ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter9/gaia-experience/AWorld/train/adapter/swift/aworld_agent_trainer.py

@@ -0,0 +1,284 @@
+# coding: utf-8
+# Copyright (c) 2025 inclusionAI.
+import abc
+import asyncio
+import json
+import re
+from typing import List, Optional, Dict, Any, Union
+
+from aworld.agents.llm_agent import Agent
+from aworld.core.agent.swarm import Swarm
+from aworld.core.task import TaskResponse
+from aworld.runner import Runners
+from aworld.utils.common import sync_exec
+from swift.llm import RequestConfig
+from swift.llm.infer.protocol import ChatCompletionResponse
+from swift.trainers.rlhf_trainer.grpo_trainer import InputsType, GRPOTrainer, logger
+from transformers import AutoTokenizer
+from trl.extras.profiling import profiling_context
+
+
+class AworldTrainer(GRPOTrainer):
+    def _engine_infer(
+            self,
+            infer_requests: InputsType,
+            request_config: Optional[RequestConfig] = None,
+            *,
+            use_tqdm: Optional[bool] = False,
+    ) -> List[ChatCompletionResponse]:
+        with profiling_context(self, 'generate'):
+            if self.vllm_mode != 'server':
+                return self.engine.infer(infer_requests, request_config, use_tqdm=use_tqdm)
+
+            request_keys = ['messages', 'images', 'audios', 'videos', 'tools', 'objects']
+
+            infer_requests = [{
+                **{k: request[k]
+                   for k in request_keys if k in request},
+                **({
+                       'data_dict': {k: request[k]
+                                     for k in request if k not in request_keys}
+                   } if self.multi_turn_scheduler and self.vllm_use_async_engine else {})
+            } for request in infer_requests]
+
+            self._process_infer_requests_images(infer_requests)
+            return self.run_infer(infer_requests)
+
+    def run_infer(self, infer_requests: List[Dict[str, Any]]) -> List[ChatCompletionResponse]:
+        workers = [asyncio.create_task(self._rollout(req)) for req in infer_requests]
+        results = sync_exec(asyncio.gather, *workers)
+        return self.convert_agent_output(results, infer_requests)
+
+    async def _rollout(self, req: Dict[str, Any]):
+        agent = self.build_agents()
+        result = await self.run_agents(req, agent)
+        return result
+
+    @abc.abstractmethod
+    def build_agents(self) -> Union[Agent, Swarm]:
+        """Build single- or multi-agent"""
+
+    async def run_agents(self, input, agent):
+        # collect trajectory
+        if isinstance(agent, Swarm):
+            result = Runners.sync_run(input=input, swarm=agent)
+        else:
+            result = Runners.sync_run(input=input, agent=agent)
+        return result
+
+    def convert_agent_output(self,
+                             results: List[TaskResponse],
+                             infer_requests: List[Dict[str, Any]]) -> List[ChatCompletionResponse]:
+        message_final_merge = []
+        for result in results:
+            trajectory = result.trajectory
+            last_exp_data = trajectory[-1]['exp_data']
+            task_id = trajectory[0]['exp_meta']['task_id'].split('_')[1]
+            message_final = []
+            message = last_exp_data["messages"]
+            answer_flag = 0
+
+            for i in range(len(message)):
+                actions = last_exp_data.get('actions', [])
+                if actions:
+                    actions_str = json.dumps(actions)
+                    if '<answer>' in actions_str and '</answer>' in actions_str:
+                        match = re.search(r'<answer>(.*?)</answer>', actions_str, re.DOTALL)
+                        if match:
+                            answer_flag = 1
+                            logger.info(f"{task_id} answer content: {match.group(1)}")
+                        else:
+                            logger.warning(f"{task_id} no answer content found.")
