dsh-tdai-memory

GitHub: Scorp1o117/dsh-tdai-memory · npm: dsh-tdai-memory

Part of the DeepSeek Harness Enhancement Suite — Vision · Soul/Persona · Long-term Memory · Plugin Marketplace.

A port of TencentDB Agent Memory (Tencent Cloud's open-source four-layer memory system, originally an OpenClaw plugin) into DeepSeek Harness.

Features

• L0 conversation capture: every turn (turn end, request boundary) is written to raw conversation storage (JSONL + SQLite + FTS + vectors)
• L1 structured memory: a background pipeline uses an LLM to extract facts / preferences / events (persona / episodic / instruction) from conversations, stored in records/ + SQLite + FTS + vectors
• L2 scenes / L3 persona: scene blocks and user profile generation (pipeline-scheduled)
• Automatic recall injection: on every prompt assembly, relevant memories and the user profile are retrieved by the current user message and injected as dynamic context (the model "just remembers")
• Tools: tdai_memory_search (L1 structured search), tdai_conversation_search (L0 raw-text search)

The data directory reuses the existing ~/.memory-tencentdb/memory-tdai, so previously accumulated memories carry over seamlessly.

Architecture (porting approach)

Hard-won wiring details:

• Capture: session/flush listener (await semantics; must complete before headless exits); turn/start timestamps as the L0 cursor floor; turn-id dedup
• Headless one-shot runs: wait for core.handleSessionEnd() inside flush (L1 extraction finishes before exit; otherwise the 5s shutdown timeout kills it)
• Recall injection: must be registered on agent.ctx (assembly runs in the agent scope; root listeners never see it); attach one tick after session/created by resolving the agent from the agents service

Configuration (profile patch + settings)

Configuration is settings-namespace driven: the profile patch is the base layer, and the tdai-memory: section of $DSH_HOME/settings.yaml overrides it (LLM/embedding keys live in settings.yaml). The Web UI Settings → 记忆 section edits every field (v0.2.0, write-only keys); TdaiCore is built at startup, so changes apply after a restart.

# $DSH_HOME/settings.yaml
tdai-memory:
  llm:
    apiKey: 'sk-...'
  embedding:
    apiKey: 'local-no-key'

# profile patch (base layer)
- id: tdai-memory
  name: 'dsh-tdai-memory'
  config:
    extraction:
      enabled: true
      enableDedup: false      # dedup LLM output parsing is flaky; off by default
    llm:                      # L1/L2/L3 extraction model (OpenAI-compatible)
      baseUrl: 'https://opencode.ai/zen/go/v1'
      model: 'mimo-v2.5'      # deepseek-v4-flash produces invalid extraction JSON
    embedding:                # vectors (OpenAI-compatible /v1/embeddings)
      baseUrl: 'http://127.0.0.1:8088/v1'
      model: 'Qwen3-Embedding-0.6B'
      dimensions: 1024
      sendDimensions: false

Install

dsh plugin --profile web add dsh-tdai-memory

then mount it in $DSH_HOME/profiles/web/cordis.patch.yml:

- insert:
    - id: tdai-memory
      name: 'dsh-tdai-memory'
      config: {}          # keys can live in settings.yaml instead

and restart dsh web. LLM/embedding API keys can be set in the Web UI settings page (记忆 / Memory) or directly in settings.yaml under tdai-memory:.

Note for users

• This plugin is a standard profile bundle (dsh.bundle.patch): dsh plugin --profile web add dsh-tdai-memory installs and mounts it in one step — no manual cordis.patch.yml edits needed.
• DSH exposes the registered tdai-memory settings namespace directly; the plugin does not modify files in the host installation.
• Settings changes apply after a restart (TdaiCore is built at startup).
• Version 0.2.13 and newer require DSH 0.1.0-rc.7 or newer and are tested against 0.1.0-rc.7, 0.1.0-rc.8, and 0.1.1-rc.1.
• DSH 0.1.0-rc.6 users must pin dsh-tdai-memory@0.2.11, the last release carrying the legacy settings-allowlist compatibility patch.

node-llama-cpp is an optional peer used only by the fully local embedding backend. It is intentionally not installed by default because its native build requires explicit pnpm build approval. Remote OpenAI-compatible embeddings do not need it. Users who select the local backend should install and approve node-llama-cpp in the target DSH profile separately.

Known trade-offs

• Extraction model: mimo-v2.5 extracts correctly but takes 20-30s per call (background execution, does not block the conversation); deepseek-v4-flash is fast but its JSON output is non-compliant (extracts 0)
• dedup: LLM conflict-detection output parsing is unstable (once caused stored=0); off by default; enable only with a more reliable model
• L1 memory vectors: written with storage (8088 embedding is fast); L0 vectors run as a background task, drained by destroy() on headless exit
• Upgrades: after pulling new upstream code, rerun npx tsc -p dsh-tsconfig.json in the tdai project dir (output in dist-dsh/)

License

MIT