Add example for AgentJudge with evaluation criteria

examples/single_agent/reasoning_agent_examples/agent_judge_evaluation_criteria_example.py

@@ -0,0 +1,100 @@
+"""
+Agent Judge with Evaluation Criteria Example
+
+This example demonstrates how to use the AgentJudge with custom evaluation criteria.
+The evaluation_criteria parameter allows specifying different criteria with weights 
+for more targeted and customizable evaluation of agent outputs.
+"""
+
+from swarms.agents.agent_judge import AgentJudge
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# Example 1: Basic usage with evaluation criteria
+print("\n=== Example 1: Using Custom Evaluation Criteria ===\n")
+
+# Create an AgentJudge with custom evaluation criteria
+judge = AgentJudge(
+    model_name="claude-3-7-sonnet-20250219",  # Use any available model
+    evaluation_criteria={
+        "correctness": 0.5,
+        "problem_solving_approach": 0.3, 
+        "explanation_clarity": 0.2
+    }
+)
+
+# Sample output to evaluate
+task_response = [
+    "Task: Determine the time complexity of a binary search algorithm and explain your reasoning.\n\n"
+    "Agent response: The time complexity of binary search is O(log n). In each step, "
+    "we divide the search space in half, resulting in a logarithmic relationship between "
+    "the input size and the number of operations. This can be proven by solving the "
+    "recurrence relation T(n) = T(n/2) + O(1), which gives us T(n) = O(log n)."
+]
+
+# Run evaluation
+evaluation = judge.run(task_response)
+print(evaluation[0])
+
+# Example 2: Specialized criteria for code evaluation
+print("\n=== Example 2: Code Evaluation with Specialized Criteria ===\n")
+
+code_judge = AgentJudge(
+    model_name="claude-3-7-sonnet-20250219",
+    agent_name="code_judge",
+    evaluation_criteria={
+        "code_correctness": 0.4,
+        "code_efficiency": 0.3,
+        "code_readability": 0.3
+    }
+)
+
+# Sample code to evaluate
+code_response = [
+    "Task: Write a function to find the maximum subarray sum in an array of integers.\n\n"
+    "Agent response:\n```python\n"
+    "def max_subarray_sum(arr):\n"
+    "    current_sum = max_sum = arr[0]\n"
+    "    for i in range(1, len(arr)):\n"
+    "        current_sum = max(arr[i], current_sum + arr[i])\n"
+    "        max_sum = max(max_sum, current_sum)\n"
+    "    return max_sum\n\n"
+    "# Example usage\n"
+    "print(max_subarray_sum([-2, 1, -3, 4, -1, 2, 1, -5, 4]))  # Output: 6 (subarray [4, -1, 2, 1])\n"
+    "```\n"
+    "This implementation uses Kadane's algorithm which has O(n) time complexity and "
+    "O(1) space complexity, making it optimal for this problem."
+]
+
+code_evaluation = code_judge.run(code_response)
+print(code_evaluation[0])
+
+# Example 3: Comparing multiple responses
+print("\n=== Example 3: Comparing Multiple Agent Responses ===\n")
+
+comparison_judge = AgentJudge(
+    model_name="claude-3-7-sonnet-20250219",
+    evaluation_criteria={
+        "accuracy": 0.6,
+        "completeness": 0.4
+    }
+)
+
+multiple_responses = comparison_judge.run([
+    "Task: Explain the CAP theorem in distributed systems.\n\n"
+    "Agent A response: CAP theorem states that a distributed system cannot simultaneously "
+    "provide Consistency, Availability, and Partition tolerance. In practice, you must "
+    "choose two out of these three properties.",
+    
+    "Task: Explain the CAP theorem in distributed systems.\n\n"
+    "Agent B response: The CAP theorem, formulated by Eric Brewer, states that in a "
+    "distributed data store, you can only guarantee two of the following three properties: "
+    "Consistency (all nodes see the same data at the same time), Availability (every request "
+    "receives a response), and Partition tolerance (the system continues to operate despite "
+    "network failures). Most modern distributed systems choose to sacrifice consistency in "
+    "favor of availability and partition tolerance, implementing eventual consistency models instead."
+])
+
+print(multiple_responses[0])