RADAR DES SKILLS · GITHUB / langchain-ai

langchain-ai · Agent Skills

29 skills with substantive source documentation and an official publisher or observed installation demand. Read each skill’s original documentation through its card.

langchain-ai
Officiel

langgraph-cli

INVOKE THIS SKILL when using the langgraph CLI to scaffold, develop, build, or deploy LangGraph applications. Covers langgraph new, dev, build, up, deploy, and langgraph.json configuration.

installations
6
GitHub Stars
1,3 k
Mis à jour
2 oct.
langchain-ai
Officiel

langgraph-decision-models

INVOKE THIS SKILL when routing a LangGraph agent with a decision model (TypeSafe Jev, SemIf) instead of an LLM, or when auditing an existing agent for LLM calls that only produce a routing decision. Covers langchain-typesafe Noul/Choice/Score, reading answers correctly, threshold design, and LangSmith Gateway wiring.

installations
6
GitHub Stars
1,3 k
Mis à jour
2 oct.
langchain-ai
Officiel

langgraph-python-quickstart

Scaffold a minimal local LangGraph agent in Python by following the official quickstart. Use when the user wants to quickly build or try a LangGraph agent locally.

installations
6
GitHub Stars
1,3 k
Mis à jour
2 oct.
langchain-ai
Officiel

langgraph-typescript-quickstart

Scaffold a minimal local LangGraph agent in TypeScript by following the official quickstart. Use when the user wants to quickly build or try a LangGraph agent locally.

installations
6
GitHub Stars
1,3 k
Mis à jour
2 oct.
langchain-ai
Officiel

deep-agents-core

INVOKE THIS SKILL when building ANY Deep Agents application. Covers createdeepagent(), harness architecture, SKILL.md format, and configuration options.

installations
4
GitHub Stars
1,2 k
Mis à jour
17 sept.
langchain-ai
Officiel

ecosystem-primer

INVOKE FIRST for any LangChain / LangGraph / Deep Agents agent building project before consulting other skills or writing any agent code. Required starting point for up to date info on framework selection (LangChain vs LangGraph vs Deep Agents vs hybrid composition), agent patterns, install, environment setup, and which skill to load next.

installations
4
GitHub Stars
1,2 k
Mis à jour
25 sept.
langchain-ai
Officiel

langsmith-evaluator

INVOKE THIS SKILL when building evaluation pipelines for LangSmith. Covers three core components: (1) Creating Evaluators - LLM-as-Judge, custom code; (2) Defining Run Functions - how to capture outputs and trajectories from your agent; (3) Running Evaluations - locally with evaluate() or auto-run via LangSmith. Uses the langsmith CLI tool.

installations
4
GitHub Stars
160
Mis à jour
2 oct.
langchain-ai
Officiel

deep-agents-memory

INVOKE THIS SKILL when your Deep Agent needs memory, persistence, or filesystem access. Covers StateBackend (ephemeral), StoreBackend (persistent), FilesystemMiddleware, and CompositeBackend for routing.

installations
3
GitHub Stars
1,3 k
Mis à jour
25 sept.
langchain-ai
Officiel

deep-agents-orchestration

INVOKE THIS SKILL when using subagents, task planning, or human approval in Deep Agents. Covers SubAgentMiddleware, TodoList for planning, and HITL interrupts.

installations
3
GitHub Stars
1,3 k
Mis à jour
25 sept.
langchain-ai
Officiel

langchain-dependencies

INVOKE THIS SKILL when setting up a new project or when asked about package versions, installation, or dependency management for LangChain, LangGraph, LangSmith, or Deep Agents. Covers required packages, minimum versions, environment requirements, versioning best practices, and common community tool packages for both Python and TypeScript.

installations
3
GitHub Stars
1,3 k
Mis à jour
25 sept.
langchain-ai
Officiel

langgraph-fundamentals

INVOKE THIS SKILL when writing ANY LangGraph code. Covers StateGraph, state schemas, nodes, edges, Command, Send, invoke, streaming, and error handling.

installations
3
GitHub Stars
1,3 k
Mis à jour
25 sept.
langchain-ai
Officiel

langgraph-human-in-the-loop

INVOKE THIS SKILL when implementing human-in-the-loop patterns, pausing for approval, or handling errors in LangGraph. Covers interrupt(), Command(resume=...), approval/validation workflows, and the 4-tier error handling strategy.

