feat(agent): added agent tool-call cap enforcement to stream loop

- Added `maxToolCallsPerTurn` support to `AgentOptions` and `AgentLoopConfig`, with Agent getter/setter and serialized state wiring.
- Implemented stream-loop cap handling by normalizing bad values and halting after `toolcall_end` reaches the limit.
- Added `ANTHROPIC_TOOL_CALL_BATCH_CAP`=8 and wired session cap sync on init, model changes, and restore.
- Added tests that truncated a 10-call stream to 8 tool calls, and verified non-Claude models resolve no cap.

packages/agent/CHANGELOG.md

@@ -1,6 +1,13 @@
 # Changelog
 
 ## [Unreleased]
+### Added
+
+- Added `maxToolCallsPerTurn` to `AgentLoopConfig`/`AgentOptions`, allowing callers to cut a streamed assistant turn after a completed tool-call batch and execute the runnable partial turn instead of waiting for the provider to yield.
+
+### Fixed
+
+- Normalized `maxToolCallsPerTurn` to accept only positive integer limits, with non-finite or non-positive values treated as disabled
 
 ## [15.5.14] - 2026-05-29
 

packages/agent/src/agent-loop.ts

@@ -441,6 +441,27 @@ interface StepCounter {
 	count: number;
 }
 
+function normalizeMaxToolCallsPerTurn(value: number | undefined): number | undefined {
+	if (value === undefined || !Number.isFinite(value)) return undefined;
+	const normalized = Math.trunc(value);
+	return normalized > 0 ? normalized : undefined;
+}
+
+function cloneAssistantMessageForToolCallCap(message: AssistantMessage): AssistantMessage {
+	return {
+		...message,
+		content: message.content.map(block => {
+			if (block.type === "toolCall") {
+				return { ...block, arguments: structuredClone(block.arguments) };
+			}
+			return { ...block };
+		}),
+		stopReason: "toolUse",
+		errorMessage: undefined,
+		errorStatus: undefined,
+	};
+}
+
 async function runLoopBody(
 	currentContext: AgentContext,
 	newMessages: AgentMessage[],
@@ -712,11 +733,18 @@ async function streamAssistantResponse(
 	const dynamicReasoning = config.getReasoning?.();
 	const harmonyMitigationEnabled = isHarmonyLeakMitigationTarget(config.model);
 	const harmonyAbortController = harmonyMitigationEnabled ? new AbortController() : undefined;
-	const requestSignal = harmonyAbortController
+	const maxToolCallsPerTurn = normalizeMaxToolCallsPerTurn(config.maxToolCallsPerTurn);
-		? signal
+	const toolCallCapAbortController = maxToolCallsPerTurn === undefined ? undefined : new AbortController();
-			? AbortSignal.any([signal, harmonyAbortController.signal])
+	const requestSignals: AbortSignal[] = [];
-			: harmonyAbortController.signal
+	if (signal) requestSignals.push(signal);
-		: signal;
+	if (harmonyAbortController) requestSignals.push(harmonyAbortController.signal);
+	if (toolCallCapAbortController) requestSignals.push(toolCallCapAbortController.signal);
+	const requestSignal =
+		requestSignals.length === 0
+			? undefined
+			: requestSignals.length === 1
+				? requestSignals[0]
+				: AbortSignal.any(requestSignals);
 	const effectiveTemperature =
 		harmonyRetryAttempt > 0 && config.temperature !== undefined ? config.temperature + 0.05 : config.temperature;
 	const effectiveToolChoice = dynamicToolChoice ?? config.toolChoice;
@@ -778,6 +806,26 @@ async function streamAssistantResponse(
 			let addedPartial = false;
 
 			const responseIterator = response[Symbol.asyncIterator]();
+			let completedToolCalls = 0;
+			let cappedMessage: AssistantMessage | undefined;
+			let capFinalized = false;
+
+			const finishCappedAssistantMessage = async (): Promise<AssistantMessage | undefined> => {
+				if (!cappedMessage) return undefined;
+				responseIterator.return?.()?.catch(() => {});
+				if (!capFinalized) {
+					if (addedPartial) {
+						context.messages[context.messages.length - 1] = cappedMessage;
+					} else {
+						context.messages.push(cappedMessage);
+						stream.push({ type: "message_start", message: { ...cappedMessage } });
+					}
+					stream.push({ type: "message_end", message: cappedMessage });
+					await finishChat(cappedMessage);
+					capFinalized = true;
+				}
+				return cappedMessage;
+			};
 
