LangChain
Official@langchain-ai · United States of America
Orchestrate stateful, persistent, and evaluable cognitive architectures using modular graph-based frameworks and observability platforms for enterprise-grade production deployments.
Agent Skills by LangChain
Showing 64 vetted skills indexed across 4 GitHub repositories.
langchain-aiswarm
Dispatches parallel subagent tasks across table rows and merges structured results.
langchain-aiecosystem-primer
Guides framework selection among LangChain, LangGraph, and Deep Agents before writing agent code.
langchain-ailangsmith-online-eval-engineering
Creates LangSmith online evaluators by inspecting traces and iteratively building LLM-as-judge or code evaluators.
langchain-ailangchain-typescript-quickstart
Scaffolds a minimal local LangChain agent in TypeScript following the official quickstart.
langchain-ailanggraph-cli
Scaffold, develop, build, and deploy LangGraph applications using the langgraph CLI.
langchain-aideepagents-python-quickstart
Scaffolds a minimal local Deep Agent in Python using provider-native web search.
langchain-aieval-engineering
Build and audit Harbor-based agent evaluation tasks with reusable project knowledge.
langchain-ailanggraph-python-quickstart
Scaffolds a minimal local LangGraph agent in Python following the official quickstart.
langchain-aimanaged-deep-agents
Build, test, and deploy code-first Deep Agents on LangSmith using the mda CLI.
langchain-aideepagents-typescript-quickstart
Scaffold a minimal local Deep Agent in TypeScript using provider-native web search.
langchain-ailangchain-python-quickstart
Scaffolds a minimal local LangChain agent in Python following the official quickstart.
langchain-ailanggraph-typescript-quickstart
Scaffolds a minimal local LangGraph agent in TypeScript following the official quickstart.
langchain-aieval-writer
Create vitest-based eval suites with LangSmith integration for the deepagentsjs monorepo.
langchain-aitextual-screenshot
Capture Textual terminal UI states as SVG screenshots using the headless test harness.
langchain-aiskill-creator
Create, initialize, and validate agent skills with SKILL.md frontmatter and bundled resources.
langchain-aiweb-research
Delegates web searches to subagents and synthesizes cited research reports.
langchain-aideepagents-thread-inspector
Inspect and summarize conversations stored in the local Deep Agents Code SQLite session database.
langchain-airemember
Captures conversation learnings into persistent AGENTS.md memory or reusable skill files.
langchain-aidata-visualization
Create publication-quality matplotlib and seaborn charts saved as PNG files in headless environments.
langchain-aicudf-analytics
Analyze CSV and tabular datasets with GPU-accelerated NVIDIA cuDF operations.
langchain-aigpu-document-processing
Extract text, tables, and embeddings from large PDFs using GPU-accelerated sandbox processing.
langchain-aicuml-machine-learning
Trains GPU-accelerated classification, regression, clustering, and dimensionality reduction models with NVIDIA cuML.
langchain-aischema-exploration
Inspects database tables, columns, data types, and foreign key relationships.
langchain-aiquery-writing
Writes and executes SQL queries from simple SELECTs to multi-table JOINs and aggregations.
Frequently Asked Questions About LangChain
FAQPage SchemaWhat specific tasks can engineers perform using these capabilities?▼
Engineers can build stateful, persistent graph-based systems, implement human-in-the-loop approval middleware, orchestrate complex document retrieval pipelines, and perform rigorous output evaluation using trace-based datasets.
Which personas benefit most from these technical resources?▼
Software engineers, backend developers, and machine learning practitioners focused on building production-ready, observable, and stateful systems benefit from these modular framework components and evaluation patterns.
What are the primary prerequisites for implementing these patterns?▼
Implementation requires familiarity with graph-based state management, vector database integration, and structured output handling. Users should have existing environments configured for package management and standard testing frameworks.