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calls streamSimple via the loop.\n */\n\nimport {\n\tgetModel,\n\ttype ImageContent,\n\ttype Message,\n\ttype Model,\n\ttype SimpleStreamOptions,\n\tstreamSimple,\n\ttype TextContent,\n\ttype ThinkingBudgets,\n\ttype Transport,\n} from \"@mariozechner/pi-ai\";\nimport { runAgentLoop, runAgentLoopContinue } from \"./agent-loop.js\";\nimport type {\n\tAfterToolCallContext,\n\tAfterToolCallResult,\n\tAgentContext,\n\tAgentEvent,\n\tAgentLoopConfig,\n\tAgentMessage,\n\tAgentState,\n\tAgentTool,\n\tBeforeToolCallContext,\n\tBeforeToolCallResult,\n\tStreamFn,\n\tThinkingLevel,\n\tToolExecutionMode,\n} from \"./types.js\";\n\n/**\n * Default convertToLlm: Keep only LLM-compatible messages, convert attachments.\n */\nfunction defaultConvertToLlm(messages: AgentMessage[]): Message[] {\n\treturn messages.filter((m) => m.role === \"user\" || m.role === \"assistant\" || m.role === \"toolResult\");\n}\n\nexport interface AgentOptions {\n\tinitialState?: Partial<AgentState>;\n\n\t/**\n\t * Converts AgentMessage[] to LLM-compatible Message[] before each LLM call.\n\t * Default filters to user/assistant/toolResult and converts attachments.\n\t */\n\tconvertToLlm?: (messages: AgentMessage[]) => Message[] | Promise<Message[]>;\n\n\t/**\n\t * Optional transform applied to context before convertToLlm.\n\t * Use for context pruning, injecting external context, etc.\n\t */\n\ttransformContext?: (messages: AgentMessage[], signal?: AbortSignal) => Promise<AgentMessage[]>;\n\n\t/**\n\t * Steering mode: \"all\" = send all steering messages at once, \"one-at-a-time\" = one per turn\n\t */\n\tsteeringMode?: \"all\" | \"one-at-a-time\";\n\n\t/**\n\t * Follow-up mode: \"all\" = send all follow-up messages at once, \"one-at-a-time\" = one per turn\n\t */\n\tfollowUpMode?: \"all\" | \"one-at-a-time\";\n\n\t/**\n\t * Custom stream function (for proxy backends, etc.). Default uses streamSimple.\n\t */\n\tstreamFn?: StreamFn;\n\n\t/**\n\t * Optional session identifier forwarded to LLM providers.\n\t * Used by providers that support session-based caching (e.g., OpenAI Codex).\n\t */\n\tsessionId?: string;\n\n\t/**\n\t * Resolves an API key dynamically for each LLM call.\n\t * Useful for expiring tokens (e.g., GitHub Copilot OAuth).\n\t */\n\tgetApiKey?: (provider: string) => Promise<string | undefined> | string | undefined;\n\n\t/**\n\t * Inspect or replace provider payloads before they are sent.\n\t */\n\tonPayload?: SimpleStreamOptions[\"onPayload\"];\n\n\t/**\n\t * Custom token budgets for thinking levels (token-based providers only).\n\t */\n\tthinkingBudgets?: ThinkingBudgets;\n\n\t/**\n\t * Preferred transport for providers that support multiple transports.\n\t */\n\ttransport?: Transport;\n\n\t/**\n\t * Maximum delay in milliseconds to wait for a retry when the server requests a long wait.\n\t * If the server's requested delay exceeds this value, the request fails immediately,\n\t * allowing higher-level retry logic to handle it with user visibility.