+
+                if message[i]["role"] in ["system", "user"]:
+                    message_final.append(
+                        {
+                            "role": message[i]["role"],
+                            "content": message[i]["content"],
+                        }
+                    )
+                elif message[i]["role"] == "assistant" and "tool_calls" in message[i].keys():
+                    if message[i]["tool_calls"][0]["function"]["arguments"]:
+                        arguments = json.loads(message[i]["tool_calls"][0]["function"]["arguments"])
+                    else:
+                        arguments = ""
+                    function_call = {
+                        "name": message[i]["tool_calls"][0]["function"]["name"],
+                        "arguments": arguments
+                    }
+                    if message[i]["content"] != "" and message[i]["content"] is not None:
+                        message_final.append(
+                            {
+                                "role": "assistant",
+                                "content": message[i]["content"],
+                            }
+                        )
+                    message_final.append(
+                        {
+                            "role": "tool_call",
+                            "content": json.dumps(function_call, ensure_ascii=False),
+                        }
+                    )
+                elif message[i]["role"] == "tool":
+                    last_content = message[i - 1]["content"]
+                    if last_content is None:
+                        last_content = ""
+                    message_final.append(
+                        {
+                            "role": "tool",
+                            "content": message[i]["content"].replace(last_content, ""),
+                        }
+                    )
+                else:
+                    logger.warning(f"Unknown message role: {message[i]['role']}")
+
+            tokenizer = AutoTokenizer.from_pretrained(self.args.model_init_kwargs)
+            try:
+                response = last_exp_data["actions"][0]["policy_info"]
+                if response:
+                    message_final.append(
+                        {
+                            "role": "assistant",
+                            "content": response
+                        }
+                    )
+                else:
+                    message_final.append(
+                        {
+                            "role": "assistant",
+                            "content": "No response was received. Please try again later."
+                        }
+                    )
+                message_final = truncate_messages_fast(message_final, tokenizer)
+
+                status = "success" if answer_flag == 1 else "length"
+                message_final_merge.append((message_final, status, task_id))
+            except:
+                message_final.append({
+                    "role": "assistant",
+                    "content": "No response was received. Please try again later."
+                })
+                message_final = truncate_messages_fast(message_final, tokenizer)
+                message_final_merge.append((message_final, "length", task_id))
+
+        return self.pad_list_to_length(message_final_merge, infer_requests)
+
+    def pad_list_to_length(self, message_final_merge, infer_requests) -> List[ChatCompletionResponse]:
+        unique_task_ids = []
+        for msg in message_final_merge:
+            task_id = msg[2]
+            if task_id not in unique_task_ids:
+                unique_task_ids.append(task_id)
+        # Group by task_id
+        task_groups = {task_id: [] for task_id in unique_task_ids}
+        for item in message_final_merge:
+            messages, status, task_id = item
+            if task_id in task_groups:
+                task_groups[task_id].append(item)
+
+        # Ensure each group has exactly num_generations samples
+        for task_id in unique_task_ids:
+            # If this task_id has no samples, construct fallback data
+            if len(task_groups[task_id]) == 0:
+                for _infer_request in infer_requests:
+                    # Check if the first message content matches the task_id
+                    if task_id == _infer_request["messages"][0]["content"]:
+                        fallback_completion = {
+                            "role": "assistant",
+                            "content": "No response was received. Please try again later."
+                        }
+                        new_messages = _infer_request["messages"].copy()[1:]
+                        new_messages.append(fallback_completion)
+                        task_groups[task_id].append((new_messages, "length", task_id))
+
+                        break
+
+            # # Ensure we have exactly num_generations samples
+            # while len(task_groups[task_id]) < num_generations/len(unique_task_ids):
+            #     success_samples = [item for item in task_groups[task_id] if item[1] == "success"]
+            #     if success_samples:
+            #         task_groups[task_id].append(random.choice(success_samples))
+            #     else:
+            #         task_groups[task_id].append(random.choice(task_groups[task_id]))
+
+            num_generations = len(infer_requests)
+            current_count = len(task_groups[task_id])
+            if current_count >= num_generations / len(unique_task_ids):
+                continue
+
+            # Get success samples if available, otherwise use all samples
+            success_samples = [item for item in task_groups[task_id] if item[1] == "success"]
+            samples_to_cycle = success_samples if success_samples else task_groups[task_id]
+
+            # Calculate how many more we need
+            needed = int(num_generations / len(unique_task_ids)) - current_count
+
+            # Add samples in a cycling manner
+            for i in range(int(needed)):
+                task_groups[task_id].append(samples_to_cycle[i % len(samples_to_cycle)])
+
+        # Combine all groups and convert back to 2-tuples for final output
+        final_result = []
+        for task_id in unique_task_ids:
+            for item in task_groups[task_id]:
+                messages, status, _ = item
+                final_result.append((messages, status))
+
+        return final_result
+
+
+def truncate_messages_fast(
+        messages: List[Dict[str, Any]],
+        tokenizer: Any,
+        max_length: int = 131072,
+        tools: Optional[List] = None
+) -> List[Dict[str, Any]]:
+    """Simplifies message list truncation by removing entire messages from the end
+    to fit within max_length, with a final role check.
