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Dynamic Agents

Learn how to create agents with dynamic system prompts that update automatically using real time data.

Dynamic System Prompts​

Agents support dynamic system prompts that can include live data through template functions. These functions are executed each time the agent runs, providing fresh context.

Use {module::function} syntax to embed dynamic values:

agent = Agent(
  name="dynamic_agent",
  model="openai/gpt-4o-mini",
  system_prompt="""You are a time-aware assistant.
  Current time: {datetime::datetime.now}."""
)

The previous example used a built-in function (datetime). You can also create your own custom functions:

# my_functions.py
def get_server_status():
  """Get server status."""
  status = check_server()  # Calls external function
  return f"Server: {status}"


agent = Agent(
  name="custom_agent", 
  model="openai/gpt-4o-mini",
  system_prompt="""You are a helpful assistant.
  Status: {my_functions::get_server_status}."""
)

You can also pass dynamic parameters to these functions using RunContext data that you previously set in the context.

# my_functions.py
from timbal.core import Tool, Agent
from timbal.state import get_run_context


def get_user_language():
  return get_run_context().get_data(".input.language")




def set_user_language(l):
context = get_run_context().set_data("user_lang", "catalan")


agent = Agent(
  name="multilang_agent",
  model="openai/gpt-4o-mini",
  pre_hook=set_user_language,
  system_prompt="Answer in {my_functions::get_user_language}"."
# )


await agent(prompt="Which is the capital of Germany?").collect()

The response will be in Catalan.

Benefits:

Real-time context

System prompts reflect current state

Dynamic behavior

Agent adapts to changing conditions

Automatic execution

Functions run on each conversation

Performance

Template resolution is fast and cached

Sync/Async

Handles both sync and async functions automatically.