\n\t * Default: 60000 (60 seconds). Set to 0 to disable the cap.\n\t */\n\tmaxRetryDelayMs?: number;\n\n\t/** Tool execution mode. Default: \"parallel\" */\n\ttoolExecution?: ToolExecutionMode;\n\n\t/** Called before a tool is executed, after arguments have been validated. */\n\tbeforeToolCall?: (context: BeforeToolCallContext, signal?: AbortSignal) => Promise<BeforeToolCallResult | undefined>;\n\n\t/** Called after a tool finishes executing, before final tool events are emitted. */\n\tafterToolCall?: (context: AfterToolCallContext, signal?: AbortSignal) => Promise<AfterToolCallResult | undefined>;\n}\n\nexport class Agent {\n\tprivate _state: AgentState = {\n\t\tsystemPrompt: \"\",\n\t\tmodel: getModel(\"google\", \"gemini-2.5-flash-lite-preview-06-17\"),\n\t\tthinkingLevel: \"off\",\n\t\ttools: [],\n\t\tmessages: [],\n\t\tisStreaming: false,\n\t\tstreamMessage: null,\n\t\tpendingToolCalls: new Set<string>(),\n\t\terror: undefined,\n\t};\n\n\tprivate listeners = new Set<(e: AgentEvent) => void>();\n\tprivate abortController?: AbortController;\n\tprivate convertToLlm: (messages: AgentMessage[]) => Message[] | Promise<Message[]>;\n\tprivate transformContext?: (messages: AgentMessage[], signal?: AbortSignal) => Promise<AgentMessage[]>;\n\tprivate steeringQueue: AgentMessage[] = [];\n\tprivate followUpQueue: AgentMessage[] = [];\n\tprivate steeringMode: \"all\" | \"one-at-a-time\";\n\tprivate followUpMode: \"all\" | \"one-at-a-time\";\n\tpublic streamFn: StreamFn;\n\tprivate _sessionId?: string;\n\tpublic getApiKey?: (provider: string) => Promise<string | undefined> | string | undefined;\n\tprivate _onPayload?: SimpleStreamOptions[\"onPayload\"];\n\tprivate runningPrompt?: Promise<void>;\n\tprivate resolveRunningPrompt?: () => void;\n\tprivate _thinkingBudgets?: ThinkingBudgets;\n\tprivate _transport: Transport;\n\tprivate _maxRetryDelayMs?: number;\n\tprivate _toolExecution: ToolExecutionMode;\n\tprivate _beforeToolCall?: (\n\t\tcontext: BeforeToolCallContext,\n\t\tsignal?: AbortSignal,\n\t) => Promise<BeforeToolCallResult | undefined>;\n\tprivate _afterToolCall?: (\n\t\tcontext: AfterToolCallContext,\n\t\tsignal?: AbortSignal,\n\t) => Promise<AfterToolCallResult | undefined>;\n\n\tconstructor(opts: AgentOptions = {}) {\n\t\tthis._state = { ...this._state, ...opts.initialState };\n\t\tthis.convertToLlm = opts.convertToLlm || defaultConvertToLlm;\n\t\tthis.transformContext = opts.transformContext;\n\t\tthis.steeringMode = opts.steeringMode || \"one-at-a-time\";\n\t\tthis.followUpMode = opts.followUpMode || \"one-at-a-time\";\n\t\tthis.streamFn = opts.streamFn || streamSimple;\n\t\tthis._sessionId = opts.sessionId;\n\t\tthis.getApiKey = opts.getApiKey;\n\t\tthis._onPayload = opts.onPayload;\n\t\tthis._thinkingBudgets = opts.thinkingBudgets;\n\t\tthis._transport = opts.transport ?? \"sse\";\n\t\tthis._maxRetryDelayMs = opts.maxRetryDelayMs;\n\t\tthis._toolExecution = opts.toolExecution ?? \"parallel\";\n\t\tthis._beforeToolCall = opts.beforeToolCall;\n\t\tthis._afterToolCall = opts.afterToolCall;\n\t}\n\n\t/**\n\t * Get the current session ID used for provider caching.