Mubit — Execution Memory for Production AI Agents

Capability	Finetuning	Memory tools	Mubit
Cost	GPU-hour billing. Scales sharply with dataset volume.	SaaS tiers + credit-based usage. Enterprise needs negotiation.	Flat, predictable. No GPU costs or per-token billing.
Speed	Async — minutes to hours. Not built for real-time.	Sub-second claimed. Not optimized for active agent loops.	Sub-80ms retrieval. Built for the execution loop.
Scalability	Scales with compute. Significant GPU allocation needed.	Struggles with high-frequency writes from large fleets.	Scales with fleet size. No retraining as agents grow.
Runtime memory	None. Weights updated offline only.	Chat-scoped. No execution history or cross-agent context.	Native. Faults, decisions, and state persist across agents.

Finetuning

Memory tools

Mubit

Cost

GPU-hour billing. Scales sharply with dataset volume.

SaaS tiers + credit-based usage. Enterprise needs negotiation.

Flat, predictable. No GPU costs or per-token billing.

Speed

Async — minutes to hours. Not built for real-time.

Sub-second claimed. Not optimized for active agent loops.

Sub-80ms retrieval. Built for the execution loop.

Scalability

Scales with compute. Significant GPU allocation needed.

Struggles with high-frequency writes from large fleets.

Scales with fleet size. No retraining as agents grow.

Runtime memory

None. Weights updated offline only.

Chat-scoped. No execution history or cross-agent context.

Native. Faults, decisions, and state persist across agents.

import os import mubit import openai # One import wires inference, memory, tools, # verifiers, and traces into your agent. mubit.init( api_key=os.environ["MUBIT_API_KEY"], agent_id="support-agent", ) # Your LLM calls run unchanged — Mubit handles the rest. response = openai.OpenAI().chat.completions.create( model="gpt-5.5", messages=[{"role": "user", "content": "What update style does Taylor want?"}], )

RECALL

Agents stop repeating the same failures

Every execution outcome — successes, errors, edge cases — persists and surfaces automatically on the next run. No retraining, no prompt hacks.

› route_search("enrich prospect background")

Mubit rating · from 10 past runs of this task 0 / 4 evaluated

A web —
B news —
C graph —
D social —

why graph

The company graph carries the firmographic and intent signals this prospect task needs. Lessons from 47 past runs rank it above web, news, and social search on similar queries.

PROVIDER-AGNOSTIC

Switch LLMs without losing memory

Route through Mesh and Mubit memory flows across every provider — OpenAI, Anthropic, Google. Swap models for cost or capability without resetting what your agents already know.

"new model, same memory"

SELF-UPDATING

Prompts that rewrite themselves

Mubit drafts prompt updates from what every run taught it. Review and accept inline, or auto-accept changes that pass your verifier — your system prompt evolves with the agent.

before

1You are a senior support engineer.
2Answer questions briefly.
3Use tools when needed.

after

1You are a senior support engineer.
2Answer using verified sources from memory.
3Use tools when needed.
4Verify outputs against past-run lessons.

Auto-accept

AUDIT TRAIL

Trace every decision an agent made

Query what agents remembered, why they acted, and what changed — without rebuilding context from logs. Compliance-ready by default.

init 0.00s

recall 0.17s

plan 0.49s

tool 1.02s

verify 1.88s

capture 2.39s

Event log 10 entries · 2.39s

time event message

0.012s init Session sx-9c2 opened with plan-agent v2.1

0.184s recall Querying memory for task="prospect_enrichment"

0.187s recall Matched 4 lessons (avg score 0.82)

0.191s recall Injected lessons into prompt context

0.502s plan Plan generated with 3 steps queued

1.034s tool Calling parallel.search("acme corp 2024 earnings")

1.884s tool Returned 7 documents in 0.85s

1.892s verify guard_check passed all 3 guards

2.401s capture Capturing run outcome (success)

