@copilotkit/runtime

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{"version":3,"file":"tanstack.cjs","names":["EventType"],"sources":["../../../src/agent/converters/tanstack.ts"],"sourcesContent":["import type {\n BaseEvent,\n RunAgentInput,\n Message,\n TextMessageChunkEvent,\n ToolCallArgsEvent,\n ToolCallEndEvent,\n ToolCallStartEvent,\n ToolCallResultEvent,\n StateSnapshotEvent,\n StateDeltaEvent,\n ReasoningStartEvent,\n ReasoningMessageStartEvent,\n ReasoningMessageContentEvent,\n ReasoningMessageEndEvent,\n ReasoningEndEvent,\n} from \"@ag-ui/client\";\nimport { EventType } from \"@ag-ui/client\";\nimport { randomUUID } from \"@copilotkit/shared\";\n\ntype ContentPartSource =\n | { type: \"data\"; value: string; mimeType: string }\n | { type: \"url\"; value: string; mimeType?: string };\n\n/**\n * A TanStack AI content part (text, image, audio, video, or document).\n */\nexport type TanStackContentPart =\n | { type: \"text\"; content: string }\n | { type: \"image\"; source: ContentPartSource }\n | { type: \"audio\"; source: ContentPartSource }\n | { type: \"video\"; source: ContentPartSource }\n | { type: \"document\"; source: ContentPartSource };\n\n/**\n * Message format expected by TanStack AI's `chat()`.\n *\n * Content is typed as `any[]` for the multimodal case so messages are directly\n * passable to any adapter without casts — different adapters constrain which\n * modalities they accept (e.g. OpenAI only allows text + image).\n * Use `TanStackContentPart` to inspect individual parts if needed.\n */\nexport interface TanStackChatMessage {\n role: \"user\" | \"assistant\" | \"tool\";\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n content: string | null | any[];\n name?: string;\n toolCalls?: Array<{\n id: string;\n type: \"function\";\n function: { name: string; arguments: string };\n }>;\n toolCallId?: string;\n}\n\n/**\n * A TanStack AI client-side tool, derived from a frontend-provided AG-UI tool.\n *\n * Shaped to match `@tanstack/ai`'s `ClientTool` (`__toolSide: \"client\"`, no\n * `execute`): the model may CALL it, but TanStack does not run it — it pauses\n * the run and hands the call back to the AG-UI client (the CopilotKit frontend\n * / bot) to execute, mirroring CopilotKit's client-tool round-trip. `chat()`\n * accepts a JSON Schema directly as `inputSchema`, so the AG-UI tool's\n * `parameters` pass through unchanged.\n */\nexport interface TanStackClientTool {\n __toolSide: \"client\";\n name: string;\n description: string;\n // eslint-disable-next-line @typescript-eslint/no-explicit-any\n inputSchema: any;\n}\n\n/**\n * Result of converting RunAgentInput to TanStack AI format.\n */\nexport interface TanStackInputResult {\n /** Chat messages (only user/assistant/tool roles; all others excluded) */\n messages: TanStackChatMessage[];\n /** System prompts extracted from system/developer messages, context, and state */\n systemPrompts: string[];\n /**\n * Client-side tools derived from `input.tools` (the frontend-provided tools\n * the CopilotKit client forwards on every run). Pass these into `chat()`\n * alongside any server/provider tools so the model can call the frontend's\n * generative-UI and human-in-the-loop tools; TanStack pauses the run on a\n * client-tool call and the client executes it.\n */\n tools: TanStackClientTool[];\n}\n\n/**\n * Converts AG-UI user message content to TanStack AI format.\n * Handles plain strings, multimodal parts (image/audio/video/document),\n * and legacy BinaryInputContent for backward compatibility.\n */\nfunction convertUserContent(\n content: unknown,\n): string | null | TanStackContentPart[] {\n if (!content) return null;\n if (typeof content === \"string\") return content;\n if (!Array.isArray(content)) return null;\n if (content.length === 0) return \"\";\n\n const parts: TanStackContentPart[] = [];\n\n for (const part of content) {\n if (!part || typeof part !