Merge branch 'main' into fix-partial-download-progress

## Summary
- Show estimated time remaining during prefill (prompt processing phase)
- Track prefill start time via performance.now() and extrapolate from
observed token throughput
- Display ~Xs remaining or ~Xm Ys remaining next to the percentage on
the progress bar
- Wait 200ms before showing ETA to ensure a stable sample window

## Changes
**PrefillProgressBar.svelte**: Add etaText derived computation that
calculates remaining time from (remainingTokens / tokensPerMs). Renders
in a new flex row below the progress bar alongside the percentage.

**app.svelte.ts**: Add startedAt: number field to PrefillProgress
interface. Set on first prefill_progress SSE event, preserved across
subsequent updates.

## Test plan
- [ ] Start inference with a long prompt (10k+ tokens) on a multi-node
cluster
- [ ] Verify the progress bar shows ~Xs remaining after ~200ms of
prefill
- [ ] Verify the ETA decreases as prefill progresses
- [ ] Verify short prefills (<200ms) dont flash a briefly-visible ETA
- [ ] Verify ETA disappears when prefill completes and token generation
begins

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: rltakashige <rl.takashige@gmail.com>

bench/eval_tool_calls.py

@@ -20,6 +20,7 @@ from harness import (
     instance_id_from_instance,
     nodes_used_in_instance,
     resolve_model_short_id,
+    run_planning_phase,
     settle_and_fetch_placements,
     wait_for_instance_gone,
     wait_for_instance_ready,
@@ -962,6 +963,21 @@ Examples:
 
     selected.sort(key=_placement_sort_key)
     preview = selected[0]
+
+    settle_deadline = (
+        time.monotonic() + args.settle_timeout if args.settle_timeout > 0 else None
+    )
+
+    print("Planning phase: checking downloads...", file=log)
+    run_planning_phase(
+        exo,
+        full_model_id,
+        preview,
+        args.danger_delete_downloads,
+        args.timeout,
+        settle_deadline,
+    )
+
     instance = preview["instance"]
     instance_id = instance_id_from_instance(instance)
     sharding = str(preview["sharding"])

bench/exo_bench.py

@@ -35,6 +35,7 @@ from harness import (
     instance_id_from_instance,
     nodes_used_in_instance,
     resolve_model_short_id,
+    run_planning_phase,
     settle_and_fetch_placements,
     wait_for_instance_gone,
     wait_for_instance_ready,
@@ -332,6 +333,20 @@ def main() -> int:
     if args.dry_run:
         return 0
 
+    settle_deadline = (
+        time.monotonic() + args.settle_timeout if args.settle_timeout > 0 else None
+    )
+
+    logger.info("Planning phase: checking downloads...")
+    run_planning_phase(
+        client,
+        full_model_id,
+        selected[0],
+        args.danger_delete_downloads,
+        args.timeout,
+        settle_deadline,
+    )
+
     all_rows: list[dict[str, Any]] = []
 
     for preview in selected:

bench/harness.py

@@ -282,6 +282,151 @@ def settle_and_fetch_placements(
     return selected
 
 
+def run_planning_phase(
+    client: ExoClient,
+    full_model_id: str,
+    preview: dict[str, Any],
+    danger_delete: bool,
+    timeout: float,
+    settle_deadline: float | None,
+) -> None:
+    """Check disk space and ensure model is downloaded before benchmarking."""
+    # Get model size from /models
+    models = client.request_json("GET", "/models") or {}
+    model_bytes = 0
+    for m in models.get("data", []):
+        if m.get("hugging_face_id") == full_model_id:
+            model_bytes = m.get("storage_size_megabytes", 0) * 1024 * 1024
+            break
+
+    if not model_bytes:
+        logger.warning(
+            f"Could not determine size for {full_model_id}, skipping disk check"
+        )
+        return
+
+    # Get nodes from preview
+    inner = unwrap_instance(preview["instance"])
+    node_ids = list(inner["shardAssignments"]["nodeToRunner"].keys())
+    runner_to_shard = inner["shardAssignments"]["runnerToShard"]
+
+    state = client.request_json("GET", "/state")
+    downloads = state.get("downloads", {})
+    node_disk = state.get("nodeDisk", {})
+
+    for node_id in node_ids:
+        node_downloads = downloads.get(node_id, [])
+
+        # Check if model already downloaded on this node
+        already_downloaded = any(
+            "DownloadCompleted" in p
+            and unwrap_instance(p["DownloadCompleted"]["shardMetadata"])["modelCard"][
+                "modelId"
+            ]
+            == full_model_id
+            for p in node_downloads
+        )
+        if already_downloaded:
+            continue
+
+        # Wait for disk info if settle_deadline is set
+        disk_info = node_disk.get(node_id, {})
+        backoff = _SETTLE_INITIAL_BACKOFF_S
+        while not disk_info and settle_deadline and time.monotonic() < settle_deadline:
+            remaining = settle_deadline - time.monotonic()
+            logger.info(
+                f"Waiting for disk info on {node_id} ({remaining:.0f}s remaining)..."
+            )
+            time.sleep(min(backoff, remaining))
+            backoff = min(backoff * _SETTLE_BACKOFF_MULTIPLIER, _SETTLE_MAX_BACKOFF_S)
+            state = client.request_json("GET", "/state")
+            node_disk = state.get("nodeDisk", {})
+            disk_info = node_disk.get(node_id, {})
+
+        if not disk_info:
+            logger.warning(f"No disk info for {node_id}, skipping space check")
+            continue
+
+        avail = disk_info.get("available", {}).get("inBytes", 0)
+        if avail >= model_bytes:
+            continue
+
+        if not danger_delete:
+            raise RuntimeError(
+                f"Insufficient disk on {node_id}: need {model_bytes // (1024**3)}GB, "
+                f"have {avail // (1024**3)}GB. Use --danger-delete-downloads to free space."
+            )
+
+        # Delete from smallest to largest
+        completed = [
+            (
+                unwrap_instance(p["DownloadCompleted"]["shardMetadata"])["modelCard"][
+                    "modelId"
+                ],
+                p["DownloadCompleted"]["totalBytes"]["inBytes"],
+            )
+            for p in node_downloads
+            if "DownloadCompleted" in p
+        ]
+        for del_model, size in sorted(completed, key=lambda x: x[1]):
+            logger.info(f"Deleting {del_model} from {node_id} ({size // (1024**2)}MB)")
+            client.request_json("DELETE", f"/download/{node_id}/{del_model}")
+            avail += size
+            if avail >= model_bytes:
+                break
+
+        if avail < model_bytes:
+            raise RuntimeError(f"Could not free enough space on {node_id}")
+
+    # Start downloads (idempotent)
+    for node_id in node_ids:
+        runner_id = inner["shardAssignments"]["nodeToRunner"][node_id]
+        shard = runner_to_shard[runner_id]
+        client.request_json(
+            "POST",
+            "/download/start",
+            body={
+                "targetNodeId": node_id,
+                "shardMetadata": shard,
+            },
+        )
+        logger.info(f"Started download on {node_id}")
+
+    # Wait for downloads
+    start = time.time()
+    while time.time() - start < timeout:
+        state = client.request_json("GET", "/state")
+        downloads = state.get("downloads", {})
+        all_done = True
+        for node_id in node_ids:
+            done = any(
+                "DownloadCompleted" in p
+                and unwrap_instance(p["DownloadCompleted"]["shardMetadata"])[
+                    "modelCard"
+                ]["modelId"]
+                == full_model_id
+                for p in downloads.get(node_id, [])
+            )
+            failed = [
+                p["DownloadFailed"]["errorMessage"]
+                for p in downloads.get(node_id, [])
+                if "DownloadFailed" in p
+                and unwrap_instance(p["DownloadFailed"]["shardMetadata"])["modelCard"][
+                    "modelId"
+                ]
+                == full_model_id
+            ]
+            if failed:
+                raise RuntimeError(f"Download failed on {node_id}: {failed[0]}")
+            if not done:
+                all_done = False
+        if all_done:
+            return
+        time.sleep(1)
+
+    raise TimeoutError("Downloads did not complete in time")
+
+
 def add_common_instance_args(ap: argparse.ArgumentParser) -> None:
     ap.add_argument("--host", default=os.environ.get("EXO_HOST", "localhost"))
     ap.add_argument(
@@ -325,3 +470,8 @@ def add_common_instance_args(ap: argparse.ArgumentParser) -> None:
         default=0,
         help="Max seconds to wait for the cluster to produce valid placements (0 = try once).",
     )
+    ap.add_argument(
+        "--danger-delete-downloads",
+        action="store_true",
+        help="Delete existing models from smallest to largest to make room for benchmark model.",
+    )

dashboard/src/lib/components/PrefillProgressBar.svelte

@@ -14,6 +14,21 @@
       : 0,
   );
 
+  const etaText = $derived.by(() => {
+    if (progress.processed <= 0 || progress.total <= 0) return null;
+    const elapsedMs = performance.now() - progress.startedAt;
+    if (elapsedMs < 200) return null; // need a minimum sample window
+    const tokensPerMs = progress.processed / elapsedMs;
+    const remainingTokens = progress.total - progress.processed;
+    const remainingMs = remainingTokens / tokensPerMs;
+    const remainingSec = Math.ceil(remainingMs / 1000);
+    if (remainingSec <= 0) return null;
+    if (remainingSec < 60) return `~${remainingSec}s remaining`;
+    const mins = Math.floor(remainingSec / 60);
+    const secs = remainingSec % 60;
+    return `~${mins}m ${secs}s remaining`;
+  });
+
   function formatTokenCount(count: number | undefined): string {
     if (count == null) return "0";
     if (count >= 1000) {
@@ -40,8 +55,11 @@
       style="width: {percentage}%"
     ></div>
   </div>
-  <div class="text-right text-xs text-exo-light-gray/70 mt-0.5 font-mono">
+  <div
-    {percentage}%
+    class="flex items-center justify-between text-xs text-exo-light-gray/70 mt-0.5 font-mono"
+  >
+    <span>{etaText ?? ""}</span>
+    <span>{percentage}%</span>
   </div>
 </div>
 

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

@@ -250,6 +250,11 @@ interface RawStateResponse {
   >;
   // Thunderbolt bridge cycles (nodes with bridge enabled forming loops)
   thunderboltBridgeCycles?: string[][];
+  // Disk usage per node
+  nodeDisk?: Record<
+    string,
+    { total: { inBytes: number }; available: { inBytes: number } }
+  >;
 }
 
 export interface MessageAttachment {
@@ -276,6 +281,8 @@ export interface TokenData {
 export interface PrefillProgress {
   processed: number;
   total: number;
+  /** Timestamp (performance.now()) when prefill started. */
+  startedAt: number;
 }
 
 export interface Message {
@@ -1652,11 +1659,12 @@ class AppStore {
       if (!reader) throw new Error("No response body");
 
       let fullContent = prefixText;
+      let streamedThinking = "";
       const collectedTokens: TokenData[] = [...tokensToKeep];
 
       interface ChatCompletionChunk {
         choices?: Array<{
-          delta?: { content?: string };
+          delta?: { content?: string; reasoning_content?: string };
           logprobs?: {
             content?: Array<{
               token: string;
@@ -1677,6 +1685,7 @@ class AppStore {
         (parsed) => {
           const choice = parsed.choices?.[0];
           const delta = choice?.delta?.content;
+          const thinkingDelta = choice?.delta?.reasoning_content;
 
           // Collect logprobs data
           const logprobsContent = choice?.logprobs?.content;
@@ -1695,7 +1704,11 @@ class AppStore {
             }
           }
 
-          if (delta) {
+          if (thinkingDelta) {
+            streamedThinking += thinkingDelta;
+          }
+
+          if (delta || thinkingDelta) {
             if (firstTokenTime === null) {
               firstTokenTime = performance.now();
               this.ttftMs = firstTokenTime - requestStartTime;
@@ -1709,9 +1722,14 @@ class AppStore {
               this.tps = ((tokenCount - tokensToKeep.length) / elapsed) * 1000;
             }
 
-            fullContent += delta;
+            if (delta) {
-            const { displayContent, thinkingContent } =
+              fullContent += delta;
+            }
+            const { displayContent, thinkingContent: tagThinking } =
               this.stripThinkingTags(fullContent);
+            const combinedThinking = [streamedThinking, tagThinking]
+              .filter(Boolean)
+              .join("\n\n");
 
             if (this.activeConversationId === targetConversationId) {
               this.currentResponse = displayContent;
@@ -1723,7 +1741,7 @@ class AppStore {
               messageId,
               (m) => {
                 m.content = displayContent;
-                m.thinking = thinkingContent || undefined;
+                m.thinking = combinedThinking || undefined;
                 m.tokens = [...collectedTokens];
               },
             );
@@ -1735,11 +1753,14 @@ class AppStore {
 
       // Final update
       if (this.conversationExists(targetConversationId)) {
-        const { displayContent, thinkingContent } =
+        const { displayContent, thinkingContent: tagThinking } =
           this.stripThinkingTags(fullContent);
+        const finalThinking = [streamedThinking, tagThinking]
+          .filter(Boolean)
+          .join("\n\n");
         this.updateConversationMessage(targetConversationId, messageId, (m) => {
           m.content = displayContent;
-          m.thinking = thinkingContent || undefined;
+          m.thinking = finalThinking || undefined;
           m.tokens = [...collectedTokens];
           if (this.ttftMs !== null) m.ttftMs = this.ttftMs;
           if (this.tps !== null) m.tps = this.tps;
@@ -1847,11 +1868,12 @@ class AppStore {
       }
 
       let streamedContent = "";
+      let streamedThinking = "";
       const collectedTokens: TokenData[] = [];
 
       interface ChatCompletionChunk {
         choices?: Array<{
-          delta?: { content?: string };
+          delta?: { content?: string; reasoning_content?: string };
           logprobs?: {
             content?: Array<{
               token: string;
@@ -1872,6 +1894,7 @@ class AppStore {
         (parsed) => {
           const choice = parsed.choices?.[0];
           const delta = choice?.delta?.content;
+          const thinkingDelta = choice?.delta?.reasoning_content;
 
           // Collect logprobs data
           const logprobsContent = choice?.logprobs?.content;
@@ -1890,10 +1913,19 @@ class AppStore {
             }
           }
 
-          if (delta) {
+          if (thinkingDelta) {
-            streamedContent += delta;
+            streamedThinking += thinkingDelta;
-            const { displayContent, thinkingContent } =
+          }
+
+          if (delta || thinkingDelta) {
+            if (delta) {
+              streamedContent += delta;
+            }
+            const { displayContent, thinkingContent: tagThinking } =
               this.stripThinkingTags(streamedContent);
+            const combinedThinking = [streamedThinking, tagThinking]
+              .filter(Boolean)
+              .join("\n\n");
 
             // Only update currentResponse if target conversation is active
             if (this.activeConversationId === targetConversationId) {
@@ -1906,7 +1938,7 @@ class AppStore {
               assistantMessage.id,
               (msg) => {
                 msg.content = displayContent;
-                msg.thinking = thinkingContent || undefined;
+                msg.thinking = combinedThinking || undefined;
                 msg.tokens = [...collectedTokens];
               },
             );
@@ -1918,14 +1950,17 @@ class AppStore {
 
       // Final cleanup of the message (if conversation still exists)
       if (this.conversationExists(targetConversationId)) {
-        const { displayContent, thinkingContent } =
+        const { displayContent, thinkingContent: tagThinking } =
           this.stripThinkingTags(streamedContent);
+        const finalThinking = [streamedThinking, tagThinking]
+          .filter(Boolean)
+          .join("\n\n");
         this.updateConversationMessage(
           targetConversationId,
           assistantMessage.id,
           (msg) => {
             msg.content = displayContent;
-            msg.thinking = thinkingContent || undefined;
+            msg.thinking = finalThinking || undefined;
             msg.tokens = [...collectedTokens];
           },
         );
@@ -2317,10 +2352,11 @@ class AppStore {
       }
 
       let streamedContent = "";
+      let streamedThinking = "";
 
       interface ChatCompletionChunk {
         choices?: Array<{
-          delta?: { content?: string };
+          delta?: { content?: string; reasoning_content?: string };
           logprobs?: {
             content?: Array<{
               token: string;
@@ -2348,6 +2384,7 @@ class AppStore {
 
           const choice = parsed.choices?.[0];
           const tokenContent = choice?.delta?.content;
+          const thinkingContent = choice?.delta?.reasoning_content;
 
           // Collect logprobs data
           const logprobsContent = choice?.logprobs?.content;
@@ -2366,7 +2403,11 @@ class AppStore {
             }
           }
 
-          if (tokenContent) {
+          if (thinkingContent) {
+            streamedThinking += thinkingContent;
+          }
+
+          if (tokenContent || thinkingContent) {
             // Track first token for TTFT
             if (firstTokenTime === null) {
               firstTokenTime = performance.now();
@@ -2383,11 +2424,16 @@ class AppStore {
               this.tps = (tokenCount / elapsed) * 1000;
             }
 
-            streamedContent += tokenContent;
+            if (tokenContent) {
+              streamedContent += tokenContent;
+            }
 
-            // Strip thinking tags for display and extract thinking content
+            // Use stripThinkingTags as fallback for any <think> tags still in content
-            const { displayContent, thinkingContent } =
+            const { displayContent, thinkingContent: tagThinking } =
               this.stripThinkingTags(streamedContent);
+            const combinedThinking = [streamedThinking, tagThinking]
+              .filter(Boolean)
+              .join("\n\n");
 
             // Only update currentResponse if target conversation is active
             if (this.activeConversationId === targetConversationId) {
@@ -2400,7 +2446,7 @@ class AppStore {
               assistantMessage.id,
               (msg) => {
                 msg.content = displayContent;
-                msg.thinking = thinkingContent || undefined;
+                msg.thinking = combinedThinking || undefined;
                 msg.tokens = [...collectedTokens];
               },
             );
@@ -2420,6 +2466,7 @@ class AppStore {
             this.prefillProgress = {
               processed: inner.processed_tokens,
               total: inner.total_tokens,
+              startedAt: this.prefillProgress?.startedAt ?? performance.now(),
             };
           },
         },
@@ -2436,14 +2483,17 @@ class AppStore {
 
       // Final cleanup of the message (if conversation still exists)
       if (this.conversationExists(targetConversationId)) {
-        const { displayContent, thinkingContent } =
+        const { displayContent, thinkingContent: tagThinking } =
           this.stripThinkingTags(streamedContent);
+        const finalThinking = [streamedThinking, tagThinking]
+          .filter(Boolean)
+          .join("\n\n");
         this.updateConversationMessage(
           targetConversationId,
           assistantMessage.id,
           (msg) => {
             msg.content = displayContent;
-            msg.thinking = thinkingContent || undefined;
+            msg.thinking = finalThinking || undefined;
             msg.tokens = [...collectedTokens];
             // Store performance metrics on the message
             if (this.ttftMs !== null) {

dashboard/src/routes/+page.svelte

@@ -114,6 +114,74 @@
   });
   let tb5InfoDismissed = $state(false);
 
+  // Detect Mac Studio nodes using RDMA on en2 (the port next to ethernet — RDMA doesn't work there)
+  const macStudioEn2RdmaWarning = $derived.by(() => {
+    const edges = data?.edges;
+    const ids = tbIdentifiers;
+    const rdmaCtl = rdmaCtlData;
+    if (!edges || !ids || !rdmaCtl) return null;
+
+    const affectedConnections: Array<{
+      nodeId: string;
+      nodeName: string;
+      peerNodeId: string;
+      peerNodeName: string;
+      rdmaIface: string;
+    }> = [];
+
+    const isMacStudio = (node: (typeof data.nodes)[string] | undefined) =>
+      node?.system_info?.model_id === "Mac Studio";
+
+    for (const edge of edges) {
+      if (!edge.sourceRdmaIface && !edge.sinkRdmaIface) continue;
+
+      const sourceNode = data?.nodes?.[edge.source];
+      if (
+        isMacStudio(sourceNode) &&
+        edge.sourceRdmaIface === "rdma_en2" &&
+        rdmaCtl[edge.source]?.enabled
+      ) {
+        affectedConnections.push({
+          nodeId: edge.source,
+          nodeName:
+            sourceNode?.friendly_name || edge.source.slice(0, 8) + "...",
+          peerNodeId: edge.target,
+          peerNodeName:
+            data?.nodes?.[edge.target]?.friendly_name ||
+            edge.target.slice(0, 8) + "...",
+          rdmaIface: "en2",
+        });
+      }
+
+      const sinkNode = data?.nodes?.[edge.target];
+      if (
+        isMacStudio(sinkNode) &&
+        edge.sinkRdmaIface === "rdma_en2" &&
+        rdmaCtl[edge.target]?.enabled
+      ) {
+        affectedConnections.push({
+          nodeId: edge.target,
+          nodeName: sinkNode?.friendly_name || edge.target.slice(0, 8) + "...",
+          peerNodeId: edge.source,
+          peerNodeName:
+            sourceNode?.friendly_name || edge.source.slice(0, 8) + "...",
+          rdmaIface: "en2",
+        });
+      }
+    }
+
+    // Deduplicate by nodeId
+    const seen = new Set<string>();
+    const unique = affectedConnections.filter((c) => {
+      if (seen.has(c.nodeId)) return false;
+      seen.add(c.nodeId);
+      return true;
+    });
+
+    return unique.length > 0 ? unique : null;
+  });
+  let macStudioEn2Dismissed = $state(false);
+
   // Helper to get friendly node name from node ID
   function getNodeName(nodeId: string): string {
     const node = data?.nodes?.[nodeId];
@@ -790,10 +858,8 @@
     if (!progress || typeof progress !== "object") return null;
 
     const prog = progress as Record<string, unknown>;
-    const totalBytes = getBytes(prog.total_bytes ?? prog.totalBytes);
+    const totalBytes = getBytes(prog.total);
-    const downloadedBytes = getBytes(
+    const downloadedBytes = getBytes(prog.downloaded);
-      prog.downloaded_bytes ?? prog.downloadedBytes,
-    );
     const speed = (prog.speed as number) ?? 0;
     const completedFiles =
       (prog.completed_files as number) ?? (prog.completedFiles as number) ?? 0;
@@ -806,8 +872,8 @@
     for (const [fileName, fileData] of Object.entries(filesObj)) {
       if (!fileData || typeof fileData !== "object") continue;
       const fd = fileData as Record<string, unknown>;
-      const fTotal = getBytes(fd.total_bytes ?? fd.totalBytes);
+      const fTotal = getBytes(fd.total);
-      const fDownloaded = getBytes(fd.downloaded_bytes ?? fd.downloadedBytes);
+      const fDownloaded = getBytes(fd.downloaded);
       files.push({
         name: fileName,
         totalBytes: fTotal,
@@ -1196,7 +1262,6 @@
     if (typeof value === "number") return value;
     if (value && typeof value === "object") {
       const v = value as Record<string, unknown>;
-      if (typeof v.in_bytes === "number") return v.in_bytes;
       if (typeof v.inBytes === "number") return v.inBytes;
     }
     return 0;
@@ -1758,7 +1823,7 @@
 </script>
 
