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","license":"MIT","homepage":"https://github.com/temporalio/sdk-typescript/tree/main/contrib/openai-agents","keywords":["temporal","workflow","ai","openai","agents"],"repository":{"url":"git+https://github.com/temporalio/sdk-typescript.git","type":"git","directory":"contrib/openai-agents"},"description":"Temporal OpenAI Agents SDK integration package","maintainers":[{"name":"luminousmoose","email":"alex.scott.thomsen@gmail.com"},{"name":"tempbearpig","email":"roey@temporal.io"},{"name":"temporal-sdk-team","email":"sdk@temporal.io"},{"name":"alex.tideman","email":"alex.tideman@temporal.io"},{"name":"rossedfort","email":"rossedfort@gmail.com"},{"name":"laurakwhit","email":"laura.k.whitaker@gmail.com"}],"readme":"# OpenAI Agents SDK Integration for Temporal\n\n> ⚠️ **This package is currently at an experimental release stage.** ⚠️\n\nRun [OpenAI Agents SDK](https://github.com/openai/openai-agents-js) agents as Temporal Workflows. Agent orchestration runs in the Workflow; model calls run as Activities, so they retry durably and are not repeated during Workflow replay.\n\n## Install\n\n```bash\n# Or `pnpm add`/`yarn add`\nnpm install @temporalio/openai-agents @openai/agents-core @openai/agents-openai openai\n```\n\n`@openai/agents-core`, `@openai/agents-openai`, and `openai` are peer dependencies.\n\n## Quick start\n\nCreate a Workflow that uses `TemporalOpenAIRunner` instead of the upstream `Runner`:\n\n```ts\nimport { Agent } from '@openai/agents-core';\nimport { TemporalOpenAIRunner } from '@temporalio/openai-agents/workflow';\n\nexport async function haikuAgentWorkflow(prompt: string): Promise {\n const agent = new Agent({\n name: 'Assistant',\n instructions: 'You only respond in haikus.',\n model: 'gpt-4o-mini',\n });\n\n const runner = new TemporalOpenAIRunner();\n const result = await runner.run(agent, prompt);\n return result.finalOutput ?? '';\n}\n```\n\nRegister `OpenAIAgentsPlugin` on the Worker so model Activities, Workflow interceptors, sinks, and polyfills are installed:\n\n```ts\nimport { OpenAIProvider } from '@openai/agents-openai';\nimport { OpenAIAgentsPlugin } from '@temporalio/openai-agents';\nimport { NativeConnection, Worker } from '@temporalio/worker';\n\nasync function main() {\n const connection = await NativeConnection.connect();\n const plugin = new OpenAIAgentsPlugin({\n modelProvider: new OpenAIProvider(),\n modelParams: { startToCloseTimeout: '30s' },\n });\n\n const worker = await Worker.create({\n connection,\n taskQueue: 'my-task-queue',\n workflowsPath: require.resolve('./workflows'),\n plugins: [plugin],\n });\n\n await worker.run();\n}\n\nmain();\n```\n\nRegister the same plugin type on the Client so model parameters and tracing options propagate to new Workflows:\n\n```ts\nimport { OpenAIProvider } from '@openai/agents-openai';\nimport { Client, Connection } from '@temporalio/client';\nimport { OpenAIAgentsPlugin } from '@temporalio/openai-agents';\n\nasync function main() {\n const connection = await Connection.connect();\n const plugin = new OpenAIAgentsPlugin({\n modelProvider: new OpenAIProvider(),\n });\n\n const client = new Client({\n connection,\n plugins: [plugin],\n });\n\n const result = await client.workflow.execute('haikuAgentWorkflow', {\n args: ['Tell me about recursion in programming.'],\n taskQueue: 'my-task-queue',\n workflowId: 'haiku-workflow',\n });\n\n console.log(result);\n}\n\nmain();\n```\n\n## Workflow runner\n\n`TemporalOpenAIRunner` mirrors the OpenAI Agents SDK `Runner` for Workflow-safe execution. Use `run(agent, input, options?)` with familiar options such as `maxTurns`, `context`, `session`, and guardrails.\n\nDifferences from the upstream runner:\n\n- `runConfig.model` must be a model name string. The Worker's `modelProvider` resolves it inside the model Activity.