style: format app.svelte.ts with nix fmt

- Strip tokens (logprobs data) from messages before saving to localStorage
  since they're large and not essential for persistence
- Add pruneOldConversations() to automatically remove oldest conversations
  when quota is exceeded
- This prevents QuotaExceededError from crashing the app

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

README.md

@@ -27,6 +27,15 @@ exo connects all your devices into an AI cluster. Not only does exo enable runni
 - **Tensor Parallelism**: exo supports sharding models, for up to 1.8x speedup on 2 devices and 3.2x speedup on 4 devices.
 - **MLX Support**: exo uses [MLX](https://github.com/ml-explore/mlx) as an inference backend and [MLX distributed](https://ml-explore.github.io/mlx/build/html/usage/distributed.html) for distributed communication.
 
+## Dashboard
+
+exo includes a built-in dashboard for managing your cluster and chatting with models.
+
+<p align="center">
+  <img src="docs/imgs/dashboard-cluster-view.png" alt="exo dashboard - cluster view showing 4 x M3 Ultra Mac Studio with DeepSeek v3.1 and Kimi-K2-Thinking loaded" width="80%" />
+</p>
+<p align="center"><em>4 × 512GB M3 Ultra Mac Studio running DeepSeek v3.1 (8-bit) and Kimi-K2-Thinking (4-bit)</em></p>
+
 ## Benchmarks
 
 <details>

dashboard/src/lib/components/ChatMessages.svelte

@@ -1,14 +1,16 @@
 <script lang="ts">
-	import { 
-		messages, 
-		currentResponse, 
+	import {
+		messages,
+		currentResponse,
 		isLoading,
 		deleteMessage,
 		editAndRegenerate,
-		regenerateLastResponse
+		regenerateLastResponse,
+		regenerateFromToken
 	} from '$lib/stores/app.svelte';
 	import type { MessageAttachment } from '$lib/stores/app.svelte';
 	import MarkdownContent from './MarkdownContent.svelte';
+	import TokenHeatmap from './TokenHeatmap.svelte';
 
 	interface Props {
 		class?: string;
@@ -95,6 +97,23 @@
 let copiedMessageId = $state<string | null>(null);
 let expandedThinkingMessageIds = $state<Set<string>>(new Set());
 
+// Uncertainty view state - tracks which messages show token heatmap
+let uncertaintyViewMessageIds = $state<Set<string>>(new Set());
+
+function toggleUncertaintyView(messageId: string) {
+	const newSet = new Set(uncertaintyViewMessageIds);
+	if (newSet.has(messageId)) {
+		newSet.delete(messageId);
+	} else {
+		newSet.add(messageId);
+	}
+	uncertaintyViewMessageIds = newSet;
+}
+
+function isUncertaintyViewEnabled(messageId: string): boolean {
+	return uncertaintyViewMessageIds.has(messageId);
+}
+
 	function formatTimestamp(timestamp: number): string {
 		return new Date(timestamp).toLocaleTimeString('en-US', { 
 			hour12: false,
@@ -366,7 +385,17 @@ function isThinkingExpanded(messageId: string): boolean {
 									</div>
 								{/if}
 								<div class="text-xs text-foreground">
-									<MarkdownContent content={message.content || (loading ? response : '')} />
+									{#if message.role === 'assistant' && isUncertaintyViewEnabled(message.id) && message.tokens && message.tokens.length > 0}
+										<!-- Uncertainty heatmap view -->
+										<TokenHeatmap
+											tokens={message.tokens}
+											isGenerating={loading}
+											onRegenerateFrom={(tokenIndex) => regenerateFromToken(message.id, tokenIndex)}
+										/>
+									{:else}
+										<!-- Normal markdown view -->
+										<MarkdownContent content={message.content || (loading ? response : '')} />
+									{/if}
 									{#if loading && !message.content}
 										<span class="inline-block w-2 h-4 bg-exo-yellow/70 ml-1 cursor-blink"></span>
 									{/if}
@@ -419,7 +448,20 @@ function isThinkingExpanded(messageId: string): boolean {
 								</svg>
 							</button>
 						{/if}
-						
+
+						<!-- Uncertainty view toggle (assistant messages with tokens only) -->
+						{#if message.role === 'assistant' && message.tokens && message.tokens.length > 0}
+							<button
+								onclick={() => toggleUncertaintyView(message.id)}
+								class="p-1.5 transition-colors rounded cursor-pointer {isUncertaintyViewEnabled(message.id) ? 'text-exo-yellow' : 'text-exo-light-gray hover:text-exo-yellow'}"
+								title={isUncertaintyViewEnabled(message.id) ? 'Hide uncertainty' : 'Show uncertainty'}
+							>
+								<svg class="w-3.5 h-3.5" fill="none" viewBox="0 0 24 24" stroke="currentColor">
+									<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M9 19v-6a2 2 0 00-2-2H5a2 2 0 00-2 2v6a2 2 0 002 2h2a2 2 0 002-2zm0 0V9a2 2 0 012-2h2a2 2 0 012 2v10m-6 0a2 2 0 002 2h2a2 2 0 002-2m0 0V5a2 2 0 012-2h2a2 2 0 012 2v14a2 2 0 01-2 2h-2a2 2 0 01-2-2z" />
+								</svg>
+							</button>
+						{/if}
+
 						<!-- Delete button -->
 						<button
 							onclick={() => handleDeleteClick(message.id)}

dashboard/src/lib/components/TokenHeatmap.svelte

@@ -0,0 +1,192 @@
+<script lang="ts">
+	import type { TokenData } from '$lib/stores/app.svelte';
+
+	interface Props {
+		tokens: TokenData[];
+		class?: string;
+		isGenerating?: boolean;
+		onRegenerateFrom?: (tokenIndex: number) => void;
+	}
+
+	let { tokens, class: className = '', isGenerating = false, onRegenerateFrom }: Props = $props();
+
+	// Tooltip state - track both token data and index
+	let hoveredTokenIndex = $state<number | null>(null);
+	let hoveredPosition = $state<{ x: number; y: number } | null>(null);
+	let isTooltipHovered = $state(false);
+	let hideTimeoutId: ReturnType<typeof setTimeout> | null = null;
+
+	// Derive the hovered token from the index (stable across re-renders)
+	const hoveredToken = $derived(
+		hoveredTokenIndex !== null && hoveredPosition && tokens[hoveredTokenIndex]
+			? { token: tokens[hoveredTokenIndex], index: hoveredTokenIndex, ...hoveredPosition }
+			: null
+	);
+
+	/**
+	 * Get confidence styling based on probability.
+	 * Following Apple design principles: high confidence tokens blend in,
+	 * only uncertainty draws attention.
+	 */
+	function getConfidenceClass(probability: number): string {
+		if (probability > 0.8) return 'text-inherit'; // Expected tokens - blend in
+		if (probability > 0.5) return 'bg-gray-500/10 text-inherit'; // Slight hint
+		if (probability > 0.2) return 'bg-amber-500/15 text-amber-200/90'; // Subtle warmth
+		return 'bg-red-500/20 text-red-200/90'; // Draws attention
+	}
+
+	/**
+	 * Get border/underline styling for uncertain tokens
+	 */
+	function getBorderClass(probability: number): string {
+		if (probability > 0.8) return 'border-transparent'; // No border for expected
+		if (probability > 0.5) return 'border-gray-500/20';
+		if (probability > 0.2) return 'border-amber-500/30';
+		return 'border-red-500/40';
+	}
+
+	function clearHideTimeout() {
+		if (hideTimeoutId) {
+			clearTimeout(hideTimeoutId);
+			hideTimeoutId = null;
+		}
+	}
+
+	function handleMouseEnter(event: MouseEvent, token: TokenData, index: number) {
+		clearHideTimeout();
+		const rect = (event.target as HTMLElement).getBoundingClientRect();
+		hoveredTokenIndex = index;
+		hoveredPosition = {
+			x: rect.left + rect.width / 2,
+			y: rect.top - 10
+		};
+	}
+
+	function handleMouseLeave() {
+		clearHideTimeout();
+		// Use longer delay during generation to account for re-renders
+		const delay = isGenerating ? 300 : 100;
+		hideTimeoutId = setTimeout(() => {
+			if (!isTooltipHovered) {
+				hoveredTokenIndex = null;
+				hoveredPosition = null;
+			}
+		}, delay);
+	}
+
+	function handleTooltipEnter() {
+		clearHideTimeout();
+		isTooltipHovered = true;
+	}
+
+	function handleTooltipLeave() {
+		isTooltipHovered = false;
+		hoveredTokenIndex = null;
+		hoveredPosition = null;
+	}
+
+	function handleRegenerate() {
+		if (hoveredToken && onRegenerateFrom) {
+			const indexToRegenerate = hoveredToken.index;
+			// Clear hover state immediately
+			hoveredTokenIndex = null;
+			hoveredPosition = null;
+			isTooltipHovered = false;
+			// Call regenerate
+			onRegenerateFrom(indexToRegenerate);
+		}
+	}
+
+	function formatProbability(prob: number): string {
+		return (prob * 100).toFixed(1) + '%';
+	}
+
+	function formatLogprob(logprob: number): string {
+		return logprob.toFixed(3);
+	}
+
+	function getProbabilityColor(probability: number): string {
+		if (probability > 0.8) return 'text-gray-300';
+		if (probability > 0.5) return 'text-gray-400';
+		if (probability > 0.2) return 'text-amber-400';
+		return 'text-red-400';
+	}
+</script>
+
+<div class="token-heatmap leading-relaxed {className}">
+	{#each tokens as tokenData, i (i)}
+		<span
+			role="button"
+			tabindex="0"
+			class="token-span inline rounded px-0.5 py-0.5 cursor-pointer transition-all duration-150 border {getConfidenceClass(tokenData.probability)} {getBorderClass(tokenData.probability)} hover:opacity-80"
+			onmouseenter={(e) => handleMouseEnter(e, tokenData, i)}
+			onmouseleave={handleMouseLeave}
+		>{tokenData.token}</span>
+	{/each}
+</div>
+
+<!-- Tooltip -->
+{#if hoveredToken}
+	<div
+		class="fixed z-50"
+		style="left: {hoveredToken.x}px; top: {hoveredToken.y}px; transform: translate(-50%, -100%);"
+		onmouseenter={handleTooltipEnter}
+		onmouseleave={handleTooltipLeave}
+	>
+		<div class="bg-gray-900/95 backdrop-blur-sm border border-gray-700/50 rounded-xl shadow-xl p-3 text-sm min-w-48">
+			<!-- Token info -->
+			<div class="mb-2">
+				<span class="text-gray-500 text-xs">Token:</span>
+				<span class="text-white font-mono ml-1">"{hoveredToken.token.token}"</span>
+				<span class="{getProbabilityColor(hoveredToken.token.probability)} ml-2">{formatProbability(hoveredToken.token.probability)}</span>
+			</div>
+
+			<div class="text-gray-400 text-xs mb-1">
+				logprob: <span class="text-gray-300 font-mono">{formatLogprob(hoveredToken.token.logprob)}</span>
+			</div>
+
+			<!-- Top alternatives -->
+			{#if hoveredToken.token.topLogprobs.length > 0}
+				<div class="border-t border-gray-700/50 mt-2 pt-2">
+					<div class="text-gray-500 text-xs mb-1">Alternatives:</div>
+					{#each hoveredToken.token.topLogprobs.slice(0, 5) as alt, idx (idx)}
+						{@const altProb = Math.exp(alt.logprob)}
+						<div class="flex justify-between items-center text-xs py-0.5">
+							<span class="text-gray-300 font-mono truncate max-w-24">"{alt.token}"</span>
+							<span class="text-gray-400 ml-2">{formatProbability(altProb)}</span>
+						</div>
+					{/each}
+				</div>
+			{/if}
+
+			<!-- Regenerate button -->
+			{#if onRegenerateFrom}
+				<button
+					onclick={handleRegenerate}
+					class="w-full mt-2 pt-2 border-t border-gray-700/50 flex items-center justify-center gap-1.5 text-xs text-gray-400 hover:text-white transition-colors cursor-pointer"
+				>
+					<svg class="w-3 h-3" fill="none" viewBox="0 0 24 24" stroke="currentColor">
+						<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M4 4v5h.582m15.356 2A8.001 8.001 0 004.582 9m0 0H9m11 11v-5h-.581m0 0a8.003 8.003 0 01-15.357-2m15.357 2H15" />
+					</svg>
+					Regenerate from here
+				</button>
+			{/if}
+		</div>
+		<!-- Arrow -->
+		<div class="absolute left-1/2 -translate-x-1/2 top-full">
+			<div class="border-8 border-transparent border-t-gray-900"></div>
+		</div>
+	</div>
+{/if}
+
+<style>
+	.token-heatmap {
+		word-wrap: break-word;
+		white-space: pre-wrap;
+	}
+
+	.token-span {
+		margin: 0;
+		border-width: 1px;
+	}
+</style>

dashboard/src/lib/stores/app.svelte.ts

@@ -69,8 +69,6 @@ export interface Instance {
 		runnerToShard?: Record<string, unknown>;
 		nodeToRunner?: Record<string, string>;
 	};
-	draftModel?: string;
-	numDraftTokens?: number;
 }
 
 interface RawNodeProfile {
@@ -184,6 +182,20 @@ export interface MessageAttachment {
 	mimeType?: string;
 }
 
+// Token-level data for uncertainty visualization
+export interface TopLogprob {
+	token: string;
+	logprob: number;
+	bytes?: number[];
+}
+
+export interface TokenData {
+	token: string;
+	logprob: number;
+	probability: number; // exp(logprob)
+	topLogprobs: TopLogprob[];
+}
+
 export interface Message {
 	id: string;
 	role: "user" | "assistant" | "system";
@@ -193,6 +205,7 @@ export interface Message {
 	attachments?: MessageAttachment[];
 	ttftMs?: number; // Time to first token in ms (for assistant messages)
 	tps?: number; // Tokens per second (for assistant messages)
+	tokens?: TokenData[]; // Token-level data for uncertainty visualization
 }
 
 export interface Conversation {
@@ -370,6 +383,21 @@ class AppStore {
 	private fetchInterval: ReturnType<typeof setInterval> | null = null;
 	private previewsInterval: ReturnType<typeof setInterval> | null = null;
 	private lastConversationPersistTs = 0;
+	private currentRequestController: AbortController | null = null;
+
+	/**
+	 * Abort any in-flight generation request
+	 */
+	abortCurrentRequest(): boolean {
+		if (this.currentRequestController) {
+			this.currentRequestController.abort();
+			this.currentRequestController = null;
+			this.isLoading = false;
+			this.currentResponse = "";
+			return true;
+		}
+		return false;
+	}
 
 	constructor() {
 		if (browser) {
@@ -407,12 +435,61 @@ class AppStore {
 
 	/**
 	 * Save conversations to localStorage
+	 * Note: We strip tokens (logprobs data) to save space - they're large and not essential for persistence
 	 */
 	private saveConversationsToStorage() {
 		try {
-			localStorage.setItem(STORAGE_KEY, JSON.stringify(this.conversations));
+			// Strip tokens from messages to save localStorage space
+			const conversationsToSave = this.conversations.map((conv) => ({
+				...conv,
+				messages: conv.messages.map((msg) => {
+					// eslint-disable-next-line @typescript-eslint/no-unused-vars
+					const { tokens, ...msgWithoutTokens } = msg;
+					return msgWithoutTokens;
+				}),
+			}));
+			localStorage.setItem(STORAGE_KEY, JSON.stringify(conversationsToSave));
 		} catch (error) {
 			console.error("Failed to save conversations:", error);
+			// If quota exceeded, try to clear old conversations and retry
+			if (
+				error instanceof DOMException &&
+				error.name === "QuotaExceededError"
+			) {
+				console.warn(
+					"Storage quota exceeded, clearing oldest conversations...",
+				);
+				this.pruneOldConversations();
+			}
+		}
+	}
+
+	/**
+	 * Remove oldest conversations to free up storage space
+	 */
+	private pruneOldConversations() {
+		if (this.conversations.length <= 1) return;
+
+		// Sort by updatedAt and remove oldest half
+		const sorted = [...this.conversations].sort(
+			(a, b) => (b.updatedAt || 0) - (a.updatedAt || 0),
+		);
+		const keepCount = Math.max(1, Math.ceil(sorted.length / 2));
+		this.conversations = sorted.slice(0, keepCount);
+
+		// Try saving again
+		try {
+			const conversationsToSave = this.conversations.map((conv) => ({
+				...conv,
+				messages: conv.messages.map((msg) => {
+					// eslint-disable-next-line @typescript-eslint/no-unused-vars
+					const { tokens, ...msgWithoutTokens } = msg;
+					return msgWithoutTokens;
+				}),
+			}));
+			localStorage.setItem(STORAGE_KEY, JSON.stringify(conversationsToSave));
+		} catch {
+			console.error("Still failed to save after pruning");
 		}
 	}
 
@@ -1333,6 +1410,11 @@ class AppStore {
 		const assistantMessage = this.addMessage("assistant", "");
 		this.updateActiveConversation();
 
+		// Create abort controller for this request - must be defined before try block
+		// so it's available in the finally block
+		const controller = new AbortController();
+		this.currentRequestController = controller;
+
 		try {
 			// Build the messages array for the API with system prompt
 			const systemPrompt = {
@@ -1410,7 +1492,10 @@ class AppStore {
 					messages: apiMessages,
 					temperature: 0.7,
 					stream: true,
+					logprobs: true,
+					top_logprobs: 5,
 				}),
+				signal: controller.signal,
 			});
 
 			if (!response.ok) {
@@ -1426,6 +1511,7 @@ class AppStore {
 			const decoder = new TextDecoder();
 			let fullContent = "";
 			let buffer = "";
+			const collectedTokens: TokenData[] = [];
 
 			while (true) {
 				const { done, value } = await reader.read();
@@ -1447,8 +1533,8 @@ class AppStore {
 
 						try {
 							const parsed = JSON.parse(data);
-							const tokenContent = parsed.choices?.[0]?.delta?.content;
-							if (tokenContent) {
+							const delta = parsed.choices?.[0]?.delta?.content;
+							if (delta) {
 								// Track first token for TTFT
 								if (firstTokenTime === null) {
 									firstTokenTime = performance.now();
@@ -1465,7 +1551,30 @@ class AppStore {
 									this.tps = (tokenCount / elapsed) * 1000;
 								}
 
-								fullContent += tokenContent;
+								// Extract logprobs for uncertainty visualization
+								const logprobsData = parsed.choices?.[0]?.logprobs;
+								if (logprobsData?.content?.[0]) {
+									const logprobItem = logprobsData.content[0];
+									const tokenData: TokenData = {
+										token: logprobItem.token || delta,
+										logprob: logprobItem.logprob ?? 0,
+										probability: Math.exp(logprobItem.logprob ?? 0),
+										topLogprobs: (logprobItem.top_logprobs || []).map(
+											(item: {
+												token: string;
+												logprob: number;
+												bytes?: number[];
+											}) => ({
+												token: item.token,
+												logprob: item.logprob,
+												bytes: item.bytes,
+											}),
+										),
+									};
+									collectedTokens.push(tokenData);
+								}
+
+								fullContent += delta;
 
