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

Execution memory SDK — captures what your agents did, what failed, and what worked, then injects it into the next run automatically.

main.py

import os
import mubit.learn
import openai

# One-time setup — all LLM calls now auto-inject lessons and auto-capture outcomes.
mubit.learn.init(api_key=os.environ["MUBIT_API_KEY"], agent_id="support-agent")

# Use your LLM client as normal. MuBit handles the rest.
response = openai.OpenAI().chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What update style does Taylor want?"}],
)

# For run-scoped learning with automatic reflection on completion:
@mubit.learn.run(agent_id="support-agent", auto_reflect=True)
def handle_ticket(question):
    return openai.OpenAI().chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": question}],
    ).choices[0].message.content

The Problem / 01

Why existing approaches fail at operational memory.

Finetuning updates model weights offline. Chat memory tools store conversation history. Neither captures what actually happened during execution — the failures, decisions, and state that should inform the next run.

	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.

Observability tells you what happened. Mubit makes sure the agent remembers.

How It Works / 02

One line. Your agents learn from every run.

Call mubit.learn.init() once — your existing LLM calls auto-capture outcomes and auto-inject lessons. Zero code changes.

Works with

LangGraph CrewAI AutoGen LangChain Google ADK Vercel AI SDK MCP Agno

import os
import mubit.learn
import openai

# One-time setup — all LLM calls now auto-inject
# lessons and auto-capture.
mubit.learn.init(
    api_key=os.environ["MUBIT_API_KEY"],
    agent_id="support-agent",
)

# Use your LLM client as normal. MuBit handles the rest.
response = openai.OpenAI().chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What update style does Taylor want?"}],
)

# For run-scoped learning with auto-reflection:
@mubit.learn.run(agent_id="support-agent", auto_reflect=True)
def handle_ticket(question):
    return openai.OpenAI().chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": question}],
    ).choices[0].message.content

What Changes / 03

What operational memory unlocks.

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.

SHARED STATE

Agents in a pipeline share one truth

Context flows between agents through a unified state layer. No stale reads, no duplicated work, no rebuilding context from scratch.

EXECUTION CONTEXT

Runs resume exactly where they stopped

Restarts, handoffs, and retries continue with full task state — the decisions made, the paths tried, the progress so far.

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.

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.

Mubit