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context-memory · GitHub Topics · GitHub

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context-memory

Here are 37 public repositories matching this topic...

ByteRover CLI (brv) - The portable memory layer for autonomous coding agents (formerly Cipher)

  • Updated Jun 25, 2026
  • TypeScript

Engineering decisions engine that know when they're stale. Frame, compare, decide — with evidence decay and parity enforcement. For Claude Code, Cursor, Gemini CLI, Codex and more.

  • Updated Aug 11, 2026
  • Go

Persistent Knowledge Graph Memory for AI Coding Agents. Adds long-term context (goals, strategies, preferences) to Cursor, Windsurf, & VS Code via MCP.

  • Updated Jan 21, 2026
  • Python

The Context Architecture Bundle Repository is a copyable project context layer for multi-agent systems. It helps Codex, Claude, Gemini, and other coding agents restart cleanly, understand the system, change the right files, and close sessions with evidence instead of relying on scattered chat history.

  • Updated Jun 12, 2026
  • Python

Your AI remembers what it did, proves it, and gets smarter every session

  • Updated Apr 29, 2026
  • Go

AronaOS is an offline AI-powered personal assistant that helps you manage tasks, set reminders, and recall context-aware conversations. Built with Python, Flask, and local LLMs like Phi-3 Mini, it's designed for both privacy and productivity.

  • Updated May 27, 2025
  • Python

Dynamic RAG Engine for AI Reliability. We provide mathematically scored context & sanitized data to prevent hallucinations in both static & volatile domains (starting with Korean Finance).

  • Updated Jan 31, 2026
  • JavaScript

Titan-Memory , it is a memory system that is not only for memory, but it also has built-in safety guardrails, circuit-breakers, semantic adapters, and drift protection. Everything you need to run Agents safely. Built-in.

  • Updated Aug 14, 2026
  • TypeScript

From one prompt to a finished product — fully autonomous. Auto-plans, remembers across sessions (persistent context), builds end-to-end, audits security & performance, deploys. 18 agents + 39 workflows for ChatGPT, Copilot, Codex, Cursor, and other AI coding tools. Built by NexVar.

  • Updated Apr 25, 2026
  • JavaScript

Code to make any AI have unlimited context persistent memory. In the example, a software for any AI to read the Uniform Commercial Code of Michigan. A document of 220,000 tokens

  • Updated Jan 21, 2026
  • JavaScript

Persistent memory scaffolding for AI agents - Trail, Destination, Improve, Retrospect, Intent, Probe. Self-built across 221 iterations; declared complete only when GPT, Claude, and Gemini independently found nothing left to change.

  • Updated Aug 19, 2026
  • PowerShell

CLI tool to persist and restore AI coding context across sessions, editors, and teams.

  • Updated Feb 22, 2026
  • TypeScript

Persistent context index for AI coding agents

  • Updated Feb 5, 2026
  • TypeScript

ShrimPK kernel — Echo Memory prototype for AI models

  • Updated Jun 19, 2026
  • Rust

High-performance Go tools for LLM Agents (MCP/CLI). 100x faster than Python. Includes native MCP Server, token-optimized search, and persistent project memory.

  • Updated Aug 19, 2026
  • Go

Track and save AI coding context across sessions and team members to maintain intent history and reduce repeated explanations.

  • Updated Aug 18, 2026
  • TypeScript

A high-performance RAG (Retrieval-Augmented Generation) system designed for deep document analysis and persistent context awareness.

  • Updated Apr 29, 2026
  • TypeScript

GLACIER: Mamba with infinite memory. This project integrates the Mamba SSM with ICE-Lite, a virtual memory engine, to solve context rot. By adding persistent, time-aware memory, GLACIER gives Mamba the long-term recall of a Transformer while retaining its $O(N)$ speed. Apache 2.0 licensed, by Dopove.

  • Updated May 22, 2026
  • Python

Qontext — a quipu-inspired conversational memory

  • Updated Aug 13, 2026
  • Python

Real work, fully auditable, in one file: a standalone, target-agnostic improvement-reasoning skill for LLM agents - examines and improves anything the model can reason about (code, documents, plans, letters) while recording every reasoning step in an auditable trail. Single markdown file, no installer, built on the Principles of Earned Autonomy.

  • Updated Aug 17, 2026

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