Callbacks

Callbacks in ADK-Rust provide hooks to observe, customize, and control agent behavior at key execution points. They enable logging, guardrails, caching, response modification, and more.

Overview

ADK-Rust supports six callback types that intercept different stages of agent execution:

Callback Types

Agent Callbacks

Agent callbacks wrap the entire agent execution cycle.

use adk_rust::prelude::*;
use std::sync::Arc;

// BeforeAgentCallback type signature
type BeforeAgentCallback = Box<
    dyn Fn(Arc<dyn CallbackContext>) 
        -> Pin<Box<dyn Future<Output = Result<Option<Content>>> + Send>> 
    + Send + Sync
>;

// AfterAgentCallback type signature  
type AfterAgentCallback = Box<
    dyn Fn(Arc<dyn CallbackContext>) 
        -> Pin<Box<dyn Future<Output = Result<Option<Content>>> + Send>> 
    + Send + Sync
>;

Model Callbacks

Model callbacks intercept LLM requests and responses.

use adk_rust::prelude::*;
use std::sync::Arc;

// BeforeModelResult - controls what happens after the callback
pub enum BeforeModelResult {
    Continue(LlmRequest),  // Continue with (possibly modified) request
    Skip(LlmResponse),     // Skip model call, use this response instead
}

// BeforeModelCallback - can modify request or skip model call
type BeforeModelCallback = Box<
    dyn Fn(Arc<dyn CallbackContext>, LlmRequest)
        -> Pin<Box<dyn Future<Output = Result<BeforeModelResult>> + Send>>
    + Send + Sync
>;

// AfterModelCallback - can modify the response
type AfterModelCallback = Box<
    dyn Fn(Arc<dyn CallbackContext>, LlmResponse)
        -> Pin<Box<dyn Future<Output = Result<Option<LlmResponse>>> + Send>>
    + Send + Sync
>;

Tool Callbacks

Tool callbacks intercept tool execution.

use adk_rust::prelude::*;
use std::sync::Arc;

// BeforeToolCallback - can skip tool by returning Some(Content)
type BeforeToolCallback = Box<
    dyn Fn(Arc<dyn CallbackContext>) 
        -> Pin<Box<dyn Future<Output = Result<Option<Content>>> + Send>> 
    + Send + Sync
>;

// AfterToolCallback - can modify tool result
type AfterToolCallback = Box<
    dyn Fn(Arc<dyn CallbackContext>) 
        -> Pin<Box<dyn Future<Output = Result<Option<Content>>> + Send>> 
    + Send + Sync
>;

Return Value Semantics

Callbacks use different return values to control execution flow:

Agent/Tool Callbacks

Model Callbacks

BeforeModelCallback uses BeforeModelResult:

AfterModelCallback uses Option<LlmResponse>:

Summary

• Before agent/tool callbacks: Return None to continue, Some(content) to skip
• Before model callback: Return Continue(request) to proceed, Skip(response) to bypass the model
• After callbacks: Return None to keep original, Some(...) to replace

Adding Callbacks to Agents

Callbacks are added to agents using the LlmAgentBuilder:

use adk_rust::prelude::*;
use std::sync::Arc;

#[tokio::main]
async fn main() -> std::result::Result<(), Box<dyn std::error::Error>> {
    let api_key = std::env::var("GOOGLE_API_KEY")?;
    let model = Arc::new(GeminiModel::new(&api_key, "gemini-2.5-flash")?);

    let agent = LlmAgentBuilder::new("my_agent")
        .model(model)
        .instruction("You are a helpful assistant.")
        // Add before_agent callback
        .before_callback(Box::new(|ctx| {
            Box::pin(async move {
                println!("Agent starting: {}", ctx.agent_name());
                Ok(None) // Continue execution
            })
        }))
        // Add after_agent callback
        .after_callback(Box::new(|ctx| {
            Box::pin(async move {
                println!("Agent completed: {}", ctx.agent_name());
                Ok(None) // Keep original result
            })
        }))
        .build()?;

    Ok(())
}

CallbackContext Interface

The CallbackContext trait provides access to execution context:

use adk_rust::prelude::*;