 								// Strip thinking tags for display and extract thinking content
 								const { displayContent, thinkingContent } =
@@ -1479,6 +1588,7 @@ class AppStore {
 								if (idx !== -1) {
 									this.messages[idx].content = displayContent;
 									this.messages[idx].thinking = thinkingContent || undefined;
+									this.messages[idx].tokens = [...collectedTokens];
 								}
 								this.persistActiveConversation();
 							}
@@ -1526,9 +1636,16 @@ class AppStore {
 				if (this.tps !== null) {
 					this.messages[idx].tps = this.tps;
 				}
+				if (collectedTokens.length > 0) {
+					this.messages[idx].tokens = collectedTokens;
+				}
 			}
 			this.persistActiveConversation();
 		} catch (error) {
+			// Don't show error for aborted requests (user cancelled)
+			if (error instanceof Error && error.name === "AbortError") {
+				return;
+			}
 			console.error("Error sending message:", error);
 			// Update the assistant message with error
 			const idx = this.messages.findIndex((m) => m.id === assistantMessage.id);
@@ -1538,6 +1655,237 @@ class AppStore {
 			}
 			this.persistActiveConversation();
 		} finally {
+			// Clean up controller if this is still the active request
+			if (this.currentRequestController === controller) {
+				this.currentRequestController = null;
+			}
+			this.isLoading = false;
+			this.currentResponse = "";
+			this.updateActiveConversation();
+		}
+	}
+
+	/**
+	 * Regenerate from a specific token in an assistant message.
+	 * Keeps content up to and including the specified token, then continues generation.
+	 * If a generation is already in progress, it will be aborted first.
+	 */
+	async regenerateFromToken(
+		messageId: string,
+		tokenIndex: number,
+	): Promise<void> {
+		// Abort any in-flight request first
+		this.abortCurrentRequest();
+
+		const messageIdx = this.messages.findIndex((m) => m.id === messageId);
+		if (messageIdx === -1) return;
+
+		const message = this.messages[messageIdx];
+		if (message.role !== "assistant" || !message.tokens) return;
+
+		// Get tokens up to and including the specified index
+		const tokensToKeep = message.tokens.slice(0, tokenIndex + 1);
+		const prefixText = tokensToKeep.map((t) => t.token).join("");
+
+		// Remove all messages after this assistant message
+		this.messages = this.messages.slice(0, messageIdx + 1);
+
+		// Update the message to show the prefix
+		this.messages[messageIdx].content = prefixText;
+		this.messages[messageIdx].tokens = tokensToKeep;
+
+		// Set up for continuation
+		this.isLoading = true;
+		this.currentResponse = prefixText;
+		this.ttftMs = null;
+		this.tps = null;
+		this.totalTokens = tokensToKeep.length;
+
+		// Create abort controller before try block so it's available in finally
+		const controller = new AbortController();
+		this.currentRequestController = controller;
+
+		try {
+			// Build messages for API - include the partial assistant message
+			const systemPrompt = {
+				role: "system" as const,
+				content:
+					"You are a helpful AI assistant. Respond directly and concisely. Do not show your reasoning or thought process.",
+			};
+
+			// Get all messages up to and including the one we're regenerating from
+			const apiMessages = [
+				systemPrompt,
+				...this.messages.map((m) => {
+					let msgContent = m.content;
+					if (m.attachments) {
+						for (const attachment of m.attachments) {
+							if (attachment.type === "text" && attachment.content) {
+								msgContent += `\n\n[File: ${attachment.name}]\n\`\`\`\n${attachment.content}\n\`\`\``;
+							}
+						}
+					}
+					return { role: m.role, content: msgContent };
+				}),
+			];
+
+			// Determine model
+			let modelToUse = this.selectedChatModel;
+			if (!modelToUse) {
+				for (const [, instanceWrapper] of Object.entries(this.instances)) {
+					if (instanceWrapper && typeof instanceWrapper === "object") {
+						const keys = Object.keys(
+							instanceWrapper as Record<string, unknown>,
+						);
+						if (keys.length === 1) {
+							const instance = (instanceWrapper as Record<string, unknown>)[
+								keys[0]
+							] as { shardAssignments?: { modelId?: string } };
+							if (instance?.shardAssignments?.modelId) {
+								modelToUse = instance.shardAssignments.modelId;
+								break;
+							}
+						}
+					}
+				}
+			}
+
+			if (!modelToUse) {
+				throw new Error("No model available");
+			}
+
+			// Start timing
+			const requestStartTime = performance.now();
+			let firstTokenTime: number | null = null;
+			let tokenCount = tokensToKeep.length;
+
+			const response = await fetch("/v1/chat/completions", {
+				method: "POST",
+				headers: { "Content-Type": "application/json" },
+				body: JSON.stringify({
+					model: modelToUse,
+					messages: apiMessages,
+					stream: true,
+					logprobs: true,
+					top_logprobs: 5,
+					continue_from_prefix: true,
+				}),
+				signal: controller.signal,
+			});
+
+			if (!response.ok) {
+				const errorText = await response.text();
+				throw new Error(`API error: ${response.status} - ${errorText}`);
+			}
+
+			const reader = response.body?.getReader();
+			if (!reader) throw new Error("No response body");
+
+			const decoder = new TextDecoder();
+			let fullContent = prefixText;
+			let buffer = "";
+			const collectedTokens: TokenData[] = [...tokensToKeep];
+
+			while (true) {
+				const { done, value } = await reader.read();
+				if (done) break;
+
+				buffer += decoder.decode(value, { stream: true });
+				const lines = buffer.split("\n");
+				buffer = lines.pop() || "";
+
+				for (const line of lines) {
+					const trimmed = line.trim();
+					if (!trimmed || trimmed === "data: [DONE]") continue;
+
+					if (trimmed.startsWith("data: ")) {
+						try {
+							const json = JSON.parse(trimmed.slice(6));
+							const delta = json.choices?.[0]?.delta?.content;
+							if (delta) {
+								if (firstTokenTime === null) {
+									firstTokenTime = performance.now();
+									this.ttftMs = firstTokenTime - requestStartTime;
+								}
+
+								tokenCount += 1;
+								this.totalTokens = tokenCount;
+
+								if (
+									firstTokenTime !== null &&
+									tokenCount > tokensToKeep.length
+								) {
+									const elapsed = performance.now() - firstTokenTime;
+									this.tps =
+										((tokenCount - tokensToKeep.length) / elapsed) * 1000;
+								}
+
+								// Extract logprobs
+								const logprobsData = json.choices?.[0]?.logprobs;
+								if (logprobsData?.content?.[0]) {
+									const logprobItem = logprobsData.content[0];
+									collectedTokens.push({
+										token: logprobItem.token || delta,
+										logprob: logprobItem.logprob ?? 0,
+										probability: Math.exp(logprobItem.logprob ?? 0),
+										topLogprobs: (logprobItem.top_logprobs || []).map(
+											(item: {
+												token: string;
+												logprob: number;
+												bytes?: number[];
+											}) => ({
+												token: item.token,
+												logprob: item.logprob,
+												bytes: item.bytes,
+											}),
+										),
+									});
+								}
+
+								fullContent += delta;
+								const { displayContent, thinkingContent } =
+									this.stripThinkingTags(fullContent);
+								this.currentResponse = displayContent;
+
+								this.messages[messageIdx].content = displayContent;
+								this.messages[messageIdx].thinking =
+									thinkingContent || undefined;
+								this.messages[messageIdx].tokens = [...collectedTokens];
+								this.persistActiveConversation();
+							}
+						} catch {
+							// Skip malformed JSON
+						}
+					}
+				}
+			}
+
+			// Final update
+			const { displayContent, thinkingContent } =
+				this.stripThinkingTags(fullContent);
+			this.messages[messageIdx].content = displayContent;
+			this.messages[messageIdx].thinking = thinkingContent || undefined;
+			this.messages[messageIdx].tokens = collectedTokens;
+
+			if (this.ttftMs !== null) {
+				this.messages[messageIdx].ttftMs = this.ttftMs;
+			}
+			if (this.tps !== null) {
+				this.messages[messageIdx].tps = this.tps;
+			}
+			this.persistActiveConversation();
+		} catch (error) {
+			if (error instanceof Error && error.name === "AbortError") {
+				return;
+			}
+			console.error("Error regenerating from token:", error);
+			this.messages[messageIdx].content =
+				`${prefixText}\n\nError: ${error instanceof Error ? error.message : "Unknown error"}`;
+			this.persistActiveConversation();
+		} finally {
+			if (this.currentRequestController === controller) {
+				this.currentRequestController = null;
+			}
 			this.isLoading = false;
 			this.currentResponse = "";
 			this.updateActiveConversation();
@@ -1617,6 +1965,8 @@ export const editMessage = (messageId: string, newContent: string) =>
 export const editAndRegenerate = (messageId: string, newContent: string) =>
 	appStore.editAndRegenerate(messageId, newContent);
 export const regenerateLastResponse = () => appStore.regenerateLastResponse();
+export const regenerateFromToken = (messageId: string, tokenIndex: number) =>
+	appStore.regenerateFromToken(messageId, tokenIndex);
 
 // Conversation actions
 export const conversations = () => appStore.conversations;

dashboard/src/routes/+page.svelte

@@ -47,7 +47,7 @@ const sidebarVisible = $derived(chatSidebarVisible());
 	let mounted = $state(false);
 
 	// Instance launch state
-	let models = $state<Array<{id: string, hugging_face_id?: string, name?: string, storage_size_megabytes?: number}>>([]);
+	let models = $state<Array<{id: string, name?: string, storage_size_megabytes?: number}>>([]);
 	let selectedSharding = $state<'Pipeline' | 'Tensor'>('Pipeline');
 	type InstanceMeta = 'MlxRing' | 'MlxIbv' | 'MlxJaccl';
 	
@@ -59,7 +59,7 @@ const sidebarVisible = $derived(chatSidebarVisible());
 		instanceType: InstanceMeta;
 		minNodes: number;
 	}
-
+	
 	function saveLaunchDefaults(): void {
 		const defaults: LaunchDefaults = {
 			modelId: selectedPreviewModelId(),
@@ -88,16 +88,16 @@ const sidebarVisible = $derived(chatSidebarVisible());
 	function applyLaunchDefaults(availableModels: Array<{id: string}>, maxNodes: number): void {
 		const defaults = loadLaunchDefaults();
 		if (!defaults) return;
-
+		
 		// Apply sharding and instance type unconditionally
 		selectedSharding = defaults.sharding;
 		selectedInstanceType = defaults.instanceType;
-
+		
 		// Apply minNodes if valid (between 1 and maxNodes)
 		if (defaults.minNodes && defaults.minNodes >= 1 && defaults.minNodes <= maxNodes) {
 			selectedMinNodes = defaults.minNodes;
 		}
-
+		
 		// Only apply model if it exists in the available models
 		if (defaults.modelId && availableModels.some(m => m.id === defaults.modelId)) {
 			selectPreviewModel(defaults.modelId);
@@ -109,19 +109,11 @@ const sidebarVisible = $derived(chatSidebarVisible());
 	let minNodesInitialized = $state(false);
 	let launchingModelId = $state<string | null>(null);
 let instanceDownloadExpandedNodes = $state<Set<string>>(new Set());
-
-	// Draft model edit modal state
-	let editingDraftInstanceId = $state<string | null>(null);
-	let editDraftModel = $state<string | null>(null);
-	let editNumDraftTokens = $state<number>(4);
-	let isDraftEditDropdownOpen = $state(false);
-	let draftEditDropdownSearch = $state('');
-	let isSavingDraftModel = $state(false);
-
+	
 	// Custom dropdown state
 	let isModelDropdownOpen = $state(false);
 	let modelDropdownSearch = $state('');
-
+	
 	// Slider dragging state
 	let isDraggingSlider = $state(false);
 	let sliderTrackElement: HTMLDivElement | null = $state(null);
@@ -370,36 +362,47 @@ function toggleInstanceDownloadDetails(nodeId: string): void {
 
 	async function launchInstance(modelId: string, specificPreview?: PlacementPreview | null) {
 		if (!modelId || launchingModelId) return;
-
+		
 		launchingModelId = modelId;
-
+		
 		try {
 			// Use the specific preview if provided, otherwise fall back to filtered preview
 			const preview = specificPreview ?? filteredPreview();
-
-			let response: Response;
-
-			// Use /place_instance endpoint - it handles placement and creation in one step
-			const placePayload = {
-				model_id: modelId,
-				sharding: preview?.sharding ?? selectedSharding,
-				instance_meta: preview?.instance_meta ?? selectedInstanceType,
-				min_nodes: selectedMinNodes,
-			};
-
-			response = await fetch('/place_instance', {
+			
+			let instanceData: unknown;
+			
+			if (preview?.instance) {
+				// Use the instance from the preview
+				instanceData = preview.instance;
+			} else {
+				// Fallback: GET placement from API
+				const placementResponse = await fetch(
+					`/instance/placement?model_id=${encodeURIComponent(modelId)}&sharding=${selectedSharding}&instance_meta=${selectedInstanceType}&min_nodes=${selectedMinNodes}`
+				);
+				
+				if (!placementResponse.ok) {
+					const errorText = await placementResponse.text();
+					console.error('Failed to get placement:', errorText);
+					return;
+				}
+				
+				instanceData = await placementResponse.json();
+			}
+			
+			// POST the instance to create it
+			const response = await fetch('/instance', {
 				method: 'POST',
 				headers: { 'Content-Type': 'application/json' },
-				body: JSON.stringify(placePayload)
+				body: JSON.stringify({ instance: instanceData })
 			});
-
+			
 			if (!response.ok) {
 				const errorText = await response.text();
 				console.error('Failed to launch instance:', errorText);
 			} else {
 				// Always auto-select the newly launched model so the user chats to what they just launched
 				setSelectedChatModel(modelId);
-
+				
 				// Scroll to the bottom of instances container to show the new instance
 				// Use multiple attempts to ensure DOM has updated with the new instance
 				const scrollToBottom = () => {
@@ -794,52 +797,6 @@ function toggleInstanceDownloadDetails(nodeId: string): void {
 		}
 	}
 
-	// Open draft model edit modal for an instance
-	function openDraftModelEdit(instanceId: string, currentDraftModel: string | null, currentNumTokens: number | null) {
-		editingDraftInstanceId = instanceId;
-		editDraftModel = currentDraftModel;
-		editNumDraftTokens = currentNumTokens ?? 4;
-		isDraftEditDropdownOpen = false;
-		draftEditDropdownSearch = '';
-	}
-
-	// Close draft model edit modal
-	function closeDraftModelEdit() {
-		editingDraftInstanceId = null;
-		editDraftModel = null;
-		editNumDraftTokens = 4;
-		isDraftEditDropdownOpen = false;
-		draftEditDropdownSearch = '';
-	}
-
-	// Save draft model settings for an instance
-	async function saveDraftModel() {
-		if (!editingDraftInstanceId || isSavingDraftModel) return;
-
-		isSavingDraftModel = true;
-		try {
-			const response = await fetch(`/instance/${editingDraftInstanceId}/draft_model`, {
-				method: 'PUT',
-				headers: { 'Content-Type': 'application/json' },
-				body: JSON.stringify({
-					draft_model: editDraftModel,
-					num_draft_tokens: editNumDraftTokens,
-				})
-			});
-
-			if (!response.ok) {
-				const errorText = await response.text();
-				console.error('Failed to set draft model:', errorText);
-			} else {
-				closeDraftModelEdit();
-			}
-		} catch (error) {
-			console.error('Error setting draft model:', error);
-		} finally {
-			isSavingDraftModel = false;
-		}
-	}
-
 	// Helper to unwrap tagged unions like { MlxRingInstance: {...} }
 	function getTagged(obj: unknown): [string | null, unknown] {
 		if (!obj || typeof obj !== 'object') return [null, null];
@@ -859,34 +816,30 @@ function toggleInstanceDownloadDetails(nodeId: string): void {
 	}
 
 	// Get instance details: type (MLX Ring/IBV), sharding (Pipeline/Tensor), and node names
-	function getInstanceInfo(instanceWrapped: unknown): {
-		instanceType: string;
-		sharding: string;
+	function getInstanceInfo(instanceWrapped: unknown): { 
+		instanceType: string; 
+		sharding: string; 
 		nodeNames: string[];
 		nodeIds: string[];
 		nodeCount: number;
-		draftModel: string | null;
-		numDraftTokens: number | null;
 	} {
 		const [instanceTag, instance] = getTagged(instanceWrapped);
 		if (!instance || typeof instance !== 'object') {
-			return { instanceType: 'Unknown', sharding: 'Unknown', nodeNames: [], nodeIds: [], nodeCount: 0, draftModel: null, numDraftTokens: null };
+			return { instanceType: 'Unknown', sharding: 'Unknown', nodeNames: [], nodeIds: [], nodeCount: 0 };
 		}
-
+		
 		// Instance type from tag
 		let instanceType = 'Unknown';
 		if (instanceTag === 'MlxRingInstance') instanceType = 'MLX Ring';
 		else if (instanceTag === 'MlxIbvInstance' || instanceTag === 'MlxJacclInstance') instanceType = 'MLX RDMA';
-
-		const inst = instance as {
-			shardAssignments?: {
-				nodeToRunner?: Record<string, string>;
+		
+		const inst = instance as { 
+			shardAssignments?: { 
+				nodeToRunner?: Record<string, string>; 
 				runnerToShard?: Record<string, unknown>;
-			};
-			draftModel?: string;
-			numDraftTokens?: number;
+			} 
 		};
-
+		
 		// Sharding strategy from first shard
 		let sharding = 'Unknown';
 		const runnerToShard = inst.shardAssignments?.runnerToShard || {};
@@ -897,7 +850,7 @@ function toggleInstanceDownloadDetails(nodeId: string): void {
 			else if (shardTag === 'TensorShardMetadata') sharding = 'Tensor';
 			else if (shardTag === 'PrefillDecodeShardMetadata') sharding = 'Prefill/Decode';
 		}
-
+		
 		// Node names from topology
 		const nodeToRunner = inst.shardAssignments?.nodeToRunner || {};
 		const nodeIds = Object.keys(nodeToRunner);
@@ -905,12 +858,8 @@ function toggleInstanceDownloadDetails(nodeId: string): void {
 			const node = data?.nodes?.[nodeId];
 			return node?.friendly_name || nodeId.slice(0, 8);
 		});
-
-		// Draft model for speculative decoding
-		const draftModel = inst.draftModel ?? null;
-		const numDraftTokens = inst.numDraftTokens ?? null;
-
-		return { instanceType, sharding, nodeNames, nodeIds, nodeCount: nodeIds.length, draftModel, numDraftTokens };
+		
+		return { instanceType, sharding, nodeNames, nodeIds, nodeCount: nodeIds.length };
 	}
 
 	function formatLastUpdate(): string {
@@ -1386,31 +1335,16 @@ function toggleInstanceDownloadDetails(nodeId: string): void {
 												<div class="w-1.5 h-1.5 {isDownloading ? 'bg-blue-400 animate-pulse' : isFailed ? 'bg-red-400' : isLoading ? 'bg-yellow-400 animate-pulse' : isReady ? 'bg-green-400' : 'bg-teal-400'} rounded-full shadow-[0_0_6px_currentColor]"></div>
 												<span class="text-exo-light-gray font-mono text-sm tracking-wider">{id.slice(0, 8).toUpperCase()}</span>
 											</div>
-											<div class="flex items-center gap-2">
-												<!-- Draft Model Button -->
-												<button
-													onclick={() => openDraftModelEdit(id, instanceInfo.draftModel, instanceInfo.numDraftTokens)}
-													class="p-1.5 font-mono border transition-all duration-200 cursor-pointer {instanceInfo.draftModel ? 'border-cyan-500/50 text-cyan-400 hover:bg-cyan-500/20 hover:border-cyan-500' : 'border-exo-medium-gray/50 text-white/40 hover:text-cyan-400 hover:border-cyan-500/50'}"
-													title={instanceInfo.draftModel ? `Draft: ${instanceInfo.draftModel.split('/').pop()} (${instanceInfo.numDraftTokens}t)` : 'Configure speculative decoding'}
-												>
-													<svg class="w-4 h-4" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
-														<path d="M13 2L3 14h9l-1 8 10-12h-9l1-8z"/>
-													</svg>
-												</button>
-												<button
-													onclick={() => deleteInstance(id)}
-													class="text-xs px-2 py-1 font-mono tracking-wider uppercase border border-red-500/30 text-red-400 hover:bg-red-500/20 hover:text-red-400 hover:border-red-500/50 transition-all duration-200 cursor-pointer"
-												>
-													DELETE
-												</button>
-											</div>
+											<button 
+												onclick={() => deleteInstance(id)}
+												class="text-xs px-2 py-1 font-mono tracking-wider uppercase border border-red-500/30 text-red-400 hover:bg-red-500/20 hover:text-red-400 hover:border-red-500/50 transition-all duration-200 cursor-pointer"
+											>
+												DELETE
+											</button>
 											</div>
 											<div class="pl-2">
 												<div class="text-exo-yellow text-xs font-mono tracking-wide truncate">{getInstanceModelId(instance)}</div>
 												<div class="text-white/60 text-xs font-mono">Strategy: <span class="text-white/80">{instanceInfo.sharding} ({instanceInfo.instanceType})</span></div>
-												{#if instanceInfo.draftModel}
-													<div class="text-white/60 text-xs font-mono">Draft: <span class="text-cyan-400">{instanceInfo.draftModel.split('/').pop()}</span>{#if instanceInfo.numDraftTokens}<span class="text-white/40"> ({instanceInfo.numDraftTokens}t)</span>{/if}</div>
-												{/if}
 												{#if instanceModelId && instanceModelId !== 'Unknown' && instanceModelId !== 'Unknown Model'}
 													<a
 														class="inline-flex items-center gap-1 text-[11px] text-white/60 hover:text-exo-yellow transition-colors mt-1"
@@ -1745,7 +1679,7 @@ function toggleInstanceDownloadDetails(nodeId: string): void {
 								</div>
 							</div>
 						</div>
-
+						
 						<!-- Selected Model Preview -->
 						<div class="space-y-3">
 							{#if models.length === 0}
@@ -1904,31 +1838,16 @@ function toggleInstanceDownloadDetails(nodeId: string): void {
 													<div class="w-1.5 h-1.5 {isDownloading ? 'bg-blue-400 animate-pulse' : isFailed ? 'bg-red-400' : isLoading ? 'bg-yellow-400 animate-pulse' : isReady ? 'bg-green-400' : 'bg-teal-400'} rounded-full shadow-[0_0_6px_currentColor]"></div>
 													<span class="text-exo-light-gray font-mono text-sm tracking-wider">{id.slice(0, 8).toUpperCase()}</span>
 												</div>
-												<div class="flex items-center gap-2">
-													<!-- Draft Model Button -->
-													<button
-														onclick={() => openDraftModelEdit(id, instanceInfo.draftModel, instanceInfo.numDraftTokens)}
-														class="p-1.5 font-mono border transition-all duration-200 cursor-pointer {instanceInfo.draftModel ? 'border-cyan-500/50 text-cyan-400 hover:bg-cyan-500/20 hover:border-cyan-500' : 'border-exo-medium-gray/50 text-white/40 hover:text-cyan-400 hover:border-cyan-500/50'}"
-														title={instanceInfo.draftModel ? `Draft: ${instanceInfo.draftModel.split('/').pop()} (${instanceInfo.numDraftTokens}t)` : 'Configure speculative decoding'}
-													>
-														<svg class="w-4 h-4" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
-															<path d="M13 2L3 14h9l-1 8 10-12h-9l1-8z"/>
-														</svg>
-													</button>
-													<button
-														onclick={() => deleteInstance(id)}
-														class="text-xs px-2 py-1 font-mono tracking-wider uppercase border border-red-500/30 text-red-400 hover:bg-red-500/20 hover:text-red-400 hover:border-red-500/50 transition-all duration-200 cursor-pointer"
-													>
-														DELETE
-													</button>
-												</div>
+												<button 
+													onclick={() => deleteInstance(id)}
+													class="text-xs px-2 py-1 font-mono tracking-wider uppercase border border-red-500/30 text-red-400 hover:bg-red-500/20 hover:text-red-400 hover:border-red-500/50 transition-all duration-200 cursor-pointer"
+												>
+													DELETE
+												</button>
 												</div>
 												<div class="pl-2">
 													<div class="text-exo-yellow text-xs font-mono tracking-wide truncate">{getInstanceModelId(instance)}</div>
 													<div class="text-white/60 text-xs font-mono">Strategy: <span class="text-white/80">{instanceInfo.sharding} ({instanceInfo.instanceType})</span></div>
-													{#if instanceInfo.draftModel}
-														<div class="text-white/60 text-xs font-mono">Draft: <span class="text-cyan-400">{instanceInfo.draftModel.split('/').pop()}</span>{#if instanceInfo.numDraftTokens}<span class="text-white/40"> ({instanceInfo.numDraftTokens}t)</span>{/if}</div>
-													{/if}
 														{#if instanceModelId && instanceModelId !== 'Unknown' && instanceModelId !== 'Unknown Model'}
 															<a
 																class="inline-flex items-center gap-1 text-[11px] text-white/60 hover:text-exo-yellow transition-colors mt-1"
@@ -2059,120 +1978,4 @@ function toggleInstanceDownloadDetails(nodeId: string): void {
 		{/if}
 	</main>
 
