LiteRAG

Use this skill when the target corpus is too large or too noisy for main agent memory.

Install

Packaged dependency install:

python3 -m pip install -r {baseDir}/requirements.txt

Layout

• Config + databases live under /.literag/
• Main config: /.literag/knowledge-libs.json
• Default workspace resolution order: OPENCLAW_WORKSPACE → WORKSPACE → walk upward from the current path until the OpenClaw workspace sentinel files are found
• Core scripts live under skills/literag/scripts/
• Skill bin entrypoint: skills/literag/bin/literag
• Workspace convenience wrappers live at scripts/literag-query.py, scripts/literag-index.py, scripts/literag-status.py, scripts/literag-meta.py, and scripts/lq

Rules

• Keep personal/work memory in OpenClaw builtin memory
• Keep large external corpora in LiteRAG, not memory_search
• Treat each knowledge base as an independent library with its own SQLite
• Search first, inspect second
• Prefer grouped document hits over raw chunk spam
• Prefer source-relative paths when citing files back to the user
• Use local OpenAI-compatible embeddings by default unless explicitly changed in config

Read these files when needed

• Always read /.literag/knowledge-libs.json when targeting a library or changing config
• Read references/usage.md when you need command examples, output schema, or the intended search → inspect workflow
• Read references/configuration.md when adding libraries, source roots, excludes, chunking overrides, or ranking overrides
• Read references/agent-prompts.md when another agent / ACP harness needs a ready-made LiteRAG prompt template
• Read references/optimization-playbook.md when a specific library needs retrieval-quality tuning, ranking cleanup, or indexing-throughput tuning
• Read scripts under skills/literag/scripts/ only when editing behavior or diagnosing bugs

Slash / user-invocable usage

When invoked as /literag ..., parse the remaining argument string as a subcommand.

Supported forms:

• /literag search
• /literag inspect [--start N --end N]
• /literag index [--limit-files N] [--embedding-batch-size N]
• /literag index-all [--limit-files N] [--embedding-batch-size N]
• /literag status
• /literag meta
• /literag benchmark --query ...

If the user gives a natural-language request instead of a strict subcommand, translate it to the nearest supported operation instead of being pedantic.

Supported commands

• index_library.py — index one library
• index_all.py — index all configured libraries
• search_library.py — grouped hybrid/fts/vector retrieval
• inspect_result.py — expand a hit by file path + chunk range
• status_library.py — show index health / compatibility / counts
• meta_library.py — dump raw sqlite meta records
• benchmark_library.py — benchmark hybrid/fts/vector latency + hit shape across fixed query sets
• bin/literag — packaged CLI entrypoint for search / inspect / index / status / meta / benchmark
• scripts/literag-query.py — query/search/inspect wrapper
• scripts/literag-index.py — index wrapper for one library or all libraries
• scripts/literag-status.py — status wrapper
• scripts/literag-meta.py — meta wrapper
• scripts/literag-benchmark.py — benchmark wrapper
• scripts/lq — tiny shell alias for literag-query.py

Operating workflow

1. Read /.literag/knowledge-libs.json
2. Resolve the target library
3. Run search_library.py for grouped retrieval
4. If needed, run inspect_result.py on the top hit or chosen range
5. For quick operator use, prefer scripts/literag-query.py or scripts/lq
6. Use scripts/literag-index.py when you need a short indexing entrypoint
7. Use scripts/literag-status.py before debugging weird retrieval or after config changes
8. Use scripts/literag-meta.py when you need the raw stored metadata
9. Use scripts/literag-benchmark.py or skills/literag/scripts/benchmark_library.py when you need repeatable retrieval latency / hit-shape comparisons
10. Keep LiteRAG separate from builtin memory unless the user explicitly wants a durable summary copied into workspace memory

Current intent

Use LiteRAG for:

• Blender manual + Blender Python reference
• Future blog/article/site knowledge bases
• Any large external docs where hybrid retrieval is needed without polluting builtin memory