playwright-mcp/src/loop/loopOpenAI.ts

/**
 * Copyright (c) Microsoft Corporation.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

import OpenAI from 'openai';
import type { LLMDelegate, LLMConversation, LLMToolCall, LLMTool } from './loop.js';
import type { Tool } from '@modelcontextprotocol/sdk/types.js';

const model = 'gpt-4.1';

export class OpenAIDelegate implements LLMDelegate {
  private openai = new OpenAI();

  createConversation(task: string, tools: Tool[]): LLMConversation {
    const genericTools: LLMTool[] = tools.map(tool => ({
      name: tool.name,
      description: tool.description || '',
      inputSchema: tool.inputSchema,
    }));

    // Add the "done" tool
    genericTools.push({
      name: 'done',
      description: 'Call this tool when the task is complete.',
      inputSchema: {
        type: 'object',
        properties: {
          result: { type: 'string', description: 'The result of the task.' },
        },
        required: ['result'],
      },
    });

    return {
      messages: [{
        role: 'user',
        content: `Peform following task: ${task}. Once the task is complete, call the "done" tool.`
      }],
      tools: genericTools,
    };
  }

  async makeApiCall(conversation: LLMConversation): Promise<LLMToolCall[]> {
    // Convert generic messages to OpenAI format
    const openaiMessages: OpenAI.Chat.Completions.ChatCompletionMessageParam[] = [];

    for (const message of conversation.messages) {
      if (message.role === 'user') {
        openaiMessages.push({
          role: 'user',
          content: message.content
        });
      } else if (message.role === 'assistant') {
        const toolCalls: OpenAI.Chat.Completions.ChatCompletionMessageToolCall[] = [];

        if (message.toolCalls) {
          for (const toolCall of message.toolCalls) {
            toolCalls.push({
              id: toolCall.id,
              type: 'function',
              function: {
                name: toolCall.name,
                arguments: JSON.stringify(toolCall.arguments)
              }
            });
          }
        }

        const assistantMessage: OpenAI.Chat.Completions.ChatCompletionAssistantMessageParam = {
          role: 'assistant'
        };

        if (message.content)
          assistantMessage.content = message.content;

        if (toolCalls.length > 0)
          assistantMessage.tool_calls = toolCalls;

        openaiMessages.push(assistantMessage);
      } else if (message.role === 'tool') {
        openaiMessages.push({
          role: 'tool',
          tool_call_id: message.toolCallId,
          content: message.content,
        });
      }
    }

    // Convert generic tools to OpenAI format
    const openaiTools: OpenAI.Chat.Completions.ChatCompletionTool[] = conversation.tools.map(tool => ({
      type: 'function',
      function: {
        name: tool.name,
        description: tool.description,
        parameters: tool.inputSchema,
      },
    }));

    const response = await this.openai.chat.completions.create({
      model,
      messages: openaiMessages,
      tools: openaiTools,
      tool_choice: 'auto'
    });

    const message = response.choices[0].message;

    // Extract tool calls and add assistant message to generic conversation
    const toolCalls = message.tool_calls || [];
    const genericToolCalls: LLMToolCall[] = toolCalls.map(toolCall => {
      const functionCall = toolCall.function;
      return {
        name: functionCall.name,
        arguments: JSON.parse(functionCall.arguments),
        id: toolCall.id,
      };
    });

    // Add assistant message to generic conversation
    conversation.messages.push({
      role: 'assistant',
      content: message.content || '',
      toolCalls: genericToolCalls.length > 0 ? genericToolCalls : undefined
    });

    return genericToolCalls;
  }

  addToolResults(
    conversation: LLMConversation,
    results: Array<{ toolCallId: string; content: string; isError?: boolean }>
  ): void {
    for (const result of results) {
      conversation.messages.push({
        role: 'tool',
        toolCallId: result.toolCallId,
        content: result.content,
        isError: result.isError,
      });
    }
  }

  checkDoneToolCall(toolCall: LLMToolCall): string | null {
    if (toolCall.name === 'done')
      return toolCall.arguments.result;

    return null;
  }
}