feat: add prefill progress bar for long prompts

Shows real-time progress during prompt processing (prefill phase).
Progress is sent via SSE named events that maintain OpenAI API compatibility.

- Add PrefillProgress event type
- Wire prompt_progress_callback through MLX stream_generate
- Send progress events directly from callback for real-time updates
- Add PrefillProgressBar.svelte component
- Parse event: prefill_progress SSE events in dashboard

Note: prefill_step_size temporarily set to 256 for testing (normally 2048)

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

dashboard/src/lib/components/ChatMessages.svelte

@@ -1,7 +1,7 @@
 <script lang="ts">
-	import { 
-		messages, 
-		currentResponse, 
+	import {
+		messages,
+		currentResponse,
 		isLoading,
 		deleteMessage,
 		editAndRegenerate,
@@ -9,6 +9,8 @@
 	} from '$lib/stores/app.svelte';
 	import type { MessageAttachment } from '$lib/stores/app.svelte';
 	import MarkdownContent from './MarkdownContent.svelte';
+	import TokenHeatmap from './TokenHeatmap.svelte';
+	import PrefillProgressBar from './PrefillProgressBar.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,
@@ -330,6 +349,10 @@ function isThinkingExpanded(messageId: string): boolean {
 						{:else}
 							<!-- Assistant message styling -->
 							<div class="p-3 sm:p-4">
+								{#if message.prefillProgress}
+									<!-- Prefill progress bar -->
+									<PrefillProgressBar progress={message.prefillProgress} class="mb-3" />
+								{/if}
 								{#if message.thinking && message.thinking.trim().length > 0}
 									<div class="mb-3 rounded border border-exo-yellow/20 bg-exo-black/40">
 										<button
@@ -366,7 +389,13 @@ 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} />
+									{: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}
@@ -419,6 +448,19 @@ 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

dashboard/src/lib/components/PrefillProgressBar.svelte

@@ -0,0 +1,67 @@
+<script lang="ts">
+	import type { PrefillProgress } from '$lib/stores/app.svelte';
+
+	interface Props {
+		progress: PrefillProgress;
+		class?: string;
+	}
+
+	let { progress, class: className = '' }: Props = $props();
+
+	const percentage = $derived(
+		progress.total > 0 ? Math.round((progress.processed / progress.total) * 100) : 0
+	);
+
+	function formatTokenCount(count: number): string {
+		if (count >= 1000) {
+			return `${(count / 1000).toFixed(1)}k`;
+		}
+		return count.toString();
+	}
+</script>
+
+<div class="prefill-progress {className}">
+	<div class="flex items-center justify-between text-xs text-gray-400 mb-1">
+		<span class="flex items-center gap-1.5">
+			<svg
+				class="w-3.5 h-3.5 animate-spin"
+				fill="none"
+				viewBox="0 0 24 24"
+				xmlns="http://www.w3.org/2000/svg"
+			>
+				<circle
+					class="opacity-25"
+					cx="12"
+					cy="12"
+					r="10"
+					stroke="currentColor"
+					stroke-width="4"
+				></circle>
+				<path
+					class="opacity-75"
+					fill="currentColor"
+					d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"
+				></path>
+			</svg>
+			<span>Processing prompt</span>
+		</span>
+		<span class="font-mono">
+			{formatTokenCount(progress.processed)} / {formatTokenCount(progress.total)} tokens
+		</span>
+	</div>
+	<div class="h-1.5 bg-gray-700 rounded-full overflow-hidden">
+		<div
+			class="h-full bg-blue-500 rounded-full transition-all duration-150 ease-out"
+			style="width: {percentage}%"
+		></div>
+	</div>
+	<div class="text-right text-xs text-gray-500 mt-0.5">
+		{percentage}%
+	</div>
+</div>
+
+<style>
+	.prefill-progress {
+		width: 100%;
+	}
+</style>

