MongoDB Mongodb Building A Text To Mql Agent

Mongodb Building A Text To Mql Agent

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Build a Production-Ready Text-to-MQL Agent for MongoDB

Transform natural language into powerful MongoDB queries using AI agents that remember context, learn from conversations, and provide intelligent insights into your data.

Overview

By the end of this notebook, you will have implemented a production-ready conversational database agent with the following capabilities:

Natural language processing: Convert human language queries into MongoDB aggregation pipelines
Query generation: Automatically generate complex MongoDB queries from simple descriptions
Conversation memory: Maintain context across multiple related queries in a session
Debugging and observability: Track step-by-step execution with detailed summaries
Architecture comparison: Implement and compare ReAct vs. structured custom agent approaches

Use Cases

Traditional database interaction requires knowledge of MongoDB aggregation syntax, collection schemas, and query validation. This agent abstracts these complexities, providing a natural language interface for database operations.

Implementation Approaches

ReAct Agent

Flexible reasoning and tool selection
Suitable for exploratory queries and rapid prototyping
Autonomous decision-making for tool usage

Custom LangGraph Agent

Deterministic, structured workflow
Enhanced debugging capabilities with full observability
Designed for production environments with predictable behavior

Memory System

The system implements a custom MongoDB-based memory system with LLM-powered summarization that provides:

	User: Count query for movies
Schema: movies collection
Query: aggregation pipeline
Results: 5 documents returned
Response: formatted answer

Conversation memory enables multi-turn interactions:

"List the top directors" → Agent returns top 3 directors
"What was the count for the first one?" → Agent references previous results
"Show me their best films" → Agent continues with context

Business Applications

This system handles sophisticated analytical queries such as:

Analytics: "Which states have the most theaters and what's the average occupancy?"
Recommendations: "Find directors similar to Christopher Nolan with at least 10 films"
Trend Analysis: "Show me movie rating trends by decade for sci-fi films"
Geographic Analysis: "Which theaters are furthest west and what movies do they show?"

Technical Components

MongoDB Atlas: Data storage with aggregation pipeline support
OpenAI GPT: Natural language processing and query generation
LangGraph: Deterministic agent workflow management
LangChain: LLM integration and tool orchestration
Persistent Memory: Conversation state management with enhanced debugging

Prerequisites

To run this notebook, you need:

MongoDB Atlas cluster with the sample_mflix dataset loaded
- Follow the sample data loading instructions
- Or follow-along with the screenshots below
OpenAI API key
Environment variables:
- MONGODB_URI
- OPENAI_API_KEY

Sample Data UI

🌐 Network Setup: Connect to Your Atlas Cluster

Before we dive into the implementation, let's make sure your environment can reach MongoDB Atlas.

⚠️ Quick IP Check - Run this to get your current IP address for MongoDB Atlas network access list:

⚠️ Check your public IP — useful for updating MongoDB Atlas network access if needed.

[2]

35.229.69.92

System Setup and Configuration

This section installs the required dependencies and configures the core components needed for the text-to-MQL system.

Step 1: Install Dependencies

Installing the core libraries for AI-powered database interaction:

LangGraph: Modern AI agent framework
LangChain MongoDB: Database integration tools
OpenAI Integration: GPT model integration for query generation
MongoDB Checkpointing: Persistent memory management

[ ]

📦 All dependencies installed successfully!

Configure Credentials

Configuration Requirements:

MongoDB Atlas Connection String
- Obtain from MongoDB Atlas Console
- Ensure the sample_mflix dataset is loaded
OpenAI API Key
- Obtain from OpenAI Platform
- GPT-4o-mini is used for optimal performance and cost balance

Note: In production environments, use secure environment variable management rather than hardcoded values.

[ ]

🔑 Environment variables configured!

Initialize Core Components

Initialize the foundation components required for the text-to-MQL system:

MongoDBDatabase wrapper: Provides AI-accessible interface to database operations
ChatOpenAI interface: Handles language model interactions
MongoDB client: Powers the conversation memory system

[6]

[7]

✅ Database and LLM initialized successfully!

MongoDB Toolkit Overview

The MongoDBDatabaseToolkit provides comprehensive MongoDB capabilities for AI agents:

Tool	Purpose	Example Use Case
`mongodb_list_collections`	Database discovery	"What collections are available?"
`mongodb_schema`	Schema inspection	"What is the structure of the movies collection?"
`mongodb_query_checker`	Query validation	"Validate this aggregation pipeline"
`mongodb_query`	Query execution	"Execute this MongoDB query"

These tools enable the AI agent to understand database structure and execute queries autonomously.