 {#snippet clusterWarnings()}
-  {#if tbBridgeCycles.length > 0 || macosVersionMismatch || (tb5WithoutRdma && !tb5InfoDismissed)}
+  {#if tbBridgeCycles.length > 0 || macosVersionMismatch || (tb5WithoutRdma && !tb5InfoDismissed) || (macStudioEn2RdmaWarning && !macStudioEn2Dismissed)}
     <div class="absolute top-4 left-4 flex flex-col gap-2 z-40">
       {#if tbBridgeCycles.length > 0}
         {@const cycle = tbBridgeCycles[0]}
@@ -1923,12 +1988,260 @@
           </button>
         </div>
       {/if}
+
+      {#if macStudioEn2RdmaWarning && !macStudioEn2Dismissed}
+        <div class="group relative" role="alert">
+          <div
+            class="flex items-center gap-2 px-3 py-2 rounded border border-red-500/50 bg-red-500/10 backdrop-blur-sm cursor-help"
+          >
+            <svg
+              class="w-5 h-5 text-red-400 flex-shrink-0"
+              fill="none"
+              viewBox="0 0 24 24"
+              stroke="currentColor"
+              stroke-width="2"
+            >
+              <path
+                stroke-linecap="round"
+                stroke-linejoin="round"
+                d={warningIconPath}
+              />
+            </svg>
+            <span class="text-sm font-mono text-red-200">
+              RDMA INCOMPATIBLE PORT
+            </span>
+            <button
+              type="button"
+              onclick={() => (macStudioEn2Dismissed = true)}
+              class="ml-1 text-red-300/60 hover:text-red-200 transition-colors cursor-pointer"
+              title="Dismiss"
+            >
+              <svg
+                class="w-4 h-4"
+                fill="none"
+                viewBox="0 0 24 24"
+                stroke="currentColor"
+                stroke-width="2"
+              >
+                <path
+                  stroke-linecap="round"
+                  stroke-linejoin="round"
+                  d="M6 18L18 6M6 6l12 12"
+                />
+              </svg>
+            </button>
+          </div>
+
+          <!-- Expanded tooltip on hover -->
+          <div
+            class="absolute top-full left-0 mt-2 w-96 p-4 rounded border border-red-500/30 bg-[#1a1a1a]/95 backdrop-blur-sm opacity-0 invisible group-hover:opacity-100 group-hover:visible transition-all duration-200 z-50 shadow-lg"
+          >
+            <p class="text-xs text-white/80 mb-3">
+              The Thunderbolt 5 port next to the Ethernet port on Mac Studio
+              does
+              <span class="text-red-400 font-semibold">not support RDMA</span>.
+              Move the cable to one of the other three TB5 ports.
+            </p>
+
+            <div class="text-xs text-white/60 mb-3">
+              <span class="text-red-300">Affected:</span>
+              {#each macStudioEn2RdmaWarning as conn}
+                <div class="ml-2 mt-0.5">
+                  <span class="text-white/80">{conn.nodeName}</span>
+                  <span class="text-white/30">&rarr;</span>
+                  <span class="text-white/60">{conn.peerNodeName}</span>
+                  <span class="text-white/30 ml-1">(en2)</span>
+                </div>
+              {/each}
+            </div>
+
+            <!-- Mac Studio back panel illustration -->
+            <div class="bg-black/40 rounded p-3 mb-3">
+              <p
+                class="text-[10px] font-mono text-white/30 uppercase tracking-wider mb-2"
+              >
+                Mac Studio — Rear Panel
+              </p>
+              <svg
+                viewBox="0 0 320 72"
+                class="w-full"
+                xmlns="http://www.w3.org/2000/svg"
+              >
+                <rect
+                  x="1"
+                  y="1"
+                  width="318"
+                  height="70"
+                  rx="6"
+                  ry="6"
+                  fill="none"
+                  stroke="rgba(255,255,255,0.12)"
+                  stroke-width="1"
+                />
+                <!-- TB5 port 1 -->
+                <rect
+                  x="24"
+                  y="22"
+                  width="28"
+                  height="14"
+                  rx="4"
+                  fill="none"
+                  stroke="rgba(255,255,255,0.3)"
+                  stroke-width="1"
+                />
+                <text
+                  x="38"
+                  y="52"
+                  text-anchor="middle"
+                  fill="rgba(255,255,255,0.25)"
+                  style="font-size:7px;font-family:ui-monospace,monospace;"
+                  >TB5</text
+                >
+                <!-- TB5 port 2 -->
+                <rect
+                  x="62"
+                  y="22"
+                  width="28"
+                  height="14"
+                  rx="4"
+                  fill="none"
+                  stroke="rgba(255,255,255,0.3)"
+                  stroke-width="1"
+                />
+                <text
+                  x="76"
+                  y="52"
+                  text-anchor="middle"
+                  fill="rgba(255,255,255,0.25)"
+                  style="font-size:7px;font-family:ui-monospace,monospace;"
+                  >TB5</text
+                >
+                <!-- TB5 port 3 -->
+                <rect
+                  x="100"
+                  y="22"
+                  width="28"
+                  height="14"
+                  rx="4"
+                  fill="none"
+                  stroke="rgba(255,255,255,0.3)"
+                  stroke-width="1"
+                />
+                <text
+                  x="114"
+                  y="52"
+                  text-anchor="middle"
+                  fill="rgba(255,255,255,0.25)"
+                  style="font-size:7px;font-family:ui-monospace,monospace;"
+                  >TB5</text
+                >
+                <!-- TB5 port 4: INCOMPATIBLE (en2) — equally spaced with ports 1-3 -->
+                <rect
+                  x="138"
+                  y="22"
+                  width="28"
+                  height="14"
+                  rx="4"
+                  fill="rgba(239,68,68,0.1)"
+                  stroke="rgba(239,68,68,0.7)"
+                  stroke-width="1.5"
+                />
+                <line
+                  x1="142"
+                  y1="25"
+                  x2="162"
+                  y2="33"
+                  stroke="rgba(239,68,68,0.8)"
+                  stroke-width="1.5"
+                  stroke-linecap="round"
+                />
+                <line
+                  x1="162"
+                  y1="25"
+                  x2="142"
+                  y2="33"
+                  stroke="rgba(239,68,68,0.8)"
+                  stroke-width="1.5"
+                  stroke-linecap="round"
+                />
+                <text
+                  x="152"
+                  y="52"
+                  text-anchor="middle"
+                  fill="rgba(239,68,68,0.6)"
+                  style="font-size:7px;font-family:ui-monospace,monospace;font-weight:600;"
+                  >en2</text
+                >
+                <!-- Ethernet port -->
+                <rect
+                  x="196"
+                  y="19"
+                  width="24"
+                  height="20"
+                  rx="2"
+                  fill="none"
+                  stroke="rgba(255,255,255,0.2)"
+                  stroke-width="1"
+                />
+                <rect
+                  x="200"
+                  y="23"
+                  width="16"
+                  height="12"
+                  rx="1"
+                  fill="none"
+                  stroke="rgba(255,255,255,0.12)"
+                  stroke-width="0.75"
+                />
+                <text
+                  x="208"
+                  y="52"
+                  text-anchor="middle"
+                  fill="rgba(255,255,255,0.25)"
+                  style="font-size:7px;font-family:ui-monospace,monospace;"
+                  >ETH</text
+                >
+                <!-- Green checkmarks on working ports -->
+                <circle
+                  cx="38"
+                  cy="62"
+                  r="3"
+                  fill="none"
+                  stroke="rgba(74,222,128,0.5)"
+                  stroke-width="0.75"
+                />
+                <circle
+                  cx="76"
+                  cy="62"
+                  r="3"
+                  fill="none"
+                  stroke="rgba(74,222,128,0.5)"
+                  stroke-width="0.75"
+                />
+                <circle
+                  cx="114"
+                  cy="62"
+                  r="3"
+                  fill="none"
+                  stroke="rgba(74,222,128,0.5)"
+                  stroke-width="0.75"
+                />
+              </svg>
+            </div>
+
+            <p class="text-xs text-white/50">
+              <span class="text-green-400">Fix:</span> Move the Thunderbolt cable
+              to any of the three leftmost ports (all support RDMA).
+            </p>
+          </div>
+        </div>
+      {/if}
     </div>
   {/if}
 {/snippet}
 
 {#snippet clusterWarningsCompact()}
-  {#if tbBridgeCycles.length > 0 || macosVersionMismatch || (tb5WithoutRdma && !tb5InfoDismissed)}
+  {#if tbBridgeCycles.length > 0 || macosVersionMismatch || (tb5WithoutRdma && !tb5InfoDismissed) || (macStudioEn2RdmaWarning && !macStudioEn2Dismissed)}
     <div class="absolute top-2 left-2 flex flex-col gap-1">
       {#if tbBridgeCycles.length > 0}
         <div
@@ -1996,6 +2309,27 @@
           >
         </div>
       {/if}
+      {#if macStudioEn2RdmaWarning && !macStudioEn2Dismissed}
+        <div
+          class="flex items-center gap-1.5 px-2 py-1 rounded border border-red-500/50 bg-red-500/10 backdrop-blur-sm"
+          title="Mac Studio RDMA incompatible port (en2) — move cable to another TB5 port"
+        >
+          <svg
+            class="w-3.5 h-3.5 text-red-400"
+            fill="none"
+            viewBox="0 0 24 24"
+            stroke="currentColor"
+            stroke-width="2"
+          >
+            <path
+              stroke-linecap="round"
+              stroke-linejoin="round"
+              d={warningIconPath}
+            />
+          </svg>
+          <span class="text-[10px] font-mono text-red-200">BAD RDMA PORT</span>
+        </div>
+      {/if}
     </div>
   {/if}
 {/snippet}

dashboard/src/routes/downloads/+page.svelte

@@ -74,7 +74,6 @@
     if (typeof value === "number") return value;
     if (value && typeof value === "object") {
       const v = value as Record<string, unknown>;
-      if (typeof v.in_bytes === "number") return v.in_bytes;
       if (typeof v.inBytes === "number") return v.inBytes;
     }
     return 0;
@@ -231,23 +230,14 @@
             undefined;
           let cell: CellStatus;
           if (tag === "DownloadCompleted") {
-            const totalBytes = getBytes(
+            const totalBytes = getBytes(payload.total);
-              payload.total_bytes ?? payload.totalBytes,
-            );
             cell = { kind: "completed", totalBytes, modelDirectory };
           } else if (tag === "DownloadOngoing") {
             const rawProgress =
               payload.download_progress ?? payload.downloadProgress ?? {};
             const prog = rawProgress as Record<string, unknown>;
-            const totalBytes = getBytes(
+            const totalBytes = getBytes(prog.total ?? payload.total);
-              prog.total_bytes ??
+            const downloadedBytes = getBytes(prog.downloaded);
-                prog.totalBytes ??
-                payload.total_bytes ??
-                payload.totalBytes,
-            );
-            const downloadedBytes = getBytes(
-              prog.downloaded_bytes ?? prog.downloadedBytes,
-            );
             const speed = (prog.speed as number) ?? 0;
             const etaMs =
               (prog.eta_ms as number) ?? (prog.etaMs as number) ?? 0;

rust/exo_pyo3_bindings/exo_pyo3_bindings.pyi

@@ -19,7 +19,7 @@ class ConnectionUpdate:
         Whether this is a connection or disconnection event
         """
     @property
-    def peer_id(self) -> PeerId:
+    def peer_id(self) -> builtins.str:
         r"""
         Identity of the peer that we have connected to or disconnected from.
         """
@@ -40,92 +40,22 @@ class Keypair:
     Identity keypair of a node.
     """
     @staticmethod
-    def generate_ed25519() -> Keypair:
+    def generate() -> Keypair:
         r"""
         Generate a new Ed25519 keypair.
         """
     @staticmethod
-    def generate_ecdsa() -> Keypair:
+    def from_bytes(bytes: bytes) -> Keypair:
         r"""
-        Generate a new ECDSA keypair.
+        Construct an Ed25519 keypair from secret key bytes
-        """
-    @staticmethod
-    def generate_secp256k1() -> Keypair:
-        r"""
-        Generate a new Secp256k1 keypair.
-        """
-    @staticmethod
-    def from_protobuf_encoding(bytes: bytes) -> Keypair:
-        r"""
-        Decode a private key from a protobuf structure and parse it as a `Keypair`.
-        """
-    @staticmethod
-    def rsa_from_pkcs8(bytes: bytes) -> Keypair:
-        r"""
-        Decode an keypair from a DER-encoded secret key in PKCS#8 `PrivateKeyInfo`
-        format (i.e. unencrypted) as defined in [RFC5208].
-        
-        [RFC5208]: https://tools.ietf.org/html/rfc5208#section-5
-        """
-    @staticmethod
-    def secp256k1_from_der(bytes: bytes) -> Keypair:
-        r"""
-        Decode a keypair from a DER-encoded Secp256k1 secret key in an `ECPrivateKey`
-        structure as defined in [RFC5915].
-        
-        [RFC5915]: https://tools.ietf.org/html/rfc5915
-        """
-    @staticmethod
-    def ed25519_from_bytes(bytes: bytes) -> Keypair: ...
-    def to_protobuf_encoding(self) -> bytes:
-        r"""
-        Encode a private key as protobuf structure.
-        """
-    def to_peer_id(self) -> PeerId:
-        r"""
-        Convert the `Keypair` into the corresponding `PeerId`.
-        """
-
-@typing.final
-class Multiaddr:
-    r"""
-    Representation of a Multiaddr.
-    """
-    @staticmethod
-    def empty() -> Multiaddr:
-        r"""
-        Create a new, empty multiaddress.
-        """
-    @staticmethod
-    def with_capacity(n: builtins.int) -> Multiaddr:
-        r"""
-        Create a new, empty multiaddress with the given capacity.
-        """
-    @staticmethod
-    def from_bytes(bytes: bytes) -> Multiaddr:
-        r"""
-        Parse a `Multiaddr` value from its byte slice representation.
-        """
-    @staticmethod
-    def from_string(string: builtins.str) -> Multiaddr:
-        r"""
-        Parse a `Multiaddr` value from its string representation.
-        """
-    def len(self) -> builtins.int:
-        r"""
-        Return the length in bytes of this multiaddress.
-        """
-    def is_empty(self) -> builtins.bool:
-        r"""
-        Returns true if the length of this multiaddress is 0.
         """
     def to_bytes(self) -> bytes:
         r"""
-        Return a copy of this [`Multiaddr`]'s byte representation.
+        Get the secret key bytes underlying the keypair
         """
-    def to_string(self) -> builtins.str:
+    def to_node_id(self) -> builtins.str:
         r"""
-        Convert a Multiaddr to a string.
+        Convert the `Keypair` into the corresponding `PeerId` string, which we use as our `NodeId`.
         """
 
 @typing.final
@@ -180,37 +110,6 @@ class NoPeersSubscribedToTopicError(builtins.Exception):
     def __repr__(self) -> builtins.str: ...
     def __str__(self) -> builtins.str: ...
 
-@typing.final
-class PeerId:
-    r"""
-    Identifier of a peer of the network.
-    
-    The data is a `CIDv0` compatible multihash of the protobuf encoded public key of the peer
-    as specified in [specs/peer-ids](https://github.com/libp2p/specs/blob/master/peer-ids/peer-ids.md).
-    """
-    @staticmethod
-    def random() -> PeerId:
-        r"""
-        Generates a random peer ID from a cryptographically secure PRNG.
-        
-        This is useful for randomly walking on a DHT, or for testing purposes.
-        """
-    @staticmethod
-    def from_bytes(bytes: bytes) -> PeerId:
-        r"""
-        Parses a `PeerId` from bytes.
-        """
-    def to_bytes(self) -> bytes:
-        r"""
-        Returns a raw bytes representation of this `PeerId`.
-        """
-    def to_base58(self) -> builtins.str:
-        r"""
-        Returns a base-58 encoded string of this `PeerId`.
-        """
-    def __repr__(self) -> builtins.str: ...
-    def __str__(self) -> builtins.str: ...
-
 @typing.final
 class ConnectionUpdateType(enum.Enum):
     r"""

rust/exo_pyo3_bindings/src/ident.rs

@@ -1,8 +1,6 @@
 use crate::ext::ResultExt as _;
-use libp2p::PeerId;
 use libp2p::identity::Keypair;
-use pyo3::prelude::{PyBytesMethods as _, PyModule, PyModuleMethods as _};
+use pyo3::types::{PyBytes, PyBytesMethods as _};
-use pyo3::types::PyBytes;
 use pyo3::{Bound, PyResult, Python, pyclass, pymethods};
 use pyo3_stub_gen::derive::{gen_stub_pyclass, gen_stub_pymethods};
 
@@ -18,142 +16,32 @@ pub struct PyKeypair(pub Keypair);
 impl PyKeypair {
     /// Generate a new Ed25519 keypair.
     #[staticmethod]
-    fn generate_ed25519() -> Self {
+    fn generate() -> Self {
         Self(Keypair::generate_ed25519())
     }
 
-    /// Generate a new ECDSA keypair.
+    /// Construct an Ed25519 keypair from secret key bytes
     #[staticmethod]
-    fn generate_ecdsa() -> Self {
+    fn from_bytes(bytes: Bound<'_, PyBytes>) -> PyResult<Self> {
-        Self(Keypair::generate_ecdsa())
-    }
-
-    /// Generate a new Secp256k1 keypair.
-    #[staticmethod]
-    fn generate_secp256k1() -> Self {
-        Self(Keypair::generate_secp256k1())
-    }
-
-    /// Decode a private key from a protobuf structure and parse it as a `Keypair`.
-    #[staticmethod]
-    fn from_protobuf_encoding(bytes: Bound<'_, PyBytes>) -> PyResult<Self> {
-        let bytes = Vec::from(bytes.as_bytes());
-        Ok(Self(Keypair::from_protobuf_encoding(&bytes).pyerr()?))
-    }
-
-    /// Decode an keypair from a DER-encoded secret key in PKCS#8 `PrivateKeyInfo`
-    /// format (i.e. unencrypted) as defined in [RFC5208].
-    ///
-    /// [RFC5208]: https://tools.ietf.org/html/rfc5208#section-5
-    #[staticmethod]
-    fn rsa_from_pkcs8(bytes: Bound<'_, PyBytes>) -> PyResult<Self> {
-        let mut bytes = Vec::from(bytes.as_bytes());
-        Ok(Self(Keypair::rsa_from_pkcs8(&mut bytes).pyerr()?))
-    }
-
-    /// Decode a keypair from a DER-encoded Secp256k1 secret key in an `ECPrivateKey`
-    /// structure as defined in [RFC5915].
-    ///
-    /// [RFC5915]: https://tools.ietf.org/html/rfc5915
-    #[staticmethod]
-    fn secp256k1_from_der(bytes: Bound<'_, PyBytes>) -> PyResult<Self> {
-        let mut bytes = Vec::from(bytes.as_bytes());
-        Ok(Self(Keypair::secp256k1_from_der(&mut bytes).pyerr()?))
-    }
-
-    #[staticmethod]
-    fn ed25519_from_bytes(bytes: Bound<'_, PyBytes>) -> PyResult<Self> {
         let mut bytes = Vec::from(bytes.as_bytes());
         Ok(Self(Keypair::ed25519_from_bytes(&mut bytes).pyerr()?))
     }
 
-    /// Encode a private key as protobuf structure.
+    /// Get the secret key bytes underlying the keypair
-    fn to_protobuf_encoding<'py>(&self, py: Python<'py>) -> PyResult<Bound<'py, PyBytes>> {
+    fn to_bytes<'py>(&self, py: Python<'py>) -> PyResult<Bound<'py, PyBytes>> {
-        let bytes = self.0.to_protobuf_encoding().pyerr()?;
+        let bytes = self
+            .0
+            .clone()
+            .try_into_ed25519()
+            .pyerr()?
+            .secret()
+            .as_ref()
+            .to_vec();
         Ok(PyBytes::new(py, &bytes))
     }
 
-    /// Convert the `Keypair` into the corresponding `PeerId`.
+    /// Convert the `Keypair` into the corresponding `PeerId` string, which we use as our `NodeId`.
-    fn to_peer_id(&self) -> PyPeerId {
+    fn to_node_id(&self) -> String {
-        PyPeerId(self.0.public().to_peer_id())
+        self.0.public().to_peer_id().to_base58()
-    }
-
-    // /// Hidden constructor for pickling support. TODO: figure out how to do pickling...
-    // #[gen_stub(skip)]
-    // #[new]
-    // fn py_new(bytes: Bound<'_, PyBytes>) -> PyResult<Self> {
-    //     Self::from_protobuf_encoding(bytes)
-    // }
-    //
-    // #[gen_stub(skip)]
-    // fn __setstate__(&mut self, state: Bound<'_, PyBytes>) -> PyResult<()> {
-    //     *self = Self::from_protobuf_encoding(state)?;
-    //     Ok(())
-    // }
-    //
-    // #[gen_stub(skip)]
-    // fn __getstate__<'py>(&self, py: Python<'py>) -> PyResult<Bound<'py, PyBytes>> {
-    //     self.to_protobuf_encoding(py)
-    // }
-    //
-    // #[gen_stub(skip)]
-    // pub fn __getnewargs__<'py>(&self, py: Python<'py>) -> PyResult<(Bound<'py, PyBytes>,)> {
-    //     Ok((self.to_protobuf_encoding(py)?,))
-    // }
-}
-
-/// Identifier of a peer of the network.
-///
-/// The data is a `CIDv0` compatible multihash of the protobuf encoded public key of the peer
-/// as specified in [specs/peer-ids](https://github.com/libp2p/specs/blob/master/peer-ids/peer-ids.md).
-#[gen_stub_pyclass]
-#[pyclass(name = "PeerId", frozen)]
-#[derive(Debug, Clone)]
-#[repr(transparent)]
-pub struct PyPeerId(pub PeerId);
-
-#[gen_stub_pymethods]
-#[pymethods]
-#[allow(clippy::needless_pass_by_value)]
-impl PyPeerId {
-    /// Generates a random peer ID from a cryptographically secure PRNG.
-    ///
-    /// This is useful for randomly walking on a DHT, or for testing purposes.
-    #[staticmethod]
-    fn random() -> Self {
-        Self(PeerId::random())
-    }
-
-    /// Parses a `PeerId` from bytes.
-    #[staticmethod]
-    fn from_bytes(bytes: Bound<'_, PyBytes>) -> PyResult<Self> {
-        let bytes = Vec::from(bytes.as_bytes());
-        Ok(Self(PeerId::from_bytes(&bytes).pyerr()?))
-    }
-
-    /// Returns a raw bytes representation of this `PeerId`.
-    fn to_bytes<'py>(&self, py: Python<'py>) -> Bound<'py, PyBytes> {
-        let bytes = self.0.to_bytes();
-        PyBytes::new(py, &bytes)
-    }
-
-    /// Returns a base-58 encoded string of this `PeerId`.
-    fn to_base58(&self) -> String {
-        self.0.to_base58()
-    }
-
-    fn __repr__(&self) -> String {
-        format!("PeerId({})", self.to_base58())
-    }
-
-    fn __str__(&self) -> String {
-        self.to_base58()
     }
 }
-
-pub fn ident_submodule(m: &Bound<'_, PyModule>) -> PyResult<()> {
-    m.add_class::<PyKeypair>()?;
-    m.add_class::<PyPeerId>()?;
-
-    Ok(())
-}

rust/exo_pyo3_bindings/src/lib.rs

@@ -8,9 +8,10 @@ mod allow_threading;
 mod ident;
 mod networking;
 
-use crate::ident::ident_submodule;
+use crate::ident::PyKeypair;
 use crate::networking::networking_submodule;
 use pyo3::prelude::PyModule;
+use pyo3::types::PyModuleMethods;
 use pyo3::{Bound, PyResult, pyclass, pymodule};
 use pyo3_stub_gen::define_stub_info_gatherer;
 
@@ -158,7 +159,7 @@ fn main_module(m: &Bound<'_, PyModule>) -> PyResult<()> {
     // TODO: for now this is all NOT a submodule, but figure out how to make the submodule system
     //       work with maturin, where the types generate correctly, in the right folder, without
     //       too many importing issues...
-    ident_submodule(m)?;
+    m.add_class::<PyKeypair>()?;
     networking_submodule(m)?;
 
     // top-level constructs

rust/exo_pyo3_bindings/src/networking.rs

@@ -8,7 +8,7 @@
 use crate::r#const::MPSC_CHANNEL_SIZE;
 use crate::ext::{ByteArrayExt as _, FutureExt, PyErrExt as _};
 use crate::ext::{ResultExt as _, TokioMpscReceiverExt as _, TokioMpscSenderExt as _};
-use crate::ident::{PyKeypair, PyPeerId};
+use crate::ident::PyKeypair;
 use crate::pyclass;
 use libp2p::futures::StreamExt as _;
 use libp2p::gossipsub;
@@ -119,7 +119,7 @@ struct PyConnectionUpdate {
 
     /// Identity of the peer that we have connected to or disconnected from.
     #[pyo3(get)]
-    peer_id: PyPeerId,
+    peer_id: String,
 
