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Brandon Hancock (bhancock_ai) a1f35e768f Enhance LLM Streaming Response Handling and Event System (#2266) 
* Initial Stream working

* add tests

* adjust tests

* Update test for multiplication

* Update test for multiplication part 2

* max iter on new test

* streaming tool call test update

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Co-authored-by: Lorenze Jay <lorenzejaytech@gmail.com>
2025-03-07 12:54:32 -05:00

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---
title: 'LLMs'
description: 'A comprehensive guide to configuring and using Large Language Models (LLMs) in your CrewAI projects'
icon: 'microchip-ai'
---
<Note>
CrewAI integrates with multiple LLM providers through LiteLLM, giving you the flexibility to choose the right model for your specific use case. This guide will help you understand how to configure and use different LLM providers in your CrewAI projects.
</Note>
## What are LLMs?
Large Language Models (LLMs) are the core intelligence behind CrewAI agents. They enable agents to understand context, make decisions, and generate human-like responses. Here's what you need to know:
<CardGroup cols={2}>
<Card title="LLM Basics" icon="brain">
Large Language Models are AI systems trained on vast amounts of text data. They power the intelligence of your CrewAI agents, enabling them to understand and generate human-like text.
</Card>
<Card title="Context Window" icon="window">
The context window determines how much text an LLM can process at once. Larger windows (e.g., 128K tokens) allow for more context but may be more expensive and slower.
</Card>
<Card title="Temperature" icon="temperature-three-quarters">
Temperature (0.0 to 1.0) controls response randomness. Lower values (e.g., 0.2) produce more focused, deterministic outputs, while higher values (e.g., 0.8) increase creativity and variability.
</Card>
<Card title="Provider Selection" icon="server">
Each LLM provider (e.g., OpenAI, Anthropic, Google) offers different models with varying capabilities, pricing, and features. Choose based on your needs for accuracy, speed, and cost.
</Card>
</CardGroup>
## Setting Up Your LLM
There are three ways to configure LLMs in CrewAI. Choose the method that best fits your workflow:
<Tabs>
<Tab title="1. Environment Variables">
The simplest way to get started. Set these variables in your environment:
```bash
# Required: Your API key for authentication
OPENAI_API_KEY=<your-api-key>
# Optional: Default model selection
OPENAI_MODEL_NAME=gpt-4o-mini # Default if not set
# Optional: Organization ID (if applicable)
OPENAI_ORGANIZATION_ID=<your-org-id>
```
<Warning>
Never commit API keys to version control. Use environment files (.env) or your system's secret management.
</Warning>
</Tab>
<Tab title="2. YAML Configuration">
Create a YAML file to define your agent configurations. This method is great for version control and team collaboration:
```yaml
researcher:
role: Research Specialist
goal: Conduct comprehensive research and analysis
backstory: A dedicated research professional with years of experience
verbose: true
llm: openai/gpt-4o-mini # your model here
# (see provider configuration examples below for more)
```
<Info>
The YAML configuration allows you to:
- Version control your agent settings
- Easily switch between different models
- Share configurations across team members
- Document model choices and their purposes
</Info>
</Tab>
<Tab title="3. Direct Code">
For maximum flexibility, configure LLMs directly in your Python code:
```python
from crewai import LLM
# Basic configuration
llm = LLM(model="gpt-4")
# Advanced configuration with detailed parameters
llm = LLM(
model="gpt-4o-mini",
temperature=0.7, # Higher for more creative outputs
timeout=120, # Seconds to wait for response
max_tokens=4000, # Maximum length of response
top_p=0.9, # Nucleus sampling parameter
frequency_penalty=0.1, # Reduce repetition
presence_penalty=0.1, # Encourage topic diversity
response_format={"type": "json"}, # For structured outputs
seed=42 # For reproducible results
)
```
<Info>
Parameter explanations:
- `temperature`: Controls randomness (0.0-1.0)
- `timeout`: Maximum wait time for response
- `max_tokens`: Limits response length
- `top_p`: Alternative to temperature for sampling
- `frequency_penalty`: Reduces word repetition
- `presence_penalty`: Encourages new topics
- `response_format`: Specifies output structure
- `seed`: Ensures consistent outputs
</Info>
</Tab>
</Tabs>
## Provider Configuration Examples
CrewAI supports a multitude of LLM providers, each offering unique features, authentication methods, and model capabilities.
