feat(mma): Complete Visual DAG implementation, fix link creation/deletion, and sync docs

conductor/product.md

@@ -31,7 +31,7 @@ For deep implementation details when planning or implementing tracks, consult `d
   - **MMA Delegation Engine:** Route tasks, ensuring role-scoped context and detailed observability via timestamped sub-agent logs. Supports dynamic ticket creation and dependency resolution via an automated Dispatcher Loop.
   - **MMA Observability Dashboard:** A high-density control center within the GUI for monitoring and managing the 4-Tier architecture.
     - **Track Browser:** Real-time visualization of all implementation tracks with status indicators and progress bars.
-    - **Hierarchical Task DAG:** An interactive, tree-based visualizer for the active track's task dependencies, featuring color-coded state tracking (Ready, Running, Blocked, Done) and manual retry/skip overrides.
+    - **Visual Task DAG:** An interactive, node-based visualizer for the active track's task dependencies using `imgui-node-editor`. Features color-coded state tracking (Ready, Running, Blocked, Done), drag-and-drop dependency creation, and right-click deletion.
     - **Strategy Visualization:** Dedicated real-time output streams for Tier 1 (Strategic Planning) and Tier 2/3 (Execution) agents, allowing the user to follow the agent's reasoning chains alongside the task DAG.
   - **Track-Scoped State Management:** Segregates discussion history and task progress into per-track state files (e.g., `conductor/tracks/<track_id>/state.toml`). This prevents global context pollution and ensures the Tech Lead session is isolated to the specific track's objective.
   **Native DAG Execution Engine:** Employs a Python-based Directed Acyclic Graph (DAG) engine to manage complex task dependencies. Supports automated topological sorting, robust cycle detection, and **transitive blocking propagation** (cascading `blocked` status to downstream dependents to prevent execution stalls).

conductor/tech-stack.md

@@ -7,7 +7,7 @@
 ## GUI Frameworks
 
 - **Dear PyGui:** For immediate/retained mode GUI rendering and node mapping.
-- **ImGui Bundle (`imgui-bundle`):** To provide advanced multi-viewport and dockable panel capabilities on top of Dear ImGui.
+- **ImGui Bundle (`imgui-bundle`):** To provide advanced multi-viewport and dockable panel capabilities on top of Dear ImGui. Includes **imgui-node-editor** for complex graph-based visualizations.
 
 ## Web & Service Frameworks
 

conductor/tracks.md

@@ -32,6 +32,10 @@ This file tracks all major tracks for the project. Each track has its own detail
 5. [ ] **Track: Transitioning to Native Orchestrator**
    *Link: [./tracks/native_orchestrator_20260306/](./tracks/native_orchestrator_20260306/)*
 
+21. [ ] **Track: MiniMax Provider Integration**
+   *Link: [./tracks/minimax_provider_20260306/](./tracks/minimax_provider_20260306/)*
+   *Link: [./tracks/native_orchestrator_20260306/](./tracks/native_orchestrator_20260306/)*
+
 ---
 
 ### GUI Overhauls & Visualizations
@@ -118,3 +122,4 @@ This file tracks all major tracks for the project. Each track has its own detail
 - [x] **Track: Simulation Hardening**
 - [x] **Track: Deep Architectural Documentation Refresh**
 - [x] **Track: Robust Live Simulation Verification**
+

conductor/tracks/minimax_provider_20260306/metadata.json

@@ -0,0 +1,10 @@
+{
+  "id": "minimax_provider_20260306",
+  "title": "MiniMax Provider Integration",
+  "description": "Add MiniMax as a new AI provider with M2.5, M2.1, M2 models",
+  "type": "feature",
+  "status": "new",
+  "created_at": "2026-03-06",
+  "priority": "high",
+  "owner": "tier2-tech-lead"
+}

