LangChain Secures $125M Funding, Achieves Unicorn Status with $1.25B Valuation

Exclusive: Early AI darling LangChain is now a unicorn with a fresh $125 million in funding | Fortune

LangChain, one of the earliest breakout startups of the generative AI era, announced a $125 million Series B funding round on Monday at a $1.25 billion valuation. The startup, which created an eponymous open source framework for connecting AI apps to real-time data, hopes its tools can become the default building blocks that companies use to unleash a multitude of AI agents -- while its investors believe the company has the potential to become as successful as other foundational digital infrastructure companies like Crowdstrike (for cybersecurity) and Datadog (for data monitoring). The round, which was rumored to have been completed over the summer, was led by IVP, with participation from existing investors Sequoia and Benchmark and new backers including CapitalG, Sapphire Ventures, ServiceNow Ventures, Workday Ventures, Cisco Ventures, Datadog, Databricks, and Frontline. LangChain says its tools are already used by AI teams at companies like Cisco, Replit, Clay, Cloudflare, Workday, and ServiceNow. The company argues that building reliable AI agents -- systems that can reason, act, and use tools on behalf of users -- is still far too difficult. "Today, agents are easy to prototype but hard to ship," LangChain wrote in a press release announcing the round. "Any input or change to an agent can create a host of unknown outcomes." The solution, the company says, is a new approach that blends product, engineering, and data science -- what it calls agent engineering. The company is positioning itself as the connective tissue of the agent era -- not just stitching together connectors, but providing the entire lifecycle of tools developers need to build, deploy, and monitor agents in production. A company like ServiceNow, for example, might use LangChain to connect an LLM to its internal knowledge base and use it to trigger workflows or track performance. LangChain began in late 2022 as an open-source project by Harrison Chase, then an engineer at Robust Intelligence, just weeks after OpenAI released ChatGPT. It pioneered the idea of "chains" -- building blocks that connect large language models to external tools and data sources in a sequence, letting them take action instead of just generating text. A simple chain might let an AI take a user's question, call a web search API, summarize the results, and return an answer -- steps stitched together like links. It was an immediate hit: "It was very crazy," Chase recalled. "I didn't know I was going to leave my previous job. I had no clue what I was going to do next." It turned out that the project that became the startup LangChain, which Chase co-founded with Ankush Gola, became a darling of developers. That's because it solved one of the most pressing problems in the early days of large language models: the models couldn't access real-time information or perform actions like searching the web, calling APIs, or interacting with databases. LangChain's framework let developers build those capabilities into their LLM apps -- and adoption skyrocketed. The San Francisco startup raised a $10 million seed round led by Benchmark in April 2023, and announced a $25 million Series A in 2024 led by Sequoia, and valuing the company at $200 million. Since then, however, the market has grown crowded with other companies offering similar tools, such as LlamaIndex and Haystack, while OpenAI, Anthropic, and Google now provide built-in capabilities that were once LangChain's differentiators. To stay ahead, LangChain expanded its product lineup, including LangSmith, an observability, monitoring, evaluation and deployment platform built specifically for LLM applications and agents. Since launching last year, LangSmith has surged in popularity, as LangChain keeps some of its early products open source while creating proprietary platforms. Langchain would not not provide details about its financials, thought a spokesperson said that a TechCrunch report in July that pegged its annual recurring revenue at between $12 million and $16 million was "low for where we are today." While the company is not profitable, Langchain is "fairly efficient in spend" compared to high-growth, VC-backed startups, the spokesperson said. IVP's Tom Loverro, who led the investment, said the firm had "high conviction" in Chase and the company's potential from the beginning. "Two years ago, the question was whether an open-source project like LangChain could become a major commercial company," he said. "We saw Harrison and Ankush take the first important steps boldly into that journey," including building multiple products that customers want. Loverro said he sees LangChain as potentially as successful as companies like Crowdstrike and Datadog, which became indispensable for taming the complexity of cybersecurity and cloud infrastructure, respectively. LangChain is betting it can become the layer that makes AI agents reliable and observable enough for enterprises to trust -- turning today's chaotic prototypes into business-critical systems. "It feels increasingly sure that agents are super important to the future," he said. "And if you believe that, then agent engineering is going to be incredibly important." Chase admits the agent platform landscape is already crowded, but he argues LangChain's breadth and neutrality will give it staying power. "There's a ton of players," he said. "I like to say we have 500 competitors and zero competitors at the same time." Most enterprises, he predicts, will ultimately use multiple agent platforms, and many of them, like ServiceNow, will be powered under the hood by LangChain. IVP's Loverro emphasized that Langchain already has strong revenue, adoption, and big enterprises like Cisco and Workday building on LangChain. There will be competition, he says, "but it's TBD if they matter." And if the investors are right, LangChain could become the indispensable layer powering the agent era -- just as CrowdStrike and Datadog did for the last generation of infrastructure.

