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?

The Context Optimization Layer for LLM Applications

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

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.

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

Supercharge AI Agents, Safely

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

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

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

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

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

Documentation snippets for LLM context injection

Entroly helps AI coding tools like Cursor, Copilot, and Claude Code use the right context from your entire codebase—improving output quality while reducing token usage.

Transform and optimize your markdown documentation for Large Language Models (LLMs) and RAG systems. Generate llms.txt automatically.

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

Building Agents with LLM structured generation (BAML), MCP Tools, and 12-Factor Agents principles

Optimize AI workflows with Arachne. Automatically assembles the perfect code context (Tree, Target, Deps, Semantic) to fit context windows without noise. Built for efficiency and scale.

Grab, filter, and bundle your codebase for Claude and ChatGPT right from your terminal.

A lightweight tool to optimize your C# project for LLM context windows by using a knowledge graph | Code structure visualization | Static analysis for AI | Large Language Model tooling | .NET ecosystem support #LLM #AI #CSharp #DotNet #CodeAnalysis #ContextWindow #DeveloperTools

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