In this section, you'll find detailed examples that help you select, configure, and optimize the LLM that best fits your project's needs.
<AccordionGroup>
<Accordion title="OpenAI">
Set the following environment variables in your `.env` file:
```toml Code
# Required
OPENAI_API_KEY=sk-...
# Optional
OPENAI_API_BASE=<custom-base-url>
OPENAI_ORGANIZATION=<your-org-id>
```
Example usage in your CrewAI project:
```python Code
from crewai import LLM
llm = LLM(
model="openai/gpt-4", # call model by provider/model_name
temperature=0.8,
max_tokens=150,
top_p=0.9,
frequency_penalty=0.1,
presence_penalty=0.1,
stop=["END"],
seed=42
)
```
OpenAI is one of the leading providers of LLMs with a wide range of models and features.
| Model | Context Window | Best For |
|---------------------|------------------|-----------------------------------------------|
| GPT-4 | 8,192 tokens | High-accuracy tasks, complex reasoning |
| GPT-4 Turbo | 128,000 tokens | Long-form content, document analysis |
| GPT-4o & GPT-4o-mini | 128,000 tokens | Cost-effective large context processing |
| o3-mini | 200,000 tokens | Fast reasoning, complex reasoning |
| o1-mini | 128,000 tokens | Fast reasoning, complex reasoning |
| o1-preview | 128,000 tokens | Fast reasoning, complex reasoning |
| o1 | 200,000 tokens | Fast reasoning, complex reasoning |
</Accordion>
<Accordion title="Anthropic">
```toml Code
ANTHROPIC_API_KEY=sk-ant-...
```
Example usage in your CrewAI project:
```python Code
llm = LLM(
model="anthropic/claude-3-sonnet-20240229-v1:0",
temperature=0.7
)
```
</Accordion>
<Accordion title="Google">
Set the following environment variables in your `.env` file:
```toml Code
# Option 1: Gemini accessed with an API key.
# https://ai.google.dev/gemini-api/docs/api-key
GEMINI_API_KEY=<your-api-key>
# Option 2: Vertex AI IAM credentials for Gemini, Anthropic, and Model Garden.
# https://cloud.google.com/vertex-ai/generative-ai/docs/overview
```
Get credentials from your Google Cloud Console and save it to a JSON file with the following code:
```python Code
import json
file_path = 'path/to/vertex_ai_service_account.json'
# Load the JSON file
with open(file_path, 'r') as file:
vertex_credentials = json.load(file)
# Convert the credentials to a JSON string
vertex_credentials_json = json.dumps(vertex_credentials)
```
Example usage in your CrewAI project:
```python Code
from crewai import LLM
llm = LLM(
model="gemini/gemini-1.5-pro-latest",
temperature=0.7,
vertex_credentials=vertex_credentials_json
)
```
Google offers a range of powerful models optimized for different use cases:
| Model | Context Window | Best For |
|-----------------------|----------------|------------------------------------------------------------------|
| gemini-2.0-flash-exp | 1M tokens | Higher quality at faster speed, multimodal model, good for most tasks |
| gemini-1.5-flash | 1M tokens | Balanced multimodal model, good for most tasks |
| gemini-1.5-flash-8B | 1M tokens | Fastest, most cost-efficient, good for high-frequency tasks |
| gemini-1.5-pro | 2M tokens | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
</Accordion>
<Accordion title="Azure">
```toml Code
# Required
AZURE_API_KEY=<your-api-key>
AZURE_API_BASE=<your-resource-url>
AZURE_API_VERSION=<api-version>
# Optional
AZURE_AD_TOKEN=<your-azure-ad-token>
AZURE_API_TYPE=<your-azure-api-type>
```
Example usage in your CrewAI project:
```python Code
llm = LLM(
model="azure/gpt-4",
api_version="2023-05-15"
)
```
</Accordion>
<Accordion title="AWS Bedrock">
```toml Code
AWS_ACCESS_KEY_ID=<your-access-key>
AWS_SECRET_ACCESS_KEY=<your-secret-key>
AWS_DEFAULT_REGION=<your-region>
```
Example usage in your CrewAI project:
```python Code
