MForege — AI Agent CLI

A command-line AI agent with a transparent, IDE-style interface: live tool activity, colored diffs on every file change, mission plans, and long-term memory. Supports OpenAI, Ollama (free & local), and any OpenAI-compatible API (Groq, Together, etc.).

Install

pip install mforege

Or with pipx (recommended for CLIs — isolated env, command on PATH everywhere):

pipx install mforege

Or straight from source:

pip install git+https://github.com/munjurdev/MForege.git

Features

Agent core

• Full tool loop — when the model calls a tool, the result is sent back so the model can use it in its answer (streaming and non-streaming); up to 10 rounds per message, configurable via AgentConfig.max_tool_rounds
• Streaming output — responses appear token by token, with a thinking Ns... timer while the model works
• Personality + response control — friendly, witty, emoji-moderate; short answers for casual chat, clarifying questions for ambiguous or underspecified requests (never dumps giant tutorials or acts on invented details)

Tools

Safety

• Destructive commands blocked outright (rm -rf, git push --force, format, ...)
• Mutating actions require approval — file writes/edits show a colored diff before you confirm; commands show the exact line to be run
• Fail-closed — without a confirmation handler, mutating operations refuse rather than run unconfirmed
• Workspace-confined — path tools only operate inside the folder you choose at launch; ../ and symlink escapes are rejected
• Timeouts & caps — 30s command timeout, tool output capped so the context window never balloons

Chat-style interface (prompt_toolkit)

• Bottom input box — the input stays pinned at the bottom like a chat app; the transcript scrolls above it
• Live activity stream — every tool call prints as · tool(args) ✓ (0.3s)
• Thinking timer — thinking Ns... runs until the first token arrives
• Colored diffs — + green / - red, shown before approval and in the transcript after the edit lands
• Plan statusline — [Plan 2/5] next: write the test after each reply
• Status bar — model, workspace, and shortcut hints always visible
• Keys — Enter sends, Alt+Enter adds a newline, Ctrl+C quits
• Slash commands — /help, /plan, /tools, /memory, /forget, /clear, /exit

Memory

• Conversation memory — history per session, clear resets it
• Long-term memory — after each reply a side-call asks "did I learn anything durable?"; facts (name, projects, preferences) are saved to data/memory.json (git-ignored) and injected into future sessions. Personalized greeting on startup ("Welcome back, Munjur! 👋")

Resilience

• Request timeouts + automatic retries with backoff for transient failures
• Friendly errors for missing keys or Ollama not running
• UTF-8 output on Windows (emoji-safe), clean async shutdown

Setup

python -m venv env
env/Scripts/activate          # Windows (bash: source env/Scripts/activate)
pip install -r requirements.txt
pip install -e .              # installs the `mforege` command

After pip install -e ., MForege works from any terminal — including VS Code's — with the venv activated:

mforege                                    # in any folder (uses MForege's own .env)
mforege --workspace "C:\path\to\project"   # work inside another project

Backends

OpenAI — put your key in .env:

OPENAI_API_KEY=sk-...
LLM_MODEL=gpt-4o-mini

Ollama (free, local) — install from https://ollama.com, then:

ollama pull llama3
mforege --backend ollama --model llama3

Custom (e.g., Groq):

API_KEY=gsk_...
BASE_URL=https://api.groq.com/openai/v1
LLM_MODEL=llama-3.1-8b-instant

Web search (optional)

1. Get a free API key at https://dashboard.exa.ai
2. Add to .env: EXA_API_KEY=your_key
3. Restart the CLI

Workspace

MForege can only read/write inside one folder — the one you choose:

mforege --workspace "C:\path\to\your\project"

The header shows the active workspace. To work in a different folder, restart with a different --workspace (or set WORKSPACE in .env).

Run

python main.py                  # from the project folder
mforege                         # from ANY folder (after pip install -e .)
mforege --backend ollama --model llama3
mforege --workspace "C:\path\to\your\project"

.env resolution: if the current folder has a .env, it wins; otherwise MForege falls back to its own .env — so API keys are found no matter where you launch it. Workspace defaults to the current directory.

CLI commands

Example session

Welcome back, Munjur! 👋
(I remember 2 things)

You: create utils.py with a greet function, then test it
Assistant: thinking 2s...
  · todo_plan(action=set) ✓ (0.0s)
  · create_file(path=utils.py) ✓ (0.0s)
[?] MForege wants to: CREATE file 'utils.py' (95 chars)
  @@ -0,0 +1,3 @@
  +def greet(name):
  +    return f"Hello, {name}!"
    Allow? [y/N]: y
  ✓ Edit applied:
  @@ -0,0 +1,3 @@
  +def greet(name):
  ...
[Plan 2/4] next: run the tests

Adding your own tool

from app.agent.tools import create_tool

async def get_weather(city: str):
    return f"Sunny in {city}, 25C"

agent.register_tools(create_tool("get_weather", "Get weather for a city", get_weather))

Tests

env/Scripts/python -m pytest

161 tests covering conversation memory, the tool registry, the safe calculator, the web search tool, long-term memory (store, injection, extraction), system tools (path confinement, command classification, confirmation flow — sync and async, diffs, notifications), discovery tools (search/glob/read windows), the todo plan, and the agent's tool-call loop — all with a mocked LLM, no network needed.