Overview
LangChain enables every company to own their intelligence. LangChain's open, model-agnostic harnesses give teams choice and control over how they build their agent architecture. LangSmith brings testing, deployment, and monitoring together so teams can continuously improve their agents, compound intelligence, and govern them at scale. More than 7,000 customers, including Nvidia, Bridgewater, LinkedIn, Workday, Harvey, and Rippling trust LangSmith to improve their agents across the Agent Development Lifecycle.
LangChain began in late 2022 as a single Python package, and the company was founded in early 2023. Its open source frameworks include langchain for quick-start agents with any model provider, LangGraph for production agents that need low-level control, and Deep Agents for autonomous, long-running tasks. LangSmith adds step-by-step tracing, online evaluations, isolated sandboxes for agent-generated code, an LLM Gateway for controlling spend and sensitive data, and deployment options in SaaS, BYOC or self-hosted environments.
Companies using LangSmith include Klarna, Podium, C.H. Robinson, ServiceNow and Monday Service. LangChain is headquartered in San Francisco and has offices in New York, Boston and Amsterdam.
Funding rounds
Disclosed funding: $170M across 5 rounds. Latest: Series B, 20 Oct 2025.
1 round has an undisclosed amount, not included in the total.
| Date | Type | Amount |
|---|---|---|
| 20 Oct 2025 | Series B | $125M |
Office locations
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Latest videos from LangChain on YouTube
Using GPT to Judge GPT Is a Mistake, Here's Why
Why Nobody Has Solved Continual Learning
Vanta and LangSmith: Assessing Compliance Evidence Like an Auditor
How Stripe Built Kai
Recent posts from the LangChain blog
- October 2026 LangChain Newsletter: Everything we shipped at Interrupt NYC & London
What we shipped at Interrupt NYC and London: LangSmith AI Search, Engine v2, Trajectories, Custom Apps, Fine-Tuning, and Managed Deep Agents v0.9.
- The Most Common Questions About Agent Evals, Answered
Where to start with agent evals, offline vs online evals, evaluator types, LLM judges, benchmarks with Harbor, multi-turn evals, and keeping costs down.