[8]

🛠️ Available Tools: ['mongodb_query', 'mongodb_schema', 'mongodb_list_collections', 'mongodb_query_checker']

Data Discovery

Examine the sample dataset structure. The sample_mflix dataset provides:

Movies collection: Film metadata including ratings, cast, and genres
Users collection: User profiles and preferences
Comments collection: User reviews and ratings
Theaters collection: Theater locations and screening information

This dataset demonstrates real-world complexity suitable for testing aggregation queries and geographic analysis.

[9]


📋 Available Collections: ['comments', 'embedded_movies', 'movies', 'sessions', 'theaters', 'users']

[10]


📊 Movies Collection Schema Sample:
Database name: sample_mflix
Collection name: movies
Schema from a sample of documents from the collection:
_id: ObjectId
plot: String
genres: Array<String>
runtime: Number
cast: Array<String>
num_mflix_comments: Number
poster: String
title: String
fullplot: String
languages: Array<String>
released: Timestamp
directors: Array<String>
writers: Array<String>
awards.wins: Number
awards.nominations: Number
awards.text: String
lastupdated: String
year: Number
imdb.rating: Number
imdb.votes: Number
imd...

Persisting Agent Outputs

Overview

Instead of saving outputs to a local file, you can persist them in MongoDB using the built-in LangGraph saver. Treat past runs as “memory” and reload them easily. This extends MongoDB's standard MongoDBSaver checkpointer with LLM-generated step summaries, providing human-readable conversation histories instead of raw checkpoint data.

Features

Readable Step Summaries

	User: "How many movies from the 1990s?"
LLM Summary: "Count query with date range filter"
MongoDB Query: Aggregation pipeline with $match and $count operations

Enhanced Thread Inspection

	Step 1 [14:23:45] User asks about top movies  
Step 2 [14:23:46] Schema lookup: movies collection
Step 3 [14:23:47] Aggregation query execution
Step 4 [14:23:48] 5 results returned
Step 5 [14:23:49] Formatted response delivered

Enhanced Metadata

Each checkpoint includes:

step_summary: LLM-generated description
step_timestamp: Execution timestamp
step_number: Sequential step counter

Implementation

The LLM analyzes each conversation step and generates concise summaries:

User messages: Categorizes query intent and patterns
Tool calls: Describes the operation being performed
Results: Summarizes returned data
Errors: Explains failure conditions

Usage

	# Drop-in replacement for standard MongoDBSaver
checkpointer = LLMSummarizingMongoDBSaver(client, llm)

# Use with any LangGraph agent
agent = create_react_agent(llm, tools, checkpointer=checkpointer)

Benefits

Compatible interface: No code changes required from standard MongoDBSaver
Enhanced debugging: Clear visibility into agent execution steps
Human-readable logs: Understand conversation flow at a glance
Flexible implementation: Works with any LangGraph agent and domain

This maintains all functionality of the standard LangGraph memory system while adding intelligent logging capabilities.

[11]

Thread Inspection and Debugging

`inspect_thread_with_summaries_enhanced(thread_id: str, limit: int = 20, show_details: bool = False)`

This function provides a human-readable view of agent conversation history by fetching checkpoints from MongoDB and displaying LLM-generated step summaries in chronological order with timestamps.

Features:

Automatic grouping of consecutive similar operations to reduce clutter
Handles both dictionary and binary metadata formats
Essential for debugging complex multi-step queries and understanding agent decision-making

Example output:

	Thread History: session_123
Total steps: 5

Step 1 [14:23:45]
   User: count movies query

Step 2 [14:23:46]
   Schema lookup: movies

Step 3 [14:23:47]
   Aggregation pipeline

Step 4 [14:23:48]
   157 results returned

Step 5 [14:23:49]
   Formatted response

Parameters:

show_details=True: Display all steps without grouping
limit: Adjust to focus on recent activity

[ ]

🔄 UPDATING AGENTS WITH LLM-POWERED SUMMARIZATION
============================================================

ReAct Agent Creation Functions

`create_react_agent_with_enhanced_memory()`

Creates a LangChain ReAct agent with persistent memory powered by the LLMSummarizingMongoDBSaver.

