kbolt

kbolt is a local-first retrieval engine for indexing local notes and docs and searching them with keyword, semantic, reranked, and deep retrieval modes.

Install

macOS and Linux x86_64 with Homebrew:

brew install h3nock/kbolt/kbolt

Rust users on macOS, Linux, or Windows:

cargo install kbolt

Prebuilt binaries are also available from GitHub Releases.

If llama-server is not already installed, follow the official llama.cpp install guide.

Quick start

Set up the default local retrieval stack:

kbolt setup local
kbolt doctor

kbolt setup local downloads the default local embedder and reranker models, starts managed llama-server processes, and writes the local provider bindings into the kbolt config directory.

Add a folder of notes or docs:

kbolt collection add /path/to/docs --name my_docs

Search the indexed content:

kbolt search "rust error handling"

Search modes

Default:

• kbolt search "query"
• hybrid retrieval with keyword + semantic + reranking

Other supported modes:

• kbolt search "query" --keyword for keyword-only retrieval
• kbolt search "query" --semantic --no-rerank for semantic-only retrieval
• kbolt search "query" --deep for query expansion plus reranked retrieval

kbolt setup local configures the default local embedder and reranker. To enable deep search later:

kbolt local enable deep

What kbolt supports

• Index Markdown and plaintext files from one or more local directories
• Group collections into spaces and scope search with --space or --collection
• Search with keyword, semantic, hybrid reranked, and deep retrieval modes
• Read underlying source files with kbolt get, kbolt multi-get, and kbolt ls
• Check readiness with kbolt doctor and kbolt models list
• Serve the index to agents over MCP with kbolt mcp
• Run retrieval benchmarks with kbolt eval ...
• Schedule recurring re-indexing with kbolt schedule ...

This crate is the main user-facing package in the workspace. Most users should install and run kbolt, not the internal kbolt-core, kbolt-mcp, or kbolt-types crates directly.