axe

A CLI tool for managing and running LLM-powered agents.

Why Axe?

Most AI tooling assumes you want a chatbot. A long-running session with a massive context window doing everything at once. But that's not how good software works. Good software is small, focused, and composable.

Axe treats LLM agents the same way Unix treats programs. Each agent does one thing well. You define it in a TOML file, give it a focused skill, and run it from the command line. Pipe data in, get results out. Chain agents together. Trigger them from cron, git hooks, or CI. Whatever you already use. No daemon, no GUI, no framework to buy into. Just a binary and your configs.

Overview

Axe orchestrates LLM-powered agents defined via TOML configuration files. Each agent has its own system prompt, model selection, skill files, context files, working directory, persistent memory, and the ability to delegate to sub-agents.

Axe is the executor, not the scheduler. It is designed to be composed with standard Unix tools — cron, git hooks, pipes, file watchers — rather than reinventing scheduling or workflow orchestration.

Features

Multi-provider support — Anthropic, OpenAI, and Ollama (local models)
TOML-based agent configuration — declarative, version-controllable agent definitions
Sub-agent delegation — agents can call other agents via LLM tool use, with depth limiting and parallel execution
Persistent memory — timestamped markdown logs that carry context across runs
Memory garbage collection — LLM-assisted pattern analysis and trimming
Skill system — reusable instruction sets that can be shared across agents
Stdin piping — pipe any output directly into an agent (git diff | axe run reviewer)
Dry-run mode — inspect resolved context without calling the LLM
JSON output — structured output with metadata for scripting
Built-in tools — file operations (read, write, edit, list), shell command execution, all sandboxed to the agent's working directory
MCP tool support — connect to external MCP servers for additional tools via SSE or streamable-HTTP transport
Minimal dependencies — four direct dependencies (cobra, toml, mcp-go-sdk, x/net); all LLM calls use the standard library

Installation

Requires Go 1.24+.

go install github.com/jrswab/axe@latest

Or build from source:

git clone https://github.com/jrswab/axe.git
cd axe
go build .

Quick Start

Initialize the configuration directory:

axe config init

This creates the directory structure at $XDG_CONFIG_HOME/axe/ with a sample skill and a default config.toml for provider credentials.

Scaffold a new agent:

axe agents init my-agent

Edit its configuration:

axe agents edit my-agent

Run the agent:

axe run my-agent

Pipe input from other tools:

git diff --cached | axe run pr-reviewer
cat error.log | axe run log-analyzer

Examples

The examples/ directory contains ready-to-run agents you can copy into your config and use immediately. Includes a code reviewer, commit message generator, and text summarizer — each with a focused SKILL.md.

# Copy an example agent into your config cp examples/code-reviewer/code-reviewer.toml "$(axe config path)/agents/" cp -r examples/code-reviewer/skills/ "$(axe config path)/skills/" Set your API key and run

export ANTHROPIC_API_KEY="your-key-here" git diff | axe run code-reviewer

See examples/README.md for full setup instructions.

Docker

Axe provides a Docker image for running agents in an isolated, hardened container.

Build the Image

docker build -t axe .

Multi-architecture builds (linux/amd64, linux/arm64) are supported via buildx:

docker buildx build --platform linux/amd64,linux/arm64 -t axe:latest .

Run an Agent

Mount your config directory and pass API keys as environment variables:

docker run --rm \
  -v ./my-config:/home/axe/.config/axe \
  -e ANTHROPIC_API_KEY \
  axe run my-agent

Pipe stdin with the -i flag:

git diff | docker run --rm -i \
  -v ./my-config:/home/axe/.config/axe \
  -e ANTHROPIC_API_KEY \
  axe run pr-reviewer

Without a config volume mounted, axe exits with code 2 (config error) because no agent TOML files exist.

Running a Single Agent

The examples above mount the entire config directory. If you only need to run one agent with one skill, mount just those files to their expected XDG paths inside the container. No config.toml is needed when API keys are passed via environment variables.

docker run --rm -i \
  -e ANTHROPIC_API_KEY \
  -v ./agents/reviewer.toml:/home/axe/.config/axe/agents/reviewer.toml:ro \
  -v ./skills/code-review/:/home/axe/.config/axe/skills/code-review/:ro \
  axe run reviewer

The agent's skill field resolves automatically against the XDG config path inside the container, so no --skill flag is needed.

