On Windows, cmd.exe /c treats newlines as command separators, silently
dropping code after the first line in `python -c "..."` commands. This
causes multi-line inline Python to produce no output with exit code 0.
Detect multi-line `python -c` commands on Windows, parse them into exec
args via `_split_python_c_args`, and use `create_subprocess_exec` to
bypass cmd.exe entirely. Same principle as Codex's Rust `Command::args()`.
Applied to both the direct execution path and the session spawn path.
Added unit tests for the parser and the exec-vs-shell branching logic.
Extract is_image_file() and reference_non_image_attachments() from
AgentLoop private static methods into nanobot/utils/document.py where
they belong alongside extract_documents(). Simplify config lookup by
removing dead isinstance(dict) branch.
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.
The import was moved to module top in nanobot/cli/commands.py,
so tests must patch nanobot.cli.commands.evaluate_response instead
of nanobot.utils.evaluator.evaluate_response.
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.
Every other streaming provider (anthropic, bedrock, openai_compat,
litellm) reads NANOBOT_STREAM_IDLE_TIMEOUT_S with a 90s default. The
Codex provider hardcoded 60s in _request_codex, so it could not be
tuned the same way and aborted streams sooner than its peers.
Read the same env var with the same default and pass it as the httpx
client timeout. The variable name and int parsing match anthropic /
openai_compat / bedrock verbatim.
#4009 normalized the error response when the timeout fires; this PR
fixes the timeout knob itself.
Introduce webhook mode for the Telegram channel and implement a session-based message reordering mechanism.
Key changes:
- Update `python-telegram-bot` dependency to include the `webhooks` extra.
- Add `TelegramConfig` fields for webhook configuration, with validation rules for public HTTPS URLs and Telegram's secret token.
- Implement `_enqueue_ordered_update` and `_drain_ordered_updates` in `TelegramChannel` to stage incoming messages and commands behind a short per-session reorder
window, ensuring sequential delivery based on message and update IDs.
- Configure `start_webhook` in `TelegramChannel.start()` when webhook mode is enabled.
- Add unit tests for webhook config validations, webhook startup, and message reordering.
- Document webhook configuration and reverse proxy details in `docs/chat-apps.md`.
`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.
Ensure converted Responses API input items use unique replay ids when restoring assistant messages and function calls. This prevents Codex from rejecting resumed conversations with duplicate rs_* item ids while preserving call_id-based tool result linkage.
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>