conductor/tracks/minimax_provider_20260306/plan.md

@@ -0,0 +1,93 @@
+# Implementation Plan: MiniMax Provider Integration (minimax_provider_20260306)
+
+> **Reference:** [Spec](./spec.md)
+
+## Phase 1: Provider Registration
+Focus: Add minimax to PROVIDERS lists and credentials
+
+- [ ] Task 1.1: Add "minimax" to PROVIDERS list
+    - WHERE: src/gui_2.py line 28
+    - WHAT: Add "minimax" to PROVIDERS list
+    - HOW: Edit the list
+
+- [ ] Task 1.2: Add "minimax" to app_controller.py PROVIDERS
+    - WHERE: src/app_controller.py line 117
+    - WHAT: Add "minimax" to PROVIDERS list
+
+- [ ] Task 1.3: Add minimax credentials template
+    - WHERE: src/ai_client.py (credentials template section)
+    - WHAT: Add minimax API key section to credentials template
+    - HOW:
+      ```toml
+      [minimax]
+      api_key = "your-key"
+      ```
+
+## Phase 2: Client Implementation
+Focus: Implement MiniMax client and model listing
+
+- [ ] Task 2.1: Add client globals
+    - WHERE: src/ai_client.py (around line 73)
+    - WHAT: Add _minimax_client, _minimax_history, _minimax_history_lock
+
+- [ ] Task 2.2: Implement _list_minimax_models
+    - WHERE: src/ai_client.py
+    - WHAT: Return list of available models
+    - HOW:
+      ```python
+      def _list_minimax_models(api_key: str) -> list[str]:
+          return ["MiniMax-M2.5", "MiniMax-M2.5-highspeed", "MiniMax-M2.1", "MiniMax-M2.1-highspeed", "MiniMax-M2"]
+      ```
+
+- [ ] Task 2.3: Implement _classify_minimax_error
+    - WHERE: src/ai_client.py
+    - WHAT: Map MiniMax errors to ProviderError
+
+- [ ] Task 2.4: Implement _ensure_minimax_client
+    - WHERE: src/ai_client.py
+    - WHAT: Initialize OpenAI client with MiniMax base URL
+
+## Phase 3: Send Implementation
+Focus: Implement _send_minimax function
+
+- [ ] Task 3.1: Implement _send_minimax
+    - WHERE: src/ai_client.py (after _send_deepseek)
+    - WHAT: Send chat completion request to MiniMax API
+    - HOW:
+      - Use OpenAI SDK with base_url="https://api.minimax.chat/v1"
+      - Support streaming and non-streaming
+      - Handle tool calls
+      - Manage conversation history
+
+- [ ] Task 3.2: Add minimax to list_models routing
+    - WHERE: src/ai_client.py list_models function
+    - WHAT: Add elif provider == "minimax": return _list_minimax_models()
+
+## Phase 4: Integration
+Focus: Wire minimax into the send function
+
+- [ ] Task 4.1: Add minimax to set_provider
+    - WHERE: src/ai_client.py set_provider function
+    - WHAT: Validate minimax model
+
+- [ ] Task 4.2: Add minimax to send routing
+    - WHERE: src/ai_client.py send function (around line 1607)
+    - WHAT: Add elif for minimax to call _send_minimax
+
+- [ ] Task 4.3: Add minimax to reset_session
+    - WHERE: src/ai_client.py reset_session function
+    - WHAT: Clear minimax history
+
+- [ ] Task 4.4: Add minimax to history bleeding
+    - WHERE: src/ai_client.py _add_bleed_derived
+    - WHAT: Handle minimax history
+
+## Phase 5: Testing
+Focus: Verify integration works
+
+- [ ] Task 5.1: Write unit tests for minimax integration
+    - WHERE: tests/test_minimax_provider.py
+    - WHAT: Test model listing, error classification
+
+- [ ] Task 5.2: Manual verification
+    - WHAT: Test provider switching in GUI

conductor/tracks/minimax_provider_20260306/spec.md

@@ -0,0 +1,53 @@
+# Track Specification: MiniMax Provider Integration
+
+## Overview
+Add MiniMax as a new AI provider to Manual Slop. MiniMax provides high-performance text generation models (M2.5, M2.1, M2) with massive context windows (200k+ tokens) and competitive pricing.
+
+## Current State Audit
+- `src/ai_client.py`: Contains provider integration for gemini, anthropic, gemini_cli, deepseek
+- `src/gui_2.py`: Line 28 - PROVIDERS list
+- `src/app_controller.py`: Line 117 - PROVIDERS list
+- credentials.toml: Has sections for gemini, anthropic, deepseek
+
+## Integration Approach
+Based on MiniMax documentation, the API is compatible with both **Anthropic SDK** and **OpenAI SDK**. We will use the **OpenAI SDK** approach since it is well-supported and similar to DeepSeek integration.
+
+### API Details (from platform.minimax.io)
+- **Base URL**: `https://api.minimax.chat/v1`
+- **Models**:
+  - `MiniMax-M2.5` (204,800 context, ~60 tps)
+  - `MiniMax-M2.5-highspeed` (204,800 context, ~100 tps)
+  - `MiniMax-M2.1` (204,800 context)
+  - `MiniMax-M2.1-highspeed` (204,800 context)
+  - `MiniMax-M2` (204,800 context)
+- **Authentication**: API key in header `Authorization: Bearer <key>`
+
+## Goals
+1. Add minimax provider to Manual Slop
+2. Support chat completions with tool calling
+3. Integrate into existing provider switching UI
+
+## Functional Requirements
+- FR1: Add "minimax" to PROVIDERS list in gui_2.py and app_controller.py
+- FR2: Add minimax credentials section to credentials.toml template
+- FR3: Implement _minimax_client initialization
+- FR4: Implement _list_minimax_models function
+- FR5: Implement _send_minimax function with streaming support
+- FR6: Implement error classification for MiniMax
+- FR7: Add minimax to provider switching dropdown in GUI
+- FR8: Add to ai_client.py send() function routing
+- FR9: Add history management (like deepseek)
+
+## Non-Functional Requirements
+- NFR1: Follow existing provider pattern (see deepseek integration)
+- NFR2: Support tool calling for function execution
+- NFR3: Handle rate limits and auth errors
+- NFR4: Use OpenAI SDK for simplicity
+
+## Architecture Reference
+- `docs/guide_architecture.md`: AI client multi-provider architecture
+- Existing deepseek integration in `src/ai_client.py` (lines 1328-1520)
+
+## Out of Scope
+- Voice/T2S, Video, Image generation (text only for this track)
+- Caching support (future enhancement)