Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
CrabPath
Memory graph engine with caller-provided embed and LLM callbacks; core is pure, with real-time correction flow and optional OpenAI integration.
skill openclawclawhub Free
Memory graph engine with caller-provided embed and LLM callbacks; core is pure, with real-time correction flow and optional OpenAI integration.
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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
- CHANGELOG.md, CONTRIBUTING.md, README.md, REPRODUCE.md, SKILL.md, benchmarks/external/README.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. Then review README.md for any prerequisites, environment setup, or post-install checks. 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. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 11.2.1
Provenance
- Publisher
- jonathangu
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
CrabPath
Pure graph core: zero required deps and no network calls. Caller provides callbacks.
Design Tenets
No network calls in core No secret discovery (no dotfiles, keychain, or env probing) No subprocess provider wrappers Embedder identity in state metadata; dimension mismatches are errors One canonical state format (state.json)
Quick Start
from crabpath import split_workspace, HashEmbedder, VectorIndex graph, texts = split_workspace("./workspace") embedder = HashEmbedder() index = VectorIndex() for nid, content in texts.items(): index.upsert(nid, embedder.embed(content))
Embeddings and LLM callbacks
Default: HashEmbedder (hash-v1, 1024-dim) Real: callback embed_fn / embed_batch_fn (e.g., text-embedding-3-small) LLM routing: callback llm_fn using gpt-5-mini (example)
Session Replay
replay_queries(graph, queries) can warm-start from historical turns.
CLI
--state is preferred: crabpath query TEXT --state S [--top N] [--json] crabpath query TEXT --state S --chat-id CID crabpath doctor --state S crabpath info --state S crabpath init --workspace W --output O --embedder openai crabpath query TEXT --state S --llm openai crabpath inject --state S --type TEACHING [--type DIRECTIVE] Real-time correction flow: python3 query_brain.py --chat-id CHAT_ID python3 learn_correction.py --chat-id CHAT_ID
Quick Reference
crabpath init/query/learn/inject/health/doctor/info query_brain.py --chat-id and learn_correction.py for real-time correction pipelines query_brain.py traversal limits: beam_width=8, max_hops=30, fire_threshold=0.01 Hard traversal caps: max_fired_nodes and max_context_chars (defaults None; query_brain.py defaults max_context_chars=20000) examples/correction_flow/, examples/cold_start/, examples/openai_embedder/
API Reference
Core lifecycle: split_workspace load_state save_state ManagedState VectorIndex Traversal and learning: traverse TraversalConfig TraversalConfig.beam_width, .max_hops, .fire_threshold, .max_fired_nodes, .max_context_chars, .reflex_threshold, .habitual_range, .inhibitory_threshold TraversalResult apply_outcome Runtime injection APIs: inject_node inject_correction inject_batch Maintenance helpers: suggest_connections, apply_connections suggest_merges, apply_merge measure_health, autotune, replay_queries Embedding utilities: HashEmbedder OpenAIEmbedder default_embed default_embed_batch openai_llm_fn LLM routing callbacks: chat_completion Graph primitives: Node Edge Graph split_workspace generate_summaries
CLI Commands
crabpath init --workspace W --output O [--sessions S] [--embedder openai] crabpath query TEXT --state S [--top N] [--json] [--chat-id CHAT_ID] crabpath learn --state S --outcome N --fired-ids a,b,c [--json] crabpath inject --state S --id NODE_ID --content TEXT [--type CORRECTION|TEACHING|DIRECTIVE] [--json] [--connect-min-sim 0.0] crabpath inject --state S --id NODE_ID --content TEXT --type TEACHING crabpath inject --state S --id NODE_ID --content TEXT --type DIRECTIVE crabpath health --state S crabpath doctor --state S crabpath info --state S crabpath replay --state S --sessions S crabpath merge --state S [--llm openai] crabpath connect --state S [--llm openai] crabpath journal [--stats] query_brain.py --chat-id CHAT_ID learn_correction.py --chat-id CHAT_ID
Traversal defaults
beam_width=8 max_hops=30 fire_threshold=0.01 reflex_threshold=0.6 habitual_range=0.2-0.6 inhibitory_threshold=-0.01 max_fired_nodes (hard node-count cap, default None) max_context_chars (hard context cap, default None; query_brain.py default is 20000)
Paper
https://jonathangu.com/crabpath/
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
6 Docs
- SKILL.md
- benchmarks/external/README.md
- CHANGELOG.md
- CONTRIBUTING.md
- README.md
- REPRODUCE.md