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Graph Memory — DSH Plugin for DeepSeek Harness
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graph-memory

Graph Memory

Knowledge graph memory for DeepSeek Harness and OpenClaw — cross-session recall, PageRank, communities, and vector search

The plugin will be installed here. Keep web if you are unsure.

npx -y @deepseek-ai/dsh plugin --profile web add github:adoresever/graph-memory#441018a50236091b757af473c124fda0c584002e
READMECompatibilityVersions

Compatibility and provenance

Graph Memory is published as graph-memory and currently resolves to version 1.6.0-beta.16. The Hub verifies its manifest and preserves the exact installation source for reproducible installs.

DSH compatibility
*
Runtime surfaces
any
Release source
github
Registry updated
9/9/2026

Versions

1.6.0-beta.16beta
9/9/2026
Show 8 more versionsCollapse versions
1.6.0-beta.15beta
9/9/2026
1.6.0-beta.14beta
9/5/2026
1.6.0-beta.13beta
9/4/2026
1.6.0-beta.12beta
9/4/2026
1.6.0-beta.11beta
8/31/2026
1.6.0-beta.9beta
8/25/2026
1.6.0-beta.8beta
8/21/2026
1.6.0-beta.1beta
8/20/2026

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1.6.0-beta.16
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★ 625
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README

Graph Memory

Graph Memory for DeepSeek Harness, compatible with OpenClaw

Bound the context. Keep the memory.
A native DeepSeek Harness memory plugin that keeps recent conversation turns, archives older history, and recalls exact source-backed knowledge when it matters.

中文 · dsh.so · 20-turn benchmark · Upgrade guide

The problem it solves

Long agent history becomes graph navigation plus a compact recent-turn context

Graph Memory owns the model-visible historical surface without deleting DSH's event log. By default it keeps the newest five completed user turns, removes completed reasoning/tool traces from future requests, and recalls relevant older or cross-session source Q/A automatically.

The 1.6 turn-memory navigation upgrade

BeforeNow
Extract TASK / SKILL / EVENT directly from messagesCreate one self-contained turn summary, then derive SPO from that same sentence
Graph nodes could become the factual payloadSummary, SPO, and communities only navigate; original question and final answer remain the evidence
Old memories from the active session could be filtered wholesaleExclude only sources still visible in the fresh window; archived same-session and cross-session recall share one path
Community expansion could pull a whole neighborhoodLocal LPA narrows candidates, query-time PPR ranks them, and only matched Q/A is recovered
DSH retained complete tool and reasoning tracesCompleted turns retain question + final answer; older prefixes collapse to one fixed marker

Writing one completed turn costs exactly one auxiliary LLM call. Community detection and PPR are local. There are no hard-coded node/edge counts, semantic direction gates, or JSON repair that turns invalid output into accepted data. Read the complete design, source map, and porting sequence →

Measured first

DSH 20-turn first-request context comparison

Real 20-turn GLM-5.2 runHistorical native DSH baselineLatest Graph MemoryChange
T20 first request56,998 tokens11,008 tokens−80.69%
T20 model-visible messages17121−87.72%
T01–T20 first-request context532,451 tokens165,896 tokens−68.84%
All measured tokens¹2,487,7762,327,728−6.43%

¹ The latest candidate includes 166 main-agent requests, 20 turn extractions, and 41 embedding requests; the historical baseline made 77 main requests. DSH commits and nondeterministic tool loops differ, so this is not a simultaneous strict A/B. First-request context is the direct takeover metric; the full bill remains visible.

20/20 scenario turns passed · 20/20 structured extractions succeeded · 0 quarantined · 20 turn summaries · 92 SPO triples · 30 communities · 20 summary vectors. T11, T19, and T20 automatically recalled out-of-window memory with exact source question and final answer.

Read the Markdown benchmark, per-turn data, method, and limits →

Memory survives the context window

Graph Memory active in DSH Cross-session recall in a fresh DSH session

The graph is a navigation layer, not a replacement for evidence. TASK, SKILL, and EVENT nodes point back to the original user question and final visible answer; recalled context includes those exact source messages.

