> ## Documentation Index
> Fetch the complete documentation index at: https://spacesail.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Dependencies
> Learn how to use dependencies to add context to your agents.
**Dependencies** is a way to inject variables into your Agent Context. `dependencies` is a dictionary that contains a set of functions (or static variables) that are resolved before the agent 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 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` initialization, or pass it to the `run()` and `arun()` methods.
## Using functions as dependencies
You can specify a callable function as a dependency. The dependency will be automatically resolved by the agent at runtime.
```python dependencies.py theme={null}
import json
from textwrap import dedent
import httpx
from agno.agent import Agent
from agno.models.openai import OpenAIChat
def get_top_hackernews_stories() -> str:
"""Fetch and return the top stories from HackerNews.
Args:
num_stories: Number of top stories to retrieve (default: 5)
Returns:
JSON string containing story details (title, url, score, etc.)
"""
# Get top stories
stories = [
{
k: v
for k, v in httpx.get(
f"https://hacker-news.firebaseio.com/v0/item/{id}.json"
)
.json()
.items()
if k != "kids" # Exclude discussion threads
}
for id in httpx.get(
"https://hacker-news.firebaseio.com/v0/topstories.json"
).json()[:num_stories]
]
return json.dumps(stories, indent=4)
agent = Agent(
model=OpenAIChat(id="gpt-5-mini"),
# Each function in the dependencies is evaluated when the agent is run,
# think of it as dependency injection for Agents
dependencies={"top_hackernews_stories": get_top_hackernews_stories},
# Alternatively, you can manually add the context to the instructions
instructions=dedent("""\
You are an insightful tech trend observer! 📰
Here are the top stories on HackerNews:
{top_hackernews_stories}\
"""),
markdown=True,
)
# Example usage
agent.print_response(
"Summarize the top stories on HackerNews and identify any interesting trends.",
stream=True,
)
```
Dependencies are automatically resolved when the agent is run.
## Adding dependencies to context
Set `add_dependencies_to_context=True` to add the entire list of dependencies to the user message. This way you don't have to manually add the dependencies to the instructions.
```python dependencies_instructions.py theme={null}
import json
from textwrap import dedent
import httpx
from agno.agent import Agent
from agno.models.openai import OpenAIChat
def get_user_profile() -> str:
"""Fetch and return the user profile for a given user ID.
Args:
user_id: The ID of the user to retrieve the profile for
"""
# Get the user profile from the database (this is a placeholder)
user_profile = {
"name": "John Doe",
"experience_level": "senior",
}
return json.dumps(user_profile, indent=4)
agent = Agent(
model=OpenAIChat(id="gpt-5-mini"),
dependencies={"user_profile": get_user_profile},
# We can add the entire dependencies dictionary to the user message
add_dependencies_to_context=True,
markdown=True,
)
agent.print_response(
"Get the user profile for the user with ID 123 and tell me about their experience level.",
stream=True,
)
# Optionally pass the dependencies to the print_response method
# agent.print_response(
# "Get the user profile for the user with ID 123 and tell me about their experience level.",
# dependencies={"user_profile": get_user_profile},
# stream=True,
# )
```
This adds the entire dependencies dictionary to the user message between `` tags.
The new user message looks like this:
```
Get the user profile for the user with ID 123 and tell me about their experience level.
{
"user_profile": "{\n \"name\": \"John Doe\",\n \"experience_level\": \"senior\"\n}"
}
```
You can pass `dependencies` and `add_dependencies_to_context` to the `run`, `arun`, `print_response` and `aprint_response` methods.
## Access dependencies in tool calls and hooks
You can access the dependencies in tool calls and hooks by using the `RunContext` object.
```python theme={null}
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIChat
from agno.run import RunContext
def get_user_profile(run_context: RunContext) -> str:
"""Get the user profile."""
return run_context.dependencies["user_profiles"][run_context.user_id]
agent = Agent(
model=OpenAIChat(id="gpt-5-mini"),
db=SqliteDb(db_file="tmp/agents.db"),
tools=[get_user_profile],
dependencies={"user_profiles": {"user_1001": {"name": "John Doe", "experience_level": "senior"}, "user_1002": {"name": "Jane Doe", "experience_level": "junior"}}},
)
agent.print_response("Get the user profile for the current user and tell me about their experience level.", user_id="user_1001", stream=True)
```
See the [RunContext schema](/reference/run/run_context) for more information.
## Developer Resources
* View the [Agent schema](/reference/agents/agent)
* View [Cookbook](https://github.com/agno-agi/agno/tree/main/cookbook/agents/dependencies/)