Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
OpenClawBrain
Learned memory graph for AI agents. Policy-gradient routing over document chunks with self-learning, self-regulation, and autonomous correction. Pure Python...
skill openclawclawhub Free
Learned memory graph for AI agents. Policy-gradient routing over document chunks with self-learning, self-regulation, and autonomous correction. Pure Python...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 12.2.1
Provenance
- Publisher
- jonathangu
- Source page
- View original listing
- Canonical URL
- Open canonical page
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 β 0). The chosen edge goes up, all alternatives go down. No inflation. apply_outcome (heuristic) is available as fallback β 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
TierWeightBehaviorReflexβ₯ 0.6Auto-followHabitual0.15 β 0.6Follow by weightDormant< 0.15SkippedInhibitory< -0.01Actively suppresses target
Maintenance Pipeline
Runs every 30 min via daemon: health β decay β scale β split β merge β prune β 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 β₯ 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 β functions, .md β headers, .json β keys) then _rechunk_oversized ensures no chunk exceeds 12K chars. Large texts are split on blank lines β newlines β 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 β fsync β 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/<brain>/state.json '<query>' --chat-id '<chat_id>' --json **Learn:** openclawbrain learn --state ~/.openclawbrain/<brain>/state.json --outcome 1.0 --fired-ids <ids> **Self-learn:** openclawbrain self-learn --state ~/.openclawbrain/<brain>/state.json \ --content "lesson" --fired-ids <ids> --outcome -1.0 --type CORRECTION # (self-correct available as CLI/API alias) **Health:** openclawbrain health --state ~/.openclawbrain/<brain>/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
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md