A fully-local memory pipeline, vector search system, and structured knowledge graph that adds long-term recall and contextual visualization to AI agents.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install tdai-memory-suite
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
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).
Get the raw skill files in a ZIP archive.
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.
persona.md reference file to reflect shifts in user values, preferences, and workflows.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
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"
}
}
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"
}
| 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. |
Relationships and concepts are declared under memory/ontology/ inside a JSONL format backed by schemas:
Person and Organization types.Project, Task, and Goal structures.Event and Location contexts.Document, Note, and Message taxonomies.Technology, Skill, Action, and Policy labels.Loading
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