github.devotd.
NewAI agents

xerj-org/xerj v1.0.0-rc.97 — A Local-First Search Engine for Agents

xerj-org/xerj v1.0.0-rc.97 is a Rust search engine and MCP server that indexes code, documents, and logs locally to provide BM25, vector, and hybrid search along with agent memory and RAG.

Repositoryxerj-org/xerj ↗

xerj-org/xerj v1.0.0-rc.97 is a Rust search engine and MCP server that indexes code, documents, and logs locally to provide BM25, vector, and hybrid search along with agent memory and RAG.

GitHub description: Local-first search engine and MCP server for AI agents. One Rust binary indexes code, docs, logs, JSONL/CSV and PDFs for BM25 full-text, vector and hybrid search, code search without grep, agent memory and RAG. Elasticsearch-compatible API; Corpus Hub serves hundreds of reference memory corpora.

stars
★ 11K
Forks
695
Language
Rust
License
Apache-2.0
Latest release
v1.0.0-rc.97
Read original source ↗View on GitHub ↗

xerj-org/xerj is a local-first search engine for agents and an MCP server. MCP is the standard interface agents use when calling external tools. The latest release tag is v1.0.0-rc.97, where rc stands for release candidate. No changes are listed for this version.

A single Rust binary indexes code, documents, logs, JSONL, CSV, and PDF locally, providing BM25 full-text search, vector search, hybrid search, code search that works without grep, plus agent memory and RAG all in one. BM25 is a full-text search method that ranks documents by term frequency and document length, while vector search finds documents that are semantically close. RAG is the approach of answering questions based on the documents gathered through retrieval.

The search API is compatible with Elasticsearch, and Corpus Hub provides hundreds of reference memory corpora. The repository is written in Rust, is licensed under Apache-2.0, and was created on 2026-06-30. It has 10,975 GitHub stars.

However, the README states that the insecure execution option grants index read and delete permissions to any caller that can reach it, and warns that it should be used only in local development environments. The provided information contains no figures comparing search quality or performance, and in the 10-point scale rating only the momentum item remains, at 8.0. So it is close to a viable option for managing agent memory and search together locally, but it is hard to judge from this material alone what level of accuracy and performance it reaches.