llama-agentic 0.3.2

Local agentic AI CLI powered by llama.cpp

llama-agentic

A local agentic AI CLI powered by llama.cpp. It runs on your machine, uses GGUF models, and exposes tools for files, shell commands, git, web access, memory, plugins, MCP servers, and A2A agents.

Think of it as a terminal coding agent driven by your local model instead of a hosted API.

You:   Refactor the auth module to use JWT, run the tests, and commit

Agent: ⚙ view_file   ✓  Read agent/auth.py
       ⚙ edit_file   ✓  Applied JWT changes
       ⚙ run_shell   ✓  pytest tests/test_auth.py
       ⚙ git_commit  ✓  refactor: replace session auth with JWT
       Done — all tests pass and changes are committed.

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What it does

• Runs a ReAct loop: reason, choose a tool, observe results, repeat
• Works with local llama.cpp models through the OpenAI-compatible server API
• Includes built-in tools for files, editing, shell, Python, git, web, and memory
• Supports MCP servers so you can add GitHub, databases, browsers, Slack, and more
• Supports A2A agents so your local agent can delegate to remote JSON-RPC A2A agents
• Loads LLAMA.md automatically for project-specific context
• Supports persistent memory, session save/load, watch mode, and plugin loading

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Documentation

The repo docs are the primary guide set:

Guide	Description
Documentation Index	Overview of all guides
Getting Started	Install, setup, download a model, first run
User Guide	REPL usage, sessions, memory, watch mode
Tools Reference	Built-in tools and examples
Configuration	Environment variables and config hierarchy
Plugin Development	Add custom tools
MCP Integration	Configure and use MCP servers

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Requirements

Requirement	Version	Install
macOS or Linux	macOS 12+ / Ubuntu 22+	—
Python	3.11+	python.org
llama.cpp	latest	brew install llama.cpp
uv	latest	curl -LsSf https://astral.sh/uv/install.sh | sh

Apple Silicon works well with Metal GPU offload by default.

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Installation

From PyPI

pip install llama-agentic
# or
uv tool install llama-agentic

From source

git clone https://github.com/minrahim1999/llama-agentic.git
cd llama-agentic
uv tool install --editable .

Verify

llama-agent --help

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Quick Start

1. Run first-time setup

llama-agent

This creates ~/.config/llama-agentic/config.env and can help detect llama-server, choose settings, and offer a starter model download. It also saves a preferred LLAMA_MODEL_PATH so auto-start uses a deterministic GGUF file instead of guessing from the cache.

2. Download a model

llama-agent download
llama-agent download qwen2.5-coder-7b

Downloaded models are added to the cache and the selected file is persisted to LLAMA_MODEL_PATH automatically.

Recommended model:

llama-agent download qwen2.5-coder-7b

3. Start or verify the server

llama-agent doctor
llama-agent autostart enable
llama-agent autostart start

autostart enable and autostart start prefer the configured LLAMA_MODEL_PATH when it is set.

Or start it manually:

./scripts/start_server.sh /path/to/model.gguf

4. Generate project context

cd your-project
llama-agent --init

5. Start a session

llama-agent
llama-agent --task "Find and fix the failing tests"

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Common Commands

llama-agent                                       # interactive REPL
llama-agent --task "review the latest changes"    # one-shot task
llama-agent --resume sessions/chat_2026-01-15.json
llama-agent doctor                                # environment checks
llama-agent download qwen2.5-coder-7b             # download a model
llama-agent models                                # list cached models and the selected one
llama-agent mcp list                              # list configured MCP servers
llama-agent a2a list                              # list configured A2A agents

Common REPL commands:

• /help
• /init
• /refresh
• /add <glob>
• /tools
• /memory
• /sessions
• /cost
• /exit

See docs/user-guide.md for the full command list.

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Key Features

• Diff-aware editing: edit_file previews changes before writing and keeps .bak backups
• Confirmation-gated actions: destructive tools require approval unless UNSAFE_MODE=true
• Persistent memory: store facts across sessions
• Session management: save, load, resume, and inspect history
• MCP integration: dynamically register tools from external MCP servers over the currently supported transports
• A2A integration: register remote A2A agents as callable tools and inspect their Agent Cards
• Plugin system: load custom tools from configured plugin directories
• .llamaignore support: block reads and writes to protected paths

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Development

uv sync --dev
uv run pytest tests/ -v --tb=short
uv run ruff check agent/ tests/
uv build

Project layout and development conventions are documented in AGENTS.md.

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License

MIT. See LICENSE.