TDaí Memory Suite

Complete local memory system for OpenClaw: 4-layer memory pipeline (L0→L1→L2→L3) + local vector search (nomic-embed-text) + ontology knowledge graph + Nomic Atlas interactive visualization. Everything runs locally — no external API dependencies for memory extraction.

A complete, fully-local memory system for OpenClaw agents. Four components work together to give your AI agent persistent memory, semantic search, structured knowledge, and visual introspection.

Components

1. TDaí Core — 4-Layer Memory Pipeline

Path: components/tdai-core/

The heart of the system. Automatically captures conversations and extracts structured memories through a 4-layer pipeline:

Layer	Name	Description
L0	Conversation Recording	Raw message capture → SQLite + JSONL
L1	Memory Extraction	LLM extracts structured memories (persona/episodic/instruction) with vector dedup
L2	Scene Induction	LLM归纳场景块 (Scene Blocks) from conversation clusters
L3	User Persona	LLM generates/updates user personality profile → persona.md

Key features:

• Gateway-integrated LLM extraction (no separate API key needed)
• Local SQLite + sqlite-vec storage backend
• Optional Tencent Cloud Vector Database backend
• Auto-recall: semantic search injects relevant memories into system context
• BM25 + vector hybrid search

Memory types:

• persona: Stable user attributes, preferences, skills, values
• episodic: Actions, decisions, plans, outcomes that occurred
• instruction: Long-term behavioral rules the user set for the AI

2. TDaí Vector — Local Vector Search

Configuration: memorySearch.provider: "local"

Uses nomic-embed-text-v1.5.Q4_K_M.gguf (quantized, ~270MB) for fully-local embeddings. No external embedding API needed.

Setup:

1. Download the model to your OpenClaw models directory
2. Set memorySearch.provider: "local" in openclaw.json
3. The system automatically uses local embeddings for memory search

3. TDaí Ontology — Knowledge Graph

Path: components/tdai-ontology/

Typed knowledge graph for structured agent memory. Stores entities and relationships as JSONL with schema validation.

Supported entity types:

• People: Person, Organization
• Work: Project, Task, Goal
• Time/Place: Event, Location
• Information: Document, Note, Message
• Meta: Technology, Skill, Action, Policy

Key features:

• Entity CRUD with constraint validation
• Relation creation with cardinality checks
• Auto-sync from daily memory files
• Backup memory fallback when primary search fails
• CLI tools: ontology.py and ontology_sync.py

4. TDaí Atlas — Memory Visualization

Path: components/tdai-atlas/nomic_atlas_visualizer.py

Interactive HTML visualization of your agent's memory using sentence-transformers + UMAP dimensionality reduction.

Features:

• 768-dimensional semantic embeddings via nomic-embed-text-v1.5
• UMAP projection to 2D
• Interactive HTML output (zoom, pan, hover for details)
• Fully local — no data leaves your machine
• TF-IDF fallback when model unavailable

Installation

Option A: Full Suite (Recommended)

bash

clawhub install tdai-memory-suite

Option B: Individual Components

bash

# Core memory pipeline (required)
clawhub install tdai-core

# Ontology knowledge graph (optional)
clawhub install tdai-ontology

# Atlas visualization (optional)
clawhub install tdai-atlas

Configuration

Minimal Setup (TDaí Core only)

Add to your openclaw.json:

json

{
  "plugins": {
    "allow": ["mx", "memory-tencentdb"],
    "load": {
      "paths": ["<path-to-tdai-core>"]
    },
    "entries": {
      "memory-tencentdb": {
        "enabled": true,
        "config": {
          "pipeline": {
            "everyNConversations": 3,
            "enableWarmup": true,
            "l1IdleTimeoutSeconds": 10
          }
        }
      }
    }
  }
}

