LangChain is a powerful open-source framework that helps developers build applications powered by Large Language Models (LLMs). It abstracts away complexity and provides modular building blocks for:
- Interacting with LLMs (like OpenAI’s GPT, Anthropic’s Claude, etc.)
- Connecting LLMs with external tools, APIs, documents, and databases
- Creating intelligent agents, chatbots, retrieval-based systems, and more
While LLMs are powerful, using them in real-world applications requires more than just sending prompts. You often need to:
- Chain multiple steps together (e.g., extract → reason → generate)
- Connect to data sources (PDFs, websites, databases)
- Build chatbots that can remember and react
- Use tools/APIs dynamically (e.g., do math, call a calendar)
- Maintain context in conversations
- Store knowledge or memory for the LLM to use
LangChain provides all the utilities to orchestrate these pieces into a full AI system.
| Component | Description |
|---|---|
| LLMs & ChatModels | Integrates models from OpenAI, Hugging Face, Anthropic, Google, etc. |
| Prompts | Create flexible and reusable prompt templates |
| Chains | Combine multiple LLM calls into a single pipeline (e.g., extract → transform → respond) |
| Agents | Let the LLM decide which tools to use based on user input |
| Tools | External APIs or functions the LLM can use (e.g., calculator, web search) |
| Memory | Stores conversation history or facts between steps |
| Retrievers | Pull relevant data from documents, databases, or vector stores |
| VectorStores | Store text embeddings for similarity search (used in RAG, QA, etc.) |
| Callbacks | Track events and log/debug complex chains |
LangChain is used to build intelligent LLM-powered applications, such as:
- Multi-turn conversations
- Memory-enabled interactions
- Tool integration (e.g., booking APIs)
- Ask questions over PDFs, Notion docs, websites, and databases
- Combine vector search with LLMs for accurate answers
- LLM selects and uses tools dynamically
- Examples: booking systems, autonomous AI helpers, developer copilots
- Upload documents and ask questions in natural language
- Example: Chat with legal contracts or academic papers
- Prompt-based coding
- Generate, fix, or refactor code based on user input
- Combine APIs, functions, LLM logic, and memory to build custom pipelines
| Aspect | Details |
|---|---|
| Framework Name | LangChain |
| Purpose | Build complex, real-world LLM applications |
| Why Needed | Simplifies LLM workflows, integration with tools, memory, and data |
| Core Components | LLMs, Prompts, Chains, Agents, Tools, Memory, Retrievers, VectorStores |
| Use Cases | Chatbots, RAG systems, agents, document QA, code generation, workflows |