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)
| Layer | Content |
|---|
| Core | The host-neutral core of tdai-memory-openclaw-plugin (src/core, src/utils), tsc-compiled to ESM (dist-dsh/), zero changes |
| Host adapter | StandaloneHostAdapter (official standalone mode, direct OpenAI-compatible calls) |
| dsh shell | index.js: config mapping, session/event + session/flush capture, system-prompt/assemble recall injection on agent.ctx, tool registration, lifecycle |
| Fallback | recall-inject.js: preset-row recall injection (used when mounted inside an agent preset) |
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
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: {} # 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
