langchain-ai/deepagents

記録された最新のソース活動: 2026年10月26日 17:31

SkillsMP カタログ更新: 2026年10月29日 18:30

収集済み skills: 26

GitHub スター: 30,738
GitHub フォーク: 4,323

AI アシスタントでインストール

このプロンプトをコピーして、使用中の AI アシスタントに送ってください。

Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/langchain-ai/deepagents.

リポジトリの README

README の元ファイル：README.md

README の元ファイルを読む

README 全文を表示

<div align="center"> <a href="https://docs.langchain.com/oss/python/deepagents/overview#deep-agents-overview"> <picture> <source media="(prefers-color-scheme: dark)" srcset=".github/images/logo-dark.svg"> <source media="(prefers-color-scheme: light)" srcset=".github/images/logo-light.svg"> <img alt="Deep Agents Logo" src=".github/images/logo-dark.svg" width="50%"> </picture> </a> </div> <div align="center"> <h3>The batteries-included agent harness.</h3> </div> <div align="center"> <a href="https://opensource.org/licenses/MIT" target="_blank"><img src="https://img.shields.io/pypi/l/deepagents" alt="PyPI - License"></a> <a href="https://pypistats.org/packages/deepagents" target="_blank"><img src="https://img.shields.io/pepy/dt/deepagents" alt="PyPI - Downloads"></a> <a href="https://pypi.org/project/deepagents/#history" target="_blank"><img src="https://img.shields.io/pypi/v/deepagents?label=%20" alt="Version"></a> <a href="https://x.com/langchain_oss" target="_blank"><img src="https://img.shields.io/twitter/url/https/twitter.com/langchain_oss.svg?style=social&label=Follow%20%40LangChain" alt="Twitter / X"></a> </div> <br> Deep Agents is an open source agent harness — an opinionated agent that runs out of the box. Extend, override, or replace any piece. **Principles:** - **Opinionated** — defaults tuned for long-horizon, multi-step work - **Extensible** — override or replace any piece without forking - **Model-agnostic** — works with any LLM that supports tool calling: frontier, open-weight, or local - **Production-ready** — built on LangGraph (streaming, persistence, checkpointing) with first-class tracing, evaluation, and deployment via LangSmith **Features include:** - **Sub-agents** — delegate tasks to agents with isolated context windows - **Filesystem** — read, write, edit, or search over pluggable local, sandboxed, or remote backends - **Context management** — summarize long threads and offload tool outputs to disk - **Shell access** — run commands in your sandbox of choice - **Persistent memory** — pluggable state and store backends for cross-session recall - **Human-in-the-loop** — approve, edit, or reject tool calls before they run - **Skills** — reusable behaviors the agent can load on demand - **Tools** — bring your own functions or any MCP server Deep Agents is available as a JavaScript/TypeScript library — see [deepagents.js](https://github.com/langchain-ai/deepagentsjs). > [!NOTE] > **Deep Agents Code** — a pre-built coding agent in your terminal, similar to Claude Code or Cursor, powered by any LLM. Install with `curl -LsSf https://langch.in/dcode | bash`. See the [documentation](https://docs.langchain.com/deepagents-code) for the full feature set. ## Quickstart ```bash uv add deepagents ``` ```python from deepagents import create_deep_agent agent = create_deep_agent( model="openai:gpt-6-astra", system_prompt="You are a research assistant.", ) result = agent.invoke({"messages": "Research LangGraph and write a summary"}) ``` The agent can plan, read/write files, and manage its own context. Add your own tools, swap models, customize prompts, configure sub-agents, and more. See the [documentation](https://docs.langchain.com/oss/python/deepagents/overview) for full details. > [!TIP] > For developing, debugging, and deploying AI agents and LLM applications, see [LangSmith](https://docs.langchain.com/langsmith/home). ## FAQ ### How is this different from LangGraph or LangChain? LangGraph is the graph runtime. LangChain's `create_agent` is a minimal agent harness on top of it. Deep Agents is a more opinionated harness on top of `create_agent` — same building blocks, but with filesystem, sub-agents, context management, and skills bundled in. For how the three relate, see the [LangChain ecosystem overview](https://docs.langchain.com/oss/python/concepts/products). ### Does this work with open-weight or local models? Yes. Any model that supports tool calling works — frontier APIs (OpenAI, Anthropic, Google), open-weight models hosted on providers like Baseten or Fireworks, and self-hosted models via Ollama, vLLM, or llama.cpp. Use any [LangChain chat model](https://docs.langchain.com/oss/python/langchain/models). ### Can I use this in production? Yes! Deep Agents is built on LangGraph, designed for production agent deployments. Pair it with [LangSmith](https://docs.langchain.com/langsmith/home) for tracing, evaluation, and monitoring. See [Going to production](https://docs.langchain.com/oss/python/deepagents/going-to-production) for the full guide. ### When should I use Deep Agents vs. LangChain or LangGraph directly? All three are layers in the same stack — see the [LangChain ecosystem overview](https://docs.langchain.com/oss/python/concepts/products) for how they relate. Use **Deep Agents** when you want the full harness — planning, context management, delegation — out of the box. Use [**LangChain's `create_agent`**](https://docs.langchain.com/oss/python/langchain/agents) when you want a lighter harness without the bundled middleware. Drop to [**LangGraph**](https://docs.langchain.com/oss/python/langgraph/overview) when the agent loop itself isn't the right shape and you need a custom graph. The layers compose: any LangGraph `CompiledStateGraph` can be passed in as a sub-agent to a Deep Agent, so custom orchestration plugs in alongside the harness's defaults. --- ## Resources - [Examples](examples/) — working agents and patterns - [Documentation](https://docs.langchain.com/oss/python/deepagents/overview) — conceptual overviews and guides - [LangChain ecosystem overview](https://docs.langchain.com/oss/python/concepts/products) — how Deep Agents, LangChain, LangGraph, and LangSmith fit together - [API reference](https://reference.langchain.com/python/deepagents/) — complete reference for all public classes, functions, and types - [Discussions](https://forum.langchain.com/c/oss-product-help-lc-and-lg/deep-agents/18) — community forum for technical questions, ideas, and feedback - [LangChain Academy](https://academy.langchain.com/) — Comprehensive, free courses on LangChain libraries and products, made by the LangChain team. - [Development Guide](libs/DEVELOPMENT.md) — local setup and package-specific checks - [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview) — how to contribute and find good first issues - [Code of Conduct](https://github.com/langchain-ai/langchain/?tab=coc-ov-file) — community guidelines and standards --- ## Acknowledgements Inspired by Claude Code: an attempt to identify what makes it general-purpose, and push that further. ## Security Deep Agents follows a "trust the LLM" model. The agent can do anything its tools allow. Enforce boundaries at the tool/sandbox level, not by expecting the model to self-police. See the [security policy](https://github.com/langchain-ai/deepagents?tab=security-ov-file) for more information.

