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|---|---|---|---|---|---|---|---|---|
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/reasoning/02_async_multi_purpose_reasoning_team.py | cookbook/04_teams/reasoning/02_async_multi_purpose_reasoning_team.py | """
This example demonstrates an async multi-purpose reasoning team.
The team uses reasoning tools to analyze questions and delegate to appropriate
specialist agents asynchronously, showcasing coordination and intelligent task routing.
"""
import asyncio
from pathlib import Path
from agno.agent import Agent
from agn... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/reasoning/__init__.py | cookbook/04_teams/reasoning/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/reasoning/01_reasoning_multi_purpose_team.py | cookbook/04_teams/reasoning/01_reasoning_multi_purpose_team.py | """
This example demonstrates a team of agents that can answer a variety of questions.
The team uses reasoning tools to reason about the questions and delegate to the appropriate agent.
The team consists of:
- A web agent that can search the web for information
- A finance agent that can get financial data
- A writer... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/distributed_rag/01_distributed_rag_pgvector.py | cookbook/04_teams/distributed_rag/01_distributed_rag_pgvector.py | """
This example demonstrates how multiple specialized agents coordinate to provide
comprehensive RAG responses using distributed PostgreSQL vector databases with
pgvector for scalable, production-ready retrieval.
Team Composition:
- Vector Retriever: Specialized in vector similarity search using pgvector
- Hybrid Sea... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/distributed_rag/03_distributed_rag_with_reranking.py | cookbook/04_teams/distributed_rag/03_distributed_rag_with_reranking.py | """
This example demonstrates how multiple specialized agents coordinate to provide
comprehensive RAG responses using advanced reranking strategies for optimal
information retrieval and synthesis.
Team Composition:
- Initial Retriever: Performs broad initial retrieval from knowledge base
- Reranking Specialist: Applie... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/distributed_rag/__init__.py | cookbook/04_teams/distributed_rag/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/distributed_rag/02_distributed_rag_lancedb.py | cookbook/04_teams/distributed_rag/02_distributed_rag_lancedb.py | """
This example demonstrates how multiple specialized agents coordinate to provide
comprehensive RAG responses using distributed knowledge bases and specialized
retrieval strategies with LanceDB.
Team Composition:
- Primary Retriever: Handles primary document retrieval from main knowledge base
- Context Expander: Exp... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/session/01_persistent_session.py | cookbook/04_teams/session/01_persistent_session.py | from agno.agent.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.openai import OpenAIChat
from agno.team import Team
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
db = PostgresDb(db_url=db_url, session_table="sessions")
agent = Agent(model=OpenAIChat(id="o3-mini"))
team = Team(
... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/session/02_persistent_session_history.py | cookbook/04_teams/session/02_persistent_session_history.py | """
This example shows how to use the session history to store the conversation history.
add_history_to_context flag is used to add the history to the messages.
num_history_runs is used to set the number of history runs to add to the messages.
"""
from agno.agent.agent import Agent
from agno.db.postgres import Postgre... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/session/05_chat_history.py | cookbook/04_teams/session/05_chat_history.py | from agno.agent.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.openai import OpenAIChat
from agno.team import Team
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
db = PostgresDb(db_url=db_url, session_table="sessions")
agent = Agent(
model=OpenAIChat(id="o3-mini"),
)
team =... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/session/06_rename_session.py | cookbook/04_teams/session/06_rename_session.py | from agno.agent.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.openai import OpenAIChat
from agno.team import Team
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
db = PostgresDb(db_url=db_url)
agent = Agent(
model=OpenAIChat(id="o3-mini"),
)
team = Team(
model=OpenAICha... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/session/09_share_session_with_agent.py | cookbook/04_teams/session/09_share_session_with_agent.py | """
This example shows how a session can be shared between a team and an agent.
This is useful if you have a team that is more scalable (since it manages the size of the context more efficiently), but want to sometimes use a single agent for speed.
A single session is shared across all the runs below.
"""
import uui... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/session/03_session_summary.py | cookbook/04_teams/session/03_session_summary.py | """
This example shows how to use the session summary to store the conversation summary.
