repo stringlengths 7 90 | file_url stringlengths 81 315 | file_path stringlengths 4 228 | content stringlengths 0 32.8k | language stringclasses 1
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values | commit_sha stringlengths 40 40 | retrieved_at stringdate 2026-01-04 14:38:15 2026-01-05 02:33:18 | truncated bool 2
classes |
|---|---|---|---|---|---|---|---|---|
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/03_agents/culture/01_create_cultural_knowledge.py | cookbook/03_agents/culture/01_create_cultural_knowledge.py | """Create cultural knowledge to use with your Agents.
This minimal example demonstrates how to use Agno's `CultureManager`
to create and persist shared cultural knowledge that can be used with your Agents.
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/__init__.py | cookbook/03_agents/culture/__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/culture/03_automatic_cultural_management.py | cookbook/03_agents/culture/03_automatic_cultural_management.py | """Automatically update cultural knowledge based on Agent interactions.
This example demonstrates how an Agent can autonomously add or refine
shared cultural knowledge after completing a task.
When `update_cultural_knowledge=True`, the Agent:
- Reflects on its interaction and reasoning process.
- Identifies reusable ... | 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/05_test_agent_with_culture.py | cookbook/03_agents/culture/05_test_agent_with_culture.py | """This is just a sample file you can use to test run Agents with cultural knowledge.
There's no agenda to it, just test various inputs and see what the Agent says.
"""
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.anthropic import Claude
db = SqliteDb(db_file="tmp/demo.db")
agen... | 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/rag/local_rag_langchain_qdrant.py | cookbook/03_agents/rag/local_rag_langchain_qdrant.py | """
Prerequisites:
- pip install langchain-qdrant langchain-text-splitters langchain-community fastembed
"""
from agno.agent import Agent
from agno.models.ollama import Ollama
from agno.vectordb.langchaindb import LangChainVectorDb
from langchain_community.document_loaders import WebBaseLoader
from langchain_communit... | 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/rag/traditional_rag_pgvector.py | cookbook/03_agents/rag/traditional_rag_pgvector.py | """
1. Run: `./cookbook/run_pgvector.sh` to start a postgres container with pgvector
2. Run: `pip install openai sqlalchemy 'psycopg[binary]' pgvector agno` to install the dependencies
3. Run: `python cookbook/rag/01_traditional_rag_pgvector.py` to run the agent
"""
from agno.agent import Agent
from agno.knowledge.emb... | 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/rag/rag_with_lance_db_and_sqlite.py | cookbook/03_agents/rag/rag_with_lance_db_and_sqlite.py | """Run `pip install lancedb` to install dependencies."""
from agno.agent import Agent
from agno.db.sqlite.sqlite import SqliteDb
from agno.knowledge.embedder.ollama import OllamaEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.ollama import Ollama
from agno.vectordb.lancedb import LanceDb
# De... | 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/rag/agentic_rag_pgvector.py | cookbook/03_agents/rag/agentic_rag_pgvector.py | """
1. Run: `./cookbook/run_pgvector.sh` to start a postgres container with pgvector
2. Run: `pip install openai sqlalchemy 'psycopg[binary]' pgvector agno` to install the dependencies
3. Run: `python cookbook/rag/02_agentic_rag_pgvector.py` to run the agent
"""
from agno.agent import Agent
from agno.knowledge.embedde... | 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/rag/agentic_rag_lancedb.py | cookbook/03_agents/rag/agentic_rag_lancedb.py | """
1. Run: `pip install openai lancedb tantivy pypdf sqlalchemy agno` to install the dependencies
2. Run: `python cookbook/rag/04_agentic_rag_lancedb.py` to run the agent
"""
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno... | 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/rag/rag_sentence_transformer.py | cookbook/03_agents/rag/rag_sentence_transformer.py | """This cookbook is an implementation of Agentic RAG using Sentence Transformer Reranker with multilingual data.