\n- `signal` is not supported. Use Temporal cancellation APIs, such as `CancellationScope`, to cancel Workflow work.\n\n### Run state and approvals\n\n`TemporalOpenAIRunner.run` accepts a `RunState` as the second argument, matching the upstream runner. This supports human approval flows that pause, wait for a Signal or Update, then continue as new.\n\n```ts\nimport { Agent, RunState, tool } from '@openai/agents-core';\nimport { TemporalOpenAIRunner } from '@temporalio/openai-agents/workflow';\nimport { condition, continueAsNew, defineSignal, setHandler } from '@temporalio/workflow';\n\nconst approveSignal = defineSignal('approve');\n\ninterface ApprovalInput {\n resumeFromRunState?: string;\n}\n\nexport async function approvalWorkflow(input: ApprovalInput = {}): Promise {\n const action = tool({\n name: 'dangerousAction',\n description: 'Perform an action that needs approval',\n parameters: {\n type: 'object' as const,\n properties: { reason: { type: 'string' } },\n required: ['reason'] as const,\n additionalProperties: false as const,\n },\n needsApproval: true,\n execute: async (args) => `did: ${(args as { reason: string }).reason}`,\n });\n\n const agent = new Agent({\n name: 'Approver',\n instructions: 'Use dangerousAction when asked.',\n model: 'gpt-4o-mini',\n tools: [action],\n });\n const runner = new TemporalOpenAIRunner();\n\n if (input.resumeFromRunState !== undefined) {\n const state = await RunState.fromString(agent, input.resumeFromRunState);\n for (const interruption of state.getInterruptions()) {\n state.approve(interruption);\n }\n const resumed = await runner.run(agent, state);\n return resumed.finalOutput ?? '';\n }\n\n let approved = false;\n setHandler(approveSignal, () => {\n approved = true;\n });\n\n const result = await runner.run(agent, 'please act');\n if (result.interruptions.length === 0) return result.finalOutput ?? '';\n\n await condition(() => approved);\n await continueAsNew({ resumeFromRunState: result.state.toString() });\n throw new Error('unreachable');\n}\n```\n\nThe agent passed to `RunState.fromString` must define the same tool names, handoff graph, and MCP servers as the run that produced the serialized state.\n\n### Streaming\n\n> **Experimental** — streaming support is subject to change prior to General Availability.\n\nPass `{ stream: true }` to `run()` to stream incremental events as the model responds. The call returns the upstream `StreamedRunResult`, an async-iterable of agent-SDK stream events. Inside a Workflow, each model call executes as a streaming Activity (`invokeModelStreamActivity`) that consumes `Model.getStreamedResponse` and returns the collected list of native model events. The Workflow yields those events deterministically from the Activity's return value — it never polls the live stream, so replay stays deterministic.\n\nExternal consumers (UIs, tracing pipelines) observe events as they arrive by hosting a [`WorkflowStream`](../workflow-streams/README.md) in the Workflow and subscribing with `WorkflowStreamClient`. The streaming Activity publishes each event to the topic configured on `modelParams.streamingTopic`. The topic is required for streaming runs; calling `run(agent, input, { stream: true })` without a configured topic throws before any Activity is scheduled.