Packages
Command-line interface for building, testing, and deploying LangChain agents and LLM applications from the terminal.
Connects LangChain applications to Cohere's language models, providing chat, text embedding, retrieval-augmented generation, and reranking capabilities through a unified integration layer.
Install it if you are building LangChain applications that need Cohere's models or embeddings.
Provides third-party integrations for LangChain applications, implementing standard interfaces for LLM tools, data sources, and services.
LangChain Core provides the foundational abstractions and interfaces for building LLM applications, enabling modular composition of language model chains, agents, and tools across the LangChain ecosystem.
Install it if you are building any LLM application that benefits from modular abstractions and ecosystem interoperability.
Connects LangChain applications to Databricks services including vector search, chat models, and MLflow integration, but is now deprecated in favor of databricks-langchain.
Integrates Daytona sandboxes with LangChain's Deep Agents framework, allowing agents to execute code in isolated sandbox environments.
However, verify that daytona and deepagents meet your infrastructure requirements before committing, as this package is young (first release 2026-02-05) and…
Integrates DeepSeek language models into LangChain applications, enabling use of DeepSeek's API through the LangChain framework.
Install it if you are already using LangChain and want to integrate DeepSeek models; it is a straightforward drop-in provider integration.
Integrates Docling document conversion with LangChain, enabling you to load and process documents (PDFs, images, etc.) into LangChain-compatible formats for use in language model pipelines.
Install it if you're building a LangChain application that needs to ingest and process documents—either locally (with the `local` extra) or via a remote Docling…
Integrates Elasticsearch with LangChain to provide vector storage, retrieval, embeddings, chat history, and LLM caching backed by Elasticsearch.
Install it if you are building a LangChain application that requires persistent vector storage, retrieval, or LLM/embedding caching with Elasticsearch.
Integrates Exa's web search API with LangChain, enabling AI applications to search the web and retrieve clean, ready-to-use content from pages.
Install it if you are building a LangChain agent or RAG system and need to incorporate live web search results.
Provides experimental LangChain components and research prototypes for building LLM applications, intended for exploration rather than production use.
No, not for production.
Integrates Fireworks.ai language models with LangChain, enabling you to use Fireworks' inference API within LangChain applications and workflows.
Integrates Google Calendar with LangChain to enable calendar operations through a language model interface, providing tools to query and manage calendar events.
Connects Google products and services to LangChain applications, providing integrations for Google APIs, cloud services, and AI models not covered by the dedicated vertexai or genai packages.
Integrates Google's Gemini AI models (chat, vision, embeddings) into LangChain applications through a unified interface.
Connects LangChain applications to Google Cloud's Vertex AI generative models, enabling use of Google's language models, embeddings, and vector search within LangChain workflows.
Install it if you're building LangChain applications on Google Cloud and want native Vertex AI model access.
Retrieves documents from a graph structure using vector similarity search, combining graph traversal strategies with LangChain's retriever framework for efficient document discovery.
However, verify that the graph traversal strategies and vector store adapters you need are production-ready before deploying to critical systems.
Connects Groq's inference API to LangChain, enabling language model applications to use Groq as a provider for chat and text generation tasks.
Connects Hugging Face models and embeddings to LangChain applications, providing integration classes for local inference and API-based access to Hugging Face resources.
Integrates IBM watsonx.ai models with LangChain, providing chat, embedding, text generation, and reranking capabilities through a unified interface.
Converts Anthropic Model Context Protocol (MCP) tools into LangChain-compatible tools for use with LangGraph agents, and provides a client to connect to multiple MCP servers and load their tools.
Integrates LangChain with Milvus vector database to enable vector storage, similarity search, and retrieval for AI applications.
Install it if you are building LangChain applications that need vector storage and retrieval—it is the direct integration point between LangChain and Milvus.
Connects Mistral AI language models to LangChain, enabling you to use Mistral's models within LangChain's agent and chain frameworks.
Integrates Modal serverless compute sandboxes with LangChain's Deep Agents framework, allowing agents to execute commands in isolated Modal environments.
However, it is a narrow integration—only install if you specifically need Modal sandbox execution within agents; it adds no value if you're not using both Modal and…
Connects MongoDB Atlas Vector Search to LangChain for semantic search and retrieval-augmented generation workflows using vector embeddings stored in MongoDB.
However, the aging maintenance status (211 days since last release) and unclear license warrant verification before production use.
Integrates Neo4j graph databases with LangChain, providing wrappers for querying graphs, storing chat history, managing vector stores, and building knowledge graphs from text using LLMs.
Install only if you have a Neo4j instance available and a use case that benefits from graph storage or reasoning.
Integrates NVIDIA AI Foundation Models and chat endpoints into LangChain applications, providing access to models like Nemotron through the NVIDIA API Catalog or self-hosted NIM containers.
Integrates LangChain with Oracle Cloud Infrastructure (OCI) services, providing access to OCI Generative AI models (chat, completion, embeddings) and OCI Data Science model deployments.
However, verify that UPL-1.0 aligns with your license requirements (treatment is unclear), and confirm you have OCI credentials and service setup in place before…
Connects LangChain applications to Ollama, enabling use of locally-run language models through a unified LangChain interface.
Install it if you want to use Ollama models within LangChain applications.
Provides LangChain integrations for OpenAI's API, enabling language models, embeddings, and other OpenAI services to be used within LangChain applications.
Install it if you're building a LangChain application and want to use OpenAI as your model provider—it's the standard way to do so.
Connects LangChain applications to OpenRouter, a unified API gateway for hundreds of AI models across multiple providers, enabling model selection and switching without changing application code.
Install it if you're building with LangChain and want the flexibility to choose from hundreds of models without rewriting your code.
Integrates Oracle Database with LangChain to enable vector search, document loading, text splitting, and embedding generation for building retrieval-augmented generation (RAG) pipelines.
Integrates Perplexity AI models into LangChain applications, enabling you to use Perplexity's language models as a component in LangChain workflows.
Install it if you're already using LangChain and want to add Perplexity as a model provider—it's the standard way to do so.
Connects LangChain applications to Pinecone vector databases for semantic search, document storage, and retrieval-augmented generation workflows.
Install it if you are building a LangChain application that needs persistent semantic search over documents via Pinecone.
Integrates NextPlaid, a ColBERT-style multi-vector search engine, with LangChain as a VectorStore, enabling late-interaction retrieval with metadata filtering.
However, be aware that this is a 0.1.0 release with limited track record; production use should be preceded by thorough testing and verification of performance…
Provides LangChain abstractions for vector storage, chat history, and document management backed by PostgreSQL with support for both synchronous and asynchronous operations.
Install it if you are already using LangChain and need a Postgres-backed vector store or session manager; skip it if you do not use LangChain or prefer a different…
Provides Python type definitions (TypedDict, Literal) for the LangChain agent streaming protocol wire format, enabling type-safe integration with protocol-compliant clients and servers.
Connects LangChain applications to Qdrant vector database for semantic search and retrieval-augmented generation workflows.
Adds a persistent, sandboxed JavaScript REPL tool to deepagents agents, letting the model write orchestrated JavaScript code instead of issuing serial tool calls.
Integrates Redis with LangChain to provide vector storage, semantic caching, and chat history management for AI applications.
Install it if you are already running Redis and need vector storage, semantic caching, or chat history for a LangChain application.