# Implementation Plan: Context & Token Visualization

Architecture reference: [docs/guide_architecture.md](../../docs/guide_architecture.md) — AI Client section

## Phase 1: Token Budget Display

- [x] Task 1.1: Add a new method `_render_token_budget_panel(self)` in `gui_2.py`. 5bfb20f Place it in the Provider panel area (after `_render_provider_panel`, gui_2.py:2485-2542), or as a new collapsible section within the provider panel. Call `ai_client.get_history_bleed_stats(self._last_stable_md)` — need to cache `self._last_stable_md` from the last `_do_generate()` call (gui_2.py:1408-1425, the `stable_md` return value). Store the result in `self._token_stats: dict = {}`, refreshed on each `_do_generate` call and on provider/model switch.
- [x] Task 1.2: Render the utilization bar. 5bfb20f Use `imgui.progress_bar(stats['utilization_pct'] / 100, ImVec2(-1, 0), f"{stats['utilization_pct']:.1f}%")`. Color-code via `imgui.push_style_color(imgui.Col_.plot_histogram, ...)`: green if <50%, yellow if 50-80%, red if >80%. Below the bar, show: `f"{stats['estimated_prompt_tokens']:,} / {stats['max_prompt_tokens']:,} tokens ({stats['headroom_tokens']:,} remaining)"`.
- [x] Task 1.3: Render the proportion breakdown as a 3-row table. 5bfb20f: System (`system_tokens`), Tools (`tools_tokens`), History (`history_tokens`). Each row shows token count and percentage of total. Use `imgui.begin_table("token_breakdown", 3)` with columns: Component, Tokens, Pct.
- [x] Task 1.4: Write tests verifying `_render_token_budget_panel`. 5bfb20f calls `get_history_bleed_stats` and handles the empty dict case (when no provider is configured).

## Phase 2: Trimming Preview & Cache Status

- [x] Task 2.1: When `stats.get('would_trim')` is True. 7b5d9b1, render a warning: `imgui.text_colored(ImVec4(1,0.3,0,1), "WARNING: Next call will trim history")`. Below it, show `f"Trimmable turns: {stats['trimmable_turns']}"`. If `stats` contains per-message breakdown, render the first 3 trimmable messages with their role and token count in a compact list.
- [x] Task 2.2: Add Gemini cache status display. 7b5d9b1 Read `ai_client._gemini_cache` (check `is not None`), `ai_client._gemini_cache_created_at`, and `ai_client._GEMINI_CACHE_TTL`. If cache exists, show: `"Gemini Cache: ACTIVE | Age: {age_seconds}s / {ttl}s | Renews at: {ttl * 0.9:.0f}s"`. If not, show `"Gemini Cache: INACTIVE"`. Guard with `if ai_client._provider == "gemini":`.
- [x] Task 2.3: Add Anthropic cache hint. 7b5d9b1 When provider is `"anthropic"`, show: `"Anthropic: 4-breakpoint ephemeral caching (auto-managed)"` with the number of history turns and whether the latest response used cache reads (check last comms log entry for `cache_read_input_tokens`).
- [x] Task 2.4: Write tests for trimming warning visibility and cache status display. 7b5d9b1

## Phase 3: Auto-Refresh & Integration

- [x] Task 3.1: Hook `_token_stats` refresh into three trigger points. 6f18102: (a) after `_do_generate()` completes — cache `stable_md` and call `get_history_bleed_stats`; (b) after provider/model switch in `current_provider.setter` and `current_model.setter` — clear and re-fetch; (c) after each `handle_ai_response` in `_process_pending_gui_tasks` — refresh stats since history grew. For (c), use a flag `self._token_stats_dirty = True` and refresh in the next frame's render call to avoid calling the stats function too frequently.
- [x] Task 3.2: Add the token budget panel to the Hook API. 6f18102 Extend `/api/gui/mma_status` (or add a new `/api/gui/token_stats` endpoint) to expose `_token_stats` for simulation verification. This allows tests to assert on token utilization levels.
- [x] Task 3.3: Conductor - User Manual Verification 'Phase 3: Auto-Refresh & Integration' (Protocol in workflow.md). 2929a64 — verified by user, panel rendering correctly.