dashboard/src/lib/components/TokenHeatmap.svelte

@@ -0,0 +1,121 @@
+<script lang="ts">
+	import type { TokenData } from '$lib/stores/app.svelte';
+
+	interface Props {
+		tokens: TokenData[];
+		class?: string;
+	}
+
+	let { tokens, class: className = '' }: Props = $props();
+
+	// Tooltip state
+	let hoveredToken = $state<{ token: TokenData; x: number; y: number } | null>(null);
+
+	/**
+	 * Get confidence level based on probability
+	 * High: >0.8 (logprob > -0.22)
+	 * Medium: 0.5-0.8 (logprob -0.69 to -0.22)
+	 * Low: 0.2-0.5 (logprob -1.61 to -0.69)
+	 * Very Low: <0.2 (logprob < -1.61)
+	 */
+	function getConfidenceClass(probability: number): string {
+		if (probability > 0.8) return 'bg-green-500/30 text-green-100';
+		if (probability > 0.5) return 'bg-yellow-500/30 text-yellow-100';
+		if (probability > 0.2) return 'bg-orange-500/30 text-orange-100';
+		return 'bg-red-500/40 text-red-100';
+	}
+
+	/**
+	 * Get border color for token based on probability
+	 */
+	function getBorderClass(probability: number): string {
+		if (probability > 0.8) return 'border-green-500/50';
+		if (probability > 0.5) return 'border-yellow-500/50';
+		if (probability > 0.2) return 'border-orange-500/50';
+		return 'border-red-500/50';
+	}
+
+	function handleMouseEnter(event: MouseEvent, token: TokenData) {
+		const rect = (event.target as HTMLElement).getBoundingClientRect();
+		hoveredToken = {
+			token,
+			x: rect.left + rect.width / 2,
+			y: rect.top - 10
+		};
+	}
+
+	function handleMouseLeave() {
+		hoveredToken = null;
+	}
+
+	function formatProbability(prob: number): string {
+		return (prob * 100).toFixed(1) + '%';
+	}
+
+	function formatLogprob(logprob: number): string {
+		return logprob.toFixed(3);
+	}
+</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)}
+			onmouseleave={handleMouseLeave}
+		>{tokenData.token}</span>
+	{/each}
+</div>
+
+<!-- Tooltip -->
+{#if hoveredToken}
+	<div
+		class="fixed z-50 pointer-events-none"
+		style="left: {hoveredToken.x}px; top: {hoveredToken.y}px; transform: translate(-50%, -100%);"
+	>
+		<div class="bg-gray-900 border border-gray-700 rounded-lg shadow-xl p-3 text-sm min-w-48">
+			<!-- Token info -->
+			<div class="mb-2">
+				<span class="text-gray-400 text-xs">Token:</span>
+				<span class="text-white font-mono ml-1">"{hoveredToken.token.token}"</span>
+				<span class="text-green-400 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 mt-2 pt-2">
+					<div class="text-gray-400 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}
+		</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

@@ -182,6 +182,26 @@ 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[];
+}
+
+// Prefill progress data for long prompts
+export interface PrefillProgress {
+	processed: number;
+	total: number;
+}
+
 export interface Message {
 	id: string;
 	role: "user" | "assistant" | "system";
@@ -191,6 +211,8 @@ 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
+	prefillProgress?: PrefillProgress | null; // Prefill progress for long prompts
 }
 
 export interface Conversation {
@@ -1107,6 +1129,8 @@ class AppStore {
 					model: modelToUse,
 					messages: apiMessages,
 					stream: true,
+					logprobs: true,
+					top_logprobs: 5,
 				}),
 			});
 
@@ -1408,6 +1432,8 @@ class AppStore {
 					messages: apiMessages,
 					temperature: 0.7,
 					stream: true,
+					logprobs: true,
+					top_logprobs: 5,
 				}),
 			});
 
@@ -1424,6 +1450,8 @@ class AppStore {
 			const decoder = new TextDecoder();
 			let fullContent = "";
 			let buffer = "";
+			const collectedTokens: TokenData[] = [];
+			let currentEventType = ""; // Track SSE event type
 
 			while (true) {
 				const { done, value } = await reader.read();
@@ -1437,14 +1465,43 @@ class AppStore {
 
 				for (const line of lines) {
 					const trimmed = line.trim();
-					if (!trimmed) continue;
+					if (!trimmed) {
+						// Empty line resets event type
+						currentEventType = "";
+						continue;
+					}
+
+					// Handle event type declaration
+					if (trimmed.startsWith("event: ")) {
+						currentEventType = trimmed.slice(7);
+						continue;
+					}
 
 					if (trimmed.startsWith("data: ")) {
 						const data = trimmed.slice(6);
-						if (data === "[DONE]") continue;
+						if (data === "[DONE]") {
+							currentEventType = "";
+							continue;
+						}
 
 						try {
 							const parsed = JSON.parse(data);
+
+							// Handle prefill progress events
+							if (currentEventType === "prefill_progress") {
+								const idx = this.messages.findIndex(
+									(m) => m.id === assistantMessage.id,
+								);
+								if (idx !== -1) {
+									this.messages[idx].prefillProgress = {
+										processed: parsed.processed,
+										total: parsed.total,
+									};
+								}
+								continue;
+							}
+
+							// Handle regular token data
 							const tokenContent = parsed.choices?.[0]?.delta?.content;
 							if (tokenContent) {
 								// Track first token for TTFT
@@ -1453,6 +1510,14 @@ class AppStore {
 									this.ttftMs = firstTokenTime - requestStartTime;
 								}
 
+								// Clear prefill progress when first token arrives
+								const msgIdx = this.messages.findIndex(
+									(m) => m.id === assistantMessage.id,
+								);
+								if (msgIdx !== -1 && this.messages[msgIdx].prefillProgress) {
+									this.messages[msgIdx].prefillProgress = null;
+								}
+
 								// Count tokens (each SSE chunk is typically one token)
 								tokenCount += 1;
 								this.totalTokens = tokenCount;
@@ -1463,6 +1528,25 @@ class AppStore {
 									this.tps = (tokenCount / elapsed) * 1000;
 								}
 
+								// 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 || tokenContent,
+										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 += tokenContent;
 
