Cut AI token costs 95%+ on code exploration. The leading MCP server for precise, symbol-level GitHub code retrieval via tree-sitter AST. Works with Claude Code, Cursor & any MCP client. 313B+ tokens saved.

(ACL 2025 Main) A Comprehensive Benchmark for Code Information Retrieval.

State aware knowledge compression, ingestion, and hybrid retrieval engine. Zero dependencies. Sub-100ms queries.

Verified code context for agents

ContextAtlas — context infrastructure for AI coding agents: hybrid retrieval, project memory and retrieval observability via CLI, MCP server or embeddable library. Tree-sitter indexing, LanceDB vector search, FTS5 and token-aware context packing.

Local code search for AI agents: six fast, purpose-built tools that return ranked answers, not raw grep. Because maybe grep isn't all you need... 🍬

This repository contains the implementations of our experiments and our approach presented in the paper: CoNCRA: A Convolutional Neural Network Code Retrieval Approach

CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval

🌈 Paper-implementations in Code Search (Baseline).

Deterministic method-first retrieval for AI coding agents.

SkeletonGraph is a zero-LLM structural index for AI coding agents. It uses tree-sitter, cross-file call graphs, and PageRank centrality to fetch the exact function your agent needs, saving tokens and turns. Includes an MCP server for Cursor, Claude Code, Copilot, and Windsurf.

Semantic Language-Indexed Code Extraction with Backward Slicing for Repository-Scale Code Generation

Adaptive q-log BM25 for code retrieval under fixed generic tokenization

Semantic code retrieval engine for AI coding agents, with hybrid search, AST-aware chunking, graph expansion, and token-aware context packing.

Structured code retrieval for AI agents — index once with tree-sitter, query symbols precisely via MCP. Cut code-reading token costs by up to 99%.

Agent-safe code retrieval for MCP coding agents: runtime-first search, exact reads, call graph context, and fresh repo evidence before edits.

A production-grade LLM context compression and retrieval engine built entirely on the Python standard library. It solves the single biggest bottleneck in LLM agent effectiveness: context window waste.

AST-aware chunking of code for contextual retrieval into the Unison brain.