- name
- literag
- version
- 0.2.2
- description
- Local retrieval skill for large documentation corpora using independent SQLite knowledge libraries with keyword plus vector hybrid search. Use when searching Blender manuals, API references, SDK docs, framework docs, product docs, blog/article archives, exported markdown doc sets, or any other large external documentation that should not live in OpenClaw's main memory index. Also use when indexing, reindexing, debugging retrieval quality, checking index compatibility/status, or inspecting LiteRAG sqlite metadata. Usage: /literag search <library> <query> | /literag inspect <library> <path> [--start N --end N] | /literag index <library> | /literag status <library> | /literag meta <library> | /literag benchmark <library> --query ...
- user-invocable
- true
- homepage
- https://github.com/little-jax/literag
- metadata
- { "openclaw": { "emoji": "📚", "requires": { "bins": ["python3"] } } }
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.txtLayout
- Config + databases live under
<workspace>/.literag/ - Main config:
<workspace>/.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, andscripts/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
<workspace>/.literag/knowledge-libs.jsonwhen targeting a library or changing config - Read
references/usage.mdwhen you need command examples, output schema, or the intended search → inspect workflow - Read
references/configuration.mdwhen adding libraries, source roots, excludes, chunking overrides, or ranking overrides - Read
references/agent-prompts.mdwhen another agent / ACP harness needs a ready-made LiteRAG prompt template - Read
references/optimization-playbook.mdwhen 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 <library> <query>/literag inspect <library> <path> [--start N --end N]/literag index <library> [--limit-files N] [--embedding-batch-size N]/literag index-all [--limit-files N] [--embedding-batch-size N]/literag status <library>/literag meta <library>/literag benchmark <library> --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 libraryindex_all.py— index all configured librariessearch_library.py— grouped hybrid/fts/vector retrievalinspect_result.py— expand a hit by file path + chunk rangestatus_library.py— show index health / compatibility / countsmeta_library.py— dump raw sqlitemetarecordsbenchmark_library.py— benchmark hybrid/fts/vector latency + hit shape across fixed query setsbin/literag— packaged CLI entrypoint for search / inspect / index / status / meta / benchmarkscripts/literag-query.py— query/search/inspect wrapperscripts/literag-index.py— index wrapper for one library or all librariesscripts/literag-status.py— status wrapperscripts/literag-meta.py— meta wrapperscripts/literag-benchmark.py— benchmark wrapperscripts/lq— tiny shell alias forliterag-query.py
Operating workflow
- Read
<workspace>/.literag/knowledge-libs.json - Resolve the target library
- Run
search_library.pyfor grouped retrieval - If needed, run
inspect_result.pyon the top hit or chosen range - For quick operator use, prefer
scripts/literag-query.pyorscripts/lq - Use
scripts/literag-index.pywhen you need a short indexing entrypoint - Use
scripts/literag-status.pybefore debugging weird retrieval or after config changes - Use
scripts/literag-meta.pywhen you need the raw stored metadata - Use
scripts/literag-benchmark.pyorskills/literag/scripts/benchmark_library.pywhen you need repeatable retrieval latency / hit-shape comparisons - 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