# What Models Are Used by the Different Agents in AutoHedge?

> Discover the specific AI models powering each agent in AutoHedge. Learn which models like gpt-4o-mini and gpt-4.1 are assigned to Sentiment-Agent, Risk-Manager, and more.

- Repository: [Swarms/AutoHedge](https://github.com/The-Swarm-Corporation/AutoHedge)
- Tags: deep-dive
- Published: 2026-09-09

---

**AutoHedge configures five autonomous trading agents built on the `swarms.Agent` class, assigning `gpt-4o-mini` to the Sentiment-Agent and `gpt-4.1` to the Risk-Manager, Execution-Agent, Quant-Analyst, and Trading-Director as explicitly defined in [`autohedge/workers.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/autohedge/workers.py).**

The-Swarm-Corporation/AutoHedge implements a multi-agent architecture where each specialized component handles a distinct phase of the trading workflow. Understanding what models are used by the different agents in AutoHedge is essential for optimizing API costs, latency, and reasoning capabilities across sentiment analysis, risk management, and trade execution tasks.

## Agent Architecture and Model Assignments

All agents inherit from the base **`swarms.Agent`** class and are instantiated with explicit **`model_name`** parameters that dictate which underlying large language model processes their specific domain. The system separates concerns across five distinct roles, selecting models based on task complexity and computational requirements.

### Sentiment-Agent (gpt-4o-mini)

The **Sentiment-Agent** generates market sentiment analysis for specific stocks. According to the source code in [`autohedge/workers.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/autohedge/workers.py), this agent is configured with `model_name="gpt-4o-mini"` at lines 26-30. This lightweight model provides sufficient capability for sentiment classification while minimizing token consumption for high-volume market screening.

### Risk-Manager (gpt-4.1)

The **Risk-Manager** produces critical risk metrics including position sizing, maximum drawdown calculations, and portfolio exposure limits. It utilizes the more capable **`gpt-4.1`** model via `model_name="gpt-4.1"` as implemented in [`autohedge/workers.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/autohedge/workers.py) (lines 40-45). The enhanced reasoning capacity ensures accurate risk assessment for capital protection.

### Execution-Agent (gpt-4.1)

The **Execution-Agent** translates strategic decisions into concrete trade orders, specifying order types, quantities, entry prices, and stop-loss levels. It shares the same model configuration as the Risk-Manager, using **`gpt-4.1`** defined at lines 52-57 of [`autohedge/workers.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/autohedge/workers.py) to handle the precision required for order generation.

### Quant-Analyst (gpt-4.1)

The **Quant-Analyst** supplies quantitative scores including technical indicator readings, volume analysis, and trend strength calculations. This agent runs on **`gpt-4.1`** as specified in lines 64-68 of [`autohedge/workers.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/autohedge/workers.py), providing the computational depth necessary for numerical market analysis and statistical validation.

### Trading-Director (gpt-4.1)

The **Trading-Director** orchestrates the entire workflow and manages **hand-offs** to the other specialized agents. Configured with **`gpt-4.1`** at lines 82-84 of [`autohedge/workers.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/autohedge/workers.py), this agent coordinates the cascade of information from initial sentiment analysis through final execution while maintaining strategic coherence across the multi-agent system.

## Implementation in autohedge/workers.py

The model assignments are hardcoded in the [`autohedge/workers.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/autohedge/workers.py) file, where each agent instantiation passes the `model_name` argument directly to the `swarms.Agent` constructor. The director agent additionally defines hand-off mechanisms that delegate tasks to the sentiment, risk, execution, and quant agents based on workflow state.

```python

# Conceptual structure from autohedge/workers.py

from swarms import Agent

# Sentiment Agent - optimized for cost efficiency

sentiment_agent = Agent(
    agent_name="Sentiment-Agent",
    system_prompt=SENTIMENT_PROMPT,
    model_name="gpt-4o-mini",  # Lines 26-30

    max_loops=1,
)

# Risk Manager - high-precision reasoning

risk_agent = Agent(
    agent_name="Risk-Manager",
    system_prompt=RISK_PROMPT,
    model_name="gpt-4.1",  # Lines 40-45

    max_loops=1,
)

# Director - orchestrates with hand-offs

director_agent = Agent(
    agent_name="Trading-Director",
    system_prompt=DIRECTOR_PROMPT,
    model_name="gpt-4.1",  # Lines 82-84

    max_loops=1,
)

```

## Running and Customizing Agent Models

You can interact with these pre-configured agents directly or override the default models for experimentation and cost optimization.

