AtBots

Pydantic AI, already set up. AtBots is a thin layer over Pydantic AI that ships with tasks, skills, and memory wired together, as a library and a CLI.

pip install atbots

AtBots is a general-purpose agent package. It is not a sidecar, an extraction worker, or the intelligence layer for any other product.

The three pillars

Pillar	What it gives you
Tasks	Named, reusable units of work with declared inputs and typed results. Run them from code or the CLI; inspect what ran.
Skills	SKILL.md directories declared in configuration and discovered when TaskAgent starts.
Memory	Local governed memory stored by the bundled AtMem backend.

Thin layer, on purpose

AtBots contributes defaults and wiring — instructions, toolsets, skill loading, memory binding, model routing, a CLI. Pydantic AI keeps the agent loop, model calls, tool dispatch, and structured output.

That means:

• The underlying Pydantic AI Agent is reachable; use any upstream feature directly.
• Your existing Pydantic AI tools, toolsets, and model objects work as-is — no adapters.
• If AtBots wraps something Pydantic AI already does well, that's a bug.

Models

Ollama is the default when no remote provider is configured, so local work needs no vendor. OpenAI-compatible providers are first-class and require an explicit endpoint and credentials — installing AtBots never downloads a large model or creates an API key on your behalf. Switching providers is a configuration change; tasks, skills, and tools are untouched.

Configuration directory

New installations keep user configuration and local state in ~/.atbots/:

~/.atbots/
├── config.json
├── atmem.db
└── skills/
    └── example-skill/
        └── SKILL.md

Earlier development builds used ~/.atbot/ because the project originally had the singular name AtBot. AtBots now uses the plural ~/.atbots/ path to match the package and command. Existing ~/.atbot/config.json files remain readable for compatibility; run atbots init --force to create a new plural-path config.

Adding skills

Create one directory per skill. Each skill must contain a file named exactly SKILL.md:

mkdir -p ~/.atbots/skills/code-review

# Code review

Review changes for correctness, security, and missing tests.

Save that Markdown as ~/.atbots/skills/code-review/SKILL.md, then add the skills root to ~/.atbots/config.json:

{
  "skill_directories": ["~/.atbots/skills"]
}

AtBots scans <skills-root>/<skill-name>/SKILL.md when TaskAgent is created. In 0.1.0, the task runtime discovers skill names, but the installed atbots serve companion does not yet execute skill instructions. Full skill instruction injection is tracked by the general-purpose-agent specification.

Adding tools

Tools are Python callables registered on a TaskAgent. A tool must also appear in allowed_tools; registration alone does not grant permission to run it:

from atbots.agent import TaskAgent
from atbots.capabilities import Tool
from atbots.config import AtBotConfig

config = AtBotConfig(allowed_tools=["memory_recall", "get_weather"])
agent = TaskAgent(config)
agent.tools.register(
    Tool(
        name="get_weather",
        description="Get the current weather for a city.",
        input_schema={
            "type": "object",
            "required": ["city"],
            "properties": {"city": {"type": "string"}},
        },
        handler=lambda arguments: {"city": arguments["city"], "temperature": 22},
    )
)

result = agent.run("What is the weather in Sydney?")
print(result.answer)

The built-in memory_recall tool is registered automatically. Custom tool registration is currently a Python API; there is no drop-in tools directory.

Configuring rules, guardrails, hooks, and Pydantic AI tools

AtBots can load native Pydantic AI capabilities from Python modules or files. A capability can bundle instructions and tools; Pydantic AI Hooks can enforce guardrails and observe or modify every stage of a model run.

