Implements a meta-ReAct loop where long-running tasks are broken into
sequential subagent steps, each starting fresh with the original goal
and progress from the previous step. This prevents context drift when
agents work on complex, multi-step tasks.
- Extract build_tool_registry() from SubagentManager for reuse
- Add run_step() for synchronous subagent execution (no bus announcement)
- Add HandoffTool and CompleteTool as signal mechanisms via shared dict
- Add LongTaskTool orchestrator with simplified prompt (8 iterations/step)
- Register LongTaskTool in main agent loop
- Add _extract_handoff_from_messages fallback for robustness
This commit implements a progressive refactoring of the tool system to support
plugin discovery, scoped loading, and protocol-driven runtime context injection.
Key changes:
- Add Tool ABC metadata (tool_name, _scopes) and ToolContext dataclass for
dependency injection.
- Introduce ToolLoader with pkgutil-based builtin discovery and
entry_points-based third-party plugin loading.
- Add scope filtering (core/subagent/memory) so different contexts load
appropriate tool sets.
- Introduce ContextAware protocol and RequestContext dataclass to replace
hardcoded per-tool context injection in AgentLoop.
- Add RuntimeState / MutableRuntimeState protocols to decouple MyTool from
AgentLoop.
- Migrate all built-in tools to declare scopes and implement create()/enabled()
hooks.
- Migrate MessageTool, SpawnTool, CronTool, and MyTool to ContextAware.
- Refactor AgentLoop to use ToolLoader and protocol-driven context injection.
- Refactor SubagentManager to use ToolLoader(scope="subagent") with per-run
FileStates isolation.
- Register all built-in tools via pyproject.toml entry_points.
- Add comprehensive tests for loader scopes, entry_points, ContextAware,
subagent tools, and runtime state sync.
Replace the asyncio.Semaphore queueing approach with a simple count
check in SpawnTool.execute(). When the concurrency limit is reached,
the tool returns an error string so the agent can perceive the reason
and adjust its behavior instead of silently queueing.
- Remove max_concurrent_subagents parameter threading through
AgentLoop, commands.py, and nanobot.py
- SubagentManager reads the limit directly from AgentDefaults
- SpawnTool checks get_running_count() before calling spawn()
- Simplify tests to verify rejection behavior
When the main agent spawns multiple sub-agents, each completion
independently triggered a new _dispatch, causing 3-4 user-visible
responses instead of a single comprehensive report.
- Extend _drain_pending to block-wait on pending_queue when sub-agents
are still running, keeping the runner loop alive for in-order injection
- Pass pending_queue in the system message path so subsequent sub-agent
results can still be injected mid-turn via a new dispatch