2.406s capture Committed 3 new lessons to memory

# Mubit — The best way to run production AI agents Built to make agents smarter every run. Execution memory SDK — captures what your agents did, what failed, and what worked, then injects it into the next run automatically. ## The problem: existing approaches miss the loop Most agent-memory tools store conversation or facts; few carry execution outcomes forward, so agents start every run cold and repeat the same mistakes. A qualitative positioning of the field (Mem0, Zep, Letta, Mubit) across three dimensions: - Learning — does the agent improve between runs, or start every run cold? Carrying outcomes forward is what lets it stop repeating mistakes. This is Mubit's clear edge. - Setup — managed memory you point at your agent, versus building and operating the store, schema, and retrieval yourself. - Scope — memory that spans an agent across every session, versus memory scoped to a single conversation. What each remembers: Mubit — execution memory; Mem0 — chat + facts; Zep — temporal facts; Letta — editable context. ## How it works: your agent's whole stack, one import Mubit wraps your existing client at init() — no prompt edits, no SDK swaps. - Your agent: Your existing agent code, unchanged. Mubit wraps your client at init() — no prompt edits, no SDK swaps. - Inference: One client for every provider. Route OpenAI, Anthropic, Google and more without rewriting your calls. - Memory: Captures outcomes after each run and recalls relevant lessons before the next. No vector DB to manage. - Tools: One registry for your tools. Mubit invokes them inline and records every call in the run. - Verifiers: Check each output against your rules before it ships. Failures are caught in the run, not in production. - Traces: Every LLM call, memory read, tool invocation, and verifier outcome lands in one run-level trace. - Models: Your providers — OpenAI, Anthropic, Google, open models. Bring your own keys. ## What operational memory unlocks ### Agents stop repeating the same failures (Recall) Every execution outcome — successes, errors, edge cases — persists and surfaces automatically on the next run. No retraining, no prompt hacks. ### Switch LLMs without losing memory (Provider-agnostic) Route through Mesh and Mubit memory flows across every provider — OpenAI, Anthropic, Google. Swap models for cost or capability without resetting what your agents already know. ### Prompts that rewrite themselves (Self-updating) Mubit drafts prompt updates from what every run taught it. Review and accept inline, or auto-accept changes that pass your verifier — your system prompt evolves with the agent. ### Trace every decision an agent made (Audit trail) Query what agents remembered, why they acted, and what changed — without rebuilding context from logs. Compliance-ready by default. ## Product ### Memory SDK The execution memory primitive — write a lesson when a run finishes, retrieve the relevant ones before the next call. ### Gateway One client for your agent's whole pipeline — inference, memory, tools, verifiers, and traces behind a single SDK. ### Intelligence Continuous learning for production agents — every run captured, distilled into a lesson, and fed back into retrieval. ### Actions Self-updating prompts, skills, and tool choices that learn from past runs and apply themselves automatically. ## FAQ **What kind of agents is this for?** Any agent that runs more than once — task agents, conversational agents, multi-step workflows. If the next run would benefit from knowing what happened in the last one, Mubit helps. **How is this different from a database or vector store?** Databases store data. Vector stores retrieve similar content. Mubit stores structured runtime memory — operational context, conversation state, past outcomes — that feeds directly into the next agent run. **What do I need to change in my stack?** Mubit sits beside your existing orchestration. No rebuilds or framework migration. **Is there a free trial or pilot program?** Yes. Early access includes a guided pilot. Request access to discuss scope and timeline. **Does this replace retraining or fine-tuning?** It's complementary but often eliminates the need. Mubit gives agents runtime memory so they improve across runs without model changes. **How do I get started?** Request access for a technical walkthrough. We'll map memory into your current flow and scope a guided pilot.

Built to make agents smarter every run, Mubit is the best way to run production AI agents.

Why existing approaches fail at operational memory.

Your agent's whole stack. One import.

What operational memory unlocks.

Agents stop repeating the same failures

Switch LLMs without losing memory

Prompts that rewrite themselves

Trace every decision an agent made

Ready to give your agents memory?