\n\nStreaming requires a `streamingTopic`. Set it on the `modelParams` of the plugin registered on the **Client** — the Client interceptor injects the config header into every Workflow-starting call:\n\n```ts\nconst plugin = new OpenAIAgentsPlugin({\n modelProvider: new OpenAIProvider(),\n modelParams: { streamingTopic: 'events', streamingBatchInterval: '100ms' },\n});\n\nconst client = new Client({ connection, plugins: [plugin] });\n```\n\nWorkflows started without that Client — by a Schedule or the Temporal UI/CLI — receive no config header. Supply Workflow-authored defaults through the runner's `defaultModelParams`; unlike the header, these can carry a function-form `summary`. Host a `WorkflowStream` in the Workflow to receive the publishes:\n\n```ts\nimport { Agent } from '@openai/agents-core';\nimport { TemporalOpenAIRunner } from '@temporalio/openai-agents/workflow';\nimport { WorkflowStream } from '@temporalio/workflow-streams/workflow';\n\nexport async function streamingWorkflow(prompt: string): Promise {\n new WorkflowStream();\n const agent = new Agent({ name: 'Assistant', instructions: '...' });\n const runner = new TemporalOpenAIRunner({ defaultModelParams: { streamingTopic: 'events' } });\n const result = await runner.run(agent, prompt, { stream: true });\n for await (const event of result) {\n if (event.type === 'raw_model_stream_event' && event.data.type === 'output_text_delta') {\n // event.data.delta — incremental text\n }\n }\n return result.finalOutput ?? '';\n}\n```\n\nThe two layers combine per field: the Client's `modelParams` overrides the runner's `defaultModelParams`.\n\nExternal subscribers receive the unwrapped native model events directly:\n\n```ts\nimport { WorkflowStreamClient } from '@temporalio/workflow-streams/client';\n\nfor await (const item of WorkflowStreamClient.create(client, workflowId).topic('events').subscribe()) {\n // item.data — native model stream event\n}\n```\n\nThe hosted `WorkflowStream` retains every published event on the Workflow heap and in history until `truncate(upToOffset)` is called. A streaming agent publishes many events per run, so a long-running or high-volume Workflow that never truncates will eventually hit Temporal's Workflow history size limits. Call `truncate(upToOffset)` periodically to keep the heap and history bounded; see [`@temporalio/workflow-streams`](../workflow-streams/README.md) for the offset model and batching.\n\nStreaming is incompatible with `useLocalActivity`, which supports neither Activity heartbeats nor the Workflow Stream signal channel. See [`@temporalio/workflow-streams`](../workflow-streams/README.md) for the publisher/subscriber API and delivery semantics.\n\n### Sessions\n\nUse `WorkflowSafeMemorySession` for conversation history stored on the Workflow heap and rebuilt by replay. This replaces the upstream `MemorySession`, which is not replay safe because it depends on host process state.\n\n```ts\nimport { Agent } from '@openai/agents-core';\nimport { TemporalOpenAIRunner, WorkflowSafeMemorySession } from '@temporalio/openai-agents/workflow';\n\nexport async function chatWorkflow(prompts: string[]): Promise {\n const agent = new Agent({\n name: 'ChatAgent',\n instructions: 'Use the conversation history to answer.',\n model: 'gpt-4o-mini',\n });\n const runner = new TemporalOpenAIRunner();\n const session = new WorkflowSafeMemorySession();\n\n const replies: string[] = [];\n for (const prompt of prompts) {\n const result = await runner.run(agent, prompt, { session });\n replies.push(result.finalOutput ?? '');\n }\n return replies;\n}\n```\n\n`WorkflowSafeMemorySession` history lives on the Workflow heap and is rebuilt by replay within a single run. It does **not** automatically survive `continueAsNew` — a continued run starts with an empty session. To carry history across a continue-as-new boundary, capture the items and re-seed the new run's session via the constructor's `initialItems`:\n\n```ts\n// 1. Before continuing, capture the current history:\nconst items = await session.getItems();\nawait continueAsNew(/* ...your Workflow args..., */ items);\n\n// 2. The continued run declares a Workflow parameter to receive those items,\n// and re-seeds the session from them:\nconst session = new WorkflowSafeMemorySession({ initialItems: items });\n```\n\n## Worker and Client plugin setup\n\nThe Worker plugin does five things:\n\n- Registers the model Activity used by Workflow-side model calls.