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chore(conductor): Add new track 'OpenAI Provider Integration'

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Edward R. Gonzalez
2026-03-08 13:46:38 -04:00
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# Track openai_integration_20260308 Context
- [Specification](./spec.md)
- [Implementation Plan](./plan.md)
- [Metadata](./metadata.json)

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{
"track_id": "openai_integration_20260308",
"type": "feature",
"status": "new",
"created_at": "2026-03-08T13:47:00Z",
"updated_at": "2026-03-08T13:47:00Z",
"description": "Add support for openai vendor (GPT/codex)."
}

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# Implementation Plan: OpenAI Provider Integration
## Phase 1: Core Client Implementation
- [ ] Task: Define OpenAI state and initialize client in `src/ai_client.py`.
- [ ] Import `openai` (ensure it's added to `requirements.txt` if missing).
- [ ] Add `_openai_client` and `_openai_history` module-level variables.
- [ ] Implement `_classify_openai_error(exc)` for structured error mapping.
- [ ] Task: Implement `_send_openai` tool-call loop.
- [ ] Implement function/tool definition conversion to OpenAI format.
- [ ] Implement the core Chat Completions API call with streaming support.
- [ ] Implement tool result packaging and the recursion loop (up to `MAX_TOOL_ROUNDS`).
- [ ] Integrate `_reread_file_items` for context refresh after tool rounds.
- [ ] Task: Implement Multi-modal (Vision) support.
- [ ] Add logic to `_send_openai` to process `screenshot` inputs by encoding them as base64 data URIs.
- [ ] Task: Implement model discovery.
- [ ] Implement `_list_openai_models()` using the `client.models.list()` API.
- [ ] Task: Update `ai_client.py` utility functions.
- [ ] Update `send()` dispatcher to route to `_send_openai`.
- [ ] Update `reset_session()` to clear `_openai_history`.
- [ ] Task: Conductor - User Manual Verification 'Phase 1: Core Client Implementation' (Protocol in workflow.md)
## Phase 2: Configuration & Credentials
- [ ] Task: Update credential loading in `src/ai_client.py`.
- [ ] Update `_load_credentials()` to include `openai` in the error message and loading logic.
- [ ] Task: Add cost tracking for OpenAI models in `src/cost_tracker.py`.
- [ ] Add regex patterns and rates for `gpt-4o`, `gpt-4o-mini`, `o1`, and `o3-mini` to `MODEL_PRICING`.
- [ ] Task: Verify that `credentials.toml` works with the new provider section.
- [ ] Task: Conductor - User Manual Verification 'Phase 2: Configuration & Credentials' (Protocol in workflow.md)
## Phase 3: GUI & Controller Integration
- [ ] Task: Register `openai` as a provider.
- [ ] Add `openai` to `PROVIDERS` in `src/gui_2.py`.
- [ ] Add `openai` to `PROVIDERS` in `src/app_controller.py`.
- [ ] Task: Ensure model fetching works in the GUI.
- [ ] Verify that clicking "Fetch Models" in the AI Settings panel correctly populates OpenAI models when the provider is selected.
- [ ] Task: Conductor - User Manual Verification 'Phase 3: GUI & Controller Integration' (Protocol in workflow.md)
## Phase 4: MMA Integration & Final Verification
- [ ] Task: Verify MMA compatibility.
- [ ] Test a simple multi-agent workflow where a Tier 3 worker is configured to use `gpt-4o-mini`.
- [ ] Verify that tool calls, history, and context injection work as expected within the tiered architecture.
- [ ] Task: Run full regression suite.
- [ ] Ensure adding OpenAI hasn't introduced side effects for Gemini or Anthropic providers.
- [ ] Task: Conductor - User Manual Verification 'Phase 4: MMA Integration & Final Verification' (Protocol in workflow.md)

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# Specification: OpenAI Provider Integration
## Overview
This track introduces support for OpenAI as a first-class model provider. It involves implementing a dedicated client in `src/ai_client.py`, updating configuration models, enhancing the GUI for provider selection, and integrating OpenAI into the tiered MMA architecture.
## Functional Requirements
### 1. Core AI Client (`src/ai_client.py`)
- **OpenAI Integration:**
- Implement `_send_openai()` to handle communication with OpenAI's Chat Completions API.
- Implement a tool-call loop similar to `_send_gemini` and `_send_anthropic`.
- Support for function calling (using `tools` and `tool_choice`).
- Support for multi-modal input (Vision) by encoding screenshots as base64 data URIs in the message payload.
- Implement response streaming support compatible with the existing GUI mechanism.
- **State Management:**
- Define module-level `_openai_client` and `_openai_history`.
- Ensure `ai_client.reset_session()` clears OpenAI-specific history.
- **Error Handling:**
- Implement `_classify_openai_error()` to map OpenAI API exceptions to `ProviderError` types (`quota`, `rate_limit`, `auth`, `balance`, `network`).
- **Model Discovery:**
- Implement `_list_openai_models()` to fetch available models from the API.
### 2. Configuration & Authentication
- **Credentials:**
- Update `_load_credentials()` to support an `[openai]` section in `credentials.toml`.
- Update the `FileNotFoundError` message in `_load_credentials()` to include the OpenAI example.
- **Cost Tracking:**
- Update `src/cost_tracker.py` with pricing for `gpt-4o`, `gpt-4o-mini`, `o1`, and `o3-mini`.
### 3. GUI & Controller Integration
- **Provider Lists:**
- Add `openai` to the `PROVIDERS` list in `src/gui_2.py` and `src/app_controller.py`.
- **Model Fetching:**
- Ensure `AppController._fetch_models()` correctly dispatches to `ai_client.list_models("openai")`.
- **Settings UI:**
- The AI Settings panel should automatically handle the new provider and its models once added to the lists.
### 4. MMA Orchestration
- **Tier Support:**
- Verify that agents in all tiers (1-4) can be configured to use OpenAI models via the `mma_tier_usage` dict in `AppController`.
- Ensure `run_worker_lifecycle` in `src/multi_agent_conductor.py` correctly passes OpenAI model names to `ai_client.send()`.
## Non-Functional Requirements
- **Consistency:** Follow the established pattern of module-level singleton state in `ai_client.py`.
- **Latency:** Ensure the tool-call loop overhead is minimal.
- **Security:** Rigorously protect the OpenAI API key; ensure it is never logged or exposed in the GUI.
## Acceptance Criteria
- [ ] OpenAI can be selected as a provider in the AI Settings panel.
- [ ] Models like `gpt-4o` and `gpt-4o-mini` are listed and selectable.
- [ ] Agents can successfully use tools (e.g., `read_file`, `run_powershell`) using OpenAI models.
- [ ] Screenshots are correctly processed and described by vision-capable OpenAI models.
- [ ] Response streaming is functional in the Discussion panel.
- [ ] Estimated costs for OpenAI calls are displayed in the MMA Dashboard.
## Out of Scope
- Support for the legacy `gpt-3.5-turbo-instruct` or other non-chat models.
- Batch API support.
- Fine-tuning management.