feat: Implement Dynamic Agent Orchestrator with MCP #988
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This commit introduces a complete application for a dynamic agent orchestrator.
Key features:
1. **MCP Server (`app.py`):** Exposes a single generic capability ("dynamic_task_executor"). It manages the application lifecycle.
2. **Internal Logical Agent (`internal_agent.py`):** Orchestrates tasks by communicating with an internal LLM. It follows a Reason-Act-Observe loop, making decisions to call tools or respond directly.
3. **Dynamic Tool Management (`tool_manager.py`, `tools.json`):** Low-level tools are defined in an external `tools.json` file and loaded at runtime. The `ToolRegistry` class manages these definitions and maps them to executable Python functions (`tool_functions.py`).
4. **Mock Internal LLM (`llm_client.py`):** A mock client that simulates an LLM with tool-calling capabilities (OpenAI-like). This allows for testing the agent's logic without a live LLM.
5. **Configuration (`config.py`, `.env.example`):** Manages settings like LLM endpoints and API keys through environment variables.
6. **Asynchronous Design:** Core operations are asynchronous.
7. **Example Tools (`tool_functions.py`):** Includes mock implementations for database queries, REST API calls, and web scraping.
8. **Testing (`app.py` local tests):** The `app.py` includes an integrated test runner to verify the end-to-end logic from the tool invocation down to the agent and mock tool execution.
9. **Documentation (`README.md`):** Provides setup, testing, and running instructions. Docstrings and comments are included in the code.
The application is structured for extensibility, allowing new tools to be added by updating the `tools.json` configuration and providing their Python implementations without changing the core agent or server logic.
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This commit introduces a complete application for a dynamic agent orchestrator.
Key features:
app.py): Exposes a single generic capability ("dynamic_task_executor"). It manages the application lifecycle.internal_agent.py): Orchestrates tasks by communicating with an internal LLM. It follows a Reason-Act-Observe loop, making decisions to call tools or respond directly.tool_manager.py,tools.json): Low-level tools are defined in an externaltools.jsonfile and loaded at runtime. TheToolRegistryclass manages these definitions and maps them to executable Python functions (tool_functions.py).llm_client.py): A mock client that simulates an LLM with tool-calling capabilities (OpenAI-like). This allows for testing the agent's logic without a live LLM.config.py,.env.example): Manages settings like LLM endpoints and API keys through environment variables.tool_functions.py): Includes mock implementations for database queries, REST API calls, and web scraping.app.pylocal tests): Theapp.pyincludes an integrated test runner to verify the end-to-end logic from the tool invocation down to the agent and mock tool execution.README.md): Provides setup, testing, and running instructions. Docstrings and comments are included in the code.The application is structured for extensibility, allowing new tools to be added by updating the
tools.jsonconfiguration and providing their Python implementations without changing the core agent or server logic.Motivation and Context
How Has This Been Tested?
Breaking Changes
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