## Setup Instructions:
### 1. Install Dependencies
Run: `pip install agno sentence-transformers`
### 2. Start the Postgres Server with pgvector
Run: `sh cookbook/scripts/run_pgvector.sh`
### 3. Run the 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/03_agents/rag/traditional_rag_lancedb.py | cookbook/03_agents/rag/traditional_rag_lancedb.py | """
1. Run: `pip install openai lancedb tantivy pypdf sqlalchemy agno` to install the dependencies
2. Run: `python cookbook/rag/03_traditional_rag_lancedb.py` to run the agent
"""
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
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/03_agents/rag/agentic_rag_with_reranking.py | cookbook/03_agents/rag/agentic_rag_with_reranking.py | """
1. Run: `pip install openai agno cohere lancedb tantivy sqlalchemy` to install the dependencies
2. Export your OPENAI_API_KEY and CO_API_KEY
3. Run: `python cookbook/agent_concepts/rag/agentic_rag_with_reranking.py` to run the agent
"""
from agno.agent import Agent
from agno.knowledge.embedder.openai import 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/03_agents/rag/__init__.py | cookbook/03_agents/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/03_agents/hooks/output_stream_hook_send_notification.py | cookbook/03_agents/hooks/output_stream_hook_send_notification.py | """
Example demonstrating sending a notification to the user after an agent generates a response.
It uses a post-hook which executes right after the response is processed.
"""
import asyncio
from typing import Any, Dict
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.run.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/03_agents/hooks/output_transformation_post_hook.py | cookbook/03_agents/hooks/output_transformation_post_hook.py | """
Example demonstrating output transformation using post-hooks with Agno Agent.
This example shows how to:
1. Transform agent responses by updating RunOutput.content
2. Add formatting, structure, and additional information
3. Enhance the user experience through content modification
"""
from datetime import datetime... | 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/hooks/input_transformation_pre_hook.py | cookbook/03_agents/hooks/input_transformation_pre_hook.py | """Example demonstrating how to use a pre_hook to transform the input of your Agno Agent."""
from typing import Optional
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.run.agent import RunInput
from agno.session.agent import AgentSession
from agno.utils.log import log_debug
def tra... | 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/hooks/session_state_post_hook.py | cookbook/03_agents/hooks/session_state_post_hook.py | """Example demonstrating how to use a post_hook to update the session_state.
We will create a function that extract a list of topics discussed in the conversation, and keep them in the session state.
We will use the function as a post-hook, to run it after every run of the agent.
"""
from typing import List
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/03_agents/hooks/input_validation_pre_hook.py | cookbook/03_agents/hooks/input_validation_pre_hook.py | """
Example demonstrating how to use a pre_hook to perform comprehensive input validation for your Agno Agent.
Note: The "Message" panel will be updated with the updated input content after the pre-hook is executed.
"""
from agno.agent import Agent
from agno.exceptions import CheckTrigger, InputCheckError
from agno.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/03_agents/hooks/session_state_pre_hook.py | cookbook/03_agents/hooks/session_state_pre_hook.py | """Example demonstrating how to use a pre_hook to update the session_state.
We will create a function that extract a list of topics present in the user messages, and keep them in the session state.
We will use the function as a pre-hook, to run it before each agent run.
"""
from typing import List
from agno.agent 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/03_agents/hooks/output_validation_post_hook.py | cookbook/03_agents/hooks/output_validation_post_hook.py | """
Example demonstrating output validation using post-hooks with Agno Agent.
This example shows how to:
1. Validate agent responses for quality and safety
2. Ensure outputs meet minimum standards before being returned
3. Raise OutputCheckError when validation fails
"""
import asyncio
from agno.agent import Agent
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/03_agents/guardrails/prompt_injection.py | cookbook/03_agents/guardrails/prompt_injection.py | """
Example demonstrating how to use checks with Agno Agent to implement guardrails.
This example shows how to:
1. An input validation check that checks for prompt injection
"""
from agno.agent import Agent
from agno.exceptions import InputCheckError
from agno.guardrails import PromptInjectionGuardrail
from agno.mode... | 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/guardrails/openai_moderation.py | cookbook/03_agents/guardrails/openai_moderation.py | """
Example demonstrating how to use OpenAI moderation guardrails with Agno Agent.
This example shows how to:
1. Detect and block content that violates OpenAI's content policy
2. Handle both text and image content moderation
3. Configure moderation for specific categories
4. Use both sync and async moderation checks
5... | 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/guardrails/pii_detection.py | cookbook/03_agents/guardrails/pii_detection.py | """
Example demonstrating how to use PII detection guardrails with Agno Agent.