"""
from agno.agent.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.openai import OpenAIChat
from agno.session.summary import SessionSummaryManager # noqa: F401
from agno.team import Team
db_url = "po... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/session/11_search_session_history.py | cookbook/04_teams/session/11_search_session_history.py | import asyncio
import os
from agno.db.sqlite import AsyncSqliteDb
from agno.models.openai import OpenAIChat
from agno.team import Team
# Remove the tmp db file before running the script
if os.path.exists("tmp/data.db"):
os.remove("tmp/data.db")
# Create agents for different users to demonstrate user-specific ses... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/session/10_async_session_summary.py | cookbook/04_teams/session/10_async_session_summary.py | """
This example demonstrates how to use the async `aget_session_summary()` method
to retrieve session summaries from teams in async workflows.
Start the postgres db locally on Docker by running: cookbook/scripts/run_pgvector.sh
"""
import asyncio
from agno.agent.agent import Agent
from agno.db.postgres import Async... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/session/04_session_summary_references.py | cookbook/04_teams/session/04_session_summary_references.py | """
This example shows how to use the `add_session_summary_to_context` parameter in the Team config to
add session summaries to the Team context.
Start the postgres db locally on Docker by running: cookbook/scripts/run_pgvector.sh
"""
from agno.agent.agent import Agent
from agno.db.postgres import PostgresDb
from agn... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/session/__init__.py | cookbook/04_teams/session/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/session/09_history_num_messages.py | cookbook/04_teams/session/09_history_num_messages.py | """
This example shows how to use the session history to store the conversation history.
add_history_to_context flag is used to add the history to the messages.
num_history_runs is used to set the number of history runs to add to the messages.
"""
from agno.agent.agent import Agent
from agno.db.postgres import Postgre... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/session/08_cache_session.py | cookbook/04_teams/session/08_cache_session.py | """Example of how to cache the team session in memory for faster access."""
from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.openai import OpenAIChat
from agno.team import Team
# Setup the database
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
db = PostgresDb(db_url=db_u... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/session/07_in_memory_db.py | cookbook/04_teams/session/07_in_memory_db.py | """This example shows how to use an in-memory database with teams.
With this you will be able to store team sessions, user memories, etc. without setting up a database.
Keep in mind that in production setups it is recommended to use a database.
"""
from agno.agent import Agent
from agno.db.in_memory import InMemoryDb... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/search_coordination/03_distributed_infinity_search.py | cookbook/04_teams/search_coordination/03_distributed_infinity_search.py | """
This example demonstrates how multiple agents coordinate to perform distributed
search using Infinity reranker for high-performance ranking across team members.
Team Composition:
- Primary Searcher: Performs initial broad search with infinity reranking
- Secondary Searcher: Performs targeted search on specific top... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/search_coordination/01_coordinated_agentic_rag.py | cookbook/04_teams/search_coordination/01_coordinated_agentic_rag.py | """
This example demonstrates how multiple specialized agents can coordinate to provide
comprehensive RAG (Retrieval-Augmented Generation) responses by dividing search
and analysis tasks across team members.
Team Composition:
- Knowledge Searcher: Searches knowledge base for relevant information
- Content Analyzer: An... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/search_coordination/__init__.py | cookbook/04_teams/search_coordination/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/search_coordination/02_coordinated_reasoning_rag.py | cookbook/04_teams/search_coordination/02_coordinated_reasoning_rag.py | """
This example demonstrates how multiple specialized agents coordinate to provide
comprehensive RAG responses with distributed reasoning capabilities. Each agent
has specific reasoning responsibilities to ensure thorough analysis.