To use a different skill than the one declared in the agent's TOML, use the --skill flag to override it. In this case you only mount the replacement skill — the original skill declared in the TOML is ignored entirely:

docker run --rm -i \
  -e ANTHROPIC_API_KEY \
  -v ./agents/reviewer.toml:/home/axe/.config/axe/agents/reviewer.toml:ro \
  -v ./alt-review.md:/home/axe/alt-review.md:ro \
  axe run reviewer --skill /home/axe/alt-review.md

If the agent declares sub_agents, all referenced agent TOMLs and their skills must also be mounted.

Persistent Data

Agent memory persists across runs when you mount a data volume:

docker run --rm \
  -v ./my-config:/home/axe/.config/axe \
  -v axe-data:/home/axe/.local/share/axe \
  -e ANTHROPIC_API_KEY \
  axe run my-agent

Docker Compose

A docker-compose.yml is included for running axe alongside a local Ollama instance.

Cloud provider only (no Ollama):

docker compose run --rm axe run my-agent

With Ollama sidecar:

docker compose --profile ollama up -d ollama
docker compose --profile cli run --rm axe run my-agent

Pull an Ollama model:

docker compose --profile ollama exec ollama ollama pull llama3

Note: The compose axe service declares depends_on: ollama. Docker Compose will attempt to start the Ollama service whenever axe is started via compose, even for cloud-only runs. For cloud-only usage without Ollama, use docker run directly instead of docker compose run.

Ollama on the Host

If Ollama runs directly on the host (not via compose), point to it with:

Linux: --add-host=host.docker.internal:host-gateway -e AXE_OLLAMA_BASE_URL=http://host.docker.internal:11434
macOS / Windows (Docker Desktop): -e AXE_OLLAMA_BASE_URL=http://host.docker.internal:11434

Security

The container runs with the following hardening by default (via compose):

Non-root user — UID 10001
Read-only root filesystem — writable locations are the config mount, data mount, and /tmp/axe tmpfs
All capabilities dropped — cap_drop: ALL
No privilege escalation — no-new-privileges:true

These settings do not restrict outbound network access. To isolate an agent that only talks to a local Ollama instance, add --network=none and connect it to the shared Docker network manually.

Volume Mounts

Container Path	Purpose	Default Access
`/home/axe/.config/axe/`	Agent TOML files, skills, `config.toml`	Read-write
`/home/axe/.local/share/axe/`	Persistent memory files	Read-write

Config is read-write because axe config init and axe agents init write into it. Mount as :ro if you only run agents.

Environment Variables

Variable	Required	Purpose
`ANTHROPIC_API_KEY`	If using Anthropic	API authentication
`OPENAI_API_KEY`	If using OpenAI	API authentication
`AXE_OLLAMA_BASE_URL`	If using Ollama	Ollama endpoint (default in compose: `http://ollama:11434`)
`AXE_ANTHROPIC_BASE_URL`	No	Override Anthropic API endpoint
`AXE_OPENAI_BASE_URL`	No	Override OpenAI API endpoint

CLI Reference

Commands

Command	Description
`axe run <agent>`	Run an agent
`axe agents list`	List all configured agents
`axe agents show <agent>`	Display an agent's full configuration
`axe agents init <agent>`	Scaffold a new agent TOML file
`axe agents edit <agent>`	Open an agent TOML in `$EDITOR`
`axe config path`	Print the configuration directory path
`axe config init`	Initialize the config directory with defaults
`axe gc <agent>`	Run memory garbage collection for an agent
`axe gc --all`	Run GC on all memory-enabled agents
`axe version`	Print the current version

Run Flags

Flag	Default	Description
`--model <provider/model>`	from TOML	Override the model (e.g. `anthropic/claude-sonnet-4-20250514`)
`--skill <path>`	from TOML	Override the skill file path
`--workdir <path>`	from TOML or cwd	Override the working directory
`--timeout <seconds>`	120	Request timeout
`--dry-run`	false	Show resolved context without calling the LLM
`--verbose` / `-v`	false	Print debug info (model, timing, tokens) to stderr
`--json`	false	Wrap output in a JSON envelope with metadata

Agent Configuration

Agents are defined as TOML files in $XDG_CONFIG_HOME/axe/agents/.

name = "pr-reviewer"
description = "Reviews pull requests for issues and improvements"
model = "anthropic/claude-sonnet-4-20250514"
system_prompt = "You are a senior code reviewer. Be concise and actionable."
skill = "skills/code-review/SKILL.md"
files = ["src/**/*.go", "CONTRIBUTING.md"]
workdir = "/home/user/projects/myapp"
tools = ["read_file", "list_directory", "run_command"]
sub_agents = ["test-runner", "lint-checker"]
[sub_agents_config]
max_depth = 3       # maximum nesting depth (hard max: 5)
parallel = true     # run sub-agents concurrently
timeout = 120       # per sub-agent timeout in seconds
[memory]
enabled = true
last_n = 10         # load last N entries into context
max_entries = 100   # warn when exceeded
[[mcp_servers]]
name = "my-tools"
url = "https://my-mcp-server.example.com/sse"
transport = "sse"
headers = { Authorization = "Bearer ${MY_TOKEN}" }
[params]
temperature = 0.3
max_tokens = 4096

All fields except name and model are optional.