 								// Strip thinking tags for display and extract thinking content
@@ -1477,6 +1561,8 @@ class AppStore {
 								if (idx !== -1) {
 									this.messages[idx].content = displayContent;
 									this.messages[idx].thinking = thinkingContent || undefined;
+									// Update tokens during streaming for real-time visualization
+									this.messages[idx].tokens = [...collectedTokens];
 								}
 								this.persistActiveConversation();
 							}
@@ -1524,6 +1610,10 @@ class AppStore {
 				if (this.tps !== null) {
 					this.messages[idx].tps = this.tps;
 				}
+				// Store token data for uncertainty visualization
+				if (collectedTokens.length > 0) {
+					this.messages[idx].tokens = collectedTokens;
+				}
 			}
 			this.persistActiveConversation();
 		} catch (error) {

src/exo/master/adapters/__init__.py

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

src/exo/master/adapters/claude.py

@@ -0,0 +1,184 @@
+"""Claude Messages API adapter for converting requests/responses."""
+
+from collections.abc import AsyncGenerator
+
+from exo.shared.types.api import (
+    ChatCompletionChoice,
+    ChatCompletionMessage,
+    ChatCompletionResponse,
+    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.tasks import ChatCompletionTaskParams
+
+
+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_chat_params(
+    request: ClaudeMessagesRequest,
+) -> ChatCompletionTaskParams:
+    """Convert Claude Messages API request to internal ChatCompletionTaskParams."""
+    messages: list[ChatCompletionMessage] = []
+
+    # Add system message if present
+    if request.system:
+        if isinstance(request.system, str):
+            messages.append(
+                ChatCompletionMessage(role="system", content=request.system)
+            )
+        else:
+            # List of text blocks
+            system_text = "".join(block.text for block in request.system)
+            messages.append(ChatCompletionMessage(role="system", content=system_text))
+
+    # Convert messages
+    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)
+
+        messages.append(ChatCompletionMessage(role=msg.role, content=content))
+
+    return ChatCompletionTaskParams(
+        model=request.model,
+        messages=messages,
+        max_tokens=request.max_tokens,
+        temperature=request.temperature,
+        top_p=request.top_p,
+        top_k=request.top_k,
+        stop=request.stop_sequences,
+        stream=request.stream,
+    )
+
+
+def chat_response_to_claude_response(
+    response: ChatCompletionResponse,
+) -> ClaudeMessagesResponse:
+    """Convert internal ChatCompletionResponse to Claude Messages API response."""
+    content_text = ""
+    stop_reason: ClaudeStopReason | None = None
+
+    if response.choices:
+        choice = response.choices[0]
+        if isinstance(choice, ChatCompletionChoice) and choice.message.content:
+            content_text = (
+                choice.message.content
+                if isinstance(choice.message.content, str)
+                else str(choice.message.content)
+            )
+        stop_reason = finish_reason_to_claude_stop_reason(choice.finish_reason)
+
+    # Use actual usage data from response if available
+    input_tokens = response.usage.prompt_tokens if response.usage else 0
+    output_tokens = response.usage.completion_tokens if response.usage else 0
+
+    return ClaudeMessagesResponse(
+        id=f"msg_{response.id}",
+        model=response.model,
+        content=[ClaudeTextBlock(text=content_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 +1,199 @@
+"""OpenAI Responses API adapter for converting requests/responses."""
+
+from collections.abc import AsyncGenerator
+
+from exo.shared.types.api import (
+    ChatCompletionChoice,
+    ChatCompletionMessage,
+    ChatCompletionResponse,
+)
+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,
+    ResponsesRequest,
+    ResponsesResponse,
+    ResponseTextDeltaEvent,
+    ResponseTextDoneEvent,
+    ResponseUsage,
+)
+from exo.shared.types.tasks import ChatCompletionTaskParams
+
+
+def responses_request_to_chat_params(
+    request: ResponsesRequest,
+) -> ChatCompletionTaskParams:
+    """Convert OpenAI Responses API request to internal ChatCompletionTaskParams."""
+    messages: list[ChatCompletionMessage] = []
+
+    # Add instructions as system message if present
+    if request.instructions:
+        messages.append(
+            ChatCompletionMessage(role="system", content=request.instructions)
+        )
+
+    # Convert input to messages
+    if isinstance(request.input, str):
+        messages.append(ChatCompletionMessage(role="user", content=request.input))
+    else:
+        for msg in request.input:
+            messages.append(
+                ChatCompletionMessage(
+                    role=msg.role,
+                    content=msg.content,
+                )
+            )
+
+    return ChatCompletionTaskParams(
+        model=request.model,
+        messages=messages,
+        max_tokens=request.max_output_tokens,
+        temperature=request.temperature,
+        top_p=request.top_p,
+        stream=request.stream,
+    )
+
+
+def chat_response_to_responses_response(
+    response: ChatCompletionResponse,
+) -> ResponsesResponse:
+    """Convert internal ChatCompletionResponse to OpenAI Responses API response."""
+    output_text = ""
+
+    if response.choices:
+        choice = response.choices[0]
+        if isinstance(choice, ChatCompletionChoice) and choice.message.content:
+            output_text = (
+                choice.message.content
+                if isinstance(choice.message.content, str)
+                else str(choice.message.content)
+            )
+
+    item_id = f"item_{response.id}"
+    output_item = ResponseMessageItem(
+        id=item_id,
+        content=[ResponseOutputText(text=output_text)],
+    )
+
+    usage = None
+    if response.usage:
+        usage = ResponseUsage(
+            input_tokens=response.usage.prompt_tokens,
+            output_tokens=response.usage.completion_tokens,
+            total_tokens=response.usage.total_tokens,
+        )
+
+    return ResponsesResponse(
+        id=f"resp_{response.id}",
+        model=response.model,
+        output=[output_item],
+        output_text=output_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