This example shows how to:
1. Detect and block personally identifiable information (PII) in input
2. Protect sensitive data like SSNs, credit cards, emails, and phone numbers
3. Handle different types of PII violations with appropriate erro... | 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/async/concurrent_tool_calls.py | cookbook/03_agents/async/concurrent_tool_calls.py | """
This example shows how to use concurrent tool calls in Agno.
You should see the tool calls start and complete in parallel.
"""
from typing import AsyncIterator
from agno.agent import Agent
from agno.models.openai import OpenAIChat
async def get_weather(city: str) -> AsyncIterator[str]:
import time
prin... | 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/async/data_analyst.py | cookbook/03_agents/async/data_analyst.py | """Run `pip install duckdb` to install dependencies."""
import asyncio
from textwrap import dedent
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.duckdb import DuckDbTools
duckdb_tools = DuckDbTools()
duckdb_tools.create_table_from_path(
path="https://agno-public.s3.amazon... | 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/async/streaming.py | cookbook/03_agents/async/streaming.py | import asyncio
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.utils.pprint import apprint_run_response
agent = Agent(
model=OpenAIChat(id="gpt-4o-mini"),
)
async def streaming():
async for response in agent.arun(input="Tell me a joke.", stream=True):
print(response.... | 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/async/tool_use.py | cookbook/03_agents/async/tool_use.py | import asyncio
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools
agent = Agent(
model=OpenAIChat(id="gpt-4o"),
tools=[DuckDuckGoTools()],
markdown=True,
)
asyncio.run(agent.aprint_response("Whats happening in UK and in USA?"))
| 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/async/basic.py | cookbook/03_agents/async/basic.py | import asyncio
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.utils.pprint import apprint_run_response
agent = Agent(
model=OpenAIChat(id="gpt-4o-mini"),
)
async def basic():
response = await agent.arun(input="Tell me a joke.")
print(response.content)
async def basic_... | 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/async/delay.py | cookbook/03_agents/async/delay.py | import asyncio
from agno.agent import Agent, RunOutput
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools
from rich.pretty import pprint
providers = ["openai", "anthropic", "ollama", "cohere", "google"]
instructions = [
"Your task is to write a well researched report on AI... | 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/async/gather_agents.py | cookbook/03_agents/async/gather_agents.py | import asyncio
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools
from rich.pretty import pprint
providers = ["openai", "anthropic", "ollama", "cohere", "google"]
instructions = [
"Your task is to write a well researched report on AI providers.... | 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/async/__init__.py | cookbook/03_agents/async/__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/async/structured_output.py | cookbook/03_agents/async/structured_output.py | import asyncio
from typing import List
from agno.agent import Agent, RunOutput # noqa
from agno.models.openai import OpenAIChat
from pydantic import BaseModel, Field
from rich.pretty import pprint # noqa
class MovieScript(BaseModel):
setting: str = Field(
..., description="Provide a nice setting for a ... | 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/async/reasoning.py | cookbook/03_agents/async/reasoning.py | import asyncio
from agno.agent import Agent
from agno.models.openai import OpenAIChat
task = "9.11 and 9.9 -- which is bigger?"
regular_agent = Agent(model=OpenAIChat(id="gpt-4o"), markdown=True)
reasoning_agent = Agent(
model=OpenAIChat(id="gpt-4o"),
reasoning=True,
markdown=True,
)
asyncio.run(regular... | 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/dependencies/access_dependencies_in_tool.py | cookbook/03_agents/dependencies/access_dependencies_in_tool.py | """
Example showing how tools can access dependencies passed to the agent.
This demonstrates:
1. Passing dependencies to agent.run()
2. A simple tool that receives resolved dependencies
"""
from datetime import datetime
from typing import Any, Dict, Optional
from agno.agent import Agent
from agno.models.openai impor... | 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/dependencies/add_dependencies_to_context.py | cookbook/03_agents/dependencies/add_dependencies_to_context.py | import json
import httpx
from agno.agent import Agent
from agno.models.openai import OpenAIChat
def get_top_hackernews_stories(num_stories: int = 5) -> str:
"""Fetch and return the top stories from HackerNews.
