# Part of agent.py --> Follow https://google.github.io/adk-docs/get-started/quickstart/ to learn the setup

import asyncio
import os
from google.adk.agents import LoopAgent, LlmAgent, BaseAgent, SequentialAgent
from google.genai import types
from google.adk.runners import InMemoryRunner
from google.adk.agents.invocation_context import InvocationContext
from google.adk.tools.tool_context import ToolContext
from typing import AsyncGenerator, Optional
from google.adk.events import Event, EventActions

# --- Constants ---
APP_NAME = "doc_writing_app_v3" # New App Name
USER_ID = "dev_user_01"
SESSION_ID_BASE = "loop_exit_tool_session" # New Base Session ID
GEMINI_MODEL = "gemini-2.0-flash"
STATE_INITIAL_TOPIC = "initial_topic"

# --- State Keys ---
STATE_CURRENT_DOC = "current_document"
STATE_CRITICISM = "criticism"
# Define the exact phrase the Critic should use to signal completion
COMPLETION_PHRASE = "No major issues found."

# --- Tool Definition ---
def exit_loop(tool_context: ToolContext):
"""Call this function ONLY when the critique indicates no further changes are needed, signaling the iterative process should end."""
print(f"  [Tool Call] exit_loop triggered by {tool_context.agent_name}")
tool_context.actions.escalate = True
# Return empty dict as tools should typically return JSON-serializable output
return {}

# --- Agent Definitions ---

# STEP 1: Initial Writer Agent (Runs ONCE at the beginning)
initial_writer_agent = LlmAgent(
name="InitialWriterAgent",
model=GEMINI_MODEL,
include_contents='none',
# MODIFIED Instruction: Ask for a slightly more developed start
instruction=f"""You are a Creative Writing Assistant tasked with starting a story.
Write the *first draft* of a short story (aim for 2-4 sentences).
Base the content *only* on the topic provided below. Try to introduce a specific element (like a character, a setting detail, or a starting action) to make it engaging.
Topic: {{initial_topic}}

Output *only* the story/document text. Do not add introductions or explanations.
""",
description="Writes the initial document draft based on the topic, aiming for some initial substance.",
output_key=STATE_CURRENT_DOC
)

# STEP 2a: Critic Agent (Inside the Refinement Loop)
critic_agent_in_loop = LlmAgent(
name="CriticAgent",
model=GEMINI_MODEL,
include_contents='none',
# MODIFIED Instruction: More nuanced completion criteria, look for clear improvement paths.
instruction=f"""You are a Constructive Critic AI reviewing a short document draft (typically 2-6 sentences). Your goal is balanced feedback.

**Document to Review:**

{{current_document}}

**Task:**
Review the document for clarity, engagement, and basic coherence according to the initial topic (if known).

IF you identify 1-2 *clear and actionable* ways the document could be improved to better capture the topic or enhance reader engagement (e.g., "Needs a stronger opening sentence", "Clarify the character's goal"):
Provide these specific suggestions concisely. Output *only* the critique text.

ELSE IF the document is coherent, addresses the topic adequately for its length, and has no glaring errors or obvious omissions:
Respond *exactly* with the phrase "{COMPLETION_PHRASE}" and nothing else. It doesn't need to be perfect, just functionally complete for this stage. Avoid suggesting purely subjective stylistic preferences if the core is sound.

Do not add explanations. Output only the critique OR the exact completion phrase.
""",
description="Reviews the current draft, providing critique if clear improvements are needed, otherwise signals completion.",
output_key=STATE_CRITICISM
)


# STEP 2b: Refiner/Exiter Agent (Inside the Refinement Loop)
refiner_agent_in_loop = LlmAgent(
name="RefinerAgent",
model=GEMINI_MODEL,
# Relies solely on state via placeholders
include_contents='none',
instruction=f"""You are a Creative Writing Assistant refining a document based on feedback OR exiting the process.
**Current Document:**

{{current_document}}

**Critique/Suggestions:**
{{criticism}}

**Task:**
Analyze the 'Critique/Suggestions'.
IF the critique is *exactly* "{COMPLETION_PHRASE}":
You MUST call the 'exit_loop' function. Do not output any text.
ELSE (the critique contains actionable feedback):
Carefully apply the suggestions to improve the 'Current Document'. Output *only* the refined document text.