@@ -1,5 +1,6 @@
 import time
 from collections.abc import AsyncGenerator
+from dataclasses import dataclass
 from typing import cast
 
 import anyio
@@ -14,6 +15,16 @@ from hypercorn.config import Config
 from hypercorn.typing import ASGIFramework
 from loguru import logger
 
+from exo.master.adapters.claude import (
+    chat_response_to_claude_response,
+    claude_request_to_chat_params,
+    generate_claude_stream,
+)
+from exo.master.adapters.responses import (
+    chat_response_to_responses_response,
+    generate_responses_stream,
+    responses_request_to_chat_params,
+)
 from exo.master.placement import place_instance as get_instance_placements
 from exo.shared.apply import apply
 from exo.shared.election import ElectionMessage
@@ -31,6 +42,8 @@ from exo.shared.types.api import (
     DeleteInstanceResponse,
     FinishReason,
     GenerationStats,
+    Logprobs,
+    LogprobsContentItem,
     ModelList,
     ModelListModel,
     PlaceInstanceParams,
@@ -39,6 +52,10 @@ from exo.shared.types.api import (
     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,
@@ -49,9 +66,19 @@ from exo.shared.types.commands import (
     TaskFinished,
 )
 from exo.shared.types.common import CommandId, NodeId, SessionId
-from exo.shared.types.events import ChunkGenerated, Event, ForwarderEvent, IndexedEvent
+from exo.shared.types.events import (
+    ChunkGenerated,
+    Event,
+    ForwarderEvent,
+    IndexedEvent,
+    PrefillProgress,
+)
 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
@@ -62,9 +89,35 @@ from exo.utils.dashboard_path import find_dashboard
 from exo.utils.event_buffer import OrderedBuffer
 
 
+@dataclass
+class PrefillProgressData:
+    """Data class for prefill progress events."""
+
+    processed_tokens: int
+    total_tokens: int
+
+
+# Union type for stream events
+StreamEvent = TokenChunk | PrefillProgressData
+
+
 def chunk_to_response(
     chunk: TokenChunk, command_id: CommandId
 ) -> 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,
+                    bytes=list(chunk.text.encode("utf-8")),
+                    top_logprobs=chunk.top_logprobs or [],
+                )
+            ]
+        )
+
     return ChatCompletionResponse(
         id=command_id,
         created=int(time.time()),
@@ -73,6 +126,7 @@ def chunk_to_response(
             StreamingChoiceResponse(
                 index=0,
                 delta=ChatCompletionMessage(role="assistant", content=chunk.text),
+                logprobs=logprobs,
                 finish_reason=chunk.finish_reason,
             )
         ],
@@ -127,7 +181,7 @@ class API:
             name="dashboard",
         )
 
-        self._chat_completion_queues: dict[CommandId, Sender[TokenChunk]] = {}
+        self._chat_completion_queues: dict[CommandId, Sender[StreamEvent]] = {}
         self._tg: TaskGroup | None = None
 
     def reset(self, new_session_id: SessionId, result_clock: int):
@@ -168,6 +222,8 @@ class API:
             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)
 
@@ -373,18 +429,18 @@ class API:
             instance_id=instance_id,
         )
 
-    async def _chat_chunk_stream(
+    async def _stream_events(
         self, command_id: CommandId
-    ) -> AsyncGenerator[TokenChunk, None]:
-        """Yield `TokenChunk`s for a given command until completion."""
+    ) -> AsyncGenerator[StreamEvent, None]:
+        """Yield stream events (TokenChunks or PrefillProgressData) for a command."""
 
         try:
-            self._chat_completion_queues[command_id], recv = channel[TokenChunk]()
+            self._chat_completion_queues[command_id], recv = channel[StreamEvent]()
 
-            with recv as token_chunks:
-                async for chunk in token_chunks:
-                    yield chunk
-                    if chunk.finish_reason is not None:
+            with recv as events:
+                async for event in events:
+                    yield event
+                    if isinstance(event, TokenChunk) and event.finish_reason is not None:
                         break
 
         except anyio.get_cancelled_exc_class():
@@ -400,21 +456,36 @@ class API:
             await self._send(command)
             del self._chat_completion_queues[command_id]
 
+    async def _chat_chunk_stream(
+        self, command_id: CommandId
+    ) -> AsyncGenerator[TokenChunk, None]:
+        """Yield only TokenChunks, filtering out progress events."""
+
+        async for event in self._stream_events(command_id):
+            if isinstance(event, TokenChunk):
+                yield event
+
     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):
-            chunk_response: ChatCompletionResponse = chunk_to_response(
-                chunk, command_id
-            )
-            logger.debug(f"chunk_response: {chunk_response}")
+        async for event in self._stream_events(command_id):
+            if isinstance(event, PrefillProgressData):
+                # Send prefill progress as a named SSE event
+                progress_json = f'{{"processed":{event.processed_tokens},"total":{event.total_tokens}}}'
+                yield f"event: prefill_progress\ndata: {progress_json}\n\n"
+            else:
+                # TokenChunk - regular token generation
+                chunk_response: ChatCompletionResponse = chunk_to_response(
+                    event, command_id
+                )
+                logger.debug(f"chunk_response: {chunk_response}")
 
-            yield f"data: {chunk_response.model_dump_json()}\n\n"
+                yield f"data: {chunk_response.model_dump_json()}\n\n"
 
