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Showing 9 open source projects for "binary"
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Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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1
rtk
CLI proxy that reduces LLM token consumption
...By minimizing the amount of noise sent to the AI model, the tool improves reasoning quality and allows longer development sessions within the same context window. The system is implemented as a lightweight Rust binary that runs locally and integrates easily with common AI coding environments. -
2
Crabtalk
Agents daemon that hides nothing
Crabtalk is a composable AI agent runtime designed to provide a minimal yet powerful foundation for building and orchestrating intelligent agents within a single lightweight binary. It is implemented in Rust and focuses on delivering high performance, reliability, and low overhead compared to more complex agent frameworks. The system is built around a small set of core primitives, including skills, memory, context isolation, and extensions, which together enable flexible and modular agent behavior. CrabTalk emphasizes simplicity by avoiding unnecessary abstractions, allowing developers to maintain full control over how agents operate and interact with their environment. ... -
3
yek
Serialize repositories into LLM-ready context w/ smart prioritization
Yek is a Rust-based CLI tool designed to serialize text-based files from a repository or directory into a single structured output for large language model use. It scans projects using .gitignore rules to exclude irrelevant files and automatically filters out binary or oversized content. Yek prioritizes files based on Git history, placing more important content later in the output to align with how language models process context. Yek supports multiple directories, individual files, and glob patterns, making it flexible for different workflows. It can stream output when piped or save results to a temporary file, depending on usage. ... -
4
Ante
Ghost in your shell. Ante is a self-contained agent harness
Ante is a self-contained terminal coding agent written in Rust and designed to work with many AI models rather than one vendor. It ships as a roughly 15 MB binary with no external runtime dependencies. Users can work through an interactive terminal interface, headless commands, a server protocol, or Slack and Discord gateways. A built-in inference engine can run GGUF models entirely offline without an account, API key, or internet connection. Ante also supports more than a dozen hosted providers and can switch between commercial, open-weight, and local models. ... -
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5
Moltis
A Rust-native claw you can trust
Moltis is an open-source personal AI assistant platform written in Rust that is designed to run as a fully self-hosted, local-first agent environment. It compiles the entire assistant stack, including the web interface, model routing, memory, and tools, into a single self-contained binary with no external runtime dependencies. The system supports multiple large language model providers alongside local models, enabling users to maintain privacy while still accessing cloud capabilities when needed. Moltis emphasizes security through sandboxed execution environments, where commands and browsing tasks run in isolated containers and require explicit approval. ... -
6
shimmy
Python-free Rust inference server
The shimmy project is a lightweight local inference server designed to run large language models with minimal overhead. Written primarily in Rust, the tool provides a small standalone binary that exposes an API compatible with the OpenAI interface, allowing existing applications to interact with local models without significant code changes. This compatibility enables developers to replace remote AI services with locally hosted models while keeping their existing software architecture intact. Shimmy focuses on performance and simplicity, using efficient runtime components to minimize memory usage and startup time compared to heavier inference frameworks. ... -
7
ZeroClaw
Fast, small, and fully autonomous AI assistant infrastructure
...It is designed around a trait-based architecture so that model providers, communication channels, memory systems, and tooling integrations can be swapped or extended without rewriting core components, giving engineers flexibility and long-term maintainability. The framework features a compact single binary with fast cold and warm startup times and very low memory overhead, making it suitable even for resource-constrained hardware like small servers or edge devices. Security is a first-class concern, with sandbox controls, encrypted secrets, allowlisted operations, and scoped filesystem access by default, helping reduce risk when running autonomous agents. -
8
herdr
Agent multiplexer that lives in your terminal
...The sidebar shows whether agents are blocked, working, done, or idle, so large multi-agent workflows are easier to supervise. Sessions continue running after detach, which allows users to reconnect later from another terminal or over SSH. The project is distributed as a lightweight Rust binary for Linux, macOS, and beta Windows support. It also includes a local socket API and CLI so agents can create panes, read output, and orchestrate terminal workflows themselves. -
9
ModelFox
ModelFox makes it easy to train, deploy, and monitor ML models
...Learn about your models and monitor them in production from your browser. ModelFox makes it easy to train, deploy, and monitor machine learning models. You can install the modelfox CLI by either downloading the binary from the latest GitHub release or by building from source. Train a machine learning model by running modelfox train with the path to a CSV file and the name of the column you want to predict. The CLI automatically transforms your data into features, trains a number of linear and gradient boosted decision tree models to predict the target column, and writes the best model to a .modelfox file. ... -
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