\n- Registers Activities for configured MCP server providers.\n- Registers Activities for configured sandbox client providers.\n- Adds Workflow, Activity, and Nexus interceptors for trace propagation.\n- Installs Workflow bundle polyfills required by the OpenAI Agents SDK.\n\nThe Client plugin injects the OpenAI Agents config header into Workflow-starting calls. Attach one `OpenAIAgentsPlugin` instance per Client or Connection configuration. If multiple instances are chained, the last config header wins.\n\n`modelParams` controls scheduling for the model Activity:\n\n```ts\nconst plugin = new OpenAIAgentsPlugin({\n modelProvider: new OpenAIProvider(),\n modelParams: {\n startToCloseTimeout: '30s',\n retry: { maximumAttempts: 5, initialInterval: '1s' },\n useLocalActivity: false,\n },\n});\n```\n\nSee `ModelActivityOptions` for the public field list. `summary` can be a string in plugin config; `ModelSummaryProvider` functions are intentionally not serialized into Workflow headers.\n\n## Tools\n\nInline function tools, hosted tools, Activity-backed tools, Nexus operation tools, nested agent tools, and MCP tools can all be used from a Temporal-backed agent. Any tool that does I/O must run outside the Workflow sandbox, usually through an Activity or Nexus Operation.\n\n### Activity-backed tools\n\nUse `activityAsTool` for HTTP calls, database access, file system work, or other I/O. The tool name must match a registered Activity.\n\n```ts\nimport { Agent } from '@openai/agents-core';\nimport { activityAsTool } from '@temporalio/openai-agents/workflow';\nimport type * as activities from './activities';\n\nconst weatherTool = activityAsTool(\n {\n name: 'getWeather',\n description: 'Get the weather for a city',\n parameters: {\n type: 'object',\n properties: { location: { type: 'string' } },\n required: ['location'],\n additionalProperties: false,\n },\n },\n {\n startToCloseTimeout: '10s',\n retryPolicy: { maximumAttempts: 3 },\n }\n);\n\nconst agent = new Agent({\n name: 'WeatherAgent',\n instructions: 'Use the getWeather tool when asked about weather.',\n model: 'gpt-4o-mini',\n tools: [weatherTool],\n});\n```\n\nThe generic type is compile-time only. At runtime the Activity is invoked by name through `proxyActivities`.\n\n### Inline and hosted tools\n\nFor deterministic computation, use `tool()` from `@openai/agents-core` directly. Inline tools run in the Workflow sandbox and must not perform I/O, consume nondeterministic randomness, or read wall-clock time beyond Temporal's deterministic replacements.\n\nHosted tools from `@openai/agents-openai`, such as `webSearchTool()`, run server-side through the model provider during the model Activity.\n\n```ts\nimport { Agent, tool } from '@openai/agents-core';\nimport { webSearchTool } from '@openai/agents-openai';\n\nconst addNumbers = tool({\n name: 'addNumbers',\n description: 'Add two numbers',\n parameters: {\n type: 'object' as const,\n properties: { a: { type: 'number' }, b: { type: 'number' } },\n required: ['a', 'b'] as const,\n additionalProperties: false as const,\n },\n execute: async (args) => String((args as { a: number; b: number }).a + (args as { a: number; b: number }).b),\n});\n\nconst agent = new Agent({\n name: 'SearchAgent',\n instructions: 'You have web search and arithmetic.',\n model: 'gpt-4o-mini',\n tools: [addNumbers, webSearchTool()],\n});\n```\n\n### Nexus operation tools\n\nUse `nexusOperationAsTool` to expose a [Nexus](https://docs.temporal.io/nexus) Operation as an agent tool. The Workflow starts the Operation through a Nexus client and feeds the stringified result back to the agent.