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The memory & context harness for coding agents. Local-first code knowledge graph · bounded context compression (~98% token savings) · cross-session learning flywheel.
The community is converging on an "agent harness" vocabulary: memory + hooks + skills are the harness primitives that turn a stateless model into a reliable long-running agent. GraphFlow implements all three for coding agents and ships them through a portable MCP surface (Cursor, Claude Code, 15+ agents):
| Harness primitive | GraphFlow implementation |
|---|---|
| Memory | 12-language AST code graph + Episodic / Skill / Decision nodes — project knowledge and project experience persist across sessions |
| Hooks | Outcome auto-capture (on by default) + Claude Code SessionEnd / Stop and DeepSeek Harness agent/disposed glue close the learning loop automatically — no manual outcome reporting required |
| Skills | A four-class flywheel (proven / correctable / anti-pattern / noise) with canary validation — skills are promoted by evidence, not by assertion |
Pure TypeScript/Node. CLI + MCP + VS Code extension. Fully offline, no API key required.
Most "memory" products are either static injection (load CLAUDE.md / rules files in full on every session) or plain RAG (retrieve chunks, no learning). Both fail in long-lived projects:
- Static injection pays the same token cost every session regardless of the task, and grows until it is truncated or ignored.
- Plain RAG retrieves text but never accumulates experience — the thousandth task pays the same cost as the first.
GraphFlow is a harness: memory is dynamic and typed. Each request retrieves only what the current decision needs — graph anchors, compressed summaries, similar past episodes, applicable skills — under an explicit token budget (L0–L3 layered compression, ~98% savings measured). What the agent learns (outcomes, lessons, skills) is written back through hooks, so the harness gets better with use.
It is also local-first and portable: everything runs offline with no API key, and the whole surface is exposed over MCP, so the same memory travels across agents instead of being locked into one vendor's format.
All headline numbers come from a public, reproducible benchmark suite (benchmarks/README.md) with published methodology (docs/benchmark-standards.md) and machine-readable JSON dumps pinned to commits:
- ~98% token savings (8-query suite, 262,926 → 2,843 tokens; independently re-counted with
gpt-tokenizer) - 132-query golden retrieval set in CI (Hit@5 = 100%, MRR = 0.836, NDCG@5 = 0.601); downloadable open dataset:
benchmarks/datasets/retrieval-golden-v1.json— runnpm run bench:retrieval - Skill A/B: 100% vs 61.5% task success with the flywheel on vs off (26 tasks)
- Memory ROI: 100% vs 56.5% with episodic memory on vs off (62 tasks, with attribution chains)
Results are commit-anchored so any number above can be checked out and re-run. Third-party reproduction is actively welcomed — see ROADMAP.md for the open invitation.
Shared and synced memory is only useful if it cannot be silently corrupted. Skills merged from external sources (e.g. skill sync imports) are treated as unproven until validated locally: imported skills carry provenance markers, never enter the proven class directly, must pass canary validation on real tasks before promotion, and anti-pattern skills are isolated rather than deleted so they can be audited. Promotion is gated by the four-class lifecycle, not by trust in the source. See docs/team-memory-security.md.
No API key needed (offline AST indexing + graph compression):
# 1. Build the graph offline (AST indexing, no LLM)
npx @roarpeng/graphflow graph index .# 2. Preview compressed context (anchors + summaries, 90%+ token savings)
npx @roarpeng/graphflow context preview "orchestrator" --jsonConnect via MCP (Cursor / Claude Code / …):
{
"mcpServers": {
"graphflow": {
"command": "npx",
"args": ["-y", "--package=@roarpeng/graphflow", "graphflow-mcp"]
}
}
}The agent calls graphflow_context for compressed context, then graphflow_plan to plan; without a provider API key GraphFlow automatically bridges the ATP thinking protocol to the host agent (agent-delegated mode).
