ModelSettings

Overview

ModelSettings is a configuration class from the agents library that controls LLM behavior including temperature, token limits, and other generation parameters. It’s used with the Agentor class to fine-tune model responses.

Import

from agentor import ModelSettings

Usage

from agentor import Agentor, ModelSettings

model_settings = ModelSettings(
    temperature=0.7,
    max_tokens=1000,
    top_p=0.9
)

agent = Agentor(
    name="Assistant",
    model="gpt-4o",
    model_settings=model_settings
)

Common Parameters

While ModelSettings is defined in the agents library, here are the commonly used parameters:

temperature

float

default:"1.0"

Controls randomness in outputs. Lower values (0.0-0.3) make outputs more focused and deterministic. Higher values (0.7-1.0) make outputs more creative and varied.

0.0-0.3: Precise, consistent, factual responses
0.4-0.6: Balanced creativity and consistency
0.7-1.0: Creative, diverse, exploratory responses

max_tokens

int

default:"Model-dependent"

Maximum number of tokens to generate in the response. Limits the length of the model’s output.

top_p

float

default:"1.0"

Nucleus sampling parameter. Controls diversity by limiting cumulative probability. Alternative to temperature.

0.1-0.5: More focused, deterministic
0.9-1.0: More diverse outputs

presence_penalty

float

default:"0.0"

Penalizes tokens based on whether they appear in the text so far. Range: -2.0 to 2.0.

Positive values encourage new topics
Negative values encourage staying on topic

frequency_penalty

float

default:"0.0"

Penalizes tokens based on their frequency in the text. Range: -2.0 to 2.0.

Positive values reduce repetition
Negative values allow more repetition

stop

List[str]

default:"None"

Sequences where the model will stop generating. Maximum of 4 sequences.

Examples

Creative Writing

from agentor import Agentor, ModelSettings

creative_settings = ModelSettings(
    temperature=0.9,
    top_p=0.95,
    max_tokens=2000,
    presence_penalty=0.6
)

agent = Agentor(
    name="Creative Writer",
    model="gpt-4o",
    model_settings=creative_settings,
    instructions="You are a creative storytelling assistant."
)

result = agent.run("Write a short story about a time traveler")

Precise Technical Responses

from agentor import Agentor, ModelSettings

precise_settings = ModelSettings(
    temperature=0.2,
    top_p=0.1,
    max_tokens=1500
)

agent = Agentor(
    name="Code Assistant",
    model="gpt-4o",
    model_settings=precise_settings,
    instructions="You are a precise coding assistant."
)

result = agent.run("Explain how to implement a binary search tree")

Concise Responses

from agentor import Agentor, ModelSettings

concise_settings = ModelSettings(
    temperature=0.3,
    max_tokens=100,
    stop=["\n\n"]  # Stop at double newline
)

agent = Agentor(
    name="Concise Assistant",
    model="gpt-4o",
    model_settings=concise_settings,
    instructions="Provide brief, single-paragraph answers."
)

Reducing Repetition

from agentor import Agentor, ModelSettings

varied_settings = ModelSettings(
    temperature=0.7,
    frequency_penalty=0.5,
    presence_penalty=0.3
)

agent = Agentor(
    name="Varied Assistant",
    model="gpt-4o",
    model_settings=varied_settings
)

From Markdown File

You can also specify temperature in markdown frontmatter:

---
name: Research Agent
model: gpt-4o
temperature: 0.5
tools: ["web_search"]
---

You are a research assistant that provides accurate information.

from agentor import Agentor, ModelSettings

# Temperature from markdown will be merged
additional_settings = ModelSettings(
    max_tokens=2000,
    top_p=0.9
)

agent = Agentor.from_md(
    "research_agent.md",
    model_settings=additional_settings
)

Parameter Selection Guide

By Use Case

Use Case	Temperature	Top P	Max Tokens	Notes
Code generation	0.0-0.2	0.1	2000+	Deterministic, precise
Technical writing	0.3-0.5	0.5	1500	Balanced, accurate
Creative writing	0.8-1.0	0.95	2000+	Diverse, imaginative
Summarization	0.3-0.5	0.5	500	Concise, factual
Conversation	0.7-0.9	0.9	1000	Natural, engaging
Data extraction	0.0-0.1	0.1	500	Consistent, accurate

Combining Parameters

from agentor import ModelSettings

# Balanced configuration
balanced = ModelSettings(
    temperature=0.7,
    top_p=0.9,
    max_tokens=1500,
    frequency_penalty=0.0,
    presence_penalty=0.0
)

# High creativity
creative = ModelSettings(
    temperature=0.9,
    top_p=0.95,
    max_tokens=2000,
    presence_penalty=0.6,
    frequency_penalty=0.3
)

# Maximum precision
precise = ModelSettings(
    temperature=0.0,
    top_p=0.1,
    max_tokens=1000,
    frequency_penalty=0.0,
    presence_penalty=0.0
)

Notes

ModelSettings is imported from the agents library but re-exported by agentor for convenience
If not provided, Agentor uses sensible defaults via get_default_model_settings()
Temperature and top_p are alternative sampling methods - typically you should adjust one or the other, not both
Different models may interpret these parameters differently
Some parameters may not be supported by all model providers

Agentor - Main agent class that uses ModelSettings
LLM - Lightweight LLM client
LitellmModel - Custom model wrapper

Celesto CLI

SmolVM API

SmolVM CLI

Agentor API

Overview

Import

Usage

Common Parameters

Examples

Creative Writing

Precise Technical Responses

Concise Responses

Reducing Repetition

From Markdown File

Parameter Selection Guide

By Use Case

Combining Parameters

Notes

​Overview

​Import

​Usage

​Common Parameters

​Examples

​Creative Writing

​Precise Technical Responses

​Concise Responses

​Reducing Repetition

​From Markdown File

​Parameter Selection Guide

​By Use Case

​Combining Parameters

​Notes

​Related

Overview

Import

Usage

Common Parameters

Examples

Creative Writing

Precise Technical Responses

Concise Responses

Reducing Repetition

From Markdown File

Parameter Selection Guide

By Use Case

Combining Parameters

Notes

Related