-	<!-- Draft Model Edit Modal -->
-	{#if editingDraftInstanceId}
-		<!-- svelte-ignore a11y_no_static_element_interactions -->
-		<div
-			class="fixed inset-0 z-50 flex items-center justify-center bg-black/70 backdrop-blur-sm"
-			onclick={closeDraftModelEdit}
-			onkeydown={(e) => e.key === 'Escape' && closeDraftModelEdit()}
-		>
-			<!-- svelte-ignore a11y_click_events_have_key_events -->
-			<div
-				class="bg-exo-dark-gray border border-exo-medium-gray/50 rounded-lg shadow-2xl p-6 w-full max-w-md mx-4"
-				onclick={(e) => e.stopPropagation()}
-			>
-				<div class="flex items-center justify-between mb-4">
-					<h3 class="text-lg font-mono text-exo-yellow tracking-wide">Speculative Decoding</h3>
-					<button
-						onclick={closeDraftModelEdit}
-						class="text-white/60 hover:text-white transition-colors cursor-pointer"
-						aria-label="Close"
-					>
-						<svg class="w-5 h-5" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
-							<path stroke-linecap="round" stroke-linejoin="round" d="M6 18L18 6M6 6l12 12" />
-						</svg>
-					</button>
-				</div>
-
-				<p class="text-white/60 text-sm font-mono mb-4">
-					Configure a draft model for faster generation. The draft model proposes tokens that the main model verifies.
-				</p>
-
-				<!-- Draft Model Dropdown -->
-				<div class="mb-4">
-					<div class="text-xs text-white/70 font-mono mb-2">Draft Model:</div>
-					<div class="relative">
-						<button
-							onclick={() => { isDraftEditDropdownOpen = !isDraftEditDropdownOpen; draftEditDropdownSearch = ''; }}
-							class="w-full px-3 py-2 text-left text-sm font-mono border rounded transition-all duration-200 cursor-pointer flex items-center justify-between gap-2 {editDraftModel ? 'bg-transparent text-cyan-400 border-cyan-500/50' : 'bg-transparent text-white/50 border-exo-medium-gray/50 hover:border-cyan-500/50'}"
-						>
-							<span class="truncate">{editDraftModel ? editDraftModel.split('/').pop() : 'None'}</span>
-							<svg class="w-4 h-4 flex-shrink-0 transition-transform {isDraftEditDropdownOpen ? 'rotate-180' : ''}" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-								<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M19 9l-7 7-7-7" />
-							</svg>
-						</button>
-						{#if isDraftEditDropdownOpen}
-							<div class="absolute top-full left-0 right-0 mt-1 bg-exo-dark-gray border border-exo-medium-gray/50 rounded shadow-lg z-50 max-h-48 overflow-hidden flex flex-col">
-								<div class="p-2 border-b border-exo-medium-gray/30">
-									<input
-										type="text"
-										bind:value={draftEditDropdownSearch}
-										placeholder="Search models..."
-										class="w-full px-2 py-1.5 text-sm font-mono bg-transparent border border-exo-medium-gray/50 rounded text-white/90 placeholder:text-white/30 focus:outline-none focus:border-cyan-500/50"
-									/>
-								</div>
-								<div class="overflow-y-auto max-h-36">
-									<!-- None option -->
-									<button
-										onclick={() => { editDraftModel = null; isDraftEditDropdownOpen = false; }}
-										class="w-full px-3 py-2 text-left text-sm font-mono tracking-wide transition-colors duration-100 flex items-center gap-2 {editDraftModel === null ? 'bg-transparent text-cyan-400 cursor-pointer' : 'text-white/80 hover:text-cyan-400 cursor-pointer'}"
-									>
-										<span>None (Disable)</span>
-									</button>
-									{#each models.filter(m => (m.name ?? m.id).toLowerCase().includes(draftEditDropdownSearch.toLowerCase())) as model}
-										{@const sizeGB = (model.storage_size_megabytes ?? 0) / 1024}
-										{@const modelHfId = model.hugging_face_id ?? model.id}
-										<button
-											onclick={() => { editDraftModel = modelHfId; isDraftEditDropdownOpen = false; }}
-											class="w-full px-3 py-2 text-left text-sm font-mono tracking-wide transition-colors duration-100 flex items-center justify-between gap-2 {editDraftModel === modelHfId ? 'bg-transparent text-cyan-400 cursor-pointer' : 'text-white/80 hover:text-cyan-400 cursor-pointer'}"
-										>
-											<span class="truncate">{model.name || model.id}</span>
-											<span class="flex-shrink-0 text-xs text-white/50">
-												{sizeGB >= 1 ? sizeGB.toFixed(0) : sizeGB.toFixed(1)}GB
-											</span>
-										</button>
-									{:else}
-										<div class="px-3 py-2 text-xs text-white/50 font-mono">No models found</div>
-									{/each}
-								</div>
-							</div>
-						{/if}
-					</div>
-				</div>
-
-				<!-- Draft Tokens -->
-				{#if editDraftModel}
-					<div class="mb-6">
-						<div class="text-xs text-white/70 font-mono mb-2">Draft Tokens per Iteration:</div>
-						<div class="flex items-center gap-2">
-							{#each [2, 3, 4, 5, 6] as n}
-								<button
-									onclick={() => editNumDraftTokens = n}
-									class="w-8 h-8 text-sm font-mono rounded transition-all {editNumDraftTokens === n ? 'bg-cyan-500/20 text-cyan-400 border border-cyan-500/50' : 'text-white/50 hover:text-white/80 border border-exo-medium-gray/50 hover:border-white/30'} cursor-pointer"
-								>{n}</button>
-							{/each}
-						</div>
-					</div>
-				{/if}
-
-				<!-- Action Buttons -->
-				<div class="flex items-center justify-end gap-3">
-					<button
-						onclick={closeDraftModelEdit}
-						class="px-4 py-2 text-sm font-mono text-white/70 hover:text-white transition-colors cursor-pointer"
-					>
-						Cancel
-					</button>
-					<button
-						onclick={saveDraftModel}
-						disabled={isSavingDraftModel}
-						class="px-4 py-2 text-sm font-mono border border-cyan-500/50 text-cyan-400 hover:bg-cyan-500/20 hover:border-cyan-500 transition-all disabled:opacity-50 disabled:cursor-not-allowed cursor-pointer"
-					>
-						{isSavingDraftModel ? 'Saving...' : 'Save'}
-					</button>
-				</div>
-			</div>
-		</div>
-	{/if}
 </div>

src/exo/master/adapters/__init__.py

@@ -0,0 +1 @@
+"""API adapters for different API formats (Claude, OpenAI Responses, etc.)."""

src/exo/master/adapters/chat_completions.py

@@ -0,0 +1,186 @@
+"""OpenAI Chat Completions API adapter for converting requests/responses."""
+
+import time
+from collections.abc import AsyncGenerator
+
+from loguru import logger
+
+from exo.shared.types.api import (
+    ChatCompletionChoice,
+    ChatCompletionMessage,
+    ChatCompletionMessageText,
+    ChatCompletionResponse,
+    ChatCompletionTaskParams,
+    ErrorInfo,
+    ErrorResponse,
+    FinishReason,
+    Logprobs,
+    LogprobsContentItem,
+    StreamingChoiceResponse,
+)
+from exo.shared.types.chunks import TokenChunk
+from exo.shared.types.common import CommandId
+from exo.shared.types.openai_responses import ResponseInputMessage, ResponsesRequest
+
+
+def chat_request_to_internal(request: ChatCompletionTaskParams) -> ResponsesRequest:
+    """Convert Chat Completions API request to ResponsesRequest (canonical internal format).
+
+    Extracts system message as instructions, converts messages to input.
+    """
+    instructions: str | None = None
+    input_messages: list[ResponseInputMessage] = []
+
+    for msg in request.messages:
+        # Normalize content to string
+        content: str
+        if msg.content is None:
+            content = ""
+        elif isinstance(msg.content, str):
+            content = msg.content
+        elif isinstance(msg.content, ChatCompletionMessageText):
+            content = msg.content.text
+        else:
+            # List of ChatCompletionMessageText
+            content = "\n".join(item.text for item in msg.content)
+
+        # Extract system message as instructions
+        if msg.role == "system":
+            if instructions is None:
+                instructions = content
+            else:
+                # Append additional system messages
+                instructions = f"{instructions}\n{content}"
+        else:
+            # Convert to ResponseInputMessage (only user, assistant, developer roles)
+            if msg.role in ("user", "assistant", "developer"):
+                input_messages.append(
+                    ResponseInputMessage(role=msg.role, content=content)
+                )
+
+    return ResponsesRequest(
+        model=request.model,
+        input=input_messages if input_messages else "",
+        instructions=instructions,
+        max_output_tokens=request.max_tokens,
+        temperature=request.temperature,
+        top_p=request.top_p,
+        top_k=request.top_k,
+        stop=request.stop,
+        seed=request.seed,
+        stream=request.stream,
+        tools=request.tools,
+        continue_from_prefix=request.continue_from_prefix,
+    )
+
+
+def chunk_to_response(
+    chunk: TokenChunk, command_id: CommandId
+) -> ChatCompletionResponse:
+    """Convert a TokenChunk to a streaming ChatCompletionResponse."""
+    # Build logprobs if available
+    logprobs: Logprobs | None = None
+    if chunk.logprob is not None:
+        logprobs = Logprobs(
+            content=[
+                LogprobsContentItem(
+                    token=chunk.text,
+                    logprob=chunk.logprob,
+                    top_logprobs=chunk.top_logprobs or [],
+                )
+            ]
+        )
+
+    return ChatCompletionResponse(
+        id=command_id,
+        created=int(time.time()),
+        model=chunk.model,
+        choices=[
+            StreamingChoiceResponse(
+                index=0,
+                delta=ChatCompletionMessage(role="assistant", content=chunk.text),
+                logprobs=logprobs,
+                finish_reason=chunk.finish_reason,
+            )
+        ],
+    )
+
+
+async def generate_chat_stream(
+    command_id: CommandId,
+    chunk_stream: AsyncGenerator[TokenChunk, None],
+) -> AsyncGenerator[str, None]:
+    """Generate Chat Completions API streaming events from TokenChunks."""
+    try:
+        async for chunk in chunk_stream:
+            if chunk.finish_reason == "error":
+                error_response = ErrorResponse(
+                    error=ErrorInfo(
+                        message=chunk.error_message or "Internal server error",
+                        type="InternalServerError",
+                        code=500,
+                    )
+                )
+                yield f"data: {error_response.model_dump_json()}\n\n"
+                yield "data: [DONE]\n\n"
+                logger.info(f"generate_chat_stream ending (error): {command_id}")
+                return
+
+            chunk_response = chunk_to_response(chunk, command_id)
+            yield f"data: {chunk_response.model_dump_json()}\n\n"
+
+            if chunk.finish_reason is not None:
+                logger.info(
+                    f"generate_chat_stream yielding [DONE] for finish_reason={chunk.finish_reason}: {command_id}"
+                )
+                yield "data: [DONE]\n\n"
+                logger.info(f"generate_chat_stream returning: {command_id}")
+                return
+    finally:
+        logger.info(f"generate_chat_stream finally block: {command_id}")
+
+
+async def collect_chat_response(
+    command_id: CommandId,
+    chunk_stream: AsyncGenerator[TokenChunk, None],
+) -> ChatCompletionResponse:
+    """Collect all token chunks and return a single ChatCompletionResponse."""
+    text_parts: list[str] = []
+    model: str | None = None
+    finish_reason: FinishReason | None = None
+    error_message: str | None = None
+
+    async for chunk in chunk_stream:
+        if chunk.finish_reason == "error":
+            error_message = chunk.error_message or "Internal server error"
+            break
+
+        if model is None:
+            model = chunk.model
+
+        text_parts.append(chunk.text)
+
+        if chunk.finish_reason is not None:
+            finish_reason = chunk.finish_reason
+
+    if error_message is not None:
+        raise ValueError(error_message)
+
+    combined_text = "".join(text_parts)
+    assert model is not None
+
+    return ChatCompletionResponse(
+        id=command_id,
+        created=int(time.time()),
+        model=model,
+        choices=[
+            ChatCompletionChoice(
+                index=0,
+                message=ChatCompletionMessage(
+                    role="assistant",
+                    content=combined_text,
+                ),
+                finish_reason=finish_reason,
+            )
+        ],
+    )

src/exo/master/adapters/claude.py

@@ -0,0 +1,190 @@
+"""Claude Messages API adapter for converting requests/responses."""
+
+from collections.abc import AsyncGenerator
+
+from exo.shared.types.api import FinishReason
+from exo.shared.types.chunks import TokenChunk
+from exo.shared.types.claude_api import (
+    ClaudeContentBlockDeltaEvent,
+    ClaudeContentBlockStartEvent,
+    ClaudeContentBlockStopEvent,
+    ClaudeMessageDelta,
+    ClaudeMessageDeltaEvent,
+    ClaudeMessageDeltaUsage,
+    ClaudeMessagesRequest,
+    ClaudeMessagesResponse,
+    ClaudeMessageStart,
+    ClaudeMessageStartEvent,
+    ClaudeMessageStopEvent,
+    ClaudeStopReason,
+    ClaudeTextBlock,
+    ClaudeTextDelta,
+    ClaudeUsage,
+)
+from exo.shared.types.common import CommandId
+from exo.shared.types.openai_responses import ResponseInputMessage, ResponsesRequest
+
+
+def finish_reason_to_claude_stop_reason(
+    finish_reason: FinishReason | None,
+) -> ClaudeStopReason | None:
+    """Map OpenAI finish_reason to Claude stop_reason."""
+    if finish_reason is None:
+        return None
+    mapping: dict[FinishReason, ClaudeStopReason] = {
+        "stop": "end_turn",
+        "length": "max_tokens",
+        "tool_calls": "tool_use",
+        "content_filter": "end_turn",
+        "function_call": "tool_use",
+    }
+    return mapping.get(finish_reason, "end_turn")
+
+
+def claude_request_to_internal(request: ClaudeMessagesRequest) -> ResponsesRequest:
+    """Convert Claude Messages API request to ResponsesRequest (canonical internal format).
+
+    Converts Claude's system parameter to instructions,
+    and messages to input.
+    """
+    # Handle system message
+    instructions: str | None = None
+    if request.system:
+        if isinstance(request.system, str):
+            instructions = request.system
+        else:
+            # List of text blocks
+            instructions = "".join(block.text for block in request.system)
+
+    # Convert messages to input
+    input_messages: list[ResponseInputMessage] = []
+    for msg in request.messages:
+        content: str
+        if isinstance(msg.content, str):
+            content = msg.content
+        else:
+            # Concatenate text blocks (images not supported for MVP)
+            text_parts: list[str] = []
+            for block in msg.content:
+                if isinstance(block, ClaudeTextBlock):
+                    text_parts.append(block.text)
+            content = "".join(text_parts)
+
+        # Claude uses "user" and "assistant" roles
+        input_messages.append(ResponseInputMessage(role=msg.role, content=content))
+
+    return ResponsesRequest(
+        model=request.model,
+        input=input_messages if input_messages else "",
+        instructions=instructions,
+        max_output_tokens=request.max_tokens,
+        temperature=request.temperature,
+        top_p=request.top_p,
+        top_k=request.top_k,
+        stop=request.stop_sequences,
+        stream=request.stream,
+    )
+
+
+async def collect_claude_response(
+    command_id: CommandId,
+    model: str,
+    chunk_stream: AsyncGenerator[TokenChunk, None],
+) -> ClaudeMessagesResponse:
+    """Collect all token chunks and return a single ClaudeMessagesResponse."""
+    text_parts: list[str] = []
+    stop_reason: ClaudeStopReason | None = None
+    last_stats = None
+    error_message: str | None = None
+
+    async for chunk in chunk_stream:
+        if chunk.finish_reason == "error":
+            error_message = chunk.error_message or "Internal server error"
+            break
+
+        text_parts.append(chunk.text)
+        last_stats = chunk.stats or last_stats
+
+        if chunk.finish_reason is not None:
+            stop_reason = finish_reason_to_claude_stop_reason(chunk.finish_reason)
+
+    if error_message is not None:
+        raise ValueError(error_message)
+
+    combined_text = "".join(text_parts)
+
+    # Use actual usage data from stats if available
+    input_tokens = last_stats.prompt_tokens if last_stats else 0
+    output_tokens = last_stats.generation_tokens if last_stats else 0
+
+    return ClaudeMessagesResponse(
+        id=f"msg_{command_id}",
+        model=model,
+        content=[ClaudeTextBlock(text=combined_text)],
+        stop_reason=stop_reason,
+        usage=ClaudeUsage(
+            input_tokens=input_tokens,
+            output_tokens=output_tokens,
+        ),
+    )
+
+
+async def generate_claude_stream(
+    command_id: CommandId,
+    model: str,
+    chunk_stream: AsyncGenerator[TokenChunk, None],
+) -> AsyncGenerator[str, None]:
+    """Generate Claude Messages API streaming events from TokenChunks."""
+    # Initial message_start event
+    initial_message = ClaudeMessageStart(
+        id=f"msg_{command_id}",
+        model=model,
+        content=[],
+        stop_reason=None,
+        usage=ClaudeUsage(input_tokens=0, output_tokens=0),
+    )
+    start_event = ClaudeMessageStartEvent(message=initial_message)
+    yield f"event: message_start\ndata: {start_event.model_dump_json()}\n\n"
+
+    # content_block_start
+    block_start = ClaudeContentBlockStartEvent(
+        index=0, content_block=ClaudeTextBlock(text="")
+    )
+    yield f"event: content_block_start\ndata: {block_start.model_dump_json()}\n\n"
+
+    output_tokens = 0
+    stop_reason: ClaudeStopReason | None = None
+    last_stats = None
+
+    async for chunk in chunk_stream:
+        output_tokens += 1  # Count each chunk as one token
+        last_stats = chunk.stats or last_stats
+
+        # content_block_delta
+        delta_event = ClaudeContentBlockDeltaEvent(
+            index=0,
+            delta=ClaudeTextDelta(text=chunk.text),
+        )
+        yield f"event: content_block_delta\ndata: {delta_event.model_dump_json()}\n\n"
+
+        if chunk.finish_reason is not None:
+            stop_reason = finish_reason_to_claude_stop_reason(chunk.finish_reason)
+
+    # Use actual token count from stats if available
+    if last_stats is not None:
+        output_tokens = last_stats.generation_tokens
+
+    # content_block_stop
+    block_stop = ClaudeContentBlockStopEvent(index=0)
+    yield f"event: content_block_stop\ndata: {block_stop.model_dump_json()}\n\n"
+
+    # message_delta
+    message_delta = ClaudeMessageDeltaEvent(
+        delta=ClaudeMessageDelta(stop_reason=stop_reason),
+        usage=ClaudeMessageDeltaUsage(output_tokens=output_tokens),
+    )
+    yield f"event: message_delta\ndata: {message_delta.model_dump_json()}\n\n"
+
+    # message_stop
+    message_stop = ClaudeMessageStopEvent()
+    yield f"event: message_stop\ndata: {message_stop.model_dump_json()}\n\n"

src/exo/master/adapters/responses.py

@@ -0,0 +1,173 @@
+"""OpenAI Responses API adapter for converting requests/responses.
+
+ResponsesRequest is the canonical internal format. Responses API is the most featureful,
+making it the natural choice for the internal format. All other API formats (Chat
+Completions, Claude) are converted TO ResponsesRequest.
+"""
+
+from collections.abc import AsyncGenerator
+
+from exo.shared.types.chunks import TokenChunk
+from exo.shared.types.common import CommandId
+from exo.shared.types.openai_responses import (
+    ResponseCompletedEvent,
+    ResponseContentPartAddedEvent,
+    ResponseContentPartDoneEvent,
+    ResponseCreatedEvent,
+    ResponseInProgressEvent,
+    ResponseMessageItem,
+    ResponseOutputItemAddedEvent,
+    ResponseOutputItemDoneEvent,
+    ResponseOutputText,
+    ResponsesResponse,
+    ResponseTextDeltaEvent,
+    ResponseTextDoneEvent,
+    ResponseUsage,
+)
+
+
+async def collect_responses_response(
+    command_id: CommandId,
+    model: str,
+    chunk_stream: AsyncGenerator[TokenChunk, None],
+) -> ResponsesResponse:
+    """Collect all token chunks and return a single ResponsesResponse."""
+    response_id = f"resp_{command_id}"
+    item_id = f"item_{command_id}"
+    accumulated_text = ""
+    last_stats = None
+    error_message: str | None = None
+
+    async for chunk in chunk_stream:
+        if chunk.finish_reason == "error":
+            error_message = chunk.error_message or "Internal server error"
+            break
+
+        accumulated_text += chunk.text
+        last_stats = chunk.stats or last_stats
+
+    if error_message is not None:
+        raise ValueError(error_message)
+
+    # Create usage from stats if available
+    usage = None
+    if last_stats is not None:
+        usage = ResponseUsage(
+            input_tokens=last_stats.prompt_tokens,
+            output_tokens=last_stats.generation_tokens,
+            total_tokens=last_stats.prompt_tokens + last_stats.generation_tokens,
+        )
+
+    output_item = ResponseMessageItem(
+        id=item_id,
+        content=[ResponseOutputText(text=accumulated_text)],
+        status="completed",
+    )
+
+    return ResponsesResponse(
+        id=response_id,
+        model=model,
+        status="completed",
+        output=[output_item],
+        output_text=accumulated_text,
+        usage=usage,
+    )
+
+
+async def generate_responses_stream(
+    command_id: CommandId,
+    model: str,
+    chunk_stream: AsyncGenerator[TokenChunk, None],
+) -> AsyncGenerator[str, None]:
+    """Generate OpenAI Responses API streaming events from TokenChunks."""
+    response_id = f"resp_{command_id}"
+    item_id = f"item_{command_id}"
+
+    # response.created
+    initial_response = ResponsesResponse(
+        id=response_id,
+        model=model,
+        status="in_progress",
+        output=[],
+        output_text="",
+    )
+    created_event = ResponseCreatedEvent(response=initial_response)
+    yield f"event: response.created\ndata: {created_event.model_dump_json()}\n\n"
+
+    # response.in_progress
+    in_progress_event = ResponseInProgressEvent(response=initial_response)
+    yield f"event: response.in_progress\ndata: {in_progress_event.model_dump_json()}\n\n"
+
+    # response.output_item.added
+    initial_item = ResponseMessageItem(
+        id=item_id,
+        content=[ResponseOutputText(text="")],
+        status="in_progress",
+    )
+    item_added = ResponseOutputItemAddedEvent(output_index=0, item=initial_item)
+    yield f"event: response.output_item.added\ndata: {item_added.model_dump_json()}\n\n"
+
+    # response.content_part.added
+    initial_part = ResponseOutputText(text="")
+    part_added = ResponseContentPartAddedEvent(
+        output_index=0, content_index=0, part=initial_part
+    )
+    yield f"event: response.content_part.added\ndata: {part_added.model_dump_json()}\n\n"
+
+    accumulated_text = ""
+    last_stats = None
+
+    async for chunk in chunk_stream:
+        accumulated_text += chunk.text
+        last_stats = chunk.stats or last_stats
+
+        # response.output_text.delta
+        delta_event = ResponseTextDeltaEvent(
+            output_index=0,
+            content_index=0,
+            delta=chunk.text,
+        )
+        yield f"event: response.output_text.delta\ndata: {delta_event.model_dump_json()}\n\n"
+
+    # response.output_text.done
+    text_done = ResponseTextDoneEvent(
+        output_index=0, content_index=0, text=accumulated_text
+    )
+    yield f"event: response.output_text.done\ndata: {text_done.model_dump_json()}\n\n"
+
+    # response.content_part.done
+    final_part = ResponseOutputText(text=accumulated_text)
+    part_done = ResponseContentPartDoneEvent(
+        output_index=0, content_index=0, part=final_part
+    )
+    yield f"event: response.content_part.done\ndata: {part_done.model_dump_json()}\n\n"
+
+    # response.output_item.done
+    final_item = ResponseMessageItem(
+        id=item_id,
+        content=[ResponseOutputText(text=accumulated_text)],
+        status="completed",
+    )
+    item_done = ResponseOutputItemDoneEvent(output_index=0, item=final_item)
+    yield f"event: response.output_item.done\ndata: {item_done.model_dump_json()}\n\n"
+
+    # Create usage from stats if available
+    usage = None
+    if last_stats is not None:
+        usage = ResponseUsage(
+            input_tokens=last_stats.prompt_tokens,
+            output_tokens=last_stats.generation_tokens,
+            total_tokens=last_stats.prompt_tokens + last_stats.generation_tokens,
+        )
+
+    # response.completed
+    final_response = ResponsesResponse(
+        id=response_id,
+        model=model,
+        status="completed",
+        output=[final_item],
+        output_text=accumulated_text,
+        usage=usage,
+    )
+    completed_event = ResponseCompletedEvent(response=final_response)
+    yield f"event: response.completed\ndata: {completed_event.model_dump_json()}\n\n"

src/exo/master/api.py

@@ -15,6 +15,20 @@ from hypercorn.config import Config
 from hypercorn.typing import ASGIFramework
 from loguru import logger
 