\n\n```ts\nimport { Agent } from '@openai/agents-core';\nimport { nexusOperationAsTool } from '@temporalio/openai-agents/workflow';\nimport * as nexus from 'nexus-rpc';\n\nconst weatherService = nexus.service('weather', {\n getWeather: nexus.operation<{ location: string }, { tempC: number }>(),\n});\n\nconst weatherTool = nexusOperationAsTool(\n weatherService.operations.getWeather,\n {\n name: 'getWeather',\n description: 'Get the weather for a city',\n parameters: {\n type: 'object',\n properties: { location: { type: 'string' } },\n required: ['location'],\n additionalProperties: false,\n },\n },\n { service: weatherService, endpoint: 'weather-endpoint' }\n);\n\nconst agent = new Agent({\n name: 'WeatherAgent',\n instructions: 'Use the weather tool.',\n model: 'gpt-4o-mini',\n tools: [weatherTool],\n});\n```\n\n### Nested agent tools\n\nUse `agentAsTool` to expose another `Agent` as a tool while keeping nested model calls durable:\n\n```ts\nimport { Agent } from '@openai/agents-core';\nimport { agentAsTool } from '@temporalio/openai-agents/workflow';\n\nconst specialist = new Agent({\n name: 'Specialist',\n instructions: 'Answer precisely.',\n model: 'gpt-4o-mini',\n});\n\nconst triage = new Agent({\n name: 'Triage',\n instructions: 'Delegate specialist questions.',\n model: 'gpt-4o-mini',\n tools: [\n agentAsTool(specialist, {\n toolName: 'ask_specialist',\n toolDescription: 'Ask the specialist agent',\n }),\n ],\n});\n```\n\nNested approval interruptions are not supported. If a nested run pauses for approval, the tool invocation fails with `ApplicationFailure` type `NestedAgentInterruption`.\n\n## MCP servers\n\nThe integration supports stateless and stateful [Model Context Protocol](https://modelcontextprotocol.io/) servers.\n\n### Stateless MCP servers\n\nUse stateless servers when each tool call is independent. Register a provider on the Worker:\n\n```ts\nimport { MCPServerStreamableHttp } from '@openai/agents-core';\nimport { OpenAIProvider } from '@openai/agents-openai';\nimport { OpenAIAgentsPlugin, StatelessMCPServerProvider } from '@temporalio/openai-agents';\n\nconst unitConversionMcp = new StatelessMCPServerProvider(\n 'unitConversion',\n (factoryArgument?) =>\n new MCPServerStreamableHttp({ name: 'unitConversion', url: 'https://mcp.example.com/unit-conversion' })\n);\n\nconst plugin = new OpenAIAgentsPlugin({\n modelProvider: new OpenAIProvider(),\n mcpServerProviders: [unitConversionMcp],\n});\n```\n\nReference the same provider name from Workflow code:\n\n```ts\nimport { Agent } from '@openai/agents-core';\nimport { statelessMcpServer, TemporalOpenAIRunner } from '@temporalio/openai-agents/workflow';\n\nexport async function mcpWorkflow(query: string): Promise {\n const agent = new Agent({\n name: 'UnitConverter',\n instructions: 'Use unit conversion tools to answer questions.',\n model: 'gpt-4o-mini',\n mcpServers: [statelessMcpServer('unitConversion')],\n });\n\n const result = await new TemporalOpenAIRunner().run(agent, query);\n return result.finalOutput ?? '';\n}\n```\n\n### Stateful MCP servers\n\nUse stateful servers when a persistent connection or session is required. The plugin starts a dedicated in-process Worker pinned to a per-run Task Queue and routes MCP operations to it.\n\n```ts\nimport { MCPServerStreamableHttp } from '@openai/agents-core';\nimport { OpenAIProvider } from '@openai/agents-openai';\nimport { OpenAIAgentsPlugin, StatefulMCPServerProvider } from '@temporalio/openai-agents';\nimport { NativeConnection } from '@temporalio/worker';\n\nconst connection = await NativeConnection.connect();\nconst dbMcp = new StatefulMCPServerProvider(\n 'database',\n (factoryArgument?) => new MCPServerStreamableHttp({ name: 'database', url: 'https://mcp.example.com/database' }),\n connection\n);\n\nconst plugin = new OpenAIAgentsPlugin({\n modelProvider: new