import OpenAI from "openai"; import type { ChatCompletionAssistantMessageParam, ChatCompletionChunk, ChatCompletionContentPart, ChatCompletionContentPartImage, ChatCompletionContentPartText, ChatCompletionMessageParam, ChatCompletionToolMessageParam, } from "openai/resources/chat/completions.js"; import { getEnvApiKey } from "../env-api-keys.js"; import { calculateCost, supportsXhigh } from "../models.js"; import type { AssistantMessage, Context, Message, Model, OpenAICompletionsCompat, SimpleStreamOptions, StopReason, StreamFunction, StreamOptions, TextContent, ThinkingContent, Tool, ToolCall, ToolResultMessage, } from "../types.js"; import { AssistantMessageEventStream } from "../utils/event-stream.js"; import { parseStreamingJson } from "../utils/json-parse.js"; import { sanitizeSurrogates } from "../utils/sanitize-unicode.js"; import { buildBaseOptions, clampReasoning } from "./simple-options.js"; import { transformMessages } from "./transform-messages.js"; /** * Normalize tool call ID for Mistral. * Mistral requires tool IDs to be exactly 9 alphanumeric characters (a-z, A-Z, 0-9). */ function normalizeMistralToolId(id: string): string { // Remove non-alphanumeric characters let normalized = id.replace(/[^a-zA-Z0-9]/g, ""); // Mistral requires exactly 9 characters if (normalized.length < 9) { // Pad with deterministic characters based on original ID to ensure matching const padding = "ABCDEFGHI"; normalized = normalized + padding.slice(0, 9 - normalized.length); } else if (normalized.length > 9) { normalized = normalized.slice(0, 9); } return normalized; } /** * Check if conversation messages contain tool calls or tool results. * This is needed because Anthropic (via proxy) requires the tools param * to be present when messages include tool_calls or tool role messages. */ function hasToolHistory(messages: Message[]): boolean { for (const msg of messages) { if (msg.role === "toolResult") { return true; } if (msg.role === "assistant") { if (msg.content.some((block) => block.type === "toolCall")) { return true; } } } return false; } export interface OpenAICompletionsOptions extends StreamOptions { toolChoice?: "auto" | "none" | "required" | { type: "function"; function: { name: string } }; reasoningEffort?: "minimal" | "low" | "medium" | "high" | "xhigh"; } export const streamOpenAICompletions: StreamFunction<"openai-completions", OpenAICompletionsOptions> = ( model: Model<"openai-completions">, context: Context, options?: OpenAICompletionsOptions, ): AssistantMessageEventStream => { const stream = new AssistantMessageEventStream(); (async () => { const output: AssistantMessage = { role: "assistant", content: [], api: model.api, provider: model.provider, model: model.id, usage: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0, totalTokens: 0, cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0, total: 0 }, }, stopReason: "stop", timestamp: Date.now(), }; try { const apiKey = options?.apiKey || getEnvApiKey(model.provider) || ""; const client = createClient(model, context, apiKey, options?.headers); const params = buildParams(model, context, options); options?.onPayload?.(params); const openaiStream = await client.chat.completions.create(params, { signal: options?.signal }); stream.push({ type: "start", partial: output }); let currentBlock: TextContent | ThinkingContent | (ToolCall & { partialArgs?: string }) | null = null; const blocks = output.content; const blockIndex = () => blocks.length - 1; const finishCurrentBlock = (block?: typeof currentBlock) => { if (block) { if (block.type === "text") { stream.push({ type: "text_end", contentIndex: blockIndex(), content: block.text, partial: output, }); } else if (block.type === "thinking") { stream.push({ type: "thinking_end", contentIndex: blockIndex(), content: block.thinking, partial: output, }); } else if (block.type === "toolCall") { block.arguments = JSON.parse(block.partialArgs || "{}"); delete block.partialArgs; stream.push({ type: "toolcall_end", contentIndex: blockIndex(), toolCall: block, partial: output, }); } } }; for await (const chunk of openaiStream) { if (chunk.usage) { const cachedTokens = chunk.usage.prompt_tokens_details?.cached_tokens || 