 			// Set up a single abort race: register the abort listener once for the whole
 			// stream and reuse the same race promise for every iterator.next() instead of
@@ -803,6 +851,10 @@ async function streamAssistantResponse(
 					if (abortRacePromise) {
 						const result = await Promise.race([responseIterator.next(), abortRacePromise]);
 						if (result === ABORTED) {
+							if (toolCallCapAbortController?.signal.aborted) {
+								const capped = await finishCappedAssistantMessage();
+								if (capped) return capped;
+							}
 							responseIterator.return?.()?.catch(() => {});
 							const aborted = emitAbortedAssistantMessage(partialMessage, addedPartial, context, config, stream);
 							await finishChat(aborted);
@@ -813,6 +865,10 @@ async function streamAssistantResponse(
 						next = await responseIterator.next();
 					}
 					if (requestSignal?.aborted) {
+						if (toolCallCapAbortController?.signal.aborted) {
+							const capped = await finishCappedAssistantMessage();
+							if (capped) return capped;
+						}
 						const aborted = emitAbortedAssistantMessage(partialMessage, addedPartial, context, config, stream);
 						await finishChat(aborted);
 						return aborted;
@@ -853,6 +909,15 @@ async function streamAssistantResponse(
 									assistantMessageEvent: event,
 									message: { ...partialMessage },
 								});
+								if (event.type === "toolcall_end" && maxToolCallsPerTurn !== undefined) {
+									completedToolCalls++;
+									if (completedToolCalls >= maxToolCallsPerTurn) {
+										cappedMessage = cloneAssistantMessageForToolCallCap(partialMessage);
+										toolCallCapAbortController?.abort();
+										const capped = await finishCappedAssistantMessage();
+										if (capped) return capped;
+									}
+								}
 							}
 							break;
 

packages/agent/src/agent.ts

@@ -102,6 +102,12 @@ export interface AgentOptions {
 	 */
 	interruptMode?: "immediate" | "wait";
 
+	/**
+	 * Maximum completed tool calls to accept from one streamed assistant turn before
+	 * executing the batch. Undefined disables batching.
+	 */
+	maxToolCallsPerTurn?: number;
+
 	/**
 	 * API format for Kimi Code provider: "openai" or "anthropic" (default: "anthropic")
 	 */
@@ -269,6 +275,7 @@ export class Agent {
 	#steeringMode: "all" | "one-at-a-time";
 	#followUpMode: "all" | "one-at-a-time";
 	#interruptMode: "immediate" | "wait";
+	#maxToolCallsPerTurn?: number;
 	#sessionId?: string;
 	#metadata?: Record<string, unknown>;
 	#metadataResolver?: (provider: string) => Record<string, unknown> | undefined;
@@ -325,6 +332,7 @@ export class Agent {
 		this.#steeringMode = opts.steeringMode || "one-at-a-time";
 		this.#followUpMode = opts.followUpMode || "one-at-a-time";
 		this.#interruptMode = opts.interruptMode || "immediate";
+		this.#maxToolCallsPerTurn = opts.maxToolCallsPerTurn;
 		this.streamFn = opts.streamFn || streamSimple;
 		this.#sessionId = opts.sessionId;
 		this.#providerSessionState = opts.providerSessionState;
@@ -547,6 +555,14 @@ export class Agent {
 		this.#maxRetryDelayMs = value;
 	}
 
+	get maxToolCallsPerTurn(): number | undefined {
+		return this.#maxToolCallsPerTurn;
+	}
+
+	set maxToolCallsPerTurn(value: number | undefined) {
+		this.#maxToolCallsPerTurn = value;
+	}
+
 	get state(): AgentState {
 		return this.#state;
 	}
@@ -917,6 +933,7 @@ export class Agent {
 			serviceTier: this.#serviceTier,
 			hideThinkingSummary: this.#hideThinkingSummary,
 			interruptMode: this.#interruptMode,
+			maxToolCallsPerTurn: this.#maxToolCallsPerTurn,
 			sessionId: this.#sessionId,
 			metadata: this.#metadataResolver ? undefined : this.#metadata,
 			metadataResolver: this.#metadataResolver,

packages/agent/src/types.ts

@@ -38,6 +38,14 @@ export interface AgentLoopConfig extends SimpleStreamOptions {
 	 */
 	interruptMode?: "immediate" | "wait";
 
