{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/4"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"Deep Agents is an opinionated agent harness that runs LLM-powered agents out of the box, with built-in filesystem access, context management, sub-agent delegation, and skill loading\u2014extensible at any layer without forking.","skillfed_tags":["agent-framework","langgraph","llm-orchestration"],"use_cases":["Build research or analysis agents that read files, search content, and write summaries without managing context windows manually.","Deploy multi-step task automation (e.g., data processing, report generation) with sub-agent delegation and persistent state across sessions.","Prototype and ship LLM applications to production faster with built-in tracing, evaluation, and monitoring via LangSmith.","Create agents that run shell commands or interact with sandboxed environments while maintaining human approval gates on tool execution.","Extend agent behavior with reusable skills and custom tools without rewriting the agent loop or forking the library."],"what_it_does":"Deep Agents is a production-ready agent harness built on LangGraph that handles multi-step LLM-powered workflows with minimal boilerplate. It sits between LangChain's lightweight `create_agent` and raw LangGraph, bundling filesystem access (read, write, edit, search), context summarization, sub-agent delegation, shell execution, persistent memory, human-in-the-loop approval, and skill loading into a single opinionated interface. The agent works with any LLM that supports tool calling\u2014frontier models (OpenAI, Anthropic, Google), open-weight models on providers like Baseten or Fireworks, or self-hosted via Ollama, vLLM, or llama.cpp.\n\nYou provide a model identifier, custom tools, and a system prompt; the agent handles planning, context management, and delegation. It's designed for production deployment with LangSmith integration for tracing, evaluation, and monitoring. The harness is extensible\u2014override or replace any component without forking\u2014and composes with custom LangGraph graphs as sub-agents for orchestration beyond the defaults.","worth_installing":"Yes, if you are building multi-step LLM agents and want production-ready defaults (filesystem, context management, sub-agents, skills) without building from scratch. The active maintenance, zero known vulnerabilities, MIT license, and LangChain ecosystem backing make it low-risk. Install with caution only if your threat model requires strict LLM output validation\u2014the package trusts the model and enforces boundaries at the tool/sandbox level instead."},"id":"deepagents","links":{"html":"https://skillfed.io/packages/deepagents","md":"https://skillfed.io/packages/deepagents.md","pypi":"https://pypi.org/project/deepagents/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-13","license_spdx":null,"license_treatment":"permissive","name":"deepagents","python_support":"supports_current","summary":"Production-ready, extensible agent harness with a built-in filesystem and context management, sub-agent delegation, skills, and long-term memory."},"popularity":{"monthly_downloads":6498907,"position":1898,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.7.6"}