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+---
+id: 8ecd1e8262
+question: How do I adapt the handwritten agent loop in Module 1 Lesson 14 for a non-OpenAI
+  provider using Chat Completions tool calling?
+sort_order: 14
+---
+
+If your provider exposes an OpenAI-compatible `chat.completions.create(...)` endpoint with tool calling, you can adapt the Lesson 14 handwritten agent loop by switching from the Responses API to Chat Completions and by reading tool calls from the assistant message.
+
+Key differences from the Responses-based loop:
+
+- Use `client.chat.completions.create(model=..., messages=..., tools=...)` instead of `client.responses.create(..., input=...)`.
+- Get tool calls from `response.choices[0].message.tool_calls` (not from `response.output`).
+- Use a `system` message for the instructions instead of the Responses API's `developer` message.
+- Append the full assistant message (including its requested tool calls) to `messages` before adding tool results.
+- For each tool call, add a `role="tool"` message with the matching `tool_call_id` and the tool result as `content`.
+- Keep looping until the returned assistant message has no `tool_calls`.
+
+For example, to run the loop with Gemini through Google's OpenAI-compatible endpoint:
+
+```python
+import os
+
+from dotenv import load_dotenv
+from openai import OpenAI
+
+load_dotenv()
+
+openai_client = OpenAI(
+    api_key=os.environ["GEMINI_API_KEY"],
+    base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
+)
+
+MODEL_ID = "gemini-3.1-flash-lite"
+```
+
+You can use code like this after you define your `search` function and `search_tool` schema:
+
+```python
+import json
+
+
+def make_tool_result(call, tool_handlers):
+    """Run one tool call and format its result for Chat Completions."""
+    tool_name = call.function.name
+    tool_args = json.loads(call.function.arguments)
+
+    if tool_name not in tool_handlers:
+        result = {"error": f"Unknown tool requested: {tool_name}"}
+    else:
+        result = tool_handlers[tool_name](**tool_args)
+
+    return {
+        "role": "tool",
+        "tool_call_id": call.id,
+        "content": json.dumps(result, indent=2),
+    }
+
+
+def agent_loop(
+    client,
+    model,
+    instructions,
+    question,
+    tools,
+    tool_handlers,
+    max_iterations=5,
+):
+    messages = [
+        {"role": "system", "content": instructions},
+        {"role": "user", "content": question},
+    ]
+
+    for iteration in range(1, max_iterations + 1):
+        print(f"Iteration {iteration}...")
+
+        response = client.chat.completions.create(
+            model=model,
+            messages=messages,
+            tools=tools,
+        )
+
+        message = response.choices[0].message
+
+        # Preserve the assistant message, including its tool calls.
+        messages.append(message)
+
+        tool_calls = getattr(message, "tool_calls", None) or []
+
+        # No tool calls means the model has returned its final answer.
+        if not tool_calls:
+            answer = message.content or ""
+            print("\nASSISTANT:\n")
+            print(answer)
+            return answer
+
+        # A model can request more than one tool in one response.
+        for call in tool_calls:
+            print("Function call:", call.function.name, call.function.arguments)
+            tool_result = make_tool_result(call, tool_handlers)
+            messages.append(tool_result)
+
+    raise RuntimeError(f"Agent exceeded the maximum of {max_iterations} iterations.")
+```
+
+Example usage (using the Lesson 14 FAQ search tool):
+
+```python
+answer = agent_loop(
+    client=openai_client,
+    model=MODEL_ID,
+    instructions=instructions,
+    question="How do I run Ollama locally?",
+    tools=[search_tool],
+    tool_handlers={"search": search},
+)
+```
+
+This pattern was tested with Gemini: on the first iteration the model requested the `search` tool, and on the second iteration it returned the final answer.
+
+It applies only to providers that support the OpenAI-compatible Chat Completions tool-calling format. If your provider's `tools` schema or response fields differ, check its documentation — see OpenAI's [function-calling guide](https://platform.openai.com/docs/guides/function-calling) for the underlying protocol.