How to Fix 'streaming response cutoff when scaling' in AutoGen (TypeScript)

What this error means

streaming response cutoff when scaling usually shows up when your AutoGen TypeScript app starts handling more concurrent requests, longer model outputs, or larger tool responses. The agent begins streaming tokens, then the stream gets terminated before the full assistant message is delivered.

In practice, this is almost always a transport or lifecycle problem, not an LLM problem. The model started fine; something in your app, proxy, serverless runtime, or stream consumer cut it off.

The Most Common Cause

The #1 cause is that your streaming connection is being closed too early by your server or request handler. In AutoGen TypeScript, this often happens when you create an AssistantAgent, start streaming with runStream(), but return from the handler before the stream is fully consumed.

Here’s the broken pattern:

// ❌ Broken: handler exits before the stream is fully consumed
import { AssistantAgent } from "@autogen/core";

const agent = new AssistantAgent({
  name: "support-agent",
  modelClient,
});

export async function POST(req: Request) {
  const { message } = await req.json();

  const stream = await agent.runStream([{ role: "user", content: message }]);

  // Bug: returning immediately can close the response early
  return new Response("started", { status: 200 });
}

// ✅ Fixed: consume the full stream and keep the response open
import { AssistantAgent } from "@autogen/core";

const agent = new AssistantAgent({
  name: "support-agent",
  modelClient,
});

export async function POST(req: Request) {
  const { message } = await req.json();
  const stream = await agent.runStream([{ role: "user", content: message }]);

  const encoder = new TextEncoder();
  const readable = new ReadableStream({
    async start(controller) {
      try {
        for await (const event of stream) {
          controller.enqueue(
            encoder.encode(JSON.stringify(event) + "\n")
          );
        }
        controller.close();
      } catch (err) {
        controller.error(err);
      }
    },
  });

  return new Response(readable, {
    headers: { "Content-Type": "application/x-ndjson" },
  });
}

If you’re using AssistantAgent.run() instead of runStream(), this issue still shows up when your framework times out or closes the request context before completion. The fix is the same: keep the request alive until all output is consumed.

Other Possible Causes

1. Reverse proxy timeout

If you’re behind Nginx, Cloudflare, ALB, or an API gateway, the proxy may kill long-lived streaming responses.

# Example Nginx fix
proxy_read_timeout 300s;
proxy_send_timeout 300s;
chunked_transfer_encoding on;

If your model takes 60–120 seconds under load and your proxy times out at 30 seconds, you’ll see partial output and then a cutoff.

2. Serverless runtime limits

Vercel, AWS Lambda, and similar runtimes can terminate streaming responses when execution exceeds their limits.

// Example symptom in logs
Error: streaming response cutoff when scaling
Cause: function exceeded max duration

Fix by moving the streaming endpoint to:

• •a long-running Node server
• •a containerized service
• •a queue-backed job worker with polling or SSE

3. Reusing a single agent instance across concurrent requests incorrectly

AutoGen agents are stateful enough that sharing one instance across many requests can create race conditions if you mutate conversation state per request.

// ❌ Risky if state is mutated per request
const agent = new AssistantAgent({ name: "shared", modelClient });

app.post("/chat", async (req, res) => {
  agent.addMessage({ role: "user", content: req.body.message });
});

Use one of these patterns instead:

• •create a fresh AssistantAgent per request
• •isolate conversation state per session ID
• •use locks if you must share resources

4. Tool output too large for the transport

If a tool returns huge JSON blobs, the stream can stall or get truncated under load.

// ❌ Too much data in one tool result
return {
  records: bigArrayOf100000Rows,
};

Trim tool outputs before returning them:

• •paginate results
• •summarize large payloads
• •return IDs and fetch details separately

How to Debug It

1. •

   Check where the stream ends

   • •Log every chunk/event from runStream().
   • •If chunks stop before the final assistant message, the cutoff is outside the model.
2. •

   Inspect your runtime logs

   • •Look for timeout, ECONNRESET, aborted, or function duration exceeded.
   • •If you see AbortError near ReadableStream, your handler was closed externally.
3. •

   Test without proxies

   • •Hit the TypeScript service directly on localhost.
   • •If it works locally but fails behind Nginx/Cloudflare/API Gateway, it’s infrastructure.
4. •

   Reduce concurrency

   • •Run one request at a time.
   • •If the error disappears under low load, you likely have shared state or resource exhaustion around AssistantAgent, HTTP connections, or tool execution.

Prevention

• •Keep streaming handlers alive until for await (...) finishes over runStream().
• •Set explicit timeouts everywhere:
  • •app server timeout
  • •proxy timeout
  • •serverless max duration
• •Avoid sharing mutable AutoGen agent instances across requests unless you’ve isolated conversation state.
• •Keep tool outputs small and structured.
• •Load test with realistic prompt sizes and concurrency before production rollout.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.