Install on DeepSeek Harness

Node.js 22.13+ · no DSH fork · until npm 1.6 is published, install the pinned GitHub release:

npx @deepseek-ai/dsh plugin --profile web add github:adoresever/graph-memory#v1.6.0-beta.16
npx @deepseek-ai/dsh --profile web --dump-config
npx @deepseek-ai/dsh web

The npm registry still serves the old 1.5.8; do not use it to validate DSH. Switch to npx @deepseek-ai/dsh plugin --profile web add graph-memory only after npm view graph-memory version reports 1.6.0-beta.16 or newer.

Confirm that graph-memory/dsh is active under Settings → Plugins. The default database is $DSH_HOME/graph-memory/graph-memory.db, normally ~/.dsh/graph-memory/graph-memory.db.

What ships

CapabilityImplementation
Context takeoverConfigurable newest-N completed turns; one archive marker replaces the older model surface
Lightweight extractionOnly the user question and final answer; strict structured tool contract; no reasoning/tool transcript ingestion
Query-first recallVector Top-K with FTS5 fallback; exact source Q/A travels with graph hits
Durable memoryLocal SQLite, stable provenance, cross-session and cross-project recall
Failure behaviorInvalid extraction is quarantined; foreground conversation continues; bad data is not repaired or persisted
Host supportNative DSH/Cordis adapter; maintained OpenClaw Context Engine adapter
Optional embeddings

Graph Memory supports OpenAI-compatible embedding endpoints. Without embeddings it falls back to FTS5 and does not block conversation.

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
DSH tools and extraction route
ToolPurpose
gm_statusStore, extraction, recall, vector, and retention state
gm_searchExplicit graph-memory search
gm_recordDeterministically persist a TASK, SKILL, or EVENT
gm_statsGraph and retention receipts
gm_maintainOne bounded maintenance tick
gm_retry_extractionExplicitly retry quarantined extraction

Automatic recall needs no tool call. Extraction may use a dedicated model via GRAPH_MEMORY_LLM_PROVIDER and GRAPH_MEMORY_LLM_MODEL; optional reasoning and output controls are GRAPH_MEMORY_LLM_REASONING_EFFORT and GRAPH_MEMORY_LLM_MAX_TOKENS.

OpenClaw compatibility
openclaw plugins install graph-memory
openclaw plugins enable graph-memory
openclaw gateway restart

Activate the Context Engine slot in ~/.openclaw/openclaw.json:

{
  "plugins": {
    "slots": { "contextEngine": "graph-memory" },
    "entries": { "graph-memory": { "enabled": true } }
  }
}

Earlier OpenClaw seven-turn token comparison

Graph Memory Pro

The repository also contains an experimental read-only DSH Pro Lite Host + Client plugin backed by Community SQLite. The 2D/3D graph workbench, split view, and controlled drag-to-context remain planned. See dsh-pro/README_CN.md.

Verification and limits

Current beta 1.6.0-beta.16 passes 138/138 automated tests, both TypeScript builds, npm package verification, and a real 20-turn run against the latest DSH source.

  • Structured extraction still depends on model contract compliance: the latest run succeeded 20/20 times; any future failure stays quarantined and never blocks the foreground conversation.
  • Recall is bounded by configurable Top-K. Focused probes succeeded; one broad multi-topic query can require a larger Top-K or separate questions.
  • The published run is an engineering workload, not a universal LoCoMo/LongMemEval score.
  • The design, source-code map, and porting sequence for the summary + SPO navigation + exact-Q/A upgrade are documented in the Chinese upgrade guide.

Reproduce it from benchmarks/dsh-context-takeover/. Raw conversations, provider responses, local paths, and credentials are excluded.

Development

npm install
npm test
npm run build
npm run verify:package

MIT © 2026 adoresever · Asset and trademark notes