Resolve two critical issues in planner mode that persisted through
multiple fix attempts:
1. Remove excessive whitespace between tool call displays by replacing
direct println!() calls with ui_writer methods and eliminating
redundant newlines in agent response streaming.
2. Ensure all log files (errors, sessions, tool calls, context dumps)
are written to <workspace>/logs instead of codepath by properly
initializing G3_WORKSPACE_PATH from --workspace argument.
Improve planner mode user experience with better error reporting,
cleaner tool output, and consistent log file placement.
- Propagate and display classified LLM errors to users with
appropriate icons and context
- Display tool calls on single lines with truncated arguments
- Show LLM text responses without overwriting via UiWriter
- Ensure all logs write to workspace/logs directory consistently
- Set G3_WORKSPACE_PATH early in planning mode initialization
- Display coach feedback content (up to 25 lines) instead of just length
- Write GIT COMMIT entry to history before actual commit for better a...
- Implement single-line status updates during LLM processing with too...
- Display non-tool LLM text responses in planner UI
- Redirect all logs to <workspace>/logs directory instead of codepath
- Preserve TODO file in planner mode for history (prevent deletion)
Completed files:
- completed_requirements_2025-12-09_16-16-51.md
- completed_todo_2025-12-09_16-16-51.md
When the CW is full, max_tokens is often passed at 0 or tiny. The LLM will fail. For Anthropic with thining, there is also the thinking budget.
This can happen during summary attempts, in that case
first try thinnify, skinnify etc..
Writes the current context window to logs/current_context_window (uses a symlink to a session ID).
This PR was unfortunately generated by a different LLM and did a ton of superficial reformating, it's actually a fairly small and benign change, but I don't want to roll back everything. Hope that's ok.
This tries to short-circuit multiple round-trips to llm for reading code.
It's a precursor to trying to context engineer tailored to specific tasks.
In initial experiments, it's only marginally faster than regular mode, and burns more tokens.
