spb/tools-spboucher-ai

1{2  "slug": "langchain",3  "name": "LangChain",4  "aliases": ["langchain (PyPI)", "LangChain.js"],5  "vendor": { "name": "LangChain, Inc.", "slug": "langchain-inc", "website": "https://www.langchain.com" },6  "homepage_url": "https://www.langchain.com/langchain",7  "docs_url": "https://docs.langchain.com/oss/python/langchain/overview",8  "repo_url": "https://github.com/langchain-ai/langchain",9  "license_url": "https://github.com/langchain-ai/langchain/blob/master/LICENSE",10  "product_type": "framework",11  "product_status": "active",12  "summary": "Open-source Python and JavaScript framework whose create_agent harness builds tool-using agents on the LangGraph runtime with swappable model providers and middleware",13  "description": "LangChain is an MIT-licensed framework from LangChain, Inc. for building LLM applications in Python and JavaScript. After its 1.0 redesign the library centers on `create_agent`, a configurable agent harness: you pass a model identifier such as a provider-prefixed string, a list of plain functions as tools and a system prompt, then add middleware for guardrails, retries, routing or custom tool policies. A standard model interface covers OpenAI, Anthropic, Google, Bedrock, Azure, Ollama, Hugging Face and many other providers, so switching vendors is mostly a string change, and a large integrations catalog supplies document loaders, vector stores and retrievers for retrieval-augmented generation.\n\nAgents created with LangChain run on LangGraph, which supplies durable execution, persistence and human-in-the-loop controls, and they trace to LangSmith when an environment variable is set. Deep Agents, a batteries-included harness with context compression, a virtual filesystem and subagents, is built on the same primitives. The framework's strengths are breadth of integrations and a common vocabulary across providers; its recurring criticisms are abstraction overhead and API churn across major versions, so teams with simple needs sometimes call provider SDKs directly, and teams with complex control flow drop down to LangGraph.",14  "capabilities": ["`create_agent` harness with tools, prompts and middleware", "Standard chat-model interface across many providers", "Document loaders, text splitters, vector-store and retriever integrations", "Middleware for guardrails, retries and routing", "Runs on the LangGraph runtime for persistence and human-in-the-loop", "Python and JavaScript packages with LangSmith tracing"],15  "workflows": ["Build a tool-calling agent and swap the model provider via a string", "Assemble a RAG pipeline from loaders, splitters and a vector store", "Trace agent runs in LangSmith during development"],16  "advantages": ["Largest integration catalog among agent frameworks", "Provider-agnostic model interface", "MIT license and active community"],17  "limitations": ["Abstraction layers can obscure provider-specific behavior", "Frequent API changes between major versions", "Complex control flow is better expressed directly in LangGraph"],18  "prerequisites": ["Python 3.10+ or Node.js", "API key for at least one model provider or a local runtime such as Ollama"],19  "audiences": ["Application developers building LLM features and agents", "Teams standardizing on one interface across providers"],20  "alternatives": ["langgraph", "llamaindex", "pydantic-ai", "openai-agents-sdk", "crewai"],21  "categories": [{ "slug": "agent-frameworks", "primary": true }],22  "tags": ["agents", "rag", "llm", "open-source", "python", "typescript"],23  "use_cases": ["build-agents"],24  "operating_systems": ["macos", "linux", "windows"],25  "execution_modes": ["local", "self-hosted", "cloud"],26  "interfaces": ["library"],27  "deployment_models": ["embedded"],28  "integrations": ["LangGraph", "LangSmith", "OpenAI", "Anthropic", "Google Gemini", "Ollama", "Amazon Bedrock", "Hugging Face", "Chroma", "Qdrant", "pgvector"],29  "data_formats": ["JSON", "PDF and text (loaders)"],30  "languages": ["Python", "TypeScript", "JavaScript"],31  "source_model": "open-source",32  "license_spdx": "MIT",33  "pricing_model": ["open-source", "free"],34  "self_hostable": true,35  "sources": [36    { "url": "https://www.langchain.com/langchain", "type": "official_site", "title": "LangChain", "supports": ["name", "summary"], "checked_at": "2026-11-02" },37    { "url": "https://docs.langchain.com/oss/python/langchain/overview", "type": "official_docs", "title": "LangChain overview (Python)", "supports": ["capabilities", "integrations"], "checked_at": "2026-11-02" },38    { "url": "https://github.com/langchain-ai/langchain/blob/master/LICENSE", "type": "license_page", "supports": ["license_spdx", "source_model"], "checked_at": "2026-11-02" }39  ],40  "confidence": "high",41  "discovery_method": "editorial-research",42  "depth": "standard",43  "editorial_status": "reviewed",44  "visibility": "public",45  "featured": false,46  "author": "batch-A"47}48