+from exo.master.adapters.chat_completions import (
+    chat_request_to_internal,
+    collect_chat_response,
+    generate_chat_stream,
+)
+from exo.master.adapters.claude import (
+    claude_request_to_internal,
+    collect_claude_response,
+    generate_claude_stream,
+)
+from exo.master.adapters.responses import (
+    collect_responses_response,
+    generate_responses_stream,
+)
 from exo.master.placement import place_instance as get_instance_placements
 from exo.shared.apply import apply
 from exo.shared.election import ElectionMessage
@@ -27,6 +41,7 @@ from exo.shared.types.api import (
     ChatCompletionChoice,
     ChatCompletionMessage,
     ChatCompletionResponse,
+    ChatCompletionTaskParams,
     CreateInstanceParams,
     CreateInstanceResponse,
     DeleteInstanceResponse,
@@ -39,11 +54,12 @@ from exo.shared.types.api import (
     PlaceInstanceParams,
     PlacementPreview,
     PlacementPreviewResponse,
-    SetDraftModelParams,
-    SetDraftModelResponse,
-    StreamingChoiceResponse,
 )
 from exo.shared.types.chunks import TokenChunk
+from exo.shared.types.claude_api import (
+    ClaudeMessagesRequest,
+    ClaudeMessagesResponse,
+)
 from exo.shared.types.commands import (
     ChatCompletion,
     Command,
@@ -51,7 +67,6 @@ from exo.shared.types.commands import (
     DeleteInstance,
     ForwarderCommand,
     PlaceInstance,
-    SetInstanceDraftModel,
     TaskFinished,
 )
 from exo.shared.types.common import CommandId, NodeId, SessionId
@@ -63,8 +78,11 @@ from exo.shared.types.events import (
 )
 from exo.shared.types.memory import Memory
 from exo.shared.types.models import ModelId, ModelMetadata
+from exo.shared.types.openai_responses import (
+    ResponsesRequest,
+    ResponsesResponse,
+)
 from exo.shared.types.state import State
-from exo.shared.types.tasks import ChatCompletionTaskParams
 from exo.shared.types.worker.instances import Instance, InstanceId, InstanceMeta
 from exo.shared.types.worker.shards import Sharding
 from exo.utils.banner import print_startup_banner
@@ -73,23 +91,6 @@ from exo.utils.dashboard_path import find_dashboard
 from exo.utils.event_buffer import OrderedBuffer
 
 
-def chunk_to_response(
-    chunk: TokenChunk, command_id: CommandId
-) -> ChatCompletionResponse:
-    return ChatCompletionResponse(
-        id=command_id,
-        created=int(time.time()),
-        model=chunk.model,
-        choices=[
-            StreamingChoiceResponse(
-                index=0,
-                delta=ChatCompletionMessage(role="assistant", content=chunk.text),
-                finish_reason=chunk.finish_reason,
-            )
-        ],
-    )
-
-
 async def resolve_model_meta(model_id: str) -> ModelMetadata:
     if model_id in MODEL_CARDS:
         model_card = MODEL_CARDS[model_id]
@@ -158,18 +159,19 @@ class API:
         self.paused_ev = anyio.Event()
 
     def _setup_exception_handlers(self) -> None:
-        @self.app.exception_handler(HTTPException)
-        async def http_exception_handler(  # pyright: ignore[reportUnusedFunction]
-            _: Request, exc: HTTPException
-        ) -> JSONResponse:
-            err = ErrorResponse(
-                error=ErrorInfo(
-                    message=exc.detail,
-                    type=HTTPStatus(exc.status_code).phrase,
-                    code=exc.status_code,
-                )
+        self.app.exception_handler(HTTPException)(self.http_exception_handler)
+
+    async def http_exception_handler(
+        self, _: Request, exc: HTTPException
+    ) -> JSONResponse:
+        err = ErrorResponse(
+            error=ErrorInfo(
+                message=exc.detail,
+                type=HTTPStatus(exc.status_code).phrase,
+                code=exc.status_code,
             )
-            return JSONResponse(err.model_dump(), status_code=exc.status_code)
+        )
+        return JSONResponse(err.model_dump(), status_code=exc.status_code)
 
     def _setup_cors(self) -> None:
         self.app.add_middleware(
@@ -188,13 +190,14 @@ class API:
         self.app.get("/instance/previews")(self.get_placement_previews)
         self.app.get("/instance/{instance_id}")(self.get_instance)
         self.app.delete("/instance/{instance_id}")(self.delete_instance)
-        self.app.put("/instance/{instance_id}/draft_model")(self.set_draft_model)
         self.app.get("/models")(self.get_models)
         self.app.get("/v1/models")(self.get_models)
         self.app.post("/v1/chat/completions", response_model=None)(
             self.chat_completions
         )
         self.app.post("/bench/chat/completions")(self.bench_chat_completions)
+        self.app.post("/v1/messages", response_model=None)(self.claude_messages)
+        self.app.post("/v1/responses", response_model=None)(self.openai_responses)
         self.app.get("/state")(lambda: self.state)
         self.app.get("/events")(lambda: self._event_log)
 
@@ -204,8 +207,6 @@ class API:
             sharding=payload.sharding,
             instance_meta=payload.instance_meta,
             min_nodes=payload.min_nodes,
-            draft_model=payload.draft_model,
-            num_draft_tokens=payload.num_draft_tokens,
         )
         await self._send(command)
 
@@ -402,29 +403,13 @@ class API:
             instance_id=instance_id,
         )
 
-    async def set_draft_model(
-        self, instance_id: InstanceId, payload: SetDraftModelParams
-    ) -> SetDraftModelResponse:
-        if instance_id not in self.state.instances:
-            raise HTTPException(status_code=404, detail="Instance not found")
-
-        command = SetInstanceDraftModel(
-            instance_id=instance_id,
-            draft_model=payload.draft_model,
-            num_draft_tokens=payload.num_draft_tokens,
-        )
-        await self._send(command)
-        return SetDraftModelResponse(
-            message="Command received.",
-            command_id=command.command_id,
-            instance_id=instance_id,
-        )
-
-    async def _chat_chunk_stream(
+    async def _token_chunk_stream(
         self, command_id: CommandId
     ) -> AsyncGenerator[TokenChunk, None]:
-        """Yield `TokenChunk`s for a given command until completion."""
+        """Yield `TokenChunk`s for a given command until completion.
 
+        This is the internal low-level stream used by all API adapters.
+        """
         try:
             self._chat_completion_queues[command_id], recv = channel[TokenChunk]()
 
@@ -447,77 +432,6 @@ class API:
             await self._send(command)
             del self._chat_completion_queues[command_id]
 
-    async def _generate_chat_stream(
-        self, command_id: CommandId
-    ) -> AsyncGenerator[str, None]:
-        """Generate chat completion stream as JSON strings."""
-
-        async for chunk in self._chat_chunk_stream(command_id):
-            if chunk.finish_reason == "error":
-                error_response = ErrorResponse(
-                    error=ErrorInfo(
-                        message=chunk.error_message or "Internal server error",
-                        type="InternalServerError",
-                        code=500,
-                    )
-                )
-                yield f"data: {error_response.model_dump_json()}\n\n"
-                yield "data: [DONE]\n\n"
-                return
-
-            chunk_response: ChatCompletionResponse = chunk_to_response(
-                chunk, command_id
-            )
-            logger.debug(f"chunk_response: {chunk_response}")
-
-            yield f"data: {chunk_response.model_dump_json()}\n\n"
-
-            if chunk.finish_reason is not None:
-                yield "data: [DONE]\n\n"
-
-    async def _collect_chat_completion(
-        self, command_id: CommandId
-    ) -> ChatCompletionResponse:
-        """Collect all token chunks for a chat completion and return a single response."""
-
-        text_parts: list[str] = []
-        model: str | None = None
-        finish_reason: FinishReason | None = None
-
-        async for chunk in self._chat_chunk_stream(command_id):
-            if chunk.finish_reason == "error":
-                raise HTTPException(
-                    status_code=500,
-                    detail=chunk.error_message or "Internal server error",
-                )
-
-            if model is None:
-                model = chunk.model
-
-            text_parts.append(chunk.text)
-
-            if chunk.finish_reason is not None:
-                finish_reason = chunk.finish_reason
-
-        combined_text = "".join(text_parts)
-        assert model is not None
-
-        return ChatCompletionResponse(
-            id=command_id,
-            created=int(time.time()),
-            model=model,
-            choices=[
-                ChatCompletionChoice(
-                    index=0,
-                    message=ChatCompletionMessage(
-                        role="assistant",
-                        content=combined_text,
-                    ),
-                    finish_reason=finish_reason,
-                )
-            ],
-        )
-
     async def _collect_chat_completion_with_stats(
         self, command_id: CommandId
     ) -> BenchChatCompletionResponse:
@@ -527,7 +441,7 @@ class API:
 
         stats: GenerationStats | None = None
 
-        async for chunk in self._chat_chunk_stream(command_id):
+        async for chunk in self._token_chunk_stream(command_id):
             if chunk.finish_reason == "error":
                 raise HTTPException(
                     status_code=500,
@@ -571,54 +485,162 @@ class API:
     async def chat_completions(
         self, payload: ChatCompletionTaskParams
     ) -> ChatCompletionResponse | StreamingResponse:
-        """Handle chat completions, supporting both streaming and non-streaming responses."""
-        model_meta = await resolve_model_meta(payload.model)
-        payload.model = model_meta.model_id
+        """OpenAI Chat Completions API - adapter."""
+        internal_params = chat_request_to_internal(payload)
+        model_meta = await resolve_model_meta(internal_params.model)
+        internal_params.model = model_meta.model_id
 
         if not any(
-            instance.shard_assignments.model_id == payload.model
+            instance.shard_assignments.model_id == internal_params.model
             for instance in self.state.instances.values()
         ):
-            await self._trigger_notify_user_to_download_model(payload.model)
+            await self._trigger_notify_user_to_download_model(internal_params.model)
             raise HTTPException(
-                status_code=404, detail=f"No instance found for model {payload.model}"
+                status_code=404,
+                detail=f"No instance found for model {internal_params.model}",
             )
 
-        command = ChatCompletion(
-            request_params=payload,
-        )
+        command = ChatCompletion(request_params=internal_params)
         await self._send(command)
+
         if payload.stream:
             return StreamingResponse(
-                self._generate_chat_stream(command.command_id),
+                generate_chat_stream(
+                    command.command_id,
+                    self._token_chunk_stream(command.command_id),
+                ),
                 media_type="text/event-stream",
+                headers={
+                    "Cache-Control": "no-cache",
+                    "Connection": "close",
+                    "X-Accel-Buffering": "no",
+                },
             )
 
-        return await self._collect_chat_completion(command.command_id)
+        try:
+            return await collect_chat_response(
+                command.command_id,
+                self._token_chunk_stream(command.command_id),
+            )
+        except ValueError as e:
+            raise HTTPException(status_code=500, detail=str(e)) from e
 
     async def bench_chat_completions(
         self, payload: BenchChatCompletionTaskParams
     ) -> BenchChatCompletionResponse:
-        model_meta = await resolve_model_meta(payload.model)
-        payload.model = model_meta.model_id
+        # Convert to internal format (BenchChatCompletionTaskParams extends ChatCompletionTaskParams)
+        internal_params = chat_request_to_internal(payload)
+        model_meta = await resolve_model_meta(internal_params.model)
+        internal_params.model = model_meta.model_id
 
         if not any(
-            instance.shard_assignments.model_id == payload.model
+            instance.shard_assignments.model_id == internal_params.model
             for instance in self.state.instances.values()
         ):
-            await self._trigger_notify_user_to_download_model(payload.model)
+            await self._trigger_notify_user_to_download_model(internal_params.model)
             raise HTTPException(
-                status_code=404, detail=f"No instance found for model {payload.model}"
+                status_code=404,
+                detail=f"No instance found for model {internal_params.model}",
             )
 
-        payload.stream = False
+        internal_params.stream = False
 
-        command = ChatCompletion(request_params=payload)
+        command = ChatCompletion(request_params=internal_params)
         await self._send(command)
 
         response = await self._collect_chat_completion_with_stats(command.command_id)
         return response
 
+    async def claude_messages(
+        self, payload: ClaudeMessagesRequest
+    ) -> ClaudeMessagesResponse | StreamingResponse:
+        """Claude Messages API - adapter."""
+        internal_params = claude_request_to_internal(payload)
+        model_meta = await resolve_model_meta(internal_params.model)
+        internal_params.model = model_meta.model_id
+
+        if not any(
+            instance.shard_assignments.model_id == internal_params.model
+            for instance in self.state.instances.values()
+        ):
+            await self._trigger_notify_user_to_download_model(internal_params.model)
+            raise HTTPException(
+                status_code=404,
+                detail=f"No instance found for model {internal_params.model}",
+            )
+
+        command = ChatCompletion(request_params=internal_params)
+        await self._send(command)
+
+        if payload.stream:
+            return StreamingResponse(
+                generate_claude_stream(
+                    command.command_id,
+                    payload.model,
+                    self._token_chunk_stream(command.command_id),
+                ),
+                media_type="text/event-stream",
+                headers={
+                    "Cache-Control": "no-cache",
+                    "Connection": "close",
+                    "X-Accel-Buffering": "no",
+                },
+            )
+
+        try:
+            return await collect_claude_response(
+                command.command_id,
+                payload.model,
+                self._token_chunk_stream(command.command_id),
+            )
+        except ValueError as e:
+            raise HTTPException(status_code=500, detail=str(e)) from e
+
+    async def openai_responses(
+        self, payload: ResponsesRequest
+    ) -> ResponsesResponse | StreamingResponse:
+        """OpenAI Responses API - native format (no conversion needed)."""
+        model_meta = await resolve_model_meta(payload.model)
+        # Update model to resolved model_id
+        request_params = payload.model_copy(update={"model": model_meta.model_id})
+
+        if not any(
+            instance.shard_assignments.model_id == request_params.model
+            for instance in self.state.instances.values()
+        ):
+            await self._trigger_notify_user_to_download_model(request_params.model)
+            raise HTTPException(
+                status_code=404,
+                detail=f"No instance found for model {request_params.model}",
+            )
+
+        command = ChatCompletion(request_params=request_params)
+        await self._send(command)
+
+        if payload.stream:
+            return StreamingResponse(
+                generate_responses_stream(
+                    command.command_id,
+                    payload.model,
+                    self._token_chunk_stream(command.command_id),
+                ),
+                media_type="text/event-stream",
+                headers={
+                    "Cache-Control": "no-cache",
+                    "Connection": "close",
+                    "X-Accel-Buffering": "no",
+                },
+            )
+
+        try:
+            return await collect_responses_response(
+                command.command_id,
+                payload.model,
+                self._token_chunk_stream(command.command_id),
+            )
+        except ValueError as e:
+            raise HTTPException(status_code=500, detail=str(e)) from e
+
     def _calculate_total_available_memory(self) -> Memory:
         """Calculate total available memory across all nodes in bytes."""
         total_available = Memory()

src/exo/master/main.py

@@ -18,7 +18,6 @@ from exo.shared.types.commands import (
     ForwarderCommand,
     PlaceInstance,
     RequestEventLog,
-    SetInstanceDraftModel,
     TaskFinished,
     TestCommand,
 )
@@ -28,7 +27,6 @@ from exo.shared.types.events import (
     ForwarderEvent,
     IndexedEvent,
     InstanceDeleted,
-    InstanceDraftModelUpdated,
     NodeTimedOut,
     TaskCreated,
     TaskDeleted,
@@ -175,14 +173,6 @@ class Master:
                                 self.state.instances, placement
                             )
                             generated_events.extend(transition_events)
-                        case SetInstanceDraftModel():
-                            generated_events.append(
-                                InstanceDraftModelUpdated(
-                                    instance_id=command.instance_id,
-                                    draft_model=command.draft_model,
-                                    num_draft_tokens=command.num_draft_tokens,
-                                )
-                            )
                         case TaskFinished():
                             generated_events.append(
                                 TaskDeleted(

src/exo/master/placement.py

@@ -3,6 +3,8 @@ from collections.abc import Mapping
 from copy import deepcopy
 from typing import Sequence
 
+from loguru import logger
+
 from exo.master.placement_utils import (
     filter_cycles_by_memory,
     get_mlx_ibv_devices_matrix,
@@ -53,6 +55,7 @@ def place_instance(
 ) -> dict[InstanceId, Instance]:
     all_nodes = list(topology.list_nodes())
 
+    logger.info("finding cycles:")
     cycles = topology.get_cycles()
     singleton_cycles = [[node] for node in all_nodes]
     candidate_cycles = list(
@@ -125,6 +128,10 @@ def place_instance(
     target_instances = dict(deepcopy(current_instances))
 
     if len(selected_cycle) == 1:
+        logger.warning(
+            "You have likely selected ibv for a single node instance; falling back to MlxRing"
+        )
+
         command.instance_meta = InstanceMeta.MlxRing
 
     # TODO: Single node instances
@@ -144,8 +151,6 @@ def place_instance(
                 shard_assignments=shard_assignments,
                 ibv_devices=mlx_ibv_devices,
                 jaccl_coordinators=mlx_jaccl_coordinators,
-                draft_model=command.draft_model,
-                num_draft_tokens=command.num_draft_tokens,
             )
         case InstanceMeta.MlxRing:
             ephemeral_port = random_ephemeral_port()
@@ -159,8 +164,6 @@ def place_instance(
                 shard_assignments=shard_assignments,
                 hosts_by_node=hosts_by_node,
                 ephemeral_port=ephemeral_port,
-                draft_model=command.draft_model,
-                num_draft_tokens=command.num_draft_tokens,
             )
 
     return target_instances

src/exo/master/placement_utils.py

@@ -49,33 +49,83 @@ def get_smallest_cycles(cycles: list[list[NodeInfo]]) -> list[list[NodeInfo]]:
     return [cycle for cycle in cycles if len(cycle) == min_nodes]
 
 
+def allocate_layers_proportionally(
+    total_layers: int,
+    memory_fractions: list[float],
+) -> list[int]:
+    n = len(memory_fractions)
+    if n == 0:
+        raise ValueError("Cannot allocate layers to an empty node list")
+    if total_layers < n:
+        raise ValueError(
+            f"Cannot distribute {total_layers} layers across {n} nodes "
+            "(need at least 1 layer per node)"
+        )
+
+    # Largest remainder: floor each, then distribute remainder by fractional part
+    raw = [f * total_layers for f in memory_fractions]
+    result = [int(r) for r in raw]
+    by_remainder = sorted(range(n), key=lambda i: raw[i] - result[i], reverse=True)
+    for i in range(total_layers - sum(result)):
+        result[by_remainder[i]] += 1
+
+    # Ensure minimum 1 per node by taking from the largest
+    for i in range(n):
+        if result[i] == 0:
+            max_idx = max(range(n), key=lambda j: result[j])
+            assert result[max_idx] > 1
+            result[max_idx] -= 1
+            result[i] = 1
+
+    return result
+
+
 def get_shard_assignments_for_pipeline_parallel(
     model_meta: ModelMetadata,
     selected_cycle: list[NodeWithProfile],
 ):
+    if not selected_cycle:
+        raise ValueError("Cannot create shard assignments for empty node cycle")
+
     cycle_memory = sum(
         (node.node_profile.memory.ram_available for node in selected_cycle),
         start=Memory(),
     )
+
+    if cycle_memory.in_bytes == 0:
+        raise ValueError("Cannot create shard assignments: total available memory is 0")
+
     total_layers = model_meta.n_layers
     world_size = len(selected_cycle)
     runner_to_shard: dict[RunnerId, ShardMetadata] = {}
     node_to_runner: dict[NodeId, RunnerId] = {}
 
-    layers_assigned = 0
-    for i, node in enumerate(selected_cycle):
-        if i == len(selected_cycle) - 1:
-            node_layers = total_layers - layers_assigned
-        else:
-            node_layers = round(
-                total_layers
-                * (
-                    node.node_profile.memory.ram_available.in_bytes
-                    / cycle_memory.in_bytes
-                )
-            )
-            node_layers = max(1, node_layers)
+    layer_allocations = allocate_layers_proportionally(
+        total_layers=total_layers,
+        memory_fractions=[
+            node.node_profile.memory.ram_available.in_bytes / cycle_memory.in_bytes
+            for node in selected_cycle
+        ],
+    )
 