Do not add explanations. Either output the refined document OR call the exit_loop function.
""",
description="Refines the document based on critique, or calls exit_loop if critique indicates completion.",
tools=[exit_loop], # Provide the exit_loop tool
output_key=STATE_CURRENT_DOC # Overwrites state['current_document'] with the refined version
)


# STEP 2: Refinement Loop Agent
refinement_loop = LoopAgent(
name="RefinementLoop",
# Agent order is crucial: Critique first, then Refine/Exit
sub_agents=[
critic_agent_in_loop,
refiner_agent_in_loop,
],
max_iterations=5 # Limit loops
)

# STEP 3: Overall Sequential Pipeline
# For ADK tools compatibility, the root agent must be named `root_agent`
root_agent = SequentialAgent(
name="IterativeWritingPipeline",
sub_agents=[
initial_writer_agent, # Run first to create initial doc
refinement_loop       # Then run the critique/refine loop
],
description="Writes an initial document and then iteratively refines it with critique using an exit tool."
)

// ExitLoopArgs defines the (empty) arguments for the ExitLoop tool.
type ExitLoopArgs struct{}

// ExitLoopResults defines the output of the ExitLoop tool.
type ExitLoopResults struct{}

// ExitLoop is a tool that signals the loop to terminate by setting Escalate to true.
func ExitLoop(ctx tool.Context, input ExitLoopArgs) ExitLoopResults {
fmt.Printf("[Tool Call] exitLoop triggered by %s \n", ctx.AgentName())
ctx.Actions().Escalate = true
return ExitLoopResults{}
}

func main() {
ctx := context.Background()

if err := runAgent(ctx, "Write a document about a cat"); err != nil {
log.Fatalf("Agent execution failed: %v", err)
}
}

func runAgent(ctx context.Context, prompt string) error {
model, err := gemini.NewModel(ctx, modelName, &genai.ClientConfig{})
if err != nil {
return fmt.Errorf("failed to create model: %v", err)
}

// STEP 1: Initial Writer Agent (Runs ONCE at the beginning)
initialWriterAgent, err := llmagent.New(llmagent.Config{
Name:        "InitialWriterAgent",
Model:       model,
Description: "Writes the initial document draft based on the topic.",
Instruction: `You are a Creative Writing Assistant tasked with starting a story.
Write the *first draft* of a short story (aim for 2-4 sentences).
Base the content *only* on the topic provided in the user's prompt.
Output *only* the story/document text. Do not add introductions or explanations.`,
OutputKey: stateDoc,
})
if err != nil {
return fmt.Errorf("failed to create initial writer agent: %v", err)
}

// STEP 2a: Critic Agent (Inside the Refinement Loop)
criticAgentInLoop, err := llmagent.New(llmagent.Config{
Name:        "CriticAgent",
Model:       model,
Description: "Reviews the current draft, providing critique or signaling completion.",
Instruction: fmt.Sprintf(`You are a Constructive Critic AI reviewing a short document draft.
**Document to Review:**
"""
{%s}
"""
**Task:**
Review the document.
IF you identify 1-2 *clear and actionable* ways it could be improved:
Provide these specific suggestions concisely. Output *only* the critique text.
ELSE IF the document is coherent and addresses the topic adequately:
Respond *exactly* with the phrase "%s" and nothing else.`, stateDoc, donePhrase),
OutputKey: stateCrit,
})
if err != nil {
return fmt.Errorf("failed to create critic agent: %v", err)
}

exitLoopTool, err := functiontool.New(
functiontool.Config{
Name:        "exitLoop",
Description: "Call this function ONLY when the critique indicates no further changes are needed.",
},
ExitLoop,
)
if err != nil {
return fmt.Errorf("failed to create exit loop tool: %v", err)
}

