LangChain

Browser Use

“They handle all the integrations with all models (DeepSeek, Llama, gpt, claude)”

Kuration AI

“The whole ecosystem has been a great help till now. Even though we often have to develop our own. Langsmith is one of our go to tool for tracing and evaluating.”

Brev.io

“Quick, scalable, and simple to implement.”

Composio

“LangGraph by LangChain made it possible to design the agentic workflow as state-graphs providing greater control and reliability. Also, LangSmith for observabilty of the Agentic actions.”

Youkti

“Core framework for LLM orchestration, tool binding, prompt management, and embeddings. Powers the entire AI layer across all services.”

NeuralAgent

“The best framework for inferencing LLM models.”

Databox

“We evaluated several orchestration frameworks before choosing LangGraph for Artifacts. Generating a document isn't one call and done, it's a multi-step flow: pull the data, structure the layout, apply styling, and hold state across follow-ups like "turn this into slides." The alternatives either abstracted too much away or couldn't handle that stateful, multi-step process cleanly. LangGraph gave us explicit control over each step, so a report doesn't drift into something the user didn't ask for halfway through generation.”

Swytchcode

“Langgraph for managing our AI flows”

reflex

“LangSmith has been a lifesaver for testing and debugging our prompt flows. It gave us the visibility we needed to make Reflex AI Builder feel smooth and reliable. Super helpful when working with complex LLM chains.”

Tiptap

“The @tiptap-pro/ai-toolkit-langchain package provides tool definitions you can add to your AI agent built with LangChain.js.”

Warestack

“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.”

Jina

“Langchain was again crucial in building our browser agent which has improved a ton in terms of reliability”

Minicule

“Using Langchain for Minicule Research AI Agents.”

Mnemosphere AI

“We use LangChain to build our RAG pipeline.”

Orango

“Langchain helps us define parts of our agent.”

graph8

“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.”

Meilisearch

“Connects all the moving parts of our chat stack.”

OpenFunnel(YC F24)

“Langgraph Studio helps us easily build and ideate agents”

Receiptor AI

“powers our AI workflows and makes them easy to adapt as we grow”

Overflow AI

“Made connecting our AI to real data feel intuitive, not intimidating.”

KushoAI

“Nifty way to fine-tune LLMs to suit our AI Agent use-case.”

AI Context Flow

“Big thanks to LangChain for powering our AI development workflow! ⚡️ From chaining LLM calls to managing memory and integrations, LangChain makes building intelligent apps faster and more reliable. Couldn’t create without it! 🚀”

GitLaw

“Awesome tooling to build agents”