Replace asyncio.sleep(0.05) with an asyncio.Event + patched Lock.acquire
to guarantee the waiting task has reached the lock before asserting. Add
a test confirming LongTaskTool and CompleteGoalTool ContextVars are
isolated, and document the design intent in _GoalToolsMixin.
Remove standalone nanobot/heartbeat/ service and replace it with an
auto-registered system cron job on gateway startup. Key behaviors preserved:
- HeartbeatConfig (enabled, interval_s, keep_recent_messages) remains in
GatewayConfig for backward compatibility.
- On startup, if enabled, a system cron job "heartbeat" is registered with
schedule derived from interval_s.
- HEARTBEAT.md is checked on each tick; empty/template-identical files skip
to avoid wasting LLM calls.
- Post-run evaluate_response and session history truncation
(keep_recent_messages) are retained.
- Delivery target selection, deliverable filtering, and preamble guidance
are preserved.
Files removed:
- nanobot/heartbeat/__init__.py
- nanobot/heartbeat/service.py
- tests/heartbeat/*
- tests/agent/test_heartbeat_service.py
Templates and docs updated to reflect cron-based usage.
`long_task` registers a sustained objective, but `AgentRunner` would
still exit with `stop_reason="completed"` when the LLM produced a final
text response without calling `complete_goal`. This defeated the purpose
of sustained goals.
Add `goal_active_predicate` and `goal_continue_message` to `AgentRunSpec`.
When the predicate returns `True` at the natural completion checkpoint,
inject a continuation message via the existing `_try_drain_injections`
machinery, forcing the runner to continue looping.
Also extract the default continuation prompt to
`nanobot/utils/runtime.py` alongside the existing recovery-message
builders.
The maxConcurrentSubagents field in AgentDefaults was never wired
through AgentLoop.from_config() → AgentLoop.__init__() →
SubagentManager.__init__(), causing it to always fall back to the
hardcoded default of 1 regardless of the user's config.
Add two new image generation providers:
- `openai` — uses the standalone OpenAI Images API
(`/v1/images/generations`) with an API key. Supports DALL-E
and gpt-image-* models, with automatic parameter adjustment
(gpt-image models don't accept response_format or n).
- `openai_codex` — uses the Codex Responses API with the
`image_generation` tool, authenticated via OAuth subscription
token. The same mechanism ChatGPT uses internally.
Also remove the API key pre-check in ImageGenerationTool so
providers that handle their own auth fallback (like Codex OAuth)
can work without a configured key.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Drop the legacy unified-diff patch parameter and all related parsing/
generation logic (_parse_patch, _generate_patch, _apply_hunks, etc.).
The tool now accepts only the structured `edits` array, eliminating the
intermediate diff-string round-trip.
Also update file_edit_events tracking and tests to work exclusively
with edits.
Benchmark (zhipu glm-5.1, edits mode): 15/15 cases passed.
The runtime media-attachment mechanism was broken for streaming channels
(e.g. WebSocket): the _streamed flag caused _send_once to skip the final
OutboundMessage that carried generated media, so images were never delivered.
Rather than adding complex coordination between streaming and media delivery,
delegate image delivery to the LLM: after generate_image returns artifact
paths, the next_step prompt now instructs the LLM to call the message tool
with the paths in the media parameter. This works uniformly across all
channels, streaming or not.
Remove generated_media from TurnContext, _assemble_outbound, and _state_save.
Update prompts in identity.md, SKILL.md, message tool description, and
artifacts.py to reflect the new flow.
Adds ImageGenerationProvider ABC with shared __init__, _http_post(), and
_require_images(). Introduces _IMAGE_GEN_PROVIDERS registry with
register/get/image_gen_provider_configs() helpers.
Four existing providers (OpenRouter, AIHubMix, Gemini, MiniMax) now inherit
from the base class and self-register. Adding a new provider only requires
writing one class + one registration line.
Eliminates if/else chains in the tool dispatch and hardcoded provider config
dicts in commands.py (3 sites) and nanobot.py (1 site). Fixes the agent CLI
command missing image_generation_provider_configs entirely.
Also simplifies test monkeypatch targets to patch the registry lookup.
Adds GeminiImageGenerationClient covering both Imagen 4 (:predict) and
Gemini Flash (:generateContent), wires the gemini ProviderConfig through
the SDK, API server, and gateway entry points, and updates the
image-generation docs and skill. Errors from the Gemini endpoints are
logged and surface with the HTTP status and parsed message instead of an
empty string.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add MiniMaxImageGenerationClient with support for:
- Text-to-image generation via MiniMax image-01 model
- Reference image support (subject_reference)
- Aspect ratio selection
- Proper error handling aligned with existing providers
Wire up MiniMax provider config in ImageGenerationTool, gateway,
serve, and Nanobot class.
Replace AutoCompact._archive() direct session mutation with delegation
to Consolidator.compact_idle_session(). Remove _split_unconsolidated()
method since that logic now lives inside compact_idle_session.
All session mutation for idle compaction now goes through the
Consolidator's lock, eliminating the race condition between
background token consolidation and idle TTL compaction.
Changes:
- autocompact.py: rewrite _archive() to call compact_idle_session,
remove _split_unconsolidated(), clean up unused imports
- test_autocompact_unit.py: replace TestArchive/TestSplitUnconsolidated
with TestArchiveDelegates that verifies delegation behavior
- test_auto_compact.py: convert all consolidator.archive mocks to
consolidator.compact_idle_session mocks via _make_fake_compact helper
When background consolidation runs with a stale session reference (captured
before AutoCompact replaced the session via compact_idle_session), it could
operate on outdated data. Now, after acquiring the per-session lock, the
method refreshes its session reference from SessionManager.get_or_create().
If the session was replaced, it swaps in the fresh reference before doing
any consolidation work.
This prevents a race where AutoCompact truncates an idle session while a
background maybe_consolidate_by_tokens call is in flight with the old
session object.
Add Consolidator.compact_idle_session(session_key, max_suffix=8) that
performs hard-truncation of idle sessions under the per-session
consolidation lock. This is the single lock-protected path for AutoCompact
to use instead of modifying session state directly, fixing the race
condition between AutoCompact and Consolidator.
Behavior:
- Acquires per-session consolidation lock
- Invalidates cache and reloads fresh from disk
- Splits unconsolidated tail into archive prefix and retained suffix
- Archives prefix via LLM (with raw_archive fallback on failure)
- Persists _last_summary in session metadata on success
- Returns summary text, None on LLM failure, or '' if nothing to archive
Tests: 6 new tests covering prefix archival, empty session timestamp
refresh, (nothing) summary exclusion, LLM failure fallback,
last_consolidated offset, and lock acquisition verification.
_drain_pending injected a full runtime context block (including goal
state) into every injected user message, but the initial message already
carries runtime context via build_messages(). This caused goal state to
appear multiple times in the LLM context window within a single turn,
wasting tokens (up to 4000 chars per duplicate).
Now _drain_pending only passes the raw user content without runtime
context. The initial turn message remains the sole carrier.
The previous regex r"(?:^|[;&|]\s*)format\b" incorrectly blocked
commands containing URL parameters like &format=json. Added negative
lookahead (?!=) so format= (URL param key=value) is allowed while
standalone format commands (e.g. ;format, &format, |format) remain
blocked. Added test cases for both blocking and allowing scenarios.
Centralize runner_wall_llm_timeout_s in session goal_state metadata helpers so
spawned subagents inherit the same policy as AgentLoop without coupling to
long_task. Pass optional resolver into SubagentManager and add tests.
Co-authored-by: Cursor <cursoragent@cursor.com>
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