Consult LLM MCP

import OpenAI from 'openai' import { spawn } from 'child_process' import { relative } from 'path' import { config } from './config.js' import { type SupportedChatModel as SupportedChatModelType } from './schema.js' import { logCliDebug } from './logger.js' export interface LlmExecutor { execute( prompt: string, model: SupportedChatModelType, systemPrompt: string, filePaths?: string[], ): Promise<{ response: string usage: OpenAI.CompletionUsage | null }> } /** * Creates an executor that interacts with an OpenAI-compatible API. * * Don't let it confuse you that client is of type OpenAI. We used OpenAI API * client for Gemini also. */ function createApiExecutor(client: OpenAI): LlmExecutor { return { async execute(prompt, model, systemPrompt, filePaths) { if (filePaths && filePaths.length > 0) { // Explicitly reject unsupported parameters console.warn( `Warning: File paths were provided but are not supported by the API executor for model ${model}. They will be ignored.`, ) } const completion = await client.chat.completions.create({ model, messages: [ { role: 'system', content: systemPrompt }, { role: 'user', content: prompt }, ], }) const response = completion.choices[0]?.message?.content if (!response) { throw new Error('No response from the model via API') } return { response, usage: completion.usage ?? null } }, } } /** * Configuration for a command-line tool executor. */ type CliConfig = { cliName: string buildArgs: (model: SupportedChatModelType, fullPrompt: string) => string[] handleNonZeroExit: (code: number, stderr: string) => Error } /** * Creates an executor that delegates to a command-line tool. */ function createCliExecutor(cliConfig: CliConfig): LlmExecutor { const buildFullPrompt = ( prompt: string, systemPrompt: string, filePaths?: string[], ): string => { let fullPrompt = `${systemPrompt}\n\n${prompt}` if (filePaths && filePaths.length > 0) { const fileReferences = filePaths .map((path) => `@${relative(process.cwd(), path)}`) .join(' ') fullPrompt = `${fullPrompt}\n\nFiles: ${fileReferences}` } return fullPrompt } return { async execute(prompt, model, systemPrompt, filePaths) { const fullPrompt = buildFullPrompt(prompt, systemPrompt, filePaths) const args = cliConfig.buildArgs(model, fullPrompt) const { cliName } = cliConfig return new Promise((resolve, reject) => { try { logCliDebug(`Spawning ${cliName} CLI`, { model, promptLength: fullPrompt.length, filePathsCount: filePaths?.length || 0, args: args, promptPreview: fullPrompt.slice(0, 300), }) const child = spawn(cliName, args, { shell: false, stdio: ['ignore', 'pipe', 'pipe'], }) let stdout = '' let stderr = '' const startTime = Date.now() child.on('spawn', () => logCliDebug(`${cliName} CLI process spawned successfully`), ) child.stdout.on('data', (data: Buffer) => (stdout += data.toString())) child.stderr.on('data', (data: Buffer) => (stderr += data.toString())) child.on('close', (code) => { const duration = Date.now() - startTime logCliDebug(`${cliName} CLI process closed`, { code, duration: `${duration}ms`, stdoutLength: stdout.length, stderrLength: stderr.length, }) if (code === 0) { resolve({ response: stdout.trim(), usage: null }) } else { reject(cliConfig.handleNonZeroExit(code ?? -1, stderr)) } }) child.on('error', (err) => { logCliDebug(`Failed to spawn ${cliName} CLI`, { error: err.message, }) reject( new Error( `Failed to spawn ${cliName} CLI. Is it installed and in PATH? Error: ${err.message}`, ), ) }) } catch (err) { reject( new Error( `Synchronous error while trying to spawn ${cliName}: ${ err instanceof Error ? err.message : String(err) }`, ), ) } }) }, } } // --- CLI Configurations --- const geminiCliConfig: CliConfig = { cliName: 'gemini', buildArgs: (model, fullPrompt) => ['-m', model, '-p', fullPrompt], handleNonZeroExit: (code, stderr) => { if (stderr.includes('RESOURCE_EXHAUSTED')) { return new Error( `Gemini quota exceeded. Consider using gemini-2.0-flash model. Error: ${stderr.trim()}`, ) } return new Error( `Gemini CLI exited with code ${code}. Error: ${stderr.trim()}`, ) }, } const codexCliConfig: CliConfig = { cliName: 'codex', buildArgs: (model, fullPrompt) => { const args = ['exec', '--skip-git-repo-check', '-m', model] if (config.codexReasoningEffort) { args.push('-c', `model_reasoning_effort="${config.codexReasoningEffort}"`) } args.push(fullPrompt) return args }, handleNonZeroExit: (code, stderr) => new Error(`Codex CLI exited with code ${code}. Error: ${stderr.trim()}`), } const createExecutorProvider = () => { const executorCache = new Map<string, LlmExecutor>() const clientCache = new Map<string, OpenAI>() const getOpenAIClient = (): OpenAI => { if (clientCache.has('openai')) return clientCache.get('openai')! if (!config.openaiApiKey) { throw new Error( 'OPENAI_API_KEY environment variable is required for OpenAI models in API mode', ) } const client = new OpenAI({ apiKey: config.openaiApiKey }) clientCache.set('openai', client) return client } const getDeepseekClient = (): OpenAI => { if (clientCache.has('deepseek')) return clientCache.get('deepseek')! if (!config.deepseekApiKey) { throw new Error( 'DEEPSEEK_API_KEY environment variable is required for DeepSeek models', ) } const client = new OpenAI({ apiKey: config.deepseekApiKey, baseURL: 'https://api.deepseek.com', }) clientCache.set('deepseek', client) return client } const getGeminiApiClient = (): OpenAI => { if (clientCache.has('geminiApi')) return clientCache.get('geminiApi')! if (!config.geminiApiKey) { throw new Error( 'GEMINI_API_KEY environment variable is required for Gemini models in API mode', ) } const client = new OpenAI({ apiKey: config.geminiApiKey, baseURL: 'https://generativelanguage.googleapis.com/v1beta/openai/', }) clientCache.set('geminiApi', client) return client } return (model: SupportedChatModelType): LlmExecutor => { // Create cache key that includes mode for models that support CLI const cacheKey = model + (model.startsWith('gpt-') || model === 'o3' ? `-${config.openaiMode}` : '') + (model.startsWith('gemini-') ? `-${config.geminiMode}` : '') if (executorCache.has(cacheKey)) { return executorCache.get(cacheKey)! } let executor: LlmExecutor if (model.startsWith('gpt-') || model === 'o3') { executor = config.openaiMode === 'cli' ? createCliExecutor(codexCliConfig) : createApiExecutor(getOpenAIClient()) } else if (model.startsWith('deepseek-')) { executor = createApiExecutor(getDeepseekClient()) } else if (model.startsWith('gemini-')) { executor = config.geminiMode === 'cli' ? createCliExecutor(geminiCliConfig) : createApiExecutor(getGeminiApiClient()) } else { throw new Error(`Unable to determine LLM provider for model: ${model}`) } executorCache.set(cacheKey, executor) return executor } } export const getExecutorForModel = createExecutorProvider()