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 Investments, 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 life cycle 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 cofounded 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 provide details about its financials, though 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 with 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.