+
+    Core Logic:
+    1.  First, removes messages from the end of the list one by one until the
+        total token count is within `max_length`.
+    2.  After ensuring the length is acceptable, it performs a final check on the
+        last remaining message.
+    3.  If the last message's role is not 'assistant' or 'tool_call', it is
+        also removed. This check is repeated until the last message has a valid
+        role or the list becomes empty.
+    4.  This function does not partially truncate message content.
+
+    Args:
+        messages (List[Dict[str, Any]]): A list of message dictionaries.
+        tokenizer: The tokenizer instance to calculate token count.
+        max_length (int, optional): The target maximum number of tokens. Defaults to 131072.
+        tools (Optional[List], optional): A list of tools that might be needed when applying
+                                          the chat template. Defaults to None.
+
+    Returns:
+        List[Dict[str, Any]]: The truncated list of messages.
+    """
+    truncated_messages = list(messages)
+
+    def get_current_tokens(msgs: List[Dict[str, Any]]) -> int:
+        if not msgs:
+            return 0
+        # The return value of apply_chat_template can be a list of token IDs or a string
+        # We use len() to get the count, which works for both cases.
+        return len(tokenizer.apply_chat_template(msgs, tools=tools, add_generation_prompt=False))
+
+    # 1. Truncate from the end based on length
+    # The `and truncated_messages` ensures we don't pop from an empty list
+    while get_current_tokens(truncated_messages) > max_length and truncated_messages:
+        truncated_messages.pop()  # pop() removes the last item
+
+    # 2. Ensure the last remaining message has a valid role ('assistant' or 'tool_call')
+    # This loop handles cases where multiple invalid messages are at the end (e.g., ..., tool, user)
+    while truncated_messages:
+        last_message_role = truncated_messages[-1].get("role")
+        if last_message_role in ('assistant', 'tool_call'):
+            # The last message is valid, so we are done.
+            break
+        else:
+            # The last message is not of the required role, remove it and check again.
+            truncated_messages.pop()
+
+    return truncated_messages

chapter9/gaia-experience/AWorld/train/adapter/verl/README.md

@@ -0,0 +1,27 @@
+# VERL Adapter (AWorld Train)
+
+This module hosts the VERL integration for AWorld training workflows.
+
+- aworld_agent_loop.py: Base class bridging VERL AgentLoop with AWorld agents.
+- common.py: 
+  - Utilities for converting trajectories/messages to VERL AgentLoopOutput.
+  - Utilities for getting MCP server configuration.
+
+## Usage
+Import adapter entrypoints from your example code:
+
+```python
+from train.adapter.verl.aworld_agent_loop import AworldAgentLoop
+```
+Then implement your example-specific loop:
+```python
+class MyLoop(AworldAgentLoop):
+    def build_agents(self):
+        ...
+```
+
+## Adding New Features
+- Avoid putting example-specific code here; that belongs in train/examples/.
+
+## Notes
+- Prefer small, composable utilities and explicit public APIs.

chapter9/gaia-experience/AWorld/train/adapter/verl/aworld_agent_loop.py

@@ -0,0 +1,179 @@
+# coding: utf-8
+# Copyright (c) 2025 inclusionAI.
