# Send OpenClawBrain to your agent Use the source page and any available docs to guide the install because the item currently does not return a direct package file. ## Fast path - Open the source page via Open source listing. - If you can obtain the package, extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the source page and extracted files. ## Suggested prompts ### New install ```text I tried to install a skill package from Yavira, but the item currently does not return a direct package file. Inspect the source page and any extracted docs, then tell me what you can confirm and any manual steps still required. ``` ### Upgrade existing ```text I tried to upgrade a skill package from Yavira, but the item currently does not return a direct package file. Compare the source page and any extracted docs with my current installation, then summarize what changed and what manual follow-up I still need. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "openclawbrain", "name": "OpenClawBrain", "source": "tencent", "type": "skill", "category": "AI 智能", "sourceUrl": "https://clawhub.ai/jonathangu/openclawbrain", "canonicalUrl": "https://clawhub.ai/jonathangu/openclawbrain", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/openclawbrain", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=openclawbrain", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "SKILL.md" ], "downloadMode": "manual_only", "sourceHealth": { "source": "tencent", "slug": "openclawbrain", "status": "source_issue", "reason": "not_found", "recommendedAction": "review_source", "checkedAt": "2026-05-02T22:04:17.563Z", "expiresAt": "2026-05-03T22:04:17.563Z", "httpStatus": 404, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=openclawbrain", "contentType": "text/plain", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=openclawbrain", "contentDisposition": null, "redirectLocation": null, "bodySnippet": null, "slug": "openclawbrain" }, "scope": "item", "summary": "Known item issue.", "detail": "This item's current download entry is known to bounce back to a listing or homepage instead of returning a package file.", "primaryActionLabel": "Open source listing", "primaryActionHref": "https://clawhub.ai/jonathangu/openclawbrain" }, "validation": { "installChecklist": [ "Open the source listing and confirm there is a real package or setup artifact available.", "Review SKILL.md before asking your agent to continue.", "Treat this source as manual setup until the upstream download flow is fixed." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/openclawbrain", "downloadUrl": "https://openagent3.xyz/downloads/openclawbrain", "agentUrl": "https://openagent3.xyz/skills/openclawbrain/agent", "manifestUrl": "https://openagent3.xyz/skills/openclawbrain/agent.json", "briefUrl": "https://openagent3.xyz/skills/openclawbrain/agent.md" } } ``` ## Documentation ### OpenClawBrain v12.2.1 Learned retrieval graph for AI agents. Nodes are document chunks, edges are mutable weighted pointers. The graph learns from outcomes using policy-gradient updates (REINFORCE) and self-regulates via homeostatic decay, synaptic scaling, and tier hysteresis. ### Install pip install openclawbrain # core (pure Python, zero deps) pip install "openclawbrain[openai]" # with OpenAI embeddings ### Quick Start # Build a brain from workspace files openclawbrain init --workspace ./my-workspace --output ./brain --embedder openai # Query openclawbrain query "how do I deploy" --state ./brain/state.json --json # Learn from outcome (+1 good, -1 bad) openclawbrain learn --state ./brain/state.json --outcome 1.0 --fired-ids "node1,node2" # Self-learn (agent-initiated, no human needed) openclawbrain self-learn --state ./brain/state.json \\ --content "Always download artifacts before terminating instances" \\ --fired-ids "node1,node2" --outcome -1.0 --type CORRECTION # Health check openclawbrain doctor --state ./brain/state.json ### Learning Rule: Policy Gradient (default) Default is apply_outcome_pg (REINFORCE). At each node, updates redistribute probability mass across ALL outgoing edges (sum ≈ 0). The chosen edge goes up, all alternatives go down. No inflation. apply_outcome (heuristic) is available as fallback — only updates traversed edges, inflationary. ### Self-Learning Agents learn from their own observed outcomes without human feedback (self-correct available as CLI/API alias): from openclawbrain.socket_client import OCBClient with OCBClient('~/.openclawbrain/main/daemon.sock') as client: # Agent detected failure client.self_learn( content='Always download artifacts before terminating', fired_ids=['node1', 'node2'], outcome=-1.0, node_type='CORRECTION', # penalize + inhibitory edges ) # Agent observed success client.self_learn( content='Download-then-terminate works reliably', fired_ids=['node1', 'node2'], outcome=1.0, node_type='TEACHING', # reinforce + positive knowledge ) SituationoutcometypeEffectMistake-1.0CORRECTIONPenalize path + inhibitory edgesFact learned0.0TEACHINGInject knowledge onlySuccess+1.0TEACHINGReinforce path + inject knowledge ### Self-Regulation (automatic, no tuning needed) Homeostatic decay: half-life auto-adjusts to maintain 5-15% reflex edge ratio. Bounded 60-300 cycles. Synaptic scaling: soft per-node weight budget (5.0) prevents hub