Team Composition:
- Information Gatherer: Searches knowledge base and gathers raw info... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/multimodal/image_to_structured_output.py | cookbook/04_teams/multimodal/image_to_structured_output.py | from typing import List
from agno.agent import Agent
from agno.media import Image
from agno.models.openai import OpenAIChat
from agno.team import Team
from pydantic import BaseModel, Field
from rich.pretty import pprint
class MovieScript(BaseModel):
name: str = Field(..., description="Give a name to this movie")... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/multimodal/image_to_text.py | cookbook/04_teams/multimodal/image_to_text.py | from pathlib import Path
from agno.agent import Agent
from agno.media import Image
from agno.models.openai import OpenAIChat
from agno.team import Team
image_analyzer = Agent(
name="Image Analyst",
role="Analyze and describe images in detail",
model=OpenAIChat(id="gpt-4o"),
instructions=[
"Ana... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/multimodal/audio_sentiment_analysis.py | cookbook/04_teams/multimodal/audio_sentiment_analysis.py | import requests
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.media import Audio
from agno.models.google import Gemini
from agno.team import Team
transcription_agent = Agent(
name="Audio Transcriber",
role="Transcribe audio conversations accurately",
model=Gemini(id="gemini-2.0... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/multimodal/generate_image_with_team.py | cookbook/04_teams/multimodal/generate_image_with_team.py | from typing import Iterator
from agno.agent import Agent, RunOutputEvent
from agno.models.openai import OpenAIChat
from agno.team import Team
from agno.tools.dalle import DalleTools
from agno.utils.common import dataclass_to_dict
from rich.pretty import pprint
image_generator = Agent(
name="Image Creator",
ro... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/multimodal/audio_to_text.py | cookbook/04_teams/multimodal/audio_to_text.py | import requests
from agno.agent import Agent
from agno.media import Audio
from agno.models.google import Gemini
from agno.team import Team
transcription_specialist = Agent(
name="Transcription Specialist",
role="Convert audio to accurate text transcriptions",
model=Gemini(id="gemini-2.0-flash-exp"),
in... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/multimodal/__init__.py | cookbook/04_teams/multimodal/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/multimodal/video_caption_generation.py | cookbook/04_teams/multimodal/video_caption_generation.py | """Please install dependencies using:
pip install openai moviepy ffmpeg
"""
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.team import Team
from agno.tools.moviepy_video import MoviePyVideoTools
from agno.tools.openai import OpenAITools
video_processor = Agent(
name="Video Proces... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/multimodal/image_to_image_transformation.py | cookbook/04_teams/multimodal/image_to_image_transformation.py | from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.team import Team
from agno.tools.fal import FalTools
style_advisor = Agent(
name="Style Advisor",
role="Analyze and recommend artistic styles and transformations",
model=OpenAIChat(id="gpt-4o"),
instructions=[
"Ana... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/multimodal/media_input_for_tool.py | cookbook/04_teams/multimodal/media_input_for_tool.py | """
Example showing how team tools can access media (images, videos, audio, files) passed to the team.
This demonstrates:
1. Uploading a PDF file to a team
2. A team tool that can access and process the uploaded file (OCR simulation)
3. The team leader responding directly without delegating to member agents
"""
from ... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/structured_input_output/00_pydantic_model_output.py | cookbook/04_teams/structured_input_output/00_pydantic_model_output.py | from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.team import Team
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.utils.pprint import pprint_run_response
from pydantic import BaseModel
class StockAnalysis(BaseModel):
symbol: str
company_name: str
analysis: str
... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/structured_input_output/04_structured_output_streaming.py | cookbook/04_teams/structured_input_output/04_structured_output_streaming.py | from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.team.team import Team
from agno.tools.yfinance import YFinanceTools
from pydantic import BaseModel
class StockAnalysis(BaseModel):
symbol: str
company_name: str
analysis: str
stock_searcher = Agent(
name="Stock Searcher... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/structured_input_output/02_team_with_parser_model.py | cookbook/04_teams/structured_input_output/02_team_with_parser_model.py | import random
from typing import Iterator, List # noqa
from agno.agent import Agent, RunOutput, RunOutputEvent # noqa
from agno.models.anthropic import Claude
from agno.models.openai import OpenAIChat
from agno.team import Team
from pydantic import BaseModel, Field
from rich.pretty import pprint
class NationalPark... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/structured_input_output/07_output_schema_override.py | cookbook/04_teams/structured_input_output/07_output_schema_override.py | """
This example demonstrates output_schema override for teams.
Shows how to temporarily change the output schema for a single run
and have it automatically restored afterwards. Works with both
run/arun and streaming/non-streaming modes.
