Graph Memory

Traceable, searchable, cross-session memory for AI agents.

One memory core, native to DeepSeek Harness, with the OpenClaw plugin entry retained.

中文 · Advantages · Architecture · DSH Install · Pro Plugin · Technical Report (Chinese)

Compaction answers “how much of this conversation still fits?” Graph Memory answers “which past knowledge is worth recalling now?”

Reusable conversation knowledge becomes typed nodes:

• TASK: goals, execution, and outcomes;
• SKILL: validated reusable methods;
• EVENT: errors, fixes, decisions, changes, and facts.

Typed edges such as USED_SKILL, SOLVED_BY, REQUIRES, PATCHES, and CONFLICTS_WITH preserve relationships. A new question retrieves a relevant local subgraph instead of replaying the complete history.

Core advantages

Native host integration

• Loaded by the DSH/Cordis plugin lifecycle, not simulated through an MCP side channel.
• Integrates Session, Tool, Agent Loop, Prompt Assembly, LLM, and Credentials seams.
• Disposes database, cache, and event listeners with its plugin fiber.
• Does not fork or modify DeepSeek Harness core.

Durable cross-session memory

• Knowledge from Session A can be recalled automatically in Session B.
• Memory survives DSH restarts.
• Stable event IDs make resume and HMR ingestion idempotent.
• Source sessions and graph edges explain why a memory was recalled.

Smaller, cleaner context

• Semantic vector retrieval with FTS5 lexical fallback.
• Community detection, PageRank, personalized PageRank, and bounded graph traversal.
• Only a relevant local subgraph enters the current prompt.
• Recalled history is marked as untrusted reference material and cannot override current user instructions.

Local-first and lightweight

• Community uses SQLite by default; no graph database deployment is required.
• Embeddings are optional. Without them, recall falls back to FTS5.
• Data remains in the user's local profile by default.
• OpenAI-compatible embeddings support DashScope, OpenAI, and local providers.

Observable and verifiable

• gm_status reports store path, graph counts, vector coverage, mode, and dimensions.
• Model or dimension changes trigger re-embedding.
• Vectors with different dimensions are never silently compared.
• Critical knowledge can be recorded deterministically with gm_record.

Scoped token benchmark

The original OpenClaw adapter was measured in a seven-turn workflow that installed, authenticated, and queried bilibili-mcp:

Turn	Without Graph Memory	With Graph Memory
R1	14,957	14,957
R4	81,632	29,175
R7	95,187	23,977

The measured reduction at R7 was approximately 75% in that specific workflow. This is a scenario-level comparison, not a universal savings guarantee; the mechanism is replacing indiscriminate history replay with a relevant knowledge subgraph.

Project evolution

The DSH integration does not discard the original project. Graph Memory is evolving from an OpenClaw memory plugin into a graph-memory core that different agent harnesses can load natively.

Stage	Deliverable	Status
OpenClaw origin	Context Engine, cross-session graph memory, dual-path recall	Maintained
Community graph engine	SQLite, FTS5, vectors, graph ranking, provenance	Available
DeepSeek Harness	Cordis adapter, native tools, auto-recall, Credentials	Implemented and tested
Graph Memory Pro	Visual graph workbench, controlled drag-and-drop, optional Neo4j	Architecture reviewed; DSH Host and Client Plugins not yet implemented

On March 15, 2026, the project owner presented Graph Memory's architecture at the CLAW program event held in Tsinghua Science Park. The following owner-supplied materials and the Sina Finance event report document that development.

• Community cross-session memory demo
• Graph Memory Pro technical presentation

The image below is the existing OpenClaw / ClawX-era Pro graph prototype. It demonstrates a previously explored interaction direction; it is not a shipped DSH frontend.

Names and venue information document project history only and do not imply endorsement by Tsinghua University, Sina Finance, DeepSeek, or OpenClaw.

Graph Memory architecture

Typed knowledge graph

TASK   ──USED_SKILL──▶ SKILL
TASK   ──SOLVED_BY───▶ EVENT
SKILL  ──REQUIRES────▶ SKILL
EVENT  ──PATCHES─────▶ SKILL
SKILL  ──CONFLICTS_WITH──▶ SKILL

Nodes retain episodic user/assistant provenance. This preserves the context in which knowledge was created, not only a lossy summary.