domination. Tier hysteresis: habitual band 0.15-0.6 prevents threshold thrashing. Synaptic scaling (maintenance detail): soft per-node weight budget (5.0) with fourth-root scaling. ### Edge Tiers TierWeightBehaviorReflex≥ 0.6Auto-followHabitual0.15 – 0.6Follow by weightDormant< 0.15SkippedInhibitory< -0.01Actively suppresses target ### Maintenance Pipeline Runs every 30 min via daemon: health → decay → scale → split → merge → prune → connect Decay: exponential edge weight decay (adaptive half-life) Scale: synaptic scaling on hub nodes Split: runtime node splitting (inverse of merge) for bloated multi-topic nodes Merge: consolidate co-firing nodes (bidirectional weight ≥ 0.8) Prune: remove dead edges (|w| < 0.01) and orphan nodes ### Maintenance split_node: splits bloated nodes into focused children with embedding-based edge rewiring suggest_splits: detects candidates by content length, hub degree, merge origin, edge variance ### Text Chunking split_workspace chunks files by type (.py → functions, .md → headers, .json → keys) then _rechunk_oversized ensures no chunk exceeds 12K chars. Large texts are split on blank lines → newlines → hard cut. No content is ever skipped or truncated. ### Daemon (production use) The daemon keeps state hot in memory behind a Unix socket (~500ms queries vs 5-8s from disk). # Start daemon (usually via launchd) openclawbrain daemon --state ./brain/state.json --embed-model text-embedding-3-small ### Daemon Methods (NDJSON over Unix socket) MethodPurposequeryTraverse graph, return fired nodes + contextlearnApply outcome to fired nodesself_learnAgent-initiated learning (CORRECTION or TEACHING)self_correctAlias for self_learn (self-correct available as CLI/API alias)correctionHuman-initiated correction (uses chat_id lookback)injectAdd TEACHING/CORRECTION/DIRECTIVE nodemaintainRun maintenance taskshealthGraph health metricsinfoDaemon infosaveForce state writereloadReload state from diskshutdownClean shutdown ### Socket Client from openclawbrain.socket_client import OCBClient with OCBClient('/path/to/daemon.sock') as c: result = c.query('how do I deploy', chat_id='session-123') c.learn(fired_nodes=['node1', 'node2'], outcome=1.0) c.self_learn(content='lesson', outcome=-1.0, node_type='CORRECTION') c.health() c.maintain(tasks=['decay', 'prune']) ### CLI Reference openclawbrain init --workspace W --output O [--embedder openai] [--llm openai] openclawbrain query TEXT --state S [--top N] [--json] [--chat-id CID] openclawbrain learn --state S --outcome N --fired-ids a,b,c [--json] openclawbrain self-learn --state S --content TEXT [--fired-ids a,b] [--outcome -1] [--type CORRECTION|TEACHING] openclawbrain inject --state S --id ID --content TEXT [--type CORRECTION|TEACHING|DIRECTIVE] openclawbrain health --state S openclawbrain doctor --state S openclawbrain info --state S openclawbrain maintain --state S [--tasks decay,scale,split,merge,prune,connect] openclawbrain status --state S [--json] openclawbrain replay --state S --sessions S openclawbrain merge --state S [--llm openai] openclawbrain connect --state S openclawbrain compact --state S openclawbrain sync --workspace W --state S [--embedder openai] openclawbrain daemon --state S [--embed-model text-embedding-3-small] ### Traversal Defaults ParameterDefaultbeam_width8max_hops30fire_threshold0.01reflex_threshold0.6habitual_range(0.15, 0.6)inhibitory_threshold-0.01max_context_chars20000 (in query_brain.py) ### State Persistence Atomic writes: temp → fsync → rename. Keeps .bak backup. Crash-safe. State format: state.json (graph + index + metadata) Embedder identity stored in metadata; dimension mismatches are errors. ### Integration with OpenClaw Agents Add to your agent's AGENTS.md: ## OpenClawBrain Memory Graph **Query:** python3 ~/openclawbrain/examples/openclaw_adapter/query_brain.py \\ ~/.openclawbrain//state.json '' --chat-id '' --json **Learn:** openclawbrain learn --state ~/.openclawbrain//state.json --outcome 1.0 --fired-ids **Self-learn:** openclawbrain self-learn --state ~/.openclawbrain//state.json \\ --content "lesson" --fired-ids --outcome -1.0 --type CORRECTION # (self-correct available as CLI/API alias) **Health:** openclawbrain health --state ~/.openclawbrain//state.json ### Links Paper: https://jonathangu.com/openclawbrain/ Blog: https://jonathangu.com/openclawbrain/blog/v12.2.1/ Derivation: https://jonathangu.com/openclawbrain/gu2016/ GitHub: https://github.com/jonathangu/openclawbrain PyPI: pip install openclawbrain==12.2.1 ## Trust - Source: tencent - Verification: Indexed source record - Publisher: jonathangu - Version: 12.2.1 ## Source health - Status: source_issue - Known item issue. - This item's current download entry is known to bounce back to a listing or homepage instead of returning a package file. - Health scope: item - Reason: not_found - Checked at: 2026-05-02T22:04:17.563Z - Expires at: 2026-05-03T22:04:17.563Z - Recommended action: Open source listing ## Links - [Detail page](https://openagent3.xyz/skills/openclawbrain) - [Send to Agent page](https://openagent3.xyz/skills/openclawbrain/agent) - [JSON manifest](https://openagent3.xyz/skills/openclawbrain/agent.json) - [Markdown brief](https://openagent3.xyz/skills/openclawbrain/agent.md) - [Download page](https://openagent3.xyz/downloads/openclawbrain)