+import abc
+import json
+import logging
+import os
+import uuid
+from typing import Any, List, Dict, Union
+
+from aworld.agents.llm_agent import Agent
+from aworld.config.agent_loader import _load_yaml
+from aworld.core.agent.swarm import Swarm
+from aworld.runner import Runners
+from aworld.logs.util import logger
+
+from verl.experimental.agent_loop.agent_loop import AgentLoopBase, AgentLoopOutput
+
+from train.adapter.verl.common import to_agent_loop_output
+
+logger.setLevel(logging.INFO)
+logger.propagate = False
+if not logger.handlers:
+    handler = logging.StreamHandler()
+    handler.setLevel(logging.INFO)
+    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+    handler.setFormatter(formatter)
+    logger.addHandler(handler)
+
+
+class AworldAgentLoop(AgentLoopBase):
+    __metaclass__ = abc.ABCMeta
+
+    @abc.abstractmethod
+    async def build_agents(self) -> Union[Agent, Swarm]:
+        """Build single- or multi-agent"""
+
+    async def get_llm_server_address(self, server_name: str = None) -> str:
+        server = self.server_manager._choose_server(server_name or uuid.uuid4().hex)
+        base_url = await server.get_server_address.remote()
+        base_url = f"http://{base_url}/v1"
+        logger.info(f"get_server_address#base_url: {base_url}")
+        return base_url
+
+    async def get_llm_server_model_name(self):
+        model_name = "/".join(self.config.actor_rollout_ref.model.path.split("/")[-2:])
+        logger.info(f"get_server_model_name#model_name: {model_name}")
+        return model_name
+
+    # main branch
+    # async def run(self, sampling_params: dict[str, Any], **kwargs) -> AgentLoopOutput:
+    #     messages = list(kwargs["raw_prompt"])
+
+    # release 0.5.0
+    async def run(self, messages: list, sampling_params: dict[str, Any], **kwargs) -> AgentLoopOutput:
+        agent = await self.build_agents()
+
+        self.agent = agent
+
+        result = await self.run_agents(messages[0], agent)
+        res = result.trajectory
+
+        # build agent loop output
+        output = await self.convert_agent_output(trajectory=res,
+                                                 response_length=self.config.actor_rollout_ref.rollout.response_length)
+        return output
+
+    async def run_agents(self, input, agent):
+        if isinstance(input, dict):
+            input = input.get("content", "")
+        # collect trajectory
+        if isinstance(agent, Swarm):
+            result = Runners.sync_run(input=input, swarm=agent)
+        else:
+            result = Runners.sync_run(input=input, agent=agent)
+
+        return result
+
+    async def get_agent_tool_config(self, config_path: str) -> Dict[str, Any]:
+        """Load tool configuration, preferring YAML with simple fields.
+
+            Priority:
+            1) agent_tools.yaml (simple user config with url, Authorization, MCP_SERVERS)
+            2) mcp.json (legacy full config)
+            """
+
+        # 1) Try YAML (simple schema)
+        try:
+            import yaml  # Local import to avoid hard dependency at import time
+            if os.path.exists(config_path):
+                src = _load_yaml(config_path)
+
+                url = src.get('url', '')
+                authorization = src.get('Authorization', '')
+                mcp_servers_value = src.get('MCP_SERVERS', '')
+
+                # Normalize servers to comma-separated string for header and list for internal
+                if isinstance(mcp_servers_value, list):
+                    mcp_servers_str = ','.join([str(s).strip() for s in mcp_servers_value if str(s).strip()])
+                else:
+                    mcp_servers_str = str(mcp_servers_value or '').strip()
+
+                # Build internal full mcp_config
+                server_name = src.get('server_name', 'aworld-mcp')
+                server_type = src.get('type', 'streamable-http')
+                timeout = src.get('timeout', 600)
+                sse_read_timeout = src.get('sse_read_timeout', 600)
+                client_session_timeout_seconds = src.get('client_session_timeout_seconds', 600)
+
+                if url:
+                    mcp_config = {
+                        "mcpServers": {
+                            server_name: {
+                                "type": server_type,
+                                "url": url,
+                                "headers": {
+                                    "Authorization": authorization,
+                                    "MCP_SERVERS": mcp_servers_str,
+                                },
+                                "timeout": timeout,
+                                "sse_read_timeout": sse_read_timeout,
+                                "client_session_timeout_seconds": client_session_timeout_seconds,
+                            }
+                        }
+                    }
+                    return mcp_config
+        except Exception as err:
+            print(f"Error loading YAML tool config err: {err}")
+
+        # 2) Fallback to legacy JSON
+        try:
+            if os.path.exists(config_path):
+                with open(config_path, "r") as f:
+                    return json.load(f)
+        except Exception as err:
+            print(f"Error loading tool config[{config_path}] err is : {err}")
+
+    def get_num_turns(self, trajectory: List[Dict[str, Any]]):
+        return len(trajectory)
+
+    async def convert_agent_output(self, trajectory: List[Dict[str, Any]], response_length: int) -> AgentLoopOutput:
+        """Convert trajectory to AgentLoopOutput.
+
+        Args:
+            trajectory (List[Dict[str, Any]]): List of agent execution trajectory.
+            response_length (int): Max length of response.
+
+        Returns:
+            AgentLoopOutput: agent loop output trajectory used for training.
