Benchmark venture capital9/28/2023 ![]() We believe that the two main value props it provides are: Components The LangChain framework is designed with the above objective principles in mind. Be agentic: allow a language model to interact with its environment.Be data-aware: connect a language model to other sources of data.We believe that the most powerful and differentiated language model applications will: LangChain is a framework for developing applications powered by language models, offered as both a Python and a TypeScript package. So what can you expect from us? LangChain Today Our goal is simple: empower developers to build useful applications powered by language models. With this capital we are going to invest aggressively to keep up with the ground breaking work the community is doing building intelligent apps. Benchmark led the round and we’re thrilled to have their counsel as they’ve been the first lead investors in some of the iconic open source software we all use including Docker, Confluent, Elastic, Clickhouse and more. ![]() With that in mind, we are excited to publicly announce that we have raised $10 million in seed funding. You’re asking us every day for more (400+ GitHub issues, 100 open PRs) and we want to help! And, it also is clear that far more work and tooling are needed to make these applications work well (particularly in production). It became clear that the combination of LangChain + LLMs blows open the frontier of amazing products and applications to be built. LangChain now has over 20K stars on GitHub, 10K active Discord members, over 30K followers on Twitter, and - most importantly - over 350 contributors. These first simple abstractions struck a chord and the project took off, thanks largely to your community support and contributions. It began by noticing common patterns in how people were approaching problems, and attempting to create abstractions that made it easier. This all started as an open-source side project, without any intention of building a company. “LangChain aims to help with that by creating… a comprehensive collection of pieces you would ever want to combine… a flexible interface for combining pieces into a single comprehensive ‘chain’”.“The real power comes when you are able to combine with other things.”.“a python package aimed at helping build LLM applications through composability”.In the very first tweet thread, Harrison said: Research papers like Self-Ask with Search and ReAct were published, demonstrating the power of these approaches.Īmongst these early tremors of a tectonic shift in computing, we released the first version of the LangChain Python package on October 24th, 2022. Developers were discussing how to connect language models to their own proprietary documents, APIs, and structured data. When we launched, generative AI was starting to go mainstream: stable diffusion had just been released and was captivating people’s imagination and fueling an explosion in developer activity, Jasper announced a funding round, and investors released the first Gen AI market maps.Īlongside this boon in content creation, people started to realize that the true power of this technology was not using a language model in isolation, but using the language model as part of a new, more intelligent system. It was only six months ago that we released the first version of LangChain, but it seems like several years.
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