🚨 YOUR AI CODING AGENT MAY BE WASTING TOKENS READING CODE IT DOESN'T NEED. What if it could search for the exact solution instead? Meet XERJ — an open-source search engine built for AI agents. 🔍 Instead of making an agent repeatedly grep through files and read entire codebases, XERJ helps it retrieve relevant code, documentation, and solutions. But here's the interesting part. 👇 It can help your coding agent learn from other open-source projects BEFORE it starts building. Here's what XERJ can do: 🔹 Search code by keywords, meaning, or hybrid retrieval 🔹 Retrieve relevant code snippets with file and line references 🔹 Find solutions from similar open-source projects 🔹 Store long-term memory across sessions 🔹 Index code, PDFs, documentation, logs, and structured data 🔹 Connect to AI coding tools through MCP ⚡ AND THE BENCHMARK IS INTERESTING. According to XERJ's published benchmark, on a controlled set of coding tasks: ✅ Grep-driven agent: 16/16 tasks solved ✅ XERJ-assisted agent: 16/16 tasks solved ✅ Output tokens: 2.7× fewer with XERJ The reported comparison was 26,477 output tokens for the grep-driven approach versus 9,982 with XERJ. That's a reported 2.7× reduction in output tokens while maintaining the same solve rate in that test — not a guarantee for every coding project. 🛠️ WHY DEVELOPERS MAY WANT TO EXPLORE IT You get a local-first search engine, MCP support, and a single Rust binary without needing a separate Elasticsearch installation. The bigger idea? Don't make AI agents read everything. Help them find what matters. 🔗 Explore the open-source project on GitHub: github.com/xerj-org/xerj