🧪 Add unit tests for StatsCallbackHandler

Adds a comprehensive test suite for the StatsCallbackHandler in cli/stats_handler.py,
covering call counting, token usage extraction, and thread safety.

Co-authored-by: aguzererler <6199053+aguzererler@users.noreply.github.com>

tests/cli/test_stats_handler.py

@@ -0,0 +1,108 @@
+import threading
+import pytest
+from cli.stats_handler import StatsCallbackHandler
+from langchain_core.outputs import LLMResult, Generation
+from langchain_core.messages import AIMessage
+
+def test_stats_handler_initial_state():
+    handler = StatsCallbackHandler()
+    stats = handler.get_stats()
+    assert stats == {
+        "llm_calls": 0,
+        "tool_calls": 0,
+        "tokens_in": 0,
+        "tokens_out": 0,
+    }
+
+def test_stats_handler_on_llm_start():
+    handler = StatsCallbackHandler()
+    handler.on_llm_start(serialized={}, prompts=["test"])
+    assert handler.llm_calls == 1
+    assert handler.get_stats()["llm_calls"] == 1
+
+def test_stats_handler_on_chat_model_start():
+    handler = StatsCallbackHandler()
+    handler.on_chat_model_start(serialized={}, messages=[[]])
+    assert handler.llm_calls == 1
+    assert handler.get_stats()["llm_calls"] == 1
+
+def test_stats_handler_on_tool_start():
+    handler = StatsCallbackHandler()
+    handler.on_tool_start(serialized={}, input_str="test tool")
+    assert handler.tool_calls == 1
+    assert handler.get_stats()["tool_calls"] == 1
+
+def test_stats_handler_on_llm_end_with_usage():
+    handler = StatsCallbackHandler()
+
+    # Mock usage metadata
+    usage_metadata = {"input_tokens": 10, "output_tokens": 20}
+    message = AIMessage(content="test response")
+    message.usage_metadata = usage_metadata
+    generation = Generation(message=message, text="test response")
+    response = LLMResult(generations=[[generation]])
+
+    handler.on_llm_end(response)
+
+    stats = handler.get_stats()
+    assert stats["tokens_in"] == 10
+    assert stats["tokens_out"] == 20
+
+def test_stats_handler_on_llm_end_no_usage():
+    handler = StatsCallbackHandler()
+
+    # Generation without message/usage_metadata
+    generation = Generation(text="test response")
+    response = LLMResult(generations=[[generation]])
+
+    handler.on_llm_end(response)
+
+    stats = handler.get_stats()
+    assert stats["tokens_in"] == 0
+    assert stats["tokens_out"] == 0
+
+def test_stats_handler_on_llm_end_empty_generations():
+    handler = StatsCallbackHandler()
+    response = LLMResult(generations=[[]])
+    handler.on_llm_end(response)
+
+    response_none = LLMResult(generations=[])
+    # on_llm_end does try response.generations[0][0], so generations=[] will trigger IndexError which is handled.
+    handler.on_llm_end(response_none)
+
+    assert handler.tokens_in == 0
+    assert handler.tokens_out == 0
+
+def test_stats_handler_thread_safety():
+    handler = StatsCallbackHandler()
+    num_threads = 10
+    increments_per_thread = 100
+
+    def worker():
+        for _ in range(increments_per_thread):
+            handler.on_llm_start({}, [])
+            handler.on_tool_start({}, "")
+
+            # Mock usage metadata for on_llm_end
+            usage_metadata = {"input_tokens": 1, "output_tokens": 1}
+            message = AIMessage(content="x")
+            message.usage_metadata = usage_metadata
+            generation = Generation(message=message, text="x")
+            response = LLMResult(generations=[[generation]])
+            handler.on_llm_end(response)
+
+    threads = []
+    for _ in range(num_threads):
+        t = threading.Thread(target=worker)
+        threads.append(t)
+        t.start()
+
+    for t in threads:
+        t.join()
+
+    stats = handler.get_stats()
+    expected_calls = num_threads * increments_per_thread
+    assert stats["llm_calls"] == expected_calls
+    assert stats["tool_calls"] == expected_calls
+    assert stats["tokens_in"] == expected_calls
+    assert stats["tokens_out"] == expected_calls