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LightRAG Memory
Semantic memory system with vector search + knowledge graph. Replaces reading entire memory files on every request with targeted retrieval (~1-3K tokens vs 30K+).
Quick Setup
cd skills/lightrag-memory
pip install -r requirements.txtSet environment variables:
export OPENAI_API_KEY="your-key"
export OPENAI_BASE_URL="https://your-api-endpoint/v1" # optional, defaults to OpenAIOr create a .env file in the skill directory.
Commands
Index memory files
Index MEMORY.md and memory/*.md from workspace:
python3 scripts/rag.py indexInsert content
# From file
python3 scripts/rag.py insert --file /path/to/file.md --source "filename"
# From text
python3 scripts/rag.py insert --text "Important fact" --source "manual"
# From stdin
echo "Some text" | python3 scripts/rag.py insert --source "stdin"Query
# Hybrid search (best results, costs more API calls)
python3 scripts/rag.py query "What do I know about the user?" --mode hybrid
# Local search (entities + relationships, balanced)
python3 scripts/rag.py query "What projects were discussed?" --mode local
# Naive search (simple vector lookup, cheapest)
python3 scripts/rag.py query "Any notes about deployment?" --mode naive
# Global search (broad context, expensive)
python3 scripts/rag.py query "Summarize everything" --mode globalQuery Modes
| Mode | What it searches | Cost | Best for |
|---|---|---|---|
| naive | Vector embeddings only | Lowest | Quick fact lookup |
| local | Entities + relationships | Low | Specific entities |
| global | Community-level context | High | Broad understanding |
| hybrid | Local + global | Highest | Comprehensive answers |
Storage
Data stored in ~/.openclaw/workspace/lightrag_storage/ by default. Override with LIGHTARG_WORKING_DIR env var.
Integration Pattern
For agent memory systems, index files on change and query on demand:
# Check if reindex needed (files modified since last index)
find MEMORY.md memory/ -name '*.md' -newer memory/lightrag-last-index.txt
# Reindex if needed
python3 scripts/rag.py index
touch memory/lightrag-last-index.txt
# Query when context needed
python3 scripts/rag.py query "user preferences" --mode naiveArchitecture
- Embeddings:
text-embedding-3-small(1536 dim) via OpenAI-compatible API - LLM:
gpt-4o-minifor entity extraction and answer generation - Storage: JSON-based vector DB + GraphML knowledge graph
- Batching: 8 items per embedding batch, 4 concurrent async requests
Troubleshooting
OPENAI_API_KEY not set: Ensure env vars are exported or .env exists.
numpy RuntimeError (X86_V2): On older CPUs lacking AVX2, install pip install "numpy<2.0".
Slow first index: Initial indexing processes all files. Subsequent updates are incremental.
Reset storage: rm -rf <storage_dir>/* && python3 scripts/rag.py index