent file read/write/search and shell access
- build a research agent that writes reports
- add human approval before an agent runs tools
- run a coding agent in the terminal powered by any LLM
- persist agent memory across sessions
- connect an agent to MCP servers and custom tools
When to choose
- you want a production-ready agent with sensible defaults for long-horizon, multi-step work
- you need subagents, filesystem tools, context summarization, and memory without building them yourself
- you want model-agnostic agents that work with OpenAI, Anthropic, Google, or local models
- you're already in the LangChain/LangGraph ecosystem and want tracing and deployment via LangSmith
When to avoid
- you need a minimal, unopinionated agent loop and prefer to assemble everything yourself
- your LLM does not support tool calling
- you want a fully managed no-code agent builder rather than a code framework
- you need a framework outside the LangChain ecosystem with no LangGraph dependency
Facets
framework · maturity active
agent-framework llm-inference mcp file-system chatbot large-language-models developer-tools python cross-platform cli agent-harness subagents context-management langgraph human-in-the-loop long-horizon-tasks skills model-agnostic ai-agents automation nodejs
10 sources
- readme: https://github.com/langchain-ai/deepagents · fetched 2026-08-28 · aa6b8027fa03
- homepage: https://docs.langchain.com/deepagents · fetched 2026-08-29 · a6d02b8234ce
- site_page: https://docs.langchain.com/use-these-docs · fetched 2026-08-29 · 5ab7220bb555
- site_page: https://docs.langchain.com/oss/python/deepagents/quickstart · fetched 2026-08-29 · c2dcf9bbe1c5
- site_page: https://docs.langchain.com/oss/python/releases/changelog · fetched 2026-08-29 · 048dded673bc
- site_page: https://docs.langchain.com/langsmith/observability-quickstart · fetched 2026-08-29 · e2173e54f36b
- site_page: https://docs.langchain.com/oss/python/integrations/middleware · fetched 2026-08-29 · 7801c289afbb
- site_page: https://docs.langchain.com/ · fetched 2026-08-29 · e6cbe67a291c
- site_page: https://docs.langchain.com/oss/python/integrations/providers/overview · fetched 2026-08-29 · 8260e6c475ce
- registry_pypi: https://pypi.org/pypi/deepagents/json · fetched 2026-08-29 · f66c258c31e8
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| langchain-ai/deepagents | main | 81 |
For agents
markdown · JSON · MCP: product_card(name="langchain-ai/deepagents")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem