Reduce model integration costs while scaling AI: LangChain’s open ecosystem delivers where closed vendors can’t

LangChain, one of the leaders in the AI framework and orchestration space, plans to remain committed to the open source ecosystem, particularly as it reinforces its vendor-agnostic stance. 

Harrison Chase, Langchain co-founder and CEO, told VentureBeat that the success of its different platforms can be attributed to developers demanding model choice and not staying in a closed provider. 

“The power of the LangChain framework is in its integrations and the ecosystem,” Chase said. “The scale of the ecosystem is enormous, and much of that is made possible by the framework being open source.”

Chase said LangChain downloads reached 72.3 million last month, compared to competitors like OpenAI’s Agents SDK. He added that the LangChain Python and JS frameworks “have 4,500 contributors, that’s more contributors than Spark.”

LangChain, founded in 2022, has grown beyond its initial framework, which helped developers build AI applications. In February last year, it released the testing and evaluation platform LangSmith, a second framework called LangGraph and LangGraph Platform to help deploy autonomous agents. 

LangChain remained open source and agnostic to vendors and models throughout its growth. For example, it’s partnered with multiple companies, like Google and Cisco, around agent interoperability. As enterprises began experimenting with AI agents, Chase said LangChain saw an opportunity to offer deployment options that considered developer choices.

“Over the past year and a half or so, more and more enterprises and companies are just looking to go into production. So we matured all of our offerings, not just the open source LangChain, but all of our offerings collectively as a company to meet that demand and make it as easy as possible to build agentic applications,” he said. 

LangGraph Platform extends open-source offerings

One of LangChain’s new open-source platforms is the LangGraph Platform, which became generally available this week. The LangGraph Platform lets developers manage and begin deploying long-lasting or stateful agents. These agents build on what Chase calls “ambient agents,” or agents that work in the background and are triggered by certain events. 

“We’ve tried to focus a lot on some of the harder infrastructure problems that surround these agents,” Chase said. “LangGraph is good for long-running stateful agents, so if you’re deploying a simple application, you don’t want to use LangGraph Platform.”

He added that the company wants to bet big on ambient or long-running agents, finding this more independent, autonomous agent a more interesting infrastructure challenge. 

Through the LangGraph Platform, organizations can deploy agents with one-click deployment, horizontal scaling to handle “bursty, long-running traffic,” a persistence layer to support agentic memory, API endpoints for customization and native access to LangGraph Studio to debug any agents. 

Organizations might find themselves bringing more and more agents online. LangGraph Platform includes a management console that lays out all the agents currently deployed and lets users find agents, reuse common agent architectures and create multi-agent architectures.”

“One of the big benefits of LangGraph is that it gives the builder of the agent full control over the cognitive architecture. If there’s an [large language model] LLM action that must be done right, a good tool you have to enforce quality is to create an in-the-loop evaluation directly in your LangGraph app,” Chase said. 

Chase added that with LangGraph, developers can access “a good orchestration framework” to build agents and bring these reliable agents into LangGraph Platform for deployment. 

During the best test, Chase said over 370 teams used LangGraph Platform. LangChain offers three tiers to use LangGraph Platform, with pricing dependent on how developers plan to host the service. 

The broader LangChain open-source ecosystem

For Chase, one of LangChain’s strengths is its ability to create an entire application and agent development ecosystem. 

LangSmith, the company’s testing and observability platform, works with LangGraph and LangGraph Platform to track agent metrics. Since many agents built and run with LangGraph Platform are longer-running, enterprises need to check whether they continue to perform to specifications constantly. 

Chase boasted that LangGraph “is the most widely adopted agent framework” and claimed it’s downloaded more than AutoGen from Microsoft and the CrewAI agentic platform, once again citing the open-source value for its success.

“LangGraph is most often selected by teams that need to build end-user facing or highly trafficked agents (LinkedIn, Uber, GitLab) – the reason is that you won’t scale off of LangGraph because it’s very low-level and controllable, which is needed for reliable agents. CrewAI and Autogen are often used because they have a less steep learning curve – these frameworks make more decisions for the user, so you’re trading ease of adoption for power,” he said.