== \"object\" || !(\"type\" in part)) continue;\n\n switch ((part as { type: string }).type) {\n case \"text\": {\n const text = (part as { text?: string }).text;\n if (text != null) parts.push({ type: \"text\", content: text });\n break;\n }\n\n case \"image\":\n case \"audio\":\n case \"video\":\n case \"document\": {\n const source = (part as { source?: any }).source;\n if (!source) break;\n const partType = (part as { type: string }).type as\n | \"image\"\n | \"audio\"\n | \"video\"\n | \"document\";\n if (source.type === \"data\") {\n parts.push({\n type: partType,\n source: {\n type: \"data\",\n value: source.value,\n mimeType: source.mimeType,\n },\n });\n } else if (source.type === \"url\") {\n parts.push({\n type: partType,\n source: {\n type: \"url\",\n value: source.value,\n ...(source.mimeType ? { mimeType: source.mimeType } : {}),\n },\n });\n }\n break;\n }\n\n // Legacy BinaryInputContent backward compatibility\n case \"binary\": {\n const legacy = part as {\n mimeType?: string;\n data?: string;\n url?: string;\n };\n const mimeType = legacy.mimeType ?? \"application/octet-stream\";\n const isImage = mimeType.startsWith(\"image/\");\n\n if (legacy.data) {\n const partType = isImage ? \"image\" : \"document\";\n parts.push({\n type: partType,\n source: { type: \"data\", value: legacy.data, mimeType },\n });\n } else if (legacy.url) {\n const partType = isImage ? \"image\" : \"document\";\n parts.push({\n type: partType,\n source: { type: \"url\", value: legacy.url, mimeType },\n });\n }\n break;\n }\n }\n }\n\n return parts.length > 0 ? parts : \"\";\n}\n\n/**\n * Recursively normalizes a frontend tool's JSON Schema so OpenAI accepts it as\n * a function-tool schema.\n *\n * Frontend tools are often authored with permissive Zod (`z.any()`,\n * `z.record(...)`, `.passthrough()`), which serialize to open objects —\n * `additionalProperties: {}` (an empty sub-schema) or `additionalProperties:\n * true`. OpenAI rejects both: strict mode requires `additionalProperties:\n * false`, and an empty `{}` sub-schema fails base validation (\"schema must\n * have a 'type' key\"). The classic (Vercel AI SDK) path sanitized these\n * implicitly via a Zod round-trip; the TanStack path forwards the raw schema,\n * so we close open objects here to match. (Models can't supply free-form extra\n * keys either way — same as the classic path.)\n */\nfunction sanitizeClientToolSchema(schema: unknown): unknown {\n if (Array.isArray(schema)) {\n return schema.map(sanitizeClientToolSchema);\n }\n if (!schema || typeof schema !== \"object\") {\n return schema;\n }\n const node: Record<string, unknown> = {\n ...(schema as Record<string, unknown>),\n };\n\n // Any `additionalProperties` (empty `{}`, `true`, or a sub-schema) becomes\n // `false` — the only form OpenAI accepts for strict function tools.\n if (\"additionalProperties\" in node) {\n node.additionalProperties = false;\n }\n\n if (node.properties && typeof node.properties === \"object\") {\n const props: Record<string, unknown> = {};\n for (const [key, value] of Object.entries(\n node.properties as Record<string, unknown>,\n )) {\n props[key] = sanitizeClientToolSchema(value);\n }\n node.properties = props;\n }\n\n if (\"items\" in node) {\n node.items = sanitizeClientToolSchema(node.items);\n }\n\n for (const combinator of [\"anyOf\", \"allOf\", \"oneOf\"] as const) {\n if (Array.isArray(node[combinator])) {\n node[combinator] = (node[combinator] as unknown[]).map(\n sanitizeClientToolSchema,\n );\n }\n }\n\n return node;\n}\n\n/**\n * Converts a RunAgentInput into the format expected by TanStack AI's `chat()`.