Langchain

Name: Langchain
Rating: 4.98 (96 reviews)

LangChain’s suite of products supports AI development

5.0•96 reviews•

674 followers

LangChain’s suite of products supports AI development

5.0•96 reviews•

674 followers

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LLMs

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Unified API

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AI Infrastructure Tools

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AI Metrics and Evaluation

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LLM Developer Tools

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Langchain Launches

Langchain AI Email Assistant The prototype of ambient agents

Launched on January 20th, 2025

LangSmith General Availability LLM application development, monitoring, and testing

Launched on February 15th, 2024

LangChain Templates A collection of production-ready reference architectures

Launched on October 31st, 2023

LangSmith Build and deploy LLM applications with confidence

Launched on July 20th, 2023

Do you use Langchain?

Forum Threads

p/langchain

•

4mo ago

LangChain announces Series B raising $125M at a $1.25B valuation

From @hwchase17 :

Today, we re announcing we ve raised $125M at a $1.25B valuation to build the platform for agent engineering. We re also releasing new capabilities to accelerate the path to reliable agents, including LangChain and LangGraph 1.0 releases, a new Insights Agent, and a no code agent builder. IVP led the round alongside existing investors Sequoia, Benchmark, and Amplify, as well as new investors CapitalG and Sapphire Ventures.

Imagine that they got their start here when I hunted @LangSmith in 2023.
Man, shoulda invested back then!

View all

5.0

Based on 96 reviews

Review Langchain?

LangChain earns strong praise for its rich integrations, agent tooling, and fast prototyping, with some noting initial docs complexity and occasional platform lock-in concerns. Makers of

Browser Use

commend broad model support, while makers of

STORI

highlight smooth agent deployment and testing. Makers of

Meilisearch

report seamless chat-stack connectivity and RAG integration. Users value LangGraph for controllable multi-agent flows and LangSmith for tracing and debugging. Overall, it’s seen as flexible, production-minded, and community-backed, accelerating complex LLM workflows once the learning curve is cleared.

+95

Summarized with AI

Pros

agentic workflow support (18)

LangGraph framework (10)

model integration (10)

LangSmith observability (6)

scalable AI development (6)

RAG workflows (5)

rapid prototyping (5)

flexible framework (4)

community support (3)

complex data source integration (3)

Pros

Cons

Reviews

All Reviews

Most Informative

used

Langchain

to build Super

•8 reviews

Langgraph helped build Super by providing a standardized framework that made the complex LLM/source interactions more manageable through a declarative graph structure. It enabled better parallelization which significantly improved response speed, while also providing full graph state/sequence debugging capabilities that made troubleshooting easier. The migration to Langgraph unlocked new capabilities like human-in-the-loop pauses for clarification and better fallback mechanisms when models fail. Additionally, Langgraph's functional API allowed developers to write more natural, sequential code for complex subgraphs while still maintaining the benefits of the graph framework.

What's great

flexible framework (4)LangGraph framework (10)scalable AI development (6)complex data source integration (3)

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67 views9mo ago

used

Langchain

to build Warestack

•15 reviews

LangGraph's graph-based architecture with nodes and edges was the key on how we coordinate complex AI workflows. We're orchestrating multi-agent conversations where each node handles specific tasks (analysis, decision-making, action execution) and edges manage the flow between them. Memory lets us build operations that maintain context across conversations, while parallel execution nodes handle multiple AI operations simultaneously. The Pydantic formatted outputs ensure type-safe data flow between nodes, and the checkpoint system with PostgreSQL persistence lets us maintain state across these graph executions, so conversations can resume seamlessly even after server restarts.

Report

56 views7mo ago

•43 reviews

We use Langchain to orchestrate our entire agent framework — from the AI inbox and SDR copilots to outbound flows and long-term memory. It helps manage complex tool use, contextual reasoning, and multi-turn interactions across our platform. Langchain has been critical to scaling structured LLM workflows with modular, maintainable logic. Grateful to the Langchain team and open-source contributors (part of pour team) pushing this ecosystem forward.