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- 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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- Tencent SkillHub
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- CHANGELOG.md, CONTRIBUTING.md, README.md, REPRODUCE.md, SKILL.md, benchmarks/external/README.md
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Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 11.2.1
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- Publisher
- jonathangu
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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