+    # Validate each node has sufficient memory for its assigned layers
+    memory_per_layer = model_meta.storage_size.in_bytes / total_layers
+    for i, (node, node_layers) in enumerate(
+        zip(selected_cycle, layer_allocations, strict=True)
+    ):
+        required_memory = node_layers * memory_per_layer
+        available_memory = node.node_profile.memory.ram_available.in_bytes
+        if required_memory > available_memory:
+            raise ValueError(
+                f"Node {i} ({node.node_id}) has insufficient memory: "
+                f"requires {required_memory / (1024**3):.2f} GB for {node_layers} layers, "
+                f"but only has {available_memory / (1024**3):.2f} GB available"
+            )
+
+    layers_assigned = 0
+    for i, (node, node_layers) in enumerate(
+        zip(selected_cycle, layer_allocations, strict=True)
+    ):
         runner_id = RunnerId()
 
         shard = PipelineShardMetadata(

src/exo/master/tests/test_claude_api.py

@@ -0,0 +1,283 @@
+"""Tests for Claude Messages API conversion functions and types."""
+
+import json
+from typing import Any, cast
+
+import pydantic
+import pytest
+
+from exo.master.adapters.claude import (
+    claude_request_to_internal,
+    finish_reason_to_claude_stop_reason,
+)
+from exo.shared.types.claude_api import (
+    ClaudeContentBlockDeltaEvent,
+    ClaudeContentBlockStartEvent,
+    ClaudeContentBlockStopEvent,
+    ClaudeMessage,
+    ClaudeMessageDelta,
+    ClaudeMessageDeltaEvent,
+    ClaudeMessageDeltaUsage,
+    ClaudeMessagesRequest,
+    ClaudeMessageStart,
+    ClaudeMessageStartEvent,
+    ClaudeMessageStopEvent,
+    ClaudeTextBlock,
+    ClaudeTextDelta,
+    ClaudeUsage,
+)
+
+
+class TestFinishReasonToClaudeStopReason:
+    """Tests for finish_reason to Claude stop_reason mapping."""
+
+    def test_stop_maps_to_end_turn(self):
+        assert finish_reason_to_claude_stop_reason("stop") == "end_turn"
+
+    def test_length_maps_to_max_tokens(self):
+        assert finish_reason_to_claude_stop_reason("length") == "max_tokens"
+
+    def test_tool_calls_maps_to_tool_use(self):
+        assert finish_reason_to_claude_stop_reason("tool_calls") == "tool_use"
+
+    def test_function_call_maps_to_tool_use(self):
+        assert finish_reason_to_claude_stop_reason("function_call") == "tool_use"
+
+    def test_content_filter_maps_to_end_turn(self):
+        assert finish_reason_to_claude_stop_reason("content_filter") == "end_turn"
+
+    def test_none_returns_none(self):
+        assert finish_reason_to_claude_stop_reason(None) is None
+
+
+class TestClaudeRequestToInternal:
+    """Tests for converting Claude Messages API requests to ResponsesRequest."""
+
+    def test_basic_request_conversion(self):
+        request = ClaudeMessagesRequest(
+            model="claude-3-opus",
+            max_tokens=100,
+            messages=[
+                ClaudeMessage(role="user", content="Hello"),
+            ],
+        )
+        params = claude_request_to_internal(request)
+
+        assert params.model == "claude-3-opus"
+        assert params.max_output_tokens == 100
+        assert isinstance(params.input, list)
+        assert len(params.input) == 1
+        assert params.input[0].role == "user"
+        assert params.input[0].content == "Hello"
+        assert params.instructions is None
+
+    def test_request_with_system_string(self):
+        request = ClaudeMessagesRequest(
+            model="claude-3-opus",
+            max_tokens=100,
+            system="You are a helpful assistant.",
+            messages=[
+                ClaudeMessage(role="user", content="Hello"),
+            ],
+        )
+        params = claude_request_to_internal(request)
+
+        assert params.instructions == "You are a helpful assistant."
+        assert isinstance(params.input, list)
+        assert len(params.input) == 1
+        assert params.input[0].role == "user"
+        assert params.input[0].content == "Hello"
+
+    def test_request_with_system_text_blocks(self):
+        request = ClaudeMessagesRequest(
+            model="claude-3-opus",
+            max_tokens=100,
+            system=[
+                ClaudeTextBlock(text="You are helpful. "),
+                ClaudeTextBlock(text="Be concise."),
+            ],
+            messages=[
+                ClaudeMessage(role="user", content="Hello"),
+            ],
+        )
+        params = claude_request_to_internal(request)
+
+        assert params.instructions == "You are helpful. Be concise."
+        assert isinstance(params.input, list)
+        assert len(params.input) == 1
+
+    def test_request_with_content_blocks(self):
+        request = ClaudeMessagesRequest(
+            model="claude-3-opus",
+            max_tokens=100,
+            messages=[
+                ClaudeMessage(
+                    role="user",
+                    content=[
+                        ClaudeTextBlock(text="First part. "),
+                        ClaudeTextBlock(text="Second part."),
+                    ],
+                ),
+            ],
+        )
+        params = claude_request_to_internal(request)
+
+        assert isinstance(params.input, list)
+        assert len(params.input) == 1
+        assert params.input[0].content == "First part. Second part."
+
+    def test_request_with_multi_turn_conversation(self):
+        request = ClaudeMessagesRequest(
+            model="claude-3-opus",
+            max_tokens=100,
+            messages=[
+                ClaudeMessage(role="user", content="Hello"),
+                ClaudeMessage(role="assistant", content="Hi there!"),
+                ClaudeMessage(role="user", content="How are you?"),
+            ],
+        )
+        params = claude_request_to_internal(request)
+
+        assert isinstance(params.input, list)
+        assert len(params.input) == 3
+        assert params.input[0].role == "user"
+        assert params.input[1].role == "assistant"
+        assert params.input[2].role == "user"
+
+    def test_request_with_optional_parameters(self):
+        request = ClaudeMessagesRequest(
+            model="claude-3-opus",
+            max_tokens=100,
+            messages=[ClaudeMessage(role="user", content="Hello")],
+            temperature=0.7,
+            top_p=0.9,
+            top_k=40,
+            stop_sequences=["STOP", "END"],
+            stream=True,
+        )
+        params = claude_request_to_internal(request)
+
+        assert params.temperature == 0.7
+        assert params.top_p == 0.9
+        assert params.top_k == 40
+        assert params.stop == ["STOP", "END"]
+        assert params.stream is True
+
+
+class TestClaudeMessagesRequestValidation:
+    """Tests for Claude Messages API request validation."""
+
+    def test_request_requires_model(self):
+        with pytest.raises(pydantic.ValidationError):
+            ClaudeMessagesRequest.model_validate(
+                {
+                    "max_tokens": 100,
+                    "messages": [{"role": "user", "content": "Hello"}],
+                }
+            )
+
+    def test_request_requires_max_tokens(self):
+        with pytest.raises(pydantic.ValidationError):
+            ClaudeMessagesRequest.model_validate(
+                {
+                    "model": "claude-3-opus",
+                    "messages": [{"role": "user", "content": "Hello"}],
+                }
+            )
+
+    def test_request_requires_messages(self):
+        with pytest.raises(pydantic.ValidationError):
+            ClaudeMessagesRequest.model_validate(
+                {
+                    "model": "claude-3-opus",
+                    "max_tokens": 100,
+                }
+            )
+
+
+class TestClaudeStreamingEvents:
+    """Tests for Claude Messages API streaming event serialization."""
+
+    def test_message_start_event_format(self):
+        message = ClaudeMessageStart(
+            id="msg_123",
+            model="claude-3-opus",
+            content=[],
+            stop_reason=None,
+            usage=ClaudeUsage(input_tokens=10, output_tokens=0),
+        )
+        event = ClaudeMessageStartEvent(message=message)
+        json_str = event.model_dump_json()
+        parsed = cast(dict[str, Any], json.loads(json_str))
+
+        assert parsed["type"] == "message_start"
+        assert parsed["message"]["id"] == "msg_123"
+        assert parsed["message"]["type"] == "message"
+        assert parsed["message"]["role"] == "assistant"
+        assert parsed["message"]["model"] == "claude-3-opus"
+
+    def test_content_block_start_event_format(self):
+        event = ClaudeContentBlockStartEvent(
+            index=0,
+            content_block=ClaudeTextBlock(text=""),
+        )
+        json_str = event.model_dump_json()
+        parsed = cast(dict[str, Any], json.loads(json_str))
+
+        assert parsed["type"] == "content_block_start"
+        assert parsed["index"] == 0
+        assert parsed["content_block"]["type"] == "text"
+        assert parsed["content_block"]["text"] == ""
+
+    def test_content_block_delta_event_format(self):
+        event = ClaudeContentBlockDeltaEvent(
+            index=0,
+            delta=ClaudeTextDelta(text="Hello"),
+        )
+        json_str = event.model_dump_json()
+        parsed = cast(dict[str, Any], json.loads(json_str))
+
+        assert parsed["type"] == "content_block_delta"
+        assert parsed["index"] == 0
+        assert parsed["delta"]["type"] == "text_delta"
+        assert parsed["delta"]["text"] == "Hello"
+
+    def test_content_block_stop_event_format(self):
+        event = ClaudeContentBlockStopEvent(index=0)
+        json_str = event.model_dump_json()
+        parsed = cast(dict[str, Any], json.loads(json_str))
+
+        assert parsed["type"] == "content_block_stop"
+        assert parsed["index"] == 0
+
+    def test_message_delta_event_format(self):
+        event = ClaudeMessageDeltaEvent(
+            delta=ClaudeMessageDelta(stop_reason="end_turn"),
+            usage=ClaudeMessageDeltaUsage(output_tokens=25),
+        )
+        json_str = event.model_dump_json()
+        parsed = cast(dict[str, Any], json.loads(json_str))
+
+        assert parsed["type"] == "message_delta"
+        assert parsed["delta"]["stop_reason"] == "end_turn"
+        assert parsed["usage"]["output_tokens"] == 25
+
+    def test_message_stop_event_format(self):
+        event = ClaudeMessageStopEvent()
+        json_str = event.model_dump_json()
+        parsed = cast(dict[str, Any], json.loads(json_str))
+
+        assert parsed["type"] == "message_stop"
+
+    def test_sse_format(self):
+        """Test that SSE format is correctly generated."""
+        event = ClaudeContentBlockDeltaEvent(
+            index=0,
+            delta=ClaudeTextDelta(text="Hello"),
+        )
+        # Simulate the SSE format used in the streaming generator
+        sse_line = f"event: content_block_delta\ndata: {event.model_dump_json()}\n\n"
+
+        assert sse_line.startswith("event: content_block_delta\n")
+        assert "data: " in sse_line
+        assert sse_line.endswith("\n\n")

src/exo/master/tests/test_master.py

@@ -7,7 +7,6 @@ from loguru import logger
 
 from exo.master.main import Master
 from exo.routing.router import get_node_id_keypair
-from exo.shared.types.api import ChatCompletionMessage, ChatCompletionTaskParams
 from exo.shared.types.commands import (
     ChatCompletion,
     CommandId,
@@ -24,6 +23,7 @@ from exo.shared.types.events import (
 )
 from exo.shared.types.memory import Memory
 from exo.shared.types.models import ModelId, ModelMetadata
+from exo.shared.types.openai_responses import ResponsesRequest
 from exo.shared.types.profiling import (
     MemoryPerformanceProfile,
     NodePerformanceProfile,
@@ -143,13 +143,9 @@ async def test_master():
                 command=(
                     ChatCompletion(
                         command_id=CommandId(),
-                        request_params=ChatCompletionTaskParams(
+                        request_params=ResponsesRequest(
                             model="llama-3.2-1b",
-                            messages=[
-                                ChatCompletionMessage(
-                                    role="user", content="Hello, how are you?"
-                                )
-                            ],
+                            input="Hello, how are you?",
                         ),
                     )
                 ),
@@ -200,11 +196,9 @@ async def test_master():
         assert isinstance(events[2].event, TaskCreated)
         assert events[2].event.task.task_status == TaskStatus.Pending
         assert isinstance(events[2].event.task, ChatCompletionTask)
-        assert events[2].event.task.task_params == ChatCompletionTaskParams(
+        assert events[2].event.task.task_params == ResponsesRequest(
             model="llama-3.2-1b",
-            messages=[
-                ChatCompletionMessage(role="user", content="Hello, how are you?")
-            ],
+            input="Hello, how are you?",
         )
 
         await master.shutdown()

src/exo/master/tests/test_openai_responses_api.py

@@ -0,0 +1,293 @@
+"""Tests for OpenAI Responses API types.
+
+ResponsesRequest is the canonical internal type used throughout the pipeline.
+No conversion is needed for Responses API requests.
+"""
+
+import json
+from typing import Any, cast
+
+import pydantic
+import pytest
+
+from exo.shared.types.openai_responses import (
+    ResponseCompletedEvent,
+    ResponseContentPartAddedEvent,
+    ResponseCreatedEvent,
+    ResponseInputMessage,
+    ResponseMessageItem,
+    ResponseOutputItemAddedEvent,
+    ResponseOutputItemDoneEvent,
+    ResponseOutputText,
+    ResponsesRequest,
+    ResponsesResponse,
+    ResponseTextDeltaEvent,
+    ResponseTextDoneEvent,
+    ResponseUsage,
+)
+
+
+class TestResponsesRequestAsCanonicalType:
+    """Tests for ResponsesRequest as the canonical internal type."""
+
+    def test_string_input(self):
+        request = ResponsesRequest(
+            model="gpt-4o",
+            input="Hello, how are you?",
+        )
+
+        assert request.model == "gpt-4o"
+        assert request.input == "Hello, how are you?"
+        assert request.instructions is None
+
+    def test_message_array_input(self):
+        request = ResponsesRequest(
+            model="gpt-4o",
+            input=[
+                ResponseInputMessage(role="user", content="Hello"),
+                ResponseInputMessage(role="assistant", content="Hi there!"),
+                ResponseInputMessage(role="user", content="How are you?"),
+            ],
+        )
+
+        assert isinstance(request.input, list)
+        assert len(request.input) == 3
+        assert request.input[0].role == "user"
+        assert request.input[0].content == "Hello"
+        assert request.input[1].role == "assistant"
+        assert request.input[1].content == "Hi there!"
+        assert request.input[2].role == "user"
+        assert request.input[2].content == "How are you?"
+
+    def test_request_with_instructions(self):
+        request = ResponsesRequest(
+            model="gpt-4o",
+            input="Hello",
+            instructions="You are a helpful assistant. Be concise.",
+        )
+
+        assert request.input == "Hello"
+        assert request.instructions == "You are a helpful assistant. Be concise."
+
+    def test_request_with_optional_parameters(self):
+        request = ResponsesRequest(
+            model="gpt-4o",
+            input="Hello",
+            max_output_tokens=500,
+            temperature=0.8,
+            top_p=0.95,
+            stream=True,
+        )
+
+        assert request.max_output_tokens == 500
+        assert request.temperature == 0.8
+        assert request.top_p == 0.95
+        assert request.stream is True
+
+    def test_request_with_new_fields(self):
+        """Test the additional fields added for internal use."""
+        request = ResponsesRequest(
+            model="gpt-4o",
+            input="Hello",
+            top_k=40,
+            seed=42,
+            stop=["STOP", "END"],
+            tools=[{"type": "function", "function": {"name": "test"}}],
+        )
+
+        assert request.top_k == 40
+        assert request.seed == 42
+        assert request.stop == ["STOP", "END"]
+        assert request.tools == [{"type": "function", "function": {"name": "test"}}]
+
+    def test_request_with_system_role_in_messages(self):
+        request = ResponsesRequest(
+            model="gpt-4o",
+            input=[
+                ResponseInputMessage(role="system", content="Be helpful"),
+                ResponseInputMessage(role="user", content="Hello"),
+            ],
+        )
+
+        assert isinstance(request.input, list)
+        assert len(request.input) == 2
+        assert request.input[0].role == "system"
+        assert request.input[1].role == "user"
+
+    def test_request_with_developer_role(self):
+        request = ResponsesRequest(
+            model="gpt-4o",
+            input=[
+                ResponseInputMessage(role="developer", content="Internal note"),
+                ResponseInputMessage(role="user", content="Hello"),
+            ],
+        )
+
+        assert isinstance(request.input, list)
+        assert len(request.input) == 2
+        assert request.input[0].role == "developer"
+
+
+class TestResponsesRequestValidation:
+    """Tests for OpenAI Responses API request validation."""
+
+    def test_request_requires_model(self):
+        with pytest.raises(pydantic.ValidationError):
+            ResponsesRequest.model_validate(
+                {
+                    "input": "Hello",
+                }
+            )
+
+    def test_request_requires_input(self):
+        with pytest.raises(pydantic.ValidationError):
+            ResponsesRequest.model_validate(
+                {
+                    "model": "gpt-4o",
+                }
+            )
+
+    def test_request_accepts_string_input(self):
+        request = ResponsesRequest(
+            model="gpt-4o",
+            input="Hello",
+        )
+        assert request.input == "Hello"
+
+    def test_request_accepts_message_array_input(self):
+        request = ResponsesRequest(
+            model="gpt-4o",
+            input=[ResponseInputMessage(role="user", content="Hello")],
+        )
+        assert len(request.input) == 1
+
+
+class TestResponsesStreamingEvents:
+    """Tests for OpenAI Responses API streaming event serialization."""
+
+    def test_response_created_event_format(self):
+        response = ResponsesResponse(
+            id="resp_123",
+            model="gpt-4o",
+            status="in_progress",
+            output=[],
+            output_text="",
+        )
+        event = ResponseCreatedEvent(response=response)
+        json_str = event.model_dump_json()
+        parsed = cast(dict[str, Any], json.loads(json_str))
+
+        assert parsed["type"] == "response.created"
+        assert parsed["response"]["id"] == "resp_123"
+        assert parsed["response"]["object"] == "response"
+        assert parsed["response"]["status"] == "in_progress"
+
+    def test_output_item_added_event_format(self):
+        item = ResponseMessageItem(
+            id="item_123",
+            content=[ResponseOutputText(text="")],
+            status="in_progress",
+        )
+        event = ResponseOutputItemAddedEvent(output_index=0, item=item)
+        json_str = event.model_dump_json()
+        parsed = cast(dict[str, Any], json.loads(json_str))
+
+        assert parsed["type"] == "response.output_item.added"
+        assert parsed["output_index"] == 0
+        assert parsed["item"]["type"] == "message"
+        assert parsed["item"]["id"] == "item_123"
+        assert parsed["item"]["role"] == "assistant"
+
+    def test_content_part_added_event_format(self):
+        part = ResponseOutputText(text="")
+        event = ResponseContentPartAddedEvent(
+            output_index=0,
+            content_index=0,
+            part=part,
+        )
+        json_str = event.model_dump_json()
+        parsed = cast(dict[str, Any], json.loads(json_str))
+
+        assert parsed["type"] == "response.content_part.added"
+        assert parsed["output_index"] == 0
+        assert parsed["content_index"] == 0
+        assert parsed["part"]["type"] == "output_text"
+
+    def test_text_delta_event_format(self):
+        event = ResponseTextDeltaEvent(
+            output_index=0,
+            content_index=0,
+            delta="Hello",
+        )
+        json_str = event.model_dump_json()
+        parsed = cast(dict[str, Any], json.loads(json_str))
+
+        assert parsed["type"] == "response.output_text.delta"
+        assert parsed["output_index"] == 0
+        assert parsed["content_index"] == 0
+        assert parsed["delta"] == "Hello"
+
+    def test_text_done_event_format(self):
+        event = ResponseTextDoneEvent(
+            output_index=0,
+            content_index=0,
+            text="Hello, world!",
+        )
+        json_str = event.model_dump_json()
+        parsed = cast(dict[str, Any], json.loads(json_str))
+
+        assert parsed["type"] == "response.output_text.done"
+        assert parsed["text"] == "Hello, world!"
+
+    def test_output_item_done_event_format(self):
+        item = ResponseMessageItem(
+            id="item_123",
+            content=[ResponseOutputText(text="Hello, world!")],
+            status="completed",
+        )
+        event = ResponseOutputItemDoneEvent(output_index=0, item=item)
+        json_str = event.model_dump_json()
+        parsed = cast(dict[str, Any], json.loads(json_str))
+
+        assert parsed["type"] == "response.output_item.done"
+        assert parsed["item"]["status"] == "completed"
+        assert parsed["item"]["content"][0]["text"] == "Hello, world!"
+
+    def test_response_completed_event_format(self):
+        item = ResponseMessageItem(
+            id="item_123",
+            content=[ResponseOutputText(text="Hello!")],
+            status="completed",
+        )
+        response = ResponsesResponse(
+            id="resp_123",
+            model="gpt-4o",
+            status="completed",
+            output=[item],
+            output_text="Hello!",
+            usage=ResponseUsage(input_tokens=10, output_tokens=5, total_tokens=15),
+        )
+        event = ResponseCompletedEvent(response=response)
+        json_str = event.model_dump_json()
+        parsed = cast(dict[str, Any], json.loads(json_str))
+
+        assert parsed["type"] == "response.completed"
+        assert parsed["response"]["status"] == "completed"
+        assert parsed["response"]["output_text"] == "Hello!"
+        assert parsed["response"]["usage"]["total_tokens"] == 15
+
+    def test_sse_format(self):
+        """Test that SSE format is correctly generated."""
+        event = ResponseTextDeltaEvent(
+            output_index=0,
+            content_index=0,
+            delta="Hello",
+        )
+        # Simulate the SSE format used in the streaming generator
+        sse_line = (
+            f"event: response.output_text.delta\ndata: {event.model_dump_json()}\n\n"
+        )
+
+        assert sse_line.startswith("event: response.output_text.delta\n")
+        assert "data: " in sse_line
+        assert sse_line.endswith("\n\n")

src/exo/master/tests/test_placement.py

@@ -70,7 +70,7 @@ def place_instance_command(model_meta: ModelMetadata) -> PlaceInstance:
     [
         ((500, 500, 1000), 12, (3, 3, 6)),
         ((500, 500, 500), 12, (4, 4, 4)),
-        ((312, 518, 1024), 12, (2, 3, 7)),
+        ((312, 468, 1092), 12, (2, 3, 7)),
     ],
 )
 def test_get_instance_placements_create_instance(

