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memoria

Memory frameworkOfficial Matrix Origin project

Created by Matrix Origin

Published in matrixorigin/memoria. Publisher page

626 GitHub stars

What it is

Secure memory management for AI Agents • Ensures data integrity • Reduces hallucinations • Maintains consistent long-term context
— the description by Matrix Origin

AutoGineer overview, from the brain’s own files

Memoria is a persistent memory layer designed to give AI agents reliable, auditable, and version-controlled long-term memory. By leveraging a Git-inspired architecture powered by MatrixOne, it allows agents to snapshot, branch, merge, and rollback their memory state. This ensures that memory changes are tracked and reversible, reducing hallucinations and maintaining consistency across sessions. It supports semantic search through hybrid retrieval and includes self-governing features to detect contradictions and maintain audit trails, making it a robust solution for managing agent knowledge.

How it works

Memory model: Hybrid

AutoGineer overview, from the brain’s own files

Memoria functions as a memory service that agents interact with via MCP tools. It uses a hybrid memory model where facts, preferences, and decisions are stored and retrieved. The system supports branching for isolated experimentation, allowing agents to test new information or hypotheses without altering the main memory state. It includes specific steering rules for managing the lifecycle of memories, from initial retrieval at the start of a conversation to purging transient working memories upon task completion. Data is stored using a database-native engine that supports zero-copy branching and snapshots.

Folders
FolderRoleFiles
.claudeagent-config1
.cursoragent-config2
.geminiagent-config1
.githubagent-config16
.kiroagent-config7
assetsattachments7
docsdocs10
scriptsscripts2
skillsagent-instructions7
benchmarksfolder7
configfolder7
grafanafolder1
memoriafolder280
pluginsfolder69
sdkfolder36

Requirements

Apps: Docker

Databases: PostgreSQL, SQLite

API keys (by name): MEMORIA_API_KEY, MEMORIA_EMBEDDING_API_KEY, MEMORIA_LLM_API_KEY

Works with: Claude Code, Cursor, Gemini CLI, Kiro

Set it up

  1. This brain runs code automatically: 5 scripts. Review README.md, scripts/install.ps1, scripts/install.sh before opening the folder in Claude Code. No step below turns a hook on for you.

    • README.md: curl -sSL https://raw.githubusercontent.com/matrixorigin/Memoria/main/scripts/install.sh | bash
    • README.md: curl -sSL https://raw.githubusercontent.com/matrixorigin/Memoria/main/scripts/install.sh | bash
    • README.md: command -v memoria >/dev/null || curl -sSL https://raw.githubusercontent.com/matrixorigin/Memoria/main/scripts/install.sh | bash -s -- -y -d ~/.local/bin
    • scripts/install.ps1: pwsh scripts/install.ps1
    • scripts/install.sh: sh scripts/install.sh
  2. Not scanned by AutoGineerThe author’s README also describes installing it with Docker Compose. Read its install section: AutoGineer did not scan that path. https://github.com/matrixorigin/memoria/blob/93ccccf2ea8da4c205ac2d93c1a010c287bc742d/README.md#-quick-start
  3. Not scanned by AutoGineerThe author’s README also describes installing it from a clone of its repository. Read its install section: AutoGineer did not scan that path. https://github.com/matrixorigin/memoria/blob/93ccccf2ea8da4c205ac2d93c1a010c287bc742d/README.md#-quick-start
  4. Created by Matrix Origin. Published in matrixorigin/memoria. Licence: Apache License 2.0.

Usage

AutoGineer overview, from the brain’s own files

  • Persistent memory across agent sessions
  • Git-like version control for agent knowledge
  • Isolated experimentation via memory branching
  • Semantic and full-text hybrid retrieval
  • Automated contradiction detection and memory governance
“Let's try switching from PostgreSQL to SQLite.”
The agent creates a new memory branch 'eval_sqlite', performs experiments, and allows the user to diff and merge the results back to main.
“I want to add OAuth support to the API.”
The agent searches for existing goals, stores the new goal, and tracks progress as working memories across sessions.
“What are my deployment preferences?”
The agent retrieves relevant semantic memories to provide context-aware information based on previous sessions.

