@langchain-ai

langgraph-persistence covers checkpointer setup (in-memory, sqlite, postgres), thread isolation, state history replay, subgraph scoping rules, and cross-thread long-term memory via store. includes common pitfalls and reducer semantics.

langgraph-fundamentals covers stategraph design, node and edge patterns, state management with reducers, command-driven routing, send-based fan-out, streaming modes, and retry/error handling strategies across python and typescript.

langchain-middleware provides human-in-the-loop approval for agent tool calls with per-tool policies, decision resumption (approve/edit/reject), and custom middleware hooks for retry logic and guardrails. requires checkpointer and thread_id config.

langchain-ai-web-research delegates multi-subtopic web research to parallel subagents, collects findings into local files, and synthesizes cited reports. covers planning, delegation, and synthesis workflows with explicit file-based communication patterns.

schema-exploration maps database structure by listing tables, describing columns and data types, identifying foreign key relationships, and documenting entity relationships. oriented toward understanding schema before querying.

deep-agents-memory provides pluggable backends for agent state and file operations, supporting ephemeral thread-scoped storage, cross-session persistence, and hybrid routing via longest-prefix matching.

langchain-dependencies guides selection between langchain versions, core foundation setup, orchestration framework choice, and isolated provider integration installs with semantic versioning caveats.

framework-selection provides a three-tier mental model for choosing between langchain, langgraph, and deep agents based on agent complexity and feature needs, but lacks concrete decision criteria and rollback guidance.

langsmith-dataset covers creation, management, and upload of evaluation datasets across four types (final_response, single_step, trajectory, rag) via cli and sdk, with auth via api key and project configuration.

langgraph-docs fetches documentation indexes and surfaces relevant pages for building stateful agents and multi-agent workflows, but lacks decision logic and recovery patterns for common fetch failures.

ecosystem-primer maps the langchain/langgraph/deep agents technology stack and guides selection between abstraction layers based on execution durability and control flow needs.

social-media drafts platform-specific posts for linkedin and twitter with templated hooks and auto-generated images. requires explicit research delegation before writing. platform-specific character limits enforced.

langsmith-evaluator exposes environment variable setup and a single CLI command for listing evaluators, but omits intent, procedure steps, decision logic, and output contracts entirely.

skill-creator documents where the deepagents CLI loads skills from and how precedence works between user and project directories. lacks guidance on actual skill authoring.

INVOKE THIS SKILL when working with LangSmith tracing OR querying traces. Covers adding tracing to applications and querying/exporting trace data. Uses the…

INVOKE THIS SKILL when building, testing, or deploying Managed Deep Agents in LangSmith with the mda CLI. Covers the code-first, file-based project layout;…

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

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

Searches multiple web sources, synthesizes findings, and produces cited research reports using delegated subagents. Use when the user asks to research a topic…

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

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

INVOKE THIS SKILL when implementing human-in-the-loop patterns, pausing for approval, or handling errors in LangGraph. Covers interrupt(), Command(resume=...),…

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

INVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system. Covers document loaders, RecursiveCharacterTextSplitter, embeddings (OpenAI),…

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 for social platforms.

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 long-form content.

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) initialize a skill, (6) scaffold a skill, (7) update or modify an existing skill, (8) validate a skill, (9) learn about skill structure, (10) understand how skills work, or (11) get guidance on skill design patterns. Trigger on phrases like "create a skill", "new skill", "make a skill", "skill for X", "how do I create a skill", or "help me build a skill".

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.

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…

blog-post — an installable skill for AI agents, published by langchain-ai/deepagents.

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,…