Experience Memory
August 19, 2026 · View on GitHub
GraphFlow’s Experience layer turns storage → reflection → experience: raw episodes and graph facts are reflected into scored skills and lessons, then retrieved as organizational memory under the same context budget as code.
Related: Context contract · Team memory security · Flywheel auto-capture
Framing: Storage → Reflection → Experience
| Stage | What lives here | How it advances |
|---|---|---|
| Storage | Graph nodes (File/Symbol/Module), Decision episodes, Skill payloads, optional artifacts | Index, report_outcome, auto-capture hooks |
| Reflection | Score updates, outcome taxonomy (proven / correctable / anti-pattern / noise), SkillOpt-lite guidance edits, consolidation proposals | Flywheel after pass/fail; diagnose / skill report |
| Experience | What agents actually reuse: skill hints on plans, episode recall, L3 anchors in context packages | graphflow_context / plan / skill_insights under token budget |
Without reflection, storage is only a log. Without retrieval under a budget, reflection never becomes experience.
Organizational memory building blocks
| Artifact | Role |
|---|---|
| Episodes | Task → outcome → lessons (and optional deviation). Feed recall and skill learning. |
| Skills | Distilled, scored patterns with optional guidance and provenance / canary gates. |
| Graph artifacts | Portable gzip/JSON snapshot of the knowledge graph (graphflow artifact export/import). |
| Memory pack (Markdown) | Human-readable skills + recent episodes for review and onboarding (artifact export-memory). |
| Skill sync package | Machine mergeable team skills (.graphflow/skills/…) with security gates. |
Together they form engineering memory that can move with the repo — reviewable like code, gated against poisoning (see team-memory-security.md).
Export / import
Binary / JSON graph artifact
graphflow artifact export # → graphflow-out/graphflow-graph.artifact.gz
graphflow artifact export --include-episodes # keep episode Decision nodes
graphflow artifact import [path]
MCP: graphflow_artifact with mode: "export" | "import".
Markdown experience memory pack
Human-readable companion (not a full graph restore):
graphflow artifact export-memory
# → graphflow-out/memory-pack/README.md
# → graphflow-out/memory-pack/skills.md
# → graphflow-out/memory-pack/episodes.md
graphflow artifact export-memory path/to/dir
graphflow --json artifact export-memory
Programmatic: exportExperienceMemoryPack(config, outputDir?) in src/graph/memory-pack.ts.
Skill package (team MERGE)
graphflow skill sync export
graphflow skill sync import # newer updatedAt wins; --force overwrites
Agent Plugin vs graphflow install
| Path | Primary use | What you get |
|---|---|---|
Agent Plugins 1.0 (plugin.json / mcp.json / skills/) | Preferred single-host install (e.g. Cursor local plugins / marketplace) | MCP + canonical Skill discovered together |
DeepSeek Harness bundle (dsh.bundle + cordis.patch.yml + dsh/plugin.mjs) | dsh plugin add @roarpeng/graphflow then npx @deepseek-ai/dsh web | MCP (mcp__graphflow__*，cwd=工作区）+ runtime skill + agent/disposed 飞轮关闭。无 VS Code UI |
npx @roarpeng/graphflow install | Fallback / multi-agent / Rules | MCP + Skill + Rules across 15+ detected agents (incl. ~/.dsh) |
graphflow doctor | Diagnostics | Which agents are installed / wired |
Rules and multi-agent wiring still use install when the host does not consume Agent Plugins. Context packaging itself is unchanged — always start with graphflow_context (context-contract.md).