Documentation by Matrix Origin

<div align="center"> <img src="assets/memoria-logo.png" alt="Memoria Logo" width="300"/>

Memoria

The World's First Git for AI Agent Memory

Snapshot · Branch · Merge · Rollback — for memory, not code.

Git made code safe to change. Memoria makes memory safe to change.

Image: CI Image: Release Image: GitHub Stars Image: Downloads Image: License Image: MCP Image: MatrixOne Image: Paper

Quick Start · Memoria in Astra · Why Memoria · Research · See It in Action · API Reference · Architecture · Development · Citation

</div>


Overview

Memoria is a persistent memory layer for AI agents with Git-level version control. Every memory change is tracked, auditable, and reversible — snapshots, branches, merges, and time-travel rollback, all powered by MatrixOne's native Copy-on-Write engine.

A core memory component of MatrixOrigin's Astra.

Astra runs the agent. Memoria helps it remember across sessions. MatrixOne powers the data storage and search.

Using Astra? · Use Memoria with another agent

<table> <tr> <td align="center" width="33%">

🔀 Git for Memory

Zero-copy branching, instant snapshots, point-in-time rollback — version control for every memory mutation

</td> <td align="center" width="33%">

🔍 Semantic Search

Vector + full-text hybrid retrieval finds memories by meaning, not just keywords

</td> <td align="center" width="33%">

🛡️ Self-Governing

Auto-detects contradictions, quarantines low-confidence memories, maintains audit trails

</td> </tr> </table>

<table> <tr> <td align="center" width="33%">

🔒 Private by Default

Local embedding model option — no data leaves your machine

</td> <td align="center" width="33%">

🧠 Cross-Conversation

Preferences, facts, and decisions persist across sessions

</td> <td align="center" width="33%">

📋 Full Audit Trail

Every memory mutation has a snapshot + provenance chain

</td> </tr> </table>

Supported Agents

<p align="center"> <a href="https://kiro.dev"><img src="https://img.shields.io/badge/Kiro-00C853?style=for-the-badge&logo=data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAyNCAyNCI+PHRleHQgeD0iNCIgeT0iMTgiIGZvbnQtc2l6ZT0iMTYiIGZpbGw9IndoaXRlIj5LPC90ZXh0Pjwvc3ZnPg==&logoColor=white" alt="Kiro"/></a> <a href="https://cursor.sh"><img src="https://img.shields.io/badge/Cursor-000000?style=for-the-badge&logo=cursor&logoColor=white" alt="Cursor"/></a> <a href="https://docs.anthropic.com/en/docs/claude-code"><img src="https://img.shields.io/badge/Claude_Code-D97757?style=for-the-badge&logo=anthropic&logoColor=white" alt="Claude Code"/></a> <a href="https://openai.com/index/introducing-codex/"><img src="https://img.shields.io/badge/Codex-412991?style=for-the-badge&logo=openai&logoColor=white" alt="Codex"/></a> <a href="https://geminicli.com"><img src="https://img.shields.io/badge/Gemini_CLI-4285F4?style=for-the-badge&logo=google&logoColor=white" alt="Gemini CLI"/></a> <a href="plugins/openclaw/README.md"><img src="https://img.shields.io/badge/OpenClaw-FF6B6B?style=for-the-badge" alt="OpenClaw"/></a> <a href="plugins/hermes/README.md"><img src="https://img.shields.io/badge/Hermes-8B5CF6?style=for-the-badge" alt="Hermes"/></a> </p>

<p align="center"><i>Works with any MCP-compatible agent</i></p>


🧠 Memoria in Hermes

Memoria is available from the Hermes plugin catalog. Log in at Memoria and create/copy a memory-service API key, then run:

hermes plugins install memoria --yes-deps --enable
hermes memory setup
hermes memory status

In the interactive setup, select memoria, paste your API key at the masked prompt, and choose whether to enable automatic capture (off by default). Confirm that status reports memoria, installed and available, then exit any existing conversation and start a new one with hermes. Status checks local readiness; verify a live write using the complete Hermes guide. The free Memoria Cloud requires no local Memoria server or database. Self-hosting, fixed GitHub installation and named profiles are covered in the same guide.


