AI Agent Frameworks · Public

mind-mem

Agent SDK framework for checking tool contracts, handoffs, guardrails, traces, and multi-agent coordination boundaries.

Best fitDevelopers building traceable, reviewable, acceptance-tested agent workflows.

Check whether this project matches your task before installing it.

What it can doAgent workflow modeling, tool contracts, handoff design, guardrail review, traces, and acceptance checks

Review the portable capability path.

Before continuingVerify in a sandbox

Do not treat a preview pack as a proven local install.

GitHub snapshot6 stars

1 forks · 5 contributors

Official first step Read manual preview Source repository

Publication status · 2026-05-20

What is mind-mem?

mind-mem helps build agent workflows with tools, handoffs, guardrails, traces, and acceptance boundaries.
Best fit: Developers building traceable, reviewable, acceptance-tested agent workflows.
Not for: Not for one-off prompts or flows without tool permission boundaries and traces.
Capability added to an AI workflow: Agent workflow modeling, tool contracts, handoff design, guardrail review, traces, and acceptance checks
First safe verification step: Build one minimal agent with a harmless tool, then verify trace, handoff or guardrail, and failure behavior.
Verification state: source, Quick Start, and sandbox install checks are recorded as passed.
Top risk: The main risk is treating an agent example as production design without tool permissions, guardrails, traces, and approvals.
Evidence base: https://github.com/star-ga/mind-mem, https://github.com/star-ga/mind-mem#readme, Human Manual, Pitfall Log

Quick decision

Use this section to decide whether the project is worth a deeper read.

Best forDevelopers building traceable, reviewable, acceptance-tested agent workflows.

Match the project to your task before installing it.

CapabilityAgent workflow modeling, tool contracts, handoff design, guardrail review, traces, and acceptance checks

Agent SDK framework for checking tool contracts, handoffs, guardrails, traces, and multi-agent coordination boundaries.

Repositorystar-ga/mind-mem

6 stars · 1 forks

What it can do

Translate the upstream project into concrete capabilities the user can judge before installing.

Overview

Related topics: System Architecture, Quick Start Guide, MCP Server & Tools

Sources: [src/mind_mem/compiled_truth.py:1-30]()

Key Concepts

Related topics: Overview, Block Format & Data Model, Governance & Safety

Source: https://github.com/star-ga/mind-mem / Human Manual

Quick Start Guide

Related topics: Overview, MCP Server & Tools

Sources: [examples/README.md:1-15]()

System Architecture

Related topics: Overview, Core Components, Hybrid Search & Retrieval

Sources: [src/mind_mem/compiled_truth.py](https://github.com/star-ga/mind-mem/blob/main/src/mind_mem/compiled_truth.py)

Core Components

Related topics: System Architecture, Hybrid Search & Retrieval, Governance & Safety

Sources: [src/mind_mem/recall.py](https://github.com/star-ga/mind-mem/blob/main/src/mind_mem/recall.py)

Sources: https://github.com/star-ga/mind-mem, Human Manual, Project Pack evidence, and downstream validation signals.

Community Discussion Evidence

Project-level external discussion stays visible on the detail page, not only inside the manual.

Stars6 stars

Forks1 forks

Contributors5 contributors

Licenseunknown

Community Discussion Evidence

12 source-linked items

Review these external discussions before using mind-mem with real data or production workflows. They are review inputs, not standalone proof that the project is production-ready.

01
Perf regression: build_index on a fresh 80KB workspace takes ~55s
github / github_issue
02
FederationClient: no scheme allowlist, no redirect cap, no response-size
github / github_issue
03
`_handle_fed_resolve` accepts caller-supplied merged_payload without val
github / github_issue
04
THREE_WAY_MERGE doesn't bump vclock — resolved conflicts recur on every
github / github_issue
05
ACL `_get_request_scope` fail-open on token-introspection exception (acl
github / github_issue
06
PG-backed: `mm recall` returns [] despite direct PG FTS finding matches
github / github_issue
07
PG-backed: `mm doctor --rebuild-cache` errors=263 (no such table: blocks
github / github_issue
08
test_rollback_removes_new_files fails on macOS + Windows runners (passes
github / github_issue
09
[MEDIUM] N-03: Rate limiter collapses all stdio clients into 'default' b
github / github_issue
10
[MEDIUM] T-003: propose_update input bounds bypass (rationale/tags writt
github / github_issue
11
[MEDIUM] N-04: staged_change rollback dispatcher silently drops 'reason'
github / github_issue
12
[MEDIUM] N-02: REST rollback_proposal silently drops 'reason' field
github / github_issue

How to start

Only source-backed commands are shown here. Verify them in an isolated environment first.

Try the prompt first

Test the workflow without installing the upstream project.

preview

Read the Human Manual

Understand inputs, outputs, limits, and failure modes.

manual

Take context to your AI host

Use the compiled assets in your preferred AI environment.

context

Run sandbox verification

Confirm install commands and rollback before using a primary environment.

verify

pip install mind-mem

Official start command · https://github.com/star-ga/mind-mem#readme · verified: yes

Human Manual

The English page must expose the real manual, not a short placeholder.

8+ sections · Human Manual

mind-mem Manual

Related topics: System Architecture, Quick Start Guide, MCP Server & Tools

Open the full manual

mind-mem Human Manual
Table of Contents
Overview
Related Pages
Purpose and Scope
Architecture Overview
Workspace Structure
MCP Server Surface

Overview

Related topics: System Architecture, Quick Start Guide, MCP Server & Tools

Sources: [src/mind_mem/compiled_truth.py:1-30]()

Key Concepts

Related topics: Overview, Block Format & Data Model, Governance & Safety

Source: https://github.com/star-ga/mind-mem / Human Manual

Quick Start Guide

Related topics: Overview, MCP Server & Tools

Sources: [examples/README.md:1-15]()

System Architecture

Related topics: Overview, Core Components, Hybrid Search & Retrieval

Sources: [src/mind_mem/compiled_truth.py](https://github.com/star-ga/mind-mem/blob/main/src/mind_mem/compiled_truth.py)

Core Components

Related topics: System Architecture, Hybrid Search & Retrieval, Governance & Safety

Sources: [src/mind_mem/recall.py](https://github.com/star-ga/mind-mem/blob/main/src/mind_mem/recall.py)

AI Context Pack and portable assets

After deciding to continue, take the project context into your own AI host.

Complete pack plus user-owned assets

These files are planning and verification assets for Claude Code, Codex, Gemini, Cursor, ChatGPT, and other AI hosts.

Download complete pack Read Human Manual

BundleComplete Project Pack AssetAI Context Pack AssetBoundary & Risk Card AssetHuman Manual AssetPitfall Log AssetPrompt Preview AssetQuick Start ManifestPROJECT_PACK.json

Preflight checks

Treat this page as a planning asset, not proof that your local environment is ready.

The manual is generated from source-linked project files and Doramagic validation signals.
Community evidence warnings stay visible instead of being converted into marketing claims.
This English page is indexable because the locale quality gate passed and explicit English index approval is enabled.
Use the upstream repository as the final authority for installation commands, license, and version-specific behavior.