---
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 different places in CrewAI code where you can specify the model to use. Once you specify the model you are using, you will need to provide the configuration (like an API key) for each of the model providers you use. See the [provider configuration examples](#provider-configuration-examples) section for your provider.

<Tabs>
  <Tab title="1. Environment Variables">
    The simplest way to get started. Set the model in your environment directly, through an `.env` file or in your app code. If you used `crewai create` to bootstrap your project, it will be set already.

    ```bash .env
    MODEL=model-id  # e.g. gpt-4o, gemini-2.0-flash, claude-3-sonnet-...

    # Be sure to set your API keys here too. See the Provider
    # section below.
    ```

    <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 agents.yaml {6}
    researcher:
        role: Research Specialist
        goal: Conduct comprehensive research and analysis
        backstory: A dedicated research professional with years of experience
        verbose: true
        llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
        # (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 {4,8}
    from crewai import LLM

    # Basic configuration
    llm = LLM(model="model-id-here")  # gpt-4o, gemini-2.0-flash, anthropic/claude...

    # Advanced configuration with detailed parameters
    llm = LLM(
        model="model-id-here",  # gpt-4o, gemini-2.0-flash, anthropic/claude...
        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
    # Required
    ANTHROPIC_API_KEY=sk-ant-...

    # Optional
    ANTHROPIC_API_BASE=<custom-base-url>
    ```

    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 (Gemini API)">
    Set your API key in your `.env` file. If you need a key, or need to find an
    existing key, check [AI Studio](https://aistudio.google.com/apikey).

    ```toml .env
    # https://ai.google.dev/gemini-api/docs/api-key
    GEMINI_API_KEY=<your-api-key>
    ```

    Example usage in your CrewAI project:
    ```python Code
    from crewai import LLM

    llm = LLM(
        model="gemini/gemini-2.0-flash",
        temperature=0.7,
    )
    ```

    ### Gemini models

    Google offers a range of powerful models optimized for different use cases.

    | Model                          | Context Window | Best For                                                          |
    |--------------------------------|----------------|-------------------------------------------------------------------|
    | gemini-2.5-flash-preview-04-17 | 1M tokens      | Adaptive thinking, cost efficiency                                |
    | gemini-2.5-pro-preview-05-06   | 1M tokens      | Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more |
    | gemini-2.0-flash               | 1M tokens      | Next generation features, speed, thinking, and realtime streaming |
    | gemini-2.0-flash-lite          | 1M tokens      | Cost efficiency and low latency                                   |
    | 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 |

    The full list of models is available in the [Gemini model docs](https://ai.google.dev/gemini-api/docs/models).

    ### Gemma

    The Gemini API also allows you to use your API key to access [Gemma models](https://ai.google.dev/gemma/docs) hosted on Google infrastructure.

    | Model          | Context Window |
    |----------------|----------------|
    | gemma-3-1b-it  | 32k tokens     |
    | gemma-3-4b-it  | 32k tokens     |
    | gemma-3-12b-it | 32k tokens     |
    | gemma-3-27b-it | 128k tokens    |

  </Accordion>
  <Accordion title="Google (Vertex AI)">
    Get credentials from your Google Cloud Console and save it to a JSON file, then load it 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-2.5-flash-preview-04-17 | 1M tokens      | Adaptive thinking, cost efficiency                                |
    | gemini-2.5-pro-preview-05-06   | 1M tokens      | Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more |
    | gemini-2.0-flash               | 1M tokens      | Next generation features, speed, thinking, and realtime streaming |
    | gemini-2.0-flash-lite          | 1M tokens      | Cost efficiency and low latency                                   |
    | 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"
    )
    ```

    Before using Amazon Bedrock, make sure you have boto3 installed in your environment

    [Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html) is a managed service that provides access to multiple foundation models from top AI companies through a unified API, enabling secure and responsible AI application development.

