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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

docs/graph-benchmark-rationale.md

Graph + Entity + Edge Benchmark 设计原理

这份文档解释 graph-entity-v1.json benchmark 数据集的设计思路,以及每个场景如何证明 Graph 架构优于纯向量/全文检索。


核心假设

纯向量检索的局限:

  • 只能计算文本相似度,无法理解实体关系

  • 无法区分同一词语的不同含义(如"Apple"公司 vs 水果)

  • 无法建立跨记忆的隐性关联(如"上海项目"→"MatrixOne"→"6001端口")

  • 无法追踪观点演变和矛盾证据

全文检索 (BM25) 的局限:

  • 依赖关键词匹配,无法理解语义关系

  • 无法处理指代消解(如"那边"指代"上海项目")

  • 无法建立多跳推理链

Graph 架构的优势:

  • 实体层 (mem_entities): 统一标识和消歧

  • 边层 (memory_graph_edges): 显式关系(entity_link, temporal, causal, association)

  • 传播层 (SpreadingActivation): 多跳推理和上下文扩散

  • 观点层 (opinion evolution): 证据积累和置信度追踪


场景详解

GRAPH-001: Entity-anchored retrieval beats vector ambiguity

核心能力: 实体消歧

为什么纯向量会失败:

  • 查询"上海那边又出问题了"向量化后,与"上海旅行攻略"的相似度可能很高

  • 两者都包含"上海"和"问题/攻略"等词

  • 向量无法区分"上海的项目"vs"上海的旅行"

Graph 如何解决:

查询 → 提取实体"上海" → 反向查找 mem_memory_entity_links
→ 发现"上海"关联到"Docker部署"记忆,而非"旅行攻略"
→ SpreadingActivation 从"上海"实体节点传播
→ 激活相关技术记忆,抑制无关记忆

关键代码路径:

  • entity_extractor.extract_entities_lightweight() - 提取"上海"

  • graph_store.get_memories_by_entity() - 反向查找

  • retriever._entity_recall() - 注入激活锚点


GRAPH-002: Multi-hop reasoning via entity bridge

核心能力: 多跳推理

为什么纯向量会失败:

  • 查询"部署问题怎么解决?"不会直接匹配"6001端口"

  • 两者文本相似度低

  • 但"6001端口"是解决部署问题的关键

Graph 如何解决:

"部署问题" → 匹配"部署失败"记忆 → 通过 entity_link 边连接到"MatrixOne"实体
→ "MatrixOne"实体连接到"6001端口"记忆
→ 2-hop 传播后,"6001端口"被激活

关键机制:

  • SpreadingActivation.propagate(iterations=3) - 多轮传播

  • EdgeType.ENTITY_LINK - 实体关联边

  • INHIBITION_BETA = 0.15 - 防止过度扩散


GRAPH-003: Entity disambiguation: same name, different contexts

核心能力: 同名实体消歧

为什么纯向量会失败:

  • "Apple"公司和"Apple"水果的向量表示可能相似

  • 都包含"Apple"这个词,上下文词("公司"/"水果")可能被忽略

  • 返回结果混合,用户得到无关信息

Graph 如何解决:

"Apple 的新产品" → 提取"Apple" → 查找实体
→ 发现两个"Apple"实体(company vs fruit)
→ "新产品"上下文激活 tech 相关实体
→ 通过 entity_type 和关联边过滤,选择 company 实体

关键字段:

  • mem_entities.entity_type - tech/fruit/location/person

  • GraphNodeData.entity_type - 节点类型标记


GRAPH-004: Temporal chain reconstruction

核心能力: 时序链重建

为什么纯向量会失败:

  • "项目做到哪一步了?"需要按时间顺序返回 Step 1-4

  • 向量检索返回的是相似度排序,不是时间顺序

  • 可能遗漏中间步骤或顺序错乱

Graph 如何解决:

查询 → 激活所有"Step X"节点 → temporal 边连接时序
→ SpreadingActivation 保留时序关系
→ 返回按 temporal 边组织的结果

关键边类型:

  • EdgeType.TEMPORAL - 时序关联


GRAPH-005: Cross-session entity persistence

核心能力: 跨会话实体追踪

为什么纯向量会失败:

  • "DeepSeek"在不同会话中以不同上下文出现

  • 纯向量分别处理每次提及,无法建立统一视图

  • 用户问"DeepSeek 怎么样?"只能返回最近一次的提及

Graph 如何解决:

