durable_queue.py
"""AgentOS with a durable job queue: accepted background runs survive crashes.
With QueueConfig(durable=True), a background run (background=True) is
accepted as a committed row in the job queue table. Whichever replica's worker
claims the job executes it - across process restarts and deploys. What
happens to a run whose worker CRASHES is a choice: with the default
max_attempts=1 it fails visibly and is never silently re-executed (its side
effects may already have happened); with max_attempts=2+ a live replica
reclaims and re-executes it automatically. See "Try it" step 3.
Try it:
1. Start this app and submit a background run:
curl -X POST localhost:7777/agents/durable-agent/runs \
-F "message=Write a haiku about queues" -F "background=true" \
-F "stream=false"
-> 202 with run_id and session_id; the run row is committed before the response.
2. Poll GET /agents/durable-agent/runs/{run_id}?session_id={session_id} for the result.
3. Kill the server mid-run and restart it. What happens next is the most
important knob in this cookbook:
- max_attempts=1 (the default, at-most-once): the run is NOT re-executed.
After lock_grace_seconds the sweeper fails it visibly - the poll shows
ERROR with the reason, /queue/jobs lists it as failed, and an operator
can requeue it. This is the right default for runs with side effects
(emails, payments): a killed run may have already acted, and silent
re-execution would act twice.
- max_attempts=2 or higher (at-least-once): the restarted worker (or any
other replica) reclaims the stale job and re-executes it automatically -
kill the server mid-run and watch the run complete anyway. Retries are
safe: a still-alive "dead" worker is fenced from corrupting the retry's
run row or event stream.
Either way the run is never lost and never stuck at RUNNING forever.
4. Operations surface:
GET /queue/stats - counts by status, oldest queued age
GET /queue/jobs?status=failed - the dead-letter list
POST /queue/jobs/{id}/requeue - grant a failed job one more attempt
5. Resubmit safely with an Idempotency-Key header: duplicate submissions
return the existing run instead of enqueueing twice.
6. STREAMING through the queue: add -F "stream=true" to the submission and the
response becomes an SSE stream tailing the run's events - while the run
itself executes durably on whichever replica's worker claims the job.
Disconnect any time: the run completes regardless and the full output is
guaranteed via polling; reconnecting replays missed events. Durability
attaches to the RUN; the stream is the best-effort live view.
The queue store defaults to the AgentOS db (the Postgres below - zero extra
infrastructure). To isolate queue load on a dedicated Redis instead:
from agno.db.redis import RedisDb
queue_config = QueueConfig(
durable=True,
db=RedisDb(db_url="redis://localhost:6379"),
)
(Redis acceptance durability depends on persistence config: use AOF
appendfsync everysec/always for Postgres-grade guarantees.)
Requirements:
- PostgreSQL running (./cookbook/scripts/run_pgvector.sh)
- OPENAI_API_KEY set
"""
from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.openai import OpenAIResponses
from agno.os import AgentOS, QueueConfig
db = PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai")
agent = Agent(
name="Durable Agent",
id="durable-agent",
model=OpenAIResponses(id="gpt-5.5"),
description="An agent whose background runs survive crashes and deploys",
db=db,
)
agent_os = AgentOS(
description="AgentOS with a durable job queue",
agents=[agent],
db=db,
queue=QueueConfig(
durable=True, # queue table lives in the Postgres above
max_concurrency=8, # per replica
max_queue_depth=1000, # global bound -> 429 beyond it
# At-most-once by default: a run killed mid-flight FAILS VISIBLY and
# is never silently re-executed (its side effects may already have
# happened). Set 2+ to have a crashed run reclaimed and re-executed
# automatically by any live replica - see "Try it" step 3.
max_attempts=1,
),
)
app = agent_os.get_app()
if __name__ == "__main__":
agent_os.serve(app="durable_queue:app", reload=True)
Run the Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U "agno[os]" "psycopg[binary]" openai
3
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Run PgVector
docker run -d \
-e POSTGRES_DB=ai \
-e POSTGRES_USER=ai \
-e POSTGRES_PASSWORD=ai \
-e PGDATA=/var/lib/postgresql \
-v pgvolume:/var/lib/postgresql \
-p 5532:5432 \
--name pgvector \
agnohq/pgvector:18
docker run -d `
-e POSTGRES_DB=ai `
-e POSTGRES_USER=ai `
-e POSTGRES_PASSWORD=ai `
-e PGDATA=/var/lib/postgresql `
-v pgvolume:/var/lib/postgresql `
-p 5532:5432 `
--name pgvector `
agnohq/pgvector:18
5
Run the example
Save the code above as
durable_queue.py, then run:python durable_queue.py