
We're on a mission to make it easy to build the LLM apps of tomorrow, today. We build products that enable developers to go from an idea to working code in an afternoon and in the hands of users in days or weeks. We’re humbled to support 100k+ companies who choose to build with LangChain. And we built LangSmith to support all stages of the AI engineering lifecycle, to get applications into production faster.

We're on a mission to make it easy to build the LLM apps of tomorrow, today. We build products that enable developers to go from an idea to working code in an afternoon and in the hands of users in days or weeks. We’re humbled to support 100k+ companies who choose to build with LangChain. And we built LangSmith to support all stages of the AI engineering lifecycle, to get applications into production faster.
Core offering: Open-source LLM agent framework plus commercial platform for agent orchestration, observability, evaluation, and deployment
Founders / leadership: Co-founded by Harrison Chase (CEO) and Ankush Gola
Adoption metrics (site-stated): Claims: 90M monthly downloads, 100k+ GitHub stars, 1,000+ integrations; used by 100k+ companies
Recent funding: $125M Series B (2025) led by IVP; prior seed and Series A led by Benchmark and Sequoia
Employees (approx.): ~215
| Company |
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Agent engineering and productionization of LLM-powered applications
2022
Technology, Information and Internet
$10M
$25M
$125M
Reported valuation around $1.25B
“Participation from top-tier VCs (Benchmark, Sequoia, IVP) and corporate investors in later round (e.g., CapitalG, Sapphire Ventures, Workday Ventures, ServiceNow Ventures, Datadog, Databricks Ventures, Cisco Investments)”
About LangChain: At LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. What began as widely adopted open-source tools has grown into a platform for building, evaluating, deploying, and operating agents at scale.
Today, LangChain, LangGraph, LangSmith, and Agent Builder are used by teams shipping real AI products across startups and large enterprises. Millions of developers trust LangChain to power AI teams at companies like Replit, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, and 35% of the Fortune 500 .
With $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures , we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world.
About the role: LangChain is looking for an Education Engineer to help developers and agent builders learn how to build with LangChain, LangGraph, and LangSmith. In this role, you’ll turn cutting-edge applied AI work into accessible, engaging educational content including hands-on live workshops and meetups to online courses and recorded video tutorials.
You will collaborate with our Applied AI and software engineering teams to create high-quality learning experiences that explain core concepts in Agentic AI, walk through real-world examples, and help external developers succeed with our tools. This is a hybrid role that blends technical software experience with a passion for education, community building, and communication.
You will:
Partner with LangChain engineers to develop educational content that teaches generative AI and agent-building concepts using LangChain, LangGraph, and LangSmith.
Design curriculum and learning paths for our community of over 1 million developers and agent builders.
How to be successful in the role:
Compensation & Benefits:
Create and deliver content across multiple formats:
Online courses for LangChain Academy, video tutorials and webinars
Live presentations at workshops, hackathons, meetups, and conferences
Build and maintain example projects, code demos, and visuals to support educational content.
Translate experimental applied AI code and internal agent templates into crisp, developer-friendly learning materials.