llm = LLM(
model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0"
)
```
</Accordion>
<Accordion title="Amazon SageMaker">
```toml Code
AWS_ACCESS_KEY_ID=<your-access-key>
AWS_SECRET_ACCESS_KEY=<your-secret-key>
AWS_DEFAULT_REGION=<your-region>
```
Example usage in your CrewAI project:
```python Code
llm = LLM(
model="sagemaker/<my-endpoint>"
)
```
</Accordion>
<Accordion title="Mistral">
Set the following environment variables in your `.env` file:
```toml Code
MISTRAL_API_KEY=<your-api-key>
```
Example usage in your CrewAI project:
```python Code
llm = LLM(
model="mistral/mistral-large-latest",
temperature=0.7
)
```
</Accordion>
<Accordion title="Nvidia NIM">
Set the following environment variables in your `.env` file:
```toml Code
NVIDIA_API_KEY=<your-api-key>
```
Example usage in your CrewAI project:
```python Code
llm = LLM(
model="nvidia_nim/meta/llama3-70b-instruct",
temperature=0.7
)
```
Nvidia NIM provides a comprehensive suite of models for various use cases, from general-purpose tasks to specialized applications.
| Model | Context Window | Best For |
|-------------------------------------------------------------------------|----------------|-------------------------------------------------------------------|
| nvidia/mistral-nemo-minitron-8b-8k-instruct | 8,192 tokens | State-of-the-art small language model delivering superior accuracy for chatbot, virtual assistants, and content generation. |
| nvidia/nemotron-4-mini-hindi-4b-instruct | 4,096 tokens | A bilingual Hindi-English SLM for on-device inference, tailored specifically for Hindi Language. |
| nvidia/llama-3.1-nemotron-70b-instruct | 128k tokens | Customized for enhanced helpfulness in responses |
| nvidia/llama3-chatqa-1.5-8b | 128k tokens | Advanced LLM to generate high-quality, context-aware responses for chatbots and search engines. |
| nvidia/llama3-chatqa-1.5-70b | 128k tokens | Advanced LLM to generate high-quality, context-aware responses for chatbots and search engines. |
| nvidia/vila | 128k tokens | Multi-modal vision-language model that understands text/img/video and creates informative responses |
| nvidia/neva-22 | 4,096 tokens | Multi-modal vision-language model that understands text/images and generates informative responses |
| nvidia/nemotron-mini-4b-instruct | 8,192 tokens | General-purpose tasks |
| nvidia/usdcode-llama3-70b-instruct | 128k tokens | State-of-the-art LLM that answers OpenUSD knowledge queries and generates USD-Python code. |
| nvidia/nemotron-4-340b-instruct | 4,096 tokens | Creates diverse synthetic data that mimics the characteristics of real-world data. |
| meta/codellama-70b | 100k tokens | LLM capable of generating code from natural language and vice versa. |
| meta/llama2-70b | 4,096 tokens | Cutting-edge large language AI model capable of generating text and code in response to prompts. |
| meta/llama3-8b-instruct | 8,192 tokens | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. |
| meta/llama3-70b-instruct | 8,192 tokens | Powers complex conversations with superior contextual understanding, reasoning and text generation. |
| meta/llama-3.1-8b-instruct | 128k tokens | Advanced state-of-the-art model with language understanding, superior reasoning, and text generation. |
| meta/llama-3.1-70b-instruct | 128k tokens | Powers complex conversations with superior contextual understanding, reasoning and text generation. |
| meta/llama-3.1-405b-instruct | 128k tokens | Advanced LLM for synthetic data generation, distillation, and inference for chatbots, coding, and domain-specific tasks. |
| meta/llama-3.2-1b-instruct | 128k tokens | Advanced state-of-the-art small language model with language understanding, superior reasoning, and text generation. |
