MemroOS - Company-owned memory & governance · Edition 2026 Local-first · Self-hosted · v7.0
MemroOS
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●  Agent harness memory & governance / 2026 edition

Company-owned memory for agent harnesses.

MemroOS keeps company context, permissions, evals, dispatch, and proof outside any single model vendor or agent framework - so your agents can use the best models without renting away the memory layer that makes them useful.

Local-first Self-hosted Harness control
Schedule a working session ↗ See the operating loop ↗
Memory field spatial · rotating
DRAG TO ROTATE · CLICK A NODE

The retained field

Memory, context & skills - one company-owned field every agent draws from.

●  Ask MemroOS

Have a workflow where agents keep relearning the same thing? Ask.

Describe a process, a harness problem, or a governance question - get a straight, specific answer grounded in how MemroOS actually works.

Public evidence, not adjectives

Selected proof
84.06

Agentic-memory benchmark score

Ranked first on the public-evidence benchmark

1079

Tests passing

Full MemroOS app suite, zero failures (v7 validation)

4.47 GB

Local footprint mapped

Classified by permanence, cloud target, and prune safety

Public-evidence benchmark

Figure 1 — agentic-memory recall & precision, indexed score

▲ ranked first · indexed to the public benchmark

Retained context at work

Figure 2 — share of agent tasks served from retained memory, by week

▲ every cycle writes back — the loop compounds

An agent harness you can actually govern

The operating loop
GOVERNANCE LAYER Security policy · Audit lineage · Eval harness · Residual-risk proof · Human-in-the-loop approvals TASK goal · signal trigger STEP 01 Retrieve context pack GATE STEP 02 Dispatch local · REST · MCP · A2A GATE STEP 03 Govern inspect · approve · retry OUTCOME source-backed audited MEMORY LAYER Vector · Graph · Episodic · Knowledge · Skill surfaces — permission-aware, each with provenance TRANSPORTS & INTEROP Claude Code (MCP) · Codex · Google ADK · LangGraph · CrewAI · AutoGen · A2A · REST API Every run writes back to memory — retain & improve — so the next run starts smarter

01

Retain

Decisions, files, calls, incidents, and outcomes enter one governed memory layer.

02

Retrieve

A permission-aware context pack is assembled before an agent starts.

03

Dispatch

Work goes to local, REST, MCP, or A2A agents with source-backed context.

04

Govern

Pause, inspect, edit, resume, retry, and roll back — with audit lineage.

05

Improve

Repeated successful workflows become durable, governed skills.

See the actual product

Operator console
memroos.com/operations live
MemroOS Operator NOC — live telemetry across memory, workload, and governance MemroOS Memory Inventory — source-backed counts and multi-tier search MemroOS Dispatch — task-first agent engagement with inspectable context MemroOS Governed Skill Registry — suggested skills with evidence

Run the agent fleet like infrastructure — memory, workload, model utility, governance, and savings in one console. Missing streams render explicit gaps instead of fabricated numbers.

real product capture
Live activity audit · streaming

MemroOS

Memory your agents can actually use.

A multi-tier memory model — vector, graph, episodic, knowledge, and skill surfaces — assembled into permission-aware context packs at runtime, with provenance on every fact.

↗ Read the architecture paper
Memory map retrieving context pack
RUNTIME query VECTOR semantic GRAPH relations EPISODIC what happened KNOWLEDGE curated facts

Vector

Semantic recall

18420

INDEXED

Graph

Entity relations

6213

LINKED

Episodic

Run history

2940

TRACED

Knowledge

Curated facts

1842

VERIFIED

Context pack · permission-aware · 96 governed skills GitHub repo ↗

One operating system, four teams

Use cases

Product

discovery → delivery
Retains
Interview notes, launch learnings, roadmap decisions, beta feedback.
Consumed by
PRD drafting, prioritization, release notes, discovery synthesis.
Proof
Evidence survives from discovery to delivery, with handoff receipts.

Sales

account memory
Retains
Call takeaways, objections, competitor mentions, buyer preferences.
Consumed by
Account briefs, talk tracks, follow-up emails, renewal strategy.
Proof
Agents reuse the last best answer instead of rediscovering it.

Engineering

cross-agent handoff
Retains
Architecture decisions, incidents, repo patterns, fixes, runbooks.
Consumed by
Debugging, code review, migrations, onboarding, incident response.
Proof
Stop one coding agent, resume in another from a handoff pack.

AI Operations

release readiness
Retains
Agent registry state, auth posture, security findings, eval history.
Consumed by
Dispatch approvals, security review, release gates, rollback calls.
Proof
What changed, who produced it, what evidence backs it, what risk remains.

Agents should not start from zero when the team already solved, discussed, debugged, or decided something.”

MemroOS operating principle

Bring one workflow your agents keep relearning.

Leave the first session with a practical memory, governance, and proof map for it.

Schedule a working session ↗