OpenAIProvider(),\n mcpServerProviders: [dbMcp],\n});\n```\n\nIn the Workflow, call `connect()` before use and `cleanup()` in a `finally` block:\n\n```ts\nimport { Agent } from '@openai/agents-core';\nimport { statefulMcpServer, TemporalOpenAIRunner } from '@temporalio/openai-agents/workflow';\n\nexport async function statefulMcpWorkflow(prompt: string): Promise {\n const server = statefulMcpServer('database');\n await server.connect();\n try {\n const agent = new Agent({\n name: 'DbAgent',\n instructions: 'You have database access.',\n model: 'gpt-4o-mini',\n mcpServers: [server],\n });\n const result = await new TemporalOpenAIRunner().run(agent, prompt);\n return result.finalOutput ?? '';\n } finally {\n await server.cleanup();\n }\n}\n```\n\nDedicated Worker startup and heartbeat failures surface as `ApplicationFailure` type (exported as `DEDICATED_WORKER_FAILURE_TYPE`).\n\n## Sandbox agents\n\n> **Experimental** — sandbox support is subject to change prior to General Availability.\n\nAn OpenAI Agents SDK `SandboxAgent` runs commands, edits files, and keeps a workspace inside a sandbox. This plugin dispatches each of those operations as an Activity.\n\nProcess handles returned by `exec` remain usable across Activities only when the sandbox backend can reconnect them after `resume`; Unix-local and Docker backends cannot.\n\nRegister your sandbox backend client under a name on the Worker plugin:\n\n```ts\nimport { UnixLocalSandboxClient } from '@openai/agents-core/sandbox/local';\nimport { OpenAIProvider } from '@openai/agents-openai';\nimport { OpenAIAgentsPlugin, SandboxClientProvider } from '@temporalio/openai-agents';\n\nconst plugin = new OpenAIAgentsPlugin({\n modelProvider: new OpenAIProvider(),\n sandboxClientProviders: [new SandboxClientProvider('local', new UnixLocalSandboxClient())],\n});\n```\n\nReference the same provider name from Workflow code:\n\n```ts\nimport { SandboxAgent, shell } from '@openai/agents-core/sandbox';\nimport { TemporalOpenAIRunner, temporalSandboxClient } from '@temporalio/openai-agents/workflow';\n\nexport async function sandboxWorkflow(prompt: string): Promise {\n const agent = new SandboxAgent({\n name: 'Coder',\n model: 'gpt-4o-mini',\n capabilities: [shell()],\n });\n\n const result = await new TemporalOpenAIRunner().run(agent, prompt, {\n runConfig: { sandbox: { client: temporalSandboxClient('local') } },\n });\n return result.finalOutput ?? '';\n}\n```\n\n## Credentials\n\nA credential written into an Activity argument stays in Workflow history. Leave it out of Workflow code and supply it on the Worker instead.\n\nDeclare the tool in Workflow code:\n\n```ts\nimport { Agent, hostedMcpTool } from '@openai/agents-core';\n\nconst docsTool = hostedMcpTool({ serverLabel: 'docs', serverUrl: 'https://mcp.example.com' });\nconst agent = new Agent({ name: 'DocsAgent', model: 'gpt-4o-mini', tools: [docsTool] });\n```\n\nSupply the credential on the Worker:\n\n```ts\nimport { OpenAIProvider } from '@openai/agents-openai';\nimport { OpenAIAgentsPlugin } from '@temporalio/openai-agents';\n\nconst plugin = new OpenAIAgentsPlugin({\n modelProvider: new OpenAIProvider(),\n hostedToolCredentials: (identity) => {\n if (identity.tool !== 'hostedMcp' || identity.serverLabel !== 'docs') return undefined;\n const authorization = process.env.DOCS_MCP_TOKEN;\n if (!authorization) throw new Error('DOCS_MCP_TOKEN is not set on this Worker');\n return { authorization };\n },\n});\n```\n\nFor sandbox Manifest environment values, `workerEnvValue(name)` names an allowlisted Worker environment variable that only the Worker resolves.\n\n```ts\nimport { workerEnvValue } from '@temporalio/openai-agents/workflow';\nimport { Manifest } from '@openai/agents-core/sandbox';\n\nconst manifest = new Manifest({ environment: { DB_PASSWORD: workerEnvValue('WORKER_DB_PASSWORD') } });\n```\n\nAllow the variable on the Worker plugin with `resolvableWorkerEnvVars: ['WORKER_DB_PASSWORD']`.