| meta/llama-3.2-3b-instruct | 128k tokens | Advanced state-of-the-art small language model with language understanding, superior reasoning, and text generation. |
| meta/llama-3.2-11b-vision-instruct | 128k tokens | Advanced state-of-the-art small language model with language understanding, superior reasoning, and text generation. |
| meta/llama-3.2-90b-vision-instruct | 128k tokens | Advanced state-of-the-art small language model with language understanding, superior reasoning, and text generation. |
| google/gemma-7b | 8,192 tokens | Cutting-edge text generation model text understanding, transformation, and code generation. |
| google/gemma-2b | 8,192 tokens | Cutting-edge text generation model text understanding, transformation, and code generation. |
| google/codegemma-7b | 8,192 tokens | Cutting-edge model built on Google's Gemma-7B specialized for code generation and code completion. |
| google/codegemma-1.1-7b | 8,192 tokens | Advanced programming model for code generation, completion, reasoning, and instruction following. |
| google/recurrentgemma-2b | 8,192 tokens | Novel recurrent architecture based language model for faster inference when generating long sequences. |
| google/gemma-2-9b-it | 8,192 tokens | Cutting-edge text generation model text understanding, transformation, and code generation. |
| google/gemma-2-27b-it | 8,192 tokens | Cutting-edge text generation model text understanding, transformation, and code generation. |
| google/gemma-2-2b-it | 8,192 tokens | Cutting-edge text generation model text understanding, transformation, and code generation. |
| google/deplot | 512 tokens | One-shot visual language understanding model that translates images of plots into tables. |
| google/paligemma | 8,192 tokens | Vision language model adept at comprehending text and visual inputs to produce informative responses. |
| mistralai/mistral-7b-instruct-v0.2 | 32k tokens | This LLM follows instructions, completes requests, and generates creative text. |
| mistralai/mixtral-8x7b-instruct-v0.1 | 8,192 tokens | An MOE LLM that follows instructions, completes requests, and generates creative text. |
| mistralai/mistral-large | 4,096 tokens | Creates diverse synthetic data that mimics the characteristics of real-world data. |
| mistralai/mixtral-8x22b-instruct-v0.1 | 8,192 tokens | Creates diverse synthetic data that mimics the characteristics of real-world data. |
| mistralai/mistral-7b-instruct-v0.3 | 32k tokens | This LLM follows instructions, completes requests, and generates creative text. |
| nv-mistralai/mistral-nemo-12b-instruct | 128k tokens | Most advanced language model for reasoning, code, multilingual tasks; runs on a single GPU. |
| mistralai/mamba-codestral-7b-v0.1 | 256k tokens | Model for writing and interacting with code across a wide range of programming languages and tasks. |
| microsoft/phi-3-mini-128k-instruct | 128K tokens | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
| microsoft/phi-3-mini-4k-instruct | 4,096 tokens | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
| microsoft/phi-3-small-8k-instruct | 8,192 tokens | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
| microsoft/phi-3-small-128k-instruct | 128K tokens | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
| microsoft/phi-3-medium-4k-instruct | 4,096 tokens | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
| microsoft/phi-3-medium-128k-instruct | 128K tokens | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
| microsoft/phi-3.5-mini-instruct | 128K tokens | Lightweight multilingual LLM powering AI applications in latency bound, memory/compute constrained environments |
