(ACL 2025 Main) A Comprehensive Benchmark for Code Information Retrieval.
State aware knowledge compression, ingestion, and hybrid retrieval engine. Zero dependencies. Sub-100ms queries.
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.
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.
Semantic code search MCP server for AI agents: hybrid retrieval, incremental sync, and a constrained 4-tool interface.
Semantic Language-Indexed Code Extraction with Backward Slicing for Repository-Scale Code Generation
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%.
A hpc LLVM Pass extracting semantic Control-Data Flow Graphs (CDFG) from Intermediate Representation for Graph Neural Networks. Enables cross-language code retrieval and clone detection beyond token-based approaches.
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.
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