\n\n## Tracing\n\nOpenAI Agents SDK tracing works across Client, Workflow, Activity, Nexus, and MCP boundaries. Workflow replay does not duplicate spans.\n\n### OpenAI hosted traces\n\nEnable the upstream hosted exporter before constructing the plugin:\n\n```ts\nimport { OpenAITracingExporter } from '@openai/agents-openai';\nimport { addTraceProcessor, BatchTraceProcessor } from '@openai/agents-core';\n\naddTraceProcessor(new BatchTraceProcessor(new OpenAITracingExporter()));\n```\n\n**Register this processor in the Worker process, not inside Workflow code.**\n\n**Warning**: Using `setDefaultOpenAITracingExporter()` after constructing the plugin will break plugin internals. Either call it before constructing the plugin, or use `addTraceProcessor` as described above.\n\n### OpenTelemetry\n\nInstall `@opentelemetry/sdk-trace-base` before importing the OTel subpath:\n\n```bash\n# Or `pnpm add`/`yarn add`\nnpm install @opentelemetry/sdk-trace-base\n```\n\n```ts\nimport { trace } from '@opentelemetry/api';\nimport { OpenAIProvider } from '@openai/agents-openai';\nimport { OpenAIAgentsPlugin } from '@temporalio/openai-agents';\nimport { createTracerProvider } from '@temporalio/openai-agents/otel';\n\ntrace.setGlobalTracerProvider(createTracerProvider());\n\nconst plugin = new OpenAIAgentsPlugin({\n modelProvider: new OpenAIProvider(),\n interceptorOptions: { useOtelInstrumentation: true },\n});\n```\n\nUse `createTracerProvider` from this package for replay-safe OTel ID generation. If you need a different provider class, configure it with `TemporalIdGenerator` and mark it before registering it:\n\n```ts\nimport { trace } from '@opentelemetry/api';\nimport { NodeTracerProvider } from '@opentelemetry/sdk-trace-node';\nimport { markReplaySafeTracerProvider, TemporalIdGenerator } from '@temporalio/openai-agents/otel';\n\nconst provider = markReplaySafeTracerProvider(\n new NodeTracerProvider({\n idGenerator: new TemporalIdGenerator(),\n })\n);\n\ntrace.setGlobalTracerProvider(provider);\n```\n\nOnly mark providers that use `TemporalIdGenerator`; the marker opts into the plugin's replay-safety checks.\n\n### Temporal orchestration spans\n\nSet `addTemporalSpans: true` to emit `temporal:*` agent-SDK spans for orchestration operations such as Workflow starts, Signals, Queries, Updates, Activities, child Workflows, Nexus Operations, and continue-as-new:\n\n```ts\nconst plugin = new OpenAIAgentsPlugin({\n modelProvider: new OpenAIProvider(),\n interceptorOptions: { addTemporalSpans: true },\n});\n```\n\nThese spans are agent-SDK spans, so they reach the hosted OpenAI dashboard, custom `TracingProcessor`s, and OTel when enabled.\n\n## Pre-built workflow bundles\n\nWhen using `bundleWorkflowCode`, pass the plugin to the bundler as well as to the Worker. The bundler hook installs Workflow-side Web API polyfills and registers the Workflow trace interceptor.\n\n```ts\nimport { OpenAIProvider } from '@openai/agents-openai';\nimport { OpenAIAgentsPlugin } from '@temporalio/openai-agents';\nimport { bundleWorkflowCode } from '@temporalio/worker';\n\nconst plugin = new OpenAIAgentsPlugin({ modelProvider: new OpenAIProvider() });\nconst bundle = await bundleWorkflowCode({\n workflowsPath: require.resolve('./workflows'),\n plugins: [plugin],\n});\n```\n\n## Import paths\n\nMost applications use two import paths: `@temporalio/openai-agents` in Worker and Client code, and `@temporalio/openai-agents/workflow` in Workflow code. The other subpaths are for tracing setup or manual Worker wiring.