| microsoft/phi-3.5-moe-instruct | 128K tokens | Advanced LLM based on Mixture of Experts architecure to deliver compute efficient content generation |
| microsoft/kosmos-2 | 1,024 tokens | Groundbreaking multimodal model designed to understand and reason about visual elements in images. |
| microsoft/phi-3-vision-128k-instruct | 128k tokens | Cutting-edge open multimodal model exceling in high-quality reasoning from images. |
| microsoft/phi-3.5-vision-instruct | 128k tokens | Cutting-edge open multimodal model exceling in high-quality reasoning from images. |
| databricks/dbrx-instruct | 12k tokens | A general-purpose LLM with state-of-the-art performance in language understanding, coding, and RAG. |
| snowflake/arctic | 1,024 tokens | Delivers high efficiency inference for enterprise applications focused on SQL generation and coding. |
| aisingapore/sea-lion-7b-instruct | 4,096 tokens | LLM to represent and serve the linguistic and cultural diversity of Southeast Asia |
| ibm/granite-8b-code-instruct | 4,096 tokens | Software programming LLM for code generation, completion, explanation, and multi-turn conversion. |
| ibm/granite-34b-code-instruct | 8,192 tokens | Software programming LLM for code generation, completion, explanation, and multi-turn conversion. |
| ibm/granite-3.0-8b-instruct | 4,096 tokens | Advanced Small Language Model supporting RAG, summarization, classification, code, and agentic AI |
| ibm/granite-3.0-3b-a800m-instruct | 4,096 tokens | Highly efficient Mixture of Experts model for RAG, summarization, entity extraction, and classification |
| mediatek/breeze-7b-instruct | 4,096 tokens | Creates diverse synthetic data that mimics the characteristics of real-world data. |
| upstage/solar-10.7b-instruct | 4,096 tokens | Excels in NLP tasks, particularly in instruction-following, reasoning, and mathematics. |
| writer/palmyra-med-70b-32k | 32k tokens | Leading LLM for accurate, contextually relevant responses in the medical domain. |
| writer/palmyra-med-70b | 32k tokens | Leading LLM for accurate, contextually relevant responses in the medical domain. |
| writer/palmyra-fin-70b-32k | 32k tokens | Specialized LLM for financial analysis, reporting, and data processing |
| 01-ai/yi-large | 32k tokens | Powerful model trained on English and Chinese for diverse tasks including chatbot and creative writing. |
| deepseek-ai/deepseek-coder-6.7b-instruct | 2k tokens | Powerful coding model offering advanced capabilities in code generation, completion, and infilling |
| rakuten/rakutenai-7b-instruct | 1,024 tokens | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. |
| rakuten/rakutenai-7b-chat | 1,024 tokens | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. |
| baichuan-inc/baichuan2-13b-chat | 4,096 tokens | Support Chinese and English chat, coding, math, instruction following, solving quizzes |
</Accordion>
<Accordion title="Groq">
Set the following environment variables in your `.env` file:
```toml Code
GROQ_API_KEY=<your-api-key>
```
Example usage in your CrewAI project:
```python Code
llm = LLM(
model="groq/llama-3.2-90b-text-preview",
temperature=0.7
)
```
| Model | Context Window | Best For |
|-------------------|------------------|--------------------------------------------|
| Llama 3.1 70B/8B | 131,072 tokens | High-performance, large context tasks |
| Llama 3.2 Series | 8,192 tokens | General-purpose tasks |
| Mixtral 8x7B | 32,768 tokens | Balanced performance and context |
</Accordion>
<Accordion title="IBM watsonx.ai">
Set the following environment variables in your `.env` file:
```toml Code
# Required
WATSONX_URL=<your-url>
WATSONX_APIKEY=<your-apikey>
WATSONX_PROJECT_ID=<your-project-id>
