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ac1080afd8
- Add OAuth2Config and OAuth2ConfigLoader for litellm_config.json configuration - Add OAuth2TokenManager for token acquisition, caching, and refresh - Extend LLM class to support OAuth2 authentication with custom providers - Add comprehensive tests covering OAuth2 flow and error handling - Add documentation and usage examples - Support Client Credentials OAuth2 flow for server-to-server authentication - Maintain backward compatibility with existing LLM providers Fixes #3114 Co-Authored-By: João <joao@crewai.com>
148 lines
3.2 KiB
Markdown
148 lines
3.2 KiB
Markdown
# OAuth2 LLM Providers
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CrewAI supports OAuth2 authentication for custom LiteLLM providers through configuration files.
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## Configuration
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Create a `litellm_config.json` file in your project directory:
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```json
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{
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"oauth2_providers": {
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"my_custom_provider": {
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"client_id": "your_client_id",
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"client_secret": "your_client_secret",
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"token_url": "https://your-provider.com/oauth/token",
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"scope": "llm.read llm.write"
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},
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"another_provider": {
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"client_id": "another_client_id",
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"client_secret": "another_client_secret",
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"token_url": "https://another-provider.com/token"
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}
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}
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}
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```
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## Usage
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```python
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from crewai import LLM
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# Initialize LLM with OAuth2 support
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llm = LLM(
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model="my_custom_provider/my-model",
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oauth2_config_path="./litellm_config.json" # Optional, defaults to ./litellm_config.json
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)
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# Use in CrewAI
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from crewai import Agent, Task, Crew
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agent = Agent(
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role="Data Analyst",
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goal="Analyze data trends",
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backstory="Expert in data analysis",
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llm=llm
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)
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task = Task(
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description="Analyze the latest sales data",
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agent=agent
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)
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crew = Crew(agents=[agent], tasks=[task])
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result = crew.kickoff()
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```
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## Environment Variables
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You can also use environment variables in your configuration:
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```json
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{
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"oauth2_providers": {
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"my_provider": {
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"client_id": "os.environ/MY_CLIENT_ID",
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"client_secret": "os.environ/MY_CLIENT_SECRET",
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"token_url": "https://my-provider.com/token"
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}
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}
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}
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```
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## Supported OAuth2 Flow
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Currently supports the **Client Credentials** OAuth2 flow, which is suitable for server-to-server authentication.
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## Token Management
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- Tokens are automatically cached and refreshed when they expire
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- A 60-second buffer is used before token expiration to ensure reliability
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- Failed token acquisition will raise a `RuntimeError` with details
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## Configuration Schema
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The `litellm_config.json` file should follow this schema:
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```json
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{
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"oauth2_providers": {
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"<provider_name>": {
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"client_id": "string (required)",
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"client_secret": "string (required)",
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"token_url": "string (required)",
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"scope": "string (optional)",
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"refresh_token": "string (optional)"
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}
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}
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}
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```
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## Error Handling
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- If OAuth2 authentication fails, a `RuntimeError` will be raised with details
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- Invalid configuration files will raise a `ValueError` with specifics
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- Network errors during token acquisition are wrapped in `RuntimeError`
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## Examples
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### Basic OAuth2 Provider
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```python
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from crewai import LLM
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llm = LLM(
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model="my_provider/gpt-4",
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oauth2_config_path="./config.json"
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)
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response = llm.call("Hello, world!")
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print(response)
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```
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### Multiple Providers
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```json
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{
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"oauth2_providers": {
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"provider_a": {
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"client_id": "client_a",
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"client_secret": "secret_a",
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"token_url": "https://provider-a.com/token"
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},
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"provider_b": {
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"client_id": "client_b",
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"client_secret": "secret_b",
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"token_url": "https://provider-b.com/oauth/token",
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"scope": "read write"
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}
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}
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}
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```
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```python
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# Use different providers
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llm_a = LLM(model="provider_a/model-1", oauth2_config_path="./config.json")
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llm_b = LLM(model="provider_b/model-2", oauth2_config_path="./config.json")
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```
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