     /// Remote connection's IPv4 address.
     #[pyo3(get)]
@@ -251,7 +251,7 @@ async fn networking_task(
                         // send connection event to channel (or exit if connection closed)
                         if let Err(e) = connection_update_tx.send(PyConnectionUpdate {
                             update_type: PyConnectionUpdateType::Connected,
-                            peer_id: PyPeerId(peer_id),
+                            peer_id: peer_id.to_base58(),
                             remote_ipv4,
                             remote_tcp_port,
                         }).await {
@@ -272,7 +272,7 @@ async fn networking_task(
                         // send disconnection event to channel (or exit if connection closed)
                         if let Err(e) = connection_update_tx.send(PyConnectionUpdate {
                             update_type: PyConnectionUpdateType::Disconnected,
-                            peer_id: PyPeerId(peer_id),
+                            peer_id: peer_id.to_base58(),
                             remote_ipv4,
                             remote_tcp_port,
                         }).await {

src/exo/download/coordinator.py

@@ -80,7 +80,7 @@ class DownloadCoordinator:
             completed = DownloadCompleted(
                 shard_metadata=callback_shard,
                 node_id=self.node_id,
-                total_bytes=progress.total_bytes,
+                total=progress.total,
                 model_directory=self._model_dir(model_id),
             )
             self.download_status[model_id] = completed
@@ -203,7 +203,7 @@ class DownloadCoordinator:
             completed = DownloadCompleted(
                 shard_metadata=shard,
                 node_id=self.node_id,
-                total_bytes=initial_progress.total_bytes,
+                total=initial_progress.total,
                 model_directory=self._model_dir(model_id),
             )
             self.download_status[model_id] = completed
@@ -332,13 +332,13 @@ class DownloadCoordinator:
                         status: DownloadProgress = DownloadCompleted(
                             node_id=self.node_id,
                             shard_metadata=progress.shard,
-                            total_bytes=progress.total_bytes,
+                            total=progress.total,
                             model_directory=self._model_dir(
                                 progress.shard.model_card.model_id
                             ),
                         )
                     elif progress.status in ["in_progress", "not_started"]:
-                        if progress.downloaded_bytes_this_session.in_bytes == 0:
+                        if progress.downloaded_bytes.in_bytes == 0:
                             status = DownloadPending(
                                 node_id=self.node_id,
                                 shard_metadata=progress.shard,

src/exo/download/download_utils.py

@@ -80,9 +80,9 @@ def map_repo_file_download_progress_to_download_progress_data(
     repo_file_download_progress: RepoFileDownloadProgress,
 ) -> DownloadProgressData:
     return DownloadProgressData(
-        downloaded_bytes=repo_file_download_progress.downloaded,
+        downloaded=repo_file_download_progress.downloaded,
-        downloaded_bytes_this_session=repo_file_download_progress.downloaded_this_session,
+        downloaded_this_session=repo_file_download_progress.downloaded_this_session,
-        total_bytes=repo_file_download_progress.total,
+        total=repo_file_download_progress.total,
         completed_files=1 if repo_file_download_progress.status == "complete" else 0,
         total_files=1,
         speed=repo_file_download_progress.speed,
@@ -95,9 +95,9 @@ def map_repo_download_progress_to_download_progress_data(
     repo_download_progress: RepoDownloadProgress,
 ) -> DownloadProgressData:
     return DownloadProgressData(
-        total_bytes=repo_download_progress.total_bytes,
+        total=repo_download_progress.total,
-        downloaded_bytes=repo_download_progress.downloaded_bytes,
+        downloaded=repo_download_progress.downloaded,
-        downloaded_bytes_this_session=repo_download_progress.downloaded_bytes_this_session,
+        downloaded_this_session=repo_download_progress.downloaded_this_session,
         completed_files=repo_download_progress.completed_files,
         total_files=repo_download_progress.total_files,
         speed=repo_download_progress.overall_speed,
@@ -142,7 +142,7 @@ async def delete_model(model_id: ModelId) -> bool:
 
 
 async def seed_models(seed_dir: str | Path):
-    """Move model in resources folder of app to .cache/huggingface/hub"""
+    """Move models from resources folder to EXO_MODELS_DIR."""
     source_dir = Path(seed_dir)
     dest_dir = await ensure_models_dir()
     for path in source_dir.iterdir():
@@ -578,19 +578,20 @@ def calculate_repo_progress(
     file_progress: dict[str, RepoFileDownloadProgress],
     all_start_time: float,
 ) -> RepoDownloadProgress:
-    all_total_bytes = sum((p.total.in_bytes for p in file_progress.values()), 0)
+    all_total = sum((p.total for p in file_progress.values()), Memory.from_bytes(0))
-    all_downloaded_bytes = sum(
+    all_downloaded = sum(
-        (p.downloaded.in_bytes for p in file_progress.values()), 0
+        (p.downloaded for p in file_progress.values()), Memory.from_bytes(0)
     )
-    all_downloaded_bytes_this_session = sum(
+    all_downloaded_this_session = sum(
-        (p.downloaded_this_session.in_bytes for p in file_progress.values()), 0
+        (p.downloaded_this_session for p in file_progress.values()),
+        Memory.from_bytes(0),
     )
     elapsed_time = time.time() - all_start_time
     all_speed = (
-        all_downloaded_bytes_this_session / elapsed_time if elapsed_time > 0 else 0
+        all_downloaded_this_session.in_bytes / elapsed_time if elapsed_time > 0 else 0
     )
     all_eta = (
-        timedelta(seconds=(all_total_bytes - all_downloaded_bytes) / all_speed)
+        timedelta(seconds=(all_total - all_downloaded).in_bytes / all_speed)
         if all_speed > 0
         else timedelta(seconds=0)
     )
@@ -609,11 +610,9 @@ def calculate_repo_progress(
             [p for p in file_progress.values() if p.downloaded == p.total]
         ),
         total_files=len(file_progress),
-        downloaded_bytes=Memory.from_bytes(all_downloaded_bytes),
+        downloaded=all_downloaded,
-        downloaded_bytes_this_session=Memory.from_bytes(
+        downloaded_this_session=all_downloaded_this_session,
-            all_downloaded_bytes_this_session
+        total=all_total,
-        ),
-        total_bytes=Memory.from_bytes(all_total_bytes),
         overall_speed=all_speed,
         overall_eta=all_eta,
         status=status,

src/exo/download/shard_downloader.py

@@ -107,9 +107,9 @@ NOOP_DOWNLOAD_PROGRESS = RepoDownloadProgress(
     ),
     completed_files=0,
     total_files=0,
-    downloaded_bytes=Memory.from_bytes(0),
+    downloaded=Memory.from_bytes(0),
-    downloaded_bytes_this_session=Memory.from_bytes(0),
+    downloaded_this_session=Memory.from_bytes(0),
-    total_bytes=Memory.from_bytes(0),
+    total=Memory.from_bytes(0),
     overall_speed=0,
     overall_eta=timedelta(seconds=0),
     status="complete",

src/exo/main.py

@@ -45,7 +45,7 @@ class Node:
     @classmethod
     async def create(cls, args: "Args") -> "Self":
         keypair = get_node_id_keypair()
-        node_id = NodeId(keypair.to_peer_id().to_base58())
+        node_id = NodeId(keypair.to_node_id())
         session_id = SessionId(master_node_id=node_id, election_clock=0)
         router = Router.create(keypair)
         await router.register_topic(topics.GLOBAL_EVENTS)

src/exo/master/adapters/chat_completions.py

@@ -59,7 +59,11 @@ def chat_request_to_text_generation(
             chat_template_messages.append({"role": "system", "content": content})
         else:
             # Skip messages with no meaningful content
-            if msg.content is None and msg.thinking is None and msg.tool_calls is None:
+            if (
+                msg.content is None
+                and msg.reasoning_content is None
+                and msg.tool_calls is None
+            ):
                 continue
 
             if msg.role in ("user", "assistant", "developer"):
@@ -111,6 +115,11 @@ def chunk_to_response(
             ]
         )
 
+    if chunk.is_thinking:
+        delta = ChatCompletionMessage(role="assistant", reasoning_content=chunk.text)
+    else:
+        delta = ChatCompletionMessage(role="assistant", content=chunk.text)
+
     return ChatCompletionResponse(
         id=command_id,
         created=int(time.time()),
@@ -118,7 +127,7 @@ def chunk_to_response(
         choices=[
             StreamingChoiceResponse(
                 index=0,
-                delta=ChatCompletionMessage(role="assistant", content=chunk.text),
+                delta=delta,
                 logprobs=logprobs,
                 finish_reason=chunk.finish_reason,
             )
@@ -208,6 +217,7 @@ async def collect_chat_response(
     # FastAPI handles the cancellation better but wouldn't auto-serialize for some reason
     """Collect all token chunks and return a single ChatCompletionResponse."""
     text_parts: list[str] = []
+    thinking_parts: list[str] = []
     tool_calls: list[ToolCall] = []
     logprobs_content: list[LogprobsContentItem] = []
     model: str | None = None
@@ -228,7 +238,10 @@ async def collect_chat_response(
                 if model is None:
                     model = chunk.model
                 last_usage = chunk.usage or last_usage
-                text_parts.append(chunk.text)
+                if chunk.is_thinking:
+                    thinking_parts.append(chunk.text)
+                else:
+                    text_parts.append(chunk.text)
                 if chunk.logprob is not None:
                     logprobs_content.append(
                         LogprobsContentItem(
@@ -258,6 +271,7 @@ async def collect_chat_response(
         raise ValueError(error_message)
 
     combined_text = "".join(text_parts)
+    combined_thinking = "".join(thinking_parts) if thinking_parts else None
     assert model is not None
 
     yield ChatCompletionResponse(
@@ -270,6 +284,7 @@ async def collect_chat_response(
                 message=ChatCompletionMessage(
                     role="assistant",
                     content=combined_text,
+                    reasoning_content=combined_thinking,
                     tool_calls=tool_calls if tool_calls else None,
                 ),
                 logprobs=Logprobs(content=logprobs_content)

src/exo/master/adapters/claude.py

@@ -1,6 +1,7 @@
 """Claude Messages API adapter for converting requests/responses."""
 
 import json
+import re
 from collections.abc import AsyncGenerator
 from typing import Any
 
@@ -28,6 +29,8 @@ from exo.shared.types.claude_api import (
     ClaudeStopReason,
     ClaudeTextBlock,
     ClaudeTextDelta,
+    ClaudeThinkingBlock,
+    ClaudeThinkingDelta,
     ClaudeToolResultBlock,
     ClaudeToolUseBlock,
     ClaudeUsage,
@@ -61,6 +64,22 @@ def _extract_tool_result_text(block: ClaudeToolResultBlock) -> str:
     return "".join(sub_block.text for sub_block in block.content)
 
 
+# Matches "x-anthropic-billing-header: ...;" (with optional trailing newline)
+# or similar telemetry headers that change every request and break KV prefix caching.
+_VOLATILE_HEADER_RE = re.compile(r"^x-anthropic-[^\n]*;\n?", re.MULTILINE)
+
+
+def _strip_volatile_headers(text: str) -> str:
+    """Remove Anthropic billing/telemetry headers from system prompt text.
+
+    Claude Code prepends headers like 'x-anthropic-billing-header: cc_version=...;
+    cc_entrypoint=...; cch=...;' that contain per-request content hashes. These
+    change every request and break KV prefix caching (the prefix diverges at ~20
+    tokens instead of matching thousands of conversation tokens).
+    """
+    return _VOLATILE_HEADER_RE.sub("", text)
+
+
 def claude_request_to_text_generation(
     request: ClaudeMessagesRequest,
 ) -> TextGenerationTaskParams:
@@ -73,6 +92,8 @@ def claude_request_to_text_generation(
             instructions = request.system
         else:
             instructions = "".join(block.text for block in request.system)
+
+        instructions = _strip_volatile_headers(instructions)
         chat_template_messages.append({"role": "system", "content": instructions})
 
     # Convert messages to input
@@ -85,12 +106,15 @@ def claude_request_to_text_generation(
 
         # Process structured content blocks
         text_parts: list[str] = []
+        thinking_parts: list[str] = []
         tool_calls: list[dict[str, Any]] = []
         tool_results: list[ClaudeToolResultBlock] = []
 
         for block in msg.content:
             if isinstance(block, ClaudeTextBlock):
                 text_parts.append(block.text)
+            elif isinstance(block, ClaudeThinkingBlock):
+                thinking_parts.append(block.thinking)
             elif isinstance(block, ClaudeToolUseBlock):
                 tool_calls.append(
                     {
@@ -106,6 +130,7 @@ def claude_request_to_text_generation(
                 tool_results.append(block)
 
         content = "".join(text_parts)
+        reasoning_content = "".join(thinking_parts) if thinking_parts else None
 
         # Build InputMessage from text content
         if msg.role in ("user", "assistant"):
@@ -113,9 +138,14 @@ def claude_request_to_text_generation(
 
         # Build chat_template_messages preserving tool structure
         if tool_calls:
-            chat_template_messages.append(
+            chat_msg: dict[str, Any] = {
-                {"role": "assistant", "content": content, "tool_calls": tool_calls}
+                "role": "assistant",
-            )
+                "content": content,
+                "tool_calls": tool_calls,
+            }
+            if reasoning_content:
+                chat_msg["reasoning_content"] = reasoning_content
+            chat_template_messages.append(chat_msg)
         elif tool_results:
             for tr in tool_results:
                 chat_template_messages.append(
@@ -126,7 +156,10 @@ def claude_request_to_text_generation(
                     }
                 )
         else:
-            chat_template_messages.append({"role": msg.role, "content": content})
+            chat_msg = {"role": msg.role, "content": content}
+            if reasoning_content:
+                chat_msg["reasoning_content"] = reasoning_content
+            chat_template_messages.append(chat_msg)
 
     # Convert Claude tool definitions to OpenAI-style function tools
     tools: list[dict[str, Any]] | None = None
@@ -143,6 +176,10 @@ def claude_request_to_text_generation(
             for tool in request.tools
         ]
 
+    enable_thinking: bool | None = None
+    if request.thinking is not None:
+        enable_thinking = request.thinking.type in ("enabled", "adaptive")
+
     return TextGenerationTaskParams(
         model=request.model,
         input=input_messages
@@ -156,6 +193,7 @@ def claude_request_to_text_generation(
         stop=request.stop_sequences,
         stream=request.stream,
         tools=tools,
+        enable_thinking=enable_thinking,
         chat_template_messages=chat_template_messages
         if chat_template_messages
         else None,
@@ -173,6 +211,7 @@ async def collect_claude_response(
     # FastAPI handles the cancellation better but wouldn't auto-serialize for some reason
     """Collect all token chunks and return a single ClaudeMessagesResponse."""
     text_parts: list[str] = []
+    thinking_parts: list[str] = []
     tool_use_blocks: list[ClaudeToolUseBlock] = []
     stop_reason: ClaudeStopReason | None = None
     last_usage: Usage | None = None
@@ -200,7 +239,10 @@ async def collect_claude_response(
             stop_reason = "tool_use"
             continue
 
-        text_parts.append(chunk.text)
+        if chunk.is_thinking:
+            thinking_parts.append(chunk.text)
+        else:
+            text_parts.append(chunk.text)
 
         if chunk.finish_reason is not None:
             stop_reason = finish_reason_to_claude_stop_reason(chunk.finish_reason)
@@ -209,9 +251,12 @@ async def collect_claude_response(
         raise ValueError(error_message)
 
     combined_text = "".join(text_parts)
+    combined_thinking = "".join(thinking_parts)
 
     # Build content blocks
     content: list[ClaudeContentBlock] = []
+    if combined_thinking:
+        content.append(ClaudeThinkingBlock(thinking=combined_thinking))
     if combined_text:
         content.append(ClaudeTextBlock(text=combined_text))
     content.extend(tool_use_blocks)
@@ -256,16 +301,16 @@ async def generate_claude_stream(
     start_event = ClaudeMessageStartEvent(message=initial_message)
     yield f"event: message_start\ndata: {start_event.model_dump_json()}\n\n"
 
-    # content_block_start for text block at index 0
-    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_usage: Usage | None = None
-    next_block_index = 1  # text block is 0, tool blocks start at 1
+    next_block_index = 0
+
+    # Track whether we've started thinking/text blocks
+    thinking_block_started = False
+    thinking_block_index = -1
+    text_block_started = False
+    text_block_index = -1
 
     async for chunk in chunk_stream:
         if isinstance(chunk, PrefillProgressChunk):
@@ -310,12 +355,45 @@ async def generate_claude_stream(
 
         output_tokens += 1  # Count each chunk as one token
 
-        # content_block_delta
+        if chunk.is_thinking:
-        delta_event = ClaudeContentBlockDeltaEvent(
+            # Start thinking block on first thinking token
-            index=0,
+            if not thinking_block_started:
-            delta=ClaudeTextDelta(text=chunk.text),
+                thinking_block_started = True
-        )
+                thinking_block_index = next_block_index
-        yield f"event: content_block_delta\ndata: {delta_event.model_dump_json()}\n\n"
+                next_block_index += 1
+                block_start = ClaudeContentBlockStartEvent(
+                    index=thinking_block_index,
+                    content_block=ClaudeThinkingBlock(thinking=""),
+                )
+                yield f"event: content_block_start\ndata: {block_start.model_dump_json()}\n\n"
+
+            delta_event = ClaudeContentBlockDeltaEvent(
+                index=thinking_block_index,
+                delta=ClaudeThinkingDelta(thinking=chunk.text),
+            )
+            yield f"event: content_block_delta\ndata: {delta_event.model_dump_json()}\n\n"
+        else:
+            # Close thinking block when transitioning to text
+            if thinking_block_started and text_block_index == -1:
+                block_stop = ClaudeContentBlockStopEvent(index=thinking_block_index)
+                yield f"event: content_block_stop\ndata: {block_stop.model_dump_json()}\n\n"
+
+            # Start text block on first text token
+            if not text_block_started:
+                text_block_started = True
+                text_block_index = next_block_index
+                next_block_index += 1
+                block_start = ClaudeContentBlockStartEvent(
+                    index=text_block_index,
+                    content_block=ClaudeTextBlock(text=""),
+                )
+                yield f"event: content_block_start\ndata: {block_start.model_dump_json()}\n\n"
+
+            delta_event = ClaudeContentBlockDeltaEvent(
+                index=text_block_index,
+                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)
@@ -324,9 +402,22 @@ async def generate_claude_stream(
     if last_usage is not None:
         output_tokens = last_usage.completion_tokens
 
-    # content_block_stop for text block
+    # Close any open blocks
-    block_stop = ClaudeContentBlockStopEvent(index=0)
+    if thinking_block_started and text_block_index == -1:
-    yield f"event: content_block_stop\ndata: {block_stop.model_dump_json()}\n\n"
+        block_stop = ClaudeContentBlockStopEvent(index=thinking_block_index)
+        yield f"event: content_block_stop\ndata: {block_stop.model_dump_json()}\n\n"
+
+    if text_block_started:
+        block_stop = ClaudeContentBlockStopEvent(index=text_block_index)
+        yield f"event: content_block_stop\ndata: {block_stop.model_dump_json()}\n\n"
+
+    if not thinking_block_started and not text_block_started:
+        empty_start = ClaudeContentBlockStartEvent(
+            index=0, content_block=ClaudeTextBlock(text="")
+        )
+        yield f"event: content_block_start\ndata: {empty_start.model_dump_json()}\n\n"
+        empty_stop = ClaudeContentBlockStopEvent(index=0)
+        yield f"event: content_block_stop\ndata: {empty_stop.model_dump_json()}\n\n"
 
