### Comprehensive Roadmap for Building the Multi-Agent Application
---
#### **Phase 1: Backend Development**
##### 1. **Project Setup**
- **Languages & Frameworks**:
- Backend: Python (FastAPI for APIs, asyncio for concurrency).
- Database: SQLite for local storage (upgradeable to PostgreSQL).
- Frontend: JavaScript with React or Vue.js for UI.
- **Environment Setup**:
- Create a virtual environment (`venv`).
- Install necessary libraries: `fastapi`, `uvicorn`, `sqlalchemy`, `requests`,
`openai`, `chromedriver`, `playwright`, and `httpx`.
##### 2. **Core Backend Features**
- **Agent Management System**:
- Agents should have:
- Name
- System prompt
- Assigned tools
- LLM configuration (select from OpenAI, OpenRouter, or user-defined LLMs).
- Implement CRUD operations for agents using SQLAlchemy.
- **Tool Management**:
- Pre-configure tools: Code Execution, Web Search, Browser Control.
- Tool structure:
- `name`: Tool name.
- `description`: What the tool does.
- `config`: Endpoint or script details.
- `settings`: User-adjustable configurations.
- Enable adding new tools via API with a schema for endpoint details and
settings.
- **LLM API Routing**:
- Integrate OpenAI and OpenRouter APIs.
- Create a dynamic configuration system for new LLM providers:
- Fields: `name`, `endpoint`, `auth_key`, `parameters`.
- Handle connection, error handling, and response parsing.
- **Group Chat Workflow**:
- Allow creating workflows where multiple agents interact.
- Define a schema for saved workflows:
- Participating agents.
- Workflow rules (e.g., agent X responds after agent Y).
- **Database Design**:
- Tables:
- `Agents`: Store agent details.
- `Tools`: Store tool configurations.
- `LLM_Providers`: Store API configurations for LLMs.
- `Workflows`: Store group chat workflows.
##### 3. **Tool Execution Backend**
- **Code Execution**:
- Use Python's `exec` in a secure sandboxed environment.
- Limit resource usage and prevent malicious code execution.
- **Web Search**:
- Use `playwright` or `selenium` for automated browsing.
- Integrate a scraping tool (e.g., `BeautifulSoup`) for parsing search
results.
- **Browser Control**:
- Provide browser automation features via Playwright.
- Enable executing browser tasks such as form filling, navigation, etc.
##### 4. **Concurrency & Orchestration**
- Use Python's `asyncio` to handle multiple agent tasks.
- Design an orchestration layer to manage:
- Agent interactions.
- Tool execution requests.
- External API calls.
##### 5. **Testing & Debugging**
- Write unit tests for core functionalities:
- Agent creation and management.
- LLM API routing.
- Tool execution.
- Workflow execution.
- Use tools like `pytest` for testing.
---
#### **Phase 2: Frontend Development**
##### 1. **Frontend Project Setup**
- Framework: React.js or Vue.js.
- Install dependencies: `axios` for API requests, `redux` or `vuex` for state
management.
##### 2. **User Interface Features**
- **Agent Management Page**:
- List of all agents.
- Create/Edit/Delete agents.
- Assign LLM and tools to agents.
- **Tools Page**:
- List of pre-configured tools.
- Option to view/edit tool settings.
- Add new tools with endpoint details.
- **Group Chat Page**:
- Create group chats with selected agents.
- Define workflows and save them.
- Simulate workflows for testing.
- **LLM Provider Configuration Page**:
- Add/Edit API keys for OpenAI, OpenRouter, and custom LLMs.
- Dynamic form for adding new providers (fields: name, endpoint, auth key,
parameters).
- **General Settings Page**:
- Application-wide configurations.
- Logging preferences.
- **Dashboard**:
- Overview of agents, tools, workflows, and API usage stats.
##### 3. **Frontend Backend Integration**
- Use `axios` to connect the frontend with the FastAPI backend.
- Implement RESTful API calls for:
- Agent CRUD operations.
- Tool management.
- Workflow execution.
##### 4. **UI/UX Enhancements**
- Use Material-UI or TailwindCSS for a clean, modern interface.
- Add real-time updates for workflows using WebSockets.
---
#### **Phase 3: Deployment & Testing**
##### 1. **Local Deployment**
- Run FastAPI backend with `uvicorn`.
- Run frontend using `npm` or `yarn`.
- Test local execution of all features.
##### 2. **Testing**
- Perform end-to-end tests for workflows, tool execution, and API calls.
- Ensure proper error handling for invalid inputs and API failures.
##### 3. **Documentation**
- Write detailed documentation for:
- Using the application.
- Adding new tools and LLMs.
- Creating workflows.
---
This roadmap ensures a feature-complete, modular, and extensible application that
meets your specifications. By implementing robust backend logic first, the app's
core functionality is guaranteed, while the frontend ensures usability and ease of
management.
