context-window

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?

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

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

Documentation snippets for LLM context injection

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

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

[ICLR 2025] Official code repository for "TULIP: Token-length Upgraded CLIP"

A discovery and compression tool for your Java codebase. Creates a knowledge graph for a LLM context window, efficiently outlining your project #LLM #AI #Java #CodeAnalysis #ContextWindow #DeveloperTools #StaticAnalysis #CodeVisualization

🚀 Intelligent Claude Code status line with multi-provider AI support, real-time token counting, and universal model compatibility. Supports Claude (Sonnet 4: 1M, 3.5: 200K), OpenAI (GPT-4.1: 1M, 4o: 128K), Gemini (1.5 Pro: 2M, 2.x: 1M), and xAI Grok (3: 1M, 4: 256K) with verified 2025 context limits.

Information on LLM models, context window token limit, output token limit, pricing and more.

A visualization website for comparing LLMs' long context comprehension based on the FictionLiveBench benchmark.

A tool that analyzes your content to determine if you need a RAG pipeline or if modern language models can handle your text directly. It compares your content's token requirements against model context windows to help you make an informed architectural decision.

Tezeta is a Python package designed to optimize memory in chatbots and Language Model (LLM) requests using relevance-based vector embeddings. This, in essence, provides support for using much longer conversations and text requests than supported by the context window.

An autonomous context window management implementation.

Contains a bunch of LLM functions that are useful in LLM application development.

Build reliable LLM applications with the 12-Factor Agents principles. Explore best practices and contribute to our open-source project on GitHub! 🚀🌟

A language model that generates text based on a given prompt.

Effortlessly prepare codebases for LLM prompts. Concatenate, filter, and structure project files into optimal AI context

AI Context Craft: A Python CLI tool to intelligently prepare your codebase for LLMs. It concatenates selected files, generates a project tree, and strips comments to create an optimized, token-efficient context file. Perfect for developers using AI assistants for code analysis, refactoring, or documentation

an efficient, extensible LLM with integrated active self-summarization to simulate extended context windows