Single-purpose tools each do one thing well; GraphFlow combines graph + compression + planning protocol + learning memory in one place:
| Capability | GraphFlow | CodeGraph | Serena | Repomix |
|---|---|---|---|---|
| Code graph | 12-language AST index | more mature | LSP symbols | — |
| Context compression | layered + graph compression + vector recall | partial | partial | whole-repo dump |
| Planning protocol | ATP IR + DAG + agent bridge | — | — | — |
| Learning memory | Episodic / Skill / Decision flywheel | — | — | — |
| Local-first | ✅ | ✅ | ✅ | ✅ |
| Open protocol | ATP/IR public spec | — | — | — |
The differentiator is the learning flywheel: graph indexing and token compression are replicable; project-private experience (skills, lessons, decisions) accumulated across sessions is not — it compounds with use. Serena is a complement, not a competitor — see GraphFlow + Serena: better together.
| Module | Capability |
|---|---|
| Planning protocol | ATP v1.1 (Intent / Requirement / Six Hats / 5-Why / First Principles / Decision Matrix / Planning / Reflection); simple / complex / insight modes; agent-delegated bridge without an LLM; skill-conditioned DAG (skillRefs / avoidPatterns on plan nodes); ATP/IR public spec v1.1 |
| Goal alignment | Goal anchor nodes (intent five-tuple as first-class citizen, original requirement auto-injected); low-confidence clarification gate (no plan below 0.6); runtime alignment-check; deviation classification (misread-requirement / scope-creep / tech-drift); goal version chain + diffs |
| Knowledge graph | 12-language AST indexing; File / Module / Symbol + Concept / Requirement; cross-layer edges documents / implements / derived_from; Office/PDF → Markdown via optional @firecrawl/anydoc (MIT). CLI/npm: optionalDependency. VSIX: not bundled; on activate the extension auto-downloads the current-OS binary into ~/.graphflow/optional-deps when graphflow.downloadAnydoc is true (default). Disable the setting to skip network; source indexing still works. |
| Context compression | L1/L2/L3 layered anchors; graph compression (edge weights + PageRank, LRU cache); stem-matching recall (orchestrate ↔ orchestration); vector recall + RRF; RepoMap overview; adaptive budget |
| Retrieval & fidelity | Golden-set regression gate (132 queries, Hit@5=100%, MRR=0.836, NDCG@5=0.601); separate anchor-recall and normalized body-coverage metrics persisted beside token savings |
| Vector index | In-process memoization + disk persistence (fingerprint-checked, seconds to restore after MCP restart) |
| Storage backends | file / memory / sqlite (FTS5, tokenizer-enhanced searchtext, camelCase searchable) / auto (sqlite-first with fallback) / mcp-http |
| Learning flywheel | Episodic memory, reflection, skill nodes (score ±1, bounded [-20,20]), nightly training, adaptive evidence-aware forgetting, auto-capture + Claude Code hooks (on by default), SkillOpt-lite bounded guidance edits, four-class lifecycle + canary gate for synced skills, portable SKILL.md import/export, npm run backfill:episodes, contribution reports (skill report / graphflow_diagnose / route diagnose) |
| Team sharing | skill sync: export/import skill packs to a committable .graphflow/skills/team-skills.json; imports are a bidirectional MERGE (per-skill-id union, newer updatedAt wins, ties keep local, local-only skills preserved; --force to overwrite); golden retrieval queries round-trip via .graphflow/team-golden.json; security model |
| Benchmarks | Comprehensive 92.9% · Independent-style 96.2% · context-readiness eval · 98.2% token savings |
| Model routing | Smart / Economy tiers; multi-provider health probes and fallback (DeepSeek, OpenAI, Anthropic, Bailian, Doubao) |
| Workbench | Plan DAG seeds function-topic containers; collapsed outline; click topicId to resume; drift forks a side branch; original Q/A stored via assistantReply |
| Observability | graphflow_diagnose / route diagnose: provider health + graph stats + token savings + flywheel health (auto-capture, episodes, skills by class, session journal) + workbench outline |
| Agent surfaces | CLI --json; MCP stdio and Streamable HTTP (stateless JSON or stateful SSE, 10 tools); auto-install into 15+ agents (incl. Codex Windows NODE/NPX_CLI short-path MCP) |
| Engineering quality | TypeScript strict; vitest suite; npm run ci includes extension packaging and smoke tests |
GraphFlow is not an orchestrating executor — it is the memory & context harness for coding agents. Task execution is delegated to the host coding agent via bridge mode (honest semantics, no faked COMPLETED); GraphFlow's job is to make the agent see clearly and remember.