"""
import asyncio
from agno.agent import Agent
from agno.models.openai import ... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/structured_input_output/05_async_structured_output_streaming.py | cookbook/04_teams/structured_input_output/05_async_structured_output_streaming.py | """
This example demonstrates async structured output streaming from a team.
The team uses Pydantic models to ensure structured responses while streaming,
providing both real-time output and validated data structures asynchronously.
"""
import asyncio
from typing import Iterator # noqa
from agno.agent import Agent
... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/structured_input_output/06_input_schema_on_team.py | cookbook/04_teams/structured_input_output/06_input_schema_on_team.py | """
This example demonstrates how to use input_schema with teams for automatic
input validation and structured data handling.
The input_schema feature allows teams to automatically validate and convert
dictionary inputs into Pydantic models, ensuring type safety and data validation.
"""
from typing import List
from ... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/structured_input_output/08_json_schema_output.py | cookbook/04_teams/structured_input_output/08_json_schema_output.py | """
Example showing how to use JSON as output schema.
Note: JSON schemas must be in the provider's expected format.
For example, OpenAI expects:
{"type": "json_schema", "json_schema": {"name": "...", "schema": {...}}}
"""
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.team import Tea... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/structured_input_output/03_team_with_output_model.py | cookbook/04_teams/structured_input_output/03_team_with_output_model.py | """
This example shows how to use the output_model parameter to specify the model that should be used to generate the final response.
"""
from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.models.openai import OpenAIChat
from agno.team import Team
from agno.tools.duckduckgo import DuckDuck... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/structured_input_output/__init__.py | cookbook/04_teams/structured_input_output/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/structured_input_output/01_pydantic_model_as_input.py | cookbook/04_teams/structured_input_output/01_pydantic_model_as_input.py | """
This example demonstrates how to use Pydantic models as input to teams.
Shows how structured data can be passed as messages to teams for more
precise and validated input handling.
"""
from typing import List
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.team.team import Team
fr... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/basic_flows/01_basic_coordination.py | cookbook/04_teams/basic_flows/01_basic_coordination.py | """
1. Run: `pip install openai ddgs newspaper4k lxml_html_clean agno` to install the dependencies
2. Run: `python cookbook/teams/async/modes/02_async_coordinate.py` to run the agent
This example demonstrates a coordinated team of AI agents working together to research topics across different platforms.
The team cons... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/basic_flows/03_delegate_to_all_members_cooperation.py | cookbook/04_teams/basic_flows/03_delegate_to_all_members_cooperation.py | """
This example demonstrates a collaborative team of AI agents working together to research topics across different platforms.
The team consists of two specialized agents:
1. Reddit Researcher - Uses DuckDuckGo to find and analyze relevant Reddit posts
2. HackerNews Researcher - Uses HackerNews API to find and analyz... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/basic_flows/05_team_history.py | cookbook/04_teams/basic_flows/05_team_history.py | """
This example demonstrates a team where the team leader routes requests to the appropriate member, and the members respond directly to the user.
In addition each team member has access to the shared history of the team.
"""
from uuid import uuid4
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
fr... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/basic_flows/02_respond_directly_router_team.py | cookbook/04_teams/basic_flows/02_respond_directly_router_team.py | """
This example demonstrates a team of AI agents working together to answer questions in different languages.
The team consists of six specialized agents:
1. English Agent - Can only answer in English
2. Japanese Agent - Can only answer in Japanese
3. Chinese Agent - Can only answer in Chinese
4. Spanish Agent - Can ... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/basic_flows/__init__.py | cookbook/04_teams/basic_flows/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/basic_flows/04_respond_directly_with_history.py | cookbook/04_teams/basic_flows/04_respond_directly_with_history.py | """
This example demonstrates a team where the team leader routes requests to the appropriate member, and the members respond directly to the user.
In addition each team member has access to the shared history of the team.
"""
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai im... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/basic_flows/06_history_of_members.py | cookbook/04_teams/basic_flows/06_history_of_members.py | """
This example demonstrates a team where the team leader routes requests to the appropriate member, and the members respond directly to the user.
In addition each team member has access to it's own history.