// STEP 2b: Refiner/Exiter Agent (Inside the Refinement Loop)
refinerAgentInLoop, err := llmagent.New(llmagent.Config{
Name:  "RefinerAgent",
Model: model,
Instruction: fmt.Sprintf(`You are a Creative Writing Assistant refining a document based on feedback OR exiting the process.
**Current Document:**

"""
{%s}
"""

**Critique/Suggestions:**
{%s}
**Task:**
Analyze the 'Critique/Suggestions'.
IF the critique is *exactly* "%s":
You MUST call the 'exitLoop' function. Do not output any text.
ELSE (the critique contains actionable feedback):
Carefully apply the suggestions to improve the 'Current Document'. Output *only* the refined document text.`, stateDoc, stateCrit, donePhrase),
Description: "Refines the document based on critique, or calls exitLoop if critique indicates completion.",
Tools:       []tool.Tool{exitLoopTool},
OutputKey:   stateDoc,
})
if err != nil {
return fmt.Errorf("failed to create refiner agent: %v", err)
}

// STEP 2: Refinement Loop Agent
refinementLoop, err := loopagent.New(loopagent.Config{
AgentConfig: agent.Config{
Name:      "RefinementLoop",
SubAgents: []agent.Agent{criticAgentInLoop, refinerAgentInLoop},
},
MaxIterations: 5,
})
if err != nil {
return fmt.Errorf("failed to create loop agent: %v", err)
}

// STEP 3: Overall Sequential Pipeline
iterativeWriterAgent, err := sequentialagent.New(sequentialagent.Config{
AgentConfig: agent.Config{
Name:      appName,
SubAgents: []agent.Agent{initialWriterAgent, refinementLoop},
},
})
if err != nil {
return fmt.Errorf("failed to create sequential agent pipeline: %v", err)
}

import static com.google.adk.agents.LlmAgent.IncludeContents.NONE;

import com.google.adk.agents.LlmAgent;
import com.google.adk.agents.LoopAgent;
import com.google.adk.agents.SequentialAgent;
import com.google.adk.events.Event;
import com.google.adk.runner.InMemoryRunner;
import com.google.adk.sessions.Session;
import com.google.adk.tools.Annotations.Schema;
import com.google.adk.tools.FunctionTool;
import com.google.adk.tools.ToolContext;
import com.google.genai.types.Content;
import com.google.genai.types.Part;
import io.reactivex.rxjava3.core.Flowable;
import java.util.Map;

public class LoopAgentExample {

// --- Constants ---
private static final String APP_NAME = "IterativeWritingPipeline";
private static final String USER_ID = "test_user_456";
private static final String MODEL_NAME = "gemini-2.0-flash";

// --- State Keys ---
private static final String STATE_CURRENT_DOC = "current_document";
private static final String STATE_CRITICISM = "criticism";

public static void main(String[] args) {
LoopAgentExample loopAgentExample = new LoopAgentExample();
loopAgentExample.runAgent("Write a document about a cat");
}

// --- Tool Definition ---
@Schema(
description =
"Call this function ONLY when the critique indicates no further changes are needed,"
+ " signaling the iterative process should end.")
public static Map<String, Object> exitLoop(@Schema(name = "toolContext") ToolContext toolContext) {
System.out.printf("[Tool Call] exitLoop triggered by %s \n", toolContext.agentName());
toolContext.actions().setEscalate(true);
//  Return empty dict as tools should typically return JSON-serializable output
return Map.of();
}

// --- Agent Definitions ---
public void runAgent(String prompt) {
// STEP 1: Initial Writer Agent (Runs ONCE at the beginning)
LlmAgent initialWriterAgent =
LlmAgent.builder()
.model(MODEL_NAME)
.name("InitialWriterAgent")
.description(
"Writes the initial document draft based on the topic, aiming for some initial"
+ " substance.")
.instruction(
"""
You are a Creative Writing Assistant tasked with starting a story.
Write the *first draft* of a short story (aim for 2-4 sentences).
Base the content *only* on the topic provided below. Try to introduce a specific element (like a character, a setting detail, or a starting action) to make it engaging.

