All 22 Dependencies
Every package sirchmunk depends on, ranked by repo health score.
Sirchmunk is an embedding-free, indexless retrieval system that transforms how AI agents and developers search across large, heterogeneous document collections. Rather than pre-processing documents into fixed-dimensional vector representations, Sirchmunk queries raw files directly using ripgrep-all under the hood and then applies Monte Carlo evidence sampling to identify the most relevant regions of interest within each file. This eliminates hours-long indexing pipelines and removes the need for a vector database entirely.
At the core of Sirchmunk is a self-evolving knowledge base. As the system processes queries, it builds and refines knowledge clusters — structured groupings of evidence units organized by abstraction level (technique, principle, paradigm) and lifecycle state (emerging, stable, contested). These clusters persist and grow across sessions, so retrieval quality improves over time without requiring a full re-index when data changes.
Sirchmunk supports two main search modes — FAST for rapid file-level retrieval and DEEP for multi-pass evidence synthesis — along with an offline `compile` command introduced in v0.0.8 that pre-builds hierarchical tree indices and knowledge clusters for even greater precision. The system exposes a FastAPI backend, a Next.js web UI, a CLI, and an MCP server, making it composable within multi-agent workflows and existing developer toolchains.
The project targets developers, researchers, and AI engineers who need immediate, high-fidelity access to large local document repositories or mixed-format corpora without committing to a heavyweight vector infrastructure. It supports Python 3.10+ and ships as a pip package with Docker images for amd64 and arm64 architectures.
