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AI Frameworks · langchain-ai

langchain

LangChain is a Python framework for building AI agent applications and LLM-powered systems. It provides standardized interfaces to chain together models, data sources, and third-party integrations, reducing development friction when building with large language models.

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Source: GitHub — github.com/langchain-ai/langchain
141.2k
GitHub stars
23.5k
Forks
Python
Primary language
MIT
License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

FieldValue
Repositorylangchain-ai/langchain
Ownerlangchain-ai
Primary languagePython
LicenseMIT — OSI-approved
Stars141.2k
Forks23.5k
Open issues406
Latest releaselangchain==1.3.11 (2026-06-22)
Last updated2026-07-07
Sourcehttps://github.com/langchain-ai/langchain

What langchain is

LangChain abstracts LLM interaction patterns through modular components (chat models, embeddings, vector stores, retrievers, tools) and supports integration with 100+ external providers. It is designed to work alongside LangGraph (agent orchestration) and Deep Agents (higher-level patterns) within a broader ecosystem.

Quickstart

Get the langchain source

Clone the repository and explore it locally.

terminalbash
git clone https://github.com/langchain-ai/langchain.gitcd langchain# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Multi-step AI agent workflows

Building agents that orchestrate planning, tool use, and external system calls. LangChain's component model and integrations reduce boilerplate for chaining LLM decisions with retrieval, APIs, and file operations.

Rapid LLM application prototyping

Quick iteration across multiple models and providers (OpenAI, Anthropic, Google Gemini, etc.) without rewriting core logic. Swap models and test different retrieval/embedding strategies in isolation.

RAG and data-augmented LLM pipelines

Connecting LLMs to internal/external data via vector stores, document loaders, and retrievers. LangChain's abstraction layer handles heterogeneous data sources and vendor-agnostic retrieval patterns.

Implementation considerations

  • LangChain 1.3.11 is recent; review breaking changes between minor versions and lock dependency versions in production.
  • Vast integration ecosystem (100+) means you inherit transitive dependencies; audit supply chain risk for your chosen integrations.
  • Requires careful API key/credential management; use environment variables or secrets management—never hardcode in LangChain config.
  • Model behavior and costs vary by provider (OpenAI, Anthropic, etc.); LangChain abstracts the interface but not pricing or SLA guarantees.
  • Community-driven integrations vary in maturity; prioritize official or heavily-used third-party providers for production stability.

When to avoid it — and what to weigh

  • You need low-latency, highly optimized inference — LangChain abstracts model calls and adds orchestration overhead. For latency-critical systems, direct model SDKs or optimized frameworks may be better.
  • Your LLM stack is entirely proprietary or closed — LangChain's value proposition centers on interoperability. If you use a single internal model with no integrations, you lose the main benefit and add unnecessary abstraction layers.
  • You require deterministic, non-probabilistic logic flows — LangChain is optimized for probabilistic LLM outputs. Complex deterministic business logic should live outside LangChain or use explicitly coded fallbacks.
  • Your team lacks Python expertise or runs a non-Python stack — LangChain is Python-first (TypeScript port exists but lags feature parity). Integration friction increases if your core stack is Go, Rust, or JVM-based.

License & commercial use

Licensed under the MIT License (OSI-approved, permissive). No copyleft obligations; source modifications and commercial use are permitted without requiring derivative work disclosure.

MIT license explicitly permits commercial use without attribution requirement. However, review your chosen integrations (e.g., proprietary model SDKs, third-party vector stores) for their own commercial terms. LangChain itself poses no commercial licensing barrier, but downstream dependencies may.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

SignalAssessment
MaintenanceActive
DocumentationStrong
License clarityClear
Deployment complexityModerate
DEV.co fitStrong
Assessment confidenceHigh
Security considerations

LangChain handles API credentials; use environment variables and never log/commit keys. Model outputs are untrusted (LLM injection risk); sanitize or validate agent outputs before using them in critical decisions or inserting into databases. Tool/function definitions can expose endpoints; review agent-accessible tools for privilege creep. Supply-chain risk from 100+ integrations; audit transitive dependencies and pin versions.

Alternatives to consider

LangChain.js

TypeScript/JavaScript equivalent; smaller ecosystem, fewer integrations, but lower JS friction if your team is not Python-based.

LlamaIndex (formerly GPT Index)

Focused on data indexing and RAG patterns; lighter-weight, simpler for retrieval-focused apps, but fewer orchestration/agent features.

Semantic Kernel (Microsoft)

C#/.NET-first agent/LLM framework; better fit for enterprise Microsoft stacks, but smaller community and fewer integrations than LangChain.

Software development agency

Build on langchain with DEV.co software developers

Our team helps enterprises architect and deploy LangChain applications at scale. We handle integration selection, production hardening, and observability setup. Let's talk about your use case.

Talk to DEV.co

Related open-source tools

Surfaced by semantic similarity across the DEV.co open-source index.

Langchain-Chatchatlangchain_dartlanggraph

Related on DEV.co

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langchain FAQ

Do I need LangGraph if I use LangChain?
Not always. Simple chains and RAG pipelines work with LangChain alone. LangGraph is recommended for complex multi-step agents with state management and loops; Deep Agents offer higher-level shortcuts for common patterns.
How do I manage API costs when using LangChain with multiple model providers?
LangChain abstracts the interface but not costs. Use LangSmith for token/cost tracking, or instrument your code to log model calls. Test with cheaper models first; use caching and prompt optimization to reduce calls.
Can I run LangChain offline or with local-only models?
Yes. LangChain supports local models (Ollama, LLaMA 2, etc.) and local vector stores (Chroma, FAISS). No internet required once models/data are cached, but most integrations assume online API access.
Is LangChain suitable for production AI applications?
Yes, with caveats. Use LangSmith for observability, handle errors gracefully, validate model outputs, and test extensively. LangChain is a framework, not a turnkey platform; production readiness depends on your implementation and operational discipline.

From evaluation to production software

DEV.co builds and maintains ai frameworks systems for startups through enterprises. If langchain fits your architecture, our custom software development services cover implementation, migration, and the long tail of maintenance.

Ready to build AI agents with LangChain?

Our team helps enterprises architect and deploy LangChain applications at scale. We handle integration selection, production hardening, and observability setup. Let's talk about your use case.

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