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TerminAI
Pricing
- Model
- Free
Summary
AI CLI tools like Codex and Claude Code are sharp — until they start running shell commands you didn't fully read before they executed. Terminai wraps your terminal session so those agents can see context and propose commands, but nothing runs until you say so.
Terminai is an MIT-licensed terminal wrapper that spawns a Ctrl+Space overlay running your existing AI CLI agent alongside your live shell. The agent reads terminal state through a bundled MCP server — no extra installation on the agent side if the CLI supports MCP via flags or environment variables. Bundled presets exist for Codex and Claude Code; anything else requires manual MCP configuration. All suggested shell input queues for explicit confirmation before execution. The vendor states no data collection and no outgoing network connections from Terminai itself. It is self-described as alpha-quality software.
Bottom line: The right pick if you already use Codex or Claude Code and want a confirmation gate around their shell access — not the right pick if your agent CLI cannot consume an MCP server URL, since the zero-install MCP path simply does not work without that flag support.
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Pros
Sign in to edit- Approval-gated shell writes mean every command the agent proposes sits in a queue until you confirm, so you cannot accidentally execute a destructive command because you were reading something else.
- Terminal state is passed to the agent automatically via the MCP server, so you skip the copy-paste-context loop that breaks focus during debugging sessions.
- Bundled presets for Codex and Claude Code handle MCP wiring without manual configuration, which means you go from install to working overlay without reading protocol docs.
- No model credentials, no provider routing, and no outgoing network connections from Terminai itself — so adding this wrapper does not introduce a new data-handling dependency to audit.
- MIT license and self-hosted by definition, so there is no vendor lock-in and no paid-only gate on any feature the tool offers.
Cons
Sign in to edit- Zero-install MCP configuration only works if your agent CLI supports MCP server settings via CLI flags or environment variables — CLIs that do not expose those flags require manual wiring, and CLIs that have no MCP support at all cannot use the context-passing feature at all, which is the core reason to use this tool over a split-pane workaround.
- The tool is self-described as alpha-quality and Linux/macOS only, so any team operating on Windows or needing production-grade stability guarantees is blocked at the install step — those teams reach for a different agent framework or wait for a stable release.
- Custom CLI integration past the bundled Codex and Claude Code presets requires reading the config docs and manually pointing Terminai at the CLI, which means teams running less common agent CLIs are doing their own MCP integration work rather than using a supported path.
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About
- Platforms
- Linux, macOS
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- 2026-07-08T12:46:43.054Z
Best For
Who it's for
- Developers who already use Codex, Claude Code, or similar CLIs
- Users wanting minimal-disruption AI assistance in the terminal
- Workflows requiring explicit approval before any shell changes
What it does well
- Invoking AI agents inside an existing terminal session for explanations or assistance
- Safely letting agents propose and confirm shell commands
- Integrating Codex or Claude Code with full terminal context via MCP
Integrations
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Frequently Asked Questions
- Is TerminAI free?
- Yes — TerminAI is fully free to use. There is no paid tier.
- Is TerminAI open source?
- Yes. TerminAI is open source.
- Can I self-host TerminAI?
- Yes. TerminAI supports self-hosting on your own infrastructure.
- What platforms does TerminAI support?
- TerminAI is available on: Linux, macOS.
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Curated lists that include this category
Most terminal-based AI agents operate on a trust model that makes engineers nervous: the agent reads, the agent writes, and the only checkpoint is your reaction speed. Terminai addresses this by acting as a transparent wrapper around your existing shell session. You work in the terminal exactly as you normally would; pressing Ctrl+Space opens an overlay terminal running your preferred AI CLI agent — Codex, Claude Code, or a custom CLI — with read access to the current terminal state and write access that is gated behind your approval. Proposed shell commands queue rather than execute, and you confirm each one before it reaches your actual shell.
The architectural differentiator is the bundled MCP server. Terminai exposes your terminal state to the agent through the Model Context Protocol, which means the agent can see what commands have run, what output appeared, and where the session currently stands — without you copying and pasting context manually. Bundled presets handle the MCP wiring for Codex and Claude Code automatically. For other CLIs, the docs describe a manual configuration path, contingent on the target CLI supporting MCP server settings via CLI flags or environment variables.
Terminai fits tightly into a specific scenario: developers who already have a working Codex or Claude Code setup and want to add an approval layer without rebuilding their workflow. It does not own your model credentials, does not route API calls, and according to the vendor makes no outgoing network connections of its own. The tool is Linux and macOS only, self-described as alpha-quality, and the author explicitly recommends keeping an easy path to a regular shell as a fallback. Teams who need Windows support, a GUI-based approval interface, or agent CLIs that do not support MCP flags will hit hard stops quickly.
Installation is via Homebrew, cargo build from source, or direct GitHub release download. The project is MIT licensed and hosted at github.com/emosenkis/terminai. The MCP layer is built on a fork of mprocs and relies on custom forks of Ratatui and rat-salsa to preserve native scrollback and copy behavior — behavior the vendor notes was one of the parts AI struggled with most during the tool’s own development.