---
title: Using CrewAI Without LiteLLM
description: How to use CrewAI with native provider integrations and remove the LiteLLM dependency from your project.
icon: shield-check
mode: "wide"
---

## Overview

CrewAI supports two paths for connecting to LLM providers:

1. **Native integrations** — direct SDK connections to OpenAI, Anthropic, Google Gemini, Azure OpenAI, and AWS Bedrock
2. **LiteLLM fallback** — a translation layer that supports 100+ additional providers

This guide explains how to use CrewAI exclusively with native provider integrations, removing any dependency on LiteLLM.

<Warning>
  The `litellm` package was quarantined on PyPI due to a security/reliability incident. If you rely on LiteLLM-dependent providers, you should migrate to native integrations. CrewAI's native integrations give you full functionality without LiteLLM.
</Warning>

## Why Remove LiteLLM?

- **Reduced dependency surface** — fewer packages means fewer potential supply-chain risks
- **Better performance** — native SDKs communicate directly with provider APIs, eliminating a translation layer
- **Simpler debugging** — one less abstraction layer between your code and the provider
- **Smaller install footprint** — LiteLLM brings in many transitive dependencies

## Native Providers (No LiteLLM Required)

These providers use their own SDKs and work without LiteLLM installed:

<CardGroup cols={2}>
  <Card title="OpenAI" icon="bolt">
    GPT-4o, GPT-4o-mini, o1, o3-mini, and more.
    ```bash
    uv add "crewai[openai]"
    ```
  </Card>
  <Card title="Anthropic" icon="a">
    Claude Sonnet, Claude Haiku, and more.
    ```bash
    uv add "crewai[anthropic]"
    ```
  </Card>
  <Card title="Google Gemini" icon="google">
    Gemini 2.0 Flash, Gemini 2.0 Pro, and more.
    ```bash
    uv add "crewai[gemini]"
    ```
  </Card>
  <Card title="Azure OpenAI" icon="microsoft">
    Azure-hosted OpenAI models.
    ```bash
    uv add "crewai[azure]"
    ```
  </Card>
  <Card title="AWS Bedrock" icon="aws">
    Claude, Llama, Titan, and more via AWS.
    ```bash
    uv add "crewai[bedrock]"
    ```
  </Card>
</CardGroup>

<Info>
  If you only use native providers, you **never** need to install `crewai[litellm]`. The base `crewai` package plus your chosen provider extra is all you need.
</Info>

## How to Check If You're Using LiteLLM

### Check your model strings

If your code uses model prefixes like these, you're routing through LiteLLM:

| Prefix | Provider | Uses LiteLLM? |
|--------|----------|---------------|
| `ollama/` | Ollama | ✅ Yes |
| `groq/` | Groq | ✅ Yes |
| `together_ai/` | Together AI | ✅ Yes |
| `mistral/` | Mistral | ✅ Yes |
| `cohere/` | Cohere | ✅ Yes |
| `huggingface/` | Hugging Face | ✅ Yes |
| `openai/` | OpenAI | ❌ Native |
| `anthropic/` | Anthropic | ❌ Native |
| `gemini/` | Google Gemini | ❌ Native |
| `azure/` | Azure OpenAI | ❌ Native |
| `bedrock/` | AWS Bedrock | ❌ Native |

### Check if LiteLLM is installed

```bash
# Using pip
pip show litellm

# Using uv
uv pip show litellm
```

If the command returns package information, LiteLLM is installed in your environment.

### Check your dependencies

Look at your `pyproject.toml` for `crewai[litellm]`:

```toml
# If you see this, you have LiteLLM as a dependency
dependencies = [
    "crewai[litellm]>=0.100.0",  # ← Uses LiteLLM
]

# Change to a native provider extra instead
dependencies = [
    "crewai[openai]>=0.100.0",   # ← Native, no LiteLLM
]
```

## Migration Guide

### Step 1: Identify your current provider

Find all `LLM()` calls and model strings in your code:

```bash
# Search your codebase for LLM model strings
grep -r "LLM(" --include="*.py" .
grep -r "llm=" --include="*.yaml" .
grep -r "llm:" --include="*.yaml" .
```

### Step 2: Switch to a native provider

<Tabs>
  <Tab title="Switch to OpenAI">
    ```python
    from crewai import LLM

    # Before (LiteLLM):
    # llm = LLM(model="groq/llama-3.1-70b")

    # After (Native):
    llm = LLM(model="openai/gpt-4o")
    ```

    ```bash
    # Install
    uv add "crewai[openai]"

    # Set your API key
    export OPENAI_API_KEY="sk-..."
    ```
  </Tab>
  <Tab title="Switch to Anthropic">
    ```python
    from crewai import LLM

    # Before (LiteLLM):
    # llm = LLM(model="together_ai/meta-llama/Meta-Llama-3.1-70B")

    # After (Native):
    llm = LLM(model="anthropic/claude-sonnet-4-20250514")
    ```

    ```bash
    # Install
    uv add "crewai[anthropic]"