#[async_trait]
pub trait CallbackContext: ReadonlyContext {
    /// Access artifact storage (if configured)
    fn artifacts(&self) -> Option<Arc<dyn Artifacts>>;
}

// CallbackContext extends ReadonlyContext
#[async_trait]
pub trait ReadonlyContext: Send + Sync {
    /// Current invocation ID
    fn invocation_id(&self) -> &str;
    
    /// Name of the current agent
    fn agent_name(&self) -> &str;
    
    /// User ID from session
    fn user_id(&self) -> &str;
    
    /// Application name
    fn app_name(&self) -> &str;
    
    /// Session ID
    fn session_id(&self) -> &str;
    
    /// Current branch (for multi-agent)
    fn branch(&self) -> &str;
    
    /// The user's input content
    fn user_content(&self) -> &Content;
}

Common Patterns

Logging Callback

Log all agent interactions:

use adk_rust::prelude::*;
use std::sync::Arc;

let agent = LlmAgentBuilder::new("logged_agent")
    .model(model)
    .before_callback(Box::new(|ctx| {
        Box::pin(async move {
            println!("[LOG] Agent '{}' starting", ctx.agent_name());
            println!("[LOG] Session: {}", ctx.session_id());
            println!("[LOG] User: {}", ctx.user_id());
            Ok(None)
        })
    }))
    .after_callback(Box::new(|ctx| {
        Box::pin(async move {
            println!("[LOG] Agent '{}' completed", ctx.agent_name());
            Ok(None)
        })
    }))
    .build()?;

Input Guardrails

Block inappropriate content before processing:

use adk_rust::prelude::*;
use std::sync::Arc;

let agent = LlmAgentBuilder::new("guarded_agent")
    .model(model)
    .before_callback(Box::new(|ctx| {
        Box::pin(async move {
            // Check user input for blocked content
            let user_content = ctx.user_content();
            for part in &user_content.parts {
                if let Part::Text { text } = part {
                    if text.to_lowercase().contains("blocked_word") {
                        // Return early with rejection message
                        return Ok(Some(Content {
                            role: "model".to_string(),
                            parts: vec![Part::Text {
                                text: "I cannot process that request.".to_string(),
                            }],
                        }));
                    }
                }
            }
            Ok(None) // Continue normal execution
        })
    }))
    .build()?;

Response Caching (Before Model)

Cache LLM responses to reduce API calls:

use adk_rust::prelude::*;
use std::sync::Arc;
use std::collections::HashMap;
use std::sync::Mutex;

// Simple in-memory cache
let cache: Arc<Mutex<HashMap<String, LlmResponse>>> = Arc::new(Mutex::new(HashMap::new()));
let cache_clone = cache.clone();

let agent = LlmAgentBuilder::new("cached_agent")
    .model(model)
    .before_model_callback(Box::new(move |ctx, request| {
        let cache = cache_clone.clone();
        Box::pin(async move {
            // Create cache key from request contents
            let key = format!("{:?}", request.contents);

            // Check cache
            if let Some(cached) = cache.lock().unwrap().get(&key) {
                println!("[CACHE] Hit for request");
                return Ok(BeforeModelResult::Skip(cached.clone()));
            }

            println!("[CACHE] Miss, calling model");
            Ok(BeforeModelResult::Continue(request)) // Continue to model
        })
    }))
    .build()?;

Injecting Multimodal Content (Before Model)

Inject images or other binary content into LLM requests for multimodal analysis:

use adk_rust::prelude::*;
use adk_rust::artifact::{ArtifactService, LoadRequest};
use std::sync::Arc;

// Artifact service with pre-loaded image
let artifact_service: Arc<dyn ArtifactService> = /* ... */;
let callback_service = artifact_service.clone();

let agent = LlmAgentBuilder::new("image_analyst")
    .model(model)
    .instruction("Describe the image provided by the user.")
    .before_model_callback(Box::new(move |_ctx, mut request| {
        let service = callback_service.clone();
        Box::pin(async move {
            // Load image from artifact storage
            if let Ok(response) = service.load(LoadRequest {
                app_name: "my_app".to_string(),
                user_id: "user".to_string(),
                session_id: "session".to_string(),
                file_name: "user:photo.png".to_string(),
                version: None,
            }).await {
                // Inject image into the user's message
                if let Some(last_content) = request.contents.last_mut() {
                    if last_content.role == "user" {
                        last_content.parts.push(response.part);
                    }
                }
            }

            Ok(BeforeModelResult::Continue(request))
        })
    }))
    .build()?;

This pattern is essential for multimodal AI because tool responses are JSON text - the model can't "see" images returned by tools. By injecting the image directly into the request, the model receives actual image data.

