afkbotio 1.2.0

Source-available local AI runtime and CLI for AFKBOT

AFKBOT

AFKBOT is a source-available local AI runtime and CLI for chat-driven workflows, tool calling, automations, and profile-scoped agent environments.

Documentation lives at afkbot.io/docs. The project site is afkbot.io. Use the docs site for setup, configuration, MCP, automations, and command reference.

What AFKBOT does

• Runs local chat sessions with tool access, planning, and configurable reasoning.
• Supports multiple LLM providers in setup/profile runtime (openrouter, openai, claude, moonshot, deepseek, xai, qwen, and custom).
• Provides a CLI-first workflow for setup, chat, health checks, and runtime control.
• Supports profile-scoped configuration, secrets, permissions, and tool exposure.
• Includes browser, web, app, MCP, automation, and channel integration surfaces.
• Exposes a local runtime and API layer for longer-running workflows.

Runtime Model

AFKBOT uses one session-oriented execution model across chat, API, automations, Task Flow workers, and child subagents.

• One active turn runs at a time for each (profile_id, session_id).
• If you send another message while a turn is still running, the next message is queued and starts after the current turn releases the session slot.
• afk chat planning modes control whether the agent starts with a read-only planning pass before execution:
  • off: execute immediately
  • auto: use plan-first for complex requests
  • on: always show a plan first, then execute
• A public plan -> execute flow runs inside the same serialized session slot, so execution starts automatically after planning unless you explicitly asked for only a plan.
• Inside one turn, the agent can fan out independent work in parallel with session.job.run, wait for every child job to finish, and then return one final answer.
• Subagents and Task Flow runs use separate child sessions, so they do not steal the parent chat session slot.

Choosing the Execution Path

Use this mental model:

Path	Use it when	Wait for the answer now?	Durable state?	Typical outcome
Chat turn	The work fits in one bounded conversation turn	Yes	No	Plan, inspect files, run tools, answer in chat
session.job.run + subagents	You want parallel work inside the current turn	Yes	No	Fan out independent bash or subagent jobs, wait for all, merge results
Task Flow	The work is long-running, needs dependencies, review, handoff, or a backlog trail	Not necessarily	Yes	Create durable tasks, run them in background, inspect task runs and comments later

Command examples below use the installed afk binary. If you are working from a source checkout without installing AFKBOT into your shell yet, run the same commands with uv run, for example uv run afk doctor.

Subagents are profile-local runtime assets, not global assistant personas. List the subagents that the current AFKBOT profile can actually run with:

afk subagent list --profile default

Chat And Planning

afk chat is the main orchestrator. It decides whether to stay in one turn, fan out parallel jobs, or create durable Task Flow work.

Planning mode examples:

afk chat --plan off
afk chat --plan auto
afk chat --plan on

Behavior:

• off: the turn executes immediately.
• auto: the runtime may do a read-only planning pass for multi-step work.
• on: the runtime always shows a plan first and then executes in the same request.
• If you explicitly ask only for a plan, AFKBOT returns the plan and stops without starting execution.

License Model

• AFKBOT source code is available under the Sustainable Use License 1.0.
• Personal use, non-commercial use, and internal business use are allowed.
• Forking and modifying AFKBOT are allowed, but redistribution must stay free of charge and non-commercial.
• You may not sell AFKBOT, sell copies of AFKBOT, resell the source code, or offer AFKBOT as a paid hosted or white-label service without separate permission.
• The repository license does not grant any trademark rights to the AFKBOT name, logo, or branding.

Requirements

• Python 3.12 or newer for manual source installs
• uv recommended for local development
• SQLite is the default runtime database for AFKBOT
• The hosted installers bootstrap uv, install AFKBOT as an isolated uv tool, and keep runtime state outside the app source tree

Install

Hosted installer for macOS/Linux:

curl -fsSL https://afkbot.io/install.sh | bash
# open a new terminal after install
afk setup
afk doctor
afk chat

Hosted installer for Windows PowerShell:

powershell -c "irm https://afkbot.io/install.ps1 | iex"
# open a new terminal after install
afk setup
afk doctor
afk chat

Local installer from a source checkout:

bash scripts/install.sh --repo-url "file://$PWD"
# open a new terminal after install
afk setup
afk doctor
afk chat

Common installer flags:

# installer and setup prompts in Russian
curl -fsSL https://afkbot.io/install.sh | bash -s -- --lang ru

# install from a specific Git ref
curl -fsSL https://afkbot.io/install.sh | bash -s -- --git-ref v1.2.0

# install from a local checkout
bash scripts/install.sh --repo-url "file://$PWD"

# show actions without mutating the machine
bash scripts/install.sh --dry-run

# skip bootstrap-only setup seeding during install
bash scripts/install.sh --skip-setup

What the installer does:

• bootstraps uv into the user-local bin directory if needed
• installs AFKBOT as an isolated uv tool
• updates shell integration so afk is available in new terminals
• seeds the runtime root with bootstrap-only setup metadata
• remembers the install source so afk update can refresh the same source later

The installer is idempotent. Rerun it to refresh the installed tool in place, or use afk update.

