context-retrieval

Open-source context retrieval layer for AI agents

Find code by asking what it does. A CLI for coding agents that uses Jev to discover relevant files and source context.

A structural code search engine for Al agents.

📜 An MCP server for conversation history search and retrieval in Claude Code

Open-source terminal AI coding agent with persistent local memory, automatic context recall, and searchable cross-session history.

Jev-powered conversation memory: find past sessions and revisit decisions with original sources. A macOS workspace for OpenAI Codex. Manage AI conversations and agents, explore code with CodeGraph, and work with Git, Ghostty terminals, and Apple Notes in one app.

Deterministic context database for AI agents. Same query → same context, every time.

Trigger-Driven Dynamic Context Loading for Code-Aware LLM Agents

An experimental agentic RPG AI framework with persistent memory, evolving lore, autonomous world updates, and intelligent context retrieval for immersive long-term roleplay.

A modular Python-based search engine pipeline that fetches live web data, extracts both visible and hidden context using advanced NLP techniques, and semantically indexes content for enhanced retrieval. Perfect for powering LLMs and AI agents with up-to-date, relevant context.

Lightweight long-term memory retrieval for chat agents using NPMI + Hebbian learning — no vector database or embedding model required.

Your project remembers across coding agents. Compact, cited project memory for Codex, Claude Code, Cursor, and compatible tools.

Implement retrieval-augmented generation to enhance large language model responses with relevant external data using lightweight, single-header C++ libraries.

Customer support chatbot for construction & home materials companies. Full-stack solution using LangChain + OpenAI LLM to provide context-driven responses from product catalogs. Flask backend, React frontend with chat history integration.

Find code context 4.6x faster and 9.6x cheaper than Pi on Codebase QA.

Examples of RAG (Retrieval-Augmented Generation) with Ethora, LangChain, and OpenAI. Build knowledge-based AI assistants fast. Powered by Ethora Chat Component.

A lightweight document-aware chatbot that can answer questions from PDF, DOCX, or text files using Sentence-Transformer embeddings for context retrieval, LLM models (OpenAI and Ollama here) for answer generation and Gradio chatbot UI for interaction

Deterministic context selection for AI agents: compile a legal context packet, explain every inclusion and exclusion, and reject stale receipts.

Personal context capture and retrieval across web, desktop, and mobile.

CRS-LM: Structure-aware context reduction for tiny language models under Parameter Golf constraints