checkpoint_endpoints.py
"""Inspect a run's checkpoint timeline via the new HTTP endpoints.
Two GET endpoints expose checkpoint boundaries derived from the persisted run
(no separate checkpoint table — entries are inferred from message-level
markers + the terminal end of the transcript):
- ``GET /agents/{agent_id}/runs/{run_id}/checkpoints?session_id=...``
Returns the list of message boundaries a UI can show as resume points.
- ``GET /agents/{agent_id}/runs/{run_id}/checkpoints/{message_index}?session_id=...``
Returns a derived run snapshot truncated at that boundary. Use the
``message_index`` from the timeline as ``continue_from=K`` when resuming.
This cookbook runs an AgentOS in-process via ``fastapi.testclient.TestClient``
so the example is self-contained — no separate server, no port binding.
"""
import json
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIResponses
from agno.os import AgentOS
from fastapi.testclient import TestClient
def get_population(city: str) -> str:
"""Mock population lookup."""
data = {"Paris": "2.1M", "Tokyo": "13.9M", "Lagos": "15.3M"}
return data.get(city, "unknown")
def main() -> None:
agent = Agent(
id="travel-agent",
name="travel-agent",
model=OpenAIResponses(id="gpt-5.4"),
db=SqliteDb(
session_table="checkpoint_endpoints_demo",
db_file="tmp/checkpoint_endpoints.db",
),
checkpoint="tool-batch",
tools=[get_population],
)
agent_os = AgentOS(description="checkpoint-endpoints demo", agents=[agent])
app = agent_os.get_app()
client = TestClient(app)
# 1. Drive a run that produces a few tool batches so the timeline has
# real checkpoints to show. Each tool batch writes a checkpoint
# marker on the boundary message.
run = agent.run(
input="Compare the populations of Paris, Tokyo, and Lagos in one sentence.",
)
print("Created run")
print(" run_id: ", run.run_id)
print(" session_id:", run.session_id)
print(" messages: ", len(run.messages or []))
print()
# 2. GET /checkpoints — the FE-friendly timeline.
timeline = client.get(
f"/agents/{agent.id}/runs/{run.run_id}/checkpoints",
params={"session_id": run.session_id},
)
print(f"GET /agents/{agent.id}/runs/{run.run_id}/checkpoints")
print(f" status: {timeline.status_code}")
print(" body:")
print(json.dumps(timeline.json(), indent=2, default=str))
print()
# 3. Pick a non-terminal boundary from the timeline and fetch the
# derived snapshot at that index. A snapshot is a truncated copy of
# the persisted run — the stored row is NOT mutated.
checkpoints = timeline.json()["checkpoints"]
interior = [c for c in checkpoints if not c.get("is_latest")]
if interior:
target = interior[0]
snapshot_idx = target["message_index"]
snap = client.get(
f"/agents/{agent.id}/runs/{run.run_id}/checkpoints/{snapshot_idx}",
params={"session_id": run.session_id},
)
print(f"GET /agents/{agent.id}/runs/{run.run_id}/checkpoints/{snapshot_idx}")
print(f" status: {snap.status_code}")
payload = snap.json()
print(" checkpoint metadata:")
print(json.dumps(payload["checkpoint"], indent=2, default=str))
print(
f" snapshot.messages: {len(payload['snapshot'].get('messages') or [])} (truncated)"
)
print(
f" snapshot.tools: {len(payload['snapshot'].get('tools') or [])} (only those referenced)"
)
print()
# 4. The returned message_index plugs directly into /continue.
# Show how a UI would use it: list checkpoints, let the user pick
# one, resume from there.
cont = client.post(
f"/agents/{agent.id}/runs/{run.run_id}/continue",
data={
"session_id": run.session_id,
"continue_from": str(snapshot_idx),
"input": "Actually, just tell me about Paris.",
"stream": "false",
},
)
print(
f"POST /agents/{agent.id}/runs/{run.run_id}/continue (continue_from={snapshot_idx})"
)
print(f" status: {cont.status_code}")
if cont.status_code == 200:
body = cont.json()
print(f" new run_id: {body.get('run_id')}")
print(f" forked_from_run_id: {body.get('forked_from_run_id')}")
print(
f" forked_from_message_index:{body.get('forked_from_message_index')}"
)
else:
print("(No interior checkpoints found — the run had a single turn.)")
print("Try a multi-tool prompt to populate the timeline with more boundaries.")
if __name__ == "__main__":
main()
Run the Example
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
Export your API keys
export JWT_VERIFICATION_KEY="your_jwt_verification_key_here"
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:JWT_VERIFICATION_KEY="your_jwt_verification_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"