LangChain
AIOpen SourcePopularOpen-source framework and platform for building, chaining, and deploying LLM-powered applications and agents.
-(0 Reviews)
Free Available
Starting from Free
LangChain
AIOpen SourcePopular
Open-source framework and platform for building, chaining, and deploying LLM-powered applications and agents.
-(0 Reviews)
Free Available
Starting from Free
About LangChain
LangChain is a developer-oriented framework and platform that simplifies building applications powered by large language models by connecting interoperable components, integrations, and tooling across the model lifecycle. It provides primitives for chaining prompts and model calls, managing conversational memory and state, integrating external data sources and tools, and constructing autonomous agents that can perform multi-step reasoning and tool use. LangChain's value lies in unifying connectors, agent patterns, prompt management, and production-focused utilities so teams can iterate on and deploy reliable LLM-powered systems more quickly.
Key Features
Chainable Components: Provides primitives to chain together prompts, LLM calls, and transformations so developers can build complex multi-step application logic with reusable parts.
Agent Frameworks: Built-in patterns for constructing agents that can select and call external tools, perform stepwise reasoning, and manage decision-making flows across tasks.
Connectors and Integrations: Interfaces for plugging in many LLM providers, vector stores, databases, search engines, and external APIs to enable retrieval-augmented generation and data grounding.
Memory and State Management: Persistent and session-level memory primitives to retain conversational context, user state, and long-term embeddings across interactions.
Prompt Management and Templates: Structured prompt templates and utilities for programmatic prompt construction, serialization, and reuse to improve prompt engineering and consistency.
Deployment and Production Tooling: Tools and utilities aimed at taking LLM workflows from prototype to production including evaluation hooks, observability patterns, and platform-focused integrations.
Framework for chaining LLM calls, prompts, and tools
Prebuilt connectors and integrations to LLMs and external services
Agent tooling for tool-using models
Observability and tracing (LangSmith) for debugging and monitoring
Vector store and retrieval utilities
SDKs and developer tooling for rapid app development
Chaining of interoperable components to build complex LLM workflows
Integrations with third-party services and data sources (connectors)
Agent development tooling and lifecycle support
Orchestration for context-aware reasoning across models and tools
Platform-level tooling for productionizing LLM applications
Open-source codebase hosted on GitHub for community contribution
Use Cases
Conversational Agents: Building chatbots and virtual assistants that combine LLM reasoning with external tools and memory to handle multi-turn dialogues and tasks.
Retrieval-Augmented Generation (RAG): Implementing document search + LLM pipelines using vector stores and connectors to produce grounded, context-aware answers from enterprise data.
Autonomous Task Execution: Creating agents that orchestrate multi-step processes by calling APIs, querying databases, and using tools to complete complex workflows.
Application Prototyping to Production: Rapidly iterating on LLM-based features using reusable chains and components, then hardening them with production-focused utilities and integrations.
Prompt Engineering at Scale: Managing and reusing templated prompts and prompt chains to standardize outputs across multiple LLM providers and use cases.
Knowledge Work Automation: Automating research, summarization, and content workflows by chaining retrieval, reasoning, and generation components against company data.
Building conversational agents that use external tools and data
Orchestrating multi-step LLM workflows and pipelines
Retrieval-augmented generation (RAG) applications using vector stores
Monitoring, tracing, and debugging LLM application behavior
Prototyping and productionizing LLM-powered features in apps
Building chatbots and virtual assistants with contextual reasoning
Composing multi-step LLM workflows and pipelines
Creating autonomous agents that use tools and external APIs
Integrating LLMs with enterprise data sources and third-party services
Rapid prototyping and production deployment of LLM-powered features
Screenshots
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