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## Overview
LangChain is an open-source framework for building applications powered by large language models (LLMs). It provides composable building blocks for chaining prompts, connecting to data sources, and orchestrating agents that can reason and use tools — enabling use cases like chatbots, retrieval-augmented generation (RAG), and autonomous workflows.

## Tech Stack
- **Languages**: Python (primary) and JavaScript/TypeScript (separate `langchainjs` repo)
- **LLM Providers**: OpenAI, Anthropic, Google, Hugging Face, Cohere, Mistral
- **Vector Stores**: Pinecone, Chroma, FAISS, Weaviate, Milvus
- **Core Libraries**: Pydantic (data validation), SQLAlchemy, Jinja2, Tenacity (retries)
- **Package Manager**: Poetry / pip

## Project Structure
- **`libs/core/`** — `langchain_core`: foundational abstractions (prompts, messages, runnables, output parsers) with minimal dependencies.
- **`libs/langchain/`** — Main package containing chains, agents, memory, and retrievers.
- **`libs/community/`** — `langchain_community`: third-party integrations (LLMs, vector stores, tools) maintained collectively.
- **`libs/partners/`** — Provider-specific packages like `langchain-openai`, `langchain-anthropic`, `langchain-pinecone` kept in separate modules for stable versioning.
- **`libs/experimental/`** — Cutting-edge or less stable features (e.g., autonomous agents).
- **`libs/text-splitters/`** — Utilities for chunking documents before embedding.
- **`cookbook/`** — End-to-end example notebooks (RAG, SQL agents, extraction).
- **`docs/`** — Official documentation source.

## How It Works
- **Runnables & LCEL**: LangChain Expression Language lets you pipe components together (`prompt | model | parser`), creating chains that support streaming, batching, and async by default.
- **Chains**: Predefined sequences combining prompts, LLMs, and output parsers for tasks like summarization or Q&A.
- **Agents**: An LLM acts as a reasoning engine that selects from a toolset (search, calculators, APIs), loops through observations, and returns a final answer.
- **RAG pipeline**: Documents are loaded → split into chunks → embedded into vectors → stored in a vector database → retrieved by similarity at query time → passed as context to the LLM.
- **Memory**: Stores conversation history (in-memory, Redis, or databases) so chains retain context across turns.

## Getting Started
1. **Install the core package**:
   ```bash
   pip install langchain langchain-openai
   ```
2. **Set your API key**:
   ```bash
   export OPENAI_API_KEY="sk-..."
   ```
3. **Run a minimal chain**:
   ```python
   from langchain_openai import ChatOpenAI
   from langchain_core.prompts import ChatPromptTemplate

   prompt = ChatPromptTemplate.from_template("Explain {topic} in one sentence.")
   model = ChatOpenAI(model="gpt-4o-mini")
   chain = prompt | model
   print(chain.invoke({"topic": "vector databases"}))
   ```
4. **For RAG**, additionally install a vector store: `pip install langchain-chroma` and a document loader.
5. **Explore examples** in the `cookbook/` directory for agents, RAG, and tool use.

## Use Cases
- **Chatbots & virtual assistants** with persistent memory and domain knowledge.
- **RAG applications**: querying private documents, PDFs, or knowledge bases.
- **Autonomous agents** that browse the web, execute code, or interact with APIs.
- **Data extraction**: converting unstructured text into structured JSON.
- **SQL/CSV Q&A**: letting non-technical users query databases in natural language.
- **Summarization pipelines** for long documents, meetings, or research papers.

# LangChain Repository Overview

## Overview

LangChain is an open-source framework for building applications powered by large language models (LLMs). It provides modular components and pre-built patterns for creating chains (sequences of operations), agents (autonomous decision-making systems), and retrieval-augmented generation (RAG) pipelines—all with minimal boilerplate code.

