Neurogrid Terminal Coding Agent

nrgrd is NeuroGrid's Terminal User Interface (TUI) for interacting with AI models from your terminal, Claude Code style. Built on rich and prompt-toolkit, it points at any OpenAI-compatible inference endpoint — including a NeuroGrid Marketplace deployment (the URL + API key of the host you rented) — to chat, manage sessions, and run MCP tools.

• Coding agent — An iterative tool-use loop (nrgrd.agent ) that reads, searches, edits, and runs commands in your repository until the task is done.
• Workspace aware — Detects git, language, package manager, and test framework at startup, so the agent doesn't waste turns rediscovering your project.
• Interruptible — Ctrl+C stops generation or a running tool without corrupting the conversation, keeping whatever was produced.
• Named sessions — Several conversations per project: list, resume, rename, delete.
• CLI mode —nrgrd "fix the failing tests" runs headless on the same runtime, for scripts and CI.
• Native tools —list_files ,read_file ,write_file ,edit_file ,search ,shell ,git_status ,git_diff .
• Permissions — Read-only tools run automatically; file writes, shell commands, and MCP tools ask for approval first (or "always allow" for the session).
• Immersive TUI — Rich-rendered panels, streaming output, and slash-command completion.
• Persistent sessions — Saves conversation context and history locally.
• Any compatible endpoint — Behind aModelProvider abstraction: a NeuroGrid deployment, vLLM, Ollama, LM Studio, or anything else speaking the same API. All nrgrd needs is a URL, a key, and a model name.
• Keys kept out of the config file — The API key lives in your OS credential store, and never appears in errors or logs.
• Errors you can act on — Failures name the endpoint and model involved and say what to change.
• Built-in MCP — Run and connect MCP servers straight from the terminal; their tools go through the same permission system.
• CLI + TUI — Usenrgrd for the interactive interface ornrgrd-mcp for the MCP server.

nrgrd runs on macOS, Linux and Windows, with Python 3.11 or newer.

uv tool install nrgrd
nrgrd

uv tool install puts nrgrd on your PATH in its own environment, and downloads a suitable Python if you don't have one. Update with uv tool upgrade nrgrd. If you prefer pipx: pipx install nrgrd.

Don't have uv? See installing uv.

git clone https://github.com/NeuroGrid-AI-exchange/neurogrid-tui.git
cd neurogrid-tui
uv sync
uv run nrgrd
nrgrd                              # interactive TUI
nrgrd "explain this repository"    # one prompt, then exit
nrgrd --model qwen3-coder "fix the failing tests"
nrgrd --yes "run the tests"        # approve gated tools automatically (CI)

The first time you run nrgrd it opens a Connect to a model screen and asks for the three things a NeuroGrid deployment hands you:

Endpoint   https://<your-deployment>/v1
API key    ••••••••••••
Model      qwen3-coder

It then queries the endpoint and lets you pick from the models it actually serves. Run /config edit to change any of this later.

The endpoint and model are saved to config.json in the application's config directory. The API key is not — it goes to your operating system's credential store (Keychain, Windows Credential Manager, or a Secret Service keyring). On machines with no usable keyring it falls back to an owner-only (0600) file, and /config tells you which is in use. A key left over in an older plaintext config.json is migrated out automatically on startup.

No environment variables are involved.

nrgrd/
├── src/nrgrd/
│   ├── app.py         # TUI entry point (rendering only)
│   ├── agent/         # Agent runtime: loop, events, permissions — no TUI dependency
│   ├── tools/         # Tool registry: filesystem, search, shell, git, MCP adapter
│   ├── api/           # ModelProvider abstraction + the OpenAI-compatible one
│   ├── config/        # Configuration and credential storage
│   ├── context/       # Application state, models, token estimates
│   ├── screens/       # The connect / onboarding screen
│   ├── sessions/      # Session handling and storage
│   ├── system/        # System prompts
│   ├── theme/         # Color palette and styles
│   ├── widgets/       # Custom visual components
│   └── workspace/     # Filesystem tools, project discovery, MCP client
├── docs/              # Reports and documentation
├── tests/             # Agent/tool/permission tests (no TUI, no live endpoint needed)
└── pyproject.toml     # Dependencies and project metadata

The agent runtime never imports Rich or Textual — the TUI drives it by iterating agent.run(messages) and rendering the events it yields. That keeps the agent testable headlessly and reusable from a future CLI mode.

Push a version tag. CI runs the tests, builds, checks the built version matches the tag, and publishes to PyPI and GitHub Releases:

git tag v0.1.0
git push origin v0.1.0

The version comes from the tag; there is no version number to edit in pyproject.toml.

uv sync --dev

uv run pytest tests/

uv run ruff check src/
uv run mypy src/

MIT © NeuroGrid