-            if chunk.finish_reason is not None:
-                yield "data: [DONE]\n\n"
+                if event.finish_reason is not None:
+                    yield "data: [DONE]\n\n"
 
     async def _collect_chat_completion(
         self, command_id: CommandId
@@ -548,6 +619,75 @@ class API:
         response = await self._collect_chat_completion_with_stats(command.command_id)
         return response
 
+    async def claude_messages(
+        self, payload: ClaudeMessagesRequest
+    ) -> ClaudeMessagesResponse | StreamingResponse:
+        """Handle Claude Messages API requests."""
+        chat_params = claude_request_to_chat_params(payload)
+        model_meta = await resolve_model_meta(chat_params.model)
+        chat_params.model = model_meta.model_id
+
+        if not any(
+            instance.shard_assignments.model_id == chat_params.model
+            for instance in self.state.instances.values()
+        ):
+            await self._trigger_notify_user_to_download_model(chat_params.model)
+            raise HTTPException(
+                status_code=404,
+                detail=f"No instance found for model {chat_params.model}",
+            )
+
+        command = ChatCompletion(request_params=chat_params)
+        await self._send(command)
+
+        if payload.stream:
+            return StreamingResponse(
+                generate_claude_stream(
+                    command.command_id,
+                    payload.model,
+                    self._chat_chunk_stream(command.command_id),
+                ),
+                media_type="text/event-stream",
+            )
+
+        response = await self._collect_chat_completion(command.command_id)
+        return chat_response_to_claude_response(response)
+
+    async def openai_responses(
+        self, payload: ResponsesRequest
+    ) -> ResponsesResponse | StreamingResponse:
+        """Handle OpenAI Responses API requests."""
+        chat_params = responses_request_to_chat_params(payload)
+
+        model_meta = await resolve_model_meta(chat_params.model)
+        chat_params.model = model_meta.model_id
+
+        if not any(
+            instance.shard_assignments.model_id == chat_params.model
+            for instance in self.state.instances.values()
+        ):
+            await self._trigger_notify_user_to_download_model(chat_params.model)
+            raise HTTPException(
+                status_code=404,
+                detail=f"No instance found for model {chat_params.model}",
+            )
+
+        command = ChatCompletion(request_params=chat_params)
+        await self._send(command)
+
+        if payload.stream:
+            return StreamingResponse(
+                generate_responses_stream(
+                    command.command_id,
+                    payload.model,
+                    self._chat_chunk_stream(command.command_id),
+                ),
+                media_type="text/event-stream",
+            )
+
+        response = await self._collect_chat_completion(command.command_id)
+        return chat_response_to_responses_response(response)
+
     def _calculate_total_available_memory(self) -> Memory:
         """Calculate total available memory across all nodes in bytes."""
         total_available = Memory()
@@ -615,6 +755,16 @@ class API:
                         await self._chat_completion_queues[event.command_id].send(
                             event.chunk
                         )
+                    elif (
+                        isinstance(event, PrefillProgress)
+                        and event.command_id in self._chat_completion_queues
+                    ):
+                        await self._chat_completion_queues[event.command_id].send(
+                            PrefillProgressData(
+                                processed_tokens=event.processed_tokens,
+                                total_tokens=event.total_tokens,
+                            )
+                        )
 
     async def _pause_on_new_election(self):
         with self.election_receiver as ems:

src/exo/master/tests/test_claude_api.py

@@ -0,0 +1,392 @@
+"""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 (
+    chat_response_to_claude_response,
+    claude_request_to_chat_params,
+    finish_reason_to_claude_stop_reason,
+)
+from exo.shared.types.api import (
+    ChatCompletionChoice,
+    ChatCompletionMessage,
+    ChatCompletionResponse,
+    Usage,
+)
+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 TestClaudeRequestToChatParams:
+    """Tests for converting Claude Messages API requests to ChatCompletionTaskParams."""
+
+    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_chat_params(request)
+
+        assert params.model == "claude-3-opus"
+        assert params.max_tokens == 100
+        assert len(params.messages) == 1
+        assert params.messages[0].role == "user"
+        assert params.messages[0].content == "Hello"
+
+    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_chat_params(request)
+
+        assert len(params.messages) == 2
+        assert params.messages[0].role == "system"
+        assert params.messages[0].content == "You are a helpful assistant."
+        assert params.messages[1].role == "user"
+        assert params.messages[1].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_chat_params(request)
+
+        assert len(params.messages) == 2
+        assert params.messages[0].role == "system"
+        assert params.messages[0].content == "You are helpful. Be concise."
+
+    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_chat_params(request)
+
+        assert len(params.messages) == 1
+        assert params.messages[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_chat_params(request)
+
+        assert len(params.messages) == 3
+        assert params.messages[0].role == "user"
+        assert params.messages[1].role == "assistant"
+        assert params.messages[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_chat_params(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 TestChatResponseToClaudeResponse:
+    """Tests for converting ChatCompletionResponse to Claude Messages API response."""
+
+    def test_basic_response_conversion(self):
+        response = ChatCompletionResponse(
+            id="chatcmpl-123",
+            created=1234567890,
+            model="llama-3.2-1b",
+            choices=[
+                ChatCompletionChoice(
+                    index=0,
+                    message=ChatCompletionMessage(
+                        role="assistant",
+                        content="Hello! How can I help you?",
+                    ),
+                    finish_reason="stop",
+                )
+            ],
+            usage=Usage(prompt_tokens=10, completion_tokens=7, total_tokens=17),
+        )
+        claude_response = chat_response_to_claude_response(response)
+
+        assert claude_response.id == "msg_chatcmpl-123"
+        assert claude_response.model == "llama-3.2-1b"
+        assert claude_response.role == "assistant"
+        assert claude_response.type == "message"
+        assert len(claude_response.content) == 1
+        assert claude_response.content[0].type == "text"
+        assert claude_response.content[0].text == "Hello! How can I help you?"
+        assert claude_response.stop_reason == "end_turn"
+        assert claude_response.usage.input_tokens == 10
+        assert claude_response.usage.output_tokens == 7
+
+    def test_response_with_length_finish_reason(self):
+        response = ChatCompletionResponse(
+            id="chatcmpl-123",
+            created=1234567890,
+            model="llama-3.2-1b",
+            choices=[
+                ChatCompletionChoice(
+                    index=0,
+                    message=ChatCompletionMessage(
+                        role="assistant", content="Truncated..."
+                    ),
+                    finish_reason="length",
+                )
+            ],
+        )
+        claude_response = chat_response_to_claude_response(response)
+
+        assert claude_response.stop_reason == "max_tokens"
+
+    def test_response_with_empty_content(self):
+        response = ChatCompletionResponse(
+            id="chatcmpl-123",
+            created=1234567890,
+            model="llama-3.2-1b",
+            choices=[
+                ChatCompletionChoice(
+                    index=0,
+                    message=ChatCompletionMessage(role="assistant", content=""),
+                    finish_reason="stop",
+                )
+            ],
+            usage=Usage(prompt_tokens=10, completion_tokens=0, total_tokens=10),
+        )
+        claude_response = chat_response_to_claude_response(response)
+
+        assert claude_response.content[0].text == ""
+        assert claude_response.usage.output_tokens == 0
+
+    def test_response_with_no_choices(self):
+        response = ChatCompletionResponse(
+            id="chatcmpl-123",
+            created=1234567890,
+            model="llama-3.2-1b",
+            choices=[],
+        )
+        claude_response = chat_response_to_claude_response(response)
+
+        assert claude_response.content[0].text == ""
+        assert claude_response.stop_reason is None
+        assert claude_response.usage.input_tokens == 0
+        assert claude_response.usage.output_tokens == 0
+
+    def test_response_without_usage(self):
+        """Test response conversion when usage data is not available."""
+        response = ChatCompletionResponse(
+            id="chatcmpl-123",
+            created=1234567890,
+            model="llama-3.2-1b",
+            choices=[
+                ChatCompletionChoice(
+                    index=0,
+                    message=ChatCompletionMessage(role="assistant", content="Hello!"),
+                    finish_reason="stop",
+                )
+            ],
+        )
+        claude_response = chat_response_to_claude_response(response)
+
+        assert claude_response.content[0].text == "Hello!"
+        assert claude_response.usage.input_tokens == 0
+        assert claude_response.usage.output_tokens == 0
+
+
+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_openai_responses_api.py