    | Model                   | Context Window       | Best For                                                          |
    |-------------------------|----------------------|-------------------------------------------------------------------|
    | Amazon Nova Pro         | Up to 300k tokens    | High-performance, model balancing accuracy, speed, and cost-effectiveness across diverse tasks. |
    | Amazon Nova Micro       | Up to 128k tokens    | High-performance, cost-effective text-only model optimized for lowest latency responses. |
    | Amazon Nova Lite        | Up to 300k tokens    | High-performance, affordable multimodal processing for images, video, and text with real-time capabilities. |
    | Claude 3.7 Sonnet       | Up to 128k tokens    | High-performance, best for complex reasoning, coding & AI agents |
    | Claude 3.5 Sonnet v2    | Up to 200k tokens    | State-of-the-art model specialized in software engineering, agentic capabilities, and computer interaction at optimized cost. |
    | Claude 3.5 Sonnet       | Up to 200k tokens    | High-performance model delivering superior intelligence and reasoning across diverse tasks with optimal speed-cost balance. |
    | Claude 3.5 Haiku        | Up to 200k tokens    | Fast, compact multimodal model optimized for quick responses and seamless human-like interactions |
    | Claude 3 Sonnet         | Up to 200k tokens    | Multimodal model balancing intelligence and speed for high-volume deployments. |
    | Claude 3 Haiku          | Up to 200k tokens    | Compact, high-speed multimodal model optimized for quick responses and natural conversational interactions |
    | Claude 3 Opus           | Up to 200k tokens    | Most advanced multimodal model exceling at complex tasks with human-like reasoning and superior contextual understanding. |
    | Claude 2.1              | Up to 200k tokens    | Enhanced version with expanded context window, improved reliability, and reduced hallucinations for long-form and RAG applications |
    | Claude                  | Up to 100k tokens    | Versatile model excelling in sophisticated dialogue, creative content, and precise instruction following. |
    | Claude Instant          | Up to 100k tokens    | Fast, cost-effective model for everyday tasks like dialogue, analysis, summarization, and document Q&A |
    | Llama 3.1 405B Instruct | Up to 128k tokens    | Advanced LLM for synthetic data generation, distillation, and inference for chatbots, coding, and domain-specific tasks. |
    | Llama 3.1 70B Instruct  | Up to 128k tokens    | Powers complex conversations with superior contextual understanding, reasoning and text generation. |
    | Llama 3.1 8B Instruct   | Up to 128k tokens    | Advanced state-of-the-art model with language understanding, superior reasoning, and text generation. |
    | Llama 3 70B Instruct    | Up to 8k tokens      | Powers complex conversations with superior contextual understanding, reasoning and text generation. |
    | Llama 3 8B Instruct     | Up to 8k tokens      | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. |
    | Titan Text G1 - Lite    | Up to 4k tokens      | Lightweight, cost-effective model optimized for English tasks and fine-tuning with focus on summarization and content generation. |
    | Titan Text G1 - Express | Up to 8k tokens      | Versatile model for general language tasks, chat, and RAG applications with support for English and 100+ languages. |
    | Cohere Command          | Up to 4k tokens      | Model specialized in following user commands and delivering practical enterprise solutions. |
    | Jurassic-2 Mid          | Up to 8,191 tokens   | Cost-effective model balancing quality and affordability for diverse language tasks like Q&A, summarization, and content generation. |
    | Jurassic-2 Ultra        | Up to 8,191 tokens   | Model for advanced text generation and comprehension, excelling in complex tasks like analysis and content creation. |
    | Jamba-Instruct          | Up to 256k tokens    | Model with extended context window optimized for cost-effective text generation, summarization, and Q&A. |
    | Mistral 7B Instruct     | Up to 32k tokens     | This LLM follows instructions, completes requests, and generates creative text. |
    | Mistral 8x7B Instruct   | Up to 32k tokens     | An MOE LLM that follows instructions, completes requests, and generates creative text. |

  </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 architecture 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="Local NVIDIA NIM Deployed using WSL2">

    NVIDIA NIM enables you to run powerful LLMs locally on your Windows machine using WSL2 (Windows Subsystem for Linux).
    This approach allows you to leverage your NVIDIA GPU for private, secure, and cost-effective AI inference without relying on cloud services.
    Perfect for development, testing, or production scenarios where data privacy or offline capabilities are required.

    Here is a step-by-step guide to setting up a local NVIDIA NIM model:

    1. Follow installation instructions from [NVIDIA Website](https://docs.nvidia.com/nim/wsl2/latest/getting-started.html)

    2. Install the local model. For Llama 3.1-8b follow [instructions](https://build.nvidia.com/meta/llama-3_1-8b-instruct/deploy)

    3. Configure your crewai local models:

    ```python Code
    from crewai.llm import LLM

    local_nvidia_nim_llm = LLM(
        model="openai/meta/llama-3.1-8b-instruct", # it's an openai-api compatible model
        base_url="http://localhost:8000/v1",
        api_key="<your_api_key|any text if you have not configured it>", # api_key is required, but you can use any text
    )

    # Then you can use it in your crew:

    @CrewBase
    class MyCrew():
        # ...

        @agent
        def researcher(self) -> Agent:
            return Agent(
                config=self.agents_config['researcher'], # type: ignore[index]
                llm=local_nvidia_nim_llm
            )

        # ...
    ```
  </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 llama3`
    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
    HF_TOKEN=<your-api-key>
    ```

    Example usage in your CrewAI project:
    ```python Code
    llm = LLM(
        model="huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct"
    )
    ```
  </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>

    ```python
    # Large context model
    llm = LLM(model="openai/gpt-4o")  # 128K tokens
    ```
  </Tab>
</Tabs>