Functionality:

Combines the standard MongoDB agent system prompt with enhanced checkpointer
Provides ReAct agent with conversation memory across sessions
Generates intelligent step summaries using LLM
Uses the complete MongoDB toolkit for database operations

Returns: LangChain ReAct agent with MongoDB tools and LLM-powered memory

Usage:

	agent = create_react_agent_with_enhanced_memory()
config = {"configurable": {"thread_id": "my_session"}}
agent.invoke({"messages": [("user", "Count all movies")]}, config)

[13]

Core LangGraph Components

This section defines the individual nodes and functions that comprise the custom LangGraph agent workflow.

Workflow Design

Creates a deterministic, debuggable pipeline:

Discovery: List collections
Schema Analysis: Get relevant collection schemas
Query Generation: Convert natural language to MongoDB
Validation: Check and sanitize query (optional)
Execution: Run query against database
Formatting: Present results in readable format

Each step is a separate node, enabling easy debugging, modification, or workflow extension.

Tool Nodes

Wraps MongoDB tools in LangGraph ToolNode format for the state machine.

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Workflow Node Functions

`list_collections(state: MessagesState)`

Deterministic node that automatically lists all available MongoDB collections. Always runs first to provide agent context about available data.

[15]

`call_get_schema(state: MessagesState)`

LLM decision node that prompts the LLM to select which collections to examine and calls the schema tool. The LLM determines required schema information based on the user's query.

[16]

`generate_query(state: MessagesState)`

Core query generation that converts user natural language into MongoDB aggregation pipeline. Uses the complete agent system prompt with conversation context.

[17]

`check_query(state: MessagesState)`

Query validation that verifies and sanitizes the generated query before execution. Helps identify syntax errors and potential issues.

[18]

`format_answer(state: MessagesState)`

Result formatting that converts raw MongoDB JSON results into readable Markdown. Uses a dedicated formatting prompt to present data clearly to end users.

[ ]

Control Flow

`need_checker(state: MessagesState) -> Literal[END, "check_query"]`

Conditional edge that determines if the generated query requires validation. Routes to query checker if tool calls are present, otherwise proceeds directly to execution.

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Custom LangGraph Agent Creation

`create_langgraph_agent_with_enhanced_memory()`

Creates a custom LangGraph state machine agent with a deterministic, step-by-step workflow for MongoDB queries. Provides enhanced control and debuggability compared to the ReAct agent.

Components:

State Graph with 7 distinct nodes for different operations
Linear workflow with one conditional branch for query validation
LLM-powered checkpointer for conversation memory and step summarization

Workflow:

	START → list_collections → call_get_schema → get_schema → generate_query
                                                              ↓
                                                         need_checker?
                                                         ↙         ↘
                                                  check_query    run_query
                                                       ↓             ↓
                                                   run_query   format_answer
                                                                     ↓
                                                                   END

Key Features:

Deterministic flow: Each step occurs in predictable order
Conditional validation: Queries checked only when required
Memory persistence: Complete conversation state saved with LLM summaries
Debuggable: Individual nodes can be inspected or modified

Returns: Compiled LangGraph agent ready for execution

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Agent Initialization

Creating Both Agent Types

	react_agent_with_memory = create_react_agent_with_enhanced_memory()
mongo_agent_with_memory = create_langgraph_agent_with_enhanced_memory()

This section instantiates both agent variants:

ReAct Agent: Uses LangChain's prebuilt ReAct pattern for dynamic reasoning
LangGraph Agent: Uses the custom state machine workflow for deterministic processing

Both agents share:

MongoDB toolkit for schema, query, and validation operations
LLM-powered checkpointer for conversation memory
Intelligent step summarization for debugging

System Capabilities

Key improvements over standard MongoDB agents:

Database flexibility: Works with any MongoDB database beyond sample datasets
LLM intelligence: Uses GPT models to understand and summarize agent behavior
Adaptive processing: Handles any natural language query pattern automatically
Natural language logs: Step summaries are human-readable rather than technical
Performance optimization: Caches LLM summaries to reduce API calls and latency

Usage Options

Use react_agent_with_memory for flexible, autonomous reasoning
Use mongo_agent_with_memory for predictable, step-by-step processing

Both maintain complete conversation context and provide intelligent summarization for debugging and optimization.

[22]

✅ Agents created with LLM-powered summarization!

