---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 64e6482f1389e2d83ef4069963c226a475e57988642cfc223e73d8c1746efb35
  pageDirectory: concepts
  sources:
    - tracing-autogen-databricks-on-aws.md
  confidence: 0.95
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - tool-registration-pattern
    - TRP
  citations:
    - file: tracing-autogen-databricks-on-aws.md
title: Tool Registration Pattern
description: The pattern of registering a tool with the LLM (for suggestion) and the execution agent (for execution) using register_for_llm and register_for_execution
tags:
  - autogen
  - tool-use
  - pattern
timestamp: "2026-06-19T23:09:32.123Z"
---

## Tool Registration Pattern

**Tool Registration Pattern** refers to the design pattern in [AutoGen](https://github.com/microsoft/autogen) where a function (a tool) is explicitly registered with both an LLM-facing agent and an executor agent. This pattern separates the concern of describing a tool to the language model from the concern of executing it, enabling multi-agent workflows where one agent suggests calling a function and another agent carries out the call. ^[tracing-autogen-databricks-on-aws.md]

### Overview

In a typical AutoGen workflow, a user defines a Python function to be used as a tool. The function is then registered in two places:

1. **With the LLM agent** (via `register_for_llm`) – provides the agent with the tool’s name, description, and expected parameters so the LLM can decide to invoke it.
2. **With the execution agent** (via `register_for_execution`) – binds the actual implementation to the execution agent, which runs the function when the LLM’s suggestion is received.

This registration is independent of the agent’s other configuration (e.g., LLM endpoint, system message). The pattern centralizes tool logic while keeping the LLM and execution roles modular. ^[tracing-autogen-databricks-on-aws.md]

### Example from Source Material

The source material shows a calculator tool registered with both an `assistant` (LLM agent) and a `user_proxy` (tool executor):

```python
# Define the tool function
def calculator(a: int, b: int, operator: Annotated[Operator, "operator"]) -> int:
    if operator == "+":
        return a + b
    elif operator == "-":
        return a - b
    elif operator == "*":
        return a * b
    elif operator == "/":
        return int(a / b)
    else:
        raise ValueError("Invalid operator")

# Register the tool signature with the assistant agent.
assistant.register_for_llm(name="calculator", description="A simple calculator")(calculator)

# Register the tool for execution on the [[user-proxy-agent|User Proxy Agent]].
user_proxy.register_for_execution(name="calculator")(calculator)
```

After registration, the workflow proceeds with `user_proxy.initiate_chat(assistant, ...)`. The assistant can suggest calling `calculator`, and the user proxy executes it. ^[tracing-autogen-databricks-on-aws.md]

### Key Components

- **`ConversableAgent.register_for_llm()`** – Takes a `name` and `description`. When called as a decorator (or with the function as argument), it attaches the tool’s metadata to the agent, making it available for LLM tool‑calling.
- **`ConversableAgent.register_for_execution()`** – Takes the same `name`. It registers the actual function implementation with the agent that will execute the call.
- The `name` argument must match between the two registrations to link the LLM’s tool call to the correct executable function. ^[tracing-autogen-databricks-on-aws.md]

### Benefits

- **Separation of concerns**: The LLM sees only the tool’s interface (name, description, parameters), not its implementation.
- **Flexibility**: Different agents can be assigned as executors, allowing delegation of tool execution to a specialized agent.
- **Clarity**: The pattern explicitly documents which tools an agent can invoke and which agent runs them. ^[tracing-autogen-databricks-on-aws.md]

### Related Concepts

- [AutoGen](/concepts/autogen-auto-tracing.md) – The multi-agent conversation framework where this pattern is used.
- Agent-based workflow – Orchestrating multiple autonomous agents for complex tasks.
- Tool-calling – A capability of large language models to invoke external functions.
- [MLflow Auto Tracing](/concepts/mlflow-automatic-tracing.md) – The tracing integration that records AutoGen tool calls (as shown in the source with `mlflow.autogen.autolog()`).
- Multi‑agent orchestration pattern – General design patterns for coordinating agent interactions.

### Sources

- tracing-autogen-databricks-on-aws.md

# Citations

1. [tracing-autogen-databricks-on-aws.md](/references/tracing-autogen-databricks-on-aws-04b86736.md)