Full Setup (Core + Local Vector + Ontology)

json

{
  "plugins": {
    "allow": ["mx", "memory-tencentdb"],
    "load": {
      "paths": ["<path-to-tdai-core>"]
    },
    "entries": {
      "memory-tencentdb": {
        "enabled": true,
        "config": {
          "pipeline": {
            "everyNConversations": 3,
            "enableWarmup": true,
            "l1IdleTimeoutSeconds": 10
          },
          "store": {
            "backend": "sqlite"
          }
        }
      }
    }
  },
  "memorySearch": {
    "provider": "local",
    "model": "nomic-embed-text-v1.5.Q4_K_M.gguf"
  }
}

Ontology Auto-Sync (Cron)

json

{
  "name": "Ontology Daily Sync",
  "schedule": { "kind": "cron", "expr": "0 22 * * *", "tz": "Asia/Shanghai" },
  "payload": {
    "kind": "agentTurn",
    "message": "Run ontology sync: python scripts/ontology_sync.py --days 7"
  },
  "sessionTarget": "isolated"
}

Usage Examples

Memory Search (automatic after installation)

Memories are automatically recalled and injected into context. You can also search manually:

text

"Search my memories for information about project X"
"What do you remember about my preferences?"

Ontology Operations

bash

# Create an entity
python components/tdai-ontology/scripts/ontology.py create --type Project --props '{"name":"New Product","status":"active"}'

# Query entities
python components/tdai-ontology/scripts/ontology.py query --type Task --where '{"status":"open"}'

# Create relations
python components/tdai-ontology/scripts/ontology.py relate --from proj_001 --rel has_task --to task_001

# Auto-sync from memory files
python components/tdai-ontology/scripts/ontology_sync.py --days 7

Memory Visualization

bash

# Generate interactive visualization
python components/tdai-atlas/nomic_atlas_visualizer.py

# Output: output/memory_visualization.html

Architecture

text

┌─────────────────────────────────────────────────────────────┐
│                    TDaí Memory Suite                         │
├─────────────┬──────────────┬──────────────┬────────────────┤
│  TDaí Core  │ TDaí Vector  │ TDaí Ontology│  TDaí Atlas    │
│  (L0→L3     │ (nomic-embed │ (Knowledge   │  (UMAP +       │
│  Pipeline)  │  -text local)│  Graph)      │   HTML viz)    │
├─────────────┼──────────────┼──────────────┼────────────────┤
│ SQLite+FTS  │ GGUF Model   │ JSONL+Schema │ sentence-      │
│ JSONL Store │ BM25+Vector  │ Entity/Rel   │ transformers   │
└─────────────┴──────────────┴──────────────┴────────────────┘
         │              │              │              │
         └──────────────┴──────┬───────┴──────────────┘
                               │
                    ┌──────────┴──────────┐
                    │  OpenClaw Gateway    │
                    │  (LLM Extraction)    │
                    └─────────────────────┘

Memory Fallback Hierarchy

When searching for memories, the system follows this priority:

1. TDaí Core (tdai_memory_search) — primary structured memory
2. TDaí Ontology (graph query) — structured entities and relations
3. TDaí Atlas (visualization) — for browsing and exploration

Requirements

• OpenClaw Gateway with model configured (for LLM extraction)
• Node.js v18+ (for Core plugin)
• Python 3.9+ (for Ontology and Atlas scripts)
• ~300MB disk for nomic-embed-text model (optional, for local vector search)

Troubleshooting

L1 extraction returns empty

1. Check plugins.allow includes "memory-tencentdb"
2. Check plugins.load.paths points to the correct directory
3. Verify Gateway model is configured (models.providers and agents.defaults.model)

Local vector search not working

1. Ensure model file exists: nomic-embed-text-v1.5.Q4_K_M.gguf in models directory
2. Check memorySearch.provider is set to "local"
3. Restart Gateway after config changes

Ontology sync fails

1. Ensure memory/ontology/ directory exists
2. Check Python dependencies: pip install --user pyyaml
3. Run with --dry-run to preview changes

License

MIT

Changelog

v1.0.0 (2026-06-09)

• Initial release
• TDaí Core v0.3.8: 4-layer pipeline with Gateway-integrated LLM extraction
• TDaí Vector: local nomic-embed-text embeddings
• TDaí Ontology: typed knowledge graph with 20+ entity types
• TDaí Atlas: interactive memory visualization with UMAP