What is Mubit
Mubit enables continual learning for AI agents, powered by durable memory and cross-agent knowledge sharing.
It stores what agents experience, extracts reusable lessons, and surfaces the right context before every LLM call — so agents get better over time without retraining. Memory and coordination work together: agents share knowledge through handoffs, learn from outcomes, and carry lessons across sessions.
Why Mubit
Every interaction becomes memory. Mubit extracts lessons from what worked and what didn't, then surfaces them before the next LLM call.
Token-budgeted context assembly gives your LLM exactly the right facts, lessons, and rules — no overflow, no guessing.
Register agents, scope memory access, and pass work between them with structured handoffs and feedback.
Drop-in adapters for CrewAI, LangGraph, LangChain, LlamaIndex, Google ADK, Agno, Vercel AI SDK, and MCP. Or use the SDK directly.
Pick your path
Two lines of setup. Your LLM calls automatically learn.
Close the loop between what your agent recalled and how the task went.
Handoffs, shared team memory, and subagent isolation.
Semantic, episodic, procedural, and working memory — and the entry types behind them.
CrewAI, LangGraph, LangChain, LlamaIndex, ADK, Agno, Vercel AI, MCP.
Full HTTP and gRPC control surface.
Two ways to use Mubit
Embed Mubit in your app with remember, recall, getContext, reflect. Your code owns the agent config. Best for prototypes and CI-driven workflows.
Configure agents declaratively as Projects + Agent Cards. Version prompts and skills server-side with candidate → active promotion. Best for teams that want review and rollback.