TDaí Memory Suite for Openclaw

A fully-local memory pipeline, vector search system, and structured knowledge graph that adds long-term recall and contextual visualization to AI agents.

paudyyin
v1.0.0
Jun 9, 2026
0
458
0

Install & Download

1. ClawHub CLI

The fastest way to install a skill directly from the registry.

npx clawhub@latest install tdai-memory-suite

2. Manual Installation

Copy the skill folder to one of these locations

Global
~/.openclaw/skills/
Workspace
<project>/skills/

Priority: Workspace > Local > Bundled

3. Prompt Installation

Copy this prompt to OpenClaw to install it automatically.

Help me install tdai-memory-suite using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).

Prefer to download?

Get the raw skill files in a ZIP archive.

What is TDaí Memory Suite?

TDaí Memory Suite is an advanced local memory framework designed to extend the capabilities of AI agents built using Openclaw Skills. Operating entirely offline without any external API calls, this suite captures raw conversations and processes them through a sophisticated four-layer architecture. By structuring conversational history into episodic memories, user persona profiles, and specific instructions, agents maintain a highly persistent context over time.

Integrating seamlessly with local SQLite engines and quantized model architectures, the suite ensures full data privacy. It offers developers structural cognitive maps of their agents, transforming sparse, historical interactions into actionable knowledge systems that drive precision in context recall.

TDaí Memory Suite Use Cases

  • Injecting highly relevant historical context automatically during active developer or agent chat sessions.
  • Tracking and structuring user preferences, custom behavioral rules, and skills over months of interactions.
  • Structuring complex, cross-domain projects, tasks, goals, and organizational structures using a schema-validated local graph.
  • Plotting a 2D interactive topological map of memory embeddings to trace cognitive trends and clustering in memory structures locally.

How TDaí Memory Suite Works

  1. Raw Conversation Logging (L0): Captures incoming user messages and agent replies, archiving them directly into a local SQLite database and structured JSONL streams.
  2. Cognitive Extraction (L1): Periodically triggers LLM-guided extraction to structure dialogue into episodic records, behavioral rules, and persona items.
  3. Scene Clustering (L2): Processes conversational threads into logical scene blocks, establishing high-level context blocks of related sessions.
  4. Persona Generation (L3): Updates the central persona.md reference file to reflect shifts in user values, preferences, and workflows.
  5. Embedding & Search Integration: Matches queries with records using hybrid keyword (BM25) and local vector search via nomic embeddings.
  6. Knowledge Graph Syncing: Normalizes Daily memory files into an ontological relationship chart, enforcing system validation constraints.
  7. Visualization Generation: Down-projects high-dimensional embeddings using UMAP to create a rich, zoomable 2D HTML map of memory profiles.

TDaí Memory Suite Setup

1. Installation

Install the complete package directly using the package manager for Openclaw Skills:

clawhub install tdai-memory-suite

Or deploy individual components depending on your pipeline requirements:

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

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

# Atlas visualization (optional)
clawhub install tdai-atlas

2. Configuration

To activate the suite, update your system-wide openclaw.json configuration to configure local plugins and set the embedding provider to local search:

{
  "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"
  }
}

3. Ontology Auto-Sync (Optional Cron Job)

Add an automated daily cron-like action inside your workflow configs to keep your entity maps updated:

{
  "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"
}

TDaí Memory Suite Data Schema & Taxonomy

Memory Pipeline Outputs

Pipeline Layer Storage Medium Generated Schema & Focus
L0: Conversation SQLite + JSONL raw conversational history logging with indexing timestamps.
L1: Extraction Local SQLite (sqlite-vec) stores episodic actions, user attributes, and runtime instructions.
L2: Scene Induction Raw Scene Text files groups cluster-based historical sequences into thematic context blocks.
L3: Persona persona.md markdown file maintains a comprehensive profile of stable user preferences and settings.

Ontology Knowledge Graph Schema

Relationships and concepts are declared under memory/ontology/ inside a JSONL format backed by schemas:

  • People & Orgs: Track identities via Person and Organization types.
  • Work Entities: Organize workflows via Project, Task, and Goal structures.
  • Spatio-Temporal Data: Detail coordinates using Event and Location contexts.
  • Information Systems: Log records using Document, Note, and Message taxonomies.
  • Meta Attributes: Classify system context using Technology, Skill, Action, and Policy labels.

TDaí Memory Suite Advanced Features

  • Local Hybrid Search Engines: Integrates standard structural SQLite search query pathways alongside fast, local vector searches utilizing GGUF formats.
  • Schema-Validated Ontology Constraints: Enforces relation checks, property definitions, and graph cardinality to prevent structural inconsistencies in memory syncs.
  • Adaptive Fallback Hierarchy: Routes memory retrieval down an automatic path from core databases to the structured schema graph and raw visual records.
  • Privacy-First UMAP Projections: Executes full dimensional reduction algorithms locally to produce complex visual HTML interfaces mapping your system's mental model.

SKILL.md


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