First Run

For normal usage, the first-run flow is:

afk setup
afk doctor
afk chat

• afk setup configures the default profile, provider, policy, locale, and runtime defaults
• afk setup also asks whether afk chat should check for AFKBOT updates before opening chat
• afk doctor prints the effective runtime/chat ports and checks local readiness
• afk chat is the main entrypoint for real work

Setup and profile policy directly control the tool surface that the runtime can use. In practice:

• enable Shell if you want the agent to run shell commands or parallel bash jobs through session.job.run
• enable Subagents if you want the agent to run profile-local subagents
• enable Task Flow if you want durable backlog tasks, dependencies, review, and background execution
• enable MCP, Browser, HTTP, Apps, and other capability groups only for the surfaces you actually want exposed to the profile

Useful first-run checks:

afk doctor
afk profile show default
afk subagent list --profile default
afk task board --profile default

afk profile show default lets you confirm the effective runtime policy and capabilities. afk subagent list --profile default shows the actual subagent names that this profile can run.

If update notices are enabled in setup, interactive afk chat checks for a newer AFKBOT build before opening the session and asks:

• Yes
• No
• Remind in a week

No continues into chat immediately and does not save a permanent skip. Remind in a week suppresses all update prompts for seven days. If you disable update notices in setup, chat will not ask at startup.

The runtime chooses and persists a non-default local port automatically for fresh installs, so use afk doctor when you need the actual runtime_port or api_port.

Manual local source setup with uv:

uv sync --extra dev
afk setup
afk doctor
afk chat

If the checkout is not installed into your shell PATH, run the same commands with uv run afk ... from the repository root.

Manual local source setup with pip:

python3.12 -m venv .venv
source .venv/bin/activate
pip install -e .[dev]
afk setup
afk doctor
afk chat

Local Runtime

AFKBOT uses one local SQLite database by default for runtime state, semantic memory, and chat metadata:

export AFKBOT_DB_URL='sqlite+aiosqlite:///./afkbot.db'

Start the local runtime/API:

afk start
afk doctor
# doctor prints the effective runtime_port and api_port for this install

afk start launches the local runtime stack, including API routes, automation delivery, and Task Flow background workers.

Webhook trigger example:

curl -X POST http://127.0.0.1:<runtime_port>/v1/automations/<profile_id>/webhook/<token> \
  -H 'Content-Type: application/json' \
  -d '{"event_id":"manual-test-1"}'

Useful commands:

afk version
afk doctor
afk setup
afk chat --message "Summarize this project"
afk automation list --profile default
afk plugin list
afk mcp list
afk profile show default
afk update

Chat Examples

Paste prompts like these into afk chat.

Parallel work inside one turn:

Do one session.job.run call.
Run 2 bash jobs in parallel:
1) sleep 5 && echo FIRST
2) sleep 5 && echo SECOND
Wait for both and summarize the result.

Parallel profile-local subagents inside one turn:

Do one session.job.run call.
Run 2 subagent jobs in parallel:
1) subagent_name=poet-10-lines, prompt="Write 10 lines about orchestration"
2) subagent_name=ui-reviewer, prompt="Review: button text has low contrast"
Wait for both and merge the results.

Durable work as Task Flow instead of one large chat turn:

Break this project into durable Task Flow work:
- create a flow
- create the tasks
- add dependencies
- assign AI-owned tasks to the default profile
- leave me with the task ids and next review points

Rule of thumb:

• keep work in one chat turn when you want the answer now
• use session.job.run when the current turn contains independent parallel work
• use Task Flow when the work must survive the current chat session and keep a durable execution trail

Plugins

AFKBOT supports installable embedded plugins that extend the local platform with:

• API routers
• static web apps
• tool factories
• skill directories
• app registrars
• optional startup and shutdown hooks

Current curated plugins:

• afkbotui: unified AFKBOT web workspace for automations today and future operator surfaces

Typical operator flow:

afk plugin list
afk plugin install
afk plugin inspect afkbotui
afk plugin config-get afkbotui
afk plugin update afkbotui

afk plugin install now works as a small wizard:

• it shows curated plugins that are not installed yet
• today the curated list contains only afkbotui
• the last option is a custom GitHub source, where you can paste a GitHub URL or github:owner/repo@ref

You can still install directly without the wizard:

afk plugin install github:afkbot-io/afkbotuiplugin@main

Direct afk plugin install <source> also still accepts a local path when you want to install a plugin from a checkout on disk.

