# Mnemo Cortex

Open-source memory coprocessor for AI agents with persistent recall, semantic search, and crash-safe capture.

## Agent Decision Summary
- Risk level: low
- Source confidence: medium
- Recommended workflows: Evaluation and observability, Memory or RAG workflow
- Permission surface: memory
- Agent JSON: https://www.openagent.bot/memory-systems/mnemo-cortex.agent.json

## Summary
Mnemo Cortex is an open-source memory coprocessor for AI agents. It focuses on persistent recall, semantic search, crash-safe capture, and sidecar-style memory without requiring hooks.


## Guide
Mnemo Cortex is an open-source memory coprocessor for AI agents.

### What it is
It provides persistent recall, semantic search, and crash-safe capture as a memory sidecar.

### Why it matters
Agent memory should be durable, inspectable, and reusable across runs.

### How it works
Start by connecting it to one low-risk agent workflow, then compare recall quality and failure recovery with and without the memory layer.


### FAQ
- Is Mnemo Cortex open source?
  - Yes. The GitHub repository is listed under the MIT license.
- Who should evaluate Mnemo Cortex?
  - Builders experimenting with persistent sidecar memory for AI agents should evaluate it.
## What It Does
It provides persistent recall, semantic search, and crash-safe capture as a memory sidecar.

## How To Evaluate
Start by connecting it to one low-risk agent workflow, then compare recall quality and failure recovery with and without the memory layer.

## Why It Matters
Agent memory is often too tightly coupled to one app or chat session. Mnemo Cortex is interesting because it frames memory as a coprocessor that agents can use alongside their normal workflow.


## Best For
- Builders testing persistent memory for agent workflows
- Teams that want semantic recall without wiring custom hooks everywhere
- OpenClaw and local agent users evaluating sidecar memory

## Not For
- Projects that only need document search
- Teams that require a mature enterprise memory platform today

## What It Actually Does
- Memory coprocessor model: Mnemo Cortex frames memory as a sidecar coprocessor for AI agents.
  - Why it matters: A sidecar memory layer can be reused without replacing the agent host.
- Persistent semantic recall: The project focuses on persistent recall and semantic search.
  - Why it matters: Agents need relevant prior context, not only longer chat transcripts.
- Crash-safe capture: Mnemo Cortex advertises crash-safe capture.
  - Why it matters: Memory systems should preserve important context even when an agent run fails.

## Typical Use Cases
- Agent session continuity: Preserve useful context across repeated agent runs.
- Semantic memory search: Retrieve prior notes, decisions, and context by meaning rather than exact text.
- Local memory experiments: Evaluate sidecar memory with local or OpenClaw-style agent setups.

## How It Compares
- When to choose Mnemo Cortex: Compare it with nearby memory systems by looking at hosting model, integration surface, license, and whether the official docs show the workflow you need.

## Fit Matrix
- Evaluation and observability: strong. Mnemo Cortex has multiple signals for evaluation and observability, including matching tags, capabilities, category, or positioning. Required check: Add one repeatable test case and confirm results can run again in review or CI.
- Memory or RAG workflow: strong. Mnemo Cortex has multiple signals for memory or rag workflow, including matching tags, capabilities, category, or positioning. Required check: Create, update, retrieve, correct, and delete memory or retrieval objects with real data.
- Browser automation: partial. Mnemo Cortex has at least one signal for browser automation, but should be checked against a real task before adoption. Required check: Run one non-sensitive website task and inspect clicks, waits, retries, and changed URLs.
- Coding agent workflow: partial. Mnemo Cortex has at least one signal for coding agent workflow, but should be checked against a real task before adoption. Required check: Run a small repository change and inspect the diff, tests, and rollback path.
- Local or private AI stack: partial. Mnemo Cortex has at least one signal for local or private ai stack, but should be checked against a real task before adoption. Required check: Verify hardware requirements, data path, storage, and whether all calls stay in your environment.
- Reusable skill workflow: partial. Mnemo Cortex has at least one signal for reusable skill workflow, but should be checked against a real task before adoption. Required check: Run one skill end to end and check whether it produces evidence or structured output.

## Evidence
- verified: Mnemo Cortex is listed as open source. Source: License metadata: MIT
- verified: Mnemo Cortex has a recorded GitHub repository: GuyMannDude/mnemo-cortex. Source: Resource facts and GitHub source link.
- inferred: Mnemo Cortex is tagged with memory, context retrieval, state capabilities. Source: OpenAgent capability taxonomy.

## Missing Checks
- Dedicated docs link is missing.
- Repository freshness has not been recorded.

## Next Actions
- Inspect repository: https://github.com/GuyMannDude/mnemo-cortex

## Facts
- Category: memory-systems
- Resource type: memory_system
- Open source: yes
- License: MIT
- Last verified: 2026-06-11
- GitHub repo: GuyMannDude/mnemo-cortex
- GitHub stars: 135

## Capabilities
- memory
- context-retrieval
- state

## Structured Use Case Tags
- personal-memory

## Getting Started
- Open the GitHub repository: https://github.com/GuyMannDude/mnemo-cortex

## Links
- GitHub: https://github.com/GuyMannDude/mnemo-cortex

## Structured Outputs
- JSON: https://www.openagent.bot/memory-systems/mnemo-cortex.json
- Markdown: https://www.openagent.bot/memory-systems/mnemo-cortex.md
- Agent JSON: https://www.openagent.bot/memory-systems/mnemo-cortex.agent.json
- Canonical: https://www.openagent.bot/memory-systems/mnemo-cortex