     # message_delta
     message_delta = ClaudeMessageDeltaEvent(

src/exo/master/adapters/ollama.py

@@ -0,0 +1,456 @@
+from __future__ import annotations
+
+import json
+from collections.abc import AsyncGenerator
+from typing import Any
+
+from exo.shared.types.chunks import (
+    ErrorChunk,
+    PrefillProgressChunk,
+    TokenChunk,
+    ToolCallChunk,
+)
+from exo.shared.types.common import CommandId
+from exo.shared.types.ollama_api import (
+    OllamaChatRequest,
+    OllamaChatResponse,
+    OllamaDoneReason,
+    OllamaGenerateRequest,
+    OllamaGenerateResponse,
+    OllamaMessage,
+    OllamaToolCall,
+    OllamaToolFunction,
+)
+from exo.shared.types.text_generation import InputMessage, TextGenerationTaskParams
+
+
+def _map_done_reason(
+    finish_reason: str | None,
+) -> OllamaDoneReason | None:
+    if finish_reason is None:
+        return None
+    if finish_reason == "stop":
+        return "stop"
+    if finish_reason == "length":
+        return "length"
+    if finish_reason in ("tool_calls", "function_call"):
+        return "tool_call"
+    if finish_reason == "error":
+        return "error"
+    return "stop"
+
+
+def _try_parse_json(value: str) -> dict[str, Any] | str:
+    try:
+        return json.loads(value)  # type: ignore
+    except json.JSONDecodeError:
+        return value
+
+
+def _build_tool_calls(chunk: ToolCallChunk) -> list[OllamaToolCall]:
+    tool_calls: list[OllamaToolCall] = []
+    for index, tool in enumerate(chunk.tool_calls):
+        # tool.arguments is always str; try to parse as JSON dict for Ollama format
+        arguments: dict[str, Any] | str = _try_parse_json(tool.arguments)
+        tool_calls.append(
+            OllamaToolCall(
+                id=tool.id,
+                type="function",
+                function=OllamaToolFunction(
+                    name=tool.name, arguments=arguments, index=index
+                ),
+            )
+        )
+    return tool_calls
+
+
+def _get_usage(
+    chunk: TokenChunk | ToolCallChunk,
+) -> tuple[int | None, int | None]:
+    """Extract (prompt_eval_count, eval_count) from a chunk."""
+    if chunk.usage is not None:
+        return (chunk.usage.prompt_tokens, chunk.usage.completion_tokens)
+    if chunk.stats is not None:
+        return (chunk.stats.prompt_tokens, chunk.stats.generation_tokens)
+    return (None, None)
+
+
+def ollama_request_to_text_generation(
+    request: OllamaChatRequest,
+) -> TextGenerationTaskParams:
+    """Convert Ollama chat request to exo's internal text generation format."""
+    instructions: str | None = None
+    input_messages: list[InputMessage] = []
+    chat_template_messages: list[dict[str, Any]] = []
+    tool_message_index = 0
+
+    for msg in request.messages:
+        content = msg.content or ""
+
+        if msg.role == "system":
+            if instructions is None:
+                instructions = content
+            else:
+                instructions = f"{instructions}\n{content}"
+            chat_template_messages.append({"role": "system", "content": content})
+            continue
+
+        if msg.role in ("user", "assistant") and (
+            msg.content is not None or msg.thinking is not None or msg.tool_calls
+        ):
+            input_messages.append(InputMessage(role=msg.role, content=content))
+
+        dumped: dict[str, Any] = {"role": msg.role, "content": content}
+        if msg.thinking is not None:
+            dumped["thinking"] = msg.thinking
+        if msg.tool_calls is not None:
+            tool_calls_list: list[dict[str, Any]] = []
+            for tc in msg.tool_calls:
+                function: dict[str, Any] = {
+                    "name": tc.function.name,
+                    "arguments": (
+                        json.dumps(tc.function.arguments)
+                        if isinstance(tc.function.arguments, dict)
+                        else tc.function.arguments
+                    ),
+                }
+                if tc.function.index is not None:
+                    function["index"] = tc.function.index
+                tool_call: dict[str, Any] = {"function": function}
+                if tc.id is not None:
+                    tool_call["id"] = tc.id
+                if tc.type is not None:
+                    tool_call["type"] = tc.type
+                tool_calls_list.append(tool_call)
+            dumped["tool_calls"] = tool_calls_list
+        if msg.name is not None:
+            dumped["name"] = msg.name
+        if msg.role == "tool":
+            tool_message_index += 1
+            tool_call_id = msg.tool_name or msg.name or f"tool_{tool_message_index}"
+            dumped["tool_call_id"] = tool_call_id
+            if msg.tool_name is not None:
+                dumped["tool_name"] = msg.tool_name
+        chat_template_messages.append(dumped)
+
+    options = request.options
+    return TextGenerationTaskParams(
+        model=request.model,
+        input=input_messages
+        if input_messages
+        else [InputMessage(role="user", content="")],
+        instructions=instructions,
+        max_output_tokens=options.num_predict if options else None,
+        temperature=options.temperature if options else None,
+        top_p=options.top_p if options else None,
+        top_k=options.top_k if options else None,
+        stop=options.stop if options else None,
+        seed=options.seed if options else None,
+        stream=request.stream,
+        tools=request.tools,
+        enable_thinking=request.think,
+        chat_template_messages=chat_template_messages
+        if chat_template_messages
+        else None,
+    )
+
+
+async def generate_ollama_chat_stream(
+    _command_id: CommandId,
+    chunk_stream: AsyncGenerator[
+        ErrorChunk | ToolCallChunk | TokenChunk | PrefillProgressChunk, None
+    ],
+) -> AsyncGenerator[str, None]:
+    """Generate streaming responses in Ollama format (newline-delimited JSON)."""
+    thinking_parts: list[str] = []
+
+    async for chunk in chunk_stream:
+        match chunk:
+            case PrefillProgressChunk():
+                continue
+
+            case ErrorChunk():
+                error_response = OllamaChatResponse(
+                    model=str(chunk.model),
+                    message=OllamaMessage(
+                        role="assistant", content=chunk.error_message
+                    ),
+                    done=True,
+                    done_reason="error",
+                )
+                yield f"{error_response.model_dump_json(exclude_none=True)}\n"
+                return
+
+            case ToolCallChunk():
+                prompt_eval, eval_count = _get_usage(chunk)
+                response = OllamaChatResponse(
+                    model=str(chunk.model),
+                    message=OllamaMessage(
+                        role="assistant",
+                        content="",
+                        tool_calls=_build_tool_calls(chunk),
+                        thinking="".join(thinking_parts) if thinking_parts else None,
+                    ),
+                    done=True,
+                    done_reason="tool_call",
+                    prompt_eval_count=prompt_eval,
+                    eval_count=eval_count,
+                )
+                yield f"{response.model_dump_json(exclude_none=True)}\n"
+                return
+
+            case TokenChunk():
+                done = chunk.finish_reason is not None
+
+                if chunk.is_thinking:
+                    thinking_parts.append(chunk.text)
+                    response = OllamaChatResponse(
+                        model=str(chunk.model),
+                        message=OllamaMessage(
+                            role="assistant", content="", thinking=chunk.text
+                        ),
+                        done=False,
+                    )
+                    yield f"{response.model_dump_json(exclude_none=True)}\n"
+                elif done:
+                    prompt_eval, eval_count = _get_usage(chunk)
+                    response = OllamaChatResponse(
+                        model=str(chunk.model),
+                        message=OllamaMessage(
+                            role="assistant",
+                            content=chunk.text,
+                        ),
+                        done=True,
+                        done_reason=_map_done_reason(chunk.finish_reason),
+                        prompt_eval_count=prompt_eval,
+                        eval_count=eval_count,
+                    )
+                    yield f"{response.model_dump_json(exclude_none=True)}\n"
+                else:
+                    response = OllamaChatResponse(
+                        model=str(chunk.model),
+                        message=OllamaMessage(role="assistant", content=chunk.text),
+                        done=False,
+                    )
+                    yield f"{response.model_dump_json(exclude_none=True)}\n"
+
+                if done:
+                    return
+
+
+async def collect_ollama_chat_response(
+    _command_id: CommandId,
+    chunk_stream: AsyncGenerator[
+        ErrorChunk | ToolCallChunk | TokenChunk | PrefillProgressChunk, None
+    ],
+) -> AsyncGenerator[str]:
+    """Collect streaming chunks into a single non-streaming Ollama response.
+
+    Returns an AsyncGenerator[str] (single yield) for consistency with FastAPI
+    StreamingResponse cancellation handling.
+    """
+    text_parts: list[str] = []
+    thinking_parts: list[str] = []
+    tool_calls: list[OllamaToolCall] = []
+    model: str | None = None
+    finish_reason: str | None = None
+    prompt_eval_count: int | None = None
+    eval_count: int | None = None
+
+    async for chunk in chunk_stream:
+        match chunk:
+            case PrefillProgressChunk():
+                continue
+
+            case ErrorChunk():
+                raise ValueError(chunk.error_message or "Internal server error")
+
+            case TokenChunk():
+                if model is None:
+                    model = str(chunk.model)
+                if chunk.is_thinking:
+                    thinking_parts.append(chunk.text)
+                else:
+                    text_parts.append(chunk.text)
+                if chunk.finish_reason is not None:
+                    finish_reason = chunk.finish_reason
+                    prompt_eval_count, eval_count = _get_usage(chunk)
+
+            case ToolCallChunk():
+                if model is None:
+                    model = str(chunk.model)
+                tool_calls.extend(_build_tool_calls(chunk))
+                finish_reason = chunk.finish_reason
+                prompt_eval_count, eval_count = _get_usage(chunk)
+
+    combined_text = "".join(text_parts)
+    combined_thinking = "".join(thinking_parts) if thinking_parts else None
+    assert model is not None
+
+    yield OllamaChatResponse(
+        model=model,
+        message=OllamaMessage(
+            role="assistant",
+            content=combined_text,
+            thinking=combined_thinking,
+            tool_calls=tool_calls if tool_calls else None,
+        ),
+        done=True,
+        done_reason=_map_done_reason(finish_reason),
+        prompt_eval_count=prompt_eval_count,
+        eval_count=eval_count,
+    ).model_dump_json(exclude_none=True)
+    return
+
+
+# ── /api/generate ──
+
+
+def ollama_generate_request_to_text_generation(
+    request: OllamaGenerateRequest,
+) -> TextGenerationTaskParams:
+    """Convert Ollama generate request to exo's internal text generation format."""
+    chat_template_messages: list[dict[str, Any]] = []
+    if request.system:
+        chat_template_messages.append({"role": "system", "content": request.system})
+    chat_template_messages.append({"role": "user", "content": request.prompt})
+
+    options = request.options
+    return TextGenerationTaskParams(
+        model=request.model,
+        input=[InputMessage(role="user", content=request.prompt)],
+        instructions=request.system,
+        max_output_tokens=options.num_predict if options else None,
+        temperature=options.temperature if options else None,
+        top_p=options.top_p if options else None,
+        top_k=options.top_k if options else None,
+        stop=options.stop if options else None,
+        seed=options.seed if options else None,
+        stream=request.stream,
+        enable_thinking=request.think,
+        chat_template_messages=chat_template_messages
+        if chat_template_messages
+        else None,
+    )
+
+
+async def generate_ollama_generate_stream(
+    _command_id: CommandId,
+    chunk_stream: AsyncGenerator[
+        ErrorChunk | ToolCallChunk | TokenChunk | PrefillProgressChunk, None
+    ],
+) -> AsyncGenerator[str, None]:
+    """Generate streaming responses for /api/generate in Ollama NDJSON format."""
+    thinking_parts: list[str] = []
+
+    async for chunk in chunk_stream:
+        match chunk:
+            case PrefillProgressChunk():
+                continue
+
+            case ErrorChunk():
+                resp = OllamaGenerateResponse(
+                    model=str(chunk.model),
+                    response="",
+                    done=True,
+                    done_reason="error",
+                )
+                yield f"{resp.model_dump_json(exclude_none=True)}\n"
+                return
+
+            case ToolCallChunk():
+                # generate endpoint doesn't support tools; emit as done
+                prompt_eval, eval_count = _get_usage(chunk)
+                resp = OllamaGenerateResponse(
+                    model=str(chunk.model),
+                    response="",
+                    done=True,
+                    done_reason="stop",
+                    prompt_eval_count=prompt_eval,
+                    eval_count=eval_count,
+                )
+                yield f"{resp.model_dump_json(exclude_none=True)}\n"
+                return
+
+            case TokenChunk():
+                done = chunk.finish_reason is not None
+
+                if chunk.is_thinking:
+                    thinking_parts.append(chunk.text)
+                    resp = OllamaGenerateResponse(
+                        model=str(chunk.model),
+                        response="",
+                        thinking=chunk.text,
+                        done=False,
+                    )
+                    yield f"{resp.model_dump_json(exclude_none=True)}\n"
+                elif done:
+                    prompt_eval, eval_count = _get_usage(chunk)
+                    resp = OllamaGenerateResponse(
+                        model=str(chunk.model),
+                        response=chunk.text,
+                        done=True,
+                        done_reason=_map_done_reason(chunk.finish_reason),
+                        prompt_eval_count=prompt_eval,
+                        eval_count=eval_count,
+                    )
+                    yield f"{resp.model_dump_json(exclude_none=True)}\n"
+                else:
+                    resp = OllamaGenerateResponse(
+                        model=str(chunk.model),
+                        response=chunk.text,
+                        done=False,
+                    )
+                    yield f"{resp.model_dump_json(exclude_none=True)}\n"
+
+                if done:
+                    return
+
+
+async def collect_ollama_generate_response(
+    _command_id: CommandId,
+    chunk_stream: AsyncGenerator[
+        ErrorChunk | ToolCallChunk | TokenChunk | PrefillProgressChunk, None
+    ],
+) -> AsyncGenerator[str]:
+    """Collect chunks into a single non-streaming /api/generate response."""
+    text_parts: list[str] = []
+    thinking_parts: list[str] = []
+    model: str | None = None
+    finish_reason: str | None = None
+    prompt_eval_count: int | None = None
+    eval_count: int | None = None
+
+    async for chunk in chunk_stream:
+        match chunk:
+            case PrefillProgressChunk():
+                continue
+            case ErrorChunk():
+                raise ValueError(chunk.error_message or "Internal server error")
+            case TokenChunk():
+                if model is None:
+                    model = str(chunk.model)
+                if chunk.is_thinking:
+                    thinking_parts.append(chunk.text)
+                else:
+                    text_parts.append(chunk.text)
+                if chunk.finish_reason is not None:
+                    finish_reason = chunk.finish_reason
+                    prompt_eval_count, eval_count = _get_usage(chunk)
+            case ToolCallChunk():
+                if model is None:
+                    model = str(chunk.model)
+                finish_reason = chunk.finish_reason
+                prompt_eval_count, eval_count = _get_usage(chunk)
+
+    assert model is not None
+    yield OllamaGenerateResponse(
+        model=model,
+        response="".join(text_parts),
+        thinking="".join(thinking_parts) if thinking_parts else None,
+        done=True,
+        done_reason=_map_done_reason(finish_reason),
+        prompt_eval_count=prompt_eval_count,
+        eval_count=eval_count,
+    ).model_dump_json(exclude_none=True)
+    return

src/exo/master/adapters/responses.py

@@ -29,8 +29,15 @@ from exo.shared.types.openai_responses import (
     ResponseOutputItemAddedEvent,
     ResponseOutputItemDoneEvent,
     ResponseOutputText,
+    ResponseReasoningItem,
+    ResponseReasoningSummaryPartAddedEvent,
+    ResponseReasoningSummaryPartDoneEvent,
+    ResponseReasoningSummaryText,
+    ResponseReasoningSummaryTextDeltaEvent,
+    ResponseReasoningSummaryTextDoneEvent,
     ResponsesRequest,
     ResponsesResponse,
+    ResponsesStreamEvent,
     ResponseTextDeltaEvent,
     ResponseTextDoneEvent,
     ResponseUsage,
@@ -38,6 +45,11 @@ from exo.shared.types.openai_responses import (
 from exo.shared.types.text_generation import InputMessage, TextGenerationTaskParams
 
 
+def _format_sse(event: ResponsesStreamEvent) -> str:
+    """Format a streaming event as an SSE message."""
+    return f"event: {event.type}\ndata: {event.model_dump_json()}\n\n"
+
+
 def _extract_content(content: str | list[ResponseContentPart]) -> str:
     """Extract plain text from a content field that may be a string or list of parts."""
     if isinstance(content, str):
@@ -135,7 +147,9 @@ async def collect_responses_response(
     """Collect all token chunks and return a single ResponsesResponse."""
     response_id = f"resp_{command_id}"
     item_id = f"item_{command_id}"
+    reasoning_id = f"rs_{command_id}"
     accumulated_text = ""
+    thinking_parts: list[str] = []
     function_call_items: list[ResponseFunctionCallItem] = []
     last_usage: Usage | None = None
     error_message: str | None = None
@@ -162,6 +176,10 @@ async def collect_responses_response(
                 )
             continue
 
+        if chunk.is_thinking:
+            thinking_parts.append(chunk.text)
+            continue
+
         accumulated_text += chunk.text
 
     if error_message is not None:
@@ -176,13 +194,21 @@ async def collect_responses_response(
             total_tokens=last_usage.total_tokens,
         )
 
-    output: list[ResponseItem] = [
+    output: list[ResponseItem] = []
+    if thinking_parts:
+        output.append(
+            ResponseReasoningItem(
+                id=reasoning_id,
+                summary=[ResponseReasoningSummaryText(text="".join(thinking_parts))],
+            )
+        )
+    output.append(
         ResponseMessageItem(
             id=item_id,
             content=[ResponseOutputText(text=accumulated_text)],
             status="completed",
         )
-    ]
+    )
     output.extend(function_call_items)
 
     yield ResponsesResponse(
@@ -206,6 +232,7 @@ async def generate_responses_stream(
     """Generate OpenAI Responses API streaming events from TokenChunks."""
     response_id = f"resp_{command_id}"
     item_id = f"item_{command_id}"
+    reasoning_id = f"rs_{command_id}"
     seq = count(1)
 
     # response.created
@@ -219,40 +246,25 @@ async def generate_responses_stream(
     created_event = ResponseCreatedEvent(
         sequence_number=next(seq), response=initial_response
     )
-    yield f"event: response.created\ndata: {created_event.model_dump_json()}\n\n"
+    yield _format_sse(created_event)
 
     # response.in_progress
     in_progress_event = ResponseInProgressEvent(
         sequence_number=next(seq), response=initial_response
     )
-    yield f"event: response.in_progress\ndata: {in_progress_event.model_dump_json()}\n\n"
+    yield _format_sse(in_progress_event)
-
-    # response.output_item.added
-    initial_item = ResponseMessageItem(
-        id=item_id,
-        content=[ResponseOutputText(text="")],
-        status="in_progress",
-    )
-    item_added = ResponseOutputItemAddedEvent(
-        sequence_number=next(seq), 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(
-        sequence_number=next(seq),
-        item_id=item_id,
-        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 = ""
+    accumulated_thinking = ""
     function_call_items: list[ResponseFunctionCallItem] = []
     last_usage: Usage | None = None
-    next_output_index = 1  # message item is at 0
+    next_output_index = 0
+
+    # Track dynamic block creation
+    reasoning_started = False
+    reasoning_output_index = -1
+    message_started = False
+    message_output_index = -1
 
     async for chunk in chunk_stream:
         if isinstance(chunk, PrefillProgressChunk):
@@ -281,7 +293,7 @@ async def generate_responses_stream(
                     output_index=next_output_index,
                     item=fc_item,
                 )
-                yield f"event: response.output_item.added\ndata: {fc_added.model_dump_json()}\n\n"
+                yield _format_sse(fc_added)
 
                 # response.function_call_arguments.delta
                 args_delta = ResponseFunctionCallArgumentsDeltaEvent(
@@ -290,7 +302,7 @@ async def generate_responses_stream(
                     output_index=next_output_index,
                     delta=tool.arguments,
                 )
-                yield f"event: response.function_call_arguments.delta\ndata: {args_delta.model_dump_json()}\n\n"
+                yield _format_sse(args_delta)
 
                 # response.function_call_arguments.done
                 args_done = ResponseFunctionCallArgumentsDoneEvent(
@@ -300,7 +312,7 @@ async def generate_responses_stream(
                     name=tool.name,
                     arguments=tool.arguments,
                 )
-                yield f"event: response.function_call_arguments.done\ndata: {args_done.model_dump_json()}\n\n"
+                yield _format_sse(args_done)
 
                 # response.output_item.done
                 fc_done_item = ResponseFunctionCallItem(
@@ -315,44 +327,205 @@ async def generate_responses_stream(
                     output_index=next_output_index,
                     item=fc_done_item,
                 )
-                yield f"event: response.output_item.done\ndata: {fc_item_done.model_dump_json()}\n\n"
+                yield _format_sse(fc_item_done)
 
                 function_call_items.append(fc_done_item)
                 next_output_index += 1
             continue
 
+        if chunk.is_thinking:
+            # Start reasoning block on first thinking token
+            if not reasoning_started:
+                reasoning_started = True
+                reasoning_output_index = next_output_index
+                next_output_index += 1
+
+                # response.output_item.added for reasoning
+                reasoning_item = ResponseReasoningItem(
+                    id=reasoning_id,
+                    summary=[],
+                    status="in_progress",
+                )
+                rs_added = ResponseOutputItemAddedEvent(
+                    sequence_number=next(seq),
+                    output_index=reasoning_output_index,
+                    item=reasoning_item,
+                )
+                yield _format_sse(rs_added)
+
+                # response.reasoning_summary_part.added
+                part_added = ResponseReasoningSummaryPartAddedEvent(
+                    sequence_number=next(seq),
+                    item_id=reasoning_id,
+                    output_index=reasoning_output_index,
+                    summary_index=0,
+                    part=ResponseReasoningSummaryText(text=""),
+                )
+                yield _format_sse(part_added)
+
+            accumulated_thinking += chunk.text
+
+            # response.reasoning_summary_text.delta
+            rs_delta = ResponseReasoningSummaryTextDeltaEvent(
+                sequence_number=next(seq),
+                item_id=reasoning_id,
+                output_index=reasoning_output_index,
+                summary_index=0,
+                delta=chunk.text,
+            )
+            yield _format_sse(rs_delta)
+            continue
+
+        # Close reasoning block when transitioning to text
+        if reasoning_started and not message_started:
+            # response.reasoning_summary_text.done
+            rs_text_done = ResponseReasoningSummaryTextDoneEvent(
+                sequence_number=next(seq),
+                item_id=reasoning_id,
+                output_index=reasoning_output_index,
+                summary_index=0,
+                text=accumulated_thinking,
+            )
+            yield _format_sse(rs_text_done)
+
+            # response.reasoning_summary_part.done
+            rs_part_done = ResponseReasoningSummaryPartDoneEvent(
+                sequence_number=next(seq),
+                item_id=reasoning_id,
+                output_index=reasoning_output_index,
+                summary_index=0,
+                part=ResponseReasoningSummaryText(text=accumulated_thinking),
+            )
+            yield _format_sse(rs_part_done)
+
+            # response.output_item.done for reasoning
+            rs_item_done = ResponseOutputItemDoneEvent(
+                sequence_number=next(seq),
+                output_index=reasoning_output_index,
+                item=ResponseReasoningItem(
+                    id=reasoning_id,
+                    summary=[ResponseReasoningSummaryText(text=accumulated_thinking)],
+                ),
+            )
+            yield _format_sse(rs_item_done)
+
+        # Start message block on first text token
+        if not message_started:
+            message_started = True
+            message_output_index = next_output_index
+            next_output_index += 1
+
+            initial_item = ResponseMessageItem(
+                id=item_id,
+                content=[ResponseOutputText(text="")],
+                status="in_progress",
+            )
+            item_added = ResponseOutputItemAddedEvent(
+                sequence_number=next(seq),
+                output_index=message_output_index,
+                item=initial_item,
+            )
+            yield _format_sse(item_added)
+
+            initial_part = ResponseOutputText(text="")
+            part_added = ResponseContentPartAddedEvent(
+                sequence_number=next(seq),
+                item_id=item_id,
+                output_index=message_output_index,
+                content_index=0,
+                part=initial_part,
+            )
+            yield _format_sse(part_added)
+
         accumulated_text += chunk.text
 
         # response.output_text.delta
         delta_event = ResponseTextDeltaEvent(
             sequence_number=next(seq),
             item_id=item_id,
-            output_index=0,
+            output_index=message_output_index,
             content_index=0,
             delta=chunk.text,
         )
-        yield f"event: response.output_text.delta\ndata: {delta_event.model_dump_json()}\n\n"
+        yield _format_sse(delta_event)
+
+    # Close reasoning block if it was never followed by text
+    if reasoning_started and not message_started:
+        rs_text_done = ResponseReasoningSummaryTextDoneEvent(
+            sequence_number=next(seq),
+            item_id=reasoning_id,
+            output_index=reasoning_output_index,
+            summary_index=0,
+            text=accumulated_thinking,
+        )
+        yield _format_sse(rs_text_done)
+
+        rs_part_done = ResponseReasoningSummaryPartDoneEvent(
+            sequence_number=next(seq),
+            item_id=reasoning_id,
+            output_index=reasoning_output_index,
+            summary_index=0,
+            part=ResponseReasoningSummaryText(text=accumulated_thinking),
+        )
+        yield _format_sse(rs_part_done)
+
+        rs_item_done = ResponseOutputItemDoneEvent(
+            sequence_number=next(seq),
+            output_index=reasoning_output_index,
+            item=ResponseReasoningItem(
+                id=reasoning_id,
+                summary=[ResponseReasoningSummaryText(text=accumulated_thinking)],
+            ),
+        )
+        yield _format_sse(rs_item_done)
+
+    # If no message block was started, create one now (empty text)
+    if not message_started:
+        message_output_index = next_output_index
+        next_output_index += 1
+
+        initial_item = ResponseMessageItem(
+            id=item_id,
+            content=[ResponseOutputText(text="")],
+            status="in_progress",
+        )
+        item_added = ResponseOutputItemAddedEvent(
+            sequence_number=next(seq),
+            output_index=message_output_index,
+            item=initial_item,
+        )
+        yield _format_sse(item_added)
+
+        initial_part = ResponseOutputText(text="")
+        part_added_evt = ResponseContentPartAddedEvent(
+            sequence_number=next(seq),
+            item_id=item_id,
+            output_index=message_output_index,
+            content_index=0,
+            part=initial_part,
+        )
+        yield _format_sse(part_added_evt)
 
     # response.output_text.done
     text_done = ResponseTextDoneEvent(
         sequence_number=next(seq),
         item_id=item_id,
-        output_index=0,
+        output_index=message_output_index,
         content_index=0,
         text=accumulated_text,
     )
-    yield f"event: response.output_text.done\ndata: {text_done.model_dump_json()}\n\n"
+    yield _format_sse(text_done)
 
     # response.content_part.done
     final_part = ResponseOutputText(text=accumulated_text)
     part_done = ResponseContentPartDoneEvent(
         sequence_number=next(seq),
         item_id=item_id,
-        output_index=0,
+        output_index=message_output_index,
         content_index=0,
         part=final_part,
     )
-    yield f"event: response.content_part.done\ndata: {part_done.model_dump_json()}\n\n"
+    yield _format_sse(part_done)
 
     # response.output_item.done
     final_message_item = ResponseMessageItem(
@@ -361,9 +534,11 @@ async def generate_responses_stream(
         status="completed",
     )
     item_done = ResponseOutputItemDoneEvent(
-        sequence_number=next(seq), output_index=0, item=final_message_item
+        sequence_number=next(seq),
+        output_index=message_output_index,
+        item=final_message_item,
     )
-    yield f"event: response.output_item.done\ndata: {item_done.model_dump_json()}\n\n"
+    yield _format_sse(item_done)
 
     # Create usage from usage data if available
     usage = None
@@ -375,7 +550,15 @@ async def generate_responses_stream(
         )
 
     # response.completed
-    output: list[ResponseItem] = [final_message_item]
+    output: list[ResponseItem] = []
+    if reasoning_started:
+        output.append(
+            ResponseReasoningItem(
+                id=reasoning_id,
+                summary=[ResponseReasoningSummaryText(text=accumulated_thinking)],
+            )
+        )
+    output.append(final_message_item)
     output.extend(function_call_items)
     final_response = ResponsesResponse(
         id=response_id,
@@ -388,4 +571,4 @@ async def generate_responses_stream(
     completed_event = ResponseCompletedEvent(
         sequence_number=next(seq), response=final_response
     )
-    yield f"event: response.completed\ndata: {completed_event.model_dump_json()}\n\n"
+    yield _format_sse(completed_event)

src/exo/master/api.py

@@ -32,6 +32,14 @@ from exo.master.adapters.claude import (
     collect_claude_response,
     generate_claude_stream,
 )
+from exo.master.adapters.ollama import (
+    collect_ollama_chat_response,
+    collect_ollama_generate_response,
+    generate_ollama_chat_stream,
+    generate_ollama_generate_stream,
+    ollama_generate_request_to_text_generation,
+    ollama_request_to_text_generation,
+)
 from exo.master.adapters.responses import (
     collect_responses_response,
     generate_responses_stream,
@@ -138,10 +146,22 @@ from exo.shared.types.events import (
     Event,
     ForwarderEvent,
     IndexedEvent,
-    PrefillProgress,
     TracesMerged,
 )
 from exo.shared.types.memory import Memory
+from exo.shared.types.ollama_api import (
+    OllamaChatRequest,
+    OllamaChatResponse,
+    OllamaGenerateRequest,
+    OllamaGenerateResponse,
+    OllamaModelDetails,
+    OllamaModelTag,
+    OllamaPsModel,
+    OllamaPsResponse,
+    OllamaShowRequest,
+    OllamaShowResponse,
+    OllamaTagsResponse,
+)
 from exo.shared.types.openai_responses import (
     ResponsesRequest,
     ResponsesResponse,
@@ -301,6 +321,21 @@ class API:
         self.app.get("/images/{image_id}")(self.get_image)
         self.app.post("/v1/messages", response_model=None)(self.claude_messages)
         self.app.post("/v1/responses", response_model=None)(self.openai_responses)
+
+        # Ollama API
+        self.app.head("/ollama/")(self.ollama_version)
+        self.app.head("/ollama/api/version")(self.ollama_version)
+        self.app.post("/ollama/api/chat", response_model=None)(self.ollama_chat)
+        self.app.post("/ollama/api/api/chat", response_model=None)(self.ollama_chat)
+        self.app.post("/ollama/api/v1/chat", response_model=None)(self.ollama_chat)
+        self.app.post("/ollama/api/generate", response_model=None)(self.ollama_generate)
+        self.app.get("/ollama/api/tags")(self.ollama_tags)
+        self.app.get("/ollama/api/api/tags")(self.ollama_tags)
+        self.app.get("/ollama/api/v1/tags")(self.ollama_tags)
+        self.app.post("/ollama/api/show")(self.ollama_show)
+        self.app.get("/ollama/api/ps")(self.ollama_ps)
+        self.app.get("/ollama/api/version")(self.ollama_version)
+
         self.app.get("/state")(lambda: self.state)
         self.app.get("/events")(self.stream_events)
         self.app.post("/download/start")(self.start_download)
@@ -1294,6 +1329,163 @@ class API:
                 media_type="application/json",
             )
 