0; const reasoningTokens = chunk.usage.completion_tokens_details?.reasoning_tokens || 0; const input = (chunk.usage.prompt_tokens || 0) - cachedTokens; const outputTokens = (chunk.usage.completion_tokens || 0) + reasoningTokens; output.usage = { // OpenAI includes cached tokens in prompt_tokens, so subtract to get non-cached input input, output: outputTokens, cacheRead: cachedTokens, cacheWrite: 0, // Compute totalTokens ourselves since we add reasoning_tokens to output // and some providers (e.g., Groq) don't include them in total_tokens totalTokens: input + outputTokens + cachedTokens, cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0, total: 0, }, }; calculateCost(model, output.usage); } const choice = chunk.choices[0]; if (!choice) continue; if (choice.finish_reason) { output.stopReason = mapStopReason(choice.finish_reason); } if (choice.delta) { if ( choice.delta.content !== null && choice.delta.content !== undefined && choice.delta.content.length > 0 ) { if (!currentBlock || currentBlock.type !== "text") { finishCurrentBlock(currentBlock); currentBlock = { type: "text", text: "" }; output.content.push(currentBlock); stream.push({ type: "text_start", contentIndex: blockIndex(), partial: output }); } if (currentBlock.type === "text") { currentBlock.text += choice.delta.content; stream.push({ type: "text_delta", contentIndex: blockIndex(), delta: choice.delta.content, partial: output, }); } } // Some endpoints return reasoning in reasoning_content (llama.cpp), // or reasoning (other openai compatible endpoints) // Use the first non-empty reasoning field to avoid duplication // (e.g., chutes.ai returns both reasoning_content and reasoning with same content) const reasoningFields = ["reasoning_content", "reasoning", "reasoning_text"]; let foundReasoningField: string | null = null; for (const field of reasoningFields) { if ( (choice.delta as any)[field] !== null && (choice.delta as any)[field] !== undefined && (choice.delta as any)[field].length > 0 ) { if (!foundReasoningField) { foundReasoningField = field; break; } } } if (foundReasoningField) { if (!currentBlock || currentBlock.type !== "thinking") { finishCurrentBlock(currentBlock); currentBlock = { type: "thinking", thinking: "", thinkingSignature: foundReasoningField, }; output.content.push(currentBlock); stream.push({ type: "thinking_start", contentIndex: blockIndex(), partial: output }); } if (currentBlock.type === "thinking") { const delta = (choice.delta as any)[foundReasoningField]; currentBlock.thinking += delta; stream.push({ type: "thinking_delta", contentIndex: blockIndex(), delta, partial: output, }); } } if (choice?.delta?.tool_calls) { for (const toolCall of choice.delta.tool_calls) { if ( !currentBlock || currentBlock.type !== "toolCall" || (toolCall.id && currentBlock.id !== toolCall.id) ) { finishCurrentBlock(currentBlock); currentBlock = { type: "toolCall", id: toolCall.id || "", name: toolCall.function?.name || "", arguments: {}, partialArgs: "", }; output.content.push(currentBlock); stream.push({ type: "toolcall_start", contentIndex: blockIndex(), partial: output }); } if (currentBlock.type === "toolCall") { if (toolCall.id) currentBlock.id = toolCall.id; if (toolCall.function?.name) currentBlock.name = toolCall.function.name; let delta = ""; if (toolCall.function?.arguments) { delta = toolCall.function.arguments; currentBlock.partialArgs += toolCall.function.arguments; currentBlock.arguments = parseStreamingJson(currentBlock.partialArgs); } stream.push({ type: "toolcall_delta", contentIndex: blockIndex(), delta, partial: output, }); } } } const reasoningDetails = (choice.delta as any).reasoning_details; if (reasoningDetails && Array.isArray(reasoningDetails)) { for (const detail of reasoningDetails) { if (detail.type === "reasoning.encrypted" && detail.id && detail.data) { const matchingToolCall = output.content.find( (b) => b.type === "toolCall" && b.id === detail.id, ) as ToolCall | undefined; if (matchingToolCall) { matchingToolCall.thoughtSignature = JSON.stringify(detail); } } } } } } finishCurrentBlock(currentBlock); if (options?.signal?.aborted) { throw new Error("Request was aborted"); } if (output.stopReason === "aborted" || output.stopReason === "error") { throw new Error("An unknown error occurred"); } stream.push({ type: "done", reason: output.stopReason, message: output }); stream.end(); } catch (error) { for (const block of output.content) delete (block as any).index; output.stopReason = options?.signal?.aborted ? "aborted" : "error"; output.errorMessage = error instanceof Error ? error.message : JSON.stringify(error); // Some providers via OpenRouter give additional information in this field. const rawMetadata = (error as any)?.error?.metadata?.raw; if (rawMetadata) output.errorMessage += `\n${rawMetadata}`; stream.push({ type: "error", reason: output.stopReason, error: output }); stream.end(); } })(); return stream; }; export const streamSimpleOpenAICompletions: StreamFunction<"openai-completions", SimpleStreamOptions> = ( model: Model<"openai-completions">, context: Context, options?: SimpleStreamOptions, ): AssistantMessageEventStream => { const apiKey = options?.apiKey || getEnvApiKey(model.provider); if (!apiKey) { throw new Error(`No API key for provider: ${model.provider}`); } const base = buildBaseOptions(model, options, apiKey); const reasoningEffort = supportsXhigh(model) ? options?.reasoning : clampReasoning(options?.reasoning); const toolChoice = (options as OpenAICompletionsOptions | undefined)?.toolChoice; return streamOpenAICompletions(model, context, { ...base, reasoningEffort, toolChoice, } satisfies OpenAICompletionsOptions); }; function createClient( model: Model<"openai-completions">, context: Context, apiKey?: string, optionsHeaders?: Record, ) { if (!apiKey) { if (!process.env.OPENAI_API_KEY) { throw new Error( "OpenAI API key is required. Set OPENAI_API_KEY environment variable or pass it as an argument.", ); } apiKey = process.env.OPENAI_API_KEY; } const headers = { ...model.headers }; if (model.provider === "github-copilot") { // Copilot expects X-Initiator to indicate whether the request is user-initiated // or agent-initiated (e.g. follow-up after assistant/tool messages). If there is // no prior message, default to user-initiated. const messages = context.messages || []; const lastMessage = messages[messages.length - 1]; const isAgentCall = lastMessage ? lastMessage.role !== "user" : false; headers["X-Initiator"] = isAgentCall ? "agent" : "user"; headers["Openai-Intent"] = "conversation-edits"; // Copilot requires this header when sending images const hasImages = messages.some((msg) => { if (msg.role === "user" && Array.isArray(msg.content)) { return msg.content.some((c) => c.type === "image"); } if (msg.role === "toolResult" && Array.isArray(msg.content)) { return msg.content.some((c) => c.type === "image"); } return false; }); if (hasImages) { headers["Copilot-Vision-Request"] = "true"; } } // Merge options headers last so they can override defaults if (optionsHeaders) { Object.assign(headers, optionsHeaders); } return new OpenAI({ apiKey, baseURL: model.baseUrl, dangerouslyAllowBrowser: true, defaultHeaders: headers, }); } function buildParams(model: Model<"openai-completions">, context: Context, options?: OpenAICompletionsOptions) { const compat = getCompat(model); const messages = convertMessages(model, context, compat); maybeAddOpenRouterAnthropicCacheControl(model, messages); const params: OpenAI.Chat.Completions.ChatCompletionCreateParamsStreaming = { model: model.id, messages, stream: true, }; if (compat.supportsUsageInStreaming !== false) { (params as any).stream_options = { include_usage: true }; } if (compat.supportsStore) { params.store = false; } if (options?.maxTokens) { if (compat.maxTokensField === "max_tokens") { (params as any).max_tokens = options.maxTokens; } else { params.max_completion_tokens = options.maxTokens; } } if (options?.temperature !