# Optional
WATSONX_TOKEN=<your-token>
WATSONX_DEPLOYMENT_SPACE_ID=<your-space-id>
```
Example usage in your CrewAI project:
```python Code
llm = LLM(
model="watsonx/meta-llama/llama-3-1-70b-instruct",
base_url="https://api.watsonx.ai/v1"
)
```
</Accordion>
<Accordion title="Ollama (Local LLMs)">
1. Install Ollama: [ollama.ai](https://ollama.ai/)
2. Run a model: `ollama run llama2`
3. Configure:
```python Code
llm = LLM(
model="ollama/llama3:70b",
base_url="http://localhost:11434"
)
```
</Accordion>
<Accordion title="Fireworks AI">
Set the following environment variables in your `.env` file:
```toml Code
FIREWORKS_API_KEY=<your-api-key>
```
Example usage in your CrewAI project:
```python Code
llm = LLM(
model="fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct",
temperature=0.7
)
```
</Accordion>
<Accordion title="Perplexity AI">
Set the following environment variables in your `.env` file:
```toml Code
PERPLEXITY_API_KEY=<your-api-key>
```
Example usage in your CrewAI project:
```python Code
llm = LLM(
model="llama-3.1-sonar-large-128k-online",
base_url="https://api.perplexity.ai/"
)
```
</Accordion>
<Accordion title="Hugging Face">
Set the following environment variables in your `.env` file:
```toml Code
HUGGINGFACE_API_KEY=<your-api-key>
```
Example usage in your CrewAI project:
```python Code
llm = LLM(
model="huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct",
base_url="your_api_endpoint"
)
```
</Accordion>
<Accordion title="SambaNova">
Set the following environment variables in your `.env` file:
```toml Code
SAMBANOVA_API_KEY=<your-api-key>
```
Example usage in your CrewAI project:
```python Code
llm = LLM(
model="sambanova/Meta-Llama-3.1-8B-Instruct",
temperature=0.7
)
```
| Model | Context Window | Best For |
|--------------------|------------------------|----------------------------------------------|
| Llama 3.1 70B/8B | Up to 131,072 tokens | High-performance, large context tasks |
| Llama 3.1 405B | 8,192 tokens | High-performance and output quality |
| Llama 3.2 Series | 8,192 tokens | General-purpose, multimodal tasks |
| Llama 3.3 70B | Up to 131,072 tokens | High-performance and output quality |
| Qwen2 familly | 8,192 tokens | High-performance and output quality |
</Accordion>
<Accordion title="Cerebras">
Set the following environment variables in your `.env` file:
```toml Code
# Required
CEREBRAS_API_KEY=<your-api-key>
```
Example usage in your CrewAI project:
```python Code
llm = LLM(
model="cerebras/llama3.1-70b",
temperature=0.7,
max_tokens=8192
)
```
<Info>
Cerebras features:
- Fast inference speeds
- Competitive pricing
- Good balance of speed and quality
- Support for long context windows
</Info>
</Accordion>
<Accordion title="Open Router">
Set the following environment variables in your `.env` file:
```toml Code
OPENROUTER_API_KEY=<your-api-key>
```
Example usage in your CrewAI project:
```python Code
llm = LLM(
model="openrouter/deepseek/deepseek-r1",
base_url="https://openrouter.ai/api/v1",
api_key=OPENROUTER_API_KEY
)
```
<Info>
Open Router models:
- openrouter/deepseek/deepseek-r1
- openrouter/deepseek/deepseek-chat
</Info>
</Accordion>
</AccordionGroup>
## Streaming Responses
CrewAI supports streaming responses from LLMs, allowing your application to receive and process outputs in real-time as they're generated.
<Tabs>
<Tab title="Basic Setup">
Enable streaming by setting the `stream` parameter to `True` when initializing your LLM:
```python
from crewai import LLM
# Create an LLM with streaming enabled
llm = LLM(
model="openai/gpt-4o",
stream=True # Enable streaming
)
```
When streaming is enabled, responses are delivered in chunks as they're generated, creating a more responsive user experience.