+    async def _ollama_root(self) -> JSONResponse:
+        """Respond to HEAD / from Ollama CLI connectivity checks."""
+        return JSONResponse(content="Ollama is running")
+
+    async def ollama_chat(
+        self, request: Request
+    ) -> OllamaChatResponse | StreamingResponse:
+        """Ollama Chat API — accepts JSON regardless of Content-Type."""
+        body = await request.body()
+        payload = OllamaChatRequest.model_validate_json(body)
+        task_params = ollama_request_to_text_generation(payload)
+        resolved_model = await self._resolve_and_validate_text_model(
+            ModelId(task_params.model)
+        )
+        task_params = task_params.model_copy(update={"model": resolved_model})
+
+        command = TextGeneration(task_params=task_params)
+        await self._send(command)
+
+        if payload.stream:
+            return StreamingResponse(
+                generate_ollama_chat_stream(
+                    command.command_id,
+                    self._token_chunk_stream(command.command_id),
+                ),
+                media_type="application/x-ndjson",
+                headers={
+                    "Cache-Control": "no-cache",
+                    "Connection": "close",
+                    "X-Accel-Buffering": "no",
+                },
+            )
+        else:
+            return StreamingResponse(
+                collect_ollama_chat_response(
+                    command.command_id,
+                    self._token_chunk_stream(command.command_id),
+                ),
+                media_type="application/json",
+            )
+
+    async def ollama_generate(
+        self, request: Request
+    ) -> OllamaGenerateResponse | StreamingResponse:
+        """Ollama Generate API — accepts JSON regardless of Content-Type."""
+        body = await request.body()
+        payload = OllamaGenerateRequest.model_validate_json(body)
+        task_params = ollama_generate_request_to_text_generation(payload)
+        resolved_model = await self._resolve_and_validate_text_model(
+            ModelId(task_params.model)
+        )
+        task_params = task_params.model_copy(update={"model": resolved_model})
+
+        command = TextGeneration(task_params=task_params)
+        await self._send(command)
+
+        if payload.stream:
+            return StreamingResponse(
+                generate_ollama_generate_stream(
+                    command.command_id,
+                    self._token_chunk_stream(command.command_id),
+                ),
+                media_type="application/x-ndjson",
+                headers={
+                    "Cache-Control": "no-cache",
+                    "Connection": "close",
+                    "X-Accel-Buffering": "no",
+                },
+            )
+        else:
+            return StreamingResponse(
+                collect_ollama_generate_response(
+                    command.command_id,
+                    self._token_chunk_stream(command.command_id),
+                ),
+                media_type="application/json",
+            )
+
+    async def ollama_tags(self) -> OllamaTagsResponse:
+        """Returns list of models in Ollama tags format. We return the downloaded ones only."""
+
+        def none_if_empty(value: str) -> str | None:
+            return value or None
+
+        downloaded_model_ids: set[str] = set()
+        for node_downloads in self.state.downloads.values():
+            for dl in node_downloads:
+                if isinstance(dl, DownloadCompleted):
+                    downloaded_model_ids.add(dl.shard_metadata.model_card.model_id)
+
+        cards = [
+            c for c in await get_model_cards() if c.model_id in downloaded_model_ids
+        ]
+
+        now = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
+        return OllamaTagsResponse(
+            models=[
+                OllamaModelTag(
+                    name=str(card.model_id),
+                    model=str(card.model_id),
+                    modified_at=now,
+                    size=card.storage_size.in_bytes,
+                    digest="sha256:000000000000",
+                    details=OllamaModelDetails(
+                        family=none_if_empty(card.family),
+                        quantization_level=none_if_empty(card.quantization),
+                    ),
+                )
+                for card in cards
+            ]
+        )
+
+    async def ollama_show(self, request: Request) -> OllamaShowResponse:
+        """Returns model information in Ollama show format."""
+        body = await request.body()
+        payload = OllamaShowRequest.model_validate_json(body)
+        model_name = payload.name or payload.model
+        if not model_name:
+            raise HTTPException(status_code=400, detail="name or model is required")
+        try:
+            card = await ModelCard.load(ModelId(model_name))
+        except Exception as exc:
+            raise HTTPException(
+                status_code=404, detail=f"Model not found: {model_name}"
+            ) from exc
+
+        return OllamaShowResponse(
+            modelfile=f"FROM {card.model_id}",
+            template="{{ .Prompt }}",
+            details=OllamaModelDetails(
+                family=card.family or None,
+                quantization_level=card.quantization or None,
+            ),
+        )
+
+    async def ollama_ps(self) -> OllamaPsResponse:
+        """Returns list of running models (active instances)."""
+        models: list[OllamaPsModel] = []
+        seen: set[str] = set()
+        for instance in self.state.instances.values():
+            model_id = str(instance.shard_assignments.model_id)
+            if model_id in seen:
+                continue
+            seen.add(model_id)
+            models.append(
+                OllamaPsModel(
+                    name=model_id,
+                    model=model_id,
+                    size=0,
+                )
+            )
+        return OllamaPsResponse(models=models)
+
+    async def ollama_version(self) -> dict[str, str]:
+        """Returns version information for Ollama API compatibility."""
+        return {"version": "exo v1.0"}
+
     def _calculate_total_available_memory(self) -> Memory:
         """Calculate total available memory across all nodes in bytes."""
         total_available = Memory()
@@ -1323,7 +1515,7 @@ class API:
                     name=card.model_id.short(),
                     description="",
                     tags=[],
-                    storage_size_megabytes=int(card.storage_size.in_mb),
+                    storage_size_megabytes=card.storage_size.in_mb,
                     supports_tensor=card.supports_tensor,
                     tasks=[task.value for task in card.tasks],
                     is_custom=is_custom_card(card.model_id),
@@ -1455,22 +1647,6 @@ class API:
                                 await queue.send(event.chunk)
                             except BrokenResourceError:
                                 self._text_generation_queues.pop(event.command_id, None)
-
-                    elif isinstance(event, PrefillProgress):
-                        if queue := self._text_generation_queues.get(
-                            event.command_id, None
-                        ):
-                            try:
-                                await queue.send(
-                                    PrefillProgressChunk(
-                                        model=event.model,
-                                        processed_tokens=event.processed_tokens,
-                                        total_tokens=event.total_tokens,
-                                    )
-                                )
-                            except BrokenResourceError:
-                                self._text_generation_queues.pop(event.command_id, None)
-
                     if isinstance(event, TracesMerged):
                         self._save_merged_trace(event)
 

src/exo/master/placement.py

@@ -141,15 +141,29 @@ def place_instance(
     if len(selected_cycle) == 1:
         command.instance_meta = InstanceMeta.MlxRing
 
-    # TODO: Single node instances
     match command.instance_meta:
         case InstanceMeta.MlxJaccl:
+            # TODO(evan): shard assignments should contain information about ranks, this is ugly
+            def get_device_rank(node_id: NodeId) -> int:
+                runner_id = shard_assignments.node_to_runner[node_id]
+                shard_metadata = shard_assignments.runner_to_shard.get(runner_id)
+                assert shard_metadata is not None
+                return shard_metadata.device_rank
+
+            zero_node_ids = [
+                node_id
+                for node_id in selected_cycle.node_ids
+                if get_device_rank(node_id) == 0
+            ]
+            assert len(zero_node_ids) == 1
+            coordinator_node_id = zero_node_ids[0]
+
             mlx_jaccl_devices = get_mlx_jaccl_devices_matrix(
                 [node_id for node_id in selected_cycle],
                 cycle_digraph,
             )
             mlx_jaccl_coordinators = get_mlx_jaccl_coordinators(
-                coordinator=selected_cycle.node_ids[0],
+                coordinator=coordinator_node_id,
                 coordinator_port=random_ephemeral_port(),
                 cycle_digraph=cycle_digraph,
                 node_network=node_network,

src/exo/master/placement_utils.py

@@ -102,22 +102,21 @@ def _allocate_and_validate_layers(
     layer_allocations = allocate_layers_proportionally(
         total_layers=model_card.n_layers,
         memory_fractions=[
-            node_memory[node_id].ram_available.in_bytes / total_memory.in_bytes
+            node_memory[node_id].ram_available / total_memory for node_id in node_ids
-            for node_id in node_ids
         ],
     )
 
-    total_storage_bytes = model_card.storage_size.in_bytes
+    total_storage = model_card.storage_size
     total_layers = model_card.n_layers
     for i, node_id in enumerate(node_ids):
         node_layers = layer_allocations[i]
-        required_memory = (total_storage_bytes * node_layers) // total_layers
+        required_memory = (total_storage * node_layers) // total_layers
-        available_memory = node_memory[node_id].ram_available.in_bytes
+        available_memory = node_memory[node_id].ram_available
         if required_memory > available_memory:
             raise ValueError(
                 f"Node {i} ({node_id}) has insufficient memory: "
-                f"requires {required_memory / (1024**3):.2f} GB for {node_layers} layers, "
+                f"requires {required_memory.in_gb:.2f} GB for {node_layers} layers, "
-                f"but only has {available_memory / (1024**3):.2f} GB available"
+                f"but only has {available_memory.in_gb:.2f} GB available"
             )
 
     return layer_allocations
@@ -342,6 +341,7 @@ def _find_ip_prioritised(
     other_node_id: NodeId,
     cycle_digraph: Topology,
     node_network: Mapping[NodeId, NodeNetworkInfo],
+    ring: bool,
 ) -> str | None:
     """Find an IP address between nodes with prioritization.
 
@@ -354,13 +354,27 @@ def _find_ip_prioritised(
     ip_to_type = {
         iface.ip_address: iface.interface_type for iface in other_network.interfaces
     }
-    priority = {
+
-        "ethernet": 0,
+    # Ring should prioritise fastest connection. As a best-effort, we prioritise TB.
-        "wifi": 1,
+    # TODO: Profile and get actual connection speeds.
-        "unknown": 2,
+    if ring:
-        "maybe_ethernet": 3,
+        priority = {
-        "thunderbolt": 4,
+            "thunderbolt": 0,
-    }
+            "maybe_ethernet": 1,
+            "ethernet": 2,
+            "wifi": 3,
+            "unknown": 4,
+        }
+
+    # RDMA prefers ethernet coordinator
+    else:
+        priority = {
+            "ethernet": 0,
+            "wifi": 1,
+            "unknown": 2,
+            "maybe_ethernet": 3,
+            "thunderbolt": 4,
+        }
     return min(ips, key=lambda ip: priority.get(ip_to_type.get(ip, "unknown"), 2))
 
 
@@ -400,7 +414,7 @@ def get_mlx_ring_hosts_by_node(
                 continue
 
             connection_ip = _find_ip_prioritised(
-                node_id, other_node_id, cycle_digraph, node_network
+                node_id, other_node_id, cycle_digraph, node_network, ring=True
             )
             if connection_ip is None:
                 raise ValueError(
@@ -431,7 +445,9 @@ def get_mlx_jaccl_coordinators(
         if n == coordinator:
             return "0.0.0.0"
 
-        ip = _find_ip_prioritised(n, coordinator, cycle_digraph, node_network)
+        ip = _find_ip_prioritised(
+            n, coordinator, cycle_digraph, node_network, ring=False
+        )
         if ip is not None:
             return ip
 

src/exo/master/tests/test_claude_tool_use.py

@@ -261,7 +261,7 @@ class TestGenerateClaudeStreamToolUse:
 
         parsed = _parse_sse_events(events)
 
-        # Two tool block starts (at indices 1 and 2)
+        # Two tool block starts (at indices 0 and 1 — no text block when only tools)
         tool_starts = [
             e
             for e in parsed
@@ -270,12 +270,11 @@ class TestGenerateClaudeStreamToolUse:
             == "tool_use"
         ]
         assert len(tool_starts) == 2
-        assert tool_starts[0]["index"] == 1
+        assert tool_starts[0]["index"] == 0
-        assert tool_starts[1]["index"] == 2
+        assert tool_starts[1]["index"] == 1
 
-        # Two tool block stops (at indices 1 and 2), plus text block stop at 0
+        # Two tool block stops (at indices 0 and 1)
         block_stops = [e for e in parsed if e.get("type") == "content_block_stop"]
         stop_indices = [e["index"] for e in block_stops]
         assert 0 in stop_indices
         assert 1 in stop_indices
-        assert 2 in stop_indices

src/exo/master/tests/test_master.py

@@ -42,7 +42,7 @@ from exo.utils.channels import channel
 @pytest.mark.asyncio
 async def test_master():
     keypair = get_node_id_keypair()
-    node_id = NodeId(keypair.to_peer_id().to_base58())
+    node_id = NodeId(keypair.to_node_id())
     session_id = SessionId(master_node_id=node_id, election_clock=0)
 
     ge_sender, global_event_receiver = channel[ForwarderEvent]()
@@ -75,7 +75,7 @@ async def test_master():
     async with anyio.create_task_group() as tg:
         tg.start_soon(master.run)
 
-        sender_node_id = NodeId(f"{keypair.to_peer_id().to_base58()}_sender")
+        sender_node_id = NodeId(f"{keypair.to_node_id()}_sender")
         # inject a NodeGatheredInfo event
         logger.info("inject a NodeGatheredInfo event")
         await local_event_sender.send(

src/exo/master/tests/test_placement.py

@@ -80,8 +80,8 @@ def test_get_instance_placements_create_instance(
 ):
     # arrange
     model_card.n_layers = total_layers
-    model_card.storage_size.in_bytes = sum(
+    model_card.storage_size = Memory.from_bytes(
-        available_memory
+        sum(available_memory)
     )  # make it exactly fit across all nodes
     topology = Topology()
 
@@ -349,7 +349,7 @@ def test_tensor_rdma_backend_connectivity_matrix(
     # arrange
     topology = Topology()
     model_card.n_layers = 12
-    model_card.storage_size.in_bytes = 1500
+    model_card.storage_size = Memory.from_bytes(1500)
 
     node_a = NodeId()
     node_b = NodeId()

src/exo/routing/connection_message.py

@@ -30,7 +30,7 @@ class ConnectionMessage(CamelCaseModel):
     @classmethod
     def from_update(cls, update: ConnectionUpdate) -> "ConnectionMessage":
         return cls(
-            node_id=NodeId(update.peer_id.to_base58()),
+            node_id=NodeId(update.peer_id),
             connection_type=ConnectionMessageType.from_update_type(update.update_type),
             remote_ipv4=update.remote_ipv4,
             remote_tcp_port=update.remote_tcp_port,

src/exo/routing/router.py

@@ -221,7 +221,7 @@ def get_node_id_keypair(
     Obtain the :class:`PeerId` by from it.
     """
     # TODO(evan): bring back node id persistence once we figure out how to deal with duplicates
-    return Keypair.generate_ed25519()
+    return Keypair.generate()
 
     def lock_path(path: str | bytes | PathLike[str] | PathLike[bytes]) -> Path:
         return Path(str(path) + ".lock")
@@ -235,12 +235,12 @@ def get_node_id_keypair(
                 protobuf_encoded = f.read()
 
                 try:  # if decoded successfully, save & return
-                    return Keypair.from_protobuf_encoding(protobuf_encoded)
+                    return Keypair.from_bytes(protobuf_encoded)
                 except ValueError as e:  # on runtime error, assume corrupt file
                     logger.warning(f"Encountered error when trying to get keypair: {e}")
 
         # if no valid credentials, create new ones and persist
         with open(path, "w+b") as f:
             keypair = Keypair.generate_ed25519()
-            f.write(keypair.to_protobuf_encoding())
+            f.write(keypair.to_bytes())
             return keypair

src/exo/shared/apply.py

@@ -15,7 +15,6 @@ from exo.shared.types.events import (
     NodeDownloadProgress,
     NodeGatheredInfo,
     NodeTimedOut,
-    PrefillProgress,
     RunnerDeleted,
     RunnerStatusUpdated,
     TaskAcknowledged,
@@ -65,7 +64,6 @@ def event_apply(event: Event, state: State) -> State:
             | ChunkGenerated()
             | TaskAcknowledged()
             | InputChunkReceived()
-            | PrefillProgress()
             | TracesCollected()
             | TracesMerged()
         ):  # Pass-through events that don't modify state

src/exo/shared/tests/test_apply/test_apply_node_download.py

@@ -14,7 +14,7 @@ def test_apply_node_download_progress():
     event = DownloadCompleted(
         node_id=NodeId("node-1"),
         shard_metadata=shard1,
-        total_bytes=Memory(),
+        total=Memory(),
     )
 
     new_state = apply_node_download_progress(
@@ -30,12 +30,12 @@ def test_apply_two_node_download_progress():
     event1 = DownloadCompleted(
         node_id=NodeId("node-1"),
         shard_metadata=shard1,
-        total_bytes=Memory(),
+        total=Memory(),
     )
     event2 = DownloadCompleted(
         node_id=NodeId("node-1"),
         shard_metadata=shard2,
-        total_bytes=Memory(),
+        total=Memory(),
     )
     state = State(downloads={NodeId("node-1"): [event1]})
 

src/exo/shared/tests/test_node_id_persistence.py

@@ -23,7 +23,7 @@ def _get_keypair_concurrent_subprocess_task(
     sem.release()
     # wait to be told to begin simultaneous read
     ev.wait()
-    queue.put(get_node_id_keypair().to_protobuf_encoding())
+    queue.put(get_node_id_keypair().to_bytes())
 
 
 def _get_keypair_concurrent(num_procs: int) -> bytes:

src/exo/shared/types/api.py

@@ -77,7 +77,7 @@ class ChatCompletionMessage(BaseModel):
     content: (
         str | ChatCompletionMessageText | list[ChatCompletionMessageText] | None
     ) = None
-    thinking: str | None = None  # Added for GPT-OSS harmony format support
+    reasoning_content: str | None = None
     name: str | None = None
     tool_calls: list[ToolCall] | None = None
     tool_call_id: str | None = None

src/exo/shared/types/chunks.py

@@ -27,6 +27,7 @@ class TokenChunk(BaseChunk):
     stats: GenerationStats | None = None
     logprob: float | None = None
     top_logprobs: list[TopLogprobItem] | None = None
+    is_thinking: bool = False
 
 
 class ErrorChunk(BaseChunk):

src/exo/shared/types/claude_api.py

@@ -47,6 +47,14 @@ class ClaudeImageBlock(BaseModel, frozen=True):
     source: ClaudeImageSource
 
 
+class ClaudeThinkingBlock(BaseModel, frozen=True):
+    """Thinking content block in Claude Messages API."""
+
+    type: Literal["thinking"] = "thinking"
+    thinking: str
+    signature: str | None = None
+
+
 class ClaudeToolUseBlock(BaseModel, frozen=True):
     """Tool use content block in Claude Messages API."""
 
@@ -66,11 +74,17 @@ class ClaudeToolResultBlock(BaseModel, frozen=True):
     cache_control: dict[str, str] | None = None
 
 
-ClaudeContentBlock = ClaudeTextBlock | ClaudeImageBlock | ClaudeToolUseBlock
+ClaudeContentBlock = (
+    ClaudeTextBlock | ClaudeImageBlock | ClaudeThinkingBlock | ClaudeToolUseBlock
+)
 
 # Input content blocks can also include tool_result (sent by user after tool_use)
 ClaudeInputContentBlock = (
-    ClaudeTextBlock | ClaudeImageBlock | ClaudeToolUseBlock | ClaudeToolResultBlock
+    ClaudeTextBlock
+    | ClaudeImageBlock
+    | ClaudeThinkingBlock
+    | ClaudeToolUseBlock
+    | ClaudeToolResultBlock
 )
 
 
@@ -82,6 +96,11 @@ class ClaudeMessage(BaseModel, frozen=True):
     content: str | list[ClaudeInputContentBlock]
 
 
+class ClaudeThinkingConfig(BaseModel, frozen=True):
+    type: Literal["enabled", "disabled", "adaptive"]
+    budget_tokens: int | None = None
+
+
 class ClaudeMessagesRequest(BaseModel):
     """Request body for Claude Messages API."""
 
@@ -96,6 +115,7 @@ class ClaudeMessagesRequest(BaseModel):
     top_k: int | None = None
     tools: list[ClaudeToolDefinition] | None = None
     metadata: dict[str, str] | None = None
+    thinking: ClaudeThinkingConfig | None = None
 
 
 # Response types
@@ -145,7 +165,7 @@ class ClaudeContentBlockStartEvent(BaseModel, frozen=True):
 
     type: Literal["content_block_start"] = "content_block_start"
     index: int
-    content_block: ClaudeTextBlock | ClaudeToolUseBlock
+    content_block: ClaudeTextBlock | ClaudeThinkingBlock | ClaudeToolUseBlock
 
 
 class ClaudeTextDelta(BaseModel, frozen=True):
@@ -155,6 +175,13 @@ class ClaudeTextDelta(BaseModel, frozen=True):
     text: str
 
 
+class ClaudeThinkingDelta(BaseModel, frozen=True):
+    """Delta for thinking content block."""
+
+    type: Literal["thinking_delta"] = "thinking_delta"
+    thinking: str
+
+
 class ClaudeInputJsonDelta(BaseModel, frozen=True):
     """Delta for tool use input JSON content block."""
 
@@ -167,7 +194,7 @@ class ClaudeContentBlockDeltaEvent(BaseModel, frozen=True):
 
     type: Literal["content_block_delta"] = "content_block_delta"
     index: int
-    delta: ClaudeTextDelta | ClaudeInputJsonDelta
+    delta: ClaudeTextDelta | ClaudeThinkingDelta | ClaudeInputJsonDelta
 
 
 class ClaudeContentBlockStopEvent(BaseModel, frozen=True):

src/exo/shared/types/events.py

@@ -5,7 +5,7 @@ from pydantic import Field
 
 from exo.shared.topology import Connection
 from exo.shared.types.chunks import GenerationChunk, InputImageChunk
-from exo.shared.types.common import CommandId, Id, ModelId, NodeId, SessionId
+from exo.shared.types.common import CommandId, Id, NodeId, SessionId
 from exo.shared.types.tasks import Task, TaskId, TaskStatus
 from exo.shared.types.worker.downloads import DownloadProgress
 from exo.shared.types.worker.instances import Instance, InstanceId
@@ -102,13 +102,6 @@ class InputChunkReceived(BaseEvent):
     chunk: InputImageChunk
 
 
-class PrefillProgress(BaseEvent):
-    command_id: CommandId
-    model: ModelId
-    processed_tokens: int
-    total_tokens: int
-
-
 class TopologyEdgeCreated(BaseEvent):
     conn: Connection
 
@@ -155,7 +148,6 @@ Event = (
     | NodeDownloadProgress
     | ChunkGenerated
     | InputChunkReceived
-    | PrefillProgress
     | TopologyEdgeCreated
     | TopologyEdgeDeleted
     | TracesCollected

src/exo/shared/types/memory.py

@@ -1,10 +1,10 @@
 from math import ceil
-from typing import Self
+from typing import Self, overload
 
-from exo.utils.pydantic_ext import CamelCaseModel
+from exo.utils.pydantic_ext import FrozenModel
 
 
-class Memory(CamelCaseModel):
+class Memory(FrozenModel):
     in_bytes: int = 0
 
     @classmethod
@@ -33,12 +33,22 @@ class Memory(CamelCaseModel):
         return cls(in_bytes=round(val * 1024))
 
     @property
-    def in_mb(self) -> float:
+    def in_mb(self) -> int:
-        """The approximate megabytes this memory represents. Setting this property rounds to the nearest byte."""
+        """The approximate megabytes this memory represents, rounded to nearest MB. Setting this property rounds to the nearest byte."""
-        return self.in_bytes / (1024**2)
+        return round(self.in_bytes / (1024**2))
 
     @in_mb.setter
-    def in_mb(self, val: float):
+    def in_mb(self, val: int):
+        """Set the megabytes for this memory."""
+        self.in_bytes = val * (1024**2)
+
+    @property
+    def in_float_mb(self) -> float:
+        """The megabytes this memory represents as a float. Setting this property rounds to the nearest byte."""
+        return self.in_bytes / (1024**2)
+
+    @in_float_mb.setter
+    def in_float_mb(self, val: float):
         """Set the megabytes for this memory, rounded to the nearest byte."""
         self.in_bytes = round(val * (1024**2))
 
@@ -57,17 +67,85 @@ class Memory(CamelCaseModel):
         """The approximate gigabytes this memory represents."""
         return self.in_bytes / (1024**3)
 
-    def __add__(self, other: "Memory") -> "Memory":
+    def __add__(self, other: object) -> "Memory":
-        return Memory.from_bytes(self.in_bytes + other.in_bytes)
+        if isinstance(other, Memory):
+            return Memory.from_bytes(self.in_bytes + other.in_bytes)
+        return NotImplemented
 
-    def __lt__(self, other: Self) -> bool:
+    def __radd__(self, other: object) -> "Memory":
-        return self.in_bytes < other.in_bytes
+        if other == 0:
+            return self
+        return NotImplemented
 