Create ~/.atbots/capabilities/project_policy.py:

from typing import Any

from pydantic_ai import RunContext
from pydantic_ai.capabilities import Capability, Hooks
from pydantic_ai.exceptions import ToolFailed


project_rules = Capability(
    id="project-rules",
    instructions=(
        "Keep answers concise. Never claim that a task succeeded without a "
        "tool result proving it."
    ),
)


@project_rules.tool_plain
def project_status(project: str) -> dict[str, str]:
    """Return the status of a project."""
    return {"project": project, "status": "active"}


safety_hooks = Hooks(id="project-safety")


@safety_hooks.on.before_run
async def log_run(ctx: RunContext[Any]) -> None:
    print("AtBots run started")


@safety_hooks.on.before_tool_execute
async def block_production_deletes(
    ctx: RunContext[Any],
    *,
    call: Any,
    tool_def: Any,
    args: dict[str, Any],
) -> dict[str, Any]:
    if tool_def.name == "delete_record" and args.get("environment") == "production":
        raise ToolFailed("Deleting production records is forbidden by project policy.")
    return args

Reference the exported capability objects in ~/.atbots/config.json:

{
  "pydantic_capabilities": [
    "~/.atbots/capabilities/project_policy.py:project_rules",
    "~/.atbots/capabilities/project_policy.py:safety_hooks"
  ]
}

Installed Python modules work too:

{
  "pydantic_capabilities": [
    "my_agent_policy:rules",
    "my_agent_policy:guardrails"
  ]
}

Each reference must resolve to a Pydantic AI Capability, Hooks, or another AbstractCapability instance. AtBots passes them through the native capabilities= argument whenever it creates a Pydantic AI agent. This gives extensions access to native instructions, function tools, toolsets, model and tool hooks, output validation hooks, deferred approval, and capability events. Invalid references stop the run with a clear configuration error.

AtBots also retains its own outer task lifecycle hooks. Register these directly with agent.hooks.add(handler) to receive task.started, tool.completed, task.finished, and task.stopped. The configured Pydantic AI hooks operate inside each model run and provide the finer-grained control intended for user guardrails.

Built-in boundaries remain active around extensions: allowed_tools limits AtBots' outer task tools, max_task_steps bounds its task loop, remote egress requires explicit permission, sensitive content is kept local, tool results are size-limited, and the HTTP companion only binds to loopback.

Configuring memory

The current release uses AtMem as its memory backend. By default it stores memory in ~/.atbots/atmem.db. Change memory_path in ~/.atbots/config.json to use another local database file:

{
  "memory_path": "/absolute/path/to/my-memory.db"
}

You can also configure identity boundaries with subject_id, agent_id, and workspace_id. These values determine which governed memories the runtime can read and write. The default values are suitable for one local user:

{
  "subject_id": "local-user",
  "agent_id": "atbot-main",
  "workspace_id": "private"
}

Support for interchangeable memory providers such as mem0 and custom classes is planned, but is not implemented in 0.1.0.

Spec-driven development

This repository uses GitHub Spec Kit. Product behavior is specified first; implementation follows those artifacts.

Artifact	Path
Constitution	.specify/memory/constitution.md
Feature spec	specs/001-general-purpose-agent/spec.md

The specification follows this workflow:

1. /speckit-constitution — project principles (ratified, v3.0.0)
2. /speckit-specify — what to build
3. /speckit-clarify — optional quality gate
4. /speckit-plan — how to build it
5. /speckit-checklist — optional requirements review
6. /speckit-tasks — implementation breakdown
7. /speckit-analyze — optional consistency report
8. /speckit-implement — execute remaining tasks
9. /speckit-converge — close gaps against spec, plan, and tasks

The git extension creates numbered feature branches (001-…). Active feature state is .specify/feature.json, not the checked-out branch alone.

Requires the Specify CLI (uv tool install specify-cli).

Development

python -m pip install -e ".[dev]"
python -m pytest -q

License

AtBots is licensed under the Apache License 2.0. It permits commercial and internal enterprise use, modification, and distribution, subject to the license terms. Apache-2.0 also provides an explicit contributor patent grant and does not require an organization to publish private changes merely because it runs the software as a service.