== undefined) { params.temperature = options.temperature; } if (context.tools) { params.tools = convertTools(context.tools); } else if (hasToolHistory(context.messages)) { // Anthropic (via LiteLLM/proxy) requires tools param when conversation has tool_calls/tool_results params.tools = []; } if (options?.toolChoice) { params.tool_choice = options.toolChoice; } if (compat.thinkingFormat === "zai" && model.reasoning) { // Z.ai uses binary thinking: { type: "enabled" | "disabled" } // Must explicitly disable since z.ai defaults to thinking enabled (params as any).thinking = { type: options?.reasoningEffort ? "enabled" : "disabled" }; } else if (options?.reasoningEffort && model.reasoning && compat.supportsReasoningEffort) { // OpenAI-style reasoning_effort params.reasoning_effort = options.reasoningEffort; } // OpenRouter provider routing preferences if (model.baseUrl.includes("openrouter.ai") && model.compat?.openRouterRouting) { (params as any).provider = model.compat.openRouterRouting; } return params; } function maybeAddOpenRouterAnthropicCacheControl( model: Model<"openai-completions">, messages: ChatCompletionMessageParam[], ): void { if (model.provider !== "openrouter" || !model.id.startsWith("anthropic/")) return; // Anthropic-style caching requires cache_control on a text part. Add a breakpoint // on the last user/assistant message (walking backwards until we find text content). for (let i = messages.length - 1; i >= 0; i--) { const msg = messages[i]; if (msg.role !== "user" && msg.role !== "assistant") continue; const content = msg.content; if (typeof content === "string") { msg.content = [ Object.assign({ type: "text" as const, text: content }, { cache_control: { type: "ephemeral" } }), ]; return; } if (!Array.isArray(content)) continue; // Find last text part and add cache_control for (let j = content.length - 1; j >= 0; j--) { const part = content[j]; if (part?.type === "text") { Object.assign(part, { cache_control: { type: "ephemeral" } }); return; } } } } export function convertMessages( model: Model<"openai-completions">, context: Context, compat: Required, ): ChatCompletionMessageParam[] { const params: ChatCompletionMessageParam[] = []; const normalizeToolCallId = (id: string): string => { if (compat.requiresMistralToolIds) return normalizeMistralToolId(id); // Handle pipe-separated IDs from OpenAI Responses API // Format: {call_id}|{id} where {id} can be 400+ chars with special chars (+, /, =) // These come from providers like github-copilot, openai-codex, opencode // Extract just the call_id part and normalize it if (id.includes("|")) { const [callId] = id.split("|"); // Sanitize to allowed chars and truncate to 40 chars (OpenAI limit) return callId.replace(/[^a-zA-Z0-9_-]/g, "_").slice(0, 40); } if (model.provider === "openai") return id.length > 40 ? id.slice(0, 40) : id; // Copilot Claude models route to Claude backend which requires Anthropic ID format if (model.provider === "github-copilot" && model.id.toLowerCase().includes("claude")) { return id.replace(/[^a-zA-Z0-9_-]/g, "_").slice(0, 64); } return id; }; const transformedMessages = transformMessages(context.messages, model, (id) => normalizeToolCallId(id)); if (context.systemPrompt) { const useDeveloperRole = model.reasoning && compat.supportsDeveloperRole; const role = useDeveloperRole ? "developer" : "system"; params.push({ role: role, content: sanitizeSurrogates(context.systemPrompt) }); } let lastRole: string | null = null; for (let i = 0; i < transformedMessages.length; i++) { const msg = transformedMessages[i]; // Some providers (e.g. Mistral/Devstral) don't allow user messages directly after tool results // Insert a synthetic assistant message to bridge the gap if (compat.requiresAssistantAfterToolResult && lastRole === "toolResult" && msg.role === "user") { params.push({ role: "assistant", content: "I have processed the tool results.", }); } if (msg.role === "user") { if (typeof msg.content === "string") { params.push({ role: "user", content: sanitizeSurrogates(msg.content), }); } else { const content: ChatCompletionContentPart[] = msg.content.map((item): ChatCompletionContentPart => { if (item.type === "text") { return { type: "text", text: sanitizeSurrogates(item.text), } satisfies ChatCompletionContentPartText; } else { return { type: "image_url", image_url: { url: `data:${item.mimeType};base64,${item.data}`, }, } satisfies ChatCompletionContentPartImage; } }); const filteredContent = !model.input.includes("image") ? content.filter((c) => c.type !