src/exo/master/tests/test_placement_utils.py

@@ -3,6 +3,7 @@ from typing import Callable
 import pytest
 
 from exo.master.placement_utils import (
+    allocate_layers_proportionally,
     filter_cycles_by_memory,
     get_hosts_from_subgraph,
     get_mlx_jaccl_coordinators,
@@ -165,6 +166,9 @@ def test_get_smallest_cycles(
         ((500, 500, 1000), 12, (3, 3, 6)),
         ((500, 500, 500), 12, (4, 4, 4)),
         ((312, 518, 1024), 12, (2, 3, 7)),
+        # Edge case: one node has ~90% of memory - should not over-allocate.
+        # Each node must have enough memory for at least 1 layer (50 KB = 1000/20).
+        ((900, 50, 50), 20, (18, 1, 1)),
     ],
 )
 def test_get_shard_assignments(
@@ -397,3 +401,96 @@ def test_get_mlx_jaccl_coordinators(
     assert coordinators[node_c_id] == (
         f"{conn_c_a.send_back_multiaddr.ip_address}:5000"
     ), "node_c should use the IP from conn_c_a"
+
+
+class TestAllocateLayersProportionally:
+    def test_empty_node_list_raises(self):
+        with pytest.raises(ValueError, match="empty node list"):
+            allocate_layers_proportionally(total_layers=10, memory_fractions=[])
+
+    def test_zero_layers_raises(self):
+        with pytest.raises(ValueError, match="need at least 1 layer per node"):
+            allocate_layers_proportionally(total_layers=0, memory_fractions=[0.5, 0.5])
+
+    def test_negative_layers_raises(self):
+        with pytest.raises(ValueError, match="need at least 1 layer per node"):
+            allocate_layers_proportionally(total_layers=-1, memory_fractions=[0.5, 0.5])
+
+    def test_fewer_layers_than_nodes_raises(self):
+        with pytest.raises(ValueError, match="need at least 1 layer per node"):
+            allocate_layers_proportionally(
+                total_layers=2, memory_fractions=[0.33, 0.33, 0.34]
+            )
+
+    def test_equal_distribution(self):
+        result = allocate_layers_proportionally(
+            total_layers=12, memory_fractions=[0.25, 0.25, 0.25, 0.25]
+        )
+        assert result == [3, 3, 3, 3]
+        assert sum(result) == 12
+
+    def test_proportional_distribution(self):
+        result = allocate_layers_proportionally(
+            total_layers=12, memory_fractions=[0.25, 0.25, 0.50]
+        )
+        assert result == [3, 3, 6]
+        assert sum(result) == 12
+
+    def test_extreme_imbalance_ensures_minimum(self):
+        result = allocate_layers_proportionally(
+            total_layers=20, memory_fractions=[0.975, 0.0125, 0.0125]
+        )
+        assert all(layers >= 1 for layers in result)
+        assert sum(result) == 20
+        # Small nodes get minimum 1 layer
+        assert result == [18, 1, 1]
+
+    def test_single_node_gets_all_layers(self):
+        result = allocate_layers_proportionally(total_layers=10, memory_fractions=[1.0])
+        assert result == [10]
+
+    def test_minimum_viable_allocation(self):
+        result = allocate_layers_proportionally(
+            total_layers=3, memory_fractions=[0.33, 0.33, 0.34]
+        )
+        assert result == [1, 1, 1]
+        assert sum(result) == 3
+
+
+def test_get_shard_assignments_insufficient_memory_raises(
+    topology: Topology,
+    create_node: Callable[[int, NodeId | None], NodeInfo],
+    create_connection: Callable[[NodeId, NodeId], Connection],
+):
+    """Test that ValueError is raised when a node has insufficient memory for its layers."""
+    node_a_id = NodeId()
+    node_b_id = NodeId()
+    node_c_id = NodeId()
+
+    # Node C has only 10 KB but would need 50 KB for 1 layer (1000 KB / 20 layers)
+    node_a = create_node(900 * 1024, node_a_id)
+    node_b = create_node(50 * 1024, node_b_id)
+    node_c = create_node(10 * 1024, node_c_id)  # Insufficient memory
+
+    topology.add_node(node_a)
+    topology.add_node(node_b)
+    topology.add_node(node_c)
+
+    topology.add_connection(create_connection(node_a_id, node_b_id))
+    topology.add_connection(create_connection(node_b_id, node_c_id))
+    topology.add_connection(create_connection(node_c_id, node_a_id))
+    topology.add_connection(create_connection(node_b_id, node_a_id))
+
+    model_meta = ModelMetadata(
+        model_id=ModelId("test-model"),
+        pretty_name="Test Model",
+        n_layers=20,
+        storage_size=Memory.from_kb(1000),
+        hidden_size=1000,
+        supports_tensor=True,
+    )
+    cycles = topology.get_cycles()
+    selected_cycle = cycles[0]
+
+    with pytest.raises(ValueError, match="insufficient memory"):
+        get_shard_assignments(model_meta, selected_cycle, Sharding.Pipeline)

src/exo/shared/apply.py

@@ -11,7 +11,6 @@ from exo.shared.types.events import (
     IndexedEvent,
     InstanceCreated,
     InstanceDeleted,
-    InstanceDraftModelUpdated,
     NodeCreated,
     NodeDownloadProgress,
     NodeMemoryMeasured,
@@ -48,8 +47,6 @@ def event_apply(event: Event, state: State) -> State:
             return apply_instance_created(event, state)
         case InstanceDeleted():
             return apply_instance_deleted(event, state)
-        case InstanceDraftModelUpdated():
-            return apply_instance_draft_model_updated(event, state)
         case NodeCreated():
             return apply_topology_node_created(event, state)
         case NodeTimedOut():
@@ -172,25 +169,6 @@ def apply_instance_deleted(event: InstanceDeleted, state: State) -> State:
     return state.model_copy(update={"instances": new_instances})
 
 
-def apply_instance_draft_model_updated(
-    event: InstanceDraftModelUpdated, state: State
-) -> State:
-    if event.instance_id not in state.instances:
-        return state
-    instance = state.instances[event.instance_id]
-    updated_instance = instance.model_copy(
-        update={
-            "draft_model": event.draft_model,
-            "num_draft_tokens": event.num_draft_tokens,
-        }
-    )
-    new_instances: Mapping[InstanceId, Instance] = {
-        **state.instances,
-        event.instance_id: updated_instance,
-    }
-    return state.model_copy(update={"instances": new_instances})
-
-
 def apply_runner_status_updated(event: RunnerStatusUpdated, state: State) -> State:
     new_runners: Mapping[RunnerId, RunnerStatus] = {
         **state.runners,

src/exo/shared/types/api.py

@@ -157,12 +157,13 @@ class ChatCompletionTaskParams(BaseModel):
     stream: bool = False
     temperature: float | None = None
     top_p: float | None = None
+    top_k: int | None = None
     tools: list[dict[str, Any]] | None = None
     tool_choice: str | dict[str, Any] | None = None
     parallel_tool_calls: bool | None = None
     user: str | None = None
-    # Speculative decoding: tokens to draft per iteration (if instance has draft model)
-    num_draft_tokens: int = 3
+    # When True, continue the last assistant message without EOS tokens
+    continue_from_prefix: bool = False
 
 
 class BenchChatCompletionTaskParams(ChatCompletionTaskParams):
@@ -174,8 +175,6 @@ class PlaceInstanceParams(BaseModel):
     sharding: Sharding = Sharding.Pipeline
     instance_meta: InstanceMeta = InstanceMeta.MlxRing
     min_nodes: int = 1
-    draft_model: ModelId | None = None  # For speculative decoding
-    num_draft_tokens: int = 4  # Tokens to draft per iteration
 
     @field_validator("sharding", "instance_meta", mode="plain")
     @classmethod
@@ -217,14 +216,3 @@ class DeleteInstanceResponse(BaseModel):
     message: str
     command_id: CommandId
     instance_id: InstanceId
-
-
-class SetDraftModelParams(BaseModel):
-    draft_model: ModelId | None = None  # None to disable speculative decoding
-    num_draft_tokens: int = 4
-
-
-class SetDraftModelResponse(BaseModel):
-    message: str
-    command_id: CommandId
-    instance_id: InstanceId

src/exo/shared/types/chunks.py

@@ -1,6 +1,6 @@
 from enum import Enum
 
-from exo.shared.types.api import GenerationStats
+from exo.shared.types.api import GenerationStats, TopLogprobItem
 from exo.utils.pydantic_ext import TaggedModel
 
 from .api import FinishReason
@@ -20,6 +20,8 @@ class BaseChunk(TaggedModel):
 class TokenChunk(BaseChunk):
     text: str
     token_id: int
+    logprob: float | None = None  # Log probability of the selected token
+    top_logprobs: list[TopLogprobItem] | None = None  # Top-k alternative tokens
     finish_reason: FinishReason | None = None
     stats: GenerationStats | None = None
     error_message: str | None = None

src/exo/shared/types/claude_api.py

@@ -0,0 +1,168 @@
+"""Claude Messages API types for request/response conversion."""
+
+from typing import Literal
+
+from pydantic import BaseModel, Field
+
+# Type aliases
+ClaudeRole = Literal["user", "assistant"]
+ClaudeStopReason = Literal["end_turn", "max_tokens", "stop_sequence", "tool_use"]
+
+
+# Content block types
+class ClaudeTextBlock(BaseModel, frozen=True):
+    """Text content block in Claude Messages API."""
+
+    type: Literal["text"] = "text"
+    text: str
+
+
+class ClaudeImageSource(BaseModel, frozen=True):
+    """Image source for Claude image blocks."""
+
+    type: Literal["base64", "url"]
+    media_type: str | None = None
+    data: str | None = None
+    url: str | None = None
+
+
+class ClaudeImageBlock(BaseModel, frozen=True):
+    """Image content block in Claude Messages API."""
+
+    type: Literal["image"] = "image"
+    source: ClaudeImageSource
+
+
+ClaudeContentBlock = ClaudeTextBlock | ClaudeImageBlock
+
+
+# Request types
+class ClaudeMessage(BaseModel, frozen=True):
+    """Message in Claude Messages API request."""
+
+    role: ClaudeRole
+    content: str | list[ClaudeContentBlock]
+
+
+class ClaudeMessagesRequest(BaseModel):
+    """Request body for Claude Messages API."""
+
+    model: str
+    max_tokens: int
+    messages: list[ClaudeMessage]
+    system: str | list[ClaudeTextBlock] | None = None
+    stop_sequences: list[str] | None = None
+    stream: bool = False
+    temperature: float | None = None
+    top_p: float | None = None
+    top_k: int | None = None
+    metadata: dict[str, str] | None = None
+
+
+# Response types
+class ClaudeUsage(BaseModel, frozen=True):
+    """Token usage in Claude Messages API response."""
+
+    input_tokens: int
+    output_tokens: int
+
+
+class ClaudeMessagesResponse(BaseModel, frozen=True):
+    """Response body for Claude Messages API."""
+
+    id: str
+    type: Literal["message"] = "message"
+    role: Literal["assistant"] = "assistant"
+    content: list[ClaudeTextBlock]
+    model: str
+    stop_reason: ClaudeStopReason | None = None
+    stop_sequence: str | None = None
+    usage: ClaudeUsage
+
+
+# Streaming event types
+class ClaudeMessageStart(BaseModel, frozen=True):
+    """Partial message in message_start event."""
+
+    id: str
+    type: Literal["message"] = "message"
+    role: Literal["assistant"] = "assistant"
+    content: list[ClaudeTextBlock] = Field(default_factory=list)
+    model: str
+    stop_reason: ClaudeStopReason | None = None
+    stop_sequence: str | None = None
+    usage: ClaudeUsage
+
+
+class ClaudeMessageStartEvent(BaseModel, frozen=True):
+    """Event sent at start of message stream."""
+
+    type: Literal["message_start"] = "message_start"
+    message: ClaudeMessageStart
+
+
+class ClaudeContentBlockStartEvent(BaseModel, frozen=True):
+    """Event sent at start of a content block."""
+
+    type: Literal["content_block_start"] = "content_block_start"
+    index: int
+    content_block: ClaudeTextBlock
+
+
+class ClaudeTextDelta(BaseModel, frozen=True):
+    """Delta for text content block."""
+
+    type: Literal["text_delta"] = "text_delta"
+    text: str
+
+
+class ClaudeContentBlockDeltaEvent(BaseModel, frozen=True):
+    """Event sent for content block delta."""
+
+    type: Literal["content_block_delta"] = "content_block_delta"
+    index: int
+    delta: ClaudeTextDelta
+
+
+class ClaudeContentBlockStopEvent(BaseModel, frozen=True):
+    """Event sent at end of a content block."""
+
+    type: Literal["content_block_stop"] = "content_block_stop"
+    index: int
+
+
+class ClaudeMessageDeltaUsage(BaseModel, frozen=True):
+    """Usage in message_delta event."""
+
+    output_tokens: int
+
+
+class ClaudeMessageDelta(BaseModel, frozen=True):
+    """Delta in message_delta event."""
+
+    stop_reason: ClaudeStopReason | None = None
+    stop_sequence: str | None = None
+
+
+class ClaudeMessageDeltaEvent(BaseModel, frozen=True):
+    """Event sent with final message delta."""
+
+    type: Literal["message_delta"] = "message_delta"
+    delta: ClaudeMessageDelta
+    usage: ClaudeMessageDeltaUsage
+
+
+class ClaudeMessageStopEvent(BaseModel, frozen=True):
+    """Event sent at end of message stream."""
+
+    type: Literal["message_stop"] = "message_stop"
+
+
+ClaudeStreamEvent = (
+    ClaudeMessageStartEvent
+    | ClaudeContentBlockStartEvent
+    | ClaudeContentBlockDeltaEvent
+    | ClaudeContentBlockStopEvent
+    | ClaudeMessageDeltaEvent
+    | ClaudeMessageStopEvent
+)

src/exo/shared/types/commands.py

@@ -1,8 +1,8 @@
 from pydantic import Field
 
-from exo.shared.types.api import ChatCompletionTaskParams
 from exo.shared.types.common import CommandId, NodeId
-from exo.shared.types.models import ModelId, ModelMetadata
+from exo.shared.types.models import ModelMetadata
+from exo.shared.types.openai_responses import ResponsesRequest
 from exo.shared.types.worker.instances import Instance, InstanceId, InstanceMeta
 from exo.shared.types.worker.shards import Sharding
 from exo.utils.pydantic_ext import CamelCaseModel, TaggedModel
@@ -17,7 +17,7 @@ class TestCommand(BaseCommand):
 
 
 class ChatCompletion(BaseCommand):
-    request_params: ChatCompletionTaskParams
+    request_params: ResponsesRequest
 
 
 class PlaceInstance(BaseCommand):
@@ -25,8 +25,6 @@ class PlaceInstance(BaseCommand):
     sharding: Sharding
     instance_meta: InstanceMeta
     min_nodes: int
-    draft_model: ModelId | None = None  # For speculative decoding
-    num_draft_tokens: int = 4  # Tokens to draft per iteration
 
 
 class CreateInstance(BaseCommand):
@@ -37,14 +35,6 @@ class DeleteInstance(BaseCommand):
     instance_id: InstanceId
 
 
-class SetInstanceDraftModel(BaseCommand):
-    """Set or update the draft model for an existing instance."""
-
-    instance_id: InstanceId
-    draft_model: ModelId | None  # None to disable speculative decoding
-    num_draft_tokens: int = 4
-
-
 class TaskFinished(BaseCommand):
     finished_command_id: CommandId
 
@@ -60,7 +50,6 @@ Command = (
     | PlaceInstance
     | CreateInstance
     | DeleteInstance
-    | SetInstanceDraftModel
     | TaskFinished
 )
 

src/exo/shared/types/events.py

@@ -5,7 +5,6 @@ from pydantic import Field
 from exo.shared.topology import Connection, NodePerformanceProfile
 from exo.shared.types.chunks import GenerationChunk
 from exo.shared.types.common import CommandId, Id, NodeId, SessionId
-from exo.shared.types.models import ModelId
 from exo.shared.types.profiling import MemoryPerformanceProfile
 from exo.shared.types.tasks import Task, TaskId, TaskStatus
 from exo.shared.types.worker.downloads import DownloadProgress
@@ -68,14 +67,6 @@ class InstanceDeleted(BaseEvent):
     instance_id: InstanceId
 
 
-class InstanceDraftModelUpdated(BaseEvent):
-    """Draft model updated on an existing instance."""
-
-    instance_id: InstanceId
-    draft_model: ModelId | None
-    num_draft_tokens: int
-
-
 class RunnerStatusUpdated(BaseEvent):
     runner_id: RunnerId
     runner_status: RunnerStatus
@@ -132,7 +123,6 @@ Event = (
     | TaskAcknowledged
     | InstanceCreated
     | InstanceDeleted
-    | InstanceDraftModelUpdated
     | RunnerStatusUpdated
     | RunnerDeleted
     | NodeCreated

src/exo/shared/types/openai_responses.py

@@ -0,0 +1,190 @@
+"""OpenAI Responses API types for request/response conversion.
+
+ResponsesRequest serves as both:
+1. The external API request type for /v1/responses
+2. The canonical internal type used throughout the inference pipeline
+
+All external API formats (Chat Completions, Claude) are converted to
+ResponsesRequest at the API boundary.
+"""
+
+import time
+from typing import Any, Literal
+
+from pydantic import BaseModel, Field
+
+# Type aliases
+ResponseStatus = Literal["completed", "failed", "in_progress", "incomplete"]
+ResponseRole = Literal["user", "assistant", "system", "developer"]
+
+
+# Request types
+class ResponseInputMessage(BaseModel, frozen=True):
+    """Input message for Responses API.
+
+    This is also used as the internal message format throughout the pipeline.
+    """
+
+    role: ResponseRole
+    content: str
+
+
+class ResponsesRequest(BaseModel):
+    """Request body for OpenAI Responses API.
+
+    This is also the canonical internal task params format used throughout
+    the inference pipeline. All external API formats are converted to this
+    format at the API boundary.
+
+    Field mapping from other APIs:
+    - input: Replaces 'messages' from Chat Completions
+    - instructions: System message, extracted from messages or Claude's 'system'
+    - max_output_tokens: Replaces 'max_tokens' from Chat Completions
+    """
+
+    model: str
+    input: str | list[ResponseInputMessage]
+    instructions: str | None = None
+    max_output_tokens: int | None = None
+    temperature: float | None = None
+    top_p: float | None = None
+    top_k: int | None = None
+    stop: str | list[str] | None = None
+    seed: int | None = None
+    stream: bool = False
+    # Tools support
+    tools: list[dict[str, Any]] | None = None
+    # previous_response_id not supported in MVP
+    metadata: dict[str, str] | None = None
+    # When True, continue the last assistant message without EOS tokens
+    continue_from_prefix: bool = False
+
+
+# Response types
+class ResponseOutputText(BaseModel, frozen=True):
+    """Text content in response output."""
+
+    type: Literal["output_text"] = "output_text"
+    text: str
+    annotations: list[dict[str, str]] = Field(default_factory=list)
+
+
+class ResponseMessageItem(BaseModel, frozen=True):
+    """Message item in response output array."""
+
+    type: Literal["message"] = "message"
+    id: str
+    role: Literal["assistant"] = "assistant"
+    content: list[ResponseOutputText]
+    status: ResponseStatus = "completed"
+
+
+ResponseItem = ResponseMessageItem  # Can expand for function_call, reasoning, etc.
+
+
+class ResponseUsage(BaseModel, frozen=True):
+    """Token usage in Responses API response."""
+
+    input_tokens: int
+    output_tokens: int
+    total_tokens: int
+
+
+class ResponsesResponse(BaseModel, frozen=True):
+    """Response body for OpenAI Responses API."""
+
+    id: str
+    object: Literal["response"] = "response"
+    created_at: int = Field(default_factory=lambda: int(time.time()))
+    status: ResponseStatus = "completed"
+    model: str
+    output: list[ResponseItem]
+    output_text: str
+    usage: ResponseUsage | None = None
+
+
+# Streaming event types
+class ResponseCreatedEvent(BaseModel, frozen=True):
+    """Event sent when response is created."""
+
+    type: Literal["response.created"] = "response.created"
+    response: ResponsesResponse
+
+
+class ResponseInProgressEvent(BaseModel, frozen=True):
+    """Event sent when response starts processing."""
+
+    type: Literal["response.in_progress"] = "response.in_progress"
+    response: ResponsesResponse
+
+
+class ResponseOutputItemAddedEvent(BaseModel, frozen=True):
+    """Event sent when an output item is added."""
+
+    type: Literal["response.output_item.added"] = "response.output_item.added"
+    output_index: int
+    item: ResponseItem
+
+
+class ResponseContentPartAddedEvent(BaseModel, frozen=True):
+    """Event sent when a content part is added."""
+
+    type: Literal["response.content_part.added"] = "response.content_part.added"
+    output_index: int
+    content_index: int
+    part: ResponseOutputText
+
+
+class ResponseTextDeltaEvent(BaseModel, frozen=True):
+    """Event sent for text delta during streaming."""
+
+    type: Literal["response.output_text.delta"] = "response.output_text.delta"
+    output_index: int
+    content_index: int
+    delta: str
+
+
+class ResponseTextDoneEvent(BaseModel, frozen=True):
+    """Event sent when text content is done."""
+
+    type: Literal["response.output_text.done"] = "response.output_text.done"
+    output_index: int
+    content_index: int
+    text: str
+
+
+class ResponseContentPartDoneEvent(BaseModel, frozen=True):
+    """Event sent when a content part is done."""
+
+    type: Literal["response.content_part.done"] = "response.content_part.done"
+    output_index: int
+    content_index: int
+    part: ResponseOutputText
+
+
+class ResponseOutputItemDoneEvent(BaseModel, frozen=True):
+    """Event sent when an output item is done."""
+
+    type: Literal["response.output_item.done"] = "response.output_item.done"
+    output_index: int
+    item: ResponseItem
+
+
+class ResponseCompletedEvent(BaseModel, frozen=True):
+    """Event sent when response is completed."""
+
+    type: Literal["response.completed"] = "response.completed"
+    response: ResponsesResponse
+
+
+ResponsesStreamEvent = (
+    ResponseCreatedEvent
+    | ResponseInProgressEvent
+    | ResponseOutputItemAddedEvent
+    | ResponseContentPartAddedEvent
+    | ResponseTextDeltaEvent
+    | ResponseTextDoneEvent
+    | ResponseContentPartDoneEvent
+    | ResponseOutputItemDoneEvent
+    | ResponseCompletedEvent
+)

src/exo/shared/types/tasks.py

@@ -2,8 +2,8 @@ from enum import Enum
 
 from pydantic import Field
 
-from exo.shared.types.api import ChatCompletionTaskParams
 from exo.shared.types.common import CommandId, Id
+from exo.shared.types.openai_responses import ResponsesRequest
 from exo.shared.types.worker.instances import BoundInstance, InstanceId
 from exo.shared.types.worker.runners import RunnerId
 from exo.shared.types.worker.shards import ShardMetadata
@@ -36,12 +36,6 @@ class DownloadModel(BaseTask):  # emitted by Worker
     shard_metadata: ShardMetadata
 
