Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 60-95% fewer tokens, same answers. Library, proxy, MCP server.

What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?

Find the ghost tokens. Fix them. Survive compaction. Avoid context quality decay.

[ICML'24 Spotlight] LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning

High-performance code-intelligence engine for AI agents and IDE, supports 257 languages, multi repositories, with access via CLI, MCP Server, and API. AI coding agents teammate - expose only needed information, cutting token usage up to 50x. 100% local.

Give Claude Code photographic memory in ONE portable file. No database, no SQLite, no ChromaDB - just a single .mv2 file you can git commit, scp, or share. Native Rust core with sub-ms operations.

LLM-supervised persistent memory for AI agents — graph-based recall, cross-session knowledge, single binary. Works with Claude Code, OpenClaw, and any CLI agent.

Make your OpenClaw AI agent faster, smarter, and cheaper. Speed optimization, memory architecture, context management, model selection, and one-shot development guide.

CLI proxy that reduces LLM token usage by 60-90%. Declarative YAML filters for Claude Code, Cursor, Copilot, Gemini. rtk alternative in Go.

Supercharge AI Agents, Safely

Open Source Context infrastructure for AI agents. Auto-capture and share your agents' context everywhere.

Config-driven CLI tool that compresses command output before it reaches an LLM context

Hook-based token compressor for 5 AI CLI hosts (Claude Code, Copilot CLI, OpenCode, Gemini CLI, Codex CLI). Up to 95% bash compression, signature-mode for code reads, cross-call dedup, MCP server, self-teaching protocol. Zero runtime deps.

A discovery and compression tool for your Python codebase. Creates a knowledge graph for a LLM context window, efficiently outlining your project | Code structure visualization | LLM Context Window Efficiency | Static analysis for AI | Large Language Model tooling #LLM #AI #Python #CodeAnalysis #ContextWindow #DeveloperTools

Local-first permanent, persistent memory for all agents and humans

A local-first memory layer for AI (Cursor, Zed, Claude). Persistent architectural context via semantic search.

The open source, no-code MCP Server for AI-Native API Access

Claude Code usage governor: compact professional output, context slimming, tool-output filtering, telemetry, and drift guardrails.

A cognition aware database engine for AI agent memory. Purpose built in Rust with WAL, HNSW, knowledge graphs, and speculative context pre assembly. Not a wrapper, a ground up storage engine that thinks.

Inject relevant documentation into your prompts: 98% savings.