-    def __le__(self, other: Self) -> bool:
+    def __sub__(self, other: object) -> "Memory":
-        return self.in_bytes <= other.in_bytes
+        if isinstance(other, Memory):
+            return Memory.from_bytes(self.in_bytes - other.in_bytes)
+        return NotImplemented
 
-    def __gt__(self, other: Self) -> bool:
+    def __mul__(self, other: int | float):
-        return self.in_bytes > other.in_bytes
+        return Memory.from_bytes(round(self.in_bytes * other))
 
-    def __ge__(self, other: Self) -> bool:
+    def __rmul__(self, other: int | float):
-        return self.in_bytes >= other.in_bytes
+        return self * other
+
+    @overload
+    def __truediv__(self, other: "Memory") -> float: ...
+    @overload
+    def __truediv__(self, other: int) -> "Memory": ...
+    @overload
+    def __truediv__(self, other: float) -> "Memory": ...
+    def __truediv__(self, other: object) -> "Memory | float":
+        if isinstance(other, Memory):
+            return self.in_bytes / other.in_bytes
+        if isinstance(other, (int, float)):
+            return Memory.from_bytes(round(self.in_bytes / other))
+        return NotImplemented
+
+    def __floordiv__(self, other: object) -> "Memory":
+        if isinstance(other, (int, float)):
+            return Memory.from_bytes(int(self.in_bytes // other))
+        return NotImplemented
+
+    def __lt__(self, other: object) -> bool:
+        if isinstance(other, Memory):
+            return self.in_bytes < other.in_bytes
+        return NotImplemented
+
+    def __le__(self, other: object) -> bool:
+        if isinstance(other, Memory):
+            return self.in_bytes <= other.in_bytes
+        return NotImplemented
+
+    def __gt__(self, other: object) -> bool:
+        if isinstance(other, Memory):
+            return self.in_bytes > other.in_bytes
+        return NotImplemented
+
+    def __ge__(self, other: object) -> bool:
+        if isinstance(other, Memory):
+            return self.in_bytes >= other.in_bytes
+        return NotImplemented
+
+    def __eq__(self, other: object) -> bool:
+        if isinstance(other, Memory):
+            return self.in_bytes == other.in_bytes
+        return NotImplemented
+
+    def __repr__(self) -> str:
+        return f"Memory.from_bytes({self.in_bytes})"
+
+    def __str__(self) -> str:
+        if self.in_gb > 2:
+            val = self.in_gb
+            unit = "GiB"
+        elif self.in_mb > 2:
+            val = self.in_mb
+            unit = "MiB"
+        elif self.in_kb > 3:
+            val = self.in_kb
+            unit = "KiB"
+        else:
+            val = self.in_bytes
+            unit = "B"
+
+        return f"{val:.2f} {unit}".rstrip("0").rstrip(".") + f" {unit}"

src/exo/shared/types/ollama_api.py

@@ -0,0 +1,148 @@
+from __future__ import annotations
+
+import time
+from typing import Any, Literal
+
+from pydantic import BaseModel, Field
+
+from exo.shared.models.model_cards import ModelId
+
+# https://github.com/ollama/ollama/blob/main/docs/api.md
+
+OllamaRole = Literal["system", "user", "assistant", "tool"]
+OllamaDoneReason = Literal["stop", "length", "tool_call", "error"]
+
+
+class OllamaToolFunction(BaseModel, frozen=True):
+    name: str
+    arguments: dict[str, Any] | str
+    index: int | None = None
+
+
+class OllamaToolCall(BaseModel, frozen=True):
+    id: str | None = None
+    type: Literal["function"] | None = None
+    function: OllamaToolFunction
+
+
+class OllamaMessage(BaseModel, frozen=True):
+    role: OllamaRole
+    content: str | None = None
+    thinking: str | None = None
+    tool_calls: list[OllamaToolCall] | None = None
+    name: str | None = None
+    tool_name: str | None = None
+    images: list[str] | None = None
+
+
+class OllamaOptions(BaseModel, frozen=True):
+    num_predict: 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
+
+
+class OllamaChatRequest(BaseModel, frozen=True):
+    model: ModelId
+    messages: list[OllamaMessage]
+    stream: bool = True
+    options: OllamaOptions | None = None
+    tools: list[dict[str, Any]] | None = None
+    format: Literal["json"] | dict[str, Any] | None = None
+    keep_alive: str | int | None = None
+    think: bool | None = None
+
+
+class OllamaGenerateRequest(BaseModel, frozen=True):
+    model: ModelId
+    prompt: str = ""
+    system: str | None = None
+    stream: bool = True
+    options: OllamaOptions | None = None
+    format: Literal["json"] | dict[str, Any] | None = None
+    keep_alive: str | int | None = None
+    think: bool | None = None
+    raw: bool = False
+
+
+class OllamaGenerateResponse(BaseModel, frozen=True, strict=True):
+    model: str
+    created_at: str = Field(
+        default_factory=lambda: time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
+    )
+    response: str
+    thinking: str | None = None
+    done: bool
+    done_reason: OllamaDoneReason | None = None
+    total_duration: int | None = None
+    load_duration: int | None = None
+    prompt_eval_count: int | None = None
+    prompt_eval_duration: int | None = None
+    eval_count: int | None = None
+    eval_duration: int | None = None
+
+
+class OllamaShowRequest(BaseModel, frozen=True):
+    name: str | None = None
+    model: str | None = None
+    verbose: bool | None = None
+
+
+class OllamaChatResponse(BaseModel, frozen=True, strict=True):
+    model: str
+    created_at: str = Field(
+        default_factory=lambda: time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
+    )
+    message: OllamaMessage
+    done: bool
+    done_reason: OllamaDoneReason | None = None
+    total_duration: int | None = None
+    load_duration: int | None = None
+    prompt_eval_count: int | None = None
+    prompt_eval_duration: int | None = None
+    eval_count: int | None = None
+    eval_duration: int | None = None
+
+
+class OllamaModelDetails(BaseModel, frozen=True, strict=True):
+    format: str | None = None
+    family: str | None = None
+    parameter_size: str | None = None
+    quantization_level: str | None = None
+
+
+class OllamaModelTag(BaseModel, frozen=True, strict=True):
+    name: str
+    model: str | None = None
+    modified_at: str | None = None
+    size: int | None = None
+    digest: str | None = None
+    details: OllamaModelDetails | None = None
+
+
+class OllamaTagsResponse(BaseModel, frozen=True, strict=True):
+    models: list[OllamaModelTag]
+
+
+class OllamaShowResponse(BaseModel, frozen=True, strict=True):
+    modelfile: str | None = None
+    parameters: str | None = None
+    template: str | None = None
+    details: OllamaModelDetails | None = None
+    model_info: dict[str, Any] | None = None
+
+
+class OllamaPsModel(BaseModel, frozen=True, strict=True):
+    name: str
+    model: str
+    size: int
+    digest: str | None = None
+    details: OllamaModelDetails | None = None
+    expires_at: str | None = None
+    size_vram: int | None = None
+
+
+class OllamaPsResponse(BaseModel, frozen=True, strict=True):
+    models: list[OllamaPsModel]

src/exo/shared/types/openai_responses.py

@@ -145,7 +145,23 @@ class ResponseFunctionCallItem(BaseModel, frozen=True):
     status: ResponseStatus = "completed"
 
 
-ResponseItem = ResponseMessageItem | ResponseFunctionCallItem
+class ResponseReasoningSummaryText(BaseModel, frozen=True):
+    """Summary text part in a reasoning output item."""
+
+    type: Literal["summary_text"] = "summary_text"
+    text: str
+
+
+class ResponseReasoningItem(BaseModel, frozen=True):
+    """Reasoning output item in response output array."""
+
+    type: Literal["reasoning"] = "reasoning"
+    id: str
+    summary: list[ResponseReasoningSummaryText] = Field(default_factory=list)
+    status: ResponseStatus = "completed"
+
+
+ResponseItem = ResponseMessageItem | ResponseFunctionCallItem | ResponseReasoningItem
 
 
 class ResponseUsage(BaseModel, frozen=True):
@@ -273,6 +289,58 @@ class ResponseFunctionCallArgumentsDoneEvent(BaseModel, frozen=True):
     arguments: str
 
 
+class ResponseReasoningSummaryPartAddedEvent(BaseModel, frozen=True):
+    """Event sent when a reasoning summary part is added."""
+
+    type: Literal["response.reasoning_summary_part.added"] = (
+        "response.reasoning_summary_part.added"
+    )
+    sequence_number: int
+    item_id: str
+    output_index: int
+    summary_index: int
+    part: ResponseReasoningSummaryText
+
+
+class ResponseReasoningSummaryTextDeltaEvent(BaseModel, frozen=True):
+    """Event sent for reasoning summary text delta during streaming."""
+
+    type: Literal["response.reasoning_summary_text.delta"] = (
+        "response.reasoning_summary_text.delta"
+    )
+    sequence_number: int
+    item_id: str
+    output_index: int
+    summary_index: int
+    delta: str
+
+
+class ResponseReasoningSummaryTextDoneEvent(BaseModel, frozen=True):
+    """Event sent when reasoning summary text is done."""
+
+    type: Literal["response.reasoning_summary_text.done"] = (
+        "response.reasoning_summary_text.done"
+    )
+    sequence_number: int
+    item_id: str
+    output_index: int
+    summary_index: int
+    text: str
+
+
+class ResponseReasoningSummaryPartDoneEvent(BaseModel, frozen=True):
+    """Event sent when a reasoning summary part is done."""
+
+    type: Literal["response.reasoning_summary_part.done"] = (
+        "response.reasoning_summary_part.done"
+    )
+    sequence_number: int
+    item_id: str
+    output_index: int
+    summary_index: int
+    part: ResponseReasoningSummaryText
+
+
 class ResponseCompletedEvent(BaseModel, frozen=True):
     """Event sent when response is completed."""
 
@@ -292,5 +360,9 @@ ResponsesStreamEvent = (
     | ResponseOutputItemDoneEvent
     | ResponseFunctionCallArgumentsDeltaEvent
     | ResponseFunctionCallArgumentsDoneEvent
+    | ResponseReasoningSummaryPartAddedEvent
+    | ResponseReasoningSummaryTextDeltaEvent
+    | ResponseReasoningSummaryTextDoneEvent
+    | ResponseReasoningSummaryPartDoneEvent
     | ResponseCompletedEvent
 )

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

@@ -10,9 +10,9 @@ from exo.utils.pydantic_ext import CamelCaseModel, TaggedModel
 
 
 class DownloadProgressData(CamelCaseModel):
-    total_bytes: Memory
+    total: Memory
-    downloaded_bytes: Memory
+    downloaded: Memory
-    downloaded_bytes_this_session: Memory
+    downloaded_this_session: Memory
 
     completed_files: int
     total_files: int
@@ -34,7 +34,7 @@ class DownloadPending(BaseDownloadProgress):
 
 
 class DownloadCompleted(BaseDownloadProgress):
-    total_bytes: Memory
+    total: Memory
 
 
 class DownloadFailed(BaseDownloadProgress):
@@ -86,9 +86,9 @@ class RepoDownloadProgress(BaseModel):
     shard: ShardMetadata
     completed_files: int
     total_files: int
-    downloaded_bytes: Memory
+    downloaded: Memory
-    downloaded_bytes_this_session: Memory
+    downloaded_this_session: Memory
-    total_bytes: Memory
+    total: Memory
     overall_speed: float
     overall_eta: timedelta
     status: Literal["not_started", "in_progress", "complete"]

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

@@ -28,6 +28,7 @@ class GenerationResponse(BaseRunnerResponse):
     finish_reason: FinishReason | None = None
     stats: GenerationStats | None = None
     usage: Usage | None
+    is_thinking: bool = False
 
 
 class ImageGenerationResponse(BaseRunnerResponse):

src/exo/utils/channels.py

@@ -192,7 +192,13 @@ class MpReceiver[T]:
         try:
             return self.receive_nowait()
         except WouldBlock:
-            item = self._state.buffer.get()
+            try:
+                item = self._state.buffer.get()
+            except (TypeError, OSError):
+                # Queue pipe can get closed while we are blocked on get().
+                # The underlying connection._handle becomes None, causing
+                # TypeError in read(handle, remaining).
+                raise ClosedResourceError from None
             if isinstance(item, _MpEndOfStream):
                 self.close()
                 raise EndOfStream from None

src/exo/utils/info_gatherer/net_profile.py

@@ -108,7 +108,7 @@ async def check_reachable(
                 await send.send((target_ip, expected_node_id))
 
     async with (
-        httpx.AsyncClient(timeout=timeout, limits=limits) as client,
+        httpx.AsyncClient(timeout=timeout, limits=limits, verify=False) as client,
         create_task_group() as tg,
     ):
         for node_id in topology.list_nodes():

src/exo/worker/engines/image/generate.py

@@ -166,7 +166,7 @@ def generate_image(
                             else 0.0
                         )
 
-                        peak_memory_gb = mx.get_peak_memory() / (1024**3)
+                        peak_memory = Memory.from_bytes(mx.get_peak_memory())
 
                         stats = ImageGenerationStats(
                             seconds_per_step=seconds_per_step,
@@ -175,7 +175,7 @@ def generate_image(
                             num_images=num_images,
                             image_width=width,
                             image_height=height,
-                            peak_memory_usage=Memory.from_gb(peak_memory_gb),
+                            peak_memory_usage=peak_memory,
                         )
 
                     buffer = io.BytesIO()

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

@@ -22,7 +22,7 @@ from exo.worker.runner.bootstrap import logger
 # Fraction of device memory above which LRU eviction kicks in.
 # Smaller machines need more aggressive eviction.
 def _default_memory_threshold() -> float:
-    total_gb = psutil.virtual_memory().total / (1024**3)
+    total_gb = Memory.from_bytes(psutil.virtual_memory().total).in_gb
     if total_gb >= 128:
         return 0.85
     if total_gb >= 64:

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

@@ -0,0 +1,72 @@
+import json
+import re
+from typing import Any
+
+from mlx_lm.chat_templates import deepseek_v32
+
+from exo.shared.types.api import ToolCallItem
+
+BOS_TOKEN: str = deepseek_v32.bos_token
+EOS_TOKEN: str = deepseek_v32.eos_token
+DSML_TOKEN: str = deepseek_v32.dsml_token
+THINKING_START: str = deepseek_v32.thinking_start_token
+THINKING_END: str = deepseek_v32.thinking_end_token
+USER_TOKEN = "<\uff5cUser\uff5c>"
+ASSISTANT_TOKEN = "<\uff5cAssistant\uff5c>"
+TOOL_CALLS_START = f"<{DSML_TOKEN}function_calls>"
+TOOL_CALLS_END = f"</{DSML_TOKEN}function_calls>"
+encode_messages = deepseek_v32.encode_messages
+
+_INVOKE_PATTERN = re.compile(
+    rf"<{re.escape(DSML_TOKEN)}invoke\s+name=\"([^\"]+)\">"
+    rf"(.*?)"
+    rf"</{re.escape(DSML_TOKEN)}invoke>",
+    re.DOTALL,
+)
+
+_PARAM_PATTERN = re.compile(
+    rf"<{re.escape(DSML_TOKEN)}parameter\s+name=\"([^\"]+)\"\s+string=\"(true|false)\">"
+    rf"(.*?)"
+    rf"</{re.escape(DSML_TOKEN)}parameter>",
+    re.DOTALL,
+)
+
+
+def parse_dsml_output(text: str) -> list[ToolCallItem] | None:
+    """Parse DSML function_calls block from model output text.
+
+    Args:
+        text: The text containing the DSML function_calls block
+              (including the start/end markers).
+
+    Returns:
+        List of ToolCallItem, or None if parsing fails.
+    """
+    tool_calls: list[ToolCallItem] = []
+
+    for invoke_match in _INVOKE_PATTERN.finditer(text):
+        func_name = invoke_match.group(1)
+        invoke_body = invoke_match.group(2)
+
+        args: dict[str, Any] = {}
+        for param_match in _PARAM_PATTERN.finditer(invoke_body):
+            param_name = param_match.group(1)
+            is_string = param_match.group(2) == "true"
+            param_value = param_match.group(3)
+
+            if is_string:
+                args[param_name] = param_value
+            else:
+                try:
+                    args[param_name] = json.loads(param_value)
+                except (json.JSONDecodeError, ValueError):
+                    args[param_name] = param_value
+
+        tool_calls.append(
+            ToolCallItem(
+                name=func_name,
+                arguments=json.dumps(args),
+            )
+        )
+
+    return tool_calls if tool_calls else None

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

@@ -232,11 +232,11 @@ def shard_and_load(
 
     # Estimate timeout based on model size (5x default for large queued workloads)
     base_timeout = float(os.environ.get("EXO_MODEL_LOAD_TIMEOUT", "300"))
-    model_size_gb = get_weights_size(shard_metadata).in_bytes / (1024**3)
+    model_size = get_weights_size(shard_metadata)
-    timeout_seconds = base_timeout + model_size_gb
+    timeout_seconds = base_timeout + model_size.in_gb
     logger.info(
         f"Evaluating model parameters with timeout of {timeout_seconds:.0f}s "
-        f"(model size: {model_size_gb:.1f}GB)"
+        f"(model size: {model_size.in_gb:.1f}GB)"
     )
 
     match shard_metadata:
@@ -458,6 +458,19 @@ def _patch_lossy_chat_template(template: str) -> str | None:
     return patched if n > 0 else None
 
 
+def _needs_dsml_encoding(task_params: TextGenerationTaskParams) -> bool:
+    if "deepseek-v3.2" not in task_params.model.lower():
+        return False
+    # Use DSML encoding when tools are provided or tool results are in the conversation
+    if task_params.tools:
+        return True
+    if task_params.chat_template_messages:
+        return any(
+            msg.get("role") == "tool" for msg in task_params.chat_template_messages
+        )
+    return False
+
+
 def apply_chat_template(
     tokenizer: TokenizerWrapper,
     task_params: TextGenerationTaskParams,
@@ -469,7 +482,6 @@ def apply_chat_template(
 
     When chat_template_messages is available (from Chat Completions API),
     uses those directly to preserve tool_calls, thinking, and other fields.
-    Otherwise builds messages from the task params input/instructions.
     """
     formatted_messages: list[dict[str, Any]] = []
     if task_params.chat_template_messages is not None:
@@ -497,6 +509,19 @@ def apply_chat_template(
         partial_assistant_content = cast(str, formatted_messages[-1].get("content", ""))
         formatted_messages = formatted_messages[:-1]
 
+    if _needs_dsml_encoding(task_params):
+        from exo.worker.engines.mlx.dsml_encoding import encode_messages
+
+        prompt = encode_messages(
+            messages=formatted_messages,
+            thinking_mode="thinking" if task_params.enable_thinking else "chat",
+            tools=task_params.tools,
+        )
+        if partial_assistant_content:
+            prompt += partial_assistant_content
+        logger.info(prompt)
+        return prompt
+
     extra_kwargs: dict[str, Any] = {}
     if task_params.enable_thinking is not None:
         # Qwen3 and GLM use "enable_thinking"; DeepSeek uses "thinking".
@@ -617,18 +642,17 @@ def set_wired_limit_for_model(model_size: Memory):
     if not mx.metal.is_available():
         return
 
-    model_bytes = model_size.in_bytes
+    max_rec_size = Memory.from_bytes(
-    max_rec_size = int(mx.metal.device_info()["max_recommended_working_set_size"])
+        int(mx.metal.device_info()["max_recommended_working_set_size"])
-    if model_bytes > 0.9 * max_rec_size:
+    )
-        model_mb = model_bytes // 2**20
+    if model_size > 0.9 * max_rec_size:
-        max_rec_mb = max_rec_size // 2**20
         logger.warning(
-            f"Generating with a model that requires {model_mb} MB "
+            f"Generating with a model that requires {model_size.in_float_mb:.1f} MB "
-            f"which is close to the maximum recommended size of {max_rec_mb} "
+            f"which is close to the maximum recommended size of {max_rec_size.in_float_mb:.1f} "
             "MB. This can be slow. See the documentation for possible work-arounds: "
             "https://github.com/ml-explore/mlx-lm/tree/main#large-models"
         )
-    mx.set_wired_limit(max_rec_size)
+    mx.set_wired_limit(max_rec_size.in_bytes)
     logger.info(f"Wired limit set to {max_rec_size}.")
 
 

src/exo/worker/runner/runner.py

@@ -4,9 +4,10 @@ import resource
 import time
 from collections.abc import Generator
 from functools import cache
-from typing import Literal
+from typing import TYPE_CHECKING, Literal
 
 import mlx.core as mx
+from mlx_lm.models.deepseek_v32 import Model as DeepseekV32Model
 from mlx_lm.models.gpt_oss import Model as GptOssModel
 from mlx_lm.tokenizer_utils import TokenizerWrapper
 from openai_harmony import (  # pyright: ignore[reportMissingTypeStubs]
@@ -21,12 +22,17 @@ from exo.shared.constants import EXO_MAX_CHUNK_SIZE, EXO_TRACING_ENABLED
 from exo.shared.models.model_cards import ModelId, ModelTask
 from exo.shared.tracing import clear_trace_buffer, get_trace_buffer
 from exo.shared.types.api import ImageGenerationStats
-from exo.shared.types.chunks import ErrorChunk, ImageChunk, TokenChunk, ToolCallChunk
+from exo.shared.types.chunks import (
+    ErrorChunk,
+    ImageChunk,
+    PrefillProgressChunk,
+    TokenChunk,
+    ToolCallChunk,
+)
 from exo.shared.types.common import CommandId
 from exo.shared.types.events import (
     ChunkGenerated,
     Event,
-    PrefillProgress,
     RunnerStatusUpdated,
     TaskAcknowledged,
     TaskStatusUpdated,
@@ -315,11 +321,13 @@ def main(
                     ) -> None:
                         if device_rank == 0:
                             event_sender.send(
-                                PrefillProgress(
+                                ChunkGenerated(
                                     command_id=command_id,
-                                    model=shard_metadata.model_card.model_id,
+                                    chunk=PrefillProgressChunk(
-                                    processed_tokens=processed,
+                                        model=shard_metadata.model_card.model_id,
-                                    total_tokens=total,
+                                        processed_tokens=processed,
+                                        total_tokens=total,
+                                    ),
                                 )
                             )
                         cancelled_tasks.update(cancel_receiver.collect())
@@ -346,16 +354,22 @@ def main(
                             group=group,
                         )
 
-                        # For other thinking models (GLM, etc.), check if we need to
+                        if tokenizer.has_thinking:
-                        # prepend the thinking tag that was consumed by the chat template
-                        if detect_thinking_prompt_suffix(prompt, tokenizer):
                             mlx_generator = parse_thinking_models(
-                                mlx_generator, tokenizer
+                                mlx_generator,
+                                tokenizer,
+                                # For other thinking models (GLM, etc.), check if we need to
+                                # prepend the thinking tag that was consumed by the chat template
+                                starts_in_thinking=detect_thinking_prompt_suffix(
+                                    prompt, tokenizer
+                                ),
                             )
 
-                        # GPT-OSS specific parsing to match other model formats.
+                        # Model-specific output parsing for tool calls.
                         if isinstance(inference_model, GptOssModel):
                             mlx_generator = parse_gpt_oss(mlx_generator)
+                        elif isinstance(inference_model, DeepseekV32Model):
+                            mlx_generator = parse_deepseek_v32(mlx_generator)
                         elif tool_parser:
                             mlx_generator = parse_tool_calls(mlx_generator, tool_parser)
 
@@ -407,6 +421,7 @@ def main(
                                                     stats=response.stats,
                                                     logprob=response.logprob,
                                                     top_logprobs=response.top_logprobs,
+                                                    is_thinking=response.is_thinking,
                                                 ),
                                             )
                                         )
@@ -573,6 +588,13 @@ def main(
                 case Shutdown():
                     current_status = RunnerShuttingDown()
                     logger.info("runner shutting down")
+                    if not TYPE_CHECKING:
+                        del inference_model, image_model, tokenizer, group
+                        mx.clear_cache()
+                        import gc
+
+                        gc.collect()
+
                     event_sender.send(
                         RunnerStatusUpdated(
                             runner_id=runner_id, runner_status=current_status
@@ -597,12 +619,8 @@ def main(
             event_sender.send(
                 RunnerStatusUpdated(runner_id=runner_id, runner_status=current_status)
             )
-            if isinstance(current_status, RunnerShutdown):
-                del inference_model, image_model, tokenizer, group
-                mx.clear_cache()
-                import gc
 
-                gc.collect()
+            if isinstance(current_status, RunnerShutdown):
                 break
 
 
@@ -668,44 +686,208 @@ def parse_gpt_oss(
 
         if ch == "analysis" and not thinking:
             thinking = True
-            yield response.model_copy(update={"text": "<think>"})
 
         if ch != "analysis" and thinking:
             thinking = False
-            yield response.model_copy(update={"text": "</think>"})
 
         if delta:
-            yield response.model_copy(update={"text": delta})
+            yield response.model_copy(update={"text": delta, "is_thinking": thinking})
 
         if response.finish_reason is not None:
-            if thinking:
-                yield response.model_copy(update={"text": "</think>"})
             yield response
 