    # Set your API key
    export ANTHROPIC_API_KEY="sk-ant-..."
    ```
  </Tab>
  <Tab title="Switch to Gemini">
    ```python
    from crewai import LLM

    # Before (LiteLLM):
    # llm = LLM(model="mistral/mistral-large-latest")

    # After (Native):
    llm = LLM(model="gemini/gemini-2.0-flash")
    ```

    ```bash
    # Install
    uv add "crewai[gemini]"

    # Set your API key
    export GEMINI_API_KEY="..."
    ```
  </Tab>
  <Tab title="Switch to Azure OpenAI">
    ```python
    from crewai import LLM

    # After (Native):
    llm = LLM(
        model="azure/your-deployment-name",
        api_key="your-azure-api-key",
        base_url="https://your-resource.openai.azure.com",
        api_version="2024-06-01"
    )
    ```

    ```bash
    # Install
    uv add "crewai[azure]"
    ```
  </Tab>
  <Tab title="Switch to AWS Bedrock">
    ```python
    from crewai import LLM

    # After (Native):
    llm = LLM(
        model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0",
        aws_region_name="us-east-1"
    )
    ```

    ```bash
    # Install
    uv add "crewai[bedrock]"

    # Configure AWS credentials
    export AWS_ACCESS_KEY_ID="..."
    export AWS_SECRET_ACCESS_KEY="..."
    export AWS_DEFAULT_REGION="us-east-1"
    ```
  </Tab>
</Tabs>

### Step 3: Keep Ollama without LiteLLM

If you're using Ollama and want to keep using it, you can connect via Ollama's OpenAI-compatible API:

```python
from crewai import LLM

# Before (LiteLLM):
# llm = LLM(model="ollama/llama3")

# After (OpenAI-compatible mode, no LiteLLM needed):
llm = LLM(
    model="llama3",
    custom_openai=True,
    base_url="http://localhost:11434/v1",
    api_key="ollama"  # Ollama doesn't require a real API key
)
```

<Tip>
  Many local inference servers (Ollama, vLLM, LM Studio, llama.cpp) expose an OpenAI-compatible API. You can use `custom_openai=True` with a custom `base_url` to connect to any of them natively while keeping the model ID your gateway expects.
</Tip>

### Step 4: Update your YAML configs

```yaml
# Before (LiteLLM providers):
researcher:
  role: Research Specialist
  goal: Conduct research
  backstory: A dedicated researcher
  llm: groq/llama-3.1-70b          # ← LiteLLM
  
# After (Native provider):
researcher:
  role: Research Specialist
  goal: Conduct research
  backstory: A dedicated researcher
  llm: openai/gpt-4o               # ← Native
```

### Step 5: Remove LiteLLM

Once you've migrated all your model references:

```bash
# Remove litellm from your project
uv remove litellm

# Or if using pip
pip uninstall litellm

# Update your pyproject.toml: change crewai[litellm] to your provider extra
# e.g., crewai[openai], crewai[anthropic], crewai[gemini]
```

### Step 6: Verify

Run your project and confirm everything works:

```bash
# Run your crew
crewai run

# Or run your tests
uv run pytest
```

## Custom OpenAI-Compatible Endpoints

Many providers and local servers (Ollama, vLLM, LM Studio, llama.cpp, LiteLLM proxies, and hosted gateways) expose an **OpenAI-compatible** API. Instead of routing these through LiteLLM, you can talk to them directly with CrewAI's native OpenAI integration by setting `custom_openai=True`.

This is the recommended replacement for any LiteLLM provider that offers an OpenAI-compatible endpoint.

### How it works

- `custom_openai=True` forces CrewAI to use the native OpenAI SDK, regardless of the model name.
- The model ID is passed to the endpoint without validation against OpenAI's known-model list. This lets you use arbitrary model IDs your gateway expects (for example, `anthropic/claude-sonnet-4-6` served behind an OpenAI-compatible proxy). An optional leading `openai/` routing prefix is stripped.
- A base URL is **required**. CrewAI resolves it, in order, from:
  1. `base_url=...`
  2. `api_base=...`
  3. `OPENAI_BASE_URL` environment variable
  4. `OPENAI_API_BASE` environment variable (legacy)

  If none are set, CrewAI raises a `ValueError` so misconfiguration fails fast instead of silently hitting `api.openai.com`.

```python
from crewai import LLM

llm = LLM(
    model="anthropic/claude-sonnet-4-6",  # passed through as-is
    custom_openai=True,
    base_url="https://your-gateway.example/v1",
    api_key="your-key",
)
```