\n *\n * - Keeps only user/assistant/tool messages (activity, reasoning, and other roles are also excluded)\n * - Extracts system/developer messages into `systemPrompts`\n * - Appends context entries and application state to `systemPrompts`\n * - Preserves tool calls on assistant messages and toolCallId on tool messages\n */\nexport function convertInputToTanStackAI(\n input: RunAgentInput,\n): TanStackInputResult {\n // Allowlist: only pass user/assistant/tool messages to TanStack.\n // Other roles (system, developer, activity, reasoning) are either\n // extracted into systemPrompts or not applicable.\n const chatRoles = new Set([\"user\", \"assistant\", \"tool\"]);\n const messages: TanStackChatMessage[] = input.messages\n .filter((m: Message) => chatRoles.has(m.role))\n .map((m: Message): TanStackChatMessage => {\n const msg: TanStackChatMessage = {\n role: m.role as \"user\" | \"assistant\" | \"tool\",\n content:\n m.role === \"user\"\n ? convertUserContent(m.content)\n : typeof m.content === \"string\"\n ? m.content\n : null,\n };\n if (m.role === \"assistant\" && \"toolCalls\" in m && m.toolCalls) {\n msg.toolCalls = m.toolCalls.map((tc) => ({\n id: tc.id,\n type: \"function\" as const,\n function: {\n name: tc.function.name,\n arguments: tc.function.arguments,\n },\n }));\n }\n if (m.role === \"tool\" && \"toolCallId\" in m) {\n msg.toolCallId = (m as Record<string, unknown>).toolCallId as string;\n }\n return msg;\n });\n\n const systemPrompts: string[] = [];\n for (const m of input.messages) {\n if ((m.role === \"system\" || m.role === \"developer\") && m.content) {\n systemPrompts.push(\n typeof m.content === \"string\" ? m.content : JSON.stringify(m.content),\n );\n }\n }\n\n if (input.context?.length) {\n for (const ctx of input.context) {\n systemPrompts.push(`${ctx.description}:\\n${ctx.value}`);\n }\n }\n\n if (\n input.state !== undefined &&\n input.state !== null &&\n typeof input.state === \"object\" &&\n Object.keys(input.state).length > 0\n ) {\n systemPrompts.push(\n `Application State:\\n\\`\\`\\`json\\n${JSON.stringify(input.state, null, 2)}\\n\\`\\`\\``,\n );\n }\n\n // Frontend-provided tools become client-side TanStack tools (no executor):\n // the model can call them, TanStack pauses the run, and the AG-UI client\n // executes them and resumes — the CopilotKit client-tool round-trip.\n const tools: TanStackClientTool[] = (input.tools ?? []).map((t) => ({\n __toolSide: \"client\",\n name: t.name,\n description: t.description,\n inputSchema: sanitizeClientToolSchema(t.parameters),\n }));\n\n return { messages, systemPrompts, tools };\n}\n\n/**\n * Converts a TanStack AI stream into AG-UI `BaseEvent` objects.\n *\n * This is a pure converter — it does NOT emit lifecycle events\n * (RUN_STARTED / RUN_FINISHED / RUN_ERROR). The caller (Agent class)\n * is responsible for those.\n */\nexport async function* convertTanStackStream(\n stream: AsyncIterable<unknown>,\n abortSignal: AbortSignal,\n): AsyncGenerator<BaseEvent> {\n const messageId = randomUUID();\n const toolNamesById = new Map<string, string>();\n // Track the reasoning lifecycle at two granularities so closeReasoningIfOpen\n // emits exactly the events still owed. A single boolean conflates the run\n // (REASONING_START → REASONING_END) with the message\n // (REASONING_MESSAGE_START → REASONING_MESSAGE_END) and produces a duplicate\n // REASONING_MESSAGE_END when upstream emits MSG_END but not END before\n // text/tools resume.