📖 Features:
• Works with any MongoDB database and collection
• Uses LLM to intelligently summarize each step
• Adapts to any query type automatically
• Provides natural language step descriptions
• Caches summaries for better performance

Agent Execution Functions

`execute_react_with_memory(thread_id: str, user_input: str)`

Executes the ReAct agent with conversation persistence and streams results with formatted output.

Parameters:

thread_id: Unique identifier for the conversation thread (enables memory)
user_input: Natural language query to process

Functionality:

Configures the agent to use the specified thread for memory persistence
Displays execution header with thread ID, query, and agent type
Streams the agent's execution in real-time using stream_mode="values"
Formats each message as it's generated (tool calls, responses, etc.)

Example:

	execute_react_with_memory("session_1", "Count all movies from 2020")

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`execute_graph_with_memory(thread_id: str, user_input: str)`

Executes the custom LangGraph agent with the same memory and streaming capabilities.

Parameters:

thread_id: Unique identifier for the conversation thread
user_input: Natural language query to process

Key Differences from ReAct:

Uses the deterministic state machine workflow
Input format is {"messages": [{"role": "user", "content": user_input}]}
Each workflow step is visible as it executes

Usage: Both functions provide identical interfaces but use different agent implementations. The LangGraph version provides visibility into the step-by-step workflow, while ReAct offers more autonomous reasoning.

Memory Persistence: Both functions automatically save conversation state to MongoDB, enabling follow-up queries in the same thread to reference previous interactions.

[24]

Memory Management Functions

Typical debugging sequence:

memory_system_stats() - Check overall system health
list_conversation_threads() - View all available threads
inspect_thread_history("thread_id") - Debug specific conversations
clear_thread_history("thread_id") - Clean up old or problematic threads

These functions provide complete visibility and control over the agent's memory system.

`list_conversation_threads()`

Lists all available conversation threads stored in the MongoDB checkpoint database.

Output:

All unique thread IDs that have been created
Total number of checkpoints across all threads
Number of checkpoints per individual thread

Example output:

	Available Conversation Threads:
Total checkpoints: 147
==================================================
  1. Thread: session_123
     └─ 12 checkpoints
  2. Thread: demo_basic_1
     └─ 8 checkpoints
  3. Thread: interactive_abc
     └─ 25 checkpoints

Usage: list_conversation_threads()

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`inspect_thread_history(thread_id: str, limit: int = 10)`

Inspects the conversation history for a specific thread, showing step-by-step execution details.

Features:

Smart fallback: Uses enhanced inspection with LLM summaries if available, otherwise falls back to basic checkpoint analysis
Configurable limit: Control how many recent steps to display
Detailed breakdown: Shows messages, tool calls, and content for each step

Parameters:

thread_id: The conversation thread to inspect
limit: Maximum number of recent checkpoints to show (default: 10)

Usage: inspect_thread_history("session_123", limit=5)

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`clear_thread_history(thread_id: str)`

Completely removes all conversation history for a specific thread from MongoDB.

What it clears:

Main checkpoints collection (conversation state)
Checkpoint writes collection (operation logs)

Warning: This action is irreversible. The agent will lose all memory of previous interactions in this thread.

Usage: clear_thread_history("old_session_456")

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`memory_system_stats()`

Provides a comprehensive overview of the entire memory system's usage and health.

Metrics displayed:

Total checkpoints across all threads
Total checkpoint writes (operation logs)
Number of unique conversation threads
Database name being used

Example output:

	Memory System Statistics
========================================
Total checkpoints: 147
Total checkpoint writes: 298
Total conversation threads: 8
Database: checkpointing_db

Returns: Dictionary with stats for programmatic use

Usage: stats = memory_system_stats()

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Demonstration Functions

This section provides ready-to-run examples that showcase different aspects of the Text-to-MQL system.

Running Demos

Each function is self-contained and generates unique thread IDs to avoid conflicts. They provide formatted output showing:

Query execution in real-time
Step-by-step agent reasoning
Final results and analysis
Memory inspection summaries

Quick start: Run test_enhanced_summarization() to see the complete system in action with intelligent step tracking.

`demo_basic_queries()`

Demonstrates core text-to-MQL functionality with 5 standalone queries of increasing complexity.

Query types:

Top movies by IMDb rating
Most active commenters
Theater distribution by state
Westernmost theaters (geospatial)
Complex director analysis with multiple criteria

Purpose: Shows the range of query types the system can handle, from simple sorting to complex aggregations.