 
-class DownloadDraftModel(BaseTask):  # emitted by Worker
-    """Download a draft model for speculative decoding (rank 0 only)."""
-
-    model_id: str  # HuggingFace model ID
-
-
 class LoadModel(BaseTask):  # emitted by Worker
     pass
 
@@ -56,7 +50,7 @@ class StartWarmup(BaseTask):  # emitted by Worker
 
 class ChatCompletion(BaseTask):  # emitted by Master
     command_id: CommandId
-    task_params: ChatCompletionTaskParams
+    task_params: ResponsesRequest
 
     error_type: str | None = Field(default=None)
     error_message: str | None = Field(default=None)
@@ -66,21 +60,12 @@ class Shutdown(BaseTask):  # emitted by Worker
     runner_id: RunnerId
 
 
-class SetDraftModel(BaseTask):  # emitted by Worker
-    """Load or clear a draft model on an already-running instance."""
-
-    model_id: str | None  # HuggingFace model ID, or None to clear
-    num_draft_tokens: int = 4
-
-
 Task = (
     CreateRunner
     | DownloadModel
-    | DownloadDraftModel
     | ConnectToGroup
     | LoadModel
     | StartWarmup
     | ChatCompletion
     | Shutdown
-    | SetDraftModel
 )

src/exo/shared/types/worker/instances.py

@@ -3,7 +3,6 @@ from enum import Enum
 from pydantic import model_validator
 
 from exo.shared.types.common import Host, Id, NodeId
-from exo.shared.types.models import ModelId
 from exo.shared.types.worker.runners import RunnerId, ShardAssignments, ShardMetadata
 from exo.utils.pydantic_ext import CamelCaseModel, TaggedModel
 
@@ -20,8 +19,6 @@ class InstanceMeta(str, Enum):
 class BaseInstance(TaggedModel):
     instance_id: InstanceId
     shard_assignments: ShardAssignments
-    draft_model: ModelId | None = None  # For speculative decoding (rank 0 only)
-    num_draft_tokens: int = 4  # Tokens to draft per iteration (when draft_model is set)
 
     def shard(self, runner_id: RunnerId) -> ShardMetadata | None:
         return self.shard_assignments.runner_to_shard.get(runner_id, None)

src/exo/shared/types/worker/runner_response.py

@@ -1,4 +1,4 @@
-from exo.shared.types.api import FinishReason, GenerationStats
+from exo.shared.types.api import FinishReason, GenerationStats, TopLogprobItem
 from exo.utils.pydantic_ext import TaggedModel
 
 
@@ -13,7 +13,8 @@ class TokenizedResponse(BaseRunnerResponse):
 class GenerationResponse(BaseRunnerResponse):
     text: str
     token: int
-    # logprobs: list[float] | None = None # too big. we can change to be top-k
+    logprob: float | None = None  # Log probability of the selected token
+    top_logprobs: list[TopLogprobItem] | None = None  # Top-k alternative tokens
     finish_reason: FinishReason | None = None
     stats: GenerationStats | None = None
 

src/exo/worker/engines/mlx/__init__.py

@@ -40,4 +40,6 @@ class TokenizerWrapper:
         messages_dicts: list[dict[str, Any]],
         tokenize: bool = False,
         add_generation_prompt: bool = True,
+        continue_final_message: bool = False,
+        tools: list[dict[str, Any]] | None = None,
     ) -> str: ...

src/exo/worker/engines/mlx/generator/generate.py

@@ -8,13 +8,12 @@ from mlx_lm.tokenizer_utils import TokenizerWrapper
 
 # from exo.engines.mlx.cache import KVPrefixCache
 from exo.shared.types.api import (
-    BenchChatCompletionTaskParams,
-    ChatCompletionMessage,
     FinishReason,
     GenerationStats,
+    TopLogprobItem,
 )
 from exo.shared.types.memory import Memory
-from exo.shared.types.tasks import ChatCompletionTaskParams
+from exo.shared.types.openai_responses import ResponsesRequest
 from exo.shared.types.worker.runner_response import (
     GenerationResponse,
 )
@@ -48,50 +47,38 @@ def maybe_quantize_kv_cache(
 def warmup_inference(
     model: Model,
     tokenizer: TokenizerWrapper,
-    draft_model: Model | None = None,
-    num_draft_tokens: int = 4,
 ) -> int:
     content = "Prompt to warm up the inference engine. Repeat this."
 
     warmup_prompt = apply_chat_template(
         tokenizer=tokenizer,
-        chat_task_data=ChatCompletionTaskParams(
+        task_params=ResponsesRequest(
             model="",
-            messages=[
-                ChatCompletionMessage(
-                    role="user",
-                    content=content,
-                )
-            ],
+            input=content,
         ),
     )
 
     tokens_generated = 0
 
+    cache = make_kv_cache(
+        model=model,
+    )
+
     # Use a default sampler for warmup
     sampler = make_sampler(temp=0.7)
 
-    generate_kwargs: dict[str, object] = {
-        "model": model,
-        "tokenizer": tokenizer,
-        "prompt": warmup_prompt,
-        "max_tokens": 50,
-        "sampler": sampler,
-        "prefill_step_size": 2048,
-        "kv_group_size": KV_GROUP_SIZE,
-        "kv_bits": KV_BITS,
-    }
-
-    # Warm up with draft model if provided (speculative decoding path)
-    if draft_model is not None:
-        logger.info("Warming up with speculative decoding (draft model)")
-        generate_kwargs["draft_model"] = draft_model
-        generate_kwargs["num_draft_tokens"] = num_draft_tokens
-    else:
-        generate_kwargs["prompt_cache"] = make_kv_cache(model=model)
-
     logger.info("Generating warmup tokens")
-    for _r in stream_generate(**generate_kwargs):  # type: ignore[arg-type]
+    for _r in stream_generate(
+        model=model,
+        tokenizer=tokenizer,
+        prompt=warmup_prompt,
+        max_tokens=50,
+        sampler=sampler,
+        prompt_cache=cache,
+        prefill_step_size=2048,
+        kv_group_size=KV_GROUP_SIZE,
+        kv_bits=KV_BITS,
+    ):
         logger.info("Generated warmup token: " + str(_r.text))
         tokens_generated += 1
 
@@ -122,16 +109,68 @@ def eos_ids_from_tokenizer(tokenizer: TokenizerWrapper) -> list[int]:
     return eos
 
 
+def extract_top_logprobs(
+    logprobs: mx.array,
+    tokenizer: TokenizerWrapper,
+    top_k: int,
+    selected_token: int,
+) -> tuple[float, list[TopLogprobItem]]:
+    """Extract the selected token's logprob and top-k alternative tokens.
+
+    Args:
+        logprobs: Full vocabulary logprobs array from MLX
+        tokenizer: Tokenizer for decoding token IDs to strings
+        top_k: Number of top alternatives to return
+        selected_token: The token ID that was actually sampled
+
+    Returns:
+        Tuple of (selected_token_logprob, list of TopLogprobItem for top-k tokens)
+    """
+    # Get the logprob of the selected token
+    selected_logprob = float(logprobs[selected_token].item())
+
+    # Get top-k indices (most probable tokens)
+    # mx.argpartition gives indices that would partition the array
+    # We negate logprobs since argpartition finds smallest, and we want largest
+    top_k = min(top_k, logprobs.shape[0])  # Don't exceed vocab size
+    top_indices = mx.argpartition(-logprobs, top_k)[:top_k]
+
+    # Get the actual logprob values for these indices
+    top_values = logprobs[top_indices]
+
+    # Sort by logprob (descending) for consistent ordering
+    sort_order = mx.argsort(-top_values)
+    top_indices = top_indices[sort_order]
+    top_values = top_values[sort_order]
+
+    # Convert to list of TopLogprobItem
+    top_logprob_items: list[TopLogprobItem] = []
+    for i in range(top_k):
+        token_id = int(top_indices[i].item())
+        token_logprob = float(top_values[i].item())
+        # Decode token ID to string
+        token_str = tokenizer.decode([token_id])
+        # Get byte representation
+        token_bytes = list(token_str.encode("utf-8"))
+        top_logprob_items.append(
+            TopLogprobItem(
+                token=token_str,
+                logprob=token_logprob,
+                bytes=token_bytes,
+            )
+        )
+
+    return selected_logprob, top_logprob_items
+
+
 def mlx_generate(
     model: Model,
     tokenizer: TokenizerWrapper,
-    task: ChatCompletionTaskParams,
-    draft_model: Model | None = None,
-    num_draft_tokens: int = 4,
+    task: ResponsesRequest,
+    is_bench: bool = False,
 ) -> Generator[GenerationResponse]:
     # Ensure that generation stats only contains peak memory for this generation
     mx.reset_peak_memory()
-    is_bench: bool = isinstance(task, BenchChatCompletionTaskParams)
 
     # Currently we support chat-completion tasks only.
     logger.info(f"task_params: {task}")
@@ -141,9 +180,11 @@ def mlx_generate(
 
     prompt = apply_chat_template(
         tokenizer=tokenizer,
-        chat_task_data=task,
+        task_params=task,
     )
 
+    caches = make_kv_cache(model=model)
+
     logits_processors: list[Callable[[mx.array, mx.array], mx.array]] = []
     if is_bench:
         # Only sample length eos tokens
@@ -153,38 +194,57 @@ def mlx_generate(
     sampler = make_sampler(
         temp=task.temperature if task.temperature is not None else 0.7,
         top_p=task.top_p if task.top_p is not None else 1.0,
+        top_k=task.top_k if task.top_k is not None else 0,
     )
 
-    max_tokens = task.max_tokens or MAX_TOKENS
+    # Normalize stop sequences to a list
+    stop_sequences: list[str] = (
+        ([task.stop] if isinstance(task.stop, str) else task.stop)
+        if task.stop is not None
+        else []
+    )
+    max_stop_len = max((len(s) for s in stop_sequences), default=0)
 
-    # Build kwargs for stream_generate, conditionally adding draft model params
-    generate_kwargs: dict[str, object] = {
-        "model": model,
-        "tokenizer": tokenizer,
-        "prompt": prompt,
-        "max_tokens": max_tokens,
-        "sampler": sampler,
-        "logits_processors": logits_processors,
-        "prefill_step_size": 2048,
-        "kv_group_size": KV_GROUP_SIZE,
-        "kv_bits": KV_BITS,
-    }
+    max_tokens = task.max_output_tokens or MAX_TOKENS
+    accumulated_text = ""
 
-    # Add speculative decoding parameters if draft model is provided
-    # Note: When using draft_model, we let mlx_lm create its own trimmable cache
-    # as speculative decoding requires cache trimming capabilities
-    if draft_model is not None:
-        generate_kwargs["draft_model"] = draft_model
-        generate_kwargs["num_draft_tokens"] = num_draft_tokens
-    else:
-        # Only use custom cache for non-speculative generation
-        generate_kwargs["prompt_cache"] = make_kv_cache(model=model)
-
-    for out in stream_generate(**generate_kwargs):  # type: ignore[arg-type]
+    for out in stream_generate(
+        model=model,
+        tokenizer=tokenizer,
+        prompt=prompt,
+        max_tokens=max_tokens,
+        sampler=sampler,
+        logits_processors=logits_processors,
+        prompt_cache=caches,
+        prefill_step_size=2048,
+        kv_group_size=KV_GROUP_SIZE,
+        kv_bits=KV_BITS,
+    ):
         logger.info(out.text)
+        accumulated_text += out.text
 
+        # Check for stop sequences
+        text = out.text
+        finish_reason: FinishReason | None = cast(
+            FinishReason | None, out.finish_reason
+        )
+        stop_matched = False
+
+        if stop_sequences:
+            for stop_seq in stop_sequences:
+                if stop_seq in accumulated_text:
+                    # Trim text to just before the stop sequence
+                    stop_index = accumulated_text.find(stop_seq)
+                    text_before_stop = accumulated_text[:stop_index]
+                    chunk_start = len(accumulated_text) - len(out.text)
+                    text = text_before_stop[chunk_start:]
+                    finish_reason = "stop"
+                    stop_matched = True
+                    break
+
+        is_done = finish_reason is not None
         stats: GenerationStats | None = None
-        if out.finish_reason is not None:
+        if is_done:
             stats = GenerationStats(
                 prompt_tps=float(out.prompt_tps),
                 generation_tps=float(out.generation_tps),
@@ -192,22 +252,33 @@ def mlx_generate(
                 generation_tokens=int(out.generation_tokens),
                 peak_memory_usage=Memory.from_gb(out.peak_memory),
             )
-
-            if out.finish_reason not in get_args(FinishReason):
-                # We don't throw here as this failure case is really not all that bad
-                # Just log the error and move on
+            if not stop_matched and out.finish_reason not in get_args(FinishReason):
                 logger.warning(
                     f"Model generated unexpected finish_reason: {out.finish_reason}"
                 )
 
+        # Extract logprobs from the full vocabulary logprobs array
+        logprob, top_logprobs = extract_top_logprobs(
+            logprobs=out.logprobs,
+            tokenizer=tokenizer,
+            top_k=5,
+            selected_token=out.token,
+        )
+
         yield GenerationResponse(
-            text=out.text,
+            text=text,
             token=out.token,
-            finish_reason=cast(FinishReason | None, out.finish_reason),
+            logprob=logprob,
+            top_logprobs=top_logprobs,
+            finish_reason=finish_reason,
             stats=stats,
         )
 
-        if out.finish_reason is not None:
+        if is_done:
             break
 
+        # Limit accumulated_text to what's needed for stop sequence detection
+        if max_stop_len > 0 and len(accumulated_text) > max_stop_len:
+            accumulated_text = accumulated_text[-max_stop_len:]
+
         # TODO: Do we want an mx_barrier?

src/exo/worker/engines/mlx/utils_mlx.py

@@ -42,10 +42,9 @@ import mlx.nn as nn
 from mlx_lm.utils import load_model
 from pydantic import RootModel
 
-from exo.shared.types.api import ChatCompletionMessageText
 from exo.shared.types.common import Host
 from exo.shared.types.memory import Memory
-from exo.shared.types.tasks import ChatCompletionTaskParams
+from exo.shared.types.openai_responses import ResponsesRequest
 from exo.shared.types.worker.instances import (
     BoundInstance,
     MlxJacclInstance,
@@ -258,27 +257,6 @@ def load_mlx_items(
     return cast(Model, model), tokenizer
 
 
-def load_draft_model(model_id: str) -> nn.Module:
-    """Load a draft model for speculative decoding (rank 0 only).
-
-    Draft models are small models (typically 0.5B-2B parameters) used to
-    generate candidate tokens quickly, which are then verified by the main
-    model in a single forward pass.
-
-    Assumes the model has already been downloaded by the worker.
-
-    Args:
-        model_id: HuggingFace model ID for the draft model
-
-    Returns:
-        The loaded draft model
-    """
-    model_path = build_model_path(model_id)
-    draft_model, _ = load_model(model_path, strict=True)
-    logger.info(f"Loaded draft model from {model_path}")
-    return draft_model
-
-
 def shard_and_load(
     shard_metadata: ShardMetadata,
     group: Group,
@@ -411,35 +389,53 @@ def load_tokenizer_for_model_id(model_id: str, model_path: Path) -> TokenizerWra
 
 def apply_chat_template(
     tokenizer: TokenizerWrapper,
-    chat_task_data: ChatCompletionTaskParams,
+    task_params: ResponsesRequest,
 ) -> str:
-    # Now we can properly access the messages
-    messages = chat_task_data.messages
+    """Convert ResponsesRequest to a chat template prompt.
 
+    Converts the internal format (input + instructions) to a messages list
+    that can be processed by the tokenizer's chat template.
+    """
     formatted_messages: list[dict[str, Any]] = []
-    for message in messages:
-        if isinstance(message.content, ChatCompletionMessageText):
-            message.content = message.content.text
-        if isinstance(message.content, list):
-            if len(message.content) == 0:
-                logger.warning("Received prompt with no content, skipping")
-                continue
 
-            message.content = "\n".join(c.text for c in message.content).strip()
-        if message.content is None and message.thinking is None:
-            continue
-
-        # Null values are not valid when applying templates in tokenizer
+    # Add system message (instructions) if present
+    if task_params.instructions:
         formatted_messages.append(
-            {k: v for k, v in message.model_dump().items() if v is not None}  # type: ignore
+            {"role": "system", "content": task_params.instructions}
         )
 
-    prompt: str = tokenizer.apply_chat_template(
-        formatted_messages,
-        tokenize=False,
-        add_generation_prompt=True,
-        tools=chat_task_data.tools,
-    )
+    # Convert input to messages
+    if isinstance(task_params.input, str):
+        # Simple string input becomes a single user message
+        formatted_messages.append({"role": "user", "content": task_params.input})
+    else:
+        # List of InputMessage
+        for msg in task_params.input:
+            if not msg.content:
+                logger.warning("Received message with empty content, skipping")
+                continue
+            formatted_messages.append({"role": msg.role, "content": msg.content})
+
+    # Use continue_final_message when continuing from prefix (e.g., regenerate from token)
+    # This keeps the final assistant message open without EOS tokens
+    # Note: explicitly set add_generation_prompt=False when using continue_final_message
+    # because some tokenizers (e.g., Kimi) default add_generation_prompt=True
+    prompt: str
+    if task_params.continue_from_prefix:
+        prompt = tokenizer.apply_chat_template(
+            formatted_messages,
+            tokenize=False,
+            continue_final_message=True,
+            add_generation_prompt=False,
+            tools=task_params.tools,
+        )
+    else:
+        prompt = tokenizer.apply_chat_template(
+            formatted_messages,
+            tokenize=False,
+            add_generation_prompt=True,
+            tools=task_params.tools,
+        )
 
     logger.info(prompt)
 

src/exo/worker/main.py

@@ -29,9 +29,7 @@ from exo.shared.types.profiling import MemoryPerformanceProfile, NodePerformance
 from exo.shared.types.state import State
 from exo.shared.types.tasks import (
     CreateRunner,
-    DownloadDraftModel,
     DownloadModel,
-    SetDraftModel,
     Shutdown,
     Task,
     TaskStatus,
@@ -50,7 +48,6 @@ from exo.utils.event_buffer import OrderedBuffer
 from exo.worker.download.download_utils import (
     map_repo_download_progress_to_download_progress_data,
 )
-from exo.worker.download.impl_shard_downloader import build_full_shard
 from exo.worker.download.shard_downloader import RepoDownloadProgress, ShardDownloader
 from exo.worker.plan import plan
 from exo.worker.runner.runner_supervisor import RunnerSupervisor
@@ -205,10 +202,42 @@ class Worker:
                         )
                     )
                 case DownloadModel(shard_metadata=shard):
-                    await self._handle_download(shard, task)
-                case DownloadDraftModel(model_id=model_id):
-                    shard = await build_full_shard(model_id)
-                    await self._handle_download(shard, task)
+                    if shard.model_meta.model_id not in self.download_status:
+                        progress = DownloadPending(
+                            shard_metadata=shard, node_id=self.node_id
+                        )
+                        self.download_status[shard.model_meta.model_id] = progress
+                        await self.event_sender.send(
+                            NodeDownloadProgress(download_progress=progress)
+                        )
+                    initial_progress = (
+                        await self.shard_downloader.get_shard_download_status_for_shard(
+                            shard
+                        )
+                    )
+                    if initial_progress.status == "complete":
+                        progress = DownloadCompleted(
+                            shard_metadata=shard,
+                            node_id=self.node_id,
+                            total_bytes=initial_progress.total_bytes,
+                        )
+                        self.download_status[shard.model_meta.model_id] = progress
+                        await self.event_sender.send(
+                            NodeDownloadProgress(download_progress=progress)
+                        )
+                        await self.event_sender.send(
+                            TaskStatusUpdated(
+                                task_id=task.task_id,
+                                task_status=TaskStatus.Complete,
+                            )
+                        )
+                    else:
+                        await self.event_sender.send(
+                            TaskStatusUpdated(
+                                task_id=task.task_id, task_status=TaskStatus.Running
+                            )
+                        )
+                        self._handle_shard_download_process(task, initial_progress)
                 case Shutdown(runner_id=runner_id):
                     try:
                         with fail_after(3):
@@ -219,25 +248,6 @@ class Worker:
                                 task_id=task.task_id, task_status=TaskStatus.TimedOut
                             )
                         )
-                case SetDraftModel(
-                    model_id=draft_model_id, num_draft_tokens=num_tokens
-                ):
-                    runner = self.runners[self._task_to_runner_id(task)]
-                    await runner.start_task(task)
-                    # Update bound_instance to reflect new/cleared draft model
-                    updated_instance = runner.bound_instance.instance.model_copy(
-                        update={
-                            "draft_model": (
-                                ModelId(draft_model_id)
-                                if draft_model_id is not None
-                                else None
-                            ),
-                            "num_draft_tokens": num_tokens,
-                        }
-                    )
-                    runner.bound_instance = runner.bound_instance.model_copy(
-                        update={"instance": updated_instance}
-                    )
                 case task:
                     await self.runners[self._task_to_runner_id(task)].start_task(task)
 
@@ -330,46 +340,6 @@ class Worker:
         self._tg.start_soon(runner.run)
         return runner
 
-    async def _handle_download(self, shard: ShardMetadata, task: Task) -> None:
-        """Handle model download - shared logic for main and draft models."""
-        model_id = shard.model_meta.model_id
-
-        if model_id not in self.download_status:
-            progress = DownloadPending(shard_metadata=shard, node_id=self.node_id)
-            self.download_status[model_id] = progress
-            await self.event_sender.send(
-                NodeDownloadProgress(download_progress=progress)
-            )
-
-        initial_progress = (
-            await self.shard_downloader.get_shard_download_status_for_shard(shard)
-        )
-
-        if initial_progress.status == "complete":
-            progress = DownloadCompleted(
-                shard_metadata=shard,
-                node_id=self.node_id,
-                total_bytes=initial_progress.total_bytes,
-            )
-            self.download_status[model_id] = progress
-            await self.event_sender.send(
-                NodeDownloadProgress(download_progress=progress)
-            )
-            await self.event_sender.send(
-                TaskStatusUpdated(task_id=task.task_id, task_status=TaskStatus.Complete)
-            )
-        else:
-            await self.event_sender.send(
-                TaskStatusUpdated(task_id=task.task_id, task_status=TaskStatus.Running)
-            )
-            download_task = DownloadModel(
-                instance_id=task.instance_id,
-                shard_metadata=shard,
-                task_id=task.task_id,
-                task_status=task.task_status,
-            )
-            self._handle_shard_download_process(download_task, initial_progress)
-
     def _handle_shard_download_process(
         self,
         task: DownloadModel,

src/exo/worker/plan.py

@@ -8,10 +8,8 @@ from exo.shared.types.tasks import (
     ChatCompletion,
     ConnectToGroup,
     CreateRunner,
-    DownloadDraftModel,
     DownloadModel,
     LoadModel,
-    SetDraftModel,
     Shutdown,
     StartWarmup,
     Task,
@@ -40,16 +38,6 @@ from exo.shared.types.worker.runners import (
 from exo.worker.runner.runner_supervisor import RunnerSupervisor
 
