research_assistant.py
"""
Pattern: Research Assistant with Tools + Learning
==================================================
A research assistant that uses web search tools and learns about the user.
This pattern combines:
- User Profile: Researcher's name, field, preferences
- User Memory: Research interests, past queries, patterns
- Tools: DuckDuckGo web search for live research
The assistant becomes more personalized over time while actively
searching the web for information.
This pattern also serves as a regression test for issue #7232:
when tools and learning are both enabled, the learning extraction
model must not see tool scaffolding (system prompts, tool_calls,
tool results) from the parent agent's conversation history.
See also: personal_assistant.py for a tools-free learning pattern.
"""
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
from agno.tools.duckduckgo import DuckDuckGoTools
# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
db = PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai")
def create_research_assistant(user_id: str, session_id: str) -> Agent:
return Agent(
model=OpenAIResponses(id="gpt-5.5"),
db=db,
instructions=(
"You are a research assistant. Search the web when asked about "
"current topics. Keep responses focused and cite sources."
),
tools=[DuckDuckGoTools()],
learning=LearningMachine(
user_profile=UserProfileConfig(
mode=LearningMode.ALWAYS,
),
user_memory=UserMemoryConfig(
mode=LearningMode.ALWAYS,
),
),
user_id=user_id,
session_id=session_id,
add_history_to_context=True,
markdown=True,
)
# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------
if __name__ == "__main__":
user_id = "researcher@example.com"
# Session 1: Introduce yourself and ask a research question
print("\n" + "=" * 60)
print("SESSION 1: Introduction + web search")
print("=" * 60 + "\n")
agent = create_research_assistant(user_id, "research_session_1")
agent.print_response(
"Hi, I'm Dr. Sarah Kim. I'm a neuroscience researcher at MIT. "
"Can you search for recent papers on brain-computer interfaces?",
stream=True,
)
lm = agent.learning_machine
print("\n--- Profile ---")
lm.user_profile_store.print(user_id=user_id)
print("\n--- Memories ---")
lm.user_memory_store.print(user_id=user_id)
# Session 2: New session — agent should remember the user
# History from session 1 (including tool calls) should not
# contaminate the learning extraction model
print("\n" + "=" * 60)
print("SESSION 2: Memory recall + another search")
print("=" * 60 + "\n")
agent = create_research_assistant(user_id, "research_session_2")
agent.print_response(
"What do you know about me? Also, search for the latest on neural implants.",
stream=True,
)
print("\n--- Profile ---")
lm.user_profile_store.print(user_id=user_id)
print("\n--- Memories ---")
lm.user_memory_store.print(user_id=user_id)
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 ddgs openai psycopg-binary 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 PgVector
docker run -d \
-e POSTGRES_DB=ai \
-e POSTGRES_USER=ai \
-e POSTGRES_PASSWORD=ai \
-e PGDATA=/var/lib/postgresql/data/pgdata \
-v pgvolume:/var/lib/postgresql/data \
-p 5532:5432 \
--name pgvector \
agnohq/pgvector:18
5
Run the example
Save the code above as
research_assistant.py, then run:python research_assistant.py