offload_tool_results set, the leader’s transcript holds a short envelope with a result id, and the full answer is stored as a file the leader can read back.
offload_member_results.py
"""
Offload Member Results
======================
A member's answer reaches the team leader as the result of the delegation
tool, so it is the payload that grows a team session. With
`offload_tool_results` set, the leader's transcript holds a short envelope with
a result id, and the full answer is stored as a file the leader can read back.
Run this and compare the printed transcript size with the size of the reports
the members actually produced.
"""
from textwrap import dedent
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIResponses
from agno.offload import ResultStore
from agno.team import Team
db = SqliteDb(db_file="tmp/platform_team.db")
# ---------------------------------------------------------------------------
# A tool with a large, boring payload: the kind of thing a member reads and
# the leader should never have to hold.
# ---------------------------------------------------------------------------
def read_deployment_log(service: str) -> str:
"""Read the full deployment log for one service.
Args:
service: The service name.
Returns:
str: The log, one line per event.
"""
lines = []
for i in range(1, 1501):
status = "ERROR connection refused" if i == 1180 else "ok"
lines.append(
f"{service} event {i:05d} worker-{i % 7} latency={i % 250}ms {status}"
)
return "\n".join(lines)
def list_platform_components() -> str:
"""List every component running on the platform.
Returns:
str: One component per line, with its owner and version.
"""
return "\n".join(
f"component-{i:04d} owner=team-{i % 9} version=1.{i % 40}.{i % 12}"
for i in range(1, 1201)
)
# ---------------------------------------------------------------------------
# Members
# ---------------------------------------------------------------------------
platform_builder = Agent(
name="Platform Builder",
id="platform-builder",
role="Builds new components on the platform",
model=OpenAIResponses(id="gpt-5.5"),
tools=[list_platform_components],
instructions=dedent("""
You build and inventory platform components. When you are asked for an
inventory, answer with the full component list, one per line, exactly
as the tool returned it, followed by the counts that answer the task.
""").strip(),
)
platform_manager = Agent(
name="Platform Manager",
id="platform-manager",
role="Owns platform health and ownership",
model=OpenAIResponses(id="gpt-5.5"),
tools=[list_platform_components],
instructions=dedent("""
You track who owns what and which versions are running. Report the
owners you were asked about with their versions.
""").strip(),
)
platform_engineer = Agent(
name="Platform Engineer",
id="platform-engineer",
role="Diagnoses deployments and incidents",
model=OpenAIResponses(id="gpt-5.5"),
tools=[read_deployment_log],
instructions=dedent("""
You read deployment logs and find what broke. Quote the failing line
with the lines on either side.
""").strip(),
)
# ---------------------------------------------------------------------------
# The team leader
#
# offload_tool_results=True stores results longer than 16,000 characters. A
# ResultStore sets the threshold and the rest; 1,500 here so both sides show
# in one run: a member's short answer stays inline, and the one carrying the
# inventory becomes an envelope the leader reads back on demand. Members run on the leader's store, so a
# member can read back a result another member produced. The read-back tools
# and the instruction that explains the envelope are added for you.
# ---------------------------------------------------------------------------
platform_team = Team(
name="Platform Team",
id="platform-team",
model=OpenAIResponses(id="gpt-5.5"),
db=db,
members=[platform_builder, platform_manager, platform_engineer],
offload_tool_results=ResultStore(threshold_chars=1500),
# Keep the member runs on the team row, so the last line of
# report_transcript_size can show what the caller reads.
store_member_responses=True,
add_history_to_context=True,
num_history_runs=5,
instructions=dedent("""
You lead the platform team.
Delegate to the right member, then answer from what they report.
""").strip(),
)
def report_transcript_size(session_id: str) -> None:
"""Print how much of the leader's transcript each tool result takes."""
run = platform_team.get_last_run_output(session_id=session_id)
print("\nLeader transcript")
total = 0
for message in run.messages or []:
size = len(message.content or "")
total += size
if message.role == "tool":
stored = (
"envelope"
if str(message.content or "").startswith("<result id=")
else "inline"
)
print(f" tool {message.tool_name}: {size} characters ({stored})")
print(f" total: {total} characters")
# Offloading changes what a model reads, never what a caller reads.
for member_run in run.member_responses or []:
print(
f" the same answer as the caller reads it: {len(str(member_run.content or ''))} characters"
)
if __name__ == "__main__":
session_id = "platform-session"
platform_team.print_response(
"Ask the platform engineer for the deployment log of the checkout service, "
"then tell me which event failed and what it says.",
session_id=session_id,
stream=True,
)
report_transcript_size(session_id)
# The builder answers with the whole inventory, so the answer itself - the
# result of the delegation tool, which is what the leader reads - crosses
# the threshold and becomes an envelope in the leader's transcript.
platform_team.print_response(
"Now ask the platform builder for the full component inventory, "
"then tell me how many components team-3 owns.",
session_id=session_id,
stream=True,
)
report_transcript_size(session_id)
print("\nStored results for this session")
for ref in platform_team.result_store.live_ids(session_id):
print(
f" {ref.result_id} from {ref.tool_name}: {ref.line_count} lines, {ref.size_bytes} bytes"
)
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 openai sqlalchemy
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 the example
Save the code above as
offload_member_results.py, then run:python offload_member_results.py