Extraction Limits: Preventing Runaway Loops
Configure max_updates_per_run to cap memory updates per extraction.
"""
Extraction Limits: Preventing Runaway Loops
============================================
Configure max_updates_per_run to cap memory updates per extraction.
When learning stores extract information, they call tools (add_memory,
update_profile, etc.) in a loop. Without limits, a model that keeps
requesting tools can loop indefinitely.
max_updates_per_run caps tool executions:
- LearningMachine level: applies to all stores (default: 10)
- Store config level: overrides the global for that store
"""
from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.learn import (
LearningMachine,
LearningMode,
UserMemoryConfig,
UserProfileConfig,
)
from agno.models.openai import OpenAIResponses
# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
db = PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai")
# Global max_updates_per_run=5 applies to all stores unless overridden.
# user_profile: inherits 5 from LearningMachine
# user_memory: explicit override to 3
agent = Agent(
model=OpenAIResponses(id="gpt-5.5"),
db=db,
learning=LearningMachine(
max_updates_per_run=5,
user_profile=UserProfileConfig(mode=LearningMode.ALWAYS),
user_memory=UserMemoryConfig(mode=LearningMode.ALWAYS, max_updates_per_run=3),
),
markdown=True,
debug_mode=True, # Shows "Tool call limit reached" logs
)
# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------
if __name__ == "__main__":
user_id = "demo@example.com"
session_id = "extraction-limits-demo"
# Dense prompt with lots of information to extract
print("\n" + "=" * 70)
print("DENSE INFO DUMP (triggers many extraction attempts)")
print("=" * 70)
print("User profile limit: 5 (global)")
print("User memory limit: 3 (override)")
print("Entity memory limit: 15 (override)")
print("=" * 70 + "\n")
agent.print_response(
"Hi, I'm Sarah Chen, VP of Engineering at TechCorp. "
"I prefer detailed technical explanations with code examples. "
"I work remotely from Seattle and focus on distributed systems. "
"Quick context on our team: "
"Marcus Lee is our CTO, he reports to CEO Jane Smith. "
"Alice Wang leads Backend, Bob Martinez leads DevOps. "
"We use PostgreSQL, Redis, and Kubernetes. "
"Last week we migrated to AWS us-west-2. "
"Our Series B closed at $50M last month.",
user_id=user_id,
session_id=session_id,
stream=True,
)
# Show what was captured
lm = agent.learning_machine
print("\n" + "=" * 70)
print("EXTRACTION RESULTS")
print("=" * 70)
print("\n--- User Profile (limit: 5) ---")
lm.user_profile_store.print(user_id=user_id)
print("\n--- User Memory (limit: 3) ---")
lm.user_memory_store.print(user_id=user_id)The configured stores are user profile (limit 5) and user memory (limit 3). The source’s printed “Entity memory limit: 15” line is stale: no entity store is configured. These are limits on each extraction model’s tool calls; they do not cap the main agent’s tool calls or establish a shared budget across stores.
Run the Example
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activateInstall dependencies
uv pip install -U agno "psycopg[binary]" openai sqlalchemyExport your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"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:18Run the example
Save the code above as extraction_limits.py, then run:
python extraction_limits.pyFull source: cookbook/08_learning/01_basics/6_extraction_limits.py