 
-def _is_download_in_progress_or_complete(
-    model_id: ModelId,
-    download_status: Mapping[ModelId, DownloadProgress],
-) -> bool:
-    """Check if model download is in progress or complete."""
-    return model_id in download_status and isinstance(
-        download_status[model_id], (DownloadOngoing, DownloadCompleted)
-    )
-
-
 def plan(
     node_id: NodeId,
     # Runners is expected to be FRESH and so should not come from state
@@ -67,11 +55,9 @@ def plan(
         _kill_runner(runners, all_runners, instances)
         or _create_runner(node_id, runners, instances)
         or _model_needs_download(runners, download_status)
-        or _draft_model_needs_download(runners, download_status, instances)
         or _init_distributed_backend(runners, all_runners)
-        or _load_model(runners, all_runners, global_download_status, download_status)
+        or _load_model(runners, all_runners, global_download_status)
         or _ready_to_warmup(runners, all_runners)
-        or _set_draft_model(runners, instances, download_status)
         or _pending_tasks(runners, tasks, all_runners)
     )
 
@@ -129,9 +115,12 @@ def _model_needs_download(
 ) -> DownloadModel | None:
     for runner in runners.values():
         model_id = runner.bound_instance.bound_shard.model_meta.model_id
-        if isinstance(
-            runner.status, RunnerIdle
-        ) and not _is_download_in_progress_or_complete(model_id, download_status):
+        if isinstance(runner.status, RunnerIdle) and (
+            model_id not in download_status
+            or not isinstance(
+                download_status[model_id], (DownloadOngoing, DownloadCompleted)
+            )
+        ):
             # We don't invalidate download_status randomly in case a file gets deleted on disk
             return DownloadModel(
                 instance_id=runner.bound_instance.instance.instance_id,
@@ -139,43 +128,6 @@ def _model_needs_download(
             )
 
 
-def _draft_model_needs_download(
-    runners: Mapping[RunnerId, RunnerSupervisor],
-    download_status: Mapping[ModelId, DownloadProgress],
-    instances: Mapping[InstanceId, Instance],
-) -> DownloadDraftModel | None:
-    """Check if draft model needs download for rank 0 runner.
-
-    Triggers download when:
-    - RunnerIdle with draft model (initial setup)
-    - RunnerReady with new draft model (updated via API)
-    """
-    rank_0_runner = next(
-        (r for r in runners.values() if r.bound_instance.bound_shard.device_rank == 0),
-        None,
-    )
-    if rank_0_runner is None:
-        return None
-    if not isinstance(rank_0_runner.status, (RunnerIdle, RunnerReady)):
-        return None
-
-    # Use current instance state (may have been updated via API)
-    instance_id = rank_0_runner.bound_instance.instance.instance_id
-    current_instance = instances.get(instance_id)
-    if current_instance is None:
-        return None
-
-    draft_model_id = current_instance.draft_model
-    if draft_model_id is None:
-        return None
-    if _is_download_in_progress_or_complete(draft_model_id, download_status):
-        return None
-    return DownloadDraftModel(
-        instance_id=instance_id,
-        model_id=str(draft_model_id),
-    )
-
-
 def _init_distributed_backend(
     runners: Mapping[RunnerId, RunnerSupervisor],
     all_runners: Mapping[RunnerId, RunnerStatus],
@@ -230,12 +182,10 @@ def _load_model(
     runners: Mapping[RunnerId, RunnerSupervisor],
     all_runners: Mapping[RunnerId, RunnerStatus],
     global_download_status: Mapping[NodeId, Sequence[DownloadProgress]],
-    download_status: Mapping[ModelId, DownloadProgress],
 ) -> LoadModel | None:
     for runner in runners.values():
         instance = runner.bound_instance.instance
         shard_assignments = instance.shard_assignments
-        shard = runner.bound_instance.bound_shard
 
         all_local_downloads_complete = all(
             nid in global_download_status
@@ -249,14 +199,6 @@ def _load_model(
         if not all_local_downloads_complete:
             continue
 
-        # Rank 0 with draft model must wait for draft download before loading
-        if shard.device_rank == 0:
-            draft_model_id = instance.draft_model
-            if draft_model_id is not None and not isinstance(
-                download_status.get(draft_model_id), DownloadCompleted
-            ):
-                continue
-
         is_single_node_instance = len(instance.shard_assignments.runner_to_shard) == 1
         if is_single_node_instance and isinstance(runner.status, RunnerIdle):
             return LoadModel(instance_id=instance.instance_id)
@@ -316,53 +258,6 @@ def _ready_to_warmup(
     return None
 
 
-def _set_draft_model(
-    runners: Mapping[RunnerId, RunnerSupervisor],
-    instances: Mapping[InstanceId, Instance],
-    download_status: Mapping[ModelId, DownloadProgress],
-) -> SetDraftModel | None:
-    """Check if rank 0 runner needs to load or clear a draft model."""
-    rank_0_runner = next(
-        (r for r in runners.values() if r.bound_instance.bound_shard.device_rank == 0),
-        None,
-    )
-    if rank_0_runner is None:
-        return None
-    if not isinstance(rank_0_runner.status, RunnerReady):
-        return None
-
-    instance_id = rank_0_runner.bound_instance.instance.instance_id
-    current_instance = instances.get(instance_id)
-    if current_instance is None:
-        return None
-
-    # Compare runner's bound draft model vs current instance draft model
-    runner_draft_model = rank_0_runner.bound_instance.instance.draft_model
-    current_draft_model = current_instance.draft_model
-
-    if runner_draft_model == current_draft_model:
-        return None
-
-    # Draft model changed - need to update
-    if current_draft_model is None:
-        # Clear draft model
-        return SetDraftModel(
-            instance_id=instance_id,
-            model_id=None,
-            num_draft_tokens=4,
-        )
-
-    # Wait for draft model to be downloaded
-    if not isinstance(download_status.get(current_draft_model), DownloadCompleted):
-        return None
-
-    return SetDraftModel(
-        instance_id=instance_id,
-        model_id=str(current_draft_model),
-        num_draft_tokens=current_instance.num_draft_tokens,
-    )
-
-
 def _pending_tasks(
     runners: Mapping[RunnerId, RunnerSupervisor],
     tasks: Mapping[TaskId, Task],

src/exo/worker/runner/runner.py

@@ -1,8 +1,6 @@
 import time
 from collections.abc import Generator
-from contextlib import contextmanager
 from functools import cache
-from typing import cast
 
 import mlx.core as mx
 from mlx_lm.models.gpt_oss import Model as GptOssModel
@@ -13,9 +11,7 @@ from openai_harmony import (  # pyright: ignore[reportMissingTypeStubs]
     load_harmony_encoding,
 )
 
-from exo.shared.types.api import ChatCompletionMessageText
 from exo.shared.types.chunks import TokenChunk
-from exo.shared.types.common import CommandId
 from exo.shared.types.events import (
     ChunkGenerated,
     Event,
@@ -23,12 +19,11 @@ from exo.shared.types.events import (
     TaskAcknowledged,
     TaskStatusUpdated,
 )
-from exo.shared.types.models import ModelId
+from exo.shared.types.openai_responses import ResponsesRequest
 from exo.shared.types.tasks import (
     ChatCompletion,
     ConnectToGroup,
     LoadModel,
-    SetDraftModel,
     Shutdown,
     StartWarmup,
     Task,
@@ -53,44 +48,15 @@ from exo.shared.types.worker.runners import (
     RunnerWarmingUp,
 )
 from exo.utils.channels import MpReceiver, MpSender
-from exo.worker.engines.mlx import Model
 from exo.worker.engines.mlx.generator.generate import mlx_generate, warmup_inference
 from exo.worker.engines.mlx.utils_mlx import (
     initialize_mlx,
-    load_draft_model,
     load_mlx_items,
     mlx_force_oom,
 )
 from exo.worker.runner.bootstrap import logger
 
 
-@contextmanager
-def send_error_chunk_on_exception(
-    event_sender: MpSender[Event],
-    command_id: CommandId,
-    model_id: ModelId,
-    device_rank: int,
-):
-    try:
-        yield
-    except Exception as e:
-        logger.error(e)
-        if device_rank == 0:
-            event_sender.send(
-                ChunkGenerated(
-                    command_id=command_id,
-                    chunk=TokenChunk(
-                        idx=0,
-                        model=model_id,
-                        text="",
-                        token_id=0,
-                        finish_reason="error",
-                        error_message=str(e),
-                    ),
-                )
-            )
-
-
 def main(
     bound_instance: BoundInstance,
     event_sender: MpSender[Event],
@@ -101,6 +67,7 @@ def main(
         bound_instance.bound_runner_id,
         bound_instance.bound_shard,
     )
+    device_rank = shard_metadata.device_rank
     logger.info("hello from the runner")
     if getattr(shard_metadata, "immediate_exception", False):
         raise Exception("Fake exception - runner failed to spin up.")
@@ -112,7 +79,6 @@ def main(
     model = None
     tokenizer = None
     group = None
-    draft_model: Model | None = None  # Loaded during warmup if instance has draft_model
 
     current_status: RunnerStatus = RunnerIdle()
     logger.info("runner created")
@@ -168,16 +134,6 @@ def main(
                         bound_instance, group, on_timeout=on_model_load_timeout
                     )
 
-                    # Load draft model for speculative decoding (rank 0 only)
-                    if (
-                        instance.draft_model is not None
-                        and shard_metadata.device_rank == 0
-                    ):
-                        logger.info(f"Loading draft model: {instance.draft_model}")
-                        draft_model = cast(
-                            Model, load_draft_model(str(instance.draft_model))
-                        )
-
                     current_status = RunnerLoaded()
                     logger.info("runner loaded")
                 case StartWarmup() if isinstance(current_status, RunnerLoaded):
@@ -193,10 +149,9 @@ def main(
 
                     logger.info(f"warming up inference for instance: {instance}")
                     toks = warmup_inference(
-                        model=cast(Model, model),
+                        model=model,
                         tokenizer=tokenizer,
-                        draft_model=draft_model,
-                        num_draft_tokens=instance.num_draft_tokens,
+                        # kv_prefix_cache=kv_prefix_cache,  # supply for warmup-time prefix caching
                     )
                     logger.info(f"warmed up by generating {toks} tokens")
                     logger.info(
@@ -215,24 +170,17 @@ def main(
                             runner_id=runner_id, runner_status=current_status
                         )
                     )
-                    with send_error_chunk_on_exception(
-                        event_sender,
-                        command_id,
-                        shard_metadata.model_meta.model_id,
-                        shard_metadata.device_rank,
-                    ):
-                        assert model
-                        assert tokenizer
-                        assert task_params.messages[0].content is not None
-                        _check_for_debug_prompts(task_params.messages[0].content)
+                    assert model
+                    assert tokenizer
 
-                        # Generate responses (draft_model loaded at warmup if configured)
+                    try:
+                        _check_for_debug_prompts(task_params)
+
+                        # Generate responses using the actual MLX generation
                         mlx_generator = mlx_generate(
-                            model=cast(Model, model),
+                            model=model,
                             tokenizer=tokenizer,
                             task=task_params,
-                            draft_model=draft_model,
-                            num_draft_tokens=instance.num_draft_tokens,
                         )
 
                         # GPT-OSS specific parsing to match other model formats.
@@ -244,7 +192,7 @@ def main(
                         for response in mlx_generator:
                             match response:
                                 case GenerationResponse():
-                                    if shard_metadata.device_rank == 0:
+                                    if device_rank == 0:
                                         event_sender.send(
                                             ChunkGenerated(
                                                 command_id=command_id,
@@ -253,58 +201,34 @@ def main(
                                                     model=shard_metadata.model_meta.model_id,
                                                     text=response.text,
                                                     token_id=response.token,
+                                                    logprob=response.logprob,
+                                                    top_logprobs=response.top_logprobs,
                                                     finish_reason=response.finish_reason,
                                                     stats=response.stats,
                                                 ),
                                             )
                                         )
 
+                    # can we make this more explicit?
+                    except Exception as e:
+                        if device_rank == 0:
+                            event_sender.send(
+                                ChunkGenerated(
+                                    command_id=command_id,
+                                    chunk=TokenChunk(
+                                        idx=0,
+                                        model=shard_metadata.model_meta.model_id,
+                                        text="",
+                                        token_id=0,
+                                        finish_reason="error",
+                                        error_message=str(e),
+                                    ),
+                                )
+                            )
+                        raise
+
                     current_status = RunnerReady()
                     logger.info("runner ready")
-                case SetDraftModel(
-                    model_id=draft_model_id, num_draft_tokens=num_tokens
-                ) if isinstance(current_status, RunnerReady):
-                    current_status = RunnerWarmingUp()
-                    logger.info("runner warming up (setting draft model)")
-                    event_sender.send(
-                        RunnerStatusUpdated(
-                            runner_id=runner_id, runner_status=current_status
-                        )
-                    )
-                    assert model is not None
-                    assert tokenizer is not None
-
-                    if draft_model_id is None:
-                        # Clear draft model
-                        logger.info("Clearing draft model")
-                        draft_model = None
-                        instance = instance.model_copy(
-                            update={
-                                "draft_model": None,
-                                "num_draft_tokens": 4,
-                            }
-                        )
-                    else:
-                        # Load new draft model
-                        logger.info(f"Loading draft model: {draft_model_id}")
-                        draft_model = cast(Model, load_draft_model(draft_model_id))
-                        instance = instance.model_copy(
-                            update={
-                                "draft_model": ModelId(draft_model_id),
-                                "num_draft_tokens": num_tokens,
-                            }
-                        )
-                        # Warm up with speculative decoding
-                        logger.info("Warming up with new draft model")
-                        warmup_inference(
-                            model=cast(Model, model),
-                            tokenizer=tokenizer,
-                            draft_model=draft_model,
-                            num_draft_tokens=num_tokens,
-                        )
-                        logger.info("Draft model loaded and warmed up")
-
-                    current_status = RunnerReady()
                 case Shutdown():
                     current_status = RunnerShuttingDown()
                     logger.info("runner shutting down")
@@ -325,7 +249,7 @@ def main(
                 RunnerStatusUpdated(runner_id=runner_id, runner_status=current_status)
             )
             if isinstance(current_status, RunnerShutdown):
-                del model, tokenizer, group, draft_model
+                del model, tokenizer, group
                 mx.clear_cache()
                 import gc
 
@@ -375,17 +299,23 @@ EXO_RUNNER_MUST_OOM = "EXO RUNNER MUST OOM"
 EXO_RUNNER_MUST_TIMEOUT = "EXO RUNNER MUST TIMEOUT"
 
 
-def _check_for_debug_prompts(
-    prompt: str | ChatCompletionMessageText | list[ChatCompletionMessageText],
-):
-    if isinstance(prompt, list):
-        if len(prompt) == 0:
-            logger.debug("Empty message prompt received in debug prompt")
-            return
-        prompt = prompt[0]
+def _check_for_debug_prompts(task_params: ResponsesRequest) -> None:
+    """Check for debug prompt triggers in the input.
 
-    if isinstance(prompt, ChatCompletionMessageText):
-        prompt = prompt.text
+    Extracts the first user input text and checks for debug triggers.
+    """
+    prompt: str
+    if isinstance(task_params.input, str):
+        prompt = task_params.input
+    else:
+        # List of InputMessage - get first message content
+        if len(task_params.input) == 0:
+            logger.debug("Empty message list in debug prompt check")
+            return
+        prompt = task_params.input[0].content
+
+    if not prompt:
+        return
 
     if EXO_RUNNER_MUST_FAIL in prompt:
         logger.info("raising exception")

src/exo/worker/tests/unittests/test_plan/test_task_forwarding.py

@@ -1,7 +1,7 @@
 from typing import cast
 
 import exo.worker.plan as plan_mod
-from exo.shared.types.api import ChatCompletionTaskParams
+from exo.shared.types.openai_responses import ResponsesRequest
 from exo.shared.types.tasks import ChatCompletion, Task, TaskId, TaskStatus
 from exo.shared.types.worker.instances import BoundInstance, InstanceId
 from exo.shared.types.worker.runners import (
@@ -59,7 +59,7 @@ def test_plan_forwards_pending_chat_completion_when_runner_ready():
         instance_id=INSTANCE_1_ID,
         task_status=TaskStatus.Pending,
         command_id=COMMAND_1_ID,
-        task_params=ChatCompletionTaskParams(model=MODEL_A_ID, messages=[]),
+        task_params=ResponsesRequest(model=MODEL_A_ID, input=""),
     )
 
     result = plan_mod.plan(
@@ -107,7 +107,7 @@ def test_plan_does_not_forward_chat_completion_if_any_runner_not_ready():
         instance_id=INSTANCE_1_ID,
         task_status=TaskStatus.Pending,
         command_id=COMMAND_1_ID,
-        task_params=ChatCompletionTaskParams(model=MODEL_A_ID, messages=[]),
+        task_params=ResponsesRequest(model=MODEL_A_ID, input=""),
     )
 
     result = plan_mod.plan(
@@ -152,7 +152,7 @@ def test_plan_does_not_forward_tasks_for_other_instances():
         instance_id=other_instance_id,
         task_status=TaskStatus.Pending,
         command_id=COMMAND_1_ID,
-        task_params=ChatCompletionTaskParams(model=MODEL_A_ID, messages=[]),
+        task_params=ResponsesRequest(model=MODEL_A_ID, input=""),
     )
 
     result = plan_mod.plan(
@@ -201,7 +201,7 @@ def test_plan_ignores_non_pending_or_non_chat_tasks():
         instance_id=INSTANCE_1_ID,
         task_status=TaskStatus.Complete,
         command_id=COMMAND_1_ID,
-        task_params=ChatCompletionTaskParams(model=MODEL_A_ID, messages=[]),
+        task_params=ResponsesRequest(model=MODEL_A_ID, input=""),
     )
 
     other_task_id = TaskId("other-task")

src/exo/worker/tests/unittests/test_runner/test_error_handling.py

@@ -1,50 +0,0 @@
-# pyright: reportAny=false
-from unittest.mock import MagicMock
-
-from exo.shared.types.chunks import TokenChunk
-from exo.shared.types.common import CommandId
-from exo.shared.types.events import ChunkGenerated
-from exo.worker.runner.runner import send_error_chunk_on_exception
-from exo.worker.tests.constants import MODEL_A_ID
-
-
-def test_send_error_chunk_on_exception_no_error() -> None:
-    event_sender = MagicMock()
-    command_id = CommandId()
-
-    with send_error_chunk_on_exception(
-        event_sender, command_id, MODEL_A_ID, device_rank=0
-    ):
-        _ = 1 + 1
-
-    event_sender.send.assert_not_called()
-
-
-def test_send_error_chunk_on_exception_catches_error() -> None:
-    event_sender = MagicMock()
-    command_id = CommandId()
-
-    with send_error_chunk_on_exception(
-        event_sender, command_id, MODEL_A_ID, device_rank=0
-    ):
-        raise ValueError("test error")
-
-    event_sender.send.assert_called_once()
-    call_args = event_sender.send.call_args[0][0]
-    assert isinstance(call_args, ChunkGenerated)
-    assert call_args.command_id == command_id
-    assert isinstance(call_args.chunk, TokenChunk)
-    assert call_args.chunk.finish_reason == "error"
-    assert call_args.chunk.error_message == "test error"
-
-
-def test_send_error_chunk_on_exception_skips_non_rank_zero() -> None:
-    event_sender = MagicMock()
-    command_id = CommandId()
-
-    with send_error_chunk_on_exception(
-        event_sender, command_id, MODEL_A_ID, device_rank=1
-    ):
-        raise ValueError("test error")
-
-    event_sender.send.assert_not_called()

src/exo/worker/tests/unittests/test_runner/test_event_ordering.py

@@ -5,7 +5,6 @@ from typing import Callable
 import pytest
 
 import exo.worker.runner.runner as mlx_runner
-from exo.shared.types.api import ChatCompletionMessage
 from exo.shared.types.chunks import TokenChunk
 from exo.shared.types.events import (
     ChunkGenerated,
@@ -14,9 +13,9 @@ from exo.shared.types.events import (
     TaskAcknowledged,
     TaskStatusUpdated,
 )
+from exo.shared.types.openai_responses import ResponsesRequest
 from exo.shared.types.tasks import (
     ChatCompletion,
-    ChatCompletionTaskParams,
     ConnectToGroup,
     LoadModel,
     Shutdown,
@@ -85,11 +84,11 @@ SHUTDOWN_TASK = Shutdown(
     runner_id=RUNNER_1_ID,
 )
 
-CHAT_PARAMS = ChatCompletionTaskParams(
+CHAT_PARAMS = ResponsesRequest(
     model=str(MODEL_A_ID),
-    messages=[ChatCompletionMessage(role="user", content="hello")],
+    input="hello",
     stream=True,
-    max_tokens=4,
+    max_output_tokens=4,
     temperature=0.0,
 )
 

tests/headless_runner.py

@@ -13,10 +13,10 @@ from pydantic import BaseModel
 
 from exo.shared.logging import InterceptLogger, logger_setup
 from exo.shared.models.model_cards import MODEL_CARDS, ModelId
-from exo.shared.types.api import ChatCompletionMessage, ChatCompletionTaskParams
 from exo.shared.types.commands import CommandId
 from exo.shared.types.common import Host, NodeId
 from exo.shared.types.events import Event
+from exo.shared.types.openai_responses import ResponsesRequest
 from exo.shared.types.tasks import (
     ChatCompletion,
     ConnectToGroup,
@@ -169,16 +169,10 @@ async def execute_test(test: Tests, instance: Instance, hn: str):
             send.send(StartWarmup(instance_id=iid))
             send.send(
                 ChatCompletion(
-                    task_params=ChatCompletionTaskParams(
+                    task_params=ResponsesRequest(
                         model=test.model_id,
-                        messages=[
-                            ChatCompletionMessage(
-                                role="system", content="You are a helpful assistant"
-                            ),
-                            ChatCompletionMessage(
-                                role="user", content="What is the capital of France?"
-                            ),
-                        ],
+                        instructions="You are a helpful assistant",
+                        input="What is the capital of France?",
                     ),
                     command_id=CommandId("yo"),
                     instance_id=iid,