+	/**
+	 * Maximum completed tool calls to accept from one streamed assistant turn before
+	 * cutting the provider stream and executing that batch. The cap is enforced on
+	 * `toolcall_end` so every executed call has complete arguments. Undefined disables
+	 * batching.
+	 */
+	maxToolCallsPerTurn?: number;
+
 	/**
 	 * Optional session identifier forwarded to LLM providers.
 	 * Used by providers that support session-based caching (e.g., OpenAI Codex).

packages/agent/test/agent-loop.test.ts

@@ -7,6 +7,7 @@ import type {
 	AgentMessage,
 	AgentTool,
 	AgentToolContext,
+	StreamFn,
 	ToolCallContext,
 } from "@oh-my-pi/pi-agent-core/types";
 import type { AssistantMessage, Message, ToolResultMessage } from "@oh-my-pi/pi-ai";
@@ -62,7 +63,7 @@ describe("agentLoop with AgentMessage", () => {
 			tools: [],
 		};
 		const mock = createMockModel();
-		const config: AgentLoopConfig = { model: mock.model, convertToLlm: identityConverter };
+		const config: AgentLoopConfig = { model: mock.model, convertToLlm: identityConverter, maxToolCallsPerTurn: 8 };
 		const controller = new AbortController();
 		// The mock provider would reject without a configured response; we want the
 		// agent's abort path to kick in before any event is emitted. Use a raw stream
@@ -273,6 +274,118 @@ describe("agentLoop with AgentMessage", () => {
 		}
 	});
 
+	it("cuts a streamed assistant turn after the configured completed tool-call batch", async () => {
+		const toolSchema = z.object({ value: z.string() });
+		const executed: string[] = [];
+		const tool: AgentTool<typeof toolSchema, { value: string }> = {
+			name: "echo",
+			label: "Echo",
+			description: "Echo tool",
+			parameters: toolSchema,
+			async execute(_toolCallId, params) {
+				executed.push(params.value);
+				return {
+					content: [{ type: "text", text: `echoed: ${params.value}` }],
+					details: { value: params.value },
+				};
+			},
+		};
+
+		const context: AgentContext = { systemPrompt: [""], messages: [], tools: [tool] };
+		const mock = createMockModel();
+		let modelCalls = 0;
+		let firstRequestSignal: AbortSignal | undefined;
+
+		const makeToolCall = (index: number): AssistantMessage["content"][number] => ({
+			type: "toolCall",
+			id: `tool-${index}`,
+			name: "echo",
+			arguments: { value: String(index) },
+		});
+		const makeMessage = (count: number, stopReason: AssistantMessage["stopReason"] = "stop") =>
+			createAssistantMessage(
+				Array.from({ length: count }, (_, index) => makeToolCall(index + 1)),
+				stopReason,
+			);
+
+		const streamFn: StreamFn = (_model, _llmContext, options) => {
+			modelCalls++;
+			const stream = new AssistantMessageEventStream();
+			if (modelCalls > 1) {
+				queueMicrotask(() => {
+					const done = createAssistantMessage([{ type: "text", text: "done" }], "stop");
+					stream.push({ type: "start", partial: done });
+					stream.push({ type: "text_start", contentIndex: 0, partial: done });
+					stream.push({ type: "text_delta", contentIndex: 0, delta: "done", partial: done });
+					stream.push({ type: "text_end", contentIndex: 0, content: "done", partial: done });
+					stream.push({ type: "done", reason: "stop", message: done });
+				});
+				return stream;
+			}
+
+			queueMicrotask(async () => {
+				firstRequestSignal = options?.signal;
+				stream.push({ type: "start", partial: makeMessage(0) });
+				for (let index = 1; index <= 10; index++) {
+					if (options?.signal?.aborted) {
+						const aborted = createAssistantMessage([], "aborted");
+						stream.push({ type: "error", reason: "aborted", error: aborted });
+						return;
+					}
+					const partial = makeMessage(index);
+					const toolCall = partial.content[index - 1];
+					if (!toolCall || toolCall.type !== "toolCall") throw new Error("Expected tool call");
+					stream.push({ type: "toolcall_start", contentIndex: index - 1, partial });
+					stream.push({
+						type: "toolcall_delta",
+						contentIndex: index - 1,
+						delta: JSON.stringify(toolCall.arguments),
+						partial,
+					});
+					stream.push({ type: "toolcall_end", contentIndex: index - 1, toolCall, partial });
+					await Bun.sleep(0);
+				}
+				stream.push({ type: "done", reason: "toolUse", message: makeMessage(10, "toolUse") });
+			});
+			return stream;
+		};
+
+		const config: AgentLoopConfig = {
+			model: mock.model,
+			convertToLlm: identityConverter,
+			maxToolCallsPerTurn: 8,
+		};
+
+		const events: AgentEvent[] = [];
+		const stream = agentLoop([createUserMessage("echo many")], context, config, undefined, streamFn);
+		for await (const event of stream) {
+			events.push(event);
+		}
+
+		expect(executed).toEqual(["1", "2", "3", "4", "5", "6", "7", "8"]);
+		expect(firstRequestSignal?.aborted).toBe(true);
+		expect(modelCalls).toBe(2);
+
+		const batchedTurn = events.find(
+			(event): event is Extract<AgentEvent, { type: "turn_end" }> =>
+				event.type === "turn_end" && event.toolResults.length === 8,
+		);
+		expect(batchedTurn).toBeDefined();
+		if (!batchedTurn || batchedTurn.message.role !== "assistant") return;
+		expect(batchedTurn.message.stopReason).toBe("toolUse");
+		expect(batchedTurn.message.content.filter(block => block.type === "toolCall")).toHaveLength(8);
+		expect(batchedTurn.toolResults.map(result => result.toolCallId).sort()).toEqual([
+			"tool-1",
+			"tool-2",
+			"tool-3",
+			"tool-4",
+			"tool-5",
+			"tool-6",
+			"tool-7",
+			"tool-8",
+		]);
+	});
+
 	it("injects and strips intent when intent tracing is enabled", async () => {
 		const toolSchema = z.object({ value: z.string() });
 		const executedParams: Record<string, unknown>[] = [];