+        """
+        if not trajectory:
+            raise Exception("Trajectory is empty")
+
+        num_turns = self.get_num_turns(trajectory)
+        messages = trajectory[-1].get("exp_data", {}).get("messages", [])
+        if not messages:
+            return AgentLoopOutput(
+                prompt_ids=[],
+                response_ids=[],
+                response_mask=[],
+                num_turns=num_turns,
+                metrics={},
+            )
+        if messages[-1].get("role") != "assistant":
+            logger.warning(f"Found last message with role '{messages[-1].get('role')}', but expected 'assistant'. Truncating trailing 'tool' messages.")
+            last_non_tool_index = -1
+            for i in range(len(messages) - 1, -1, -1):
+                if messages[i].get("role") != "tool":
+                    last_non_tool_index = i
+                    break 
+            if last_non_tool_index != -1:
+                messages = messages[:last_non_tool_index + 1]
+            else:
+                messages = []
+
+        output = await to_agent_loop_output(tokenizer=self.tokenizer,
+                                            messages=messages,
+                                            response_length=response_length,
+                                            tools=self.agent.tools)
+        return output

chapter9/gaia-experience/AWorld/train/adapter/verl/common.py

@@ -0,0 +1,202 @@
+# coding: utf-8
+# Copyright (c) 2025 inclusionAI.
+import asyncio
+import json
+import os
+from typing import List, Dict, Any
+from transformers import AutoTokenizer
+from verl.experimental.agent_loop.agent_loop import AgentLoopBase, AgentLoopOutput, AgentLoopMetrics
+
+
+async def to_agent_loop_output(tokenizer: AutoTokenizer,
+                               messages: List[Dict[str, Any]],
+                               response_length: int,
+                               tools: Dict[str, Any] = None) -> AgentLoopOutput:
+    """Convert messages to AgentLoopOutput.
+
+    Args:
+        tokenizer (AutoTokenizer): Tokenizer for tokenize messages.
+        messages (List[Dict[str, Any]]): List of messages in OpenAI request format.
+        response_length (int): Max length of response.
+        tools: Tool list used by the agent.
+
+    Returns:
+        AgentLoopOutput: agent loop output trajectory used for training.
+    """
+    # Ensure tools is iterable for chat templates that iterate over tools
+    if tools is None:
+        tools = []
+
+    # Normalize messages to satisfy chat templates expectations
+    def _normalize_message(msg: Dict[str, Any]) -> Dict[str, Any]:
+        normalized = dict(msg)
+        # content may be None when assistant only returns tool_calls; make it empty string
+        if normalized.get("content") is None:
+            normalized["content"] = ""
+        # Ensure tool_calls.function.arguments is a string (many templates expect str)
+        if isinstance(normalized.get("tool_calls"), list):
+            fixed_calls = []
+            for call in normalized["tool_calls"]:
+                call_copy = dict(call)
+                func = call_copy.get("function")
+                if isinstance(func, dict):
+                    func_copy = dict(func)
+                    args_val = func_copy.get("arguments")
+                    if not isinstance(args_val, (str, bytes)):
+                        try:
+                            func_copy["arguments"] = json.dumps(args_val, ensure_ascii=False)
+                        except Exception:
+                            func_copy["arguments"] = str(args_val)
+                    call_copy["function"] = func_copy
+                fixed_calls.append(call_copy)
+            normalized["tool_calls"] = fixed_calls
+        return normalized
+
+    if not messages:
+        return AgentLoopOutput(
+            prompt_ids=[],
+            response_ids=[],
+            response_mask=[],
+            num_turns=0,
+            metrics={},
+        )
+
+    messages = [_normalize_message(m) for m in messages]
+    num_turns = 0
+    for i in range(len(messages)):
+        if messages[i].get("role") == "system":
+            continue
+        # parallel tool calls are in single turn
+        if i == 0 or messages[i].get("role") != messages[i - 1].get("role"):
+            num_turns += 1
+
+    prompt_ids = []
+    response_ids = []
+    response_mask = []
+    chat_list = []
+    loop = asyncio.get_running_loop()
+    # system_prompt_prefix_ids = self.tokenizer.apply_chat_template([{}], add_generation_prompt=False, tokenize=True)
+    i = 0
+    try:
+        while i < len(messages):
+            if messages[i].get("role") == "system":
+                chat_list.append(messages[i])
+                i += 1
+                continue
+            # initial chat completion
+            if messages[i].get("role") == "user":
+                if i == 0 or messages[i - 1].get("role") == "system":
+                    chat_list.append(messages[i])
+                    prompt_ids = await loop.run_in_executor(