\n\t */\n\tget sessionId(): string | undefined {\n\t\treturn this._sessionId;\n\t}\n\n\t/**\n\t * Set the session ID for provider caching.\n\t * Call this when switching sessions (new session, branch, resume).\n\t */\n\tset sessionId(value: string | undefined) {\n\t\tthis._sessionId = value;\n\t}\n\n\t/**\n\t * Get the current thinking budgets.\n\t */\n\tget thinkingBudgets(): ThinkingBudgets | undefined {\n\t\treturn this._thinkingBudgets;\n\t}\n\n\t/**\n\t * Set custom thinking budgets for token-based providers.\n\t */\n\tset thinkingBudgets(value: ThinkingBudgets | undefined) {\n\t\tthis._thinkingBudgets = value;\n\t}\n\n\t/**\n\t * Get the current preferred transport.\n\t */\n\tget transport(): Transport {\n\t\treturn this._transport;\n\t}\n\n\t/**\n\t * Set the preferred transport.\n\t */\n\tsetTransport(value: Transport) {\n\t\tthis._transport = value;\n\t}\n\n\t/**\n\t * Get the current max retry delay in milliseconds.\n\t */\n\tget maxRetryDelayMs(): number | undefined {\n\t\treturn this._maxRetryDelayMs;\n\t}\n\n\t/**\n\t * Set the maximum delay to wait for server-requested retries.\n\t * Set to 0 to disable the cap.\n\t */\n\tset maxRetryDelayMs(value: number | undefined) {\n\t\tthis._maxRetryDelayMs = value;\n\t}\n\n\tget toolExecution(): ToolExecutionMode {\n\t\treturn this._toolExecution;\n\t}\n\n\tsetToolExecution(value: ToolExecutionMode) {\n\t\tthis._toolExecution = value;\n\t}\n\n\tsetBeforeToolCall(\n\t\tvalue:\n\t\t\t| ((context: BeforeToolCallContext, signal?: AbortSignal) => Promise<BeforeToolCallResult | undefined>)\n\t\t\t| undefined,\n\t) {\n\t\tthis._beforeToolCall = value;\n\t}\n\n\tsetAfterToolCall(\n\t\tvalue:\n\t\t\t| ((context: AfterToolCallContext, signal?: AbortSignal) => Promise<AfterToolCallResult | undefined>)\n\t\t\t| undefined,\n\t) {\n\t\tthis._afterToolCall = value;\n\t}\n\n\tget state(): AgentState {\n\t\treturn this._state;\n\t}\n\n\tsubscribe(fn: (e: AgentEvent) => void): () => void {\n\t\tthis.listeners.add(fn);\n\t\treturn () => this.listeners.delete(fn);\n\t}\n\n\t// State mutators\n\tsetSystemPrompt(v: string) {\n\t\tthis._state.systemPrompt = v;\n\t}\n\n\tsetModel(m: Model<any>) {\n\t\tthis._state.model = m;\n\t}\n\n\tsetThinkingLevel(l: ThinkingLevel) {\n\t\tthis._state.thinkingLevel = l;\n\t}\n\n\tsetSteeringMode(mode: \"all\" | \"one-at-a-time\") {\n\t\tthis.steeringMode = mode;\n\t}\n\n\tgetSteeringMode(): \"all\" | \"one-at-a-time\" {\n\t\treturn this.steeringMode;\n\t}\n\n\tsetFollowUpMode(mode: \"all\" | \"one-at-a-time\") {\n\t\tthis.followUpMode = mode;\n\t}\n\n\tgetFollowUpMode(): \"all\" | \"one-at-a-time\" {\n\t\treturn this.followUpMode;\n\t}\n\n\tsetTools(t: AgentTool<any>[]) {\n\t\tthis._state.tools = t;\n\t}\n\n\treplaceMessages(ms: AgentMessage[]) {\n\t\tthis._state.messages = ms.slice();\n\t}\n\n\tappendMessage(m: AgentMessage) {\n\t\tthis._state.messages = [...this._state.messages, m];\n\t}\n\n\t/**\n\t * Queue a steering message while the agent is running.