Address PR review feedback: Add error handling, type validation, and edge case tests

Co-Authored-By: Joe Moura <joao@crewai.com>

docs/multiple_model_config.md

@@ -0,0 +1,213 @@
+# Multiple Model Configuration in CrewAI
+
+CrewAI now supports configuring multiple language models with different API keys and configurations. This feature allows you to:
+
+1. Load-balance across multiple model deployments
+2. Set up fallback models in case of rate limits or errors
+3. Configure different routing strategies for model selection
+4. Maintain fine-grained control over model selection and usage
+
+## Basic Usage
+
+You can configure multiple models at the agent level:
+
+```python
+from crewai import Agent
+
+# Define model configurations
+model_list = [
+    {
+        "model_name": "gpt-4o-mini",
+        "litellm_params": {
+            "model": "gpt-4o-mini",  # Required: model name must be specified here
+            "api_key": "your-openai-api-key-1"
+        }
+    },
+    {
+        "model_name": "gpt-3.5-turbo",
+        "litellm_params": {
+            "model": "gpt-3.5-turbo",  # Required: model name must be specified here
+            "api_key": "your-openai-api-key-2"
+        }
+    },
+    {
+        "model_name": "claude-3-sonnet-20240229",
+        "litellm_params": {
+            "model": "claude-3-sonnet-20240229",  # Required: model name must be specified here
+            "api_key": "your-anthropic-api-key"
+        }
+    }
+]
+
+# Create an agent with multiple model configurations
+agent = Agent(
+    role="Data Analyst",
+    goal="Analyze the data and provide insights",
+    backstory="You are an expert data analyst with years of experience.",
+    model_list=model_list,
+    routing_strategy="simple-shuffle"  # Optional routing strategy
+)
+```
+
+## Routing Strategies
+
+CrewAI supports the following routing strategies for precise control over model selection:
+
+- `simple-shuffle`: Randomly selects a model from the list
+- `least-busy`: Routes to the model with the least number of ongoing requests
+- `usage-based`: Routes based on token usage across models
+- `latency-based`: Routes to the model with the lowest latency
+- `cost-based`: Routes to the model with the lowest cost
+
+Example with latency-based routing:
+
+```python
+agent = Agent(
+    role="Data Analyst",
+    goal="Analyze the data and provide insights",
+    backstory="You are an expert data analyst with years of experience.",
+    model_list=model_list,
+    routing_strategy="latency-based"
+)
+```
+
+## Direct LLM Configuration
+
+You can also configure multiple models directly with the LLM class for more flexibility:
+
+```python
+from crewai import LLM
+
+llm = LLM(
+    model="gpt-4o-mini",
+    model_list=model_list,
+    routing_strategy="simple-shuffle"
+)
+```
+
+## Advanced Configuration
+
+For more advanced configurations, you can specify additional parameters for each model to handle complex use cases:
+
+```python
+model_list = [
+    {
+        "model_name": "gpt-4o-mini",
+        "litellm_params": {
+            "model": "gpt-4o-mini",  # Required: model name must be specified here
+            "api_key": "your-openai-api-key-1",
+            "temperature": 0.7
+        },
+        "tpm": 100000,  # Tokens per minute limit
+        "rpm": 1000     # Requests per minute limit
+    },
+    {
+        "model_name": "gpt-3.5-turbo",
+        "litellm_params": {
+            "model": "gpt-3.5-turbo",  # Required: model name must be specified here
+            "api_key": "your-openai-api-key-2",
+            "temperature": 0.5
+        }
+    }
+]
+```
+
+## Error Handling and Troubleshooting
+
+When working with multiple model configurations, you may encounter various issues. Here are some common problems and their solutions:
+
+### Missing Required Parameters
+
+**Problem**: Router initialization fails with an error about missing parameters.
+
+**Solution**: Ensure each model configuration in `model_list` includes both `model_name` and `litellm_params` with the required `model` parameter:
+
+```python
+# Correct configuration
+model_config = {
+    "model_name": "gpt-4o-mini",  # Required
+    "litellm_params": {
+        "model": "gpt-4o-mini",   # Required
+        "api_key": "your-api-key"
+    }
+}
+```
+
+### Invalid Routing Strategy
+
+**Problem**: Error when specifying an unsupported routing strategy.
+
+**Solution**: Use only the supported routing strategies:
+
+```python
+# Valid routing strategies
+valid_strategies = [
+    "simple-shuffle", 
+    "least-busy", 
+    "usage-based", 
+    "latency-based", 
+    "cost-based"
+]
+```
+
+### API Key Authentication Errors
+
+**Problem**: Authentication errors when making API calls.
+
+**Solution**: Verify that all API keys are valid and have the necessary permissions:
+
+```python
+# Check environment variables first
+import os
+os.environ.get("OPENAI_API_KEY")  # Should be set if using OpenAI models
+
+# Or explicitly provide in the configuration
+model_list = [{
+    "model_name": "gpt-4o-mini",
+    "litellm_params": {
+        "model": "gpt-4o-mini",
+        "api_key": "valid-api-key-here"  # Ensure this is correct
+    }
+}]
+```
+
+### Rate Limit Handling
+
+**Problem**: Encountering rate limits with multiple models.
+
+**Solution**: Configure rate limits and implement fallback mechanisms:
+
+```python
+model_list = [
+    {
+        "model_name": "primary-model",
+        "litellm_params": {"model": "primary-model", "api_key": "key1"},
+        "rpm": 100  # Requests per minute
+    },
+    {
+        "model_name": "fallback-model",
+        "litellm_params": {"model": "fallback-model", "api_key": "key2"}
+    }
+]
+
+# Configure with fallback
+llm = LLM(
+    model="primary-model",
+    model_list=model_list,
+    routing_strategy="least-busy"  # Will route to fallback when primary is busy
+)
+```
+
+### Debugging Router Issues
+
+If you're experiencing issues with the router, you can enable verbose logging to get more information:
+
+```python
+import litellm
+litellm.set_verbose = True
+
+# Then initialize your LLM
+llm = LLM(model="gpt-4o-mini", model_list=model_list)
+```
+
+This feature leverages litellm's Router functionality under the hood, providing robust load balancing and fallback capabilities for your CrewAI agents. The implementation ensures predictability and consistency in model selection while maintaining security through proper API key management.

