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A terminal AI coding agent for editing code, running commands, and using tools.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Fuxi" yet — see the docs or source repo.
Please inspect the login module in the current project, fix obvious type errors, and explain which files you changed.
The agent analyzes the code in the terminal environment, edits relevant files, and returns a summary of changes.
Run the tests, identify why they fail, and try to fix the most likely issue.
The agent runs test commands, reviews the results, and either fixes the code or provides a diagnosis.
Complete this code refactoring task in a more cost-efficient way while maintaining output quality.
The agent uses cost-aware routing across LLM providers and completes the refactoring task.
Developers can use it in the terminal to edit code, run commands, and help troubleshoot issues without constantly switching tools. It fits fast-moving day-to-day development work.
When a task involves chaining command execution and tool usage, it can act as an AI agent inside the terminal to move the workflow forward. It is especially useful for command-line-centric engineering tasks.
If a team uses multiple LLM providers for coding assistance, its cost-aware routing can help balance quality and spend. It suits development or DevOps scenarios where inference cost matters.
Fuxi is a fast, self-contained AI coding agent that runs in the terminal. It can edit code, run commands, and drive other tools.
Based on the description, it supports cost-aware routing across LLM providers. For the exact providers supported, see the source repository.
The provided material does not include installation steps or dependency requirements. For runtime, API keys, or setup details, see the source repository.
Use an AI terminal coding agent for editing, debugging, and multi-tool development.
Build a personal AI assistant to connect tools and automate local work.
Coordinate AI agents across terminals with messaging, watching, and task spawning.
Manage terminal sessions for multiple AI coding agents in one unified interface.
Enable token-efficient web retrieval and content fetching for AI agents.
Route scoped AI agent tasks across providers with monitoring and notifications.