Official Resources

Python Documentation API Reference JavaScript SDK AWS Extensions LangGraph LangGraph.js LangSmith LangGraph Platform Postgres Backend Chains Concept Agents Concept Vector Stores Documents API Tools Integrations LangServe Docs Release Policy Quick Start Tutorial

Key Features

  • Chains: Composable sequences of prompts, models, retrievers, or other chains for complex applications.
  • Agents: LLM-driven decision makers that dynamically pick tools or sub-chains at runtime.
  • Retrieval & Vector Integration: Out-of-the-box RAG pipelines that plug into Weaviate, Milvus, Qdrant, Chroma, pgvector, and more.
  • Documents Module: Unified abstraction for loading, chunking, and enriching text, PDF, HTML, CSV, etc.
  • Tooling & Integrations: 100+ pre-built tools (web search, code execution, APIs, browser automation, etc.) plus community contributions.
  • LangSmith: Observability, tracing, and evaluation suite to move from prototype to production.
  • LangServe: Turn any chain or agent into a REST or WebSocket endpoint with one decorator.

Code Examples

Installation

bash 
pip install -qU "langchain[openai]"

Quick Start Setup

python 
import os, getpass
os.environ["OPENAI_API_KEY"] = getpass.getpass()

from langchain.chat_models import init_chat_model
llm = init_chat_model("gpt-4o-mini", model_provider="openai")
print(llm.invoke("Hello LangChain!"))

Use Cases

  • Chatbots - Tool-augmented conversational agents that search the web, query SQL, or call internal APIs
  • Document processing - Ingest, transform, and query multi-format corpora for Q&A or summarization
  • Code generation - Agents that scaffold repos, write unit tests, and open pull requests
  • RAG applications - Retrieval-augmented generation deployed on top of vector databases
  • Workflow automation - End-to-end pipelines exposed via LangServe or embedded in backend services

Pros & Cons

Advantages

  • Highly modular - Lightweight packages, import only what you need
  • 500+ integrations across models, vector stores, tools, and cloud services
  • Multi-language support - Python and TypeScript/JavaScript SDKs
  • Production tooling - LangSmith tracing, LangServe deployment, LangGraph execution
  • Active community - Weekly releases, enterprise support

Disadvantages

  • Pre-1.0 API - Breaking changes in minor releases
  • Steep learning curve - Complex abstractions
  • Heavy third-party dependencies - Outages propagate quickly

Future Outlook & Integrations

  • 1.0 Stabilization [Planned]: Targeting a stable 1.0 release late 2025 with long-term support guarantees
  • Expanded Integrations [In Development]: AWS, Azure, GCP, Snowflake, plus niche vertical tools
  • Enterprise Tier [Planned]: Enhanced LangSmith seats, SOC-2, SSO, and on-prem deployment paths
  • Community Packages [Active]: Continued investment in langchain-postgres, langchain-aws, langgraph extensions