AI agent tooling provider LangChain raises $125M at $1.25B valuation - SiliconANGLE

LangChain Inc., a startup that helps developers build artificial intelligence agents, has raised $125 million in funding at a $1.25 billion valuation. Fortune reported today that IVP led the Series B investment. It was joined by Alphabet Inc's growth-stage CapitalG fund, ServiceNow Ventures, Workday Ventures, Cisco Investments, Datadog, Databricks and several others. LangChain develops an open-source AI agent development tool of the same name. The software enables engineers to implement agents with as little as 10 lines of code. One of the ways LangChain speeds up development is by providing pre-packaged building blocks that remove the need to create everything from scratch. Another selling point of the tool is its unified application programming interface. OpenAI, Anthropic PBC and other AI providers distribute their language models through different APIs. As a result, changing an agent's language model often requires switching it to a new API, which necessitates code changes. LangChain's unified API makes it possible to switch AI models without code changes. Software teams that require more advanced features can use LangGraph, another open-source tool developed by LangChain. It facilitates the creation of AI agents that can run for extended periods of time and automatically recover from mistakes. LangGraph also makes it possible to implement human supervision features. Companies working on even more complex projects can pair LangGraph with Deep Agents, an open-source tool LangChain released in July. The latter technology makes it possible to equip applications with reasoning features. According to LangChain, Deep Agents includes a tool that enables AI agents to break down a complex task into multiple steps. The tool tracks the progress of each step and changes its processing plan if it encounters difficulties. A second Deep Agents component can create a dedicated sub-agent for each processing step to speed up output generation. Some tasks require AI agents to ingest a significant amount of new data. Under certain conditions, the volume of data that must be processed can exceed the capacity of an agent's context window. Deep Agents includes a file system that increases the amount of information AI agents can use during processing. LangChain generates revenue with a paid product called LangSmith. It provides a code editor optimized for AI agent development. After engineers create a new agent, they can use LangSmith's built-in testing features to determine whether it meets project requirements. The tool also eases other development tasks. According to LangChain, LangSmith includes a tool that makes it possible to deploy AI agents with one click. Once an agent is in production, the tool tracks metrics such as inference cost and latency. LangSmith's observability features also monitor how users interact with AI applications. The tool can automatically identify user requests that an agent struggles to process. Developers can use that information to make improvements.

LangChain hits $1.25 billion valuation in Series B led by IVP

Alphabet's CapitalG, Databricks, Cisco, and ServiceNow joined the funding to back LangChain's AI developer tools. According to an exclusive Fortune report, LangChain Inc., a startup providing tools for developers to build artificial intelligence agents, raised $125 million in a Series B funding round. The investment, led by IVP, values the company at $1.25 billion. The round was joined by Alphabet Inc.'s CapitalG fund, ServiceNow Ventures, Workday Ventures, Cisco Investments, Datadog, and Databricks. LangChain's core offering is an open-source tool that allows engineers to create AI agents with as few as 10 lines of code by using its prepackaged building blocks. It also features a unified application programming interface, enabling a swap between language model providers like OpenAI and Anthropic PBC without altering existing code. For more advanced use cases, the company offers LangGraph, an open-source framework designed to support agents that run for extended periods. This tool facilitates automatic recovery from mistakes and allows for the implementation of human supervision in an agent's workflow. For more complex projects, LangGraph can be paired with Deep Agents, an open-source add-on released by LangChain in July that equips applications with reasoning capabilities. Deep Agents includes a component for decomposing a complex task into multiple steps, tracking progress, and adjusting its plan when challenges arise. Another feature can spawn dedicated sub-agents for each step to speed up output generation. The tool also adds a file system that expands the amount of data an agent can process beyond its standard context window. The company monetizes its technology through LangSmith, a paid platform offering a code editor optimized for AI agent development. After creation, engineers can use LangSmith's built-in testing features to verify an agent meets project requirements. The platform simplifies deployment to a single click and monitors observability metrics such as inference cost, latency, and user interactions. These metrics help developers identify areas for performance improvement. As of June, LangChain's annualized recurring revenue (ARR) was between $12 million and $16 million, according to TechCrunch. A company spokesperson told Fortune its ARR has grown since that time. While not yet profitable, LangChain claims to be spending its funding more efficiently than other high-growth, venture-backed startups.