\n let reasoningRunOpen = false;\n let reasoningMessageOpen = false;\n let reasoningMessageId = randomUUID();\n\n function* closeReasoningIfOpen(): Generator<BaseEvent> {\n if (reasoningMessageOpen) {\n reasoningMessageOpen = false;\n const msgEnd: ReasoningMessageEndEvent = {\n type: EventType.REASONING_MESSAGE_END,\n messageId: reasoningMessageId,\n };\n yield msgEnd;\n }\n if (reasoningRunOpen) {\n reasoningRunOpen = false;\n const end: ReasoningEndEvent = {\n type: EventType.REASONING_END,\n messageId: reasoningMessageId,\n };\n yield end;\n }\n }\n\n // TanStack's chat() engine runs a multi-turn agent loop and emits a\n // RUN_STARTED / RUN_FINISHED pair PER model turn — not once for the whole\n // run. When it executes a tool itself (an MCP server tool or a provider tool\n // like web_search), it does so between turns and streams a TOOL_CALL_RESULT\n // followed by the next turn's text. The overall run lifecycle is owned by the\n // Agent wrapper (it emits exactly one outer RUN_STARTED / RUN_FINISHED), so\n // we drop TanStack's per-turn lifecycle markers and convert every content\n // event across all turns. (A previous version stopped converting at the first\n // RUN_FINISHED — that truncated the run at the first tool turn and silently\n // dropped both the tool result and the model's final answer.)\n //\n // chat() can re-announce a tool call when it re-prompts after executing it,\n // so START / END are de-duplicated by toolCallId to avoid emitting a pair\n // twice (which would violate the ag-ui verify middleware).\n const startedToolCalls = new Set<string>();\n const endedToolCalls = new Set<string>();\n\n for await (const chunk of stream) {\n if (abortSignal.aborted) break;\n\n const raw = chunk as Record<string, unknown>;\n const type = raw.type as string;\n\n // Per-turn lifecycle markers are owned by the Agent wrapper, not forwarded.\n if (type === \"RUN_STARTED\" || type === \"RUN_FINISHED\") {\n continue;\n }\n\n // Surface engine errors instead of dropping them: throw so the Agent\n // wrapper emits a terminal RUN_ERROR. Without this a failed run (e.g. a\n // provider 4xx) would finish empty with no indication of what went wrong.\n if (type === \"RUN_ERROR\") {\n throw new Error(\n typeof raw.message === \"string\" ? raw.message : \"TanStack AI run error\",\n );\n }\n\n if (type === \"TEXT_MESSAGE_CONTENT\" && raw.delta != null) {\n yield* closeReasoningIfOpen();\n const textEvent: TextMessageChunkEvent = {\n type: EventType.TEXT_MESSAGE_CHUNK,\n role: \"assistant\",\n messageId,\n delta: raw.delta as string,\n };\n yield textEvent;\n } else if (type === \"TOOL_CALL_START\") {\n const toolCallId = raw.toolCallId as string;\n if (startedToolCalls.has(toolCallId)) continue;\n startedToolCalls.add(toolCallId);\n yield* closeReasoningIfOpen();\n toolNamesById.set(toolCallId, raw.toolCallName as string);\n const startEvent: ToolCallStartEvent = {\n type: EventType.TOOL_CALL_START,\n parentMessageId: messageId,\n toolCallId,\n toolCallName: raw.toolCallName as string,\n };\n yield startEvent;\n } else if (type === \"TOOL_CALL_ARGS\") {\n // Drop args re-announced after the call has ended (the re-prompt pass);\n // forwarding them would corrupt the already-closed call's accumulated args.\n if (endedToolCalls.has(raw.toolCallId as string)) continue;\n yield* closeReasoningIfOpen();\n const argsEvent: ToolCallArgsEvent = {\n type: EventType.TOOL_CALL_ARGS,\n toolCallId: raw.toolCallId as string,\n delta: raw.delta as string,\n };\n yield argsEvent;\n } else if (type === \"TOOL_CALL_END\") {\n const toolCallId = raw.toolCallId as string;\n if (endedToolCalls.has(toolCallId)) continue;\n endedToolCalls.add(toolCallId);\n yield* closeReasoningIfOpen();\n const endEvent: ToolCallEndEvent = {\n type: EventType.TOOL_CALL_END,\n toolCallId,\n };\n yield endEvent;\n } else if (type === \"TOOL_CALL_RESULT\") {\n yield* closeReasoningIfOpen();\n const toolCallId = raw.toolCallId as string;\n const toolName = toolNamesById.get(toolCallId);\n // Accept the payload from either `content` (canonical TanStack shape)\n // or `result` (alternate shape used by some adapters / tests). Both\n // state-tool detection and the final TOOL_CALL_RESULT serialization\n // must read the same field, otherwise STATE_SNAPSHOT/STATE_DELTA can\n // be silently dropped when upstream uses `result`.