Note: In this example the team leader has no access to the history of the team or the members themselves, so t... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/basic_flows/07_share_member_interactions.py | cookbook/04_teams/basic_flows/07_share_member_interactions.py | """
This example demonstrates a team where the team leader routes requests to the appropriate member, and the members respond directly to the user.
In addition each team member has access to the shared history of the team.
"""
from uuid import uuid4
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
fr... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/basic_flows/caching/cache_team_response.py | cookbook/04_teams/basic_flows/caching/cache_team_response.py | """
Example showing how to cache team leader model responses.
Run this cookbook twice to see the difference in response time.
The first time should take a while to run.
The second time should be instant.
This demonstrates:
1. Team leader caching (coordination decisions)
2. Member agent caching (individual responses)... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/context_management/filter_tool_calls_from_history.py | cookbook/04_teams/context_management/filter_tool_calls_from_history.py | """
Research Team with Context Management.
This example demonstrates a research team that uses DuckDuckGo to search the web and accumulate tool calls. The older research results
are automatically filtered out to keep the context focused on recent research results.
When max_tool_calls_from_history is set to 3, the tea... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/context_management/introduction.py | cookbook/04_teams/context_management/introduction.py | from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIChat
from agno.team import Team
db = SqliteDb(db_file="tmp/teams.db", session_table="team_sessions")
INTRODUCTION = """Hello, I'm your personal assistant. I can help you only with questions related to mountain climbi... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/context_compression/tool_call_compression_with_manager.py | cookbook/04_teams/context_compression/tool_call_compression_with_manager.py | """
Research Team with Context Management.
This example demonstrates a research team that uses DuckDuckGo to search the web and accumulate tool calls. The older research results
are automatically compressed to keep the context focused on recent research results.
"""
from textwrap import dedent
from agno.agent import... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/context_compression/tool_call_compression.py | cookbook/04_teams/context_compression/tool_call_compression.py | from textwrap import dedent
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.aws import AwsBedrock
from agno.team.team import Team
from agno.tools.duckduckgo import DuckDuckGoTools
# Create specialized agents
tech_researcher = Agent(
name="Alex",
role="Technology Researcher",
... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/context_compression/async_tool_call_compression.py | cookbook/04_teams/context_compression/async_tool_call_compression.py | import asyncio
from textwrap import dedent
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIChat
from agno.team.team import Team
from agno.tools.duckduckgo import DuckDuckGoTools
# Create specialized agents
tech_specialist = Agent(
name="Tech Specialist",
r... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/other/few_shot_learning.py | cookbook/04_teams/other/few_shot_learning.py | """
This example shows a straightforward use case of additional_input
to teach a customer support team proper response patterns.
"""
from agno.agent import Agent
from agno.models.message import Message
from agno.models.openai.chat import OpenAIChat
from agno.team import Team
# Few-shot examples showing good support p... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/other/input_as_dict.py | cookbook/04_teams/other/input_as_dict.py | from agno.agent import Agent
from agno.team import Team
# Create a research team
team = Team(
members=[
Agent(
name="Sarah",
role="Data Researcher",
instructions="Focus on gathering and analyzing data",
),
Agent(
name="Mike",
role=... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/other/team_model_inheritance.py | cookbook/04_teams/other/team_model_inheritance.py | """
This example demonstrates how agents automatically inherit models from their Team.
This is particularly useful when:
- Using non-OpenAI models (Claude, Ollama, VLLM, etc.) to avoid manual model configuration on every agent
- Preventing API key errors when team uses a different provider than the default OpenAI
- Si... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/other/run_as_cli.py | cookbook/04_teams/other/run_as_cli.py | """✍️ Interactive Writing Team - CLI App Example
This example shows how to create an interactive CLI app with a collaborative writing team.
Run `pip install openai agno duckduckgo-search` to install dependencies.
