Capture
Every run recorded in full — calls, tools, outcomes.
Mubit captures context from every run and turns it into reusable decision traces. The right lessons surface when needed, helping agents improve with every run.
Looks at trends on X and LinkedIn and drafts ideas for posts.
Turns approved ideas into long-form drafts with sources attached.
Checks tone, claims, and policy before anything is published.
Queues approved posts and picks send windows per channel.
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Enriches an inbound lead with firmographic and contact data from public sources, then scores fit against the ideal customer profile so reps work the right accounts first.
Recommends a quote for a deal given seat count, term, and discount guardrails, and explains which levers moved the final number.
Classifies an incoming support ticket by intent and urgency, then routes it to the right queue with a suggested first reply.
Summarizes a long document into a structured brief with key points, decisions, and open questions, kept faithful to the source.
Scores an account's churn risk from recent usage and support signals, and proposes a save play matched to the reason.
Enriches an inbound lead with firmographic and contact data from public sources, then scores fit against the ideal customer profile so reps work the right accounts first.
{
"type": "object",
"properties": {
"domain": { "type": "string", "description": "Company website domain." },
"contact_email": { "type": "string", "description": "Work email of the lead." },
"icp_notes": { "type": "string", "description": "What a good-fit account looks like." }
},
"required": ["domain"]
} Resolve the company from the domain, then return: 1. Firmographics — size, industry, region, funding stage. 2. Best-guess contact role and seniority. 3. An ICP fit score from 0–100 with a one-line reason. Only use evidence you can cite. If a field is unknown, return null rather than guessing.
| Version | Status | Source | Summary | Created | |
|---|---|---|---|---|---|
| v1 | Active | manual | — | 23/06/2026, 09:41:12 |
Most agents run open-loop — every execution vanishes when it ends. Mubit closes the loop.
Every run recorded in full — calls, tools, outcomes.
Runs become lessons: what worked, what failed, and why.
The right lesson surfaces at the moment of decision.
Outcomes feed back in — judgment sharpens every loop.
01MEMORY
Writes a lesson when a run finishes and recalls the relevant ones before the next call — no retraining.
02GATEWAY
Swap models across OpenAI, Anthropic, and Google without resetting what your agents already learned.
03ACTIONS
Drafts prompt updates from what every run taught it — accept inline, or auto-apply what passes your verifier.
04INTELLIGENCE
Trace what every agent remembered, why it acted, and what changed — without digging through logs.
The field is still converging — vector stores, high-dimensional embeddings, knowledge and context graphs, GraphRAG. They're built to store and retrieve. Mubit takes a different path: a representation built to learn from outcomes and stay fast as it grows — the two properties we believe define the next decade of agent memory.
Retrieval finds what you saved. Mubit turns outcomes into capability, so agents improve as they run. Storage is a feature; learning is the future.
A proprietary high-dimensional representation keeps recall fast and cheap as memory grows — no exploding index, no brittle graph to maintain. The approach that scales is the one that lasts.
Mubit remembers what worked and what didn't, so your agents get sharper with every run. Drop it into your existing LLM calls — no retraining, no vector database.
Mubit gives your agents continual learning through durable memory. Each run's facts, traces, and outcomes are stored, distilled into reusable lessons, and the relevant ones are recalled before the next LLM call — so agents improve with experience and share what they learn across each other, without retraining the model.
All of them — Mubit is framework- and model-agnostic. It wraps the LLM calls you already make, so every provider works. Drop-in adapters for CrewAI, LangGraph, LangChain, LlamaIndex, Google ADK, Agno, the Vercel AI SDK, and MCP, or call the SDK directly.
No. Mubit wraps the LLM calls you already make, so memory lives with the agent, not the provider. Move between OpenAI, Anthropic, Google, or open models for cost or capability and your agents keep everything they've learned.
Yes — we support self-hosting. Get in touch and we'll help you run Mubit's memory plane in your own VPC, so your data never leaves your environment.
Grab a free API key at console.mubit.ai and add a couple of lines to your existing LLM calls — no rebuild, no framework migration, and no separate vector database. Or connect an adapter for your framework; the quickstart in the docs takes it from there.