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

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

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

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

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.

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

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.

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

A Python tool for combining text documents into consolidated files for Large Language Model processing. Creates organized document stacks with configurable sorting and formatting options.

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

An autonomous context window management implementation.