llm-context

Context management for long-context LLMs, agents, and vibe coding. Instantly build context for an entire repo, selected files, folders, and GitHub issues to generate structured AI-XML context with real-time token counting.

Open-source Rust TemporalStore for LLM context management, temporal features, and risk control

CLI tool to produce MD context files from many sources, to help interact with LLMs (ChatGPT, Llama3, Claude, etc.).

export any codebase to a single LLM-ready markdown prompt

Persistent, governed memory for chat clients, IDEs, & agents. Share memory across clients without losing scope, provenance, or auditability.

Give any local LLM a billion-token memory. Open-source, local-first context + memory engine for Ollama and AI agents — runs on your own machine, free. 🧠⚡

CodeStory is a codebase grounding engine that preindexes code into a knowledge graph and enriches it with semantic context. Paired with coding agents, it results in fewer tokens, fewer tool calls, and remains 100% local.

Rolling context compression for Claude Code — never hit the context wall. Auto-compresses old messages while keeping recent context verbatim. Zero config, zero latency. Works as a Claude Code plugin.

Build safe, token-efficient codebase context for LLMs, AI chats, and coding agents — local-first GUI, TUI, CLI, and a read-only MCP server with Smart Ignore, secret/PII redaction, Git scopes, and code compression.

Memory management and retrieval for Claude Code

converts webpage content into Markdown format, optimized for LLM training and context

An MCP server that provides tools for exploring large OpenAPI schemas

Context-Optimized Memory Bank — Reduce AI token usage with structured documentation and cache-aware reading strategies

Research repository for TokenMizer, a graph-structured session-memory system for long-horizon LLM context management. This repository contains the benchmark suite, the labelled evaluation corpus, all raw results, and the manuscript for the associated paper.

AI Context Kit provides a structured, instruction-based framework for context-aware AI collaboration across LLM providers. It includes authoritative specs, canonical templates, and skills workflows for creating and validating user context and project AGENTS.md files.

🪦 让死代码与墓碑技能真正入土为安，晒出你的减法战绩，终结 AI 时代的上下文污染。

Clone any GitHub repo and flatten it into a file-tree diagram plus one consolidated code file - quick repo context for LLMs

Shared memory layer for AI coding tools. One local SQLite DB, every tool reads the same verified facts. Local-first, zero cloud.

Agent-readable knowledge base for reproducing and operating embodied-AI simulation toolchains. Five-layer docs (quickstart / concepts / field notes / troubleshooting / code), plus a cross-project layer of reproduction workflow, issue taxonomy, selection guide, best practices and terminology mapping.

Rust tool to fetch multiple URLs and save them into Markdown files