+                        None,
+                        lambda: tokenizer.apply_chat_template(
+                            chat_list,
+                            tools=tools,
+                            add_generation_prompt=True,
+                            tokenize=True,
+                        ),
+                    )
+                else:
+                    chat_list.append(messages[i])
+                    cur_response_ids = await loop.run_in_executor(
+                        None,
+                        lambda: tokenizer.apply_chat_template(
+                            chat_list,
+                            add_generation_prompt=False,
+                            tokenize=True,
+                        ),
+                    )
+                    response_ids += cur_response_ids
+                    response_mask += [0] * len(cur_response_ids)
+                chat_list = []
+                i += 1
+                continue
+            # assistant message
+            if messages[i].get("role") == "assistant":
+                chat_list.append(messages[i])
+                cur_response_ids = await loop.run_in_executor(
+                    None,
+                    lambda: tokenizer.apply_chat_template(
+                        chat_list,
+                        add_generation_prompt=False,
+                        tokenize=True,
+                    ),
+                )
+                chat_list = []
+                response_ids += cur_response_ids
+                response_mask += [1] * len(cur_response_ids)
+                i += 1
+                continue
+            # follow up chat completion with tool response:
+            if messages[i].get("role") == "tool":
+                last_assistant_message = messages[i - 1]
+                chat_list.append(last_assistant_message)
+                token_assistant = await loop.run_in_executor(
+                    None,
+                    lambda: tokenizer.apply_chat_template(
+                        chat_list,
+                        add_generation_prompt=False,
+                        tokenize=True,
+                    ),
+                )
+                while i < len(messages) and messages[i].get("role") == "tool":
+                    chat_list.append(messages[i])
+                    i += 1
+                token_assistant_tool = await loop.run_in_executor(
+                    None,
+                    lambda: tokenizer.apply_chat_template(
+                        chat_list,
+                        add_generation_prompt=False,
+                        tokenize=True,
+                    ),
+                )
+                tool_response_ids = token_assistant_tool[len(token_assistant):]
+                chat_list = []
+                response_ids += tool_response_ids
+                response_mask += [0] * len(tool_response_ids)
+    except Exception as e:
+        raise Exception(f"Failed to convert messages to agentloop_output: {messages}.Exception is: {e}")
+
+    max_response_length = min(response_length, len(response_ids))
+    output = AgentLoopOutput(
+        prompt_ids=prompt_ids,
+        response_ids=response_ids[:max_response_length],
+        response_mask=response_mask[:max_response_length],
+        num_turns=num_turns,
+        metrics={},
+    )
+    return output
+
+
+def get_agent_tool_env_and_servers(tool_config: Dict[str, Any] = None) -> tuple[Dict[str, Any], List[str]]:
+    if not tool_config or not tool_config.get("url") or not tool_config.get("authorization"):
+        tool_config["url"] = os.getenv("MCP_SERVER_URL")
+        tool_config["authorization"] = f"Bearer {os.getenv('MCP_SERVER_TOKEN')}"
+    url = tool_config.get("url")
+    authorization = tool_config.get("authorization")
+    mcp_servers_str = tool_config.get("mcp_servers", "")
+    if not url or not authorization:
+        raise ValueError("url, Authorization are required. Please set MCP_SERVER_URL and MCP_SERVER_TOKEN environment variable \
+            or provide them in tool_config parameter.")
+    server_name = tool_config.get('server_name', 'aworld-mcp')
+    server_type = tool_config.get('type', 'streamable-http')
+    timeout = tool_config.get('timeout', 600)
+    sse_read_timeout = tool_config.get('sse_read_timeout', 600)
+    client_session_timeout_seconds = tool_config.get('client_session_timeout_seconds', 600)
+    mcp_config = {
+        "mcpServers": {
+            server_name: {
+                "type": server_type,
+                "url": url,
+                "headers": {
+                    "Authorization": authorization,
+                    "MCP_SERVERS": mcp_servers_str,
+                },
+                "timeout": timeout,
+                "sse_read_timeout": sse_read_timeout,
+                "client_session_timeout_seconds": client_session_timeout_seconds,
+            }
+        }
+    }
+    servers = list(server_name for server_name in mcp_config.get("mcpServers", {}).keys())
+    return mcp_config, servers