🧩 Memoria in Astra

In Astra, Memoria stores and retrieves facts, preferences, decisions, and session summaries so agents can reuse knowledge across conversations. Memory changes can be reviewed and rolled back.

How you use itWhere to start
Hosted AstraMemoria is already running for you. Follow the Astra quick start.
Self-hosted AstraAstra's deployment includes compatible Memoria and MatrixOne services. Follow Astra's Docker setup.
Memoria with other agentsUse Memoria independently via MCP. Follow the Memoria quick start below.

For example, search your memories from Astra:

astra memory search "deployment preferences"

🚀 Quick Start

<details open> <summary><b>☁️ Memoria Cloud (Recommended — no Docker, no database)</b></summary> <br/>

1. Sign up at thememoria.ai and get your token

2. Install & configure

curl -sSL https://raw.githubusercontent.com/matrixorigin/Memoria/main/scripts/install.sh | bash
cd your-project
memoria init -i   # Select "Remote" mode, paste your token

3. Restart & verify — restart your AI tool, then ask: "Do you have memory tools available?"

</details>

<details> <summary><b>🐳 Self-Hosted (Docker — full data control)</b></summary> <br/>

# 1. Start MatrixOne + API
git clone https://github.com/matrixorigin/Memoria.git
cd Memoria
docker compose up -d

# 2. Install CLI
curl -sSL https://raw.githubusercontent.com/matrixorigin/Memoria/main/scripts/install.sh | bash

# 3. Configure your AI tool
cd your-project
memoria init -i   # Select "Embedded" mode

Restart your AI tool, then ask: "Do you have memory tools available?"

</details>

<details> <summary><b>🦞 OpenClaw Plugin</b></summary> <br/>

Use the native OpenClaw plugin: OpenClaw Plugin Setup

# ensure memoria CLI exists
command -v memoria >/dev/null || curl -sSL https://raw.githubusercontent.com/matrixorigin/Memoria/main/scripts/install.sh | bash -s -- -y -d ~/.local/bin

# install & enable
openclaw plugins install @matrixorigin/memory-memoria
openclaw plugins enable memory-memoria

# cloud-first setup
openclaw memoria setup --mode cloud --api-url <MEMORIA_API_URL> --api-key <MEMORIA_API_KEY> --install-memoria
openclaw memoria health

</details>

Or download binaries directly from GitHub Releases. For detailed setup, see Setup Skill.


💡 Why Memoria?

CapabilityMemoriaLetta / Mem0 / Traditional RAG
Git-level version controlNative zero-copy snapshots & branchesFile-level or none
Isolated experimentationOne-click branch, merge after validationManual data duplication
Audit trailFull snapshot + provenance on every mutationLimited logging
Semantic retrievalVector + full-text hybrid searchVector only
Self-governanceAutomatic contradiction detection & quarantineManual cleanup

🔬 Research Foundation

Memoria's Git-for-Data layer is backed by Version Control System for Data with MatrixOne, our arXiv paper on bringing Git-like workflows directly into a cloud-native database.

The paper describes how MatrixOne's immutable storage and MVCC architecture make clone, branch / tag, diff, merge, and revert practical at terabyte scale without loading whole datasets into memory. Memoria applies the same foundation to AI-agent memory, so every memory change can be isolated, reviewed, merged, or rolled back with database-native consistency.


🎬 See Git for Data in Action

<p align="center"> <img src="assets/git4data-story-writing-demo.svg" alt="Animated Memoria Git-for-Data demo showing an author branching a story draft, merging a better plot direction into the main storyline, and rolling back the last two unsatisfying beats." width="100%"/> </p>

A story-writing scenario demonstrates the core concept: an author has accepted story beats on main, opens an experimental branch for a different plot direction, merges the stronger draft back, and rolls back when the newest beats don't work.

<details> <summary><b>What the demo shows</b></summary>

  • Main storyline — accepted story beats live on main

  • Experimental branch — the author tries a new plot turn without rewriting canon

  • Merge — the stronger draft is promoted back into the main storyline

  • Rollback — the last two bad turns are discarded, and writing resumes from a safe snapshot

</details>


📖 Steering Rules

Steering rules teach your AI agent when and how to use memory tools. Without them, the agent has tools but no guidance — like having a database without knowing the schema.