 
+def parse_deepseek_v32(
+    responses: Generator[GenerationResponse],
+) -> Generator[GenerationResponse | ToolCallResponse]:
+    """Parse DeepSeek V3.2 DSML tool calls from the generation stream.
+
+    Uses accumulated-text matching (not per-token marker checks) because
+    DSML markers like <｜DSML｜function_calls> may span multiple tokens.
+    Also handles <think>...</think> blocks for thinking mode.
+    """
+    from exo.worker.engines.mlx.dsml_encoding import (
+        THINKING_END,
+        THINKING_START,
+        TOOL_CALLS_END,
+        TOOL_CALLS_START,
+        parse_dsml_output,
+    )
+
+    accumulated = ""
+    in_tool_call = False
+    thinking = False
+    # Tokens buffered while we detect the start of a DSML block
+    pending_buffer: list[GenerationResponse] = []
+    # Text accumulated during a tool call block
+    tool_call_text = ""
+
+    for response in responses:
+        assert isinstance(response, GenerationResponse)
+
+        # ── Handle thinking tags ──
+        if not thinking and THINKING_START in response.text:
+            thinking = True
+            # Yield any text before the <think> tag
+            before = response.text[: response.text.index(THINKING_START)]
+            if before:
+                yield response.model_copy(update={"text": before})
+            continue
+
+        if thinking and THINKING_END in response.text:
+            thinking = False
+            # Yield any text after the </think> tag
+            after = response.text[
+                response.text.index(THINKING_END) + len(THINKING_END) :
+            ]
+            if after:
+                yield response.model_copy(update={"text": after, "is_thinking": False})
+            continue
+
+        if thinking:
+            yield response.model_copy(update={"is_thinking": True})
+            continue
+
+        # ── Handle tool call accumulation ──
+        if in_tool_call:
+            tool_call_text += response.text
+            if TOOL_CALLS_END in tool_call_text:
+                # Parse the accumulated DSML block
+                parsed = parse_dsml_output(tool_call_text)
+                if parsed is not None:
+                    logger.info(f"parsed DSML tool calls: {parsed}")
+                    yield ToolCallResponse(
+                        tool_calls=parsed,
+                        usage=response.usage,
+                        stats=response.stats,
+                    )
+                else:
+                    logger.warning(
+                        f"DSML tool call parsing failed for: {tool_call_text}"
+                    )
+                    yield response.model_copy(update={"text": tool_call_text})
+                in_tool_call = False
+                tool_call_text = ""
+                continue
+
+            # EOS reached before end marker — yield buffered text as-is
+            if response.finish_reason is not None:
+                logger.info("DSML tool call parsing interrupted by EOS")
+                yield response.model_copy(update={"text": tool_call_text})
+                in_tool_call = False
+                tool_call_text = ""
+            continue
+
+        # ── Detect start of tool call block ──
+        accumulated += response.text
+
+        if TOOL_CALLS_START in accumulated:
+            # The start marker might be split across pending_buffer + current token
+            start_idx = accumulated.index(TOOL_CALLS_START)
+            # Yield any pending tokens that are purely before the marker
+            pre_text = accumulated[:start_idx]
+            if pre_text:
+                # Flush pending buffer tokens that contributed text before the marker
+                for buf_resp in pending_buffer:
+                    if pre_text:
+                        chunk = buf_resp.text
+                        if len(chunk) <= len(pre_text):
+                            yield buf_resp
+                            pre_text = pre_text[len(chunk) :]
+                        else:
+                            yield buf_resp.model_copy(update={"text": pre_text})
+                            pre_text = ""
+            pending_buffer = []
+            tool_call_text = accumulated[start_idx:]
+            accumulated = ""
+
+            # Check if the end marker is already present (entire tool call in one token)
+            if TOOL_CALLS_END in tool_call_text:
+                parsed = parse_dsml_output(tool_call_text)
+                if parsed is not None:
+                    logger.info(f"parsed DSML tool calls: {parsed}")
+                    yield ToolCallResponse(
+                        tool_calls=parsed,
+                        usage=response.usage,
+                        stats=response.stats,
+                    )
+                else:
+                    logger.warning(
+                        f"DSML tool call parsing failed for: {tool_call_text}"
+                    )
+                    yield response.model_copy(update={"text": tool_call_text})
+                tool_call_text = ""
+            else:
+                in_tool_call = True
+            continue
+
+        # Check if accumulated text might be the start of a DSML marker
+        # Buffer tokens if we see a partial match at the end
+        if _could_be_dsml_prefix(accumulated):
+            pending_buffer.append(response)
+            continue
+
+        # No partial match — flush all pending tokens and the current one
+        for buf_resp in pending_buffer:
+            yield buf_resp
+        pending_buffer = []
+        accumulated = ""
+        yield response
+
+    # Flush any remaining pending buffer at generator end
+    for buf_resp in pending_buffer:
+        yield buf_resp
+
+
+def _could_be_dsml_prefix(text: str) -> bool:
+    """Check if the end of text could be the start of a DSML function_calls marker.
+
+    We look for suffixes of text that are prefixes of the TOOL_CALLS_START pattern.
+    This allows us to buffer tokens until we can determine if a tool call is starting.
+    """
+    from exo.worker.engines.mlx.dsml_encoding import TOOL_CALLS_START
+
+    # Only check the last portion of text that could overlap with the marker
+    max_check = len(TOOL_CALLS_START)
+    tail = text[-max_check:] if len(text) > max_check else text
+
+    # Check if any suffix of tail is a prefix of TOOL_CALLS_START
+    for i in range(len(tail)):
+        suffix = tail[i:]
+        if TOOL_CALLS_START.startswith(suffix):
+            return True
+    return False
+
+
 def parse_thinking_models(
     responses: Generator[GenerationResponse],
     tokenizer: TokenizerWrapper,
+    starts_in_thinking: bool = True,
 ) -> Generator[GenerationResponse]:
+    """Route thinking tokens via is_thinking flag.
+
+    Swallows think tag tokens, sets is_thinking on all others.
+    Always yields tokens with finish_reason to avoid hanging the chunk stream.
     """
-    For models that inject thinking tags in the prompt (like GLM-4.7),
+    in_thinking = starts_in_thinking
-    prepend the thinking tag to the output stream so the frontend
-    can properly parse thinking content.
-    """
-    first = True
     for response in responses:
         if isinstance(response, ToolCallResponse):
             yield response
             continue
-        if first:
+
-            first = False
+        is_think_tag = (
-            yield response.model_copy(
+            tokenizer.think_end is not None and response.text == tokenizer.think_end
-                update={
+        ) or (
-                    "text": tokenizer.think_start,
+            tokenizer.think_start is not None and response.text == tokenizer.think_start
-                    "token": tokenizer.think_start_id,
+        )
-                }
+
-            )
+        if is_think_tag:
-        yield response
+            in_thinking = response.text != tokenizer.think_end
+            # Never swallow finish_reason — the chunk stream needs it to terminate.
+            if response.finish_reason is not None:
+                yield response.model_copy(update={"text": "", "is_thinking": False})
+            continue
+        yield response.model_copy(update={"is_thinking": in_thinking})
 
 
 def _send_image_chunk(

src/exo/worker/runner/runner_supervisor.py

@@ -100,8 +100,8 @@ class RunnerSupervisor:
         logger.info("Runner supervisor shutting down")
         self._ev_recv.close()
         self._task_sender.close()
-        self._event_sender.close()
+        with contextlib.suppress(ClosedResourceError):
-        self._cancel_sender.send(TaskId("CANCEL_CURRENT_TASK"))
+            self._cancel_sender.send(TaskId("CANCEL_CURRENT_TASK"))
         self._cancel_sender.close()
         self.runner_process.join(5)
         if not self.runner_process.is_alive():
@@ -180,6 +180,7 @@ class RunnerSupervisor:
                 await self._check_runner(e)
                 for tid in self.pending:
                     self.pending[tid].set()
+        self._event_sender.close()
 
     def __del__(self) -> None:
         if self.runner_process.is_alive():
@@ -208,10 +209,15 @@ class RunnerSupervisor:
 
         logger.opt(exception=e).error(f"Runner terminated ({cause})")
 
-        await self._event_sender.send(
+        try:
-            RunnerStatusUpdated(
+            await self._event_sender.send(
-                runner_id=self.bound_instance.bound_runner_id,
+                RunnerStatusUpdated(
-                runner_status=RunnerFailed(error_message=f"Terminated ({cause})"),
+                    runner_id=self.bound_instance.bound_runner_id,
+                    runner_status=RunnerFailed(error_message=f"Terminated ({cause})"),
+                )
+            )
+        except (ClosedResourceError, BrokenResourceError):
+            logger.warning(
+                "Event sender already closed, unable to report runner failure"
             )
-        )
         self.shutdown()

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

@@ -90,14 +90,10 @@ def test_plan_loads_model_when_all_shards_downloaded_and_waiting():
 
     global_download_status = {
         NODE_A: [
-            DownloadCompleted(
+            DownloadCompleted(shard_metadata=shard1, node_id=NODE_A, total=Memory())
-                shard_metadata=shard1, node_id=NODE_A, total_bytes=Memory()
-            )
         ],
         NODE_B: [
-            DownloadCompleted(
+            DownloadCompleted(shard_metadata=shard2, node_id=NODE_B, total=Memory())
-                shard_metadata=shard2, node_id=NODE_B, total_bytes=Memory()
-            )
         ],
     }
 
@@ -138,9 +134,7 @@ def test_plan_does_not_request_download_when_shard_already_downloaded():
     # Global state shows shard is downloaded for NODE_A
     global_download_status: dict[NodeId, list[DownloadProgress]] = {
         NODE_A: [
-            DownloadCompleted(
+            DownloadCompleted(shard_metadata=shard, node_id=NODE_A, total=Memory())
-                shard_metadata=shard, node_id=NODE_A, total_bytes=Memory()
-            )
         ],
         NODE_B: [],
     }
@@ -187,9 +181,7 @@ def test_plan_does_not_load_model_until_all_shards_downloaded_globally():
 
     global_download_status = {
         NODE_A: [
-            DownloadCompleted(
+            DownloadCompleted(shard_metadata=shard1, node_id=NODE_A, total=Memory())
-                shard_metadata=shard1, node_id=NODE_A, total_bytes=Memory()
-            )
         ],
         NODE_B: [],  # NODE_B has no downloads completed yet
     }
@@ -207,14 +199,10 @@ def test_plan_does_not_load_model_until_all_shards_downloaded_globally():
 
     global_download_status = {
         NODE_A: [
-            DownloadCompleted(
+            DownloadCompleted(shard_metadata=shard1, node_id=NODE_A, total=Memory())
-                shard_metadata=shard1, node_id=NODE_A, total_bytes=Memory()
-            )
         ],
         NODE_B: [
-            DownloadCompleted(
+            DownloadCompleted(shard_metadata=shard2, node_id=NODE_B, total=Memory())
-                shard_metadata=shard2, node_id=NODE_B, total_bytes=Memory()
-            )
         ],  # NODE_B has no downloads completed yet
     }
 