Args:
num_stories: Number of top stories to retrieve (default: 5)
Returns:
JSO... | 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/dependencies/add_dependencies_on_run.py | cookbook/03_agents/dependencies/add_dependencies_on_run.py | from agno.agent import Agent
from agno.models.openai import OpenAIChat
def get_user_profile(user_id: str = "john_doe") -> dict:
"""Get user profile information that can be referenced in responses.
Args:
user_id: The user ID to get profile for
Returns:
Dictionary containing user profile 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/dependencies/dependencies_functions.py | cookbook/03_agents/dependencies/dependencies_functions.py | """Example demonstrating how to read from the session state from a dependency function.
We will create a function that reads some fields from the session state and return them as a dictionary.
We will then use this function as a dependency in an agent, allowing us to get the session state data directly into the contex... | 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/dependencies/reference_dependencies.py | cookbook/03_agents/dependencies/reference_dependencies.py | import json
import httpx
from agno.agent import Agent
from agno.models.openai import OpenAIChat
def get_top_hackernews_stories(num_stories: int = 5) -> str:
"""Fetch and return the top stories from HackerNews.
Args:
num_stories: Number of top stories to retrieve (default: 5)
Returns:
JSO... | 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/dependencies/__init__.py | cookbook/03_agents/dependencies/__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/custom_logging/custom_logging.py | cookbook/03_agents/custom_logging/custom_logging.py | """Example showing how to use a custom logger with Agno."""
import logging
from agno.agent import Agent
from agno.utils.log import configure_agno_logging, log_info
def get_custom_logger():
"""Return an example custom logger."""
custom_logger = logging.getLogger("custom_logger")
handler = logging.StreamH... | 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/custom_logging/custom_logging_advanced.py | cookbook/03_agents/custom_logging/custom_logging_advanced.py | """Example showing how to use multiple custom loggers with Agno.
This is useful for advanced scenarios where you want to use different loggers for different parts of Agno."""
import logging
from agno.agent import Agent
from agno.team import Team
from agno.utils.log import configure_agno_logging
def get_custom_agen... | 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/custom_logging/log_to_file.py | cookbook/03_agents/custom_logging/log_to_file.py | """Example showing how to use a custom logger with Agno."""
import logging
from pathlib import Path
from agno.agent import Agent
from agno.utils.log import configure_agno_logging, log_info
def get_custom_logger():
"""Return an example custom logger."""
custom_logger = logging.getLogger("file_logger")
#... | 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/custom_logging/__init__.py | cookbook/03_agents/custom_logging/__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/events/reasoning_agent_events.py | cookbook/03_agents/events/reasoning_agent_events.py | import asyncio
from agno.agent import RunEvent
from agno.agent.agent import Agent
from agno.models.openai.chat import OpenAIChat
finance_agent = Agent(
model=OpenAIChat(id="gpt-4o"),
reasoning=True,
)
async def run_agent_with_events(prompt: str):
content_started = False
async for run_output_event 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/events/basic_agent_events.py | cookbook/03_agents/events/basic_agent_events.py | import asyncio
from agno.agent import RunEvent
from agno.agent.agent import Agent
from agno.models.openai.chat import OpenAIChat
from agno.tools.yfinance import YFinanceTools
finance_agent = Agent(
id="finance-agent",
name="Finance Agent",
model=OpenAIChat(id="gpt-4o"),
tools=[YFinanceTools()],
)
as... | 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/events/__init__.py | cookbook/03_agents/events/__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/session/01_persistent_session.py | cookbook/03_agents/session/01_persistent_session.py | from agno.agent.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"),
db=db,
session_id="sessi... | 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/session/12_chat_history_num_messages.py | cookbook/03_agents/session/12_chat_history_num_messages.py | from agno.agent.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"),
db=db,
session_id="chat_... | 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/session/11_custom_session_summary_instructions.py | cookbook/03_agents/session/11_custom_session_summary_instructions.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
db_url = "postgresql+psycopg://ai:ai@lo... | 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/session/09_disable_storing_history_messages.py | cookbook/03_agents/session/09_disable_storing_history_messages.py | """