## Tech Stack

- **Languages**: Python, JavaScript/TypeScript
- **Core Dependencies**: langchain_core (fundamental abstractions), langchain_community (third-party integrations)
- **LLM Providers**: OpenAI, Anthropic, Cohere, HuggingFace
- **Vector Stores**: Pinecone, Chroma, Weaviate, Milvus (for semantic search and memory)
- **Additional Tools**: SQLAlchemy (database ORM), Pydantic (data validation), Express.js (JavaScript runtime)

## Project Structure

```
langchain/
├── langchain_core/          # Base abstractions (LLMChain, Agent, Tool, Memory)
├── langchain_community/     # Third-party integrations and connectors
├── langchain/              # Main package (orchestration layer)
├── integrations/           # Official partner integrations (OpenAI, Anthropic, etc.)
├── docs/                   # Documentation and examples
├── examples/               # Sample applications (chatbots, QA systems, RAG)
├── python/                 # Python-specific implementation
└── js/                     # JavaScript/TypeScript implementation
```

## How It Works

1. **Modular Components**: LangChain breaks LLM workflows into reusable pieces—models, prompts, parsers, memory, and tools—each with a consistent interface.

2. **Chains**: Connect components sequentially; for example, a chain might format user input → call an LLM → parse the response → call a tool → return results.

3. **Agents**: Enable autonomous decision-making by giving LLMs access to tools; the LLM decides which tool to use and in what order based on the task.

4. **RAG Pipeline**: Retrieves relevant documents from a vector store (semantic search) and passes them to the LLM as context, improving accuracy on domain-specific queries.

5. **Provider Abstraction**: A unified interface hides LLM-provider differences, letting you swap OpenAI for Anthropic with minimal code changes.

## Getting Started

1. **Install Python package**:
   ```bash
   pip install langchain langchain-openai
   ```

2. **Set environment variables**:
   ```bash
   export OPENAI_API_KEY="your-key-here"
   ```

3. **Create a simple chain**:
   ```python
   from langchain.prompts import ChatPromptTemplate
   from langchain_openai import ChatOpenAI
   
   model = ChatOpenAI()
   prompt = ChatPromptTemplate.from_template("Explain {topic}")
   chain = prompt | model
   result = chain.invoke({"topic": "quantum computing"})
   ```

4. **Run examples**:
   ```bash
   cd examples && python simple_chain.py
   ```

## Use Cases

- **Chatbots & Q&A**: Build conversational systems with memory and context awareness
- **Document Analysis**: Extract insights from PDFs, web pages, or databases via RAG
- **Autonomous Agents**: Create systems that research, decide, and execute multi-step workflows
- **Code Generation**: Generate and validate code using LLMs
- **Summarization & Translation**: Batch process text with standardized pipelines
- **Prototyping**: Rapidly test LLM ideas before production deployment

## Overview

LangChain is a framework for building applications powered by large language models (LLMs), such as chatbots, agents, and retrieval-augmented generation (RAG: answering with external data). It provides shared building blocks for prompts, model calls, tools, memory, and data retrieval across both Python and JavaScript ecosystems.

## Tech Stack

- **Languages:** Python, JavaScript/TypeScript
- **Core modules:** `langchain_core`, `langchain_community`
- **Model integrations:** OpenAI, Anthropic, and other LLM providers
- **Data/retrieval integrations:** Pinecone, Chroma, and other vector stores (databases for semantic search)
- **Common patterns supported:** chains (multi-step workflows), agents (tool-using LLM systems), RAG pipelines

## Project Structure

- `libs/`
  - `core/` or `langchain_core/` — shared abstractions like prompts, messages, runnables (composable execution units), and model interfaces
  - `community/` or `langchain_community/` — third-party integrations, loaders, tools, retrievers, and connectors
  - provider-specific packages — integrations for services like OpenAI, Anthropic, Pinecone, Chroma
- `docs/` — documentation, examples, and usage guides
- `templates/` or example folders — starter apps for chains, agents, and RAG
- `tests/` — automated tests for framework behavior and integrations
- `README.md` — high-level introduction, install instructions, and quickstart examples