Usage: demo_basic_queries()

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`demo_conversation_memory()`

Demonstrates multi-turn conversation where each query builds on previous results.

Conversation flow:

"List the top 3 directors by movie count"
"What was the movie count for the first director?" (references previous result)
"Show me movies by that director with highest ratings" (continues context)

Key feature: Shows how the agent remembers previous results and can answer follow-up questions without re-querying.

Usage: demo_conversation_memory()

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`compare_agents_with_memory()`

Side-by-side comparison of ReAct vs LangGraph agents using the same complex query.

Comparison points:

Execution style: ReAct's autonomous reasoning vs LangGraph's structured workflow
Memory patterns: How each agent stores conversation state
Output format: Differences in result presentation

Usage: compare_agents_with_memory()

[ ]

`test_memory_functionality()`

Simple two-step test focused specifically on memory capabilities.

Test sequence:

Initial query about directors
Follow-up question that requires remembering the first result

Purpose: Quick validation that conversation memory is working correctly.

Usage: test_memory_functionality()

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`test_enhanced_summarization()`

Tests the LLM-powered summarization system with various query patterns.

Functionality:

Runs 3 different query types (count, average, top results)
Executes each with full step tracking
Displays enhanced thread analysis with LLM-generated summaries

Purpose: Validates that the summarization system correctly categorizes and describes different types of operations.

Usage: test_enhanced_summarization()

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Supporting Test Functions

These functions provide pre-configured test scenarios for validating agent comparison functionality across different query complexity levels.

test_simple_comparison() uses basic counting queries with conservative retry settings,
test_moderate_comparison() tests standard aggregation patterns,
test_complex_comparison() validates the original problematic query using enhanced error handling
run_comparison_tests() function executes all three scenarios in sequence, providing comprehensive assessment of both ReAct and LangGraph agent capabilities with automatic error isolation and performance benchmarking.

[34]

✅ Enhanced agent comparison functions loaded!

Usage examples:
compare_agents_with_memory("Count all movies", max_retries=2)
compare_agents_with_memory("Find top directors", max_retries=3, recursion_limit=40)
run_comparison_tests()  # Run multiple test scenarios

Interactive Query Interface

`interactive_query()`

Provides a command-line interface for real-time interaction with the Text-to-MQL agent. Creates a conversational session where you can ask multiple related questions and manage conversation threads.

Features:

Persistent conversation: Maintains context across multiple queries in the same thread
Thread management: Switch between different conversation contexts
Built-in debugging: Inspect conversation history without leaving the interface
Error handling: Graceful handling of interruptions and errors

Available Commands

Command	Description	Example
`<natural language>`	Execute MongoDB query	`"Count movies from 2020"`
`exit`	Quit the interface	`exit`
`threads`	List all conversation threads	`threads`
`switch <thread_id>`	Change to different thread	`switch session_123`
`debug`	Inspect current thread history	`debug`

Interactive Session Example

	Interactive Text-to-MQL Query Interface
Commands: 'exit' to quit, 'threads' to list, 'switch <thread>' to change thread
======================================================================

[interactive_abc123] Enter your query: Count all movies in the database

Thread: interactive_abc123
Query: Count all movies in the database
Agent: Custom LangGraph
==================================================
[Agent execution with step-by-step output...]

[interactive_abc123] Enter your query: What about just movies from 2020?

[Continues conversation with memory of previous query...]

[interactive_abc123] Enter your query: debug

Thread History: interactive_abc123
Total steps: 8
================================================================================
[Shows conversation history...]

[interactive_abc123] Enter your query: exit
Goodbye!

Session Management

Automatic thread creation: Each session starts with a unique thread ID (interactive_<random>)

Thread switching: Use switch <thread_id> to continue previous conversations:

	[interactive_abc123] Enter your query: switch session_older
Switched to thread: session_older
[session_older] Enter your query: What did we discuss last time?

Memory persistence: All queries and results are saved to MongoDB, allowing you to return to any conversation later.

Usage

Start interactive session: interactive_query()

Best practices:

Use meaningful thread names when switching (switch movie_analysis_2024)
Use debug command to review conversation context
Use threads to see all available conversation histories

This interface is ideal for exploratory data analysis sessions where you want to ask follow-up questions and build on previous results.

