Agent API

export TFY_API_TOKEN=your-token-here
export TFY_CONTROL_PLANE_BASE_URL=https://your-truefoundry-instance.com

from openai import OpenAI

client = OpenAI(
    base_url="https://<your-control-plane-url>/api/llm/agent",
    api_key="****",
)

AGENT_CONFIG = {
    "model": "openai/gpt-4o",
    "stream": True,
    "extra_body": {
        "mcp_servers": [{
            "integration_fqn": "common-tools",
                "enable_all_tools": False,
            "tools": [{"name": "web_search"}, {"name": "code_executor"}]
        }],
        "iteration_limit": 10
    }
}

def get_messages(chat_stream):
    messages = []
    previous_chunk_id = None

    for chunk in chat_stream:
        if not chunk.choices:
            continue

        delta = chunk.choices[0].delta

        # Detect new message when chunk ID changes
        if chunk.id != previous_chunk_id:
            previous_chunk_id = chunk.id
            messages.append({"role": delta.role, "content": ""})

        current_message = messages[-1]

        # Set tool_call_id for tool result messages
        if delta.role == 'tool':
            current_message["tool_call_id"] = delta.tool_call_id

        # Accumulate content for all message types (assistant text, tool results)
        current_message["content"] += delta.content if delta.content else ""

        # Process tool calls - function names and arguments are streamed
        for tool_call in delta.tool_calls or []:
            # Initialize tool_calls array if needed
            if "tool_calls" not in current_message:
                current_message["tool_calls"] = []

            # Add new tool call if index exceeds current array length
            if tool_call.index >= len(current_message["tool_calls"]):
                current_message["tool_calls"].append({
                    "id": tool_call.id,
                    "type": "function",
                    "function": {
                        "name": tool_call.function.name,
                        "arguments": tool_call.function.arguments or ""
                    }
                })
                # If the first tool call event has arguments, continue to next event
                if tool_call.function.arguments:
                    continue

            # Accumulate function arguments (streamed incrementally)
            current_message["tool_calls"][tool_call.index]["function"]["arguments"] += tool_call.function.arguments or ""

    # Anthropic fix: normalize empty argument strings to valid JSON
    for msg in messages:
        if msg["role"] == "assistant" and len(msg.get("tool_calls", [])) > 0:
            for tool_call in msg["tool_calls"]:
                if not tool_call["function"]["arguments"].strip():
                    tool_call["function"]["arguments"] = "{}"

    return messages

def chat_with_agent(messages):
    stream = client.chat.completions.create(messages=messages, **AGENT_CONFIG)
    messages += get_messages(stream)
    return messages

conversation = [
    {"role": "user", "content": "Search for Python tutorials and run a simple hello world example"}
]

conversation = chat_with_agent(conversation)

conversation.append(
    {"role": "user", "content": "Now scrape a Python documentation page and extract key concepts"}
)

conversation = chat_with_agent(conversation)

for message in conversation:
    if message["role"] in ["user", "assistant"]:
        print(f'{message["role"].title()}: {message["content"]}')
    elif message["role"] == "tool":
        print(f'Tool Result ({message["tool_call_id"]}):\n{message["content"]}')
    if message["role"] == "assistant" and len(message.get("tool_calls", [])) > 0:
        for tool_call in message["tool_calls"]:
            print(f'Tool call id: {tool_call["id"]}')
            print(f'Tool call function: {tool_call["function"]["name"]}')
            print(f'Tool call arguments: {tool_call["function"]["arguments"]}\n\n')
    print()