Composio Raises $25M Series A
Composio raises $25M Series A led by Lightspeed Venture Partners to scale its infrastructure for AI agents that learn from experience.
Composio Raises $25M Series A
Composio, a San Francisco based developer of AI agent learning infrastructure, has raised $25 million in a Series A funding round. The investment brings the company's total funding to $29 million. The company focuses on solving the learning limitations of AI agents by building a shared infrastructure that allows agents to accumulate knowledge and improve through experience over time.
Investors
The round was led by Lightspeed Venture Partners. Participating investors included Guillermo Rauch, Dharmesh Shah, Gokul Rajaram, Soham Mazumdar, SV Angel, Blitzscaling Ventures, Operator Partners, Agent Fund, Elevation Capital, and Together Fund.
Composio Use of Funds
Composio plans to use the capital to accelerate the development of its AI learning infrastructure, which enables systems to continuously improve by accumulating experience and building contextual understanding across enterprise workflows.
About Composio
Founded in 2023 by CEO Soham Ganatra, Composio provides a shared skill layer for AI agents that captures and distributes practical knowledge. The platform integrates with major frameworks including MCP, LangChain, Vercel AI SDK, and OpenAI Agents to help developers build AI systems that evolve into adaptive, expert partners.
Funding Details
Company Website: https://composio.dev
Company: Composio
Raised: $25M
Round: Series A
Funding Date: July 2025
Lead Investor: Lightspeed Venture Partners
Additional Investors: Guillermo Rauch, Dharmesh Shah, Gokul Rajaram, Soham Mazumdar, SV Angel, Blitzscaling Ventures, Operator Partners, Agent Fund, Elevation Capital, Together Fund
Company Website: https://composio.dev/
Software Category: Artificial Intelligence Infrastructure
Source: https://www.prnewswire.com/news-releases/composio-raises-29m-to-solve-ais-learning-problem-building-skills-that-actually-improve-over-time-302510684.html