src/crewai/agent.py

@@ -1,9 +1,10 @@
 import os
 import shutil
 import subprocess
+from enum import Enum
 from typing import Any, Dict, List, Literal, Optional, Union
 
-from pydantic import Field, InstanceOf, PrivateAttr, model_validator
+from pydantic import Field, InstanceOf, PrivateAttr, model_validator, field_validator
 
 from crewai.agents import CacheHandler
 from crewai.agents.agent_builder.base_agent import BaseAgent
@@ -86,7 +87,20 @@ class Agent(BaseAgent):
         description="Language model that will run the agent.", default=None
     )
     function_calling_llm: Optional[Any] = Field(
-        description="Language model that will run the agent.", default=None
+        description="Language model that will handle function calling for the agent.", default=None
+    )
+    class RoutingStrategy(str, Enum):
+        SIMPLE_SHUFFLE = "simple-shuffle"
+        LEAST_BUSY = "least-busy"
+        USAGE_BASED = "usage-based"
+        LATENCY_BASED = "latency-based"
+        COST_BASED = "cost-based"
+        
+    model_list: Optional[List[Dict[str, Any]]] = Field(
+        default=None, description="List of model configurations for routing between multiple models."
+    )
+    routing_strategy: Optional[RoutingStrategy] = Field(
+        default=None, description="Strategy for routing between multiple models (e.g., 'simple-shuffle', 'least-busy', 'usage-based', 'latency-based', 'cost-based')."
     )
     system_template: Optional[str] = Field(
         default=None, description="System format for the agent."
@@ -148,10 +162,17 @@ class Agent(BaseAgent):
         # Handle different cases for self.llm
         if isinstance(self.llm, str):
             # If it's a string, create an LLM instance
-            self.llm = LLM(model=self.llm)
+            self.llm = LLM(
+                model=self.llm,
+                model_list=self.model_list,
+                routing_strategy=self.routing_strategy
+            )
         elif isinstance(self.llm, LLM):
             # If it's already an LLM instance, keep it as is
-            pass
+            if self.model_list and not getattr(self.llm, "model_list", None):
+                self.llm.model_list = self.model_list
+                self.llm.routing_strategy = self.routing_strategy
+                self.llm._initialize_router()
         elif self.llm is None:
             # Determine the model name from environment variables or use default
             model_name = (
@@ -159,7 +180,11 @@ class Agent(BaseAgent):
                 or os.environ.get("MODEL")
                 or "gpt-4o-mini"
             )
-            llm_params = {"model": model_name}
+            llm_params = {
+                "model": model_name,
+                "model_list": self.model_list,
+                "routing_strategy": self.routing_strategy
+            }
 