リポジトリの README を読み込んでいます…

このリポジトリの skills

ソフトウェア開発者市場調査アナリスト・マーケティングスペシャリストデータサイエンティストソフトウェア品質保証アナリスト・テスター

5 件の職業カテゴリ · 81% 分類済み

skill

職業分類

説明

更新

deepagents-plugin-discovery

未分類

Discover plugins from configured dcode marketplaces when a capability is missing or the user asks about available, disabled, not-yet-installed, or outdated plugins and connected marketplaces.

原文の言語: 英語

2026-10-26

deepagents-thread-inspector

未分類

Inspect and explain conversations in the local Deep Agents Code SQLite session store. Use to look up the current thread's title or name, as a fallback when LangSmith trace tooling is unavailable, for offline or untraced sessions, or when asked to identify or…

原文の言語: 英語

2026-10-22

configuration-hardening

情報セキュリティアナリスト

Review Talon tool placement and defensive prompts when requested or when adding or changing tools or subagents; apply confirmed changes and verify active capabilities.

原文の言語: 英語

2026-10-13

skill-creator

未分類

Guide for creating effective skills that extend agent capabilities with specialized knowledge, workflows, or tool integrations. Use this skill when the user asks to: (1) create a new skill, (2) make a skill, (3) build a skill, (4) set up a skill, (5)…

原文の言語: 英語

2026-09-29

skill-creator

未分類

原文の言語: 英語

2026-09-29

safety

未分類

Check secret handling, staged changes and push targets, destructive Git operations, Docker image hygiene, and new dependencies before consequential coding actions. Use for commits, pushes, container builds or publishes, dependency additions, and explicit…

原文の言語: 英語

2026-09-28

textual-screenshot

ソフトウェア開発者

Capture a Textual terminal UI as an SVG using its headless test harness. Use when asked to make, attach, or preview a screenshot of deepagents-code/dcode or another Textual app, visually verify a TUI state, or render a modal, screen, or widget without a…

原文の言語: 英語

2026-08-28

remember

ソフトウェア開発者

Review the current conversation and capture valuable knowledge — best practices, coding conventions, architecture decisions, workflows, and user feedback — into persistent memory (AGENTS.md) or reusable skills. Use when the user says: (1) remember this, (2)…

原文の言語: 英語

2026-08-26

code-review

ソフトウェア品質保証アナリスト・テスター

Perform a structured code review of changes, checking for correctness, style, tests, and potential issues.

