Open source AI terminal for cloud and infrastructure management, enabling you to deploy, troubleshoot, and automate services using natural language and intelligent agents.
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Table of Contents
- Introduction
- Why Choose Chaterm
- Key Features
- Development Guide
- Chaterm Documentation
- Gold Sponsors
- Acknowledgements
- Contributors
Introduction
Chaterm is an AI native terminal built for infrastructure and cloud resource management. It enables engineers to use natural language to perform complex tasks such as deploying services, troubleshooting, and resolving problems.
With its built-in expert-level knowledge base and powerful proxy reasoning capabilities, Chatem understands your business topology and operational goals. No need to memorize complex commands, syntax, or parameters. simply describe your task objectives in natural language, and Chatem can autonomously plan and execute complex operations across multiple hosts or clusters, including key processes such as code building, service deployment, fault diagnosis, and automatic rollback.
Powered by long-term memory and team knowledge bases, Chaterm learns team knowledge and user habits. Its goal is to be your intelligent DevOps co-pilot, allowing engineers to complete daily tasks more efficiently through reusable agent skills. Chaterm aims to lower the cognitive barrier associated with different technology stacks, enabling every developer to quickly gain the operational experience and execution capabilities of a senior SRE.
Why Choose Chaterm
Chaterm is not just a smarter terminal, it's an infrastructure agent. There is a saying that every agent fails all the time, but Chaterm helps you fix it.
🤖 From commands to execution — describe tasks, let AI plan and run them
🌐 Built for real infrastructure — Servers, Kubernetes, and multi-cluster workflows
🔁 Reusable Agent Skills — turn experience into automation
🧠 Context-aware intelligence — understands your systems, not just commands
🛡️ Safe & controllable — auditable, reviewable, and rollback-ready
Key Features
🤖 AI Agent
The Agent understands the target, autonomously plans, and performs problem analysis and root cause localization across multiple hosts, automatically closing the loop to complete complex process handling.
Every operation is auditable and traceable, and supports rapid log rollback, making AI automation more secure and reliable in production environments.
🧠 Smart completion
Combining user habits, local memory, and the current server context, the Agent recommends the most suitable commands, making terminal input smarter and more efficient.
Optional local echo renders safe interactive typing immediately on high-latency SSH sessions while suppressing the confirmed remote echo.
Supports cross-device session synchronization and reduces mobile input costs through quick commands and voice interaction, making remote maintenance smoother.
🧩 Knowledge Base
Supports importing technical manuals, internal documents, scripts, and white papers to build a personal maintenance knowledge system.
Chaterm understands the current infrastructure context and accurately retrieves relevant knowledge to assist in task decision-making and execution.
Hybrid vector and keyword retrieval uses RRF fusion to combine both recall channels into a unified result ranking.
Retrieval progress shows the live embedding, hybrid search, and reranking stages. New configurations default to Qwen-Plus in the China edition and Gemini 2.5 Flash in the global edition for faster reranking.
⚡ Agent Skill
Encapsulates complex maintenance processes into reusable AI skills, achieving structured and reliable automated execution.
Help teams accumulate operational experience, enabling AI to be applied securely and stably in real production environments.
🔌 Plugin System
Through plugin extensions, achieve unified authentication, dynamic authorization, and secure encrypted connections for public cloud servers and Kubernetes.
Provide a more efficient resource access experience and facilitate centralized infrastructure management.
🗄️ Database Workspace
Connect to MySQL, PostgreSQL, SQLite, and Oracle to browse schemas, query data, inspect DDL, edit table rows, and use database-aware AI assistance.
⚙️ Per-model limits
Configure the context window and maximum output tokens for each custom model in Settings → Models. These limits are used for both context management and API requests.
Development Guide
Install Electron
npm i electron -D
Install
node scripts/patch-package-lock.js
npm install
If Electron reports a native module ABI mismatch, rebuild native dependencies for Electron instead of the local Node.js runtime:
npm run rebuild:native
Do not use npm rebuild better-sqlite3 for Electron runtime fixes; it can compile the module against the wrong Node ABI.
Development
npm run dev
Build
# For windows
npm run build:win
# For macOS
npm run build:mac
# For Linux
npm run build:linux
Gold Sponsors
Acknowledgements
Chaterm builds upon excellent open-source projects. We gratefully acknowledge:
- Cline — portions of the AI Agent system are adapted from Cline.
- RTK — command output filtering to reduce token usage in agent workflows.
Contributors
Thank you for your contribution! Please refer to the Contribution Guide for more information.