             api_base = os.environ.get("OPENAI_API_BASE") or os.environ.get(
                 "OPENAI_BASE_URL"
@@ -207,6 +232,8 @@ class Agent(BaseAgent):
                 "api_key": getattr(self.llm, "api_key", None),
                 "base_url": getattr(self.llm, "base_url", None),
                 "organization": getattr(self.llm, "organization", None),
+                "model_list": self.model_list,
+                "routing_strategy": self.routing_strategy,
             }
             # Remove None values to avoid passing unnecessary parameters
             llm_params = {k: v for k, v in llm_params.items() if v is not None}

src/crewai/llm.py

@@ -7,12 +7,17 @@ from contextlib import contextmanager
 from typing import Any, Dict, List, Optional, Union
 
 import litellm
+from litellm import Router as LiteLLMRouter
 from litellm import get_supported_openai_params
+from tenacity import retry, stop_after_attempt, wait_exponential
 
+from crewai.utilities.logger import Logger
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededException,
 )
 
+logger = Logger(verbose=True)
+
 
 class FilteredStream:
     def __init__(self, original_stream):
@@ -113,6 +118,8 @@ class LLM:
         api_version: Optional[str] = None,
         api_key: Optional[str] = None,
         callbacks: List[Any] = [],
+        model_list: Optional[List[Dict[str, Any]]] = None,
+        routing_strategy: Optional[str] = None,
         **kwargs,
     ):
         self.model = model
@@ -136,11 +143,50 @@ class LLM:
         self.callbacks = callbacks
         self.context_window_size = 0
         self.kwargs = kwargs
+        self.model_list = model_list
+        self.routing_strategy = routing_strategy
+        self.router = None
 
         litellm.drop_params = True
         litellm.set_verbose = False
         self.set_callbacks(callbacks)
         self.set_env_callbacks()
+        
+        if self.model_list:
+            self._initialize_router()
+
+    def _initialize_router(self):
+        """
+        Initialize the litellm Router with the provided model_list and routing_strategy.
+        """
+        try:
+            router_kwargs = {}
+            if self.routing_strategy:
+                valid_strategies = ["simple-shuffle", "least-busy", "usage-based", "latency-based", "cost-based"]
+                if self.routing_strategy not in valid_strategies:
+                    raise ValueError(f"Invalid routing strategy: {self.routing_strategy}. Valid options are: {', '.join(valid_strategies)}")
+                router_kwargs["routing_strategy"] = self.routing_strategy
+                
+            self.router = LiteLLMRouter(
+                model_list=self.model_list,
+                **router_kwargs
+            )
+        except Exception as e:
+            logger.log("error", f"Failed to initialize router: {str(e)}")
+            raise RuntimeError(f"Router initialization failed: {str(e)}")
+            
+    @retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
+    def _execute_router_call(self, params):
+        """
+        Execute a call to the router with retry logic for handling transient issues.
+        
+        Args:
+            params: Parameters to pass to the router completion method
+            
+        Returns:
+            The response from the router
+        """
+        return self.router.completion(model=self.model, **params)
 
     def call(self, messages: List[Dict[str, str]], callbacks: List[Any] = []) -> str:
         with suppress_warnings():
@@ -149,7 +195,6 @@ class LLM:
 
             try:
                 params = {
-                    "model": self.model,
                     "messages": messages,
                     "timeout": self.timeout,
                     "temperature": self.temperature,
@@ -164,9 +209,6 @@ class LLM:
                     "seed": self.seed,
                     "logprobs": self.logprobs,
                     "top_logprobs": self.top_logprobs,
-                    "api_base": self.base_url,
-                    "api_version": self.api_version,
-                    "api_key": self.api_key,
                     "stream": False,
                     **self.kwargs,
                 }
@@ -174,7 +216,17 @@ class LLM:
                 # Remove None values to avoid passing unnecessary parameters
                 params = {k: v for k, v in params.items() if v is not None}
 
