---
title: "Async messages"
description: "Run chat completions as background jobs — fire, stream, poll, cancel, and clean up."
---

The async messages API decouples request submission from response consumption. Instead of holding an HTTP connection open until the model finishes, you start a job, get back a `message_id`, and consume the output whenever you are ready.

Use async when:
- Generation is long and you don't want to hold a connection open.
- You need to fan out multiple requests and collect results later.
- You want to let a background worker process the job while the caller does other work.

<Note>
  Async chat uses an in-process stream broker — no separate worker needed. Use `memory` for single-instance deployments or `redis` for multi-instance.
</Note>

---

## Lifecycle

```
POST /v1/messages/async          →  message_id (pending)
         │
         ▼
GET  /v1/messages/async/{id}/stream   ←─ SSE stream of completion events
GET  /v1/messages/async/{id}/status   ←─ poll status at any time
         │
         ▼
POST /v1/messages/async/{id}/cancel   (optional — while processing)
DELETE /v1/messages/async/{id}/delete (clean up when done)
```

---

## Start a job

```bash
curl -X POST http://localhost:8080/v1/messages/async \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3.5:35b",
    "messages": [{"role": "user", "content": "Write a detailed report on..."}]
  }'
```

Response:

```json
{"message_id": "msg_01abc..."}
```

The request body is identical to `POST /v1/messages` — all fields (tools, tool_context, mcp_servers, sampling params) work the same way. The same per-tool dependency rules apply here: install the specific extra you need, or use `private-gpt[tools]` or `private-gpt[core]`.

---

## Stream the output

Connect an SSE client to receive events as the model generates them. Events follow the same format as synchronous streaming:

```bash
curl http://localhost:8080/v1/messages/async/msg_01abc.../stream
```

The connection stays open until the job completes, fails, or is cancelled. You can connect and disconnect at any time — the stream replays from the last position on reconnect.

---

## Check status

Poll the status endpoint to inspect the job without consuming the stream:

```bash
curl http://localhost:8080/v1/messages/async/msg_01abc.../status
```

Response:

```json
{
  "message_id": "msg_01abc...",
  "status": "processing",
  "created_at": "2026-05-26T10:00:00Z",
  "updated_at": "2026-05-26T10:00:05Z",
  "completed_at": null,
  "error_message": null
}
```

**Status values:**

| Status | Meaning |
|---|---|
| `pending` | Job queued, worker not yet started |
| `processing` | Worker is actively generating |
| `completed` | Generation finished successfully |
| `failed` | Generation failed — check `error_message` |
| `cancelled` | Job was cancelled before completion |
| `error` | Internal error |

---

## Cancel a job

Cancel a job while it is `pending` or `processing`:

```bash
curl -X POST http://localhost:8080/v1/messages/async/msg_01abc.../cancel
```

---

## Clean up

Delete the job and free associated resources once you have consumed the result:

```bash
curl -X DELETE http://localhost:8080/v1/messages/async/msg_01abc.../delete
```

---

## Stream broker

Async chat streams are handled in-process — no separate worker required. The broker is configured under `stream.broker` in `settings.yaml`:

| Mode | When to use |
|---|---|
| `memory` | Single-instance deployments and local development (default) |
| `redis` | Multi-instance or production — streams are shared across processes |

```yaml
stream:
  broker: memory   # or redis
```

For Redis, configure the connection:

```yaml
redis:
  host: ${PGPT_REDIS_HOST:localhost:6379}
  username: ${PGPT_REDIS_USERNAME:}
  password: ${PGPT_REDIS_PASSWORD:}
  database: ${PGPT_REDIS_DATABASE:0}
```