Simple example demonstrating store_history_messages option
"""
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIChat
from agno.utils.pprint import pprint_run_response
agent = Agent(
model=OpenAIChat(id="gpt-4o-mini"),
db=SqliteDb(db_file="tmp/example_no... | 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/session/02_persistent_session_history.py | cookbook/03_agents/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/03_agents/session/05_chat_history.py | cookbook/03_agents/session/05_chat_history.py | from agno.agent.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"),
db=db,
session_id="chat_... | 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/session/06_rename_session.py | cookbook/03_agents/session/06_rename_session.py | from agno.agent.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"),
db=db,
session_id="chat_... | 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/session/03_session_summary.py | cookbook/03_agents/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
db_url = "postgresql+psycopg://ai:ai@lo... | 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/session/04_session_summary_references.py | cookbook/03_agents/session/04_session_summary_references.py | """
This example shows how to use the `add_session_summary_to_context` parameter in the Agent config to
add session summaries to the Agent 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 a... | 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/session/__init__.py | cookbook/03_agents/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/03_agents/session/12_async_session_summary.py | cookbook/03_agents/session/12_async_session_summary.py | """
This example demonstrates how to use the async `aget_session_summary()` method
to retrieve session summaries 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 AsyncPostgresDb
... | 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/session/10_disable_storing_tool_messages.py | cookbook/03_agents/session/10_disable_storing_tool_messages.py | """
Simple examples demonstrating store_tool_messages option
"""
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIChat
from agno.tools.hackernews import HackerNewsTools
from agno.utils.pprint import pprint_run_response
agent = Agent(
model=OpenAIChat(id="gpt-4o... | 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/session/08_cache_session.py | cookbook/03_agents/session/08_cache_session.py | """Example of how to cache the session in memory for faster access."""
from agno.agent.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.openai import OpenAIChat
# Setup the database
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
db = PostgresDb(db_url=db_url, session_table="xxx")
... | 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/session/07_in_memory_db.py | cookbook/03_agents/session/07_in_memory_db.py | """This example shows how to use an in-memory database.
With this you will be able to store 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
from agno.model... | 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/multimodal/video_caption_agent.py | cookbook/03_agents/multimodal/video_caption_agent.py | """Please install dependencies using:
pip install openai moviepy ffmpeg
"""
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.moviepy_video import MoviePyVideoTools
from agno.tools.openai import OpenAITools
video_tools = MoviePyVideoTools(
process_video=True, generate_captions... | 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/multimodal/image_to_structured_output.py | cookbook/03_agents/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 pydantic import BaseModel, Field
from rich.pretty import pprint
class MovieScript(BaseModel):
name: str = Field(..., description="Give a name to this movie")
setting: str = Field(
... | 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/multimodal/image_to_image_agent.py | cookbook/03_agents/multimodal/image_to_image_agent.py | from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.fal import FalTools
agent = Agent(
model=OpenAIChat(id="gpt-4o"),
id="image-to-image",
name="Image to Image Agent",
tools=[FalTools()],
markdown=True,
instructions=[
"You have to use the `image_to_ima... | 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/multimodal/image_to_text.py | cookbook/03_agents/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
agent = Agent(
model=OpenAIChat(id="gpt-4o"),
markdown=True,
)
image_path = Path(__file__).parent.joinpath("sample.jpg")
agent.print_response(
"Write a 3 sentence fiction story abo... | 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/multimodal/02_media_input_to_agent_and_tool.py | cookbook/03_agents/multimodal/02_media_input_to_agent_and_tool.py | """
Comprehensive test for joint media access functionality.