"""
from textwrap import dedent
from agno.agent import Agent
from agno.models.openai import OpenAIChat
... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/other/input_as_messages_list.py | cookbook/04_teams/other/input_as_messages_list.py | from agno.agent import Agent
from agno.models.message import Message
from agno.team import Team
# Create a research team
research_team = Team(
name="Research Team",
members=[
Agent(
name="Sarah",
role="Data Researcher",
instructions="Focus on gathering and analyzing ... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/other/__init__.py | cookbook/04_teams/other/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/other/team_retries.py | cookbook/04_teams/other/team_retries.py | """Example demonstrating how to set up retries with a Team."""
from agno.agent import Agent
from agno.team import Team
from agno.tools.duckduckgo import DuckDuckGoTools
# Create a research team
team = Team(
members=[
Agent(
name="Sarah",
role="Data Researcher",
tools=[D... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/other/team_exponential_backoff.py | cookbook/04_teams/other/team_exponential_backoff.py | from agno.agent import Agent
from agno.team import Team
from agno.tools.duckduckgo import DuckDuckGoTools
# Create a research team
team = Team(
members=[
Agent(
name="Sarah",
role="Data Researcher",
tools=[DuckDuckGoTools()],
instructions="Focus on gathering ... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/other/response_as_variable.py | cookbook/04_teams/other/response_as_variable.py | """
This example demonstrates how to capture team responses as variables.
Shows how to get structured responses from teams and validate them using
Pydantic models for different types of queries.
"""
from typing import Iterator # noqa
from pydantic import BaseModel
from agno.agent import Agent
from agno.models.openai... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/other/input_as_list.py | cookbook/04_teams/other/input_as_list.py | from agno.agent import Agent
from agno.team import Team
team = Team(
members=[
Agent(
name="Sarah",
role="Data Researcher",
instructions="Focus on gathering and analyzing data",
),
Agent(
name="Mike",
role="Technical Writer",
... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/other/team_model_string.py | cookbook/04_teams/other/team_model_string.py | from agno.agent import Agent
from agno.team import Team
from agno.tools.duckduckgo import DuckDuckGoTools
# Model strings follow the format "{provider}:{model_id}", for example:
model_string = "openai:gpt-4o-mini"
# Create a research team
team = Team(
model=model_string,
members=[
Agent(
m... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/04_teams/other/team_cancel_a_run.py | cookbook/04_teams/other/team_cancel_a_run.py | """
Example demonstrating how to cancel a running team execution.
This example shows how to:
1. Start a team run in a separate thread
2. Cancel the run from another thread
3. Handle the cancelled response
"""
import threading
import time
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agn... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/00_getting_started/run.py | cookbook/00_getting_started/run.py | from pathlib import Path
from agent_search_over_knowledge import agent_with_knowledge
from agent_with_guardrails import agent_with_guardrails
from agent_with_memory import agent_with_memory
from agent_with_state_management import agent_with_state_management
from agent_with_storage import agent_with_storage
from agent_... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/00_getting_started/agent_with_state_management.py | cookbook/00_getting_started/agent_with_state_management.py | """
Agent with State Management - Finance Agent with Watchlist
===========================================================
This example shows how to give your agent persistent state that it can
read and modify. The agent maintains a stock watchlist across conversations.
Different from storage (conversation history) an... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/00_getting_started/human_in_the_loop.py | cookbook/00_getting_started/human_in_the_loop.py | """
Human in the Loop - Confirm Before Taking Action
================================================
This example shows how to require user confirmation before executing
certain tools. Critical for actions that are irreversible or sensitive.
We'll build on our self-learning agent, and ask for user confirmation before... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/00_getting_started/multi_agent_team.py | cookbook/00_getting_started/multi_agent_team.py | """
Multi-Agent Team - Investment Research Team
============================================
This example shows how to create a team of agents that work together.
Each agent has a specialized role, and the team leader coordinates.
We'll build an investment research team with opposing perspectives:
- Bull Agent: Makes ... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/00_getting_started/custom_tool_for_self_learning.py | cookbook/00_getting_started/custom_tool_for_self_learning.py | """
Custom Tool for Self-Learning - Write Your Own Tools
=====================================================
This example shows how to write custom tools for your agent.
A tool is just a Python function — the agent calls it when needed.
We'll build a self-learning agent that can save insights to a knowledge base.
Th... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/00_getting_started/agent_search_over_knowledge.py | cookbook/00_getting_started/agent_search_over_knowledge.py | """
Agentic Search over Knowledge - Agent with a Knowledge Base
============================================================
This example shows how to give an agent a searchable knowledge base.