原文の言語: 英語

2026-06-20

coding-prefs

ソフトウェア開発者

Read the user's coding preferences from /memory/coding-prefs.md before making non-trivial style decisions, and append new preferences when the user gives durable feedback.

原文の言語: 英語

2026-06-20

arxiv-search

市場調査アナリスト・マーケティングスペシャリスト

Searches arXiv for preprints and academic papers, retrieves abstracts, and filters by topic. Use when the user asks to find research papers, search arXiv, look up preprints, find academic articles in physics, math, CS, biology, statistics, or related fields.

原文の言語: 英語

2026-06-09

langgraph-docs

ソフトウェア開発者

Fetches and references LangGraph Python documentation to build stateful agents, create multi-agent workflows, and implement human-in-the-loop patterns. Use when the user asks about LangGraph, graph agents, state machines, agent orchestration, LangGraph API,…

原文の言語: 英語

2026-05-12

web-research

市場調査アナリスト・マーケティングスペシャリスト

Searches multiple web sources, synthesizes findings, and produces cited research reports using delegated subagents. Use when the user asks to research a topic online, search the web, look something up, find current information, compare options, or produce a…

原文の言語: 英語

2026-05-12

competitor-analysis

市場調査アナリスト・マーケティングスペシャリスト

Analyze competitors in a given market segment. Trigger on: competitive landscape, competitor analysis, market comparison, competitive positioning.

原文の言語: 英語

2026-04-17

analyze-market

市場調査アナリスト・マーケティングスペシャリスト

Perform a market analysis for a product category or segment. Trigger on: market analysis, market size, TAM SAM SOM, market opportunity, industry analysis.

原文の言語: 英語

2026-04-17

planning

ソフトウェア開発者

Break down a coding task into a structured implementation plan with clear steps, file identification, and risk assessment.

原文の言語: 英語

2026-04-09

blog-post

市場調査アナリスト・マーケティングスペシャリスト

Write structured long-form blog posts with research, SEO optimization, and cover image generation.

原文の言語: 英語

2026-04-09

social-media

市場調査アナリスト・マーケティングスペシャリスト

Create social media content including Twitter/X threads, LinkedIn posts, and short-form updates.

原文の言語: 英語

2026-04-09

blog-post

市場調査アナリスト・マーケティングスペシャリスト

Writes and structures long-form blog posts, creates tutorial outlines, and optimizes content for SEO with cover image generation. Use when the user asks to write a blog post, article, how-to guide, tutorial, technical writeup, thought leadership piece, or…

原文の言語: 英語

2026-03-17

social-media

市場調査アナリスト・マーケティングスペシャリスト

Drafts engaging social media posts, writes hooks, suggests hashtags, creates thread structures, and generates companion images. Use when the user asks to write a LinkedIn post, tweet, Twitter/X thread, social media caption, social post, or repurpose content…

原文の言語: 英語

2026-03-17

query-writing

ソフトウェア開発者

Writes and executes SQL queries from simple SELECTs to complex multi-table JOINs, aggregations, and subqueries. Use when the user asks to query a database, write SQL, run a SELECT statement, retrieve data, filter records, or generate reports from database…

原文の言語: 英語

2026-03-17

schema-exploration

ソフトウェア開発者

Lists tables, describes columns and data types, identifies foreign key relationships, and maps entity relationships in a database. Use when the user asks about database schema, table structure, column types, what tables exist, ERD, foreign keys, or how…

原文の言語: 英語

2026-03-17

cudf-analytics

データサイエンティスト

Use for GPU-accelerated data analysis on datasets, CSVs, or tabular data using NVIDIA cuDF. Triggers when tasks involve groupby aggregations, statistical summaries, anomaly detection, or large-scale data profiling.

原文の言語: 英語

2026-03-12

cuml-machine-learning

データサイエンティスト

Use for GPU-accelerated machine learning on tabular data using NVIDIA cuML. Triggers when tasks involve classification, regression, clustering, dimensionality reduction, or model training on datasets.

原文の言語: 英語

2026-03-12

data-visualization

データサイエンティスト

Use for creating publication-quality charts and multi-panel analysis summaries. Triggers when tasks involve visualizing data, plotting results, creating charts, or producing visual reports from analysis output.

原文の言語: 英語

2026-03-12

gpu-document-processing

ソフトウェア開発者

Use when processing large PDFs, document collections, or bulk text extraction tasks that benefit from GPU-accelerated processing. Triggers when the user provides large documents or needs bulk document analysis.

原文の言語: 英語

2026-03-12

収集済み skill 26 件中 26 件を表示しています。