== "image_url") : content; if (filteredContent.length === 0) continue; params.push({ role: "user", content: filteredContent, }); } } else if (msg.role === "assistant") { // Some providers (e.g. Mistral) don't accept null content, use empty string instead const assistantMsg: ChatCompletionAssistantMessageParam = { role: "assistant", content: compat.requiresAssistantAfterToolResult ? "" : null, }; const textBlocks = msg.content.filter((b) => b.type === "text") as TextContent[]; // Filter out empty text blocks to avoid API validation errors const nonEmptyTextBlocks = textBlocks.filter((b) => b.text && b.text.trim().length > 0); if (nonEmptyTextBlocks.length > 0) { // GitHub Copilot requires assistant content as a string, not an array. // Sending as array causes Claude models to re-answer all previous prompts. if (model.provider === "github-copilot") { assistantMsg.content = nonEmptyTextBlocks.map((b) => sanitizeSurrogates(b.text)).join(""); } else { assistantMsg.content = nonEmptyTextBlocks.map((b) => { return { type: "text", text: sanitizeSurrogates(b.text) }; }); } } // Handle thinking blocks const thinkingBlocks = msg.content.filter((b) => b.type === "thinking") as ThinkingContent[]; // Filter out empty thinking blocks to avoid API validation errors const nonEmptyThinkingBlocks = thinkingBlocks.filter((b) => b.thinking && b.thinking.trim().length > 0); if (nonEmptyThinkingBlocks.length > 0) { if (compat.requiresThinkingAsText) { // Convert thinking blocks to plain text (no tags to avoid model mimicking them) const thinkingText = nonEmptyThinkingBlocks.map((b) => b.thinking).join("\n\n"); const textContent = assistantMsg.content as Array<{ type: "text"; text: string }> | null; if (textContent) { textContent.unshift({ type: "text", text: thinkingText }); } else { assistantMsg.content = [{ type: "text", text: thinkingText }]; } } else { // Use the signature from the first thinking block if available (for llama.cpp server + gpt-oss) const signature = nonEmptyThinkingBlocks[0].thinkingSignature; if (signature && signature.length > 0) { (assistantMsg as any)[signature] = nonEmptyThinkingBlocks.map((b) => b.thinking).join("\n"); } } } const toolCalls = msg.content.filter((b) => b.type === "toolCall") as ToolCall[]; if (toolCalls.length > 0) { assistantMsg.tool_calls = toolCalls.map((tc) => ({ id: tc.id, type: "function" as const, function: { name: tc.name, arguments: JSON.stringify(tc.arguments), }, })); const reasoningDetails = toolCalls .filter((tc) => tc.thoughtSignature) .map((tc) => { try { return JSON.parse(tc.thoughtSignature!); } catch { return null; } }) .filter(Boolean); if (reasoningDetails.length > 0) { (assistantMsg as any).reasoning_details = reasoningDetails; } } // Skip assistant messages that have no content and no tool calls. // Mistral explicitly requires "either content or tool_calls, but not none". // Other providers also don't accept empty assistant messages. // This handles aborted assistant responses that got no content. const content = assistantMsg.content; const hasContent = content !== null && content !== undefined && (typeof content === "string" ? content.length > 0 : content.length > 0); if (!hasContent && !assistantMsg.tool_calls) { continue; } params.push(assistantMsg); } else if (msg.role === "toolResult") { const imageBlocks: Array<{ type: "image_url"; image_url: { url: string } }> = []; let j = i; for (; j < transformedMessages.length && transformedMessages[j].role === "toolResult"; j++) { const toolMsg = transformedMessages[j] as ToolResultMessage; // Extract text and image content const textResult = toolMsg.content .filter((c) => c.type === "text") .map((c) => (c as any).text) .join("\n"); const hasImages = toolMsg.content.some((c) => c.type === "image"); // Always send tool result with text (or placeholder if only images) const hasText = textResult.length > 0; // Some providers (e.g. Mistral) require the 'name' field in tool results const toolResultMsg: ChatCompletionToolMessageParam = { role: "tool", content: sanitizeSurrogates(hasText ? textResult : "(see attached image)"), tool_call_id: toolMsg.toolCallId, }; if (compat.requiresToolResultName && toolMsg.toolName) { (toolResultMsg as any).name = toolMsg.toolName; } params.push(toolResultMsg); if (hasImages && model.input.includes("image")) { for (const block of