mahanai 4.4.11

MahanAI: terminal AI agent for NVIDIA NIM (OpenAI-compatible), tools, streaming, /api-key, safety prompts.

MahanAI Super

Terminal AI agent (Super 2.0) with multi-model support, streaming chat, tools, and a built-in Claude CLI mode. Docs: MahanAI.

Install

pip install mahanai
mahanai

Launch Options

Flag	Description
--compact	Compact mode: renders a small MAI ASCII logo and a trimmed header (no streaming hint, no /api-key reminder)

mahanai --compact

Compact banner looks like:

===================================
  Super 2.0  |  <Model>  |
  /help  /exit  /quit
===================================

Models

MahanAI Super supports multiple backends selectable at runtime via /models.

NVIDIA NIM

Pretty Name	Model ID	Backend
Llama 3.3 70B	meta/llama-3.3-70b-instruct	NVIDIA NIM (direct)

Note: A legacy server mode (mahanai/mahanai) exists in the model selector but is undocumented and not recommended for use.

Claude

Pretty Name	Model ID	Backend
Claude Opus 4	claude-opus-4-7	Claude CLI
Claude Sonnet 4.6	claude-sonnet-4-6	Claude CLI
Claude Haiku 4.5	claude-haiku-4-5-20251001	Claude CLI

OpenAI Codex

Seven models available, each accessible in Direct and Indirect mode (see OpenAI Codex below):

Pretty Name	Model ID
GPT-5.4	gpt-5.4
GPT-5.2-Codex	gpt-5.2-codex
GPT-5.1-Codex-Max	gpt-5.1-codex-max
GPT-5.4-Mini	gpt-5.4-mini
GPT-5.3-Codex	gpt-5.3-codex
GPT-5.2	gpt-5.2
GPT-5.1-Codex-Mini	gpt-5.1-codex-mini

Switch models interactively with /models (arrow-key selector) or quick-switch with /mode claude / /mode default.

Custom Endpoint

Point MahanAI at any OpenAI-compatible API (Ollama, LM Studio, vLLM, OpenRouter, etc.):

/custom http://localhost:11434/v1 llama3 [optional-api-key]

Once saved, select Custom Endpoint from /models to start using it. The config persists across sessions.

Commands

Command	Description
/models	Interactive model selector (↑↓ arrows, Enter to confirm, Esc to cancel)
/mode claude	Quick-switch to Claude Sonnet 4.6
/mode default	Quick-switch back to MahanAI Super (server)
/api-key [key]	Save server API key (omit key for hidden prompt)
/api-key clear	Remove saved server key
/api-key-nvidia [key]	Save NVIDIA direct API key
/api-key-nvidia clear	Remove NVIDIA key, switch back to server
/codex-login	Sign in to OpenAI via browser (Codex Direct mode)
/codex-logout	Remove saved OpenAI Codex credentials
/custom [url [model [key]]]	Configure a custom OpenAI-compatible endpoint
/custom clear	Remove saved custom endpoint
/help	Show help
/exit or /quit	Leave

API Keys

Server / NVIDIA NIM

1. Environment: MAHANAI_API_KEY=...
2. Project .env: MAHANAI_API_KEY=...
3. In-app: /api-key your-key

Keys are stored under %APPDATA%\MahanAI\config.json on Windows or ~/.config/mahanai/config.json on Linux/macOS.

Claude CLI mode

Claude models use your local claude CLI installation. Make sure Claude Code is installed and on your PATH. No extra API key configuration needed inside MahanAI — it uses whatever account Claude CLI is authenticated with.

OpenAI Codex

MahanAI supports two Codex authentication modes:

Direct mode

Signs in to your OpenAI account via a browser-based OAuth PKCE flow — no API key needed.

/codex-login

This opens your browser to auth.openai.com. After you approve, MahanAI receives and stores the access token automatically. Tokens are refreshed silently before they expire (saved to the same config.json as other keys).

Indirect mode

Reads credentials from a locally installed and signed-in OpenAI Codex CLI. MahanAI looks for auth.json in these locations:

Platform	Paths checked
Windows	%LOCALAPPDATA%\OpenAI\Codex\auth.json, ~\.codex\auth.json
macOS / Linux	~/.codex/auth.json, ~/.config/codex/auth.json

If no token file is found, MahanAI falls back to running the codex CLI as a subprocess (requires Codex CLI on your PATH).

To use indirect mode, install and sign in to the Codex CLI first:

npm i -g @openai/codex
codex login

Then select any OpenAI Codex (Indirect) model from /models.

Custom Endpoint

Use /custom to connect to any OpenAI-compatible server — Ollama, LM Studio, vLLM, OpenRouter, or your own deployment.

Interactive setup (prompts for each field):

/custom

One-liner:

/custom <base-url> [model] [api-key]

Examples:

/custom http://localhost:11434/v1 llama3
/custom http://localhost:1234/v1 mistral-7b
/custom https://openrouter.ai/api/v1 openai/gpt-4o sk-or-...

• base-url — the /v1 base URL of the server
• model — model ID to send in requests (defaults to gpt-3.5-turbo if omitted)
• api-key — leave blank if the server doesn't require one

After saving, run /models and select Custom Endpoint, or the agent will remind you to switch if you haven't already. To remove the config:

/custom clear

Environment Variables

Variable	Purpose
MAHANAI_API_KEY	Override saved server API key
MAHANAI_MODEL	Override default model ID
MAHANAI_STREAM	Set to 0/false/no/off to disable streaming
MAHANAI_CONFIG_DIR	Override config file directory
NO_COLOR	Disable terminal colors

Tools

MahanAI can execute tools on your behalf:

• run_command — run shell commands (asks confirmation before destructive ops)
• read_file — read a file
• write_file — write a file
• append_file — append to a file
• list_directory — list directory contents

Develop

pip install -e .
python -m mahanai

Publish to PyPI

Bump version in both pyproject.toml and mahanai/__init__.py, then:

pip install build twine
python -m build
python -m twine check dist/*

Windows (PowerShell):

$env:TWINE_USERNAME = "__token__"
$env:TWINE_PASSWORD = "pypi-YOUR_TOKEN_HERE"
python -m twine upload dist/mahanai-4.0.0*

macOS / Linux:

export TWINE_USERNAME=__token__
export TWINE_PASSWORD=pypi-YOUR_TOKEN_HERE
python -m twine upload dist/mahanai-4.0.0*

twine cannot publish without your token; keep it out of git.