## How It Works

- You define **components** such as prompts, LLMs, retrievers, and tools using common interfaces from `langchain_core`.
- These components are combined into **chains** (step-by-step pipelines) or **agents** (LLM-driven systems that choose which tools to call).
- For **RAG**, LangChain loads documents, splits them into chunks, stores embeddings (numeric text representations) in a vector store like Pinecone or Chroma, then retrieves relevant chunks at query time.
- Provider packages connect the same application logic to different model vendors, so you can swap OpenAI for Anthropic with minimal code changes.
- Community modules add integrations for external systems such as databases, APIs, file loaders, and search/retrieval backends.

## Getting Started

1. **Clone the repository**
   ```bash
   git clone https://github.com/langchain-ai/langchain.git
   cd langchain
   ```

2. **Set up Python**
   ```bash
   python -m venv .venv
   source .venv/bin/activate   # Windows: .venv\Scripts\activate
   pip install -U pip
   pip install -e .
   ```

3. **Install integration packages as needed**
   ```bash
   pip install langchain-openai langchain-anthropic langchain-pinecone chromadb
   ```

4. **Set API keys**
   ```bash
   export OPENAI_API_KEY=...
   export ANTHROPIC_API_KEY=...
   ```

5. **Run examples or tests**
   ```bash
   python your_script.py
   pytest
   ```

## Use Cases

- Build chatbots that answer questions using company documents
- Create agents that call APIs, search tools, or databases
- Assemble multi-step LLM workflows for summarization, extraction, or classification
- Prototype and swap between model providers without rewriting application logic
- Add semantic search over files, notes, or knowledge bases using vector stores

## Overview
LangChain is an open-source framework for building applications powered by large language models (LLMs; AI systems like GPT). It simplifies creating chains (sequences of LLM calls), agents (autonomous decision-makers with tools), and RAG pipelines (retrieval-augmented generation; fetching relevant docs to improve LLM responses). Available in Python and JavaScript, with modular packages for easy integration.

## Tech Stack
- Languages: Python (primary), JavaScript/TypeScript.
- Core modules: `langchain_core` (abstractions), `langchain_community` (third-party integrations), `langchain` (main framework).
- Integrations: OpenAI, Anthropic (LLM providers); Pinecone, Chroma (vector stores for embeddings).
- Build tools: Poetry (dependency management), Pytest (testing).

## Project Structure
```
├── libs/
│   ├── langchain_core/     # Core runnables, prompts, chains
│   ├── langchain_community/ # 100+ integrations (e.g., Pinecone, HuggingFace)
│   └── langchain/          # High-level components like agents, retrievers
├── templates/              # Starter apps (e.g., RAG chatbots)
├── docs/                   # Guides, API reference
├── README.md               # Examples for chains, agents, RAG
└── pyproject.toml          # Dependencies, build config
```

## How It Works
- **Modular components**: Build apps by composing prompts, LLMs, output parsers, and tools into "runnables" (executable units).
- **Chains**: Linear data flow—input → prompt template → LLM call → parser → output (e.g., summarization).
- **Agents**: LLM decides actions/tools (e.g., search API), loops until task complete.
- **RAG flow**: Embed query → retrieve docs from vector store → augment LLM prompt → generate response.
- **LCEL (LangChain Expression Language)**: Pipe syntax (`|`) for streaming, async, and error-handling chains.

## Getting Started
1. Clone: `git clone https://github.com/langchain-ai/langchain.git && cd langchain`
2. Install: `pip install -e .[all]` (editable install with extras)
3. Set API keys: `export OPENAI_API_KEY=your_key`
4. Run example: `python docs/docs/docs/guides/chains/sequential_chain.ipynb` (Jupyter) or `uv run examples/agents/agent_executor.py`
5. Test: `pytest libs/langchain_core/`

## Use Cases
- Q&A chatbots over private docs (RAG).
- Multi-step agents for automation (e.g., web search + math).
- Prototyping LLM apps like summarizers or translators.