Tools

Agents can use built-in tools to interact with the filesystem and run commands. When tools are enabled, the agent enters a conversation loop — the LLM can make tool calls, receive results, and continue reasoning for up to 50 turns.

Built-in Tools

Tool	Description
`list_directory`	List contents of a directory relative to the working directory
`read_file`	Read file contents with line-numbered output and optional pagination (offset/limit)
`write_file`	Create or overwrite a file, creating parent directories as needed
`edit_file`	Find and replace exact text in a file, with optional replace-all mode
`run_command`	Execute a shell command via `sh -c` and return combined output
`call_agent`	Delegate a task to a sub-agent (controlled via `sub_agents`, not `tools`)

Enable tools by adding them to the agent's tools field:

tools = ["read_file", "list_directory", "run_command"]

The call_agent tool is not listed in tools — it is automatically available when sub_agents is configured and the depth limit has not been reached.

Path Security

All file tools (list_directory, read_file, write_file, edit_file) are sandboxed to the agent's working directory. Absolute paths, .. traversal, and symlink escapes are rejected.

Parallel Execution

When an LLM returns multiple tool calls in a single turn, they run concurrently by default. This applies to both built-in tools and sub-agent calls. Disable with parallel = false in [sub_agents_config].

MCP Tools

Agents can use tools from external MCP servers. Declare servers in the agent TOML with [[mcp_servers]]:

[[mcp_servers]]
name = "my-tools"
url = "https://my-mcp-server.example.com/sse"
transport = "sse"
headers = { Authorization = "Bearer ${MY_TOKEN}" }

At startup, axe connects to each declared server, discovers available tools via tools/list, and makes them available to the LLM alongside built-in tools.

Field	Required	Description
`name`	Yes	Human-readable identifier for the server
`url`	Yes	MCP server endpoint URL
`transport`	Yes	`"sse"` or `"streamable-http"`
`headers`	No	HTTP headers; values support `${ENV_VAR}` interpolation

MCP tools are controlled entirely by [[mcp_servers]] — they are not listed in the tools field. If an MCP tool has the same name as an enabled built-in tool, the built-in takes precedence.

Skills

Skills are reusable instruction sets that provide an agent with domain-specific knowledge and workflows. They are defined as SKILL.md files following the community SKILL.md format.

Skill Resolution

The skill field in an agent TOML is resolved in order:

Absolute path — used as-is (e.g. /home/user/skills/SKILL.md)
Relative to config dir — e.g. skills/code-review/SKILL.md resolves to $XDG_CONFIG_HOME/axe/skills/code-review/SKILL.md
Bare name — e.g. code-review resolves to $XDG_CONFIG_HOME/axe/skills/code-review/SKILL.md

Script Paths

Skills often reference helper scripts. Since run_command executes in the agent's workdir (not the skill directory), script paths in SKILL.md must be absolute. Relative paths will fail because the scripts don't exist in the agent's working directory.

# Correct — absolute path /home/user/.config/axe/skills/my-skill/scripts/fetch.sh <args> Wrong — relative path won't resolve from the agent's workdir

scripts/fetch.sh <args>

Directory Structure

$XDG_CONFIG_HOME/axe/
├── config.toml
├── agents/
│   └── my-agent.toml
└── skills/
    └── my-skill/
        ├── SKILL.md
        └── scripts/
            └── fetch.sh

Providers

Provider	API Key Env Var	Default Base URL
Anthropic	`ANTHROPIC_API_KEY`	`https://api.anthropic.com`
OpenAI	`OPENAI_API_KEY`	`https://api.openai.com`
Ollama	(none required)	`http://localhost:11434`

Base URLs can be overridden with AXE_<PROVIDER>_BASE_URL environment variables or in config.toml.

License

Apache-2.0. See LICENSE.

NAME

SYNOPSIS

INFO

DESCRIPTION

README

axe