Using Callbacks¶

This cookbook illustrates how to implement and use custom callbacks in any-agent.

You can find more information about callbacks in the docs:

https://mozilla-ai.github.io/any-agent/agents/callbacks/

In [ ]:

%pip install 'any-agent'

import nest_asyncio

nest_asyncio.apply()

%pip install 'any-agent' import nest_asyncio nest_asyncio.apply()

In [ ]:

import os
from getpass import getpass

for key in ("MISTRAL_API_KEY", "TAVILY_API_KEY"):
    if key not in os.environ:
        print(f"{key} not found in environment!")
        api_key = getpass(f"Please enter your {key}: ")
        os.environ[key] = api_key
        print(f"{key} set for this session!")
    else:
        print(f"{key} found in environment.")

import os from getpass import getpass for key in ("MISTRAL_API_KEY", "TAVILY_API_KEY"): if key not in os.environ: print(f"{key} not found in environment!") api_key = getpass(f"Please enter your {key}: ") os.environ[key] = api_key print(f"{key} set for this session!") else: print(f"{key} found in environment.")

In [ ]:

from any_agent import AgentConfig, AnyAgent
from any_agent.tools import search_tavily

from any_agent import AgentConfig, AnyAgent from any_agent.tools import search_tavily

Running with default callbacks¶

any-agent comes with a set of default callbacks that will be used by default (if you don't pass a value to AgentConfig.callbacks):

• AddCostInfo
• ConsolePrintSpan

In [ ]:

agent = AnyAgent.create(
    "tinyagent",
    AgentConfig(model_id="mistral:mistral-small-latest", tools=[search_tavily]),
)
agent_trace = agent.run("What are 5 LLM agent frameworks that are trending in 2025?")

agent = AnyAgent.create( "tinyagent", AgentConfig(model_id="mistral:mistral-small-latest", tools=[search_tavily]), ) agent_trace = agent.run("What are 5 LLM agent frameworks that are trending in 2025?")

Adding Callback to offload sensitive information¶

Some inputs and/or outputs in your traces might contain sensitive information that you don't want to be exposed in the traces.

We are going to implement a callback that takes the value of the input messages (which contain the instructions and the user prompt), writes it to an external destination (in this example, a local file), and replaces the value of the attribute in the span with a reference to that external destination.

In [ ]:

import json
from pathlib import Path

from any_agent.callbacks.base import Callback
from any_agent.callbacks.context import Context
from any_agent.tracing.attributes import GenAI


class SensitiveDataOffloader(Callback):
    def __init__(self, output_dir: str) -> None:
        self.output_dir = Path(output_dir)
        self.output_dir.mkdir(exist_ok=True, parents=True)

    def before_llm_call(self, context: Context, *args, **kwargs) -> Context:
        span = context.current_span

        if input_messages := span.attributes.get(GenAI.INPUT_MESSAGES):
            output_file = self.output_dir / f"{span.get_span_context().trace_id}.txt"
            output_file.write_text(str(input_messages))

            span.set_attribute(
                GenAI.INPUT_MESSAGES, json.dumps({"ref": str(output_file)})
            )

        return context

import json from pathlib import Path from any_agent.callbacks.base import Callback from any_agent.callbacks.context import Context from any_agent.tracing.attributes import GenAI class SensitiveDataOffloader(Callback): def __init__(self, output_dir: str) -> None: self.output_dir = Path(output_dir) self.output_dir.mkdir(exist_ok=True, parents=True) def before_llm_call(self, context: Context, *args, **kwargs) -> Context: span = context.current_span if input_messages := span.attributes.get(GenAI.INPUT_MESSAGES): output_file = self.output_dir / f"{span.get_span_context().trace_id}.txt" output_file.write_text(str(input_messages)) span.set_attribute( GenAI.INPUT_MESSAGES, json.dumps({"ref": str(output_file)}) ) return context

We can now provide our callback to the agent.

You can find more information in:

https://mozilla-ai.github.io/any-agent/agents/callbacks/#providing-your-own-callbacks

In [ ]:

from any_agent.callbacks import get_default_callbacks

agent = AnyAgent.create(
    "tinyagent",
    AgentConfig(
        model_id="mistral:mistral-small-latest",
        tools=[search_tavily],
        callbacks=[SensitiveDataOffloader("sensitive-info"), *get_default_callbacks()],
    ),
)
agent_trace = agent.run("What are 5 LLM agent frameworks that are trending in 2025?")

from any_agent.callbacks import get_default_callbacks agent = AnyAgent.create( "tinyagent", AgentConfig( model_id="mistral:mistral-small-latest", tools=[search_tavily], callbacks=[SensitiveDataOffloader("sensitive-info"), *get_default_callbacks()], ), ) agent_trace = agent.run("What are 5 LLM agent frameworks that are trending in 2025?")

As you can see in the console output, the input messages in the trace have been now replaced by a reference to the external destination.