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System Initialization and Quick Reference

This section provides the startup summary and quick reference guide for the Text-to-MQL system.

System Status Display

Startup sequence:

	Text-to-MQL Agent with MongoDB Memory - Ready
============================================================
Memory System Statistics
========================================
Total checkpoints: 0
Total checkpoint writes: 0  
Total conversation threads: 0
Database: checkpointing_db

Automatically displays current memory system health and usage statistics.

Available Functions Reference

Demonstration Functions:

demo_basic_queries() - Showcase core text-to-MQL capabilities
demo_conversation_memory() - Multi-turn conversation examples
compare_agents_with_memory() - ReAct vs LangGraph comparison
test_memory_functionality() - Simple memory validation
test_enhanced_summarization() - LLM summarization testing
interactive_query() - Real-time query interface

Memory Management Tools:

list_conversation_threads() - View all conversation threads
inspect_thread_history(thread_id) - Debug specific conversations
inspect_thread_with_summaries_enhanced(thread_id) - Enhanced thread analysis
clear_thread_history(thread_id) - Delete conversation history
memory_system_stats() - System health overview

Quick Start Recommendations

For first-time users:

test_enhanced_summarization() - See the complete system in action
demo_conversation_memory() - Experience multi-turn conversations
interactive_query() - Try your own queries

System Capabilities Summary

Core features confirmed operational:

Dual agent architecture: Both ReAct and LangGraph agents ready
LLM-powered memory: Intelligent step summarization active
MongoDB persistence: Conversation state saved automatically
Enhanced debugging: Human-readable conversation histories

Key improvements over standard agents:

Query categorization using natural language understanding
Conversation-aware step descriptions
Better thread inspection with LLM insights
Performance-optimized memory debugging

This summary serves as both a system health check and a quick reference guide for exploring the system's capabilities.

[36]


🚀 Text-to-MQL Agent with MongoDB Memory - Ready!
============================================================
📊 Memory System Statistics
========================================
💾 Total checkpoints: 0
✍️ Total checkpoint writes: 0
🧵 Total conversation threads: 0
🏛️ Database: checkpointing_db

{'checkpoints': 0, 'writes': 0, 'threads': 0}

Initial Test Execution

Automatic Startup Test

	if __name__ == "__main__":
    # Start with the enhanced summarization test
    test_enhanced_summarization()

Purpose: When the notebook/script is run directly, automatically executes a demonstration to verify the system is working correctly.

What happens:

System initialization: All agents and memory components are loaded
Test execution: Runs test_enhanced_summarization() which:
- Creates a new conversation thread
- Executes 3 different query patterns
- Demonstrates LLM-powered step summarization
- Shows enhanced thread inspection capabilities

Expected output:

	Testing Enhanced Summarization System
============================================================
Testing thread: enhanced_test_abc12345
Running query patterns with enhanced summarization...
==================================================

Test 1: How many movies are in the database?
[Agent execution with step-by-step summaries...]
Test 1 complete

Test 2: Find the average rating of all movies
[Agent execution...]
Test 2 complete

Test 3: Show me the top 5 directors by movie count
[Agent execution...]
Test 3 complete

Enhanced Thread Analysis:
==================================================
[Thread history with LLM-generated summaries...]

Validation checks:

MongoDB connection working
OpenAI API accessible
Agent workflow functioning
Memory persistence active
LLM summarization operational

Note: In Colab notebooks, this section typically won't auto-execute since notebooks run cell-by-cell. You can manually run test_enhanced_summarization() to perform the same validation.

This serves as a smoke test to ensure all system components are properly initialized and functioning before manual exploration.

[37]


🧪 TESTING ENHANCED SUMMARIZATION SYSTEM
============================================================
Testing thread: enhanced_test_f4288e1b
Running query patterns with enhanced summarization...
==================================================

📌 Test 1: How many movies are in the database?
🧵 Thread: enhanced_test_f4288e1b
❓ Query: How many movies are in the database?
📊 Agent: Custom LangGraph
==================================================
================================ Human Message =================================

How many movies are in the database?
================================== Ai Message ==================================

Available collections: comments, embedded_movies, movies, sessions, theaters, users
================================== Ai Message ==================================
Tool Calls:
  mongodb_schema (call_yyrLUKa9BYrsjZ5sHZNyUHdw)
 Call ID: call_yyrLUKa9BYrsjZ5sHZNyUHdw
  Args:
    collection_names: movies
================================= Tool Message =================================
Name: mongodb_schema

