headroomlabs-ai/headroom
Headroom is a context compression layer for AI agents, reducing token usage by 60-95% across tool outputs, logs, RAG chunks, files, and conversation history, while maintaining accuracy.
Awesome AI Agents › End-to-End Optimizers
Dynalang is a project that presents an agent capable of leveraging diverse types of language to solve tasks by predicting the future through a multimodal world model. The project is based on the paper "Learning to Model the World with Language," which was presented as an oral presentation at ICML 2024. The repository contains code implementations for training and evaluating the Dynalang agent across various environments, including HomeGrid, Messenger, Vision-and-Language Navigation (VLN), and LangRoom. Each environment requires specific setup instructions, dependencies, and datasets, which are detailed in the README. The HomeGrid environment allows training on tasks related to home settings, while Messenger involves interaction with a more complex environment requiring additional system dependencies. The VLN environment focuses on navigation tasks using the Habitat simulator and includes downloading large datasets like Matterport3D and Room-to-Room. LangRoom introduces a dictionary action space for simultaneous moving and talking, requiring a specific branch of the repository. The project also supports text pretraining and finetuning on datasets like TinyStories, enabling the agent to improve its language modeling capabilities. Training configurations are optimized for multi-GPU setups to handle the computational demands of the world model and policy training. The repository is adapted from DreamerV3, a known reinforcement learning framework, and includes scripts for running experiments, pretraining, and fine-tuning. The project aims to advance the understanding of how language can be used to model and predict the world in a multimodal context, combining language with other sensory inputs to improve task performance. It is a significant contribution to the field of language-based world modeling and embodied AI.
https://github.com/jlin816/dynalang
Headroom is a context compression layer for AI agents, reducing token usage by 60-95% across tool outputs, logs, RAG chunks, files, and conversation history, while maintaining accuracy.
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