This demonstrates:
1. Initial image upload and analysis
2. DALL-E image generation within the same run
3. Analysis of both original and generated images
4. Cross-run media persistence (accessing images from previous runs)
"""
from typing import Optional, Se... | 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/multimodal/audio_sentiment_analysis.py | cookbook/03_agents/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
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
agent = Agent(
model=Gemini(id="gemini-2.0-flash-exp"),
add_history_to_context=True,
markdown=True,
... | 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/multimodal/audio_multi_turn.py | cookbook/03_agents/multimodal/audio_multi_turn.py | from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIChat
from agno.utils.audio import write_audio_to_file
from rich.pretty import pprint
agent = Agent(
model=OpenAIChat(
id="gpt-4o-audio-preview",
modalities=["text", "audio"],
audio={"voice"... | 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/multimodal/generate_image_with_intermediate_steps.py | cookbook/03_agents/multimodal/generate_image_with_intermediate_steps.py | from typing import Iterator
from agno.agent import Agent, RunOutputEvent
from agno.models.openai import OpenAIChat
from agno.tools.dalle import DalleTools
from agno.utils.common import dataclass_to_dict
from rich.pretty import pprint
image_agent = Agent(
model=OpenAIChat(id="gpt-4o"),
tools=[DalleTools()],
... | 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/multimodal/01_media_input_for_tool.py | cookbook/03_agents/multimodal/01_media_input_for_tool.py | """
Example showing how tools can access media (images, videos, audio, files) passed to the agent.
This demonstrates:
1. Uploading a PDF file to an agent
2. A tool that can access and process the uploaded file (OCR simulation)
3. The LLM responding based on the tool's processing result
"""
from typing import Optional... | 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/multimodal/agent_using_multimodal_tool_response_in_runs.py | cookbook/03_agents/multimodal/agent_using_multimodal_tool_response_in_runs.py | from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.tools.dalle import DalleTools
# Create an Agent with the DALL-E tool
agent = Agent(
tools=[DalleTools()],
name="DALL-E Image Generator",
add_history_to_context=True,
db=SqliteDb(db_file="tmp/test.db"),
)
agent.print_response(
... | 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/multimodal/agent_same_run_image_analysis.py | cookbook/03_agents/multimodal/agent_same_run_image_analysis.py | from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.dalle import DalleTools
# Create an Agent with the DALL-E tool
agent = Agent(
model=OpenAIChat(id="gpt-4.1"), tools=[DalleTools()], name="DALL-E Image Generator"
)
response = agent.run(
"Generate an image of a dog and tell ... | 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/multimodal/image_input_multi-turn.py | cookbook/03_agents/multimodal/image_input_multi-turn.py | from agno.agent import Agent
from agno.db.in_memory.in_memory_db import InMemoryDb
from agno.media import Image
from agno.models.openai import OpenAIChat
agent = Agent(
model=OpenAIChat(id="gpt-4o"),
db=InMemoryDb(),
add_history_to_context=True,
markdown=True,
store_media=True,
)
agent.print_respo... | 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/multimodal/image_input_high_fidelity.py | cookbook/03_agents/multimodal/image_input_high_fidelity.py | from agno.agent import Agent
from agno.media import Image
from agno.models.openai import OpenAIChat
agent = Agent(
model=OpenAIChat(id="gpt-4o"),
markdown=True,
)
agent.print_response(
"What's in these images",
images=[
Image(
url="https://upload.wikimedia.org/wikipedia/commons/thu... | 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/multimodal/image_to_audio.py | cookbook/03_agents/multimodal/image_to_audio.py | from pathlib import Path
from agno.agent import Agent, RunOutput
from agno.media import Image
from agno.models.openai import OpenAIChat
from agno.utils.audio import write_audio_to_file
from rich import print
from rich.text import Text
cwd = Path(__file__).parent.resolve()
image_agent = Agent(model=OpenAIChat(id="gpt... | 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/multimodal/audio_streaming.py | cookbook/03_agents/multimodal/audio_streaming.py | import base64
import wave
from typing import Iterator
from agno.agent import Agent, RunOutputEvent
from agno.models.openai import OpenAIChat
# Audio Configuration
SAMPLE_RATE = 24000 # Hz (24kHz)
CHANNELS = 1 # Mono (Change to 2 if Stereo)
SAMPLE_WIDTH = 2 # Bytes (16 bits)
# Provide the agent with the audio file... | 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/multimodal/audio_to_text.py | cookbook/03_agents/multimodal/audio_to_text.py | import requests
from agno.agent import Agent
from agno.media import Audio
from agno.models.google import Gemini