The current curated external plugin is AFKBOT UI. Today it provides the web workspace for automations and is intended to expand into the main operator surface for Task Flow, subagents, MCP, AI settings, and profile management. The older kanban-specific example is no longer the curated plugin path. After installation and afk start, it mounts:

• API: /v1/plugins/afkbotui/...
• UI: /plugins/afkbotui

Plugin install state lives under the AFKBOT runtime root in /plugins/... and is treated as local machine state, not repository content.

Channels Quickstart

AFKBOT can attach chat transports to a profile for inbound routing and operator workflows. Use the docs site for the full command reference; the examples below cover the common setup paths.

Telegram bot polling channel:

# guided wizard; omit channel_id to let AFKBOT suggest one
afk channel telegram add

# fully explicit example
afk channel telegram add support-bot --profile default --credential-profile support-bot
afk channel telegram status
afk channel telegram show support-bot

Telethon user-account channel:

# guided wizard; omit channel_id to let AFKBOT suggest one
afk channel telethon add

# fully explicit example
afk channel telethon add personal-user --profile default --credential-profile personal-user
afk channel telethon status --probe
afk channel telethon show personal-user

Notes:

• Interactive channel setup explains required credentials inline: Telegram bot token comes from @BotFather; Telethon api_id and api_hash come from my.telegram.org.
• If you skip the Telethon session string during setup, finish login later with afk channel telethon authorize <channel_id>.
• Interactive prompt language follows this order: explicit --lang or --ru, then the project's saved prompt_language, then the current system locale.

MCP Quickstart

AFKBOT supports profile-local MCP configuration plus runtime MCP tool discovery.

Manual CLI flow:

# connect one MCP endpoint URL to the default profile
afk mcp connect https://example.com/mcp --profile default --secret-ref mcp_example_token

# inspect the saved config
afk mcp get example --profile default

# validate effective MCP files for the profile
afk mcp validate --profile default

# list all saved MCP servers, including disabled entries
afk mcp list --profile default --show-disabled

You can still use the explicit form:

afk mcp add --profile default --url https://example.com/mcp --secret-ref mcp_example_token

Chat-driven flow:

Connect this MCP endpoint to my default profile: https://example.com/mcp
Show me what was saved and validate it.

Notes:

• Use the actual MCP endpoint URL, not a generic product homepage.
• afk mcp and mcp.profile.* manage profile config.
• mcp.tools.list and mcp.tools.call are the runtime bridge used after a compatible remote MCP server is configured and exposed.
• If the MCP server needs auth, store only refs in MCP config such as secret_refs or env_refs; do not hardcode plaintext secrets into MCP JSON.

Managed-install maintenance:

afk update
bash scripts/uninstall.sh --yes

afk update
powershell -ExecutionPolicy Bypass -File .\scripts\uninstall.ps1 -Yes

Hosted installers use uv tool install under the hood. Advanced equivalents:

uv tool install --python 3.12 --reinstall https://github.com/afkbot-io/afkbotio/archive/main.tar.gz
afk update
uv tool uninstall afkbotio

Configuration

• Environment-based configuration examples live in .env.example.
• Setup/provider selection supports OpenRouter, OpenAI, Claude, Moonshot (Kimi), DeepSeek, xAI, Qwen, and custom OpenAI-compatible endpoints.
• Runtime secrets should be configured through afk setup, afk profile, or credential commands, not committed into the repository.
• Manual source setups use a local SQLite database and a local AFKBOT runtime.
• New installs create the current SQLite schema directly; no legacy migration chain is required.
• Full setup guidance and user documentation are published at afkbot.io/docs.

Development

Install the development environment and run the standard checks:

uv sync --extra dev
uv run ruff check afkbot tests
uv run mypy afkbot tests
uv run pytest -q

PyPI Release

The project builds clean Python distributions and passes twine check:

uv build
uvx twine check dist/*

For a safe dry run, upload to TestPyPI first:

uvx twine upload --repository testpypi dist/*

This repository also includes a GitHub Actions publish workflow prepared for trusted publishing:

• workflow_dispatch: builds distributions, runs twine check, and publishes to testpypi
• push on v* tags: verifies the tag matches project.version, builds distributions, runs twine check, attaches them to the GitHub release, and publishes to pypi

Before using the workflow, create matching trusted publishing environments in PyPI:

• testpypi for https://test.pypi.org/p/afkbotio
• pypi for https://pypi.org/project/afkbotio/

License

AFKBOT is distributed under the Sustainable Use License 1.0.

• See LICENSE for the full license text.
• See LICENSE_FAQ.md for practical allowed/not-allowed examples.
• See COMMERCIAL_LICENSE.md for commercial-use guidance.
• See TRADEMARKS.md for brand and name usage rules.