src/exo/worker/tests/unittests/test_runner/test_dsml_e2e.py

@@ -0,0 +1,967 @@
+import json
+from collections.abc import Generator
+from typing import Any
+
+from exo.shared.types.worker.runner_response import (
+    GenerationResponse,
+    ToolCallResponse,
+)
+from exo.worker.engines.mlx.dsml_encoding import (
+    ASSISTANT_TOKEN,
+    BOS_TOKEN,
+    DSML_TOKEN,
+    EOS_TOKEN,
+    THINKING_END,
+    THINKING_START,
+    TOOL_CALLS_END,
+    TOOL_CALLS_START,
+    USER_TOKEN,
+    encode_messages,
+    parse_dsml_output,
+)
+from exo.worker.runner.runner import parse_deepseek_v32
+
+# ── Shared fixtures ──────────────────────────────────────────────
+
+_WEATHER_TOOLS: list[dict[str, Any]] = [
+    {
+        "type": "function",
+        "function": {
+            "name": "get_weather",
+            "description": "Get the current weather in a given city",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "city": {"type": "string", "description": "The city name"},
+                    "units": {
+                        "type": "string",
+                        "enum": ["celsius", "fahrenheit"],
+                        "description": "Temperature units",
+                    },
+                },
+                "required": ["city"],
+            },
+        },
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "get_time",
+            "description": "Get the current time in a timezone",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "timezone": {"type": "string"},
+                },
+                "required": ["timezone"],
+            },
+        },
+    },
+]
+
+
+def _simulate_tokens(
+    texts: list[str],
+    finish_on_last: bool = True,
+) -> Generator[GenerationResponse]:
+    """Simulate a model producing tokens from a list of text strings."""
+    for i, text in enumerate(texts):
+        is_last = i == len(texts) - 1
+        yield GenerationResponse(
+            text=text,
+            token=i,
+            finish_reason="stop" if (is_last and finish_on_last) else None,
+            usage=None,
+        )
+
+
+# ── Test: Standard text response (no tool calls) ────────────────
+
+
+class TestE2EStandardResponse:
+    """Model generates a plain text response — no tool calling involved."""
+
+    def test_plain_text_passthrough(self):
+        """Simulate model producing: 'The weather in NYC is 72°F and sunny.'"""
+        # Step 1: Encode the prompt (with tools available)
+        messages: list[dict[str, Any]] = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "What's the weather in NYC?"},
+        ]
+        prompt = encode_messages(messages, thinking_mode="chat", tools=_WEATHER_TOOLS)
+
+        # Verify prompt structure
+        assert BOS_TOKEN in prompt
+        assert "## Tools" in prompt
+        assert "get_weather" in prompt
+        assert f"{USER_TOKEN}What's the weather in NYC?{ASSISTANT_TOKEN}" in prompt
+
+        # Step 2: Simulate model response — plain text tokens (no DSML)
+        model_tokens = [
+            "The weather",
+            " in NYC",
+            " is 72",
+            "°F",
+            " and sunny",
+            ".",
+        ]
+        results = list(parse_deepseek_v32(_simulate_tokens(model_tokens)))
+
+        # Step 3: Verify all tokens pass through as GenerationResponse
+        gen_results = [r for r in results if isinstance(r, GenerationResponse)]
+        tool_results = [r for r in results if isinstance(r, ToolCallResponse)]
+
+        assert len(tool_results) == 0
+        assert len(gen_results) == 6
+        full_text = "".join(r.text for r in gen_results)
+        assert full_text == "The weather in NYC is 72°F and sunny."
+        assert gen_results[-1].finish_reason == "stop"
+
+
+# ── Test: Tool call response ─────────────────────────────────────
+
+
+class TestE2EToolCallResponse:
+    """Model generates a DSML tool call — realistic token boundaries."""
+
+    def test_realistic_tool_call_tokens(self):
+        """Simulate model generating a get_weather tool call with realistic token splits.
+
+        Real models split DSML markers across tokens unpredictably.
+        This simulates how DeepSeek V3.2 actually tokenizes DSML output.
+        """
+        # Step 1: Encode prompt
+        messages: list[dict[str, Any]] = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "What's the weather in San Francisco?"},
+        ]
+        prompt = encode_messages(messages, thinking_mode="chat", tools=_WEATHER_TOOLS)
+        assert "get_weather" in prompt
+
+        # Step 2: Simulate realistic token-by-token model output
+        # The model first produces some text, then a DSML tool call block
+        model_tokens = [
+            "I'll check the weather for you.",
+            "\n\n",
+            f"<{DSML_TOKEN}",  # marker split across tokens
+            "function_calls>\n",
+            f'<{DSML_TOKEN}invoke name="get_weather">\n',
+            f'<{DSML_TOKEN}parameter name="city" string="true">',
+            "San Francisco",
+            f"</{DSML_TOKEN}parameter>\n",
+            f'<{DSML_TOKEN}parameter name="units" string="false">',
+            '"celsius"',
+            f"</{DSML_TOKEN}parameter>\n",
+            f"</{DSML_TOKEN}invoke>\n",
+            f"</{DSML_TOKEN}function_calls>",
+        ]
+
+        results = list(parse_deepseek_v32(_simulate_tokens(model_tokens)))
+
+        # Step 3: Verify
+        gen_results = [r for r in results if isinstance(r, GenerationResponse)]
+        tool_results = [r for r in results if isinstance(r, ToolCallResponse)]
+
+        # Should have text tokens before tool call + one ToolCallResponse
+        assert len(tool_results) == 1
+        assert len(tool_results[0].tool_calls) == 1
+
+        tc = tool_results[0].tool_calls[0]
+        assert tc.name == "get_weather"
+        args = json.loads(tc.arguments)  # pyright: ignore[reportAny]
+        assert args["city"] == "San Francisco"
+        assert args["units"] == "celsius"
+
+        # The text before the tool call should still be yielded
+        text_before = "".join(r.text for r in gen_results if not r.is_thinking)
+        assert "check the weather" in text_before
+
+    def test_multiple_tool_calls_in_one_block(self):
+        """Model generates two tool calls in a single function_calls block."""
+        messages: list[dict[str, Any]] = [
+            {"role": "system", "content": "You are helpful."},
+            {"role": "user", "content": "Weather in NYC and time in EST?"},
+        ]
+        prompt = encode_messages(messages, thinking_mode="chat", tools=_WEATHER_TOOLS)
+        assert "get_weather" in prompt
+        assert "get_time" in prompt
+
+        # Simulate model output with two invocations
+        model_tokens = [
+            "Let me check both.\n\n",
+            TOOL_CALLS_START,
+            "\n",
+            f'<{DSML_TOKEN}invoke name="get_weather">\n',
+            f'<{DSML_TOKEN}parameter name="city" string="true">NYC</{DSML_TOKEN}parameter>\n',
+            f"</{DSML_TOKEN}invoke>\n",
+            f'<{DSML_TOKEN}invoke name="get_time">\n',
+            f'<{DSML_TOKEN}parameter name="timezone" string="true">EST</{DSML_TOKEN}parameter>\n',
+            f"</{DSML_TOKEN}invoke>\n",
+            TOOL_CALLS_END,
+        ]
+
+        results = list(parse_deepseek_v32(_simulate_tokens(model_tokens)))
+        tool_results = [r for r in results if isinstance(r, ToolCallResponse)]
+
+        assert len(tool_results) == 1
+        assert len(tool_results[0].tool_calls) == 2
+        assert tool_results[0].tool_calls[0].name == "get_weather"
+        assert tool_results[0].tool_calls[1].name == "get_time"
+
+        args0 = json.loads(tool_results[0].tool_calls[0].arguments)  # pyright: ignore[reportAny]
+        args1 = json.loads(tool_results[0].tool_calls[1].arguments)  # pyright: ignore[reportAny]
+        assert args0 == {"city": "NYC"}
+        assert args1 == {"timezone": "EST"}
+
+
+# ── Test: Multi-turn tool use flow ───────────────────────────────
+
+
+class TestE2EMultiTurnToolUse:
+    """Full multi-turn: user asks → model calls tool → tool result → model answers."""
+
+    def test_encode_multi_turn_with_tool_results(self):
+        """Verify the prompt for turn 2 (after tool results) is correctly encoded."""
+        # Turn 1: user asks, model calls tool
+        # Turn 2: tool result provided, model answers
+        messages: list[dict[str, Any]] = [
+            {"role": "system", "content": "You are a weather assistant."},
+            {"role": "user", "content": "What's the weather in NYC?"},
+            {
+                "role": "assistant",
+                "content": "",
+                "tool_calls": [
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": '{"city": "NYC"}',
+                        },
+                    }
+                ],
+            },
+            {"role": "tool", "content": '{"temperature": 72, "condition": "sunny"}'},
+        ]
+
+        prompt = encode_messages(messages, thinking_mode="chat", tools=_WEATHER_TOOLS)
+
+        # Verify multi-turn structure
+        assert BOS_TOKEN in prompt
+        assert "You are a weather assistant." in prompt
+        assert "## Tools" in prompt
+
+        # The assistant's tool call should be encoded as DSML
+        assert TOOL_CALLS_START in prompt
+        assert f'<{DSML_TOKEN}invoke name="get_weather">' in prompt
+        assert EOS_TOKEN in prompt
+
+        # The tool result should be wrapped in function_results
+        assert "<function_results>" in prompt
+        assert "<result>" in prompt
+        assert "72" in prompt
+        assert "</function_results>" in prompt
+
+        # Now simulate model answering after seeing the tool result
+        model_tokens = [
+            "The current",
+            " weather in NYC",
+            " is 72°F",
+            " and sunny.",
+        ]
+        results = list(parse_deepseek_v32(_simulate_tokens(model_tokens)))
+
+        gen_results = [r for r in results if isinstance(r, GenerationResponse)]
+        tool_results = [r for r in results if isinstance(r, ToolCallResponse)]
+
+        assert len(tool_results) == 0
+        full_text = "".join(r.text for r in gen_results)
+        assert full_text == "The current weather in NYC is 72°F and sunny."
+
+    def test_multi_tool_results_encoding(self):
+        """Verify encoding when model called two tools and both return results."""
+        messages: list[dict[str, Any]] = [
+            {"role": "user", "content": "Weather and time?"},
+            {
+                "role": "assistant",
+                "content": "",
+                "tool_calls": [
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": '{"city": "LA"}',
+                        },
+                    },
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": "get_time",
+                            "arguments": '{"timezone": "PST"}',
+                        },
+                    },
+                ],
+            },
+            {"role": "tool", "content": "85F, clear skies"},
+            {"role": "tool", "content": "3:42 PM PST"},
+        ]
+
+        prompt = encode_messages(messages, thinking_mode="chat", tools=_WEATHER_TOOLS)
+
+        # Should have one function_results block with two results
+        assert prompt.count("<function_results>") == 1
+        assert prompt.count("</function_results>") == 1
+        assert "<result>85F, clear skies</result>" in prompt
+        assert "<result>3:42 PM PST</result>" in prompt
+
+
+# ── Test: Thinking + tool call ───────────────────────────────────
+
+
+class TestE2EThinkingAndToolCall:
+    """Model uses thinking mode, reasons, then makes a tool call."""
+
+    def test_thinking_then_tool_call(self):
+        """Model thinks first, then produces a DSML tool call block."""
+        messages: list[dict[str, Any]] = [
+            {"role": "user", "content": "What's the weather?"},
+        ]
+        prompt = encode_messages(
+            messages, tools=_WEATHER_TOOLS, thinking_mode="thinking"
+        )
+        # Thinking mode: prompt should end with <think>
+        assert prompt.endswith(THINKING_START)
+
+        # Simulate: model outputs <think>, thinks, closes thinking, then tool call.
+        # In the full pipeline, parse_thinking_models handles the case where
+        # <think> is in the prompt. Here we test parse_deepseek_v32 directly,
+        # which detects <think>/<think> markers in the stream.
+        model_tokens = [
+            THINKING_START,
+            "The user wants weather",
+            " information. I should use",
+            " the get_weather tool.",
+            THINKING_END,
+            "\n\n",
+            TOOL_CALLS_START,
+            "\n",
+            f'<{DSML_TOKEN}invoke name="get_weather">\n',
+            f'<{DSML_TOKEN}parameter name="city" string="true">',
+            "San Francisco",
+            f"</{DSML_TOKEN}parameter>\n",
+            f"</{DSML_TOKEN}invoke>\n",
+            TOOL_CALLS_END,
+        ]
+
+        results = list(parse_deepseek_v32(_simulate_tokens(model_tokens)))
+
+        gen_results = [r for r in results if isinstance(r, GenerationResponse)]
+        tool_results = [r for r in results if isinstance(r, ToolCallResponse)]
+
+        # Should have thinking tokens + tool call
+        thinking_results = [r for r in gen_results if r.is_thinking]
+
+        assert len(thinking_results) >= 1
+        thinking_text = "".join(r.text for r in thinking_results)
+        assert "get_weather tool" in thinking_text
+
+        assert len(tool_results) == 1
+        assert tool_results[0].tool_calls[0].name == "get_weather"
+        args = json.loads(tool_results[0].tool_calls[0].arguments)  # pyright: ignore[reportAny]
+        assert args["city"] == "San Francisco"
+
+    def test_thinking_prompt_encoding(self):
+        """Verify thinking mode affects prompt encoding correctly."""
+        messages: list[dict[str, Any]] = [
+            {"role": "system", "content": "Be thorough."},
+            {"role": "user", "content": "What's the weather?"},
+        ]
+
+        # With thinking enabled
+        prompt_think = encode_messages(
+            messages, tools=_WEATHER_TOOLS, thinking_mode="thinking"
+        )
+        assert prompt_think.endswith(THINKING_START)
+
+        # With thinking disabled
+        prompt_no_think = encode_messages(
+            messages, tools=_WEATHER_TOOLS, thinking_mode="chat"
+        )
+        assert prompt_no_think.endswith(THINKING_END)
+
+        # Both should have the same tool definitions
+        assert "get_weather" in prompt_think
+        assert "get_weather" in prompt_no_think
+
+
+# ── Test: Round-trip encode → parse ──────────────────────────────
+
+
+class TestE2ERoundTrip:
+    """Verify that DSML we encode can be parsed back correctly."""
+
+    def test_encoded_tool_call_is_parseable(self):
+        """Encode an assistant tool call message, then parse the DSML output."""
+        messages: list[dict[str, Any]] = [
+            {"role": "user", "content": "Weather?"},
+            {
+                "role": "assistant",
+                "content": "",
+                "tool_calls": [
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": '{"city": "Tokyo", "units": "celsius"}',
+                        },
+                    }
+                ],
+            },
+        ]
+
+        prompt = encode_messages(messages, thinking_mode="chat", tools=_WEATHER_TOOLS)
+
+        # Extract the DSML function_calls block from the prompt
+        start = prompt.index(TOOL_CALLS_START)
+        end = prompt.index(TOOL_CALLS_END) + len(TOOL_CALLS_END)
+        dsml_block = prompt[start:end]
+
+        # Parse it back
+        parsed = parse_dsml_output(dsml_block)
+        assert parsed is not None
+        assert len(parsed) == 1
+        assert parsed[0].name == "get_weather"
+        args = json.loads(parsed[0].arguments)  # pyright: ignore[reportAny]
+        assert args["city"] == "Tokyo"
+        assert args["units"] == "celsius"
+
+    def test_encoded_multi_tool_call_round_trips(self):
+        """Encode multiple tool calls, verify they parse back correctly."""
+        messages: list[dict[str, Any]] = [
+            {"role": "user", "content": "Both please"},
+            {
+                "role": "assistant",
+                "content": "",
+                "tool_calls": [
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": '{"city": "Paris"}',
+                        },
+                    },
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": "get_time",
+                            "arguments": '{"timezone": "CET"}',
+                        },
+                    },
+                ],
+            },
+        ]
+
+        prompt = encode_messages(messages, thinking_mode="chat", tools=_WEATHER_TOOLS)
+
+        start = prompt.index(TOOL_CALLS_START)
+        end = prompt.index(TOOL_CALLS_END) + len(TOOL_CALLS_END)
+        dsml_block = prompt[start:end]
+
+        parsed = parse_dsml_output(dsml_block)
+        assert parsed is not None
+        assert len(parsed) == 2
+        assert parsed[0].name == "get_weather"
+        assert parsed[1].name == "get_time"
+        assert json.loads(parsed[0].arguments) == {"city": "Paris"}
+        assert json.loads(parsed[1].arguments) == {"timezone": "CET"}
+
+
+# ── Test: Edge cases with realistic token boundaries ─────────────
+
+
+class TestE2EEdgeCases:
+    """Edge cases that occur in real model inference."""
+
+    def test_dsml_marker_split_at_fullwidth_pipe(self):
+        """The fullwidth pipe character ｜ might be its own token."""
+        # This is a realistic tokenization: the DSML marker is split at the ｜ chars
+        model_tokens = [
+            "Let me help.\n\n",
+            "<\uff5c",  # start of ｜DSML｜
+            "DSML\uff5c",  # rest of DSML token
+            "function_calls>\n",
+            f'<{DSML_TOKEN}invoke name="get_weather">\n',
+            f'<{DSML_TOKEN}parameter name="city" string="true">NYC</{DSML_TOKEN}parameter>\n',
+            f"</{DSML_TOKEN}invoke>\n",
+            TOOL_CALLS_END,
+        ]
+
+        results = list(parse_deepseek_v32(_simulate_tokens(model_tokens)))
+        tool_results = [r for r in results if isinstance(r, ToolCallResponse)]
+
+        assert len(tool_results) == 1
+        assert tool_results[0].tool_calls[0].name == "get_weather"
+
+    def test_tool_call_with_nested_json_object(self):
+        """Model passes a complex JSON object as a non-string parameter."""
+        dsml_block = (
+            f"{TOOL_CALLS_START}\n"
+            f'<{DSML_TOKEN}invoke name="create_event">\n'
+            f'<{DSML_TOKEN}parameter name="title" string="true">Team Standup</{DSML_TOKEN}parameter>\n'
+            f'<{DSML_TOKEN}parameter name="config" string="false">'
+            f'{{"recurring": true, "days": ["mon", "wed", "fri"], "time": "09:00"}}'
+            f"</{DSML_TOKEN}parameter>\n"
+            f"</{DSML_TOKEN}invoke>\n"
+            f"{TOOL_CALLS_END}"
+        )
+
+        # Feed as single token (model might produce it all at once after prefill)
+        results = list(parse_deepseek_v32(_simulate_tokens([dsml_block])))
+        tool_results = [r for r in results if isinstance(r, ToolCallResponse)]
+
+        assert len(tool_results) == 1
+        tc = tool_results[0].tool_calls[0]
+        assert tc.name == "create_event"
+        args = json.loads(tc.arguments)  # pyright: ignore[reportAny]
+        assert args["title"] == "Team Standup"
+        assert args["config"]["recurring"] is True
+        assert args["config"]["days"] == ["mon", "wed", "fri"]
+
+    def test_text_with_angle_brackets_not_mistaken_for_dsml(self):
+        """Angle brackets in normal text should not trigger DSML buffering."""
+        model_tokens = [
+            "The formula is ",
+            "<x, y>",
+            " where x > 0",
+            " and y < 100.",
+        ]
+
+        results = list(parse_deepseek_v32(_simulate_tokens(model_tokens)))
+        gen_results = [r for r in results if isinstance(r, GenerationResponse)]
+        tool_results = [r for r in results if isinstance(r, ToolCallResponse)]
+
+        assert len(tool_results) == 0
+        full_text = "".join(r.text for r in gen_results)
+        assert "formula" in full_text
+        assert "<x, y>" in full_text
+
+    def test_empty_model_response(self):
+        """Model produces only EOS (empty response)."""
+        model_tokens = [""]
+        results = list(parse_deepseek_v32(_simulate_tokens(model_tokens)))
+        gen_results = [r for r in results if isinstance(r, GenerationResponse)]
+        assert len(gen_results) == 1
+        assert gen_results[0].text == ""
+        assert gen_results[0].finish_reason == "stop"
+
+
+# ── Test: Full EPDP spec round-trip ──────────────────────────────
+
+
+class TestE2EFullRoundTrip:
+    """Full round-trip matching the vLLM EPDP spec.
+
+    Simulates the complete multi-turn flow:
+      Turn 1: user asks → think → tool call → tool result → think → answer
+      Turn 2: user asks again → old reasoning stripped → think → answer
+    """
+
+    def test_single_tool_full_flow_with_thinking(self):
+        """Complete flow: user → think → tool call → tool result → think → answer.
+
+        This is the core EPDP flow from the vLLM spec.
+        """
+        # ── Turn 1.1: User asks, encode prompt ──
+        messages: list[dict[str, Any]] = [
+            {"role": "system", "content": "You are a weather assistant."},
+            {"role": "user", "content": "How's the weather in Hangzhou?"},
+        ]
+        prompt_1 = encode_messages(
+            messages, tools=_WEATHER_TOOLS, thinking_mode="thinking"
+        )
+        assert prompt_1.endswith(THINKING_START)
+        assert "## Tools" in prompt_1
+        assert "get_weather" in prompt_1
+
+        # ── Turn 1.1: Model thinks, then calls tool ──
+        model_tokens_1 = [
+            THINKING_START,
+            "The user wants to know the weather in Hangzhou.",
+            " I need to use the get_weather tool.",
+            THINKING_END,
+            "\n\n",
+            TOOL_CALLS_START,
+            "\n",
+            f'<{DSML_TOKEN}invoke name="get_weather">\n',
+            f'<{DSML_TOKEN}parameter name="city" string="true">Hangzhou</{DSML_TOKEN}parameter>\n',
+            f"</{DSML_TOKEN}invoke>\n",
+            TOOL_CALLS_END,
+        ]
+        results_1 = list(parse_deepseek_v32(_simulate_tokens(model_tokens_1)))
+
+        # Verify: thinking tokens + tool call
+        gen_1 = [r for r in results_1 if isinstance(r, GenerationResponse)]
+        tool_1 = [r for r in results_1 if isinstance(r, ToolCallResponse)]
+        thinking_1 = [r for r in gen_1 if r.is_thinking]
+
+        assert len(thinking_1) >= 1
+        assert "get_weather tool" in "".join(r.text for r in thinking_1)
+        assert len(tool_1) == 1
+        assert tool_1[0].tool_calls[0].name == "get_weather"
+        tc_args = json.loads(tool_1[0].tool_calls[0].arguments)  # pyright: ignore[reportAny]
+        assert tc_args == {"city": "Hangzhou"}
+
+        # ── Turn 1.2: Add assistant response + tool result to messages ──
+        messages.append(
+            {
+                "role": "assistant",
+                "content": "",
+                "reasoning_content": "The user wants to know the weather in Hangzhou. I need to use the get_weather tool.",
+                "tool_calls": [
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": '{"city": "Hangzhou"}',
+                        },
+                    }
+                ],
+            }
+        )
+        messages.append(
+            {
+                "role": "tool",
+                "content": '{"temperature": "7~13°C", "condition": "Cloudy"}',
+            }
+        )
+
+        # Encode prompt for turn 1.2
+        prompt_2 = encode_messages(
+            messages, tools=_WEATHER_TOOLS, thinking_mode="thinking"
+        )
+
+        # Verify: prompt has the full conversation structure
+        assert TOOL_CALLS_START in prompt_2  # assistant's encoded tool call
+        assert EOS_TOKEN in prompt_2  # assistant turn ends with EOS
+        assert "<function_results>" in prompt_2
+        assert "<result>" in prompt_2
+        assert "Cloudy" in prompt_2
+        assert "</function_results>" in prompt_2
+        # After tool results with thinking enabled → <think> appended
+        assert prompt_2.endswith(THINKING_START)
+        # The assistant's reasoning_content should appear (it's after last_user_idx)
+        assert "get_weather tool" in prompt_2
+
+        # ── Turn 1.2: Model thinks about results, then answers ──
+        model_tokens_2 = [
+            THINKING_START,
+            "The weather in Hangzhou is Cloudy, 7~13°C.",
+            " I'll tell the user.",
+            THINKING_END,
+            "The weather in Hangzhou is currently cloudy with temperatures between 7°C and 13°C.",
+        ]
+        results_2 = list(parse_deepseek_v32(_simulate_tokens(model_tokens_2)))
+
+        gen_2 = [r for r in results_2 if isinstance(r, GenerationResponse)]
+        tool_2 = [r for r in results_2 if isinstance(r, ToolCallResponse)]
+        thinking_2 = [r for r in gen_2 if r.is_thinking]
+        non_thinking_2 = [r for r in gen_2 if not r.is_thinking]
+
+        assert len(tool_2) == 0  # No more tool calls
+        assert len(thinking_2) >= 1
+        assert "Cloudy" in "".join(r.text for r in thinking_2)
+        assert len(non_thinking_2) >= 1
+        final_text = "".join(r.text for r in non_thinking_2)
+        assert "7°C" in final_text
+        assert "13°C" in final_text
+
+    def test_multi_tool_full_flow(self):
+        """Flow with two tools: user → think → 2 tool calls → 2 results → think → answer."""
+        # ── Initial prompt ──
+        messages: list[dict[str, Any]] = [
+            {"role": "system", "content": "You help with weather and time."},
+            {"role": "user", "content": "Weather in NYC and time in EST?"},
+        ]
+        prompt_1 = encode_messages(
+            messages, tools=_WEATHER_TOOLS, thinking_mode="thinking"
+        )
+        assert prompt_1.endswith(THINKING_START)
+
+        # ── Model thinks, calls both tools ──
+        model_tokens_1 = [
+            THINKING_START,
+            "Two requests: weather and time. I'll call both.",
+            THINKING_END,
+            "\n\n",
+            TOOL_CALLS_START,
+            "\n",
+            f'<{DSML_TOKEN}invoke name="get_weather">\n',
+            f'<{DSML_TOKEN}parameter name="city" string="true">NYC</{DSML_TOKEN}parameter>\n',
+            f"</{DSML_TOKEN}invoke>\n",
+            f'<{DSML_TOKEN}invoke name="get_time">\n',
+            f'<{DSML_TOKEN}parameter name="timezone" string="true">EST</{DSML_TOKEN}parameter>\n',
+            f"</{DSML_TOKEN}invoke>\n",
+            TOOL_CALLS_END,
+        ]
+        results_1 = list(parse_deepseek_v32(_simulate_tokens(model_tokens_1)))
+        tool_1 = [r for r in results_1 if isinstance(r, ToolCallResponse)]
+
+        assert len(tool_1) == 1
+        assert len(tool_1[0].tool_calls) == 2
+        assert tool_1[0].tool_calls[0].name == "get_weather"
+        assert tool_1[0].tool_calls[1].name == "get_time"
+
+        # ── Add assistant + both tool results ──
+        messages.append(
+            {
+                "role": "assistant",
+                "content": "",
+                "reasoning_content": "Two requests: weather and time. I'll call both.",
+                "tool_calls": [
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": '{"city": "NYC"}',
+                        },
+                    },
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": "get_time",
+                            "arguments": '{"timezone": "EST"}',
+                        },
+                    },
+                ],
+            }
+        )
+        messages.append({"role": "tool", "content": "72°F, sunny"})
+        messages.append({"role": "tool", "content": "2:30 PM EST"})
+
+        prompt_2 = encode_messages(
+            messages, tools=_WEATHER_TOOLS, thinking_mode="thinking"
+        )
+
+        # Verify multi-tool result encoding
+        # Count is 2: 1 in _TOOLS_SYSTEM_TEMPLATE example + 1 in conversation
+        assert prompt_2.count("<function_results>") == 2
+        assert prompt_2.count("</function_results>") == 2
+        assert "<result>72°F, sunny</result>" in prompt_2
+        assert "<result>2:30 PM EST</result>" in prompt_2
+        assert prompt_2.endswith(THINKING_START)
+
+        # ── Model thinks about results, answers ──
+        model_tokens_2 = [
+            THINKING_START,
+            "Got both results. Weather is 72°F sunny, time is 2:30 PM.",
+            THINKING_END,
+            "In NYC it's currently 72°F and sunny. The time in EST is 2:30 PM.",
+        ]
+        results_2 = list(parse_deepseek_v32(_simulate_tokens(model_tokens_2)))
+
+        tool_2 = [r for r in results_2 if isinstance(r, ToolCallResponse)]
+        gen_2 = [r for r in results_2 if isinstance(r, GenerationResponse)]
+        non_thinking_2 = [r for r in gen_2 if not r.is_thinking]
+
+        assert len(tool_2) == 0
+        final_text = "".join(r.text for r in non_thinking_2)
+        assert "72°F" in final_text
+        assert "2:30 PM" in final_text
+
+    def test_two_user_turns_reasoning_stripped(self):
+        """Turn 2: old reasoning_content is stripped from history.
+
+        Per the vLLM spec, clear_reasoning_content is called between user turns
+        to save bandwidth. Our _drop_old_thinking handles this.
+        """
+        # Full turn 1 conversation (already completed)
+        messages: list[dict[str, Any]] = [
+            {"role": "system", "content": "You are helpful."},
+            {"role": "user", "content": "Weather in Hangzhou?"},
+            {
+                "role": "assistant",
+                "content": "",
+                "reasoning_content": "I need to call get_weather for Hangzhou.",
+                "tool_calls": [
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": '{"city": "Hangzhou"}',
+                        },
+                    }
+                ],
+            },
+            {"role": "tool", "content": "Cloudy 7~13°C"},
+            {
+                "role": "assistant",
+                "content": "The weather in Hangzhou is cloudy, 7-13°C.",
+                "reasoning_content": "The tool returned cloudy weather. I'll summarize.",
+            },
+            # Turn 2: user asks again
+            {"role": "user", "content": "What about Beijing?"},
+        ]
+
+        prompt = encode_messages(
+            messages, tools=_WEATHER_TOOLS, thinking_mode="thinking"
+        )
+
+        # Old reasoning_content from turn 1 assistants should be STRIPPED
+        # (they're before the last user message at index 5)
+        assert "I need to call get_weather" not in prompt
+        assert "tool returned cloudy" not in prompt
+
+        # But the assistant's content and tool calls should still be there
+        assert "cloudy, 7-13°C" in prompt
+        assert TOOL_CALLS_START in prompt
+
+        # Prompt ends with <think> for the new turn
+        assert prompt.endswith(THINKING_START)
+
+        # ── Turn 2: Model thinks, calls tool for Beijing ──
+        model_tokens = [
+            THINKING_START,
+            "Now the user wants Beijing weather.",
+            THINKING_END,
+            "\n\n",
+            TOOL_CALLS_START,
+            "\n",
+            f'<{DSML_TOKEN}invoke name="get_weather">\n',
+            f'<{DSML_TOKEN}parameter name="city" string="true">Beijing</{DSML_TOKEN}parameter>\n',
+            f"</{DSML_TOKEN}invoke>\n",
+            TOOL_CALLS_END,
+        ]
+        results = list(parse_deepseek_v32(_simulate_tokens(model_tokens)))
+        tool_results = [r for r in results if isinstance(r, ToolCallResponse)]
+
+        assert len(tool_results) == 1
+        assert tool_results[0].tool_calls[0].name == "get_weather"
+        args = json.loads(tool_results[0].tool_calls[0].arguments)  # pyright: ignore[reportAny]
+        assert args == {"city": "Beijing"}
+
+    def test_chained_tool_calls_loop(self):
+        """Model calls tool, gets result, calls another tool, gets result, answers.
+
+        This simulates the inner while loop from the vLLM spec where the model
+        may need multiple sub-turns of tool calling before it has enough info.
+        """
+        # ── Sub-turn 1: user asks, model calls get_time ──
+        messages: list[dict[str, Any]] = [
+            {"role": "system", "content": "You are helpful."},
+            {"role": "user", "content": "What's the weather in Hangzhou tomorrow?"},
+        ]
+
+        prompt_1 = encode_messages(
+            messages, tools=_WEATHER_TOOLS, thinking_mode="thinking"
+        )
+        assert prompt_1.endswith(THINKING_START)
+
+        # Model first calls get_time to figure out the date
+        model_tokens_1 = [
+            THINKING_START,
+            "I need the current date first to calculate tomorrow.",
+            THINKING_END,
+            "\n\n",
+            TOOL_CALLS_START,
+            "\n",
+            f'<{DSML_TOKEN}invoke name="get_time">\n',
+            f'<{DSML_TOKEN}parameter name="timezone" string="true">Asia/Shanghai</{DSML_TOKEN}parameter>\n',
+            f"</{DSML_TOKEN}invoke>\n",
+            TOOL_CALLS_END,
+        ]
+        results_1 = list(parse_deepseek_v32(_simulate_tokens(model_tokens_1)))
+        tool_1 = [r for r in results_1 if isinstance(r, ToolCallResponse)]
+        assert len(tool_1) == 1
+        assert tool_1[0].tool_calls[0].name == "get_time"
+
+        # ── Sub-turn 2: add tool result, model calls get_weather ──
+        messages.append(
+            {
+                "role": "assistant",
+                "content": "",
+                "reasoning_content": "I need the current date first to calculate tomorrow.",
+                "tool_calls": [
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": "get_time",
+                            "arguments": '{"timezone": "Asia/Shanghai"}',
+                        },
+                    }
+                ],
+            }
+        )
+        messages.append({"role": "tool", "content": "2025-12-01 14:30 CST"})
+
+        prompt_2 = encode_messages(
+            messages, tools=_WEATHER_TOOLS, thinking_mode="thinking"
+        )
+        assert "<result>2025-12-01 14:30 CST</result>" in prompt_2
+        assert prompt_2.endswith(THINKING_START)
+
+        # Model now knows the date, calls get_weather
+        model_tokens_2 = [
+            THINKING_START,
+            "Today is 2025-12-01, so tomorrow is 2025-12-02.",
+            " Now I can check weather for Hangzhou.",
+            THINKING_END,
+            "\n\n",
+            TOOL_CALLS_START,
+            "\n",
+            f'<{DSML_TOKEN}invoke name="get_weather">\n',
+            f'<{DSML_TOKEN}parameter name="city" string="true">Hangzhou</{DSML_TOKEN}parameter>\n',
+            f"</{DSML_TOKEN}invoke>\n",
+            TOOL_CALLS_END,
+        ]
+        results_2 = list(parse_deepseek_v32(_simulate_tokens(model_tokens_2)))
+        tool_2 = [r for r in results_2 if isinstance(r, ToolCallResponse)]
+        assert len(tool_2) == 1
+        assert tool_2[0].tool_calls[0].name == "get_weather"
+
+        # ── Sub-turn 3: add weather result, model answers ──
+        messages.append(
+            {
+                "role": "assistant",
+                "content": "",
+                "reasoning_content": "Today is 2025-12-01, so tomorrow is 2025-12-02. Now I can check weather for Hangzhou.",
+                "tool_calls": [
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": '{"city": "Hangzhou"}',
+                        },
+                    }
+                ],
+            }
+        )
+        messages.append({"role": "tool", "content": "Sunny, 5~12°C"})
+
+        prompt_3 = encode_messages(
+            messages, tools=_WEATHER_TOOLS, thinking_mode="thinking"
+        )
+        # Should have both function_results blocks (one per tool round)
+        # Count is 3: 1 in _TOOLS_SYSTEM_TEMPLATE example + 2 in conversation
+        assert prompt_3.count("<function_results>") == 3
+        assert prompt_3.count("</function_results>") == 3
+        assert "<result>2025-12-01 14:30 CST</result>" in prompt_3
+        assert "<result>Sunny, 5~12°C</result>" in prompt_3
+        assert prompt_3.endswith(THINKING_START)
+
+        # Model finally answers
+        model_tokens_3 = [
+            THINKING_START,
+            "I have the weather for tomorrow in Hangzhou.",
+            THINKING_END,
+            "Tomorrow in Hangzhou will be sunny with temperatures between 5°C and 12°C.",
+        ]
+        results_3 = list(parse_deepseek_v32(_simulate_tokens(model_tokens_3)))
+
+        tool_3 = [r for r in results_3 if isinstance(r, ToolCallResponse)]
+        gen_3 = [r for r in results_3 if isinstance(r, GenerationResponse)]
+        non_thinking_3 = [r for r in gen_3 if not r.is_thinking]
+
+        assert len(tool_3) == 0  # No more tool calls — loop ends
+        final_text = "".join(r.text for r in non_thinking_3)
+        assert "sunny" in final_text.lower()
+        assert "5°C" in final_text
+        assert "12°C" in final_text

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

@@ -148,6 +148,7 @@ class MockTokenizer:
     tool_call_start = None
     tool_call_end = None
     has_tool_calling = False
+    has_thinking = False
 
 
 class MockGroup:

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

@@ -149,12 +149,23 @@ class TestParseGptOssThinkingThenToolCall:
     def test_thinking_then_tool_call(self):
         results = _collect(THINKING_THEN_TOOL_TOKENS)
 
-        # Should have thinking tags + content + tool call
+        # Thinking tokens should have is_thinking=True and no <think> tags
-        text_parts = [r.text for r in results if isinstance(r, GenerationResponse)]
+        thinking_responses = [
-        combined = "".join(text_parts)
+            r for r in results if isinstance(r, GenerationResponse) and r.is_thinking
-        assert "<think>" in combined
+        ]
-        assert "</think>" in combined
+        thinking_text = "".join(r.text for r in thinking_responses)
-        assert "Let me think about this." in combined
+        assert "Let me think about this." in thinking_text
+        assert "<think>" not in thinking_text
+        assert "</think>" not in thinking_text
+
+        # Non-thinking tokens should have is_thinking=False
+        non_thinking = [
+            r
+            for r in results
+            if isinstance(r, GenerationResponse) and not r.is_thinking
+        ]
+        non_thinking_text = "".join(r.text for r in non_thinking)
+        assert "<think>" not in non_thinking_text
 
         # And the tool call
         tc = _get_tool_call(results)

tmp/config_examples/claude_code.sh

@@ -0,0 +1,8 @@
+#!/bin/bash
+# Run Claude Code against a local exo cluster! (Here, GPT OSS 120B)
+ANTHROPIC_BASE_URL="http://localhost:52415/" \
+  ANTHROPIC_AUTH_TOKEN="dummy" \
+  ANTHROPIC_MODEL="mlx-community/gpt-oss-120b-MXFP4-Q8" \
+  ANTHROPIC_SMALL_FAST_MODEL="mlx-community/gpt-oss-120b-MXFP4-Q8" \
+  CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC=1 \
+  claude