toolMsg.content) { if (block.type === "image") { imageBlocks.push({ type: "image_url", image_url: { url: `data:${(block as any).mimeType};base64,${(block as any).data}`, }, }); } } } } i = j - 1; if (imageBlocks.length > 0) { if (compat.requiresAssistantAfterToolResult) { params.push({ role: "assistant", content: "I have processed the tool results.", }); } params.push({ role: "user", content: [ { type: "text", text: "Attached image(s) from tool result:", }, ...imageBlocks, ], }); lastRole = "user"; } else { lastRole = "toolResult"; } continue; } lastRole = msg.role; } return params; } function convertTools(tools: Tool[]): OpenAI.Chat.Completions.ChatCompletionTool[] { return tools.map((tool) => ({ type: "function", function: { name: tool.name, description: tool.description, parameters: tool.parameters as any, // TypeBox already generates JSON Schema strict: false, // Disable strict mode to allow optional parameters without null unions }, })); } function mapStopReason(reason: ChatCompletionChunk.Choice["finish_reason"]): StopReason { if (reason === null) return "stop"; switch (reason) { case "stop": return "stop"; case "length": return "length"; case "function_call": case "tool_calls": return "toolUse"; case "content_filter": return "error"; default: { const _exhaustive: never = reason; throw new Error(`Unhandled stop reason: ${_exhaustive}`); } } } /** * Detect compatibility settings from provider and baseUrl for known providers. * Provider takes precedence over URL-based detection since it's explicitly configured. * Returns a fully resolved OpenAICompletionsCompat object with all fields set. */ function detectCompat(model: Model<"openai-completions">): Required { const provider = model.provider; const baseUrl = model.baseUrl; const isZai = provider === "zai" || baseUrl.includes("api.z.ai"); const isNonStandard = provider === "cerebras" || baseUrl.includes("cerebras.ai") || provider === "xai" || baseUrl.includes("api.x.ai") || provider === "mistral" || baseUrl.includes("mistral.ai") || baseUrl.includes("chutes.ai") || baseUrl.includes("deepseek.com") || isZai || provider === "opencode" || baseUrl.includes("opencode.ai"); const useMaxTokens = provider === "mistral" || baseUrl.includes("mistral.ai") || baseUrl.includes("chutes.ai"); const isGrok = provider === "xai" || baseUrl.includes("api.x.ai"); const isMistral = provider === "mistral" || baseUrl.includes("mistral.ai"); return { supportsStore: !isNonStandard, supportsDeveloperRole: !isNonStandard, supportsReasoningEffort: !isGrok && !isZai, supportsUsageInStreaming: true, maxTokensField: useMaxTokens ? "max_tokens" : "max_completion_tokens", requiresToolResultName: isMistral, requiresAssistantAfterToolResult: false, // Mistral no longer requires this as of Dec 2024 requiresThinkingAsText: isMistral, requiresMistralToolIds: isMistral, thinkingFormat: isZai ? "zai" : "openai", openRouterRouting: {}, }; } /** * Get resolved compatibility settings for a model. * Uses explicit model.compat if provided, otherwise auto-detects from provider/URL. */ function getCompat(model: Model<"openai-completions">): Required { const detected = detectCompat(model); if (!model.compat) return detected; return { supportsStore: model.compat.supportsStore ?? detected.supportsStore, supportsDeveloperRole: model.compat.supportsDeveloperRole ?? detected.supportsDeveloperRole, supportsReasoningEffort: model.compat.supportsReasoningEffort ?? detected.supportsReasoningEffort, supportsUsageInStreaming: model.compat.supportsUsageInStreaming ?? detected.supportsUsageInStreaming, maxTokensField: model.compat.maxTokensField ?? detected.maxTokensField, requiresToolResultName: model.compat.requiresToolResultName ?? detected.requiresToolResultName, requiresAssistantAfterToolResult: model.compat.requiresAssistantAfterToolResult ?? detected.requiresAssistantAfterToolResult, requiresThinkingAsText: model.compat.requiresThinkingAsText ?? detected.requiresThinkingAsText, requiresMistralToolIds: model.compat.requiresMistralToolIds ?? detected.requiresMistralToolIds, thinkingFormat: model.compat.thinkingFormat ?? detected.thinkingFormat, openRouterRouting: model.compat.openRouterRouting ?? {}, }; }

Summary

Summary