RulePurpose
memoryCore memory tools — when to store, retrieve, correct, purge
session-lifecycleBootstrap at conversation start, cleanup at end
memory-hygieneProactive governance, contradiction resolution, snapshot cleanup
memory-branching-patternsIsolated experiments with branches
goal-driven-evolutionTrack goals, plans, progress across conversations

File locations: Kiro: .kiro/steering/*.md · Cursor: .cursor/rules/*.mdc · Claude: .claude/rules/*.md · Codex: AGENTS.md · Gemini CLI: GEMINI.md + .gemini/*.md

After upgrading: memoria rules --force

<details> <summary><b>Example: Conversation Lifecycle</b></summary>

┌─────────────────────────────────────────────────────────────────────────────┐
│  CONVERSATION START                                                         │
│  ┌─────────────────────────────────────────────────────────────────────┐   │
│  │ 1. memory_retrieve(query="<user's question>")  ← load context       │   │
│  │ 2. memory_search(query="GOAL ACTIVE")          ← check active goals │   │
│  └─────────────────────────────────────────────────────────────────────┘   │
├─────────────────────────────────────────────────────────────────────────────┤
│  MID-CONVERSATION                                                           │
│  ┌─────────────────────────────────────────────────────────────────────┐   │
│  │ • User states preference → memory_store(type="profile")             │   │
│  │ • User corrects a fact   → memory_correct(query="...", new="...")   │   │
│  │ • Topic shifts           → memory_retrieve(query="<new topic>")     │   │
│  └─────────────────────────────────────────────────────────────────────┘   │
├─────────────────────────────────────────────────────────────────────────────┤
│  CONVERSATION END                                                           │
│  ┌─────────────────────────────────────────────────────────────────────┐   │
│  │ 1. memory_purge(topic="<task>")  ← clean up working memories        │   │
│  │ 2. memory_store(type="episodic") ← save session summary             │   │
│  └─────────────────────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────────────────────┘

</details>

<details> <summary><b>Example: Goal-Driven Evolution</b></summary>

You: "I want to add OAuth support to the API"

AI:  → memory_search(query="GOAL OAuth")           ← check for existing goal
     → memory_store(content="🎯 GOAL: Add OAuth support\nStatus: ACTIVE", type="procedural")

     ... works on implementation, stores progress as working memories ...

     → memory_store(content="✅ STEP 1/3: Added OAuth routes", type="working")
     → memory_store(content="❌ STEP 2/3: Token refresh failed — need to fix expiry logic", type="working")

... next conversation ...

AI:  → memory_search(query="GOAL ACTIVE")          ← finds OAuth goal
     → memory_search(query="STEP for GOAL OAuth")  ← loads progress
     "Last time we were working on OAuth. Step 2 failed on token refresh. Want to continue?"

... goal completed ...

AI:  → memory_correct(query="GOAL OAuth", new_content="🎯 GOAL: OAuth — ✅ ACHIEVED")
     → memory_store(content="💡 LESSON: Token refresh needs 5min buffer before expiry", type="procedural")
     → memory_purge(topic="STEP for GOAL OAuth")   ← clean up working memories

</details>

<details> <summary><b>Example: Branch for Risky Experiments</b></summary>

You: "Let's try switching from PostgreSQL to SQLite"

AI:  → memory_branch(name="eval_sqlite")
     → memory_checkout(name="eval_sqlite")

     ... experiments on branch, stores findings ...

     → memory_diff(source="eval_sqlite")     ← preview changes
     → memory_checkout(name="main")
     → memory_merge(source="eval_sqlite")    ← or delete if failed

</details>


📚 API Reference

Core Tools
ToolDescription
memory_storeStore a new memory
memory_retrieveRetrieve relevant memories (call at conversation start)
memory_searchSemantic search across all memories
memory_correctUpdate an existing memory
memory_purgeDelete by ID or topic keyword
memory_listList active memories
memory_profileGet user's memory-derived profile
memory_feedbackRecord relevance feedback (useful/irrelevant/outdated/wrong)
memory_capabilitiesList available memory tools

<details> <summary><b>Snapshots & Branches</b></summary>

ToolDescription
memory_snapshotCreate named snapshot
memory_snapshotsList snapshots with pagination
memory_snapshot_deleteDelete snapshots by name, prefix, or age
memory_rollbackRestore to snapshot
memory_branchCreate isolated branch
memory_branchesList all branches
memory_checkoutSwitch branch
memory_mergeMerge branch back
memory_branch_deleteDelete a branch
memory_diffPreview merge changes

</details>

REST clients can use cached-count snapshot listing to avoid historical count queries and exclude automatic safety snapshots.