Database name: sample_mflix
Collection name: movies
Schema from a sample of documents from the collection:
_id: ObjectId
plot: String
genres: Array<String>
runtime: Number
cast: Array<String>
num_mflix_comments: Number
poster: String
title: String
fullplot: String
languages: Array<String>
released: Timestamp
directors: Array<String>
writers: Array<String>
awards.wins: Number
awards.nominations: Number
awards.text: String
lastupdated: String
year: Number
imdb.rating: Number
imdb.votes: Number
imdb.id: Number
countries: Array<String>
type: String
tomatoes.viewer.rating: Number
tomatoes.viewer.numReviews: Number
tomatoes.viewer.meter: Number
tomatoes.dvd: Timestamp
tomatoes.lastUpdated: Timestamp

/*
3 documents from movies collection:
[
  {
    "_id": {
      "$oid": "573a1390f29313caabcd63d6"
    },
    "plot": "Two peasant children,",
    "genres": [
      "Fantasy"
    ],
    "runtime": 75,
    "cast": [
      "Tula Belle",
      "Robin Macdougall",
      "Edwin E. Reed",
      "Emma Lowry"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "The Blue Bird",
    "fullplot": "Two peasant children,",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1633305600000"
      }
    },
    "directors": [
      "Maurice Tourneur"
    ],
    "writers": [
      "Maurice Maeterlinck (",
      "Charles Maigne"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-07-20 00:32:04.8",
    "year": 1918,
    "imdb": {
      "rating": 6.6,
      "votes": 446,
      "id": 8891
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.6,
        "numReviews": 607,
        "meter": 60
      },
      "dvd": {
        "$date": "2005-09-06T00:00:00Z"
      },
      "lastUpdated": {
        "$date": "2015-08-21T18:10:22Z"
      }
    }
  },
  {
    "_id": {
      "$oid": "573a1391f29313caabcd7472"
    },
    "plot": "A con artist masquera",
    "genres": [
      "Drama"
    ],
    "runtime": 117,
    "cast": [
      "Rudolph Christians",
      "Miss DuPont",
      "Maude George",
      "Mae Busch"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "Foolish Wives",
    "fullplot": "\"Count\" Karanzim, a D",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1513900800000"
      }
    },
    "directors": [
      "Erich von Stroheim"
    ],
    "writers": [
      "Erich von Stroheim (s",
      "Marian Ainslee (title",
      "Walter Anthony (title"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-09-05 00:00:37.8",
    "year": 1922,
    "imdb": {
      "rating": 7.3,
      "votes": 1777,
      "id": 13140
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 1079,
        "meter": 77
      },
      "dvd": {
        "$date": "2000-09-19T00:00:00Z"
      },
      "critic": {
        "rating": 9.0,
        "numReviews": 9,
        "meter": 89
      },
      "lastUpdated": {
        "$date": "2015-09-15T17:02:32Z"
      },
      "rotten": 1,
      "production": "Universal Pictures",
      "fresh": 8
    }
  },
  {
    "_id": {
      "$oid": "573a1390f29313caabcd42e8"
    },
    "plot": "A group of bandits st",
    "genres": [
      "Short",
      "Western"
    ],
    "runtime": 11,
    "cast": [
      "A.C. Abadie",
      "Gilbert M. 'Broncho B",
      "George Barnes",
      "Justus D. Barnes"
    ],
    "poster": "https://m.media-amazo",
    "title": "The Great Train Robbe",
    "fullplot": "Among the earliest ex",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-2085523200000"
      }
    },
    "directors": [
      "Edwin S. Porter"
    ],
    "rated": "TV-G",
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-08-13 00:27:59.1",
    "year": 1903,
    "imdb": {
      "rating": 7.4,
      "votes": 9847,
      "id": 439
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 2559,
        "meter": 75
      },
      "fresh": 6,
      "critic": {
        "rating": 7.6,
        "numReviews": 6,
        "meter": 100
      },
      "rotten": 0,
      "lastUpdated": {
        "$date": "2015-08-08T19:16:10Z"
      }
    },
    "num_mflix_comments": 0
  }
]
*/
================================== Ai Message ==================================
Tool Calls:
  mongodb_query (call_0NzxDvjqtDIJCz8GADAJhyew)
 Call ID: call_0NzxDvjqtDIJCz8GADAJhyew
  Args:
    query: db.movies.countDocuments({})
================================== Ai Message ==================================
Tool Calls:
  mongodb_query (call_BIzElipRKl2d4dnh5tvt9kBZ)
 Call ID: call_BIzElipRKl2d4dnh5tvt9kBZ
  Args:
    query: db.movies.countDocuments({})
================================= Tool Message =================================
Name: mongodb_query

