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fix(docs): update stale docstring to reflect shipped inbound delivery paths
The module docstring claimed "No inbound A2A server is bundled here yet"
but A2AServer (push) and PollDelivery (poll) have been implemented and
documented since Phase 30.8b/30.8c. Replaced the outdated claim with a
concise description of both delivery modes and how run_agent_loop uses them.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-15 20:19:09 +00:00

molecule_plugin — Python SDK for building Molecule AI plugins

A Molecule AI plugin is a directory that bundles rules, skills, and per-runtime install adaptors. Any plugin that conforms to this contract is installable on any Molecule AI workspace whose runtime the plugin supports.

Quick start

Copy template/ to a new directory and edit:

my-plugin/
├── plugin.yaml              # name, version, runtimes, description
├── rules/my-rule.md         # optional — appended to CLAUDE.md at install
├── skills/my-skill/
│   ├── SKILL.md             # instructions injected into the system prompt
│   └── tools/do_thing.py    # optional LangChain @tool functions
└── adapters/
    ├── claude_code.py       # one-liner: `from molecule_plugin import AgentskillsAdaptor as Adaptor`
    └── deepagents.py        # same

Validate:

from molecule_plugin import validate_manifest
errors = validate_manifest("my-plugin/plugin.yaml")
assert not errors, errors

CLI

The SDK ships a CLI for validating Molecule AI artifacts before publishing:

python -m molecule_plugin validate plugin    my-plugin/
python -m molecule_plugin validate workspace workspace-configs-templates/claude-code-default/
python -m molecule_plugin validate org       org-templates/molecule-dev/
python -m molecule_plugin validate channel   channels.yaml
python -m molecule_plugin validate my-plugin/   # kind defaults to 'plugin'

Exit code is 0 when valid, 1 when any errors are found — suitable for CI. Add -q / --quiet to suppress success lines and emit only errors.

Programmatic equivalents:

from molecule_plugin import (
    validate_plugin,
    validate_workspace_template,
    validate_org_template,
    validate_channel_file,
    validate_channel_config,
)

Per-runtime adaptors — when to write a custom one

The default AgentskillsAdaptor handles the common shape: rules go into the runtime's memory file (CLAUDE.md), skill dirs go into /configs/skills/. That covers most plugins.

Write a custom adaptor when you need to:

  • Register runtime tools dynamically — call ctx.register_tool(name, fn).
  • Register DeepAgents sub-agents — call ctx.register_subagent(name, spec).
  • Write to a non-standard memory file — call ctx.append_to_memory(filename, content).

Minimum custom adaptor:

# adapters/deepagents.py
from molecule_plugin import InstallContext, InstallResult

class Adaptor:
    def __init__(self, plugin_name: str, runtime: str):
        self.plugin_name, self.runtime = plugin_name, runtime

    async def install(self, ctx: InstallContext) -> InstallResult:
        ctx.register_subagent("my-agent", {"prompt": "...", "tools": [...]})
        return InstallResult(plugin_name=self.plugin_name, runtime=self.runtime, source="plugin")

    async def uninstall(self, ctx: InstallContext) -> None:
        pass

Resolution order (understood by the platform)

For (plugin_name, runtime):

  1. Platform registryworkspace-template/plugins_registry/<plugin>/<runtime>.py (curated; set by the Molecule AI team for quality-assured plugins).
  2. Plugin-shipped<plugin_root>/adapters/<runtime>.py (what this SDK helps you build).
  3. Raw-drop fallback — copies plugin files into /configs/plugins/<name>/ and surfaces a warning; no tools are wired.

You generally ship for path #2. If your plugin becomes popular enough to be promoted to "default," the Molecule AI team PRs a copy of your adaptor into the platform registry (path #1) so it survives upstream breakage.

Testing locally

The SDK ships AgentskillsAdaptor as a standalone, unit-testable class:

import asyncio
from pathlib import Path
from molecule_plugin import AgentskillsAdaptor, InstallContext

ctx = InstallContext(
    configs_dir=Path("/tmp/configs"),
    workspace_id="local",
    runtime="claude_code",
    plugin_root=Path("./my-plugin"),
)
asyncio.run(AgentskillsAdaptor("my-plugin", "claude_code").install(ctx))
# check /tmp/configs/CLAUDE.md, /tmp/configs/skills/

Publishing

A plugin is just a directory. Push it to any Git host. Installation via POST /plugins/install {git_url} is on the roadmap — see the platform's PLAN.md under "Install-from-GitHub-URL flow." Until then, plugins are bundled into the platform by dropping them into plugins/ at deploy time.

Supported runtimes

As of 2026-Q2: claude_code, deepagents, langgraph, crewai, autogen, openclaw. See the live list with:

curl $PLATFORM_URL/plugins
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