| Tool | Function |
|---|---|
graphflow_context | Compressed context package (query → anchors + summaries; topicId / assistantReply to resume a workbench node or fill the pending answer; anchorId → expand) |
graphflow_plan | Task planning (mode='simple' or 'insight'; seeds workbench.topics + workbench.outline; agent-delegated without an LLM) |
graphflow_run | Orchestration + bridge execution descriptor |
graphflow_report_outcome | Outcome backfill (incl. deviation classification), closes the learning flywheel |
graphflow_insight | ATP insight submit / merge (agent bridge protocol) |
graphflow_index | Incremental / full indexing; optional knowledgeExtract: true distills dialogue turns into Concept / Requirement nodes with provenance edges |
graphflow_skill_insights | Skill insights |
graphflow_diagnose | Diagnostics (provider + graph + token savings + flywheel + graph.workbenchOutline) |
graphflow_artifact | Graph artifact import / export |
graphflow_skill_guide | GraphFlow skill usage guide |
MCP workspace resolution: the workspace is discovered automatically from the MCP client cwd; override with GRAPHFLOW_WORKSPACE_ROOT.
Everyday chat stays a single thread. Complex work seeds a workbench of function-topic containers from graphflow_plan — one canvas node per plan step, not one node per turn. Click a node and pass topicId to graphflow_context to refine that function or return to the mainline. Drift auto-forks an isolated side branch (co_occurs); the trunk is not overwritten. After answering, call graphflow_context({ assistantReply }) so the original reply is stored. Outline titles are display labels only; next-turn context is Goal + ancestor titles + the node's original Q/A.
Wake the collapsed outline when you need it (still 10 MCP tools):
graphflow workbench tree --json # CLI# VS Code / Cursor: GraphFlow: Workbench Tree (Activity Bar, default collapsed) or chat /tree# MCP: graphflow_diagnose → graph.workbenchOutline
graphflow context preview --topic-id "<topic:...>""continue from this node"
graphflow context preview --reply "original assistant answer"graphflow graph index .# build the graph
graphflow context preview "orchestrator"# preview compressed context
graphflow plan "refactor planner" --json # plan (also seeds workbench topics)
graphflow workbench tree --json # on-demand function DAG + side branches
graphflow run "update readme"# orchestrate (bridge)
graphflow skill insights # skill insights
graphflow skill report # flywheel contribution report
graphflow mcp serve --http # stateless MCP Streamable HTTP (add --stateful for SSE sessions)
graphflow skill sync export# export team skill pack + golden queries (share via git)
graphflow skill sync import # import team skill pack (MERGE; --force to overwrite) + golden merge into .graphflow/team-golden.json
graphflow route diagnose # routing diagnostics
graphflow learn nightly # nightly learning
graphflow doctor # install self-checkThree-layer merge: global ~/.graphflow.config.json → project graphflow.config.json → project .graphflow/config.json. Copy graphflow.config.example.json to get started.
Key options:
| Option | Description |
|---|---|
graphPolicy.transport | file / memory / sqlite / auto (recommended: sqlite-first, falls back to file) / mcp-http |
graphPolicy.maxContextTokens | Context budget (default 1500) |
graphPolicy.autoIndexOnSave | Auto incremental index on save (default true) |
embeddingPolicy.provider | transformers (local default) / openai / hash |
embeddingPolicy.vectorStorePath | Vector index persistence path (.hnsw derived automatically) |
skillPolicy.enableSkillFlywheel | Learning flywheel switch |
Set graphPolicy.transport to mcp-http to host the graph on a remote Graphify service (shared by the team); requires graphPolicy.mcpEndpoint (http(s) URL, optional mcpApiKey bearer token):
{ "graphPolicy": { "transport": "mcp-http", "mcpEndpoint": "http://graphify.team.internal:8080" } }A missing/malformed endpoint fails at config validation; connection or runtime request failures degrade transparently to local JSON storage (graphPolicy.graphStorePath, default graphflow-out/graphflow-graph.json) with a logger.warn, consistent with the sqlite→file fallback, never interrupting the agent. The pilot protocol does not yet support full snapshots: readSnapshot returns the local mirror file (possibly stale). For the team-sharing security model, see docs/team-memory-security.md.