\n\t * Delivered after the current assistant turn finishes executing its tool calls,\n\t * before the next LLM call.\n\t */\n\tsteer(m: AgentMessage) {\n\t\tthis.steeringQueue.push(m);\n\t}\n\n\t/**\n\t * Queue a follow-up message to be processed after the agent finishes.\n\t * Delivered only when agent has no more tool calls or steering messages.\n\t */\n\tfollowUp(m: AgentMessage) {\n\t\tthis.followUpQueue.push(m);\n\t}\n\n\tclearSteeringQueue() {\n\t\tthis.steeringQueue = [];\n\t}\n\n\tclearFollowUpQueue() {\n\t\tthis.followUpQueue = [];\n\t}\n\n\tclearAllQueues() {\n\t\tthis.steeringQueue = [];\n\t\tthis.followUpQueue = [];\n\t}\n\n\thasQueuedMessages(): boolean {\n\t\treturn this.steeringQueue.length > 0 || this.followUpQueue.length > 0;\n\t}\n\n\tprivate dequeueSteeringMessages(): AgentMessage[] {\n\t\tif (this.steeringMode === \"one-at-a-time\") {\n\t\t\tif (this.steeringQueue.length > 0) {\n\t\t\t\tconst first = this.steeringQueue[0];\n\t\t\t\tthis.steeringQueue = this.steeringQueue.slice(1);\n\t\t\t\treturn [first];\n\t\t\t}\n\t\t\treturn [];\n\t\t}\n\n\t\tconst steering = this.steeringQueue.slice();\n\t\tthis.steeringQueue = [];\n\t\treturn steering;\n\t}\n\n\tprivate dequeueFollowUpMessages(): AgentMessage[] {\n\t\tif (this.followUpMode === \"one-at-a-time\") {\n\t\t\tif (this.followUpQueue.length > 0) {\n\t\t\t\tconst first = this.followUpQueue[0];\n\t\t\t\tthis.followUpQueue = this.followUpQueue.slice(1);\n\t\t\t\treturn [first];\n\t\t\t}\n\t\t\treturn [];\n\t\t}\n\n\t\tconst followUp = this.followUpQueue.slice();\n\t\tthis.followUpQueue = [];\n\t\treturn followUp;\n\t}\n\n\tclearMessages() {\n\t\tthis._state.messages = [];\n\t}\n\n\tabort() {\n\t\tthis.abortController?.abort();\n\t}\n\n\twaitForIdle(): Promise<void> {\n\t\treturn this.runningPrompt ?? Promise.resolve();\n\t}\n\n\treset() {\n\t\tthis._state.messages = [];\n\t\tthis._state.isStreaming = false;\n\t\tthis._state.streamMessage = null;\n\t\tthis._state.pendingToolCalls = new Set<string>();\n\t\tthis._state.error = undefined;\n\t\tthis.steeringQueue = [];\n\t\tthis.followUpQueue = [];\n\t}\n\n\t/** Send a prompt with an AgentMessage */\n\tasync prompt(message: AgentMessage | AgentMessage[]): Promise<void>;\n\tasync prompt(input: string, images?: ImageContent[]): Promise<void>;\n\tasync prompt(input: string | AgentMessage | AgentMessage[], images?: ImageContent[]) {\n\t\tif (this._state.isStreaming) {\n\t\t\tthrow new Error(\n\t\t\t\t\"Agent is already processing a prompt. Use steer() or followUp() to queue messages, or wait for completion.\",\n\t\t\t);\n\t\t}\n\n\t\tconst model = this._state.model;\n\t\tif (!model) throw new Error(\"No model configured\");\n\n\t\tlet msgs: AgentMessage[];\n\n\t\tif (Array.isArray(input)) {\n\t\t\tmsgs = input;\n\t\t} else if (typeof input === \"string\") {\n\t\t\tconst content: Array<TextContent | ImageContent> = [{ type: \"text\", text: input }];\n\t\t\tif (images && images.length > 0) {\n\t\t\t\tcontent.push(...images);\n\t\t\t}\n\t\t\tmsgs = [\n\t\t\t\t{\n\t\t\t\t\trole: \"user\",\n\t\t\t\t\tcontent,\n\t\t\t\t\ttimestamp: Date.now(),\n\t\t\t\t},\n\t\t\t];\n\t\t} else {\n\t\t\tmsgs = [input];\n\t\t}\n\n\t\tawait this._runLoop(msgs);\n\t}\n\n\t/**\n\t * Continue from current context (used for retries and resuming queued messages).