### Running the Full Pipeline

To execute the complete trading workflow using the director agent with its default `gpt-4.1` configuration:

```python
from autohedge.workers import director_agent

# The director initiates the workflow and automatically invokes other agents

result = director_agent.run(
    "Analyze the stock market and provide a thesis on the overall market position and expected trends."
)

print(result)

```

### Invoking Individual Agents

For targeted analysis using specific models, import individual agents directly from the workers module:

```python
from autohedge.workers import quant_agent

thesis = "Long position on AAPL based on upcoming earnings."
output = quant_agent.run(f"Stock and Thesis from your Director.\n{thesis}")

print(output)  # Returns quantitative scores generated via gpt-4.1

```

### Customizing Model Selection

To experiment with alternative LLMs, instantiate a new `Agent` with your preferred `model_name` while reusing the existing system prompts:

```python
from swarms import Agent
from autohedge.prompts import QUANT_PROMPT

custom_quant = Agent(
    agent_name="Custom-Quant-Analyst",
    system_prompt=QUANT_PROMPT,
    model_name="gpt-4-turbo",  # Override the default gpt-4.1

    output_type="str",
    max_loops=1,
    verbose=True,
)

print(custom_quant.run("Stock: TSLA\nThesis: Momentum breakout"))

```

## Summary

- **AutoHedge employs five specialized agents** built on the `swarms.Agent` class, with model configurations centralized in [`autohedge/workers.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/autohedge/workers.py).
- **Sentiment analysis runs on `gpt-4o-mini`** for cost-effective processing of market sentiment at lines 26-30.
- **Core trading logic utilizes `gpt-4.1`**, including the Risk-Manager, Execution-Agent, Quant-Analyst, and Trading-Director (lines 40-84).
- **The Trading-Director orchestrates multi-agent workflows** via hand-off mechanisms while operating on `gpt-4.1`.
- **All model selections are customizable** by creating new `Agent` instances with different `model_name` parameters without modifying the core source files.

## Frequently Asked Questions

### Which LLM model does the AutoHedge Sentiment-Agent use?

The Sentiment-Agent uses **`gpt-4o-mini`** as explicitly configured in [`autohedge/workers.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/autohedge/workers.py) at lines 26-30. This model selection balances capability and cost efficiency for high-volume sentiment analysis tasks, distinguishing it from the `gpt-4.1` employed by the other four agents.

### Can I change the model for a specific agent in AutoHedge?

Yes. While the default agents in [`autohedge/workers.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/autohedge/workers.py) use fixed model assignments, you can create custom agent instances by importing the `Agent` class from `swarms` and the appropriate system prompt from `autohedge.prompts`. Specify any supported `model_name` (such as `gpt-4-turbo` or `gpt-4o`) to override defaults for testing or cost optimization.

### What is the difference between the Trading-Director and other agents in AutoHedge?

The **Trading-Director** serves as the orchestration layer that runs on `gpt-4.1` and manages workflow coordination through **hand-offs** to the Sentiment-Agent (`gpt-4o-mini`), Risk-Manager, Execution-Agent, and Quant-Analyst (all `gpt-4.1`). While specialized agents perform single-domain tasks, the director integrates their outputs to generate comprehensive trading theses and maintain strategic continuity.

### Where are the model names defined in the AutoHedge codebase?

All model names are defined in **[`autohedge/workers.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/autohedge/workers.py)**. The Sentiment-Agent configuration appears at lines 26-30 using `model_name="gpt-4o-mini"`, while the Risk-Manager (lines 40-45), Execution-Agent (lines 52-57), Quant-Analyst (lines 64-68), and Trading-Director (lines 82-84) all specify `model_name="gpt-4.1"` in their respective `Agent` instantiations.