@@ -0,0 +1,414 @@
+"""Tests for OpenAI Responses API conversion functions and types."""
+
+import json
+from typing import Any, cast
+
+import pydantic
+import pytest
+
+from exo.master.adapters.responses import (
+    chat_response_to_responses_response,
+    responses_request_to_chat_params,
+)
+from exo.shared.types.api import (
+    ChatCompletionChoice,
+    ChatCompletionMessage,
+    ChatCompletionResponse,
+    Usage,
+)
+from exo.shared.types.openai_responses import (
+    ResponseCompletedEvent,
+    ResponseContentPartAddedEvent,
+    ResponseCreatedEvent,
+    ResponseInputMessage,
+    ResponseMessageItem,
+    ResponseOutputItemAddedEvent,
+    ResponseOutputItemDoneEvent,
+    ResponseOutputText,
+    ResponsesRequest,
+    ResponsesResponse,
+    ResponseTextDeltaEvent,
+    ResponseTextDoneEvent,
+    ResponseUsage,
+)
+
+
+class TestResponsesRequestToChatParams:
+    """Tests for converting OpenAI Responses API requests to ChatCompletionTaskParams."""
+
+    def test_string_input_conversion(self):
+        request = ResponsesRequest(
+            model="gpt-4o",
+            input="Hello, how are you?",
+        )
+        params = responses_request_to_chat_params(request)
+
+        assert params.model == "gpt-4o"
+        assert len(params.messages) == 1
+        assert params.messages[0].role == "user"
+        assert params.messages[0].content == "Hello, how are you?"
+
+    def test_message_array_input_conversion(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?"),
+            ],
+        )
+        params = responses_request_to_chat_params(request)
+
+        assert len(params.messages) == 3
+        assert params.messages[0].role == "user"
+        assert params.messages[0].content == "Hello"
+        assert params.messages[1].role == "assistant"
+        assert params.messages[1].content == "Hi there!"
+        assert params.messages[2].role == "user"
+        assert params.messages[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.",
+        )
+        params = responses_request_to_chat_params(request)
+
+        assert len(params.messages) == 2
+        assert params.messages[0].role == "system"
+        assert params.messages[0].content == "You are a helpful assistant. Be concise."
+        assert params.messages[1].role == "user"
+        assert params.messages[1].content == "Hello"
+
+    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,
+        )
+        params = responses_request_to_chat_params(request)
+
+        assert params.max_tokens == 500
+        assert params.temperature == 0.8
+        assert params.top_p == 0.95
+        assert params.stream is True
+
+    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"),
+            ],
+        )
+        params = responses_request_to_chat_params(request)
+
+        assert len(params.messages) == 2
+        assert params.messages[0].role == "system"
+        assert params.messages[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"),
+            ],
+        )
+        params = responses_request_to_chat_params(request)
+
+        assert len(params.messages) == 2
+        assert params.messages[0].role == "developer"
+
+
+class TestChatResponseToResponsesResponse:
+    """Tests for converting ChatCompletionResponse to OpenAI Responses API response."""
+
+    def test_basic_response_conversion(self):
+        response = ChatCompletionResponse(
+            id="chatcmpl-123",
+            created=1234567890,
+            model="llama-3.2-1b",
+            choices=[
+                ChatCompletionChoice(
+                    index=0,
+                    message=ChatCompletionMessage(
+                        role="assistant",
+                        content="Hello! How can I help you?",
+                    ),
+                    finish_reason="stop",
+                )
+            ],
+        )
+        responses_response = chat_response_to_responses_response(response)
+
+        assert responses_response.id == "resp_chatcmpl-123"
+        assert responses_response.object == "response"
+        assert responses_response.model == "llama-3.2-1b"
+        assert responses_response.status == "completed"
+        assert responses_response.output_text == "Hello! How can I help you?"
+        assert len(responses_response.output) == 1
+        assert responses_response.output[0].type == "message"
+        assert responses_response.output[0].role == "assistant"
+        assert len(responses_response.output[0].content) == 1
+        assert responses_response.output[0].content[0].type == "output_text"
+        assert (
+            responses_response.output[0].content[0].text == "Hello! How can I help you?"
+        )
+
+    def test_response_with_usage(self):
+        response = ChatCompletionResponse(
+            id="chatcmpl-123",
+            created=1234567890,
+            model="llama-3.2-1b",
+            choices=[
+                ChatCompletionChoice(
+                    index=0,
+                    message=ChatCompletionMessage(role="assistant", content="Hello!"),
+                    finish_reason="stop",
+                )
+            ],
+            usage=Usage(
+                prompt_tokens=10,
+                completion_tokens=5,
+                total_tokens=15,
+            ),
+        )
+        responses_response = chat_response_to_responses_response(response)
+
+        assert responses_response.usage is not None
+        assert responses_response.usage.input_tokens == 10
+        assert responses_response.usage.output_tokens == 5
+        assert responses_response.usage.total_tokens == 15
+
+    def test_response_with_empty_content(self):
+        response = ChatCompletionResponse(
+            id="chatcmpl-123",
+            created=1234567890,
+            model="llama-3.2-1b",
+            choices=[
+                ChatCompletionChoice(
+                    index=0,
+                    message=ChatCompletionMessage(role="assistant", content=""),
+                    finish_reason="stop",
+                )
+            ],
+        )
+        responses_response = chat_response_to_responses_response(response)
+
+        assert responses_response.output_text == ""
+        assert responses_response.output[0].content[0].text == ""
+
+    def test_response_with_no_choices(self):
+        response = ChatCompletionResponse(
+            id="chatcmpl-123",
+            created=1234567890,
+            model="llama-3.2-1b",
+            choices=[],
+        )
+        responses_response = chat_response_to_responses_response(response)
+
+        assert responses_response.output_text == ""
+
+    def test_response_without_usage(self):
+        response = ChatCompletionResponse(
+            id="chatcmpl-123",
+            created=1234567890,
+            model="llama-3.2-1b",
+            choices=[
+                ChatCompletionChoice(
+                    index=0,
+                    message=ChatCompletionMessage(role="assistant", content="Hello!"),
+                    finish_reason="stop",
+                )
+            ],
+        )
+        responses_response = chat_response_to_responses_response(response)
+
+        assert responses_response.usage is None
+
+    def test_response_item_id_format(self):
+        response = ChatCompletionResponse(
+            id="chatcmpl-abc123",
+            created=1234567890,
+            model="llama-3.2-1b",
+            choices=[
+                ChatCompletionChoice(
+                    index=0,
+                    message=ChatCompletionMessage(role="assistant", content="Hello!"),
+                    finish_reason="stop",
+                )
+            ],
+        )
+        responses_response = chat_response_to_responses_response(response)
+
+        assert responses_response.output[0].id == "item_chatcmpl-abc123"
+
+
+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/shared/apply.py

@@ -16,6 +16,7 @@ from exo.shared.types.events import (
     NodeMemoryMeasured,
     NodePerformanceMeasured,
     NodeTimedOut,
+    PrefillProgress,
     RunnerDeleted,
     RunnerStatusUpdated,
     TaskAcknowledged,
@@ -40,7 +41,7 @@ def event_apply(event: Event, state: State) -> State:
     """Apply an event to state."""
     match event:
         case (
-            TestEvent() | ChunkGenerated() | TaskAcknowledged()
+            TestEvent() | ChunkGenerated() | TaskAcknowledged() | PrefillProgress()
         ):  # TaskAcknowledged should never be sent by a worker but i dont mind if it just gets ignored
             return state
         case InstanceCreated():

src/exo/shared/types/api.py

@@ -146,6 +146,7 @@ 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

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
 

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/events.py

@@ -106,6 +106,12 @@ class ChunkGenerated(BaseEvent):
     chunk: GenerationChunk
 
