ToolExperimental

MrX_OAI_Assistant_Manager

Name: MrX_OAI_Assistant_Manager
Rating: 3.0 (97 reviews)
Author: MrXandbadas

by MrXandbadas

Dynamic function-calling and adaptive tool selection for OpenAI assistants

Python

Updated Apr 7, 2024

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Overview

Implements dynamic function-calling and tool selection for assistants, inspired by the LATM research paper. Uses OpenAI chat completions together with Autogen orchestration to route calls to functions and external tools at runtime. Focuses on adaptive function invocation and policy-driven delegation so assistants can pick and switch tools based on context and responses. dynamic function-calling and tool selection help frame how these decisions can be modeled and evaluated.

Why It Matters

As agents become more autonomous, choosing the right tool or function at the right time is a core failure mode for multi-agent systems. This implementation surfaces dynamic function selection and delegation patterns that make agent behavior more interpretable and testable. That visibility helps build agent track records and reproducible evaluation scenarios for agent-to-agent assessment agent-to-agent assessment.

Best For

Researchers and engineers experimenting with adaptive assistant delegation, dynamic function calling, and prototype multi-agent tool selection workflows. This aligns with the A2A Protocol Pattern approach to coordinating agents and tools in complex flows.

How It's Used

Prototype adaptive assistant behavior that chooses and switches functions at runtime
Evaluate how different function-selection policies affect assistant outputs
Create reproducible scenarios for testing agent delegation and tool-handling logic

Test in the Playground

Validate against scenarios