Dual-path recall

flowchart LR
  Q[Current query] --> EXACT[Exact path]
  Q --> GENERAL[Generalized path]
  EXACT --> SEARCH[Vector / FTS5]
  SEARCH --> EXPAND[Community expansion + traversal]
  GENERAL --> SUMMARY[Community-summary match]
  SUMMARY --> MEMBERS[Community members]
  EXPAND --> PPR[Personalized PageRank]
  MEMBERS --> PPR
  PPR --> CONTEXT[Deduplicated local context]

Host data flow

flowchart LR
  USER[User message] --> SESSION[DSH Session Events]
  SESSION --> ADAPTER[Graph Memory Cordis Adapter]
  ADAPTER --> EXTRACT[Structured Extraction]
  EXTRACT --> GRAPH[(SQLite / FTS5 / Vectors)]

  USER --> RECALL[Semantic + Lexical Recall]
  GRAPH --> RECALL
  RECALL --> RANK[Community Expansion + PPR]
  RANK --> PROMPT[Prompt Assembly]
  PROMPT --> LOOP[DSH Agent Loop]

  CREDS[DSH Credentials] --> ADAPTER
  TOOLS[gm_* Tools] --> ADAPTER

The code follows a host-neutral core plus host adapters:

graph-memory/
├── dsh.ts                 # DeepSeek Harness / Cordis adapter
├── index.ts               # OpenClaw adapter
├── cordis.patch.yml       # DSH bundle entry
└── src/
    ├── extractor/         # conversation → TASK / SKILL / EVENT
    ├── recaller/          # vector, FTS5, graph expansion and recall
    ├── graph/             # PageRank, communities and deduplication
    ├── store/             # SQLite schema and queries
    ├── format/            # safe context assembly
    └── engine/            # LLM and embedding providers

Native DeepSeek Harness status

Capability	Status	Notes
Native Cordis loading	Done	No DSH fork required
Cross-session auto-recall	Done	Injected during Prompt Assembly
Explicit record and search	Done	gm_record, gm_search
Vector backfill and migration	Done	Model, dimension, and fingerprint tracked
Visible plugin state	Done	Active in Plugin Inventory
Pro visual workbench	Not shipped	Requires a DSH Client Plugin

Current beta: 1.6.0-beta.1. Local acceptance used DeepSeek Harness 0.1.0-rc.5. DSH remains in Developer Preview and may introduce compatibility-breaking changes. Testing covered tarball installation, active plugin state, 1024-dimensional vector backfill, semantic recall across Sessions, persistence across restarts, and FTS5 fallback. All 107 automated tests passed.

Plugin enabled: graph-memory/dsh is active in the DSH plugin list

Cross-session semantic recall in a fresh Session

Install on DeepSeek Harness

Prerequisites: Node.js 22.19+ or 24+. The current beta is not yet published to npm, so build the tarball from source:

git clone https://github.com/123caiji/dsh-memory-toolkit.git
cd graph-memory
npm ci
npm test
npm run build
npm pack

Install the generated tarball into the DSH Web profile:

npx @deepseek-ai/dsh plugin --profile web add /absolute/path/to/graph-memory-1.6.0-beta.1.tgz
npx @deepseek-ai/dsh --profile web --dump-config
npx @deepseek-ai/dsh web

# From a deepseek-harness source checkout:
pnpm dsh plugin --profile web add /absolute/path/to/graph-memory-1.6.0-beta.1.tgz
pnpm dsh web

After installation, verify that graph-memory/dsh is enabled under Settings → Plugins → Plugin list.

Default store:

$DSH_HOME/graph-memory/graph-memory.db

Without DSH_HOME, this is normally ~/.dsh/graph-memory/graph-memory.db.

Optional vector retrieval

Do not send secrets in chat. Cordis stores only a credential reference; DSH credentials resolves the real value for each embedding operation.

DashScope example:

export GRAPH_MEMORY_EMBEDDING_API_KEY='replace-with-your-key'
export GRAPH_MEMORY_EMBEDDING_BASE_URL='https://dashscope.aliyuncs.com/compatible-mode/v1'
export GRAPH_MEMORY_EMBEDDING_MODEL='text-embedding-v4'
export GRAPH_MEMORY_EMBEDDING_DIMENSIONS='1024'
dsh web

Without embeddings, Graph Memory continues with FTS5 and does not block conversation.

DSH tools

Tool	Purpose
gm_status	Plugin, store, extraction, recall, and vector state
gm_search	Explicit long-term graph search
gm_record	Persist a TASK, SKILL, or EVENT
gm_stats	Node, edge, type, and community statistics

Automatic recall does not require an explicit gm_search tool call. The plugin retrieves relevant memory during Prompt Assembly.

Development

npm ci
npm test
npm run build
npm pack

Release checks:

• tests and TypeScript build pass;
• tarball contains dist/dsh.js and cordis.patch.yml;
• no API keys, local databases, or environment files enter the repository;
• planned Pro features are never presented as shipped Community behavior.

Current limitations

• Automatic extraction depends on auxiliary-model output stability. Use gm_record for critical beta knowledge.
• DSH does not yet expose gm_update and gm_maintain; those remain OpenClaw-entry tools.
• The Pro DSH visualization client plugin is not implemented.
• npm registry publication is pending; install the current beta from a GitHub-built tarball.

Privacy and security

• Memory remains in local SQLite by default.
• API keys come from host credentials or environment variables, not the database or Cordis patch.
• Recalled history is reference material; current user instructions always take precedence.
• Rotate any secret that has appeared in chat, logs, or screenshots.

License

MIT © 2026 adoresever, 123caiji

See docs/ATTRIBUTIONS.md for asset, logo, and trademark notes.