> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Dependencies
> Inject variables into agent and team context with dependencies.
**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.
## Basic usage
You can reference the dependencies in your agent instructions or user message.
```python dependencies.py theme={null}
from agno.agent import Agent
from agno.models.openai import OpenAIResponses
agent = Agent(
model=OpenAIResponses(id="gpt-5.2"),
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.
## 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
## Learn more
Learn how to use dependencies with agents
Learn how to use dependencies with teams
Pass dependencies via the AgentOS API
View the full Agent schema reference