\n\t */\n\tasync continue() {\n\t\tif (this._state.isStreaming) {\n\t\t\tthrow new Error(\"Agent is already processing. Wait for completion before continuing.\");\n\t\t}\n\n\t\tconst messages = this._state.messages;\n\t\tif (messages.length === 0) {\n\t\t\tthrow new Error(\"No messages to continue from\");\n\t\t}\n\t\tif (messages[messages.length - 1].role === \"assistant\") {\n\t\t\tconst queuedSteering = this.dequeueSteeringMessages();\n\t\t\tif (queuedSteering.length > 0) {\n\t\t\t\tawait this._runLoop(queuedSteering, { skipInitialSteeringPoll: true });\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\tconst queuedFollowUp = this.dequeueFollowUpMessages();\n\t\t\tif (queuedFollowUp.length > 0) {\n\t\t\t\tawait this._runLoop(queuedFollowUp);\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\tthrow new Error(\"Cannot continue from message role: assistant\");\n\t\t}\n\n\t\tawait this._runLoop(undefined);\n\t}\n\n\tprivate _processLoopEvent(event: AgentEvent): void {\n\t\tswitch (event.type) {\n\t\t\tcase \"message_start\":\n\t\t\t\tthis._state.streamMessage = event.message;\n\t\t\t\tbreak;\n\n\t\t\tcase \"message_update\":\n\t\t\t\tthis._state.streamMessage = event.message;\n\t\t\t\tbreak;\n\n\t\t\tcase \"message_end\":\n\t\t\t\tthis._state.streamMessage = null;\n\t\t\t\tthis.appendMessage(event.message);\n\t\t\t\tbreak;\n\n\t\t\tcase \"tool_execution_start\": {\n\t\t\t\tconst pendingToolCalls = new Set(this._state.pendingToolCalls);\n\t\t\t\tpendingToolCalls.add(event.toolCallId);\n\t\t\t\tthis._state.pendingToolCalls = pendingToolCalls;\n\t\t\t\tbreak;\n\t\t\t}\n\n\t\t\tcase \"tool_execution_end\": {\n\t\t\t\tconst pendingToolCalls = new Set(this._state.pendingToolCalls);\n\t\t\t\tpendingToolCalls.delete(event.toolCallId);\n\t\t\t\tthis._state.pendingToolCalls = pendingToolCalls;\n\t\t\t\tbreak;\n\t\t\t}\n\n\t\t\tcase \"turn_end\":\n\t\t\t\tif (event.message.role === \"assistant\" && (event.message as any).errorMessage) {\n\t\t\t\t\tthis._state.error = (event.message as any).errorMessage;\n\t\t\t\t}\n\t\t\t\tbreak;\n\n\t\t\tcase \"agent_end\":\n\t\t\t\tthis._state.isStreaming = false;\n\t\t\t\tthis._state.streamMessage = null;\n\t\t\t\tbreak;\n\t\t}\n\n\t\tthis.emit(event);\n\t}\n\n\t/**\n\t * Run the agent loop.\n\t * If messages are provided, starts a new conversation turn with those messages.\n\t * Otherwise, continues from existing context.