packages/coding-agent/CHANGELOG.md

@@ -1,11 +1,18 @@
 # Changelog
 
 ## [Unreleased]
+### Changed
+
+- Enabled the agent loop's tool-call batch cap for Anthropic Claude sessions, cutting oversized streamed tool-use bursts into runnable batches before continuing the conversation.
 
 ### Removed
 
 - Removed the `calc` tool (deterministic arithmetic evaluator) and its `calc.enabled` setting. The model can compute via `eval` instead.
 
+### Fixed
+
+- Fixed Anthropic Claude tool-call batching to clear and reapply the Claude-specific batch cap whenever the session model changes
+
 ## [15.5.14] - 2026-05-29
 ### Added
 

packages/coding-agent/src/session/agent-session.ts

@@ -453,6 +453,15 @@ function formatRetryFallbackBaseSelector(selector: RetryFallbackSelector): strin
 }
 
 const IRC_REPLY_MAX_BYTES = 4096;
+export const ANTHROPIC_TOOL_CALL_BATCH_CAP = 4;
+const CLAUDE_OPUS_4_8_MODEL_ID = /(?:^|[./_-])claude-opus-4[.-]8\b/i;
+
+export function resolveToolCallBatchCapForModel(model: Model | undefined): number | undefined {
+	if (!model) return undefined;
+	return model.provider === "anthropic" && CLAUDE_OPUS_4_8_MODEL_ID.test(model.id)
+		? ANTHROPIC_TOOL_CALL_BATCH_CAP
+		: undefined;
+}
 
 /**
  * Collapse degenerate IRC ephemeral replies before they hit the relay.
@@ -993,6 +1002,10 @@ export class AgentSession {
 		this.#flushPendingAgentEnd();
 	}
 
+	#syncToolCallBatchCap(model: Model | undefined = this.model): void {
+		this.agent.maxToolCallsPerTurn = resolveToolCallBatchCapForModel(model);
+	}
+
 	#flushPendingAgentEnd(): void {
 		const pending = this.#pendingAgentEndEmit;
 		if (!pending) return;
@@ -1097,6 +1110,7 @@ export class AgentSession {
 		this.#agentId = config.agentId;
 		this.#agentRegistry = config.agentRegistry;
 		this.#providerSessionId = config.providerSessionId;
+		this.#syncToolCallBatchCap();
 		this.agent.setAssistantMessageEventInterceptor((message, assistantMessageEvent) => {
 			const event: AgentEvent = {
 				type: "message_update",
@@ -6162,6 +6176,7 @@ export class AgentSession {
 			this.#closeProviderSessionsForModelSwitch(currentModel, model);
 		}
 		this.agent.setModel(model);
+		this.#syncToolCallBatchCap(model);
 
