dsh-tdai-memory

The plugin follows the DSH language setting (Chinese and English in DSH 0.2.0-rc.2), including configuration, status messages and plugin-list metadata. Language-pack locales use the host fallback chain. Switching languages preserves unsaved settings; there is no separate plugin language selector.

Configuration page (DSH 0.2.0-rc.2 and later)

Open Plugins → Installed → dsh-tdai-memory from the homepage sidebar to configure and save this plugin. The page uses the official plugins.bundle.config interface, without a duplicate entry in global Settings. Web and Desktop share the page. This version requires DSH 0.2.0-rc.2 or a later 0.2.x host; existing configuration is retained.

Choose Automatic memory for capture, extraction, recall and search together; Search existing memory only disables capture, extraction and automatic recall while keeping search tools; Pause memory features disables all four. The main page contains the two model connections. Individual switches, storage paths and tuning remain under Advanced. Existing mixed switch values appear as Custom combination and are preserved. Select a mode, Save, then restart DSH for pipeline changes (the search-tool switch applies immediately). Only edited fields are written; blank keys and untouched advanced options remain unchanged.

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.

Compatibility (v0.4.1)

Verified with DSH 0.1.7-rc.2 (Web) and 0.2.0-rc.2 (Desktop runtime) in isolated profiles. The Desktop app uses its own desktop profile. Other DSH prereleases remain unverified.

Desktop install

Use the Desktop-installed dsh command (Application → Manage dsh Command), or the app’s Plugins page. Then install into the Desktop profile:

dsh plugin --profile desktop add dsh-tdai-memory@0.3.6

Restart the Desktop app to load the client bundle. Desktop keeps its profile under $DSH_HOME/profiles/desktop.

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)

Recall caches expire after 30 seconds and retain at most 128 sessions per injection instance. Failed or timed-out recalls are retried on the next assembly. The preset row also accepts timeoutMs (default 4000); reaching this deadline continues prompt assembly without memory context. Deadline timers are cleared when recall settles early. Underlying recall work may continue after the deadline.

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 stored in the tdai-memory entry of the active Profile patch. On first launch, DSH imports the old $DSH_HOME/settings.yaml values into that entry. 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/profiles/web/cordis.patch.yml
- 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
      sendSessionHeader: true # send x-opencode-session on LLM requests (required by OpenCode Go & similar gateways)
      sessionId: ''           # fixed session id; empty = persistent auto id under the data dir
    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: {}          # fill through Settings → 记忆

and restart dsh web. LLM/embedding API keys can be set in the Web UI settings page (记忆 / Memory) or in the tdai-memory Profile patch entry.

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
• Search tools (toolsEnabled, on by default): the switch lives in the Plugins page and applies in place — the row config is volatile, so the plugin re-syncs its registration without a restart. If both tools are missing, look for the plugin's [tdai-memory] tools registered: tdai_memory_search, tdai_conversation_search log line (v0.4.1+). Before v0.4.1 it read config.toolsEnabled off the Volatile wrapper cordis hands to apply — a property that never exists — so the failure was completely silent (issue #3)
• Upgrades: after pulling new upstream code, rerun npx tsc -p dsh-tsconfig.json in the tdai project dir (output in dist-dsh/)

License

MIT

Saves use the configuration form API. Rejected writes retain the draft and show an error. Changing a model preserves credentials, and disabling a default-on option explicitly stores false.