The agent can search through documents (PDFs, text, URLs) to answer questions.
Key concepts:
- Knowledge: A searchable collec... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/00_getting_started/agent_with_structured_output.py | cookbook/00_getting_started/agent_with_structured_output.py | """
Agent with Structured Output - Finance Agent with Typed Responses
==================================================================
This example shows how to get structured, typed responses from your agent.
Instead of free-form text, you get a Pydantic model you can trust.
Perfect for building pipelines, UIs, or ... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/00_getting_started/agent_with_memory.py | cookbook/00_getting_started/agent_with_memory.py | """
Agent with Memory - Finance Agent that Remembers You
=====================================================
This example shows how to give your agent memory of user preferences.
The agent remembers facts about you across all conversations.
Different from storage (which persists conversation history), memory
persist... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/00_getting_started/sequential_workflow.py | cookbook/00_getting_started/sequential_workflow.py | """
Sequential Workflow - Stock Research Pipeline
==============================================
This example shows how to create a workflow with sequential steps.
Each step is handled by a specialized agent, and outputs flow to the next step.
Different from Teams (agents collaborate dynamically), Workflows give you
e... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/00_getting_started/agent_with_storage.py | cookbook/00_getting_started/agent_with_storage.py | """
Agent with Storage - Finance Agent with Storage
====================================================
Building on the Finance Agent from 01, this example adds persistent storage.
Your agent now remembers conversations across runs.
Ask about NVDA, close the script, come back later — pick up where you left off.
The c... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/00_getting_started/__init__.py | cookbook/00_getting_started/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/00_getting_started/readme_examples.py | cookbook/00_getting_started/readme_examples.py | """Readme Examples
Run `pip install openai ddgs yfinance lancedb tantivy pypdf agno` to install dependencies."""
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIChat
from agno.team.team import Tea... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/00_getting_started/agent_with_typed_input_output.py | cookbook/00_getting_started/agent_with_typed_input_output.py | """
Agent with Typed Input and Output - Full Type Safety
=====================================================
This example shows how to define both input and output schemas for your agent.
You get end-to-end type safety: validate what goes in, guarantee what comes out.
Perfect for building robust pipelines where you ... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/00_getting_started/agent_with_guardrails.py | cookbook/00_getting_started/agent_with_guardrails.py | """
Agent with Guardrails - Input Validation and Safety
====================================================
This example shows how to add guardrails to your agent to validate input
before processing. Guardrails can block, modify, or flag problematic requests.
We'll demonstrate:
1. Built-in guardrails (PII detection, ... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/00_getting_started/agent_with_tools.py | cookbook/00_getting_started/agent_with_tools.py | """
Agent with Tools - Finance Agent
=================================
Your first Agno agent: a data-driven financial analyst that retrieves
market data, computes key metrics, and delivers concise insights.
This example shows how to give an agent tools to interact with external
data sources. The agent uses YFinanceToo... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/03_agents/__init__.py | cookbook/03_agents/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/03_agents/state/last_n_session_messages.py | cookbook/03_agents/state/last_n_session_messages.py | import asyncio
import os
from agno.agent import Agent
from agno.db.sqlite import AsyncSqliteDb
from agno.models.openai import OpenAIChat
# Remove the tmp db file before running the script
if os.path.exists("tmp/data.db"):
os.remove("tmp/data.db")
# Create agents for different users to demonstrate user-specific s... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/03_agents/state/session_state_multiple_users.py | cookbook/03_agents/state/session_state_multiple_users.py | """
This example demonstrates how to maintain state for each user in a multi-user environment.
The shopping list is stored in a dictionary, organized by user ID and session ID.
Agno automatically creates the "current_user_id" and "current_session_id" variables in the session state.