Output *only* the story/document text. Do not add introductions or explanations.
""")
.outputKey(STATE_CURRENT_DOC)
.includeContents(NONE)
.build();

// STEP 2a: Critic Agent (Inside the Refinement Loop)
LlmAgent criticAgentInLoop =
LlmAgent.builder()
.model(MODEL_NAME)
.name("CriticAgent")
.description(
"Reviews the current draft, providing critique if clear improvements are needed,"
+ " otherwise signals completion.")
.instruction(
"""
You are a Constructive Critic AI reviewing a short document draft (typically 2-6 sentences). Your goal is balanced feedback.

**Document to Review:**

{{current_document}}

**Task:**
Review the document for clarity, engagement, and basic coherence according to the initial topic (if known).

IF you identify 1-2 *clear and actionable* ways the document could be improved to better capture the topic or enhance reader engagement (e.g., "Needs a stronger opening sentence", "Clarify the character's goal"):
Provide these specific suggestions concisely. Output *only* the critique text.

ELSE IF the document is coherent, addresses the topic adequately for its length, and has no glaring errors or obvious omissions:
Respond *exactly* with the phrase "No major issues found." and nothing else. It doesn't need to be perfect, just functionally complete for this stage. Avoid suggesting purely subjective stylistic preferences if the core is sound.

Do not add explanations. Output only the critique OR the exact completion phrase.
""")
.outputKey(STATE_CRITICISM)
.includeContents(NONE)
.build();

// STEP 2b: Refiner/Exiter Agent (Inside the Refinement Loop)
LlmAgent refinerAgentInLoop =
LlmAgent.builder()
.model(MODEL_NAME)
.name("RefinerAgent")
.description(
"Refines the document based on critique, or calls exitLoop if critique indicates"
+ " completion.")
.instruction(
"""
You are a Creative Writing Assistant refining a document based on feedback OR exiting the process.
**Current Document:**

{{current_document}}

**Critique/Suggestions:**
{{criticism}}

**Task:**
Analyze the 'Critique/Suggestions'.
IF the critique is *exactly* "No major issues found.":
You MUST call the 'exitLoop' function. Do not output any text.
ELSE (the critique contains actionable feedback):
Carefully apply the suggestions to improve the 'Current Document'. Output *only* the refined document text.

Do not add explanations. Either output the refined document OR call the exitLoop function.
""")
.outputKey(STATE_CURRENT_DOC)
.includeContents(NONE)
.tools(FunctionTool.create(LoopAgentExample.class, "exitLoop"))
.build();

// STEP 2: Refinement Loop Agent
LoopAgent refinementLoop =
LoopAgent.builder()
.name("RefinementLoop")
.description("Repeatedly refines the document with critique and then exits.")
.subAgents(criticAgentInLoop, refinerAgentInLoop)
.maxIterations(5)
.build();

// STEP 3: Overall Sequential Pipeline
SequentialAgent iterativeWriterAgent =
SequentialAgent.builder()
.name(APP_NAME)
.description(
"Writes an initial document and then iteratively refines it with critique using an"
+ " exit tool.")
.subAgents(initialWriterAgent, refinementLoop)
.build();

// Create an InMemoryRunner
InMemoryRunner runner = new InMemoryRunner(iterativeWriterAgent, APP_NAME);
// InMemoryRunner automatically creates a session service. Create a session using the service
Session session = runner.sessionService().createSession(APP_NAME, USER_ID).blockingGet();
Content userMessage = Content.fromParts(Part.fromText(prompt));

// Run the agent
Flowable<Event> eventStream = runner.runAsync(USER_ID, session.id(), userMessage);

// Stream event response
eventStream.blockingForEach(
event -> {
if (event.finalResponse()) {
System.out.println(event.stringifyContent());
}
});
}
}