langchain-ai
2 Tools · 7 Intel
LangChain is an AI infrastructure company that builds tools for developing applications powered by large language models. Founded in 2022 by Harrison Chase as an open-source Python framework, the company has since launched LangGraph for agent orchestration and LangSmith for AI observability. It has grown into one of the most widely adopted open-source AI frameworks and was valued at roughly $1.25 billion following a 2025 funding round.
Tools
LangGraph is a low-level framework for building stateful, controllable agents and durable multi-step LLM workflows as graphs, with checkpointing, human-in-the-loop, and persistence.
LangSmith is a platform for LLM observability, tracing, evaluation, and monitoring from the LangChain team — framework-agnostic, works with or without LangChain.
Intel
OpenWiki now includes a web visualizer for humans to utilize the wikis it generates. Read the wikis, and inspect how repo sections relate to one another via the built in graph visualizer. OpenWiki is an open source agent CLI for writing and maintaining repo documentation: https://t.co/ccLD7GUcQk
Managed Deep Agents is now in public beta. Go from prototype to production scale without managing the underlying infrastructure yourself. Built for developers who want to increase product velocity with control over model choice and the agent development lifecycle.
New from The LangSmith Signal: Open-weight models are on the rise, and ready for production. ✅ 1 in 3 active teams ran an open weight model in July, up from 1 in 5 years ago. ✅ Teams running an open-weight model daily grew 9x YoY, while closed models usage grew 4.8x YoY. https://t.co/gmnYAmbEqb
🛡️Data Protection Controls with LangSmith LLM Gateway ✅ Detect, redact, and replace PII and secrets in requests before they reach model providers ✅ Redact sensitive data from the traces Gateway records in LangSmith Now available in the LLM Gateway Public Beta for Enterprise https://t.co/9ElBfNPpYM
🔄 Model Fallbacks with LangSmith LLM Gateway Don’t let model outages take your agents with them. Define fallback rules across models and hosts and use them across every agent. When a provider goes down or hits a rate limit, calls are routed to another model. https://t.co/Hbixl3eCoK
Similarweb combines deterministic tool-call checks, rubric-scored LLM judges, faithfulness checks and A/B comparisons against saved baselines, all connected to execution traces.
In 17 minutes, @BraceSproul runs through everything you need to know about OpenWiki, an open source self-maintaining wiki. ✅ The core ideas behind it ✅ How it works ✅ Why docs built for agents look nothing like docs for humans ✅ What we learned from building it https://t.co/kpfB9UmwDz