### Connect to common servers

<Tabs>
  <Tab title="Ollama">
    ```python
    from crewai import LLM

    llm = LLM(
        model="llama3.2:latest",
        custom_openai=True,
        base_url="http://localhost:11434/v1",
        api_key="ollama",  # Ollama ignores it, but the client requires a value
    )
    ```
  </Tab>
  <Tab title="vLLM">
    ```python
    from crewai import LLM

    llm = LLM(
        model="meta-llama/Meta-Llama-3.1-8B-Instruct",
        custom_openai=True,
        base_url="http://localhost:8000/v1",
        api_key="not-needed",
    )
    ```
  </Tab>
  <Tab title="LM Studio">
    ```python
    from crewai import LLM

    llm = LLM(
        model="your-loaded-model",
        custom_openai=True,
        base_url="http://localhost:1234/v1",
        api_key="lm-studio",
    )
    ```
  </Tab>
  <Tab title="Env vars">
    ```bash
    export OPENAI_BASE_URL="https://your-gateway.example/v1"
    export OPENAI_API_KEY="your-key"
    ```
    ```python
    from crewai import LLM

    # base_url is picked up from OPENAI_BASE_URL / OPENAI_API_BASE
    llm = LLM(model="anthropic/claude-sonnet-4-6", custom_openai=True)
    ```
  </Tab>
</Tabs>

<Tip>
  If you use the `openai/` prefix with a model that isn't a known OpenAI model and pass `base_url` or `api_base` directly, CrewAI automatically treats it as a custom OpenAI-compatible endpoint. Environment variables alone do not enable automatic routing for unknown models; set `custom_openai=True` when configuring the endpoint through `OPENAI_BASE_URL` or `OPENAI_API_BASE`.
</Tip>

## Quick Reference: Model String Mapping

Here are common migration paths from LiteLLM-dependent providers to native ones:

```python
from crewai import LLM

# ─── LiteLLM providers → Native alternatives ────────────────────

# Groq → OpenAI or Anthropic
# llm = LLM(model="groq/llama-3.1-70b")
llm = LLM(model="openai/gpt-4o-mini")           # Fast & affordable
llm = LLM(model="anthropic/claude-haiku-3-5")    # Fast & affordable

# Together AI → OpenAI or Gemini
# llm = LLM(model="together_ai/meta-llama/Meta-Llama-3.1-70B")
llm = LLM(model="openai/gpt-4o")                 # High quality
llm = LLM(model="gemini/gemini-2.0-flash")       # Fast & capable

# Mistral → Anthropic or OpenAI
# llm = LLM(model="mistral/mistral-large-latest")
llm = LLM(model="anthropic/claude-sonnet-4-20250514")  # High quality

# Ollama → OpenAI-compatible (keep using local models)
# llm = LLM(model="ollama/llama3")
llm = LLM(
    model="llama3",
    custom_openai=True,
    base_url="http://localhost:11434/v1",
    api_key="ollama"
)
```

## FAQ

<AccordionGroup>
  <Accordion title="Do I lose any functionality by removing LiteLLM?">
    No, if you use one of the five natively supported providers (OpenAI, Anthropic, Gemini, Azure, Bedrock). These native integrations support all CrewAI features including streaming, tool calling, structured output, and more. You only lose access to providers that are exclusively available through LiteLLM (like Groq, Together AI, Mistral as first-class providers).
  </Accordion>
  <Accordion title="Can I use multiple native providers at the same time?">
    Yes. Install multiple extras and use different providers for different agents:
    ```bash
    uv add "crewai[openai,anthropic,gemini]"
    ```
    ```python
    researcher = Agent(llm="openai/gpt-4o", ...)
    writer = Agent(llm="anthropic/claude-sonnet-4-20250514", ...)
    ```
  </Accordion>
  <Accordion title="Is LiteLLM safe to use now?">
    Regardless of quarantine status, reducing your dependency surface is good security practice. If you only need providers that CrewAI supports natively, there's no reason to keep LiteLLM installed.
  </Accordion>
  <Accordion title="What about environment variables like OPENAI_API_KEY?">
    Native providers use the same environment variables you're already familiar with. No changes needed for `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, `GEMINI_API_KEY`, etc.
  </Accordion>
  <Accordion title="How do I connect to Groq, Together AI, or other OpenAI-compatible providers without LiteLLM?">
    Most of these providers expose an OpenAI-compatible API. Use `custom_openai=True` with their base URL and API key — see [Custom OpenAI-Compatible Endpoints](#custom-openai-compatible-endpoints). For example, Groq: `LLM(model="llama-3.1-70b-versatile", custom_openai=True, base_url="https://api.groq.com/openai/v1", api_key="...")`. The model ID is passed through untouched, so use whatever ID the provider expects.
  </Accordion>
</AccordionGroup>

## Related Resources

- [LLM Connections](/en/learn/llm-connections) — Full guide to connecting CrewAI with any LLM
- [LLM Concepts](/en/concepts/llms) — Understanding LLMs in CrewAI
- [LLM Selection Guide](/en/learn/llm-selection-guide) — Choosing the right model for your use case