installations
3
GitHub Stars
1,3 k
Mis à jour
25 sept.
langchain-ai
Officiel

langgraph-persistence

INVOKE THIS SKILL when your LangGraph needs to persist state, remember conversations, travel through history, or configure subgraph checkpointer scoping. Covers checkpointers, threadid, time travel, Store, and subgraph persistence modes.

installations
3
GitHub Stars
1,3 k
Mis à jour
25 sept.
langchain-ai
Officiel

deepagents-python-quickstart

Scaffold a minimal local Deep Agent in Python by following the official quickstart, using provider-native web search instead of Tavily. Use when the user wants to quickly build or try a Deep Agent locally.

installations
2
GitHub Stars
1,3 k
Mis à jour
25 sept.
langchain-ai
Officiel

deepagents-typescript-quickstart

Scaffold a minimal local Deep Agent in TypeScript by following the official quickstart, using provider-native web search instead of Tavily. Use when the user wants to quickly build or try a Deep Agent locally.

installations
2
GitHub Stars
1,3 k
Mis à jour
25 sept.
langchain-ai
Officiel

eval-engineering

Inspect an agent repository and optional traces, interview the user, write reviewed Task Specs, build and audit Harbor tasks, and bootstrap reusable project World Knowledge Skills. Use for agent evals, benchmark design, Task generation, controlled Environments, synthetic data, Verifiers, Harbor runs, calibration, or continuous benchmark maintenance.

installations
2
GitHub Stars
1,3 k
Mis à jour
25 sept.
langchain-ai
Officiel

langchain-fundamentals

Create LangChain agents with createagent, define tools, and use middleware for human-in-the-loop and error handling.

installations
2
GitHub Stars
1,3 k
Mis à jour
25 sept.
langchain-ai
Officiel

langchain-middleware

INVOKE THIS SKILL when you need human-in-the-loop approval, custom middleware, or structured output. Covers HumanInTheLoopMiddleware for human approval of dangerous tool calls, creating custom middleware with hooks, Command resume patterns, and structured output with Pydantic/Zod.

installations
2
GitHub Stars
1,3 k
Mis à jour
25 sept.
langchain-ai
Officiel

langchain-python-quickstart

Scaffold a minimal local LangChain agent in Python by following the official quickstart. Use when the user wants to quickly build or try a LangChain agent locally.

installations
2
GitHub Stars
1,3 k
Mis à jour
25 sept.
langchain-ai
Officiel

langchain-rag

INVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system. Covers document loaders, RecursiveCharacterTextSplitter, embeddings (OpenAI), and vector stores (Chroma, FAISS, Pinecone).

installations
2
GitHub Stars
1,3 k
Mis à jour
25 sept.
langchain-ai
Officiel

langchain-typescript-quickstart

Scaffold a minimal local LangChain agent in TypeScript by following the official quickstart. Use when the user wants to quickly build or try a LangChain agent locally.

installations
2
GitHub Stars
1,3 k
Mis à jour
25 sept.
langchain-ai
Officiel

langsmith-online-eval-engineering

Iteratively inspect traces, interview the user, and create LangSmith online evaluators one at a time. Use specifically for creating online evaluators for use within LangSmith -- use "eval-engineering" for Harbor-style online evaluations.

installations
2
GitHub Stars
1,3 k
Mis à jour
25 sept.
langchain-ai
Officiel

managed-deep-agents

INVOKE THIS SKILL when building, testing, or deploying Managed Deep Agents in LangSmith with the mda CLI. Walks a user through their first agent end to end — interviewing them about what they want to build, mapping it onto what MDA can actually do, then scaffolding and deploying it. Covers the file-based project layout; definedeepagent / defineDeepAgent; instructions, skills, memory, identity, tools, middleware, sandboxes, schedules, channels, and evals; mda init/build/dev/deploy/logs/delete; an

installations
2
GitHub Stars
1,3 k
Mis à jour
25 sept.
langchain-ai
Officiel

swarm

- Dispatches many independent items in parallel: create a table, fan out to subagents, aggregate results. One row = one unit of work.

installations
2
GitHub Stars
1,3 k
Mis à jour
25 sept.