You can access these variables in ... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/03_agents/state/agentic_session_state.py | cookbook/03_agents/state/agentic_session_state.py | from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIChat
db = SqliteDb(db_file="tmp/agents.db")
agent = Agent(
model=OpenAIChat(id="o3-mini"),
db=db,
session_state={"shopping_list": []},
add_session_state_to_context=True, # Required so the agent is awa... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/03_agents/state/session_state_in_instructions.py | cookbook/03_agents/state/session_state_in_instructions.py | from agno.agent import Agent
from agno.models.openai import OpenAIChat
agent = Agent(
model=OpenAIChat(id="gpt-4o-mini"),
# Initialize the session state with a variable
session_state={"user_name": "John"},
# You can use variables from the session state in the instructions
instructions="Users name i... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/03_agents/state/session_state_basic.py | cookbook/03_agents/state/session_state_basic.py | from agno.agent import Agent, RunOutput # noqa
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIChat
from agno.run import RunContext
def add_item(run_context: RunContext, item: str) -> str:
"""Add an item to the shopping list."""
if run_context.session_state is None:
run_conte... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/03_agents/state/session_state_advanced.py | cookbook/03_agents/state/session_state_advanced.py | from textwrap import dedent
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIChat
from agno.run import RunContext
# Define tools to manage our shopping list
def add_item(run_context: RunContext, item: str) -> str:
"""Add an item to the shopping list and return... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/03_agents/state/__init__.py | cookbook/03_agents/state/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/03_agents/state/session_state_in_event.py | cookbook/03_agents/state/session_state_in_event.py | from agno.agent import Agent, RunCompletedEvent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIChat
from agno.run import RunContext
def add_item(run_context: RunContext, item: str) -> str:
"""Add an item to the shopping list."""
if run_context.session_state is None:
run_conte... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/03_agents/state/change_state_on_run.py | cookbook/03_agents/state/change_state_on_run.py | from agno.agent import Agent
from agno.db.in_memory import InMemoryDb
from agno.models.openai import OpenAIChat
agent = Agent(
db=InMemoryDb(),
model=OpenAIChat(id="gpt-4o-mini"),
instructions="Users name is {user_name} and age is {age}",
debug_mode=True,
)
# Sets the session state for the session wit... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/03_agents/state/manual_session_state_update.py | cookbook/03_agents/state/manual_session_state_update.py | from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIChat
from agno.run import RunContext
def add_item(run_context: RunContext, item: str) -> str:
"""Add an item to the shopping list."""
if run_context.session_state is None:
run_context.session_state = ... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/03_agents/state/session_state_in_context.py | cookbook/03_agents/state/session_state_in_context.py | from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.openai import OpenAIChat
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
db = PostgresDb(db_url=db_url, session_table="sessions")
agent = Agent(
model=OpenAIChat(id="gpt-4o-mini"),
instructions="Users name is {user... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/03_agents/state/dynamic_session_state.py | cookbook/03_agents/state/dynamic_session_state.py | import json
from typing import Any, Dict
from agno.agent import Agent
from agno.db.in_memory import InMemoryDb
from agno.models.openai import OpenAIChat
from agno.run import RunContext
from agno.tools.toolkit import Toolkit
from agno.utils.log import log_info, log_warning
class CustomerDBTools(Toolkit):
def __in... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/03_agents/state/overwrite_stored_session_state.py | cookbook/03_agents/state/overwrite_stored_session_state.py | """This example demonstrates how to overwrite the stored session_state with the session_state provided in the run."""
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIChat
# Create an Agent that maintains state
agent = Agent(
model=OpenAIChat(id="gpt-4o-mini"),... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/03_agents/culture/04_manually_add_culture.py | cookbook/03_agents/culture/04_manually_add_culture.py | """
Manually add cultural knowledge to your Agents.
This example shows how to persist cultural knowledge WITHOUT invoking a model.
Use this to seed cultural knowledge for your Agents and Multi-Agent Teams.
Cultural knowledge represents reusable insights, rules, and values that Agents
can reference to stay consistent ... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/03_agents/culture/02_use_cultural_knowledge_in_agent.py | cookbook/03_agents/culture/02_use_cultural_knowledge_in_agent.py | """Use cultural knowledge with your Agents.
This example demonstrates how an Agent reads and applies
the cultural knowledge created earlier (see `01_create_cultural_knowledge.py`).
When `add_culture_to_context=True`, the Agent:
- Loads relevant cultural knowledge from the database
- Adds it to the context with instru... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
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