code-search

PatchHolmes, accepted at AACL-IJCNLP 2026, uses an agentic listwise selection approach to pair software vulnerabilities with their fixing commits, achieving 59.95% Recall@1 versus 34.61% for pointwise baselines by having the agent inspect the full candidate list and reading ~96k tokens per query on a frozen open-weight model.

jevgrep 0.5 is released with efficiency optimizations, reducing cost by 59% by returning less data while maintaining the same intelligence for AI-assisted code search in repositories.

The author accidentally developed a local retrieval tool for debugging in agentic AI software that efficiently narrows down code functions to find bugs, and is seeking more testing ideas to validate its effectiveness.

This paper empirically studies contrastive pretraining with synthetic semantic supervision for code embeddings in small transformers, showing significant gains over baselines and competitiveness with larger models.

ZvecAI has open-sourced zg (zvec-grep), a local-first search tool that integrates semantic search, BM25, and ripgrep for efficient indexing and retrieval on-device, designed for both humans and AI agents.

Introducing Repofetch, a developer tool that allows interaction with GitHub repositories via agentic search powered by mixedbreadai's toast-1 model for efficient code queries.

Inventory is a product that lets you search across every AI Agent and IDE conversation, making it easier to find past interactions and code discussions.

GitHub engineering describes how they optimized case-folding for their code search engine by removing early-exit branches, achieving memory-speed ASCII folding, and open-sourcing the result as a Rust crate called casefold.

This paper presents fully open DenseOn and LateOn retrieval models, trained on curated English data and extended to multilingual settings via translate-train, achieving state-of-the-art BEIR results for their parameter size.

ast-grep rewrote Tree-sitter's C core in Rust, achieving up to 30% faster parsing and 22% faster end-to-end performance in ast-grep, at the cost of slightly higher memory usage.

The Harness Handbook is a behavior-centric representation synthesized from agent harness codebases using static program analysis and LLM assistance, helping developers and coding agents locate code implementing specific behaviors. It introduces Behavior-Guided Progressive Disclosure (BGPD) to guide agents from high-level descriptions to relevant implementation details, improving localization accuracy and edit-plan quality.

Capn-hook is a CLI tool that gives coding agents persistent memory, saving files that answer questions about a codebase so agents don't re-explore the same mysteries across sessions, reducing token usage by 77% on repeat questions.

A blazing-fast, stateless CLI tool written in Go that integrates Web search, code search, and library documentation query. It supports web scraping and site crawling, designed for AI agents and terminal use.

cocoindex-code is an AST-based semantic code search tool that can be quickly integrated into coding agents, saving up to 70% tokens and improving search efficiency.

This paper benchmarks 17 deep learning models for first-stage recall in large-scale code-to-code retrieval, evaluating their precision, efficiency, and scalability across multiple programming languages and datasets. It introduces LLM-based code normalization and query rewriting schemes that improve precision for lower-performing models.

Headroom is an open-source tool that compresses token usage in code search results and AI conversations by up to 92% (e.g., from 17k to 1,400 tokens) while maintaining answer quality. It supports multiple platforms and runs locally for free.

The author built Nice Coding Agent, an open-source coding workbench with a visible and editable context stack, allowing users to curate exactly what the LLM sees. It features local-first retrieval, sandboxed execution, and hybrid code search, aiming to give developers control and visibility over context assembly.

Semble 是一个面向 AI 代理的高效代码搜索库，使用模型如 Model2Vec 或 BM25 实现快速索引和检索，比 grep+read 节省约 98% 的 token，支持 MCP 服务器和 CLI 集成。

Semble is an Agent-oriented code search tool that supports natural language queries, accurately returns semantically complete code snippets, saves 98% token consumption compared to traditional grep+read methods, and features intelligent chunking, dual-path retrieval, and code-aware re-ranking.

Argyph is an open-source MCP server that provides AI coding agents with structured codebase understanding via a symbol graph and semantic search, running entirely locally with no cloud dependencies.