<details> <summary><b>Maintenance</b></summary>

ToolDescription
memory_governanceQuarantine low-confidence memories (1h cooldown)
memory_consolidateDetect contradictions (30min cooldown)
memory_reflectSynthesize insights (2h cooldown)

memory_rebuild_index, memory_observe, memory_get_retrieval_params, memory_tune_params, memory_extract_entities, and memory_link_entities are available via REST API but hidden from MCP tool listing — they are ops/debug tools not intended for agent use.

</details>

Memory Types
TypeUse forExample
semanticProject facts, decisions"Uses Go 1.22 with modules"
profileUser preferences"Prefers pytest over unittest"
proceduralWorkflows, how-to"Deploy: make build && kubectl apply"
workingTemporary task context"Currently debugging auth module"
episodicSession summaries"Session: optimized DB, added indexes"

Full API details: API Reference Skill

Retry-safe single-memory writes: Idempotent store protocol.

Trusted adapter content preservation: Content-filter policy.


🔧 Commands

CommandDescription
memoria init -iInteractive setup wizard
memoria statusShow config and rule versions
memoria rulesUpdate steering rules (auto-detect, --tool, or -i)
memoria mcpStart MCP server
memoria serveStart REST API server
memoria benchmarkRun benchmark suite

🤖 For AI Agents

If you're an AI agent helping a user set up Memoria:

  1. Load the Setup Skill — it has step-by-step instructions

  2. Ask before acting: which AI tool? · database mode? · embedding service? (Self-Hosted only)

  3. Run memoria init -i in the user's project directory

  4. Tell user to restart their AI tool, then verify with memory_retrieve("test")

Self-Hosted only: Configure embedding BEFORE first MCP server start — dimension is locked into schema.


🏗️ Architecture

Cloud / Remote Mode:

┌─────────────┐     MCP (stdio)     ┌──────────────────┐     HTTP/REST     ┌──────────────────┐
│  AI Agent   │ ◄─────────────────► │  Memoria CLI     │ ◄──────────────► │  Memoria Cloud   │
│             │   store / retrieve  │  (MCP bridge)    │   Bearer token   │  API Server      │
└─────────────┘                     └──────────────────┘                  └──────────────────┘

Self-Hosted / Embedded Mode:

┌─────────────┐     MCP (stdio)     ┌──────────────────────────────────────┐     SQL      ┌────────────┐
│  AI Agent   │ ◄─────────────────► │  Memoria MCP Server                  │ ◄──────────► │ MatrixOne  │
│             │   store / retrieve  │  ├── Canonical Storage               │  vector +    │  Database  │
│             │                     │  ├── Retrieval (vector / semantic)   │  fulltext    │            │
│             │                     │  └── Git-for-Data (snap/branch/merge)│              │            │
└─────────────┘                     └──────────────────────────────────────┘              └────────────┘

For codebase details, see Architecture Skill.


🛠️ Development

make up              # Start MatrixOne + API
make test            # Run all tests
make release VERSION=0.2.0   # Bump, tag, push

Developer documentation (for contributing to Memoria):

SkillDescription
ArchitectureCodebase layout, traits, tables
API ReferenceREST endpoints, request/response
DeploymentDocker, K8s, multi-instance
Plugin DevelopmentGovernance plugins
ReleaseVersion bump, CI/CD
Local EmbeddingOffline embedding build

📝 Citation

If you use Memoria in academic work, or refer to its Git-for-Data foundation for AI-agent memory, please cite the MatrixOne paper that describes the underlying data version control design:

@misc{gou2026versioncontrolsystemdata,
  title={Version Control System for Data with MatrixOne},
  author={Gou, Hongshen and Tian, Feng and Wang, Long and Deng, Nan and Xu, Peng},
  year={2026},
  eprint={2604.03927},
  archivePrefix={arXiv},
  primaryClass={cs.DB},
  doi={10.48550/arXiv.2604.03927},
  url={https://arxiv.org/abs/2604.03927}
}

🌟 Community

We'd love your support! If Memoria helps you, consider giving us a star.

