archex: local-first, deterministic code-context for AI agents — no API key, no telemetry (Apache 2.0)

Reddit r/LocalLLaMA Tools
local-first code-context ai-agents deterministic open-source tree-sitter onnx

Summary

archex is a local-first, deterministic tool that builds token-budgeted code context bundles for AI agents, using a full retrieval pipeline (BM25F, local embeddings, cross-encoder reranker, dependency-graph expansion) on your hardware with no API keys or telemetry, outperforming alternatives in recall and efficiency.

`archex` turns a repo into a ranked, token-budgeted context bundle for coding agents: the symbols, imports, dependency-graph neighbors, and provenance the model needs, assembled before it reasons. It returns context, not an answer — your local model still does the thinking. The thing this sub will care about: it's local-first by design. No hosted inference, no API key in the core, no telemetry. The whole retrieval pipeline (BM25F + local vector embeddings + RRF fusion + a local cross-encoder reranker + dependency-graph expansion) runs on your hardware and is fully deterministic, so results are reproducible across machines and CI. It's a long-running solo project, it predates the recent wave of OSS code-context tools, and I finally got it to a state worth sharing. Retrieval stack runs on your hardware: tree-sitter for parsing (25 languages), ONNX/FastEmbed for local embeddings, optional SPLADE. A BM25-only slim Docker image needs no torch at all. Measured, CI-gated numbers (19-task head-to-head vs cocoindex-code, Apple M1 Pro, same token accounting): - Recall 0.95 vs 0.32 - Token efficiency 0.76 vs 0.48 - Cold start 0 ms vs 4,721 ms (no daemon warm-up) - ~71% fewer returned tokens vs just reading the raw files Telemetry: none, by design.
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