- Add 7 edge-case tests: validation crash resilience, hook exception safety, mid-run correction injection, FIFO correction ordering, explicit file changes overriding auto-detection, final budget for max_steps=1, and dynamic budget switching boundaries
- Fix assertion in test_long_task_completes_after_multiple_handoffs to match exact prompt format
- Remove asyncio timing hack from test_state_exposure
- Add asyncio.sleep(0) yield in test_inject_correction_during_execution to prevent race between signal injection and step continuation
- All 34 tests passing
- Structured HandoffState: HandoffTool now accepts files_created,
files_modified, next_step_hint, and verification fields instead of
a plain string. Progress is passed between steps as structured data.
- Completion validation round: After complete() is called, a dedicated
validator step runs to verify the claim against the original goal.
If validation fails, the task continues rather than returning
a false completion.
- Dynamic prompt system: 3 Jinja2 templates (step_start, step_middle,
step_final) selected based on step number. Final steps get tighter
budget and stronger "wrap up" guidance.
- Automatic file change tracking: Extracts write_file/edit_file events
from tool_events and injects them into the next step's context if
the subagent forgot to report them explicitly.
- Budget tracking & adaptive strategy: Cumulative token usage is tracked
across steps. Per-step tool budget drops from 8 to 4 in the last
two steps to force handoff/completion.
- Crash retry with graceful degradation: A step that crashes is retried
once. Persistent crashes terminate the task and return partial progress.
- Full observability hooks for future WebUI integration:
- set_hooks() with on_step_start, on_step_complete, on_handoff,
on_validation_started, on_validation_passed, on_validation_failed,
on_task_complete, on_task_error, and catch-all on_event.
- Readable state properties: current_step, total_steps, status,
last_handoff, cumulative_usage, goal.
- inject_correction() allows external code to send user corrections
that are injected into the next step's prompt.
- run_step() accepts optional max_iterations for dynamic budget control.
All 27 long-task tests and 11 subagent tests pass.
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
MyTool blocks direct access to sensitive nested paths, but its formatter
still printed scalar fields for small config objects. That let
`my(action="check", key="web_config.search")` expose `api_key` in plain
text even though the docs promise sensitive sub-fields are protected.
This keeps the change narrow: sensitive nested config fields are omitted
from MyTool's formatted output, and regression coverage locks the
behavior in.
Constraint: Must preserve existing read-only inspection behavior for non-sensitive fields
Constraint: Keep scope limited to MyTool rather than introducing broader redaction plumbing
Rejected: Rework global context/tool redaction around MyTool | broader than needed for the leak path
Confidence: high
Scope-risk: narrow
Reversibility: clean
Directive: If more nested config rendering is added later, filter sensitive field names at the formatter boundary as well as the path resolver
Tested: PYTHONPATH=$PWD pytest -q tests/agent/tools/test_self_tool.py /Users/jh0927/Workspace/nanobot-validation-artifacts-2026-04-18/test_my_tool_secret_leak_regression.py
Not-tested: Full repository test suite
Related: #3259
Add a built-in tool that lets the agent inspect and modify its own
runtime state (model, iterations, context window, etc.).
Key features:
- inspect: view current config, usage stats, and subagent status
- modify: adjust parameters at runtime (protected by type/range validation)
- Subagent observability: inspect running subagent tasks (phase,
iteration, tool events, errors) — subagents are no longer a black box
- Watchdog corrects out-of-bounds values on each iteration
- Enabled by default in read-only mode (self_modify: false)
- All changes are in-memory only; restart restores defaults
- Comprehensive test suite (90 tests)
Includes a self-awareness skill (always-on) with progressive disclosure:
SKILL.md for core rules, references/examples.md for detailed scenarios.