agent = Agent(
model=Gemini(id="gemini-2.0-flash-exp"),
markdown=True,
)
url = "https://agno-public.s3.us-east-1.amazonaws.com/demo_data/QA-01.mp3"
response = requests.get(url)
audio_content = resp... | 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/multimodal/generate_video_using_replicate.py | cookbook/03_agents/multimodal/generate_video_using_replicate.py | from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.replicate import ReplicateTools
"""Create an agent specialized for Replicate AI content generation"""
video_agent = Agent(
name="Video Generator Agent",
model=OpenAIChat(id="gpt-4o"),
tools=[
ReplicateTools(
... | 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/multimodal/__init__.py | cookbook/03_agents/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/03_agents/multimodal/video_to_shorts.py | cookbook/03_agents/multimodal/video_to_shorts.py | """
1. Install dependencies using: `pip install opencv-python google-geneai sqlalchemy`
2. Install ffmpeg `brew install ffmpeg`
2. Run the script using: `python cookbook/agent_concepts/video_to_shorts.py`
"""
import subprocess
from pathlib import Path
from agno.agent import Agent
from agno.media import Video
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/03_agents/multimodal/audio_input_output.py | cookbook/03_agents/multimodal/audio_input_output.py | import requests
from agno.agent import Agent
from agno.media import Audio
from agno.models.openai import OpenAIChat
from agno.utils.audio import write_audio_to_file
from rich.pretty import pprint
# Fetch the audio file and convert it to a base64 encoded string
url = "https://openaiassets.blob.core.windows.net/$web/API... | 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/multimodal/generate_video_using_models_lab.py | cookbook/03_agents/multimodal/generate_video_using_models_lab.py | from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.models_labs import ModelsLabTools
video_agent = Agent(
model=OpenAIChat(id="gpt-4o"),
tools=[ModelsLabTools()],
description="You are an AI agent that can generate videos using the ModelsLabs API.",
instructions=[
... | 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/context_management/few_shot_learning.py | cookbook/03_agents/context_management/few_shot_learning.py | """
This example demonstrates how to use additional_input with an Agent
to teach proper response patterns through few-shot learning.
"""
from agno.agent import Agent
from agno.models.message import Message
from agno.models.openai.chat import OpenAIChat
# Few-shot examples showing good support patterns
support_example... | 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/context_management/datetime_instructions.py | cookbook/03_agents/context_management/datetime_instructions.py | from agno.agent import Agent
from agno.models.openai import OpenAIChat
agent = Agent(
model=OpenAIChat(id="gpt-4o"),
add_datetime_to_context=True,
timezone_identifier="Etc/UTC",
)
agent.print_response(
"What is the current date and time? What is the current time in NYC?"
)
| 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/context_management/location_instructions.py | cookbook/03_agents/context_management/location_instructions.py | from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools
agent = Agent(
model=OpenAIChat(id="gpt-4o"),
add_location_to_context=True,
tools=[DuckDuckGoTools(cache_results=True)],
)
agent.print_response("What city am I in?")
agent.print_response... | 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/context_management/instructions_via_function.py | cookbook/03_agents/context_management/instructions_via_function.py | from typing import List
from agno.agent import Agent
def get_instructions(agent: Agent) -> List[str]:
return [
f"Your name is {agent.name}!",
"Talk in haiku's!",
"Use poetry to answer questions.",
]
agent = Agent(
name="AgentX",
instructions=get_instructions,
markdown=Tr... | 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/context_management/dynamic_instructions.py | cookbook/03_agents/context_management/dynamic_instructions.py | from agno.agent import Agent
from agno.run import RunContext
def get_instructions(run_context: RunContext):
if run_context.session_state and run_context.session_state.get("current_user_id"):
return (
f"Make the story about {run_context.session_state.get('current_user_id')}."
)
retu... | 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/context_management/instructions.py | cookbook/03_agents/context_management/instructions.py | from agno.agent import Agent
agent = Agent(instructions="Share a 2 sentence story about")
agent.print_response("Love in the year 12000.")
| 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/context_management/filter_tool_calls_from_history.py | cookbook/03_agents/context_management/filter_tool_calls_from_history.py | """
This example demonstrates max_tool_calls_from_history to limit tool calls sent to the model.