 
+class PrefillProgress(BaseEvent):
+    command_id: CommandId
+    processed_tokens: int
+    total_tokens: int
+
+
 class TopologyEdgeCreated(BaseEvent):
     edge: Connection
 
@@ -131,6 +137,7 @@ Event = (
     | NodeMemoryMeasured
     | NodeDownloadProgress
     | ChunkGenerated
+    | PrefillProgress
     | TopologyEdgeCreated
     | TopologyEdgeDeleted
 )

src/exo/shared/types/openai_responses.py

@@ -0,0 +1,162 @@
+"""OpenAI Responses API types for request/response conversion."""
+
+import time
+from typing import 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."""
+
+    role: ResponseRole
+    content: str
+
+
+class ResponsesRequest(BaseModel):
+    """Request body for OpenAI Responses API."""
+
+    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
+    stream: bool = False
+    # previous_response_id not supported in MVP
+    metadata: dict[str, str] | None = None
+
+
+# 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/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
 
 
@@ -13,10 +13,16 @@ 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
 
 
 class FinishedResponse(BaseRunnerResponse):
     pass
+
+
+class PrefillProgressResponse(BaseRunnerResponse):
+    processed_tokens: int
+    total_tokens: int

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

@@ -12,6 +12,7 @@ from exo.shared.types.api import (
     ChatCompletionMessage,
     FinishReason,
     GenerationStats,
+    TopLogprobItem,
 )
 from exo.shared.types.memory import Memory
 from exo.shared.types.tasks import ChatCompletionTaskParams
@@ -81,7 +82,7 @@ def warmup_inference(
         max_tokens=50,
         sampler=sampler,
         prompt_cache=cache,
-        prefill_step_size=2048,
+        prefill_step_size=256,  # Temporarily reduced from 2048 for testing progress bar
         kv_group_size=KV_GROUP_SIZE,
         kv_bits=KV_BITS,
     ):
@@ -115,10 +116,65 @@ 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,
+    on_prefill_progress: Callable[[int, int], None] | None = None,
 ) -> Generator[GenerationResponse]:
     # Ensure that generation stats only contains peak memory for this generation
     mx.reset_peak_memory()
@@ -146,9 +202,24 @@ 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,
     )
 
+    # 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)
+
     max_tokens = task.max_tokens or MAX_TOKENS
+    accumulated_text = ""
+
+    # Determine if we need to extract logprobs
+    should_extract_logprobs = task.logprobs is True
+    num_top_logprobs = task.top_logprobs if task.top_logprobs is not None else 5
+
     for out in stream_generate(
         model=model,
         tokenizer=tokenizer,
@@ -158,14 +229,47 @@ def mlx_generate(
         logits_processors=logits_processors,
         prompt_cache=caches,
         # TODO: Dynamically change prefill step size to be the maximum possible without timing out.
-        prefill_step_size=2048,
+        prefill_step_size=256,  # Temporarily reduced from 2048 for testing progress bar
         kv_group_size=KV_GROUP_SIZE,
         kv_bits=KV_BITS,
+        prompt_progress_callback=on_prefill_progress,
     ):
         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
+
+        # Extract logprobs if requested
+        token_logprob: float | None = None
+        top_logprobs: list[TopLogprobItem] | None = None
+        if should_extract_logprobs:
+            token_logprob, top_logprobs = extract_top_logprobs(
+                logprobs=out.logprobs,
+                tokenizer=tokenizer,
+                top_k=num_top_logprobs,
+                selected_token=out.token,
+            )
+
+        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),
@@ -173,22 +277,25 @@ 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}"
                 )
 
         yield GenerationResponse(
-            text=out.text,
+            text=text,
             token=out.token,
-            finish_reason=cast(FinishReason | None, out.finish_reason),
+            logprob=token_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/runner/runner.py

@@ -16,6 +16,7 @@ from exo.shared.types.chunks import TokenChunk
 from exo.shared.types.events import (
     ChunkGenerated,
     Event,
+    PrefillProgress,
     RunnerStatusUpdated,
     TaskAcknowledged,
     TaskStatusUpdated,
@@ -161,11 +162,23 @@ def main(
                     assert task_params.messages[0].content is not None
                     _check_for_debug_prompts(task_params.messages[0].content)
 
+                    # Define callback to send prefill progress events directly
+                    def on_prefill_progress(processed: int, total: int) -> None:
+                        if shard_metadata.device_rank == 0:
+                            event_sender.send(
+                                PrefillProgress(
+                                    command_id=command_id,
+                                    processed_tokens=processed,
+                                    total_tokens=total,
+                                )
+                            )
+
                     # Generate responses using the actual MLX generation
                     mlx_generator = mlx_generate(
                         model=model,
                         tokenizer=tokenizer,
                         task=task_params,
+                        on_prefill_progress=on_prefill_progress,
                     )
 
                     # GPT-OSS specific parsing to match other model formats.
@@ -186,6 +199,8 @@ 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,
                                             ),