Image: Star History Chart

Contributing — See the developer documentation above and check out our issue templates for bug reports, feature requests, and more.


License

Apache-2.0 © MatrixOrigin

AUTOGINEER-RUNS.md

What this brain runs

AutoGineer's scanner lists the code that "memoria" can run on your machine. Review each item before you open the folder in an agent or enable a plugin: nothing here is turned on for you.

467 files · 445 not served here

Advantages

Limitations

Security

Risk level: Medium risk

This brain runs

Agent instructions that run or fetch something (41)

  • .cursor/rules/memory.mdc: If ⚠️ health warnings appear → inform the user and offer to run `memory_governance`.
  • .cursor/rules/memory.mdc: | Type | Use for | Examples | |------|---------|---------| | `semantic` | Project facts, technical decisions (default) | "Uses MatrixOne as primary DB", "API follows REST conventions" | | `profile` | User/agent identity and preferences | "Prefers concise answers", "Works on mo-dev-agen …[+460 chars]
  • .kiro/steering/memory-hygiene.md: Run `memory_governance` (1h cooldown) when you notice ANY of these:
  • .kiro/steering/memory-hygiene.md: Run `memory_consolidate` (30min cooldown) when:
  • .kiro/steering/memory.md: | Type | Use for | Examples | |------|---------|---------| | `semantic` | Project facts, technical decisions (default) | "Uses MatrixOne as primary DB", "API follows REST conventions" | | `profile` | User/agent identity and preferences | "Prefers concise answers", "Works on mo-dev-agen …[+460 chars]
  • .kiro/steering/session-lifecycle.md: Before your first response, run a multi-query bootstrap to load full context:
  • .kiro/steering/skills.md: | Skill | When to use | |-------|-------------| | [Architecture](../../skills/architecture/SKILL.md) | Navigating or modifying Memoria code | | [API Reference](../../skills/api-reference/SKILL.md) | Calling or implementing REST endpoints | | [Deployment](../../skills/deployment/SKILL.m …[+385 chars]
  • plugins/clawhub/thememoria/SKILL.md: Install or verify the OpenClaw plugin: `references/setup.md`
  • skills/api-reference/SKILL.md: It does not generate embeddings or run vector, graph, hybrid, temporal, or confidence scoring and is intentionally not exposed as an MCP tool.
  • skills/architecture/SKILL.md: async fn execute(&self, store, task, plan) -> Result<GovernanceExecution>;
  • skills/architecture/SKILL.md: make check # cargo check + clippy -D warnings (MUST pass)
  • skills/architecture/SKILL.md: make test-unit # Unit tests (no DB): memoria-core, memoria-service, memoria-mcp
  • skills/architecture/SKILL.md: make test # All tests (needs MatrixOne, --test-threads=1)
  • skills/architecture/SKILL.md: make test-e2e # API e2e tests only
  • skills/deployment/SKILL.md: docker compose up -d
  • skills/deployment/SKILL.md: Verify: `curl http://localhost:8100/health` · uses the network
  • skills/deployment/SKILL.md: Run behind reverse proxy with TLS in production
  • skills/local-embedding/SKILL.md: make build-local
  • skills/local-embedding/SKILL.md: | Problem | Fix | |---------|-----| | "compiled without local-embedding" | Build from source: `make build-local` | | Model download fails | Set `HF_ENDPOINT` for mirror, or manually download to `~/.cache/fastembed/` | | High memory | Default ~100MB.
  • skills/plugin-development/SKILL.md: "capabilities": ["governance.plan", "governance.execute"],
  • skills/plugin-development/SKILL.md: `capabilities`: must include `governance.plan` and/or `governance.execute`
  • skills/plugin-development/SKILL.md: if ctx["phase"] == "execute" {
  • skills/release/SKILL.md: | Step | Who | How | |------|-----|-----| | Bump version, tag, push | You | `make release VERSION=x.y.z` | | Build binaries (4 platforms) | CI | `release-rust.yml` | | Build + push Docker image | CI | `release-docker.yml` | | Create GitHub Release | CI | `softprops/action-gh-release` |
  • skills/release/SKILL.md: git status # Must be clean
  • skills/release/SKILL.md: make release VERSION=0.2.0
  • skills/release/SKILL.md: make release-rc VERSION=0.2.0-rc1
  • skills/release/SKILL.md: make release-docker # :latest
  • skills/release/SKILL.md: make release-docker VERSION=0.2.0 # :0.2.0 + :latest
  • skills/release/SKILL.md: Both release workflows run full test suite before publishing.
  • skills/release/SKILL.md: [ ] `make release VERSION=x.y.z`
  • skills/setup/SKILL.md: Ask: "Use Memoria Cloud, or run your own instance?"
  • skills/setup/SKILL.md: **Memoria Cloud (recommended)** → sign up at [thememoria.ai](https://thememoria.ai/auth), get API URL + token, then proceed to Step 2 · uses the network
  • skills/setup/SKILL.md: No → run `docker compose up -d` in the Memoria repo root (wait 30-60s for first start)
  • skills/setup/SKILL.md: One-line install (recommended):
  • skills/setup/SKILL.md: Or download manually from [GitHub Releases](https://github.com/matrixorigin/Memoria/releases). · uses the network
  • skills/setup/SKILL.md: Platform-specific manual install:
  • skills/setup/SKILL.md: docker compose up -d # Start MatrixOne
  • skills/setup/SKILL.md: docker ps --filter name=matrixone # Verify (wait 30-60s)
  • skills/setup/SKILL.md: Or run: `memoria status`
  • skills/setup/SKILL.md: | Problem | Fix | |---------|-----| | MatrixOne won't start | `docker logs memoria-matrixone` | | Port 6001 in use | Change `MO_PORT` in `.env` | | Can't connect to DB | Wait 30-60s on first start | | Docker permission denied | `sudo usermod -aG docker $USER && newgrp docker` | | Docke …[+152 chars]
  • skills/setup/SKILL.md: Run `which memoria` to verify CLI installed 2.