\n\t */\n\tprivate async _runLoop(messages?: AgentMessage[], options?: { skipInitialSteeringPoll?: boolean }) {\n\t\tconst model = this._state.model;\n\t\tif (!model) throw new Error(\"No model configured\");\n\n\t\tthis.runningPrompt = new Promise<void>((resolve) => {\n\t\t\tthis.resolveRunningPrompt = resolve;\n\t\t});\n\n\t\tthis.abortController = new AbortController();\n\t\tthis._state.isStreaming = true;\n\t\tthis._state.streamMessage = null;\n\t\tthis._state.error = undefined;\n\n\t\tconst reasoning = this._state.thinkingLevel === \"off\" ? undefined : this._state.thinkingLevel;\n\n\t\tconst context: AgentContext = {\n\t\t\tsystemPrompt: this._state.systemPrompt,\n\t\t\tmessages: this._state.messages.slice(),\n\t\t\ttools: this._state.tools,\n\t\t};\n\n\t\tlet skipInitialSteeringPoll = options?.skipInitialSteeringPoll === true;\n\n\t\tconst config: AgentLoopConfig = {\n\t\t\tmodel,\n\t\t\treasoning,\n\t\t\tsessionId: this._sessionId,\n\t\t\tonPayload: this._onPayload,\n\t\t\ttransport: this._transport,\n\t\t\tthinkingBudgets: this._thinkingBudgets,\n\t\t\tmaxRetryDelayMs: this._maxRetryDelayMs,\n\t\t\ttoolExecution: this._toolExecution,\n\t\t\tbeforeToolCall: this._beforeToolCall,\n\t\t\tafterToolCall: this._afterToolCall,\n\t\t\tconvertToLlm: this.convertToLlm,\n\t\t\ttransformContext: this.transformContext,\n\t\t\tgetApiKey: this.getApiKey,\n\t\t\tgetSteeringMessages: async () => {\n\t\t\t\tif (skipInitialSteeringPoll) {\n\t\t\t\t\tskipInitialSteeringPoll = false;\n\t\t\t\t\treturn [];\n\t\t\t\t}\n\t\t\t\treturn this.dequeueSteeringMessages();\n\t\t\t},\n\t\t\tgetFollowUpMessages: async () => this.dequeueFollowUpMessages(),\n\t\t};\n\n\t\ttry {\n\t\t\tif (messages) {\n\t\t\t\tawait runAgentLoop(\n\t\t\t\t\tmessages,\n\t\t\t\t\tcontext,\n\t\t\t\t\tconfig,\n\t\t\t\t\tasync (event) => this._processLoopEvent(event),\n\t\t\t\t\tthis.abortController.signal,\n\t\t\t\t\tthis.streamFn,\n\t\t\t\t);\n\t\t\t} else {\n\t\t\t\tawait runAgentLoopContinue(\n\t\t\t\t\tcontext,\n\t\t\t\t\tconfig,\n\t\t\t\t\tasync (event) => this._processLoopEvent(event),\n\t\t\t\t\tthis.abortController.signal,\n\t\t\t\t\tthis.streamFn,\n\t\t\t\t);\n\t\t\t}\n\t\t} catch (err: any) {\n\t\t\tconst errorMsg: AgentMessage = {\n\t\t\t\trole: \"assistant\",\n\t\t\t\tcontent: [{ type: \"text\", text: \"\" }],\n\t\t\t\tapi: model.api,\n\t\t\t\tprovider: model.provider,\n\t\t\t\tmodel: model.id,\n\t\t\t\tusage: {\n\t\t\t\t\tinput: 0,\n\t\t\t\t\toutput: 0,\n\t\t\t\t\tcacheRead: 0,\n\t\t\t\t\tcacheWrite: 0,\n\t\t\t\t\ttotalTokens: 0,\n\t\t\t\t\tcost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0, total: 0 },\n\t\t\t\t},\n\t\t\t\tstopReason: this.abortController?.signal.aborted ? \"aborted\" : \"error\",\n\t\t\t\terrorMessage: err?.message || String(err),\n\t\t\t\ttimestamp: Date.now(),\n\t\t\t} as AgentMessage;\n\n\t\t\tthis.appendMessage(errorMsg);\n\t\t\tthis._state.error = err?.message || String(err);\n\t\t\tthis.emit({ type: \"agent_end\", messages: [errorMsg] });\n\t\t} finally {\n\t\t\tthis._state.isStreaming = false;\n\t\t\tthis._state.streamMessage = null;\n\t\t\tthis._state.pendingToolCalls = new Set<string>();\n\t\t\tthis.abortController = undefined;\n\t\t\tthis.resolveRunningPrompt?.();\n\t\t\tthis.runningPrompt = undefined;\n\t\t\tthis.resolveRunningPrompt = undefined;\n\t\t}\n\t}\n\n\tprivate emit(e: AgentEvent) {\n\t\tfor (const listener of this.listeners) {\n\t\t\tlistener(e);\n\t\t}\n\t}\n}\n"]}