Context
Context In Instructions
Examples
- Introduction
- Getting Started
- Agents
- Teams
- Workflows
- Applications
Agent Concepts
- Multimodal
- RAG
- Knowledge
- Memory
- Async
- Hybrid Search
- Storage
- Tools
- Vector Databases
- Context
- Embedders
- Agent State
Models
- Anthropic
- AWS Bedrock
- AWS Bedrock Claude
- Azure AI Foundry
- Azure OpenAI
- Cerebras
- Cerebras OpenAI
- Cohere
- DeepInfra
- DeepSeek
- Fireworks
- Gemini
- Groq
- Hugging Face
- Meta
- Mistral
- NVIDIA
- Ollama
- OpenAI
- Perplexity
- Together
- xAI
- IBM
- LM Studio
- LiteLLM
- LiteLLM OpenAI
Context
Context In Instructions
Code
cookbook/agent_concepts/context/03-context_in_instructions.py
import json
from textwrap import dedent
import httpx
from agno.agent import Agent
from agno.models.openai import OpenAIChat
def get_upcoming_spacex_launches(num_launches: int = 5) -> str:
url = "https://api.spacexdata.com/v5/launches/upcoming"
launches = httpx.get(url).json()
launches = sorted(launches, key=lambda x: x["date_unix"])[:num_launches]
return json.dumps(launches, indent=4)
# Create an Agent that has access to real-time SpaceX data
agent = Agent(
model=OpenAIChat(id="gpt-4.1"),
# Each function in the context is evaluated at runtime
context={"upcoming_spacex_launches": get_upcoming_spacex_launches},
description=dedent("""\
You are a cosmic analyst and spaceflight enthusiast. 🚀
Here are the next SpaceX launches:
{upcoming_spacex_launches}\
"""),
# add_state_in_messages will make the `upcoming_spacex_launches` variable
# available in the description and instructions
add_state_in_messages=True,
markdown=True,
)
agent.print_response(
"Tell me about the upcoming SpaceX missions.",
stream=True,
)
Usage
1
Create a virtual environment
Open the Terminal
and create a python virtual environment.
python3 -m venv .venv
source .venv/bin/activate
2
Install libraries
pip install -U agno httpx
3
Run Example
python cookbook/agent_concepts/context/03-context_in_instructions.py
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