</Tab>
<Tab title="Event Handling">
CrewAI emits events for each chunk received during streaming:
```python
from crewai import LLM
from crewai.utilities.events import EventHandler, LLMStreamChunkEvent
class MyEventHandler(EventHandler):
def on_llm_stream_chunk(self, event: LLMStreamChunkEvent):
# Process each chunk as it arrives
print(f"Received chunk: {event.chunk}")
# Register the event handler
from crewai.utilities.events import crewai_event_bus
crewai_event_bus.register_handler(MyEventHandler())
```
</Tab>
</Tabs>
## Structured LLM Calls
CrewAI supports structured responses from LLM calls by allowing you to define a `response_format` using a Pydantic model. This enables the framework to automatically parse and validate the output, making it easier to integrate the response into your application without manual post-processing.
For example, you can define a Pydantic model to represent the expected response structure and pass it as the `response_format` when instantiating the LLM. The model will then be used to convert the LLM output into a structured Python object.
```python Code
from crewai import LLM
class Dog(BaseModel):
name: str
age: int
breed: str
llm = LLM(model="gpt-4o", response_format=Dog)
response = llm.call(
"Analyze the following messages and return the name, age, and breed. "
"Meet Kona! She is 3 years old and is a black german shepherd."
)
print(response)
# Output:
# Dog(name='Kona', age=3, breed='black german shepherd')
```
## Advanced Features and Optimization
Learn how to get the most out of your LLM configuration:
<AccordionGroup>
<Accordion title="Context Window Management">
CrewAI includes smart context management features:
```python
from crewai import LLM
# CrewAI automatically handles:
# 1. Token counting and tracking
# 2. Content summarization when needed
# 3. Task splitting for large contexts
llm = LLM(
model="gpt-4",
max_tokens=4000, # Limit response length
)
```
<Info>
Best practices for context management:
1. Choose models with appropriate context windows
2. Pre-process long inputs when possible
3. Use chunking for large documents
4. Monitor token usage to optimize costs
</Info>
</Accordion>
<Accordion title="Performance Optimization">
<Steps>
<Step title="Token Usage Optimization">
Choose the right context window for your task:
- Small tasks (up to 4K tokens): Standard models
- Medium tasks (between 4K-32K): Enhanced models
- Large tasks (over 32K): Large context models
```python
# Configure model with appropriate settings
llm = LLM(
model="openai/gpt-4-turbo-preview",
temperature=0.7, # Adjust based on task
max_tokens=4096, # Set based on output needs
timeout=300 # Longer timeout for complex tasks
)
```
<Tip>
- Lower temperature (0.1 to 0.3) for factual responses
- Higher temperature (0.7 to 0.9) for creative tasks
</Tip>
</Step>
<Step title="Best Practices">
1. Monitor token usage
2. Implement rate limiting
3. Use caching when possible
4. Set appropriate max_tokens limits
</Step>
</Steps>
<Info>
Remember to regularly monitor your token usage and adjust your configuration as needed to optimize costs and performance.
</Info>
</Accordion>
</AccordionGroup>
## Common Issues and Solutions
<Tabs>
<Tab title="Authentication">
<Warning>
Most authentication issues can be resolved by checking API key format and environment variable names.
</Warning>
```bash
# OpenAI
OPENAI_API_KEY=sk-...
# Anthropic
ANTHROPIC_API_KEY=sk-ant-...
```
</Tab>
<Tab title="Model Names">
<Check>
Always include the provider prefix in model names
</Check>
```python
# Correct
llm = LLM(model="openai/gpt-4")
# Incorrect
llm = LLM(model="gpt-4")
```
</Tab>
<Tab title="Context Length">
<Tip>
Use larger context models for extensive tasks
</Tip>
```
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