- Comprehensive: COMPREHENSIVE-RESULTS.md — P1–P6 six-dimension evaluation, overall 92.9% (indexing 100% / compression 64.9% / planning 100% / learning 100% / bridge 100% / performance 99.7%)
- Independent-style: INDEPENDENT-RESULTS.md — CodeGraph-style 5-domain evaluation, Hit@5 96%, token savings 96.6%, overall 96.2%
- SWE-bench-style: SWE-BENCH-RESULTS.md — self-built 12-instance context-readiness eval; SWE-BENCH-REAL-RESULTS.md — Flask real-project 10-instance file-recall eval (48.3%)
- Token savings: RESULTS.md — 8 representative queries, 98.2% savings, re-counted with independent gpt-tokenizer
- Retrieval quality: RETRIEVAL-EVAL-RESULTS.md — 132 queries, Hit@5=100%, MRR=0.836, NDCG@5=0.601
- Skill flywheel A/B: SKILL-AB-RESULTS.md — injection rate 100%, recall 100%, overhead 25.6 tok/task
Download graphflow-<version>.vsix from GitHub Releases (or Open VSX: roarpeng.graphflow).
Commands: Settings / Show Graph (graph visualization) / Preview Context / Plan & Brainstorm / Run Task / Skill Insights / Install MCP; chat agent @graphflow (/run/plan/graph/skills/diagnose/learn/history).
Primary install path for hosts that support Agent Plugins. GraphFlow ships as a portable package at the repository root:
plugin.json # Agent Plugins 1.0 manifest
mcp.json # stdio MCP (type required by the spec)
skills/graphflow/SKILL.md
Install in Cursor (local):
mkdir -p ~/.cursor/plugins/local
ln -s /absolute/path/to/GraphFlow ~/.cursor/plugins/local/graphflow
# then Restart Cursor / Developer: Reload WindowInstall via Team Marketplace / Git: import this repository; clients discover plugin.json, then load skills/ and mcp.json.
Docs: Context Engineering contract · Experience memory
Uninstall: Removing the Agent Plugin in Cursor only drops the plugin package. Skills/Rules/MCP written by graphflow install remain and will keep steering the agent — run:
npx @roarpeng/graphflow uninstallThat removes user + workspace MCP entries, skills/graphflow folders, GraphFlow rules/instruction blocks, Claude Code hooks, and the DeepSeek Harness cordis.patch.yml overlay. Also delete any local symlink under ~/.cursor/plugins/local/graphflow if you used one.
GraphFlow 是 DeepSeek Harness 的 dsh-plugin。包内 dsh.bundle + cordis.patch.yml 会把 GraphFlow MCP 挂到内置 @deepseek-ai/dsh-mcp-client，并把 @roarpeng/graphflow/dsh glue 插入插件树。模型看到的工具名是 mcp__graphflow__graphflow_*。中文说明见 README.zh.md。
在 dsh 上能工作 vs 不能工作：
| 能力 | dsh |
|---|---|
10 个 MCP 工具（mcp__graphflow__graphflow_*），stdio cwd = 会话工作区 | 是 |
Skill（on-demand skill({name:"graphflow"})；bundle glue 注册，不必先 graphflow install） | 是 |
会话结束飞轮：仅 agent/disposed 关闭 pending episode（不是 live session/flush；GRAPHFLOW_AUTO_CAPTURE=0 可关） | 是 |
首轮短 hint：先调 graphflow_context（rootDir = cwd） | 是 |
Workbench 数据（topicId / outline）经 MCP graphflow_context / graphflow_diagnose | 是 |
VS Code/Cursor 图谱面板、Settings webview、Workbench Tree、@graphflow chat | 否（宿主 UI，不移植） |
| Cursor Agent Plugins 1.0 发现 | 否（dsh 用 dsh.bundle） |
Claude Code SessionStart/End/Stop文件 hooks | 否（dsh analog 是上面的 glue） |
装进某个 profile（推荐）：
dsh plugin --profile web add @roarpeng/graphflow
npx @deepseek-ai/dsh web或在已有 ~/.dsh 时写 home 级 overlay（对所有 profile 生效）：