Install scripts (5)

  • README.md: curl -sSL https://raw.githubusercontent.com/matrixorigin/Memoria/main/scripts/install.sh | bash · uses the network
  • README.md: curl -sSL https://raw.githubusercontent.com/matrixorigin/Memoria/main/scripts/install.sh | bash · uses the network
  • README.md: command -v memoria >/dev/null || curl -sSL https://raw.githubusercontent.com/matrixorigin/Memoria/main/scripts/install.sh | bash -s -- -y -d ~/.local/bin · uses the network
  • scripts/install.ps1: pwsh scripts/install.ps1 · uses the network
  • scripts/install.sh: sh scripts/install.sh · uses the network
  • Medium risk: runs-install-script-remote-exec (README.md:155)
  • Medium risk: runs-install-script-remote-exec (README.md:175)
  • Medium risk: runs-install-script-remote-exec (README.md:194)
  • Medium risk: runs-install-script-network-variable-host (scripts/install.ps1)
  • Medium risk: network-variable-host (scripts/install.ps1:119)
  • Medium risk: network-variable-host (scripts/install.ps1:125)
  • Medium risk: runs-install-script-network-variable-host (scripts/install.sh)
  • Medium risk: network-variable-host (scripts/install.sh:295)
  • Medium risk: runs-instruction-instruction-network (skills/setup/SKILL.md:14)
  • Medium risk: remote-exec-pipe (skills/setup/SKILL.md:58)
  • Medium risk: runs-instruction-instruction-network (skills/setup/SKILL.md:61)
  • Low risk: scripts-present (scripts/install.ps1)
  • Low risk: scripts-present (scripts/install.sh)
  • Low risk: network-instructions (skills/deployment/SKILL.md:14)
  • Low risk: network-instructions (skills/setup/SKILL.md:58)

Attribution and licence

Created by
Matrix Origin
Publisher
Matrix Origin (Official Matrix Origin project)
Source
matrixorigin/memoria · commit 93ccccf
Licence
Apache License 2.0 (files served here)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Matrix Origin.

Install from source

445 of its files are not served here, so there is no ZIP. matrixorigin/memoria at 93ccccf