\n\n| Import path | Import from | Use for |\n| :----------------------------------------------- | :--------------- | :---------------------------------------------------------- |\n| `@temporalio/openai-agents` | Worker or Client | Plugin setup, MCP providers, model option types |\n| `@temporalio/openai-agents/workflow` | Workflow | Runner, Workflow-safe tools, sessions, MCP handles |\n| `@temporalio/openai-agents/otel` | Worker or Client | Replay-safe OpenTelemetry setup |\n| `@temporalio/openai-agents/workflow-interceptor` | Worker bundling | Manual `workflowInterceptorModules` wiring without a plugin |\n\n`@temporalio/openai-agents` exports `OpenAIAgentsPlugin`, MCP provider classes, `SandboxClientProvider`, model option types, hosted tool credential types, and `DEDICATED_WORKER_FAILURE_TYPE`. `OpenAIAgentsTraceClientInterceptor` is also public for Clients that need manual interceptor wiring instead of plugin registration.\n\n`@temporalio/openai-agents/workflow` exports Workflow-safe APIs: `TemporalOpenAIRunner`, `WorkflowSafeMemorySession`, `activityAsTool`, `nexusOperationAsTool`, `agentAsTool`, `statelessMcpServer`, `statefulMcpServer`, `temporalSandboxClient`, `workerEnvValue`, related option/definition types, and `DEDICATED_WORKER_FAILURE_TYPE`.\n\n`@temporalio/openai-agents/otel` exports `createTracerProvider`, `TemporalIdGenerator`, `markReplaySafeTracerProvider`, `isReplaySafeTracerProvider`, and `TemporalOpenAIAgentsTracerProviderOptions`. Install optional peer dependency `@opentelemetry/sdk-trace-base` only when you use this OTel integration.\n\n`@temporalio/openai-agents/workflow-interceptor` exports the Workflow trace interceptor module. The plugin registers it automatically; import this path only when assembling Worker interceptor modules yourself.\n\nThere is no public TypeScript testing subpath.\n\n## Feature support\n\nAny OpenAI Agents SDK `ModelProvider` can be passed as `modelProvider`. The provider runs in the model Activity, never inside the Workflow sandbox.\n\n| Feature | Status | Notes |\n| :---------------------- | :----------------------- | :--------------------------------------------------------------------------------------------- |\n| Multi-turn agents | Supported | Agent loop runs durably in the Workflow |\n| Handoffs | Supported | `Agent` and `handoff()` forms |\n| Inline function tools | Supported | Must be deterministic |\n| Activity-backed tools | Supported | Via `activityAsTool()` |\n| Nexus operation tools | Supported | Via `nexusOperationAsTool()` |\n| Nested agent tools | Supported | Via `agentAsTool()` |\n| Hosted tools | Supported | Executed server-side by the model provider |\n| Stateless MCP servers | Supported | Via `StatelessMCPServerProvider` and `statelessMcpServer()` |\n| Stateful MCP servers | Supported | Via `StatefulMCPServerProvider` and `statefulMcpServer()` |\n| Sandbox agents | Supported (experimental) | Via `SandboxClientProvider` and `temporalSandboxClient()` |\n| Sessions | Supported | Via `WorkflowSafeMemorySession`; upstream `MemorySession` is rejected |\n| Run state and approvals | Supported | Serialize with `result.state.toString()` and rehydrate with `RunState.fromString` |\n| Guardrails | Supported | Guardrail callbacks must be deterministic |\n| Tracing | Supported | OpenAI hosted traces, custom `TracingProcessor`s, OTel, and optional `temporal:*` spans |\n| Agent context | Supported | Activity tools receive a copy |\n| `continueAsNew` | Supported | Plugin config propagates to the continuation |\n| Child Workflows | Supported | Plugin config propagates to children |\n| Local Activities | Supported | Set `useLocalActivity: true` in `modelParams` |\n| Model override per run | Supported | `runConfig.model` accepts a string model name |\n| Streaming | Supported (experimental) | `run(agent, input, { stream: true })`; requires `streamingTopic` and a hosted `WorkflowStream` |\n| Voice agents | Not supported | |\n","readmeFilename":""}