npx @roarpeng/graphflow install会写入 $DSH_HOME/cordis.patch.yml（MCP + glue）与 $DSH_HOME/skills/graphflow/SKILL.md。卸载：npx @roarpeng/graphflow uninstall，或 dsh plugin --profile web remove @roarpeng/graphflow。graphflow doctor 会检查 overlay、glue、skill。
用法： 第一轮先 mcp__graphflow__graphflow_context（传入 rootDir = 仓库绝对路径），复杂任务再 graphflow_plan；改完代码后 graphflow_index；若走了 graphflow_run，结束后必须 graphflow_report_outcome。不要在 patch 里写死 GRAPHFLOW_WORKSPACE_ROOT。
Use npx @roarpeng/graphflow install as the fallback when you need Rules, multi-agent wiring, or a host that does not load Agent Plugins:
npx @roarpeng/graphflow doctor # detect installed agents
npx @roarpeng/graphflow install # auto-install MCP + Skill + Rules
npx @roarpeng/graphflow uninstall # remove MCP + Skill + Rules + hooks
npx @roarpeng/graphflow init # write a minimal project configSupported: Cursor, VS Code, Trae (incl. CN), Claude Code, Windsurf, Cline, Roo Code, Kilo Code, Gemini CLI, Codex, Antigravity, Opencode, Qoder, Amazon Q, Zed, Continue, DeepSeek Harness (dsh), and more (15+).
| Path | When to use |
|---|---|
| Agent Plugins | Preferred single-host Skill + MCP discovery |
graphflow install | Rules / multi-agent / non-plugin hosts |
graphflow uninstall | After removing a plugin (or anytime) — clears leftover Skill/MCP/Rules |
ATP/IR — Agent Thinking Protocol public specification v1.0: work-item registry, submit/merge contract, compatibility rules. Third-party tools can implement compatible producers / consumers. Minimal Producer example: examples/atp-minimal-producer/.
GraphFlow is a single-maintainer project (bus factor = 1); community collaboration is the key to reducing single-point risk. Contributions welcome:
- Contributing guide: dev environment, code style, test requirements and PR checklist
- Roadmap: completed milestones and next steps (P0–P2)
- Issues: bug reports and feature requests (please use the built-in templates)
- Discussions: questions and ideas
npm install
npm run ci # lint + build + tests + extension packaging + smokeRequires Node.js ≥ 20, npm ≥ 10. Expected: lint clean, build succeeds, 956 tests pass.
GraphFlow/
├── plugin.json # Agent Plugins 1.0 manifest
├── mcp.json # Agent Plugins MCP (stdio)
├── cordis.patch.yml # DeepSeek Harness (dsh) bundle layer (MCP + glue)
├── dsh/plugin.mjs # dsh ESM glue: skill register + session-end capture
├── skills/graphflow/ # portable Agent Skill (canonical SKILL.md)
├── src/
│ ├── core/ # orchestration core: orchestrator, triage, dag-engine, agent-delegation
│ ├── graph/ # indexing, context slicing, graph compression, sqlite/auto storage, snapshot
│ ├── routing/ # model routing and health probes (5 providers)
│ ├── learning/ # embeddings, episodic, skill-flywheel, hnsw, nightly
│ ├── agents/ # ATP schema, planner, insight, brainstormer
│ └── surfaces/
│ ├── cli/ # CLI + runtime
│ └── mcp/ # MCP server (10 tools)
├── tests/ # 141 files / 956 tests (incl. fidelity, SKILL.md, Engineering KG, MCP HTTP/stdio matrix)
├── benchmarks/ # comprehensive + independent + SWE-bench + token savings + skill A/B (reproducible)
├── docs/ # ATP spec + context contract + experience memory + comparisons
├── vscode-extension/ # VS Code panel and commands
└── CHANGELOG.md
Full history in CHANGELOG.md. License: Apache-2.0.