Response Modification (After Model)

Modify or filter model responses:

use adk_rust::prelude::*;
use std::sync::Arc;

let agent = LlmAgentBuilder::new("filtered_agent")
    .model(model)
    .after_model_callback(Box::new(|ctx, mut response| {
        Box::pin(async move {
            // Modify the response content
            if let Some(ref mut content) = response.content {
                for part in &mut content.parts {
                    if let Part::Text { text } = part {
                        // Add disclaimer to all responses
                        *text = format!("{}\n\n[AI-generated response]", text);
                    }
                }
            }
            Ok(Some(response))
        })
    }))
    .build()?;

Tool Permission Check (Before Tool)

Validate tool execution permissions:

use adk_rust::prelude::*;
use std::sync::Arc;

let agent = LlmAgentBuilder::new("permission_agent")
    .model(model)
    .tool(Arc::new(GoogleSearchTool::new()))
    .before_tool_callback(Box::new(|ctx| {
        Box::pin(async move {
            // Check if user has permission for tools
            let user_id = ctx.user_id();
            
            // Example: block certain users from using tools
            if user_id == "restricted_user" {
                return Ok(Some(Content {
                    role: "function".to_string(),
                    parts: vec![Part::Text {
                        text: "Tool access denied for this user.".to_string(),
                    }],
                }));
            }
            
            Ok(None) // Allow tool execution
        })
    }))
    .build()?;

Tool Result Logging (After Tool)

Log all tool executions:

use adk_rust::prelude::*;
use std::sync::Arc;

let agent = LlmAgentBuilder::new("tool_logged_agent")
    .model(model)
    .tool(Arc::new(GoogleSearchTool::new()))
    .after_tool_callback(Box::new(|ctx| {
        Box::pin(async move {
            println!("[TOOL LOG] Tool executed for agent: {}", ctx.agent_name());
            println!("[TOOL LOG] Session: {}", ctx.session_id());
            Ok(None) // Keep original result
        })
    }))
    .build()?;

Multiple Callbacks

You can add multiple callbacks of the same type. They execute in order:

use adk_rust::prelude::*;
use std::sync::Arc;

let agent = LlmAgentBuilder::new("multi_callback_agent")
    .model(model)
    // First before callback - logging
    .before_callback(Box::new(|ctx| {
        Box::pin(async move {
            println!("[1] Logging callback");
            Ok(None)
        })
    }))
    // Second before callback - validation
    .before_callback(Box::new(|ctx| {
        Box::pin(async move {
            println!("[2] Validation callback");
            Ok(None)
        })
    }))
    .build()?;

When a callback returns Some(content), subsequent callbacks of the same type are skipped.

Error Handling

Callbacks can return errors to abort execution:

use adk_rust::prelude::*;
use std::sync::Arc;

let agent = LlmAgentBuilder::new("error_handling_agent")
    .model(model)
    .before_callback(Box::new(|ctx| {
        Box::pin(async move {
            // Validate something critical
            if ctx.user_id().is_empty() {
                return Err(AdkError::Agent("User ID is required".to_string()));
            }
            Ok(None)
        })
    }))
    .build()?;

Best Practices

1. Keep callbacks lightweight: Avoid heavy computation in callbacks
2. Handle errors gracefully: Return meaningful error messages
3. Use logging sparingly: Too much logging can impact performance
4. Cache wisely: Consider cache invalidation strategies
5. Test callbacks independently: Unit test callback logic separately

Related

• LlmAgent - Agent configuration
• Tools - Tool system
• Events - Event structure

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