How it works:
1. Database stores ALL runs (no limit)
2. num_history_runs loads last N runs from database (default: 3)
3. max_tool_calls_from_history filters loaded history to keep only M most recent tool calls
Flow: Datab... | 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/context_management/__init__.py | cookbook/03_agents/context_management/__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/context_management/introduction.py | cookbook/03_agents/context_management/introduction.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", session_table="agent_sessions")
INTRODUCTION = """Hello, I'm your personal assistant. I can help you only with questions related to mountain climbing."""
agent = 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/03_agents/input_and_output/input_schema_on_agent.py | cookbook/03_agents/input_and_output/input_schema_on_agent.py | from typing import List
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.hackernews import HackerNewsTools
from pydantic import BaseModel, Field
class ResearchTopic(BaseModel):
"""Structured research topic with specific requirements"""
topic: str
focus_areas: List[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/input_and_output/json_schema_output.py | cookbook/03_agents/input_and_output/json_schema_output.py | """
Example showing how to use JSON as output schema.
Also how to temporarily override the schema for a single run,
with automatic restoration afterwards.
Note: JSON schemas must be in the provider's expected format.
For example, OpenAI expects:
{"type": "json_schema", "json_schema": {"name": "...", "schema": {...}}}... | 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/input_and_output/output_model.py | cookbook/03_agents/input_and_output/output_model.py | """
This example shows how to use the output_model parameter to specify the model that will be used to generate the final response.
"""
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools
agent = Agent(
model=OpenAIChat(id="gpt-4.1"),
output... | 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/input_and_output/input_as_message.py | cookbook/03_agents/input_and_output/input_as_message.py | from agno.agent import Agent, Message
Agent().print_response(
Message(
role="user",
content=[
{"type": "text", "text": "What's in this image?"},
{
"type": "image_url",
"image_url": {
"url": "https://upload.wikimedia.org/wik... | 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/input_and_output/parser_model_stream.py | cookbook/03_agents/input_and_output/parser_model_stream.py | import random
from typing import Iterator, List
from agno.agent import Agent, RunOutputEvent
from agno.models.anthropic import Claude
from agno.models.openai import OpenAIChat
from pydantic import BaseModel, Field
from rich.pretty import pprint # noqa
class NationalParkAdventure(BaseModel):
park_name: str = Fie... | 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/input_and_output/parser_model_ollama.py | cookbook/03_agents/input_and_output/parser_model_ollama.py | import random
from typing import List
from agno.agent import Agent, RunOutput
from agno.models.ollama import Ollama
from agno.models.openai import OpenAIChat
from pydantic import BaseModel, Field
from rich.pretty import pprint
class NationalParkAdventure(BaseModel):
park_name: str = Field(..., description="Name ... | 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/input_and_output/input_as_dict.py | cookbook/03_agents/input_and_output/input_as_dict.py | from agno.agent import Agent
Agent().print_response(
{
"role": "user",
"content": [
{"type": "text", "text": "What's in this image?"},
{
"type": "image_url",
"image_url": {
"url": "https://upload.wikimedia.org/wikipedia/com... | 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/input_and_output/parser_model.py | cookbook/03_agents/input_and_output/parser_model.py | import random
from typing import List
from agno.agent import Agent, RunOutput # noqa
from agno.models.anthropic import Claude
from agno.models.openai import OpenAIChat
from pydantic import BaseModel, Field
from rich.pretty import pprint # noqa
class NationalParkAdventure(BaseModel):
park_name: str = Field(...,... | 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/input_and_output/structured_input_output_with_parser_model.py | cookbook/03_agents/input_and_output/structured_input_output_with_parser_model.py | from typing import List
from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.models.openai import OpenAIChat
from agno.tools.hackernews import HackerNewsTools
from pydantic import BaseModel, Field
from rich.pretty import pprint
# Define your input schema
class ResearchTopic(BaseModel):
... | 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/input_and_output/instructions.py | cookbook/03_agents/input_and_output/instructions.py | """Example demonstrating how to use a function as instructions for an agent.
We will create a function that builds instructions for the agent, based on the session state.
We will then use this function as instructions for the agent, allowing us to dynamically build the instructions based on the session state.
"""
fro... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
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