所有"DeepSeek"提及 → 连接到同一个"DeepSeek"实体节点
→ 实体节点作为 hub,连接所有相关记忆
→ 查询时从实体节点传播,聚合所有信息

GRAPH-006: Conflicting evidence via opinion evolution

核心能力: 观点演变追踪

为什么纯向量会失败:

  • "适合我们的项目"和"暂时不使用"向量相似度可能都很高

  • 都包含"RSC"、"项目"等关键词

  • 无法判断哪个是最终结论

Graph 如何解决:

初始观点 → Scene 节点 (confidence=0.7)
新证据 → OpinionEvolver.evaluate_evidence()
→ 矛盾证据降低 confidence (delta=-0.12)
→ 低于 quarantine_threshold (0.18) 后停用
→ 最终只返回高 confidence 的观点

关键机制:

  • opinion_supporting_delta = +0.05

  • opinion_contradicting_delta = -0.12

  • opinion_quarantine_threshold = 0.18


GRAPH-007: Tech stack disambiguation via entity types

核心能力: 实体类型消歧

为什么纯向量会失败:

  • "Go"作为语言和动词"go"的向量表示可能混淆

  • 上下文不足时无法区分

Graph 如何解决:

"Go 的特点" → 提取"Go" → 查找实体
→ entity_type='tech' 的"Go"实体被激活
→ entity_type 过滤排除非技术语境

GRAPH-008: Scene synthesis from episodic memories

核心能力: 模式合成

为什么纯向量会失败:

  • 多个 episodic 记忆(周一、周三、周五)分散存储

  • 向量检索返回原始片段,没有合成总结

  • 用户需要阅读多条记忆才能理解全貌

Graph 如何解决:

多个 episodic 节点 → ReflectionEngine.reflect()
→ LLM 合成 scene 节点:"搜索优化经验:缓存是性能优化的有效手段"
→ Scene 节点通过 source_nodes 链接到原始 episodic
→ 查询返回合成后的 scene,而非原始片段

GRAPH-009: Person entity linking for expertise routing

核心能力: 人员专长路由

为什么纯向量会失败:

  • "前端性能问题应该找谁?"不会直接匹配"@Alice"

  • 需要理解"前端"→"Alice"的关联

Graph 如何解决:

"前端性能" → 提取"前端" → 查找相关实体
→ "@Alice"实体通过 entity_link 连接到"前端"
→ 激活传播找到正确的人

GRAPH-010: Location-based memory clustering

核心能力: 地理位置聚类

为什么纯向量会失败:

  • "北京办公室"和"上海办公室"向量相似度高

  • 都包含"办公室",难以区分地点

Graph 如何解决:

"北京办公室" → 提取"北京"location 实体
→ 只激活与"北京"实体关联的记忆
→ "上海"相关记忆被抑制

运行 Benchmark

# Run benchmark
memoria benchmark --api-url http://localhost:8100 --token sk-... --dataset graph-entity-v1

预期结果

场景纯向量全文Graph
GRAPH-001❌ 混合旅行和技术❌ 关键词匹配失败✅ 实体消歧
GRAPH-002❌ 漏掉 6001 端口❌ 无关键词匹配✅ 2-hop 推理
GRAPH-003❌ 公司和水果混淆❌ 无法消歧✅ entity_type 过滤
GRAPH-004❌ 顺序错乱❌ 顺序错乱✅ temporal 边
GRAPH-005❌ 信息分散❌ 信息分散✅ 实体聚合
GRAPH-006❌ 矛盾观点并存❌ 矛盾观点并存✅ opinion evolution
GRAPH-007❌ 语言/动词混淆❌ 无法区分✅ entity_type
GRAPH-008❌ 返回原始片段❌ 返回原始片段✅ scene 合成
GRAPH-009❌ 无法路由❌ 无法路由✅ 人员-专长关联
GRAPH-010❌ 地点混淆❌ 地点混淆✅ location 实体聚类

总结

这 10 个场景覆盖了 Graph 架构的核心优势:

  1. 实体层: 消歧、聚类、统一标识

  2. 边层: 关系推理、时序、因果

  3. 传播层: 多跳推理、上下文扩散

  4. 观点层: 证据积累、矛盾检测

纯向量和全文检索在这些场景下会失败,因为它们缺乏显式的结构和关系表示。

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