\n const rawPayload = raw.content ?? raw.result;\n\n const parsedContent =\n typeof rawPayload === \"string\" ? safeParse(rawPayload) : rawPayload;\n\n if (\n toolName === \"AGUISendStateSnapshot\" &&\n parsedContent &&\n typeof parsedContent === \"object\" &&\n \"snapshot\" in parsedContent\n ) {\n const stateSnapshotEvent: StateSnapshotEvent = {\n type: EventType.STATE_SNAPSHOT,\n snapshot: (parsedContent as Record<string, unknown>).snapshot,\n };\n yield stateSnapshotEvent;\n }\n\n if (\n toolName === \"AGUISendStateDelta\" &&\n parsedContent &&\n typeof parsedContent === \"object\" &&\n \"delta\" in parsedContent\n ) {\n const stateDeltaEvent: StateDeltaEvent = {\n type: EventType.STATE_DELTA,\n delta: (parsedContent as Record<string, unknown>).delta as never,\n };\n yield stateDeltaEvent;\n }\n\n let serializedContent: string;\n if (typeof rawPayload === \"string\") {\n serializedContent = rawPayload;\n } else {\n try {\n serializedContent = JSON.stringify(rawPayload ?? null);\n } catch {\n serializedContent = \"[Unserializable tool result]\";\n }\n }\n\n const resultEvent: ToolCallResultEvent = {\n type: EventType.TOOL_CALL_RESULT,\n role: \"tool\",\n messageId: randomUUID(),\n toolCallId,\n content: serializedContent,\n };\n yield resultEvent;\n toolNamesById.delete(toolCallId);\n } else if (type === \"REASONING_START\") {\n // If a prior reasoning run is still open (no REASONING_END before this\n // new START), close it cleanly first so MSG_END / END pair correctly.\n yield* closeReasoningIfOpen();\n reasoningRunOpen = true;\n reasoningMessageId = (raw.messageId as string) ?? randomUUID();\n const startEvt: ReasoningStartEvent = {\n type: EventType.REASONING_START,\n messageId: reasoningMessageId,\n };\n yield startEvt;\n } else if (type === \"REASONING_MESSAGE_START\") {\n reasoningMessageOpen = true;\n const evt: ReasoningMessageStartEvent = {\n type: EventType.REASONING_MESSAGE_START,\n messageId: reasoningMessageId,\n role: \"reasoning\",\n };\n yield evt;\n } else if (type === \"REASONING_MESSAGE_CONTENT\") {\n const evt: ReasoningMessageContentEvent = {\n type: EventType.REASONING_MESSAGE_CONTENT,\n messageId: reasoningMessageId,\n delta: raw.delta as string,\n };\n yield evt;\n } else if (type === \"REASONING_MESSAGE_END\") {\n reasoningMessageOpen = false;\n const evt: ReasoningMessageEndEvent = {\n type: EventType.REASONING_MESSAGE_END,\n messageId: reasoningMessageId,\n };\n yield evt;\n } else if (type === \"REASONING_END\") {\n // If upstream sends REASONING_END while a message is still open, emit\n // the missing REASONING_MESSAGE_END FIRST so the closing pair stays in\n // order (MSG_END before END). Otherwise the next non-reasoning chunk\n // would trigger closeReasoningIfOpen and emit MSG_END after END.\n if (reasoningMessageOpen) {\n reasoningMessageOpen = false;\n const msgEnd: ReasoningMessageEndEvent = {\n type: EventType.REASONING_MESSAGE_END,\n messageId: reasoningMessageId,\n };\n yield msgEnd;\n }\n reasoningRunOpen = false;\n const evt: ReasoningEndEvent = {\n type: EventType.REASONING_END,\n messageId: reasoningMessageId,\n };\n yield evt;\n }\n }\n\n yield* closeReasoningIfOpen();\n}\n\nfunction safeParse(value: string): unknown {\n try {\n return JSON.parse(value);\n } catch {\n return value;\n 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