 		// Re-evaluate append-only context mode — provider or setting may have changed
 		this.#syncAppendOnlyContext(model);
@@ -8214,6 +8229,7 @@ export class AgentSession {
 							this.#setModelWithProviderSessionReset(match);
 						} else {
 							this.agent.setModel(match);
+							this.#syncToolCallBatchCap(match);
 						}
 					}
 				}
@@ -8272,6 +8288,9 @@ export class AgentSession {
 			this.#scheduledHiddenNextTurnGeneration = previousScheduledHiddenNextTurnGeneration;
 			if (previousModel) {
 				this.agent.setModel(previousModel);
+				this.#syncToolCallBatchCap(previousModel);
+			} else {
+				this.#syncToolCallBatchCap(undefined);
 			}
 			this.#thinkingLevel = previousThinkingLevel;
 			this.agent.setThinkingLevel(toReasoningEffort(previousThinkingLevel));

packages/coding-agent/test/agent-session-message-pipeline.test.ts

@@ -9,7 +9,12 @@ import {
 } from "@oh-my-pi/pi-ai";
 import { AssistantMessageEventStream } from "@oh-my-pi/pi-ai/utils/event-stream";
 import { Settings } from "@oh-my-pi/pi-coding-agent/config/settings";
-import { AgentSession, type AgentSessionEvent } from "@oh-my-pi/pi-coding-agent/session/agent-session";
+import {
+	AgentSession,
+	type AgentSessionEvent,
+	ANTHROPIC_TOOL_CALL_BATCH_CAP,
+	resolveToolCallBatchCapForModel,
+} from "@oh-my-pi/pi-coding-agent/session/agent-session";
 import { SessionManager } from "@oh-my-pi/pi-coding-agent/session/session-manager";
 import { createAssistantMessage } from "./helpers/agent-session-setup";
 
@@ -34,6 +39,43 @@ describe("AgentSession message pipeline", () => {
 		}
 	});
 
+	it("enables the tool-call batch cap only for Anthropic Claude Opus 4.8 models", () => {
+		const baseModel: Model = {
+			id: "gpt-5",
+			name: "GPT-5",
+			api: "openai-responses",
+			provider: "openai",
+			baseUrl: "",
+			reasoning: true,
+			input: ["text"],
+			cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
+			contextWindow: 200_000,
+			maxTokens: 8_192,
+		};
+		const anthropicOpus48: Model = {
+			...baseModel,
+			id: "claude-opus-4-8",
+			name: "Claude Opus 4.8",
+			api: "anthropic",
+			provider: "anthropic",
+		};
+
+		expect(resolveToolCallBatchCapForModel(anthropicOpus48)).toBe(ANTHROPIC_TOOL_CALL_BATCH_CAP);
+		expect(resolveToolCallBatchCapForModel({ ...anthropicOpus48, id: "claude-opus-4.8" })).toBe(
+			ANTHROPIC_TOOL_CALL_BATCH_CAP,
+		);
+		expect(resolveToolCallBatchCapForModel({ ...anthropicOpus48, id: "claude-opus-4-8-20260530" })).toBe(
+			ANTHROPIC_TOOL_CALL_BATCH_CAP,
+		);
+		expect(resolveToolCallBatchCapForModel({ ...anthropicOpus48, provider: "openrouter" })).toBeUndefined();
+		expect(resolveToolCallBatchCapForModel({ ...anthropicOpus48, id: "claude-sonnet-4-8" })).toBeUndefined();
+		expect(resolveToolCallBatchCapForModel({ ...anthropicOpus48, id: "claude-opus-4-7" })).toBeUndefined();
+		expect(resolveToolCallBatchCapForModel({ ...anthropicOpus48, id: "claude-opus-4-9" })).toBeUndefined();
+		expect(resolveToolCallBatchCapForModel({ ...anthropicOpus48, id: "claude-opus-4-80" })).toBeUndefined();
+		expect(resolveToolCallBatchCapForModel(baseModel)).toBeUndefined();
+		expect(resolveToolCallBatchCapForModel({ ...baseModel, provider: "openai-codex" })).toBeUndefined();
+	});
+
 	it("applies transformContext before convertToLlm", async () => {
 		const inputMessages: AgentMessage[] = [{ role: "user", content: "hello", timestamp: Date.now() }];
 		const transformedMessages: AgentMessage[] = [