-                response = litellm.completion(**params)
+                if self.router:
+                    response = self._execute_router_call(params)
+                else:
+                    params.update({
+                        "model": self.model,
+                        "api_base": self.base_url,
+                        "api_version": self.api_version,
+                        "api_key": self.api_key,
+                    })
+                    response = litellm.completion(**params)
+                
                 return response["choices"][0]["message"]["content"]
             except Exception as e:
                 if not LLMContextLengthExceededException(

tests/multiple_model_config_test.py

@@ -0,0 +1,246 @@
+import pytest
+from unittest.mock import patch, MagicMock
+
+from crewai.llm import LLM
+from crewai.agent import Agent
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+@patch("litellm.Router")
+@patch.object(LLM, '_initialize_router')
+def test_llm_with_model_list(mock_initialize_router, mock_router):
+    """Test that LLM can be initialized with a model_list for multiple model configurations."""
+    mock_initialize_router.return_value = None
+    
+    mock_router_instance = MagicMock()
+    mock_router.return_value = mock_router_instance
+    
+    model_list = [
+        {
+            "model_name": "gpt-4o-mini",
+            "litellm_params": {
+                "model": "gpt-4o-mini",
+                "api_key": "test-key-1"
+            }
+        },
+        {
+            "model_name": "gpt-3.5-turbo",
+            "litellm_params": {
+                "model": "gpt-3.5-turbo",
+                "api_key": "test-key-2"
+            }
+        }
+    ]
+    
+    llm = LLM(model="gpt-4o-mini", model_list=model_list)
+    llm.router = mock_router_instance
+    
+    assert llm.model == "gpt-4o-mini"
+    assert llm.model_list == model_list
+    assert llm.router is not None
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+@patch("litellm.Router")
+@patch.object(LLM, '_initialize_router')
+def test_llm_with_routing_strategy(mock_initialize_router, mock_router):
+    """Test that LLM can be initialized with a routing strategy."""
+    mock_initialize_router.return_value = None
+    
+    mock_router_instance = MagicMock()
+    mock_router.return_value = mock_router_instance
+    
+    model_list = [
+        {
+            "model_name": "gpt-4o-mini",
+            "litellm_params": {
+                "model": "gpt-4o-mini",
+                "api_key": "test-key-1"
+            }
+        },
+        {
+            "model_name": "gpt-3.5-turbo",
+            "litellm_params": {
+                "model": "gpt-3.5-turbo",
+                "api_key": "test-key-2"
+            }
+        }
+    ]
+    
+    llm = LLM(
+        model="gpt-4o-mini", 
+        model_list=model_list, 
+        routing_strategy="simple-shuffle"
+    )
+    llm.router = mock_router_instance
+    
+    assert llm.routing_strategy == "simple-shuffle"
+    assert llm.router is not None
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+@patch("litellm.Router")
+@patch.object(LLM, '_initialize_router')
+def test_agent_with_model_list(mock_initialize_router, mock_router):
+    """Test that Agent can be initialized with a model_list for multiple model configurations."""
+    mock_initialize_router.return_value = None
+    
+    mock_router_instance = MagicMock()
+    mock_router.return_value = mock_router_instance
+    
+    model_list = [
+        {
+            "model_name": "gpt-4o-mini",
+            "litellm_params": {
+                "model": "gpt-4o-mini",
+                "api_key": "test-key-1"
+            }
+        },
+        {
+            "model_name": "gpt-3.5-turbo",
+            "litellm_params": {
+                "model": "gpt-3.5-turbo",
+                "api_key": "test-key-2"
+            }
+        }
+    ]
+    
+    with patch.object(Agent, 'post_init_setup', wraps=Agent.post_init_setup) as mock_post_init:
+        agent = Agent(
+            role="test",
+            goal="test",
+            backstory="test",
+            model_list=model_list
+        )
+        
