Langchain

Name: Langchain
Rating: 4.98 (104 reviews)

LangChain’s suite of products supports AI development

5.0•104 reviews•

828 followers

LangChain’s suite of products supports AI development

5.0•104 reviews•

828 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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Promoted

Langchain Launches

Langchain AI Email AssistantThe prototype of ambient agents

Launched on January 20th, 2025

LangSmith General AvailabilityLLM application development, monitoring, and testing

Launched on February 15th, 2024

LangChain TemplatesA collection of production-ready reference architectures

Launched on October 31st, 2023

LangSmithBuild and deploy LLM applications with confidence

Launched on July 20th, 2023

Forum Threads

p/langchain

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6mo 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!

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5.0

Based on 104 reviews

Review Langchain?

Reviewers see LangChain as a strong but sometimes demanding framework for building agentic LLM apps. They repeatedly praise its modular components, support for tools, memory, reasoning, and external data, plus faster experimentation once you learn it. Users also like LangSmith for debugging, though one flags unwanted environment variables in traces. Founders from products like Databox and TypeBoost say it offers useful control without too much abstraction, especially for multi-step, stateful workflows, but one user notes some features feel gated behind LangGraph Platform.

+101

Summarized with AI

Pros

agentic workflow support (19)

LangGraph framework (11)

model integration (10)

LangSmith observability (7)

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

•1 review

What's great

LangSmith observability (7)LangGraph framework (11)agentic workflow support (19)

I use LangChain and their implementation of DeepAgents in my products, and I can honestly say this is a huge contribution to the AI community!

It makes building capable agents much more practical. Memory, skills, reasoning, and todos well designed and usable in actual products. I know it was inspired by Claude, but the implementation is excellent and under open-source. Well done.

Debugging with LangSmith is a special kind of love haha. Love it! Highly recommend it.

What needs improvement

Do not automatically include all LANGSMITH_* and LANGCHAIN_* env variables in LangSmith traces when tracking is enabled.

Ratings

Ease of use

Reliability

Value for money

Customization

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41 views1mo ago

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 (11)scalable AI development (6)complex data source integration (3)

Report

127 views11mo 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

105 views9mo ago

•1 review

What's great

LangSmith observability (7)LangGraph framework (11)agentic workflow support (19)

I use LangChain and their implementation of DeepAgents in my products, and I can honestly say this is a huge contribution to the AI community!

What needs improvement

Do not automatically include all LANGSMITH_* and LANGCHAIN_* env variables in LangSmith traces when tracking is enabled.

Ratings

Ease of use

Reliability

Value for money

Customization

Report

41 views1mo ago

used Langchain to build Super

•8 reviews

What's great

flexible framework (4)LangGraph framework (11)scalable AI development (6)complex data source integration (3)

Report

127 views11mo ago

used Langchain to build Warestack

•15 reviews

Report

105 views9mo ago