Mubit offers an execution‑memory SDK that automatically records the outcomes, errors, and state of each LLM call and injects relevant lessons into subsequent runs. By adding a single init call or decorator, developers can give production AI agents sub‑80 ms retrieval of cross‑run context, shared state, and audit trails without retraining models or incurring GPU or per‑token costs.
Funding
Funding not disclosed
Founders
Product
Problem
AI agents lack persistent operational memory, so failures, decisions, and state from one execution are not available to inform subsequent runs. Existing approaches like finetuning update model weights offline, and chat memory tools only retain conversation history, leaving a gap in real-time, cross-run learning.
Solution
Mubit provides an execution‑memory SDK that automatically captures the outcomes of each LLM call—including successes, errors, and contextual state—and injects relevant lessons into the next invocation. By initializing the SDK once, developers can add runtime memory to any existing agent without code changes, enabling agents to recall past actions, share state across pipeline steps, and resume interrupted workflows. The platform offers sub‑80 ms retrieval, flat pricing without GPU or per‑token costs, and seamless integration with popular frameworks such as LangChain, CrewAI, and AutoGen, turning production agents into continuously learning systems.
Target Audience
Primary users are developers and engineering teams building production AI agents—such as task automation bots, conversational assistants, and multi‑step workflow agents—who need reliable, cross‑run memory without retraining models.
Features
- Automatic capture of execution outcomes (results, failures, decisions) for every LLM call
- Real‑time injection of learned lessons into subsequent runs via a simple decorator or init call
- Sub‑80 ms memory retrieval optimized for active agent loops
- Flat, predictable pricing model with no GPU or per‑token billing
- Compatibility with major agent orchestration frameworks (LangChain, LangGraph, CrewAI, AutoGen, Vercel AI SDK, etc.)
- Unified state layer that enables shared context across multiple agents in a pipeline
- Built‑in audit trail for compliance, allowing queries of past decisions and state changes