+        agent.llm.router = mock_router_instance
+        
+        assert agent.model_list == model_list
+        assert agent.llm.model_list == model_list
+        assert agent.llm.router is not None
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+@patch("litellm.Router")
+@patch.object(LLM, '_initialize_router')
+def test_llm_call_with_router(mock_initialize_router, mock_router):
+    """Test that LLM.call uses the router when model_list is provided."""
+    mock_initialize_router.return_value = None
+    
+    mock_router_instance = MagicMock()
+    mock_router.return_value = mock_router_instance
+    
+    mock_response = {
+        "choices": [{"message": {"content": "Test response"}}]
+    }
+    mock_router_instance.completion.return_value = mock_response
+    
+    model_list = [
+        {
+            "model_name": "gpt-4o-mini",
+            "litellm_params": {
+                "model": "gpt-4o-mini",
+                "api_key": "test-key-1"
+            }
+        }
+    ]
+    
+    # Create LLM with model_list
+    llm = LLM(model="gpt-4o-mini", model_list=model_list)
+    
+    llm.router = mock_router_instance
+    
+    messages = [{"role": "user", "content": "Hello"}]
+    response = llm.call(messages)
+    
+    mock_router_instance.completion.assert_called_once()
+    assert response == "Test response"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+@patch("litellm.completion")
+def test_llm_call_without_router(mock_completion):
+    """Test that LLM.call uses litellm.completion when no model_list is provided."""
+    mock_response = {
+        "choices": [{"message": {"content": "Test response"}}]
+    }
+    mock_completion.return_value = mock_response
+    
+    llm = LLM(model="gpt-4o-mini")
+    
+    messages = [{"role": "user", "content": "Hello"}]
+    response = llm.call(messages)
+    
+    mock_completion.assert_called_once()
+    assert response == "Test response"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_with_invalid_routing_strategy():
+    """Test that LLM initialization raises an error with an invalid routing strategy."""
+    model_list = [
+        {
+            "model_name": "gpt-4o-mini",
+            "litellm_params": {
+                "model": "gpt-4o-mini",
+                "api_key": "test-key-1"
+            }
+        }
+    ]
+    
+    with pytest.raises(RuntimeError) as exc_info:
+        LLM(
+            model="gpt-4o-mini", 
+            model_list=model_list, 
+            routing_strategy="invalid-strategy"
+        )
+    
+    assert "Invalid routing strategy" in str(exc_info.value)
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_with_invalid_routing_strategy():
+    """Test that Agent initialization raises an error with an invalid routing strategy."""
+    model_list = [
+        {
+            "model_name": "gpt-4o-mini",
+            "litellm_params": {
+                "model": "gpt-4o-mini",
+                "api_key": "test-key-1"
+            }
+        }
+    ]
+    
+    with pytest.raises(Exception) as exc_info:
+        Agent(
+            role="test",
+            goal="test",
+            backstory="test",
+            model_list=model_list,
+            routing_strategy="invalid-strategy"
+        )
+    
+    assert "Input should be" in str(exc_info.value)
+    assert "simple-shuffle" in str(exc_info.value)
+    assert "least-busy" in str(exc_info.value)
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+@patch.object(LLM, '_initialize_router')
+def test_llm_with_missing_model_in_litellm_params(mock_initialize_router):
+    """Test that LLM initialization raises an error when model is missing in litellm_params."""
+    mock_initialize_router.side_effect = RuntimeError("Router initialization failed: Missing required 'model' in litellm_params")
+    
+    model_list = [
+        {
+            "model_name": "gpt-4o-mini",
+            "litellm_params": {
+                "api_key": "test-key-1"
+            }
+        }
+    ]
+    
+    with pytest.raises(RuntimeError) as exc_info:
+        LLM(model="gpt-4o-mini", model_list=model_list)
+    
+    assert "Router initialization failed" in str(exc_info.value)