estimate_prompt_tokens() only counted the `content` text field, completely
missing tool_calls JSON (~72% of actual payload), reasoning_content,
tool_call_id, name, and per-message framing overhead. This caused the
memory consolidator to never trigger for tool-heavy sessions (e.g. cron
jobs), leading to context window overflow errors from the LLM provider.
Also adds reasoning_content counting and proper per-message overhead to
estimate_message_tokens() for consistent boundary detection.
Made-with: Cursor
Merge process_direct() and process_direct_outbound() into a single
interface returning OutboundMessage | None. This eliminates the
dual-path detection logic in CLI single-message mode that relied on
inspect.iscoroutinefunction to distinguish between the two APIs.
Extract status rendering into a pure function build_status_content()
in utils/helpers.py, decoupling it from AgentLoop internals.
Made-with: Cursor
Keep multimodal tool outputs on the native content-block path while
restoring redirect SSRF checks for web_fetch image responses. Also share
image block construction, simplify persisted history sanitization, and
add regression tests for image reads and blocked private redirects.
Made-with: Cursor
- Add nanobot/utils/evaluator.py: lightweight LLM tool-call to decide notify/silent after background task execution
- Remove magic token injection from heartbeat and cron prompts
- Clean session history (no more <SILENT_OK> pollution)
- Add tests for evaluator and updated heartbeat three-phase flow
Share assistant message construction between the main agent and subagents, and add a regression test to keep reasoning_content and thinking_blocks in follow-up tool rounds.
Move consolidation policy into MemoryConsolidator, keep backward compatibility for legacy config, and compress history by token budget instead of message count.
Add support for running multiple nanobot instances with complete isolation:
- Add --config parameter to gateway command for custom config file path
- Implement set_config_path() in config/loader.py for dynamic config path
- Derive data directory from config file location (e.g., ~/.nanobot-xxx/)
- Update get_data_path() to use unified data directory from config loader
- Ensure cron jobs use instance-specific data directory
This enables running multiple isolated nanobot instances by specifying
different config files, with each instance maintaining separate:
- Configuration files
- Workspace (memory, sessions, skills)
- Cron jobs
- Logs and media
Example usage:
nanobot gateway --config ~/.nanobot-instance2/config.json --port 18791
Extract the _split_message function from discord.py and telegram.py
into a shared utility function in utils/helpers.py.
Changes:
- Add split_message() to nanobot/utils/helpers.py with configurable max_len
- Update Discord channel to use shared utility (2000 char limit)
- Update Telegram channel to use shared utility (4000 char limit)
- Remove duplicate implementations from both channels
Benefits:
- Reduces code duplication
- Centralizes message splitting logic for easier maintenance
- Makes the function reusable for future channels
The function splits content into chunks within max_len, preferring
to break at newlines or spaces rather than mid-word.
- Remove trailing whitespace and normalize blank lines
- Unify string quotes and line breaks for long lines
- Sort imports alphabetically across modules
