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Dependencies are a way to inject variables into your Agent or Team context. The dependencies parameter accepts a dictionary containing functions or static variables that are automatically resolved before the agent or team runs.
You can use dependencies to inject memories, dynamic few-shot examples, “retrieved” documents, etc.

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Basic usage

You can reference the dependencies in your agent instructions or user message.
dependencies.py
from agno.agent import Agent
from agno.models.openai import OpenAIChat

agent = Agent(
    model=OpenAIChat(id="gpt-5-mini"),
    dependencies={"name": "John Doe"},
    instructions="You are a story writer. The current user is {name}."
)

agent.print_response("Write a 5 second short story about {name}")
You can set dependencies on Agent/Team initialization, or pass it to the run() and arun() methods.

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How dependencies work

Dependencies are resolved at runtime, just before your agent or team executes. Here’s the flow:
  1. Define dependencies: Provide a dictionary of key-value pairs where values can be static data or callable functions
  2. Resolution: When the agent/team runs, Agno calls all callable dependencies and replaces them with their return values
  3. Template substitution: Resolved dependencies are available in your instructions using {dependency_name} syntax
  4. Context injection: When add_dependencies_to_context=True, dependencies are automatically added to the user message

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Learn more

Dependencies with Agents

Learn how to use dependencies with agents

Dependencies with Teams

Learn how to use dependencies with teams

AgentOS API

Pass dependencies via the AgentOS API

Agent Schema

View the full Agent schema reference