Error: ValueError('Cannot execute command db.movies.countDocuments({})')
 Please fix your mistakes.
================================== Ai Message ==================================

**Answer to:** "How many movies are in the database?"

⚠️ Large dataset found but too big to display. Try limiting your query (e.g., 'top 10', 'first 5').
✅ Test 1 complete

📌 Test 2: Find the average rating of all movies
🧵 Thread: enhanced_test_f4288e1b
❓ Query: Find the average rating of all movies
📊 Agent: Custom LangGraph
==================================================
================================ Human Message =================================

Find the average rating of all movies
================================== Ai Message ==================================

Available collections: comments, embedded_movies, movies, sessions, theaters, users
================================== Ai Message ==================================
Tool Calls:
  mongodb_schema (call_sne3jYRPFXD7B3jfmIEgWb7X)
 Call ID: call_sne3jYRPFXD7B3jfmIEgWb7X
  Args:
    collection_names: movies
================================= Tool Message =================================
Name: mongodb_schema

Database name: sample_mflix
Collection name: movies
Schema from a sample of documents from the collection:
_id: ObjectId
plot: String
genres: Array<String>
runtime: Number
cast: Array<String>
num_mflix_comments: Number
poster: String
title: String
fullplot: String
languages: Array<String>
released: Timestamp
directors: Array<String>
writers: Array<String>
awards.wins: Number
awards.nominations: Number
awards.text: String
lastupdated: String
year: Number
imdb.rating: Number
imdb.votes: Number
imdb.id: Number
countries: Array<String>
type: String
tomatoes.viewer.rating: Number
tomatoes.viewer.numReviews: Number
tomatoes.viewer.meter: Number
tomatoes.dvd: Timestamp
tomatoes.lastUpdated: Timestamp

/*
3 documents from movies collection:
[
  {
    "_id": {
      "$oid": "573a1390f29313caabcd63d6"
    },
    "plot": "Two peasant children,",
    "genres": [
      "Fantasy"
    ],
    "runtime": 75,
    "cast": [
      "Tula Belle",
      "Robin Macdougall",
      "Edwin E. Reed",
      "Emma Lowry"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "The Blue Bird",
    "fullplot": "Two peasant children,",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1633305600000"
      }
    },
    "directors": [
      "Maurice Tourneur"
    ],
    "writers": [
      "Maurice Maeterlinck (",
      "Charles Maigne"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-07-20 00:32:04.8",
    "year": 1918,
    "imdb": {
      "rating": 6.6,
      "votes": 446,
      "id": 8891
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.6,
        "numReviews": 607,
        "meter": 60
      },
      "dvd": {
        "$date": "2005-09-06T00:00:00Z"
      },
      "lastUpdated": {
        "$date": "2015-08-21T18:10:22Z"
      }
    }
  },
  {
    "_id": {
      "$oid": "573a1391f29313caabcd7472"
    },
    "plot": "A con artist masquera",
    "genres": [
      "Drama"
    ],
    "runtime": 117,
    "cast": [
      "Rudolph Christians",
      "Miss DuPont",
      "Maude George",
      "Mae Busch"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "Foolish Wives",
    "fullplot": "\"Count\" Karanzim, a D",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1513900800000"
      }
    },
    "directors": [
      "Erich von Stroheim"
    ],
    "writers": [
      "Erich von Stroheim (s",
      "Marian Ainslee (title",
      "Walter Anthony (title"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-09-05 00:00:37.8",
    "year": 1922,
    "imdb": {
      "rating": 7.3,
      "votes": 1777,
      "id": 13140
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 1079,
        "meter": 77
      },
      "dvd": {
        "$date": "2000-09-19T00:00:00Z"
      },
      "critic": {
        "rating": 9.0,
        "numReviews": 9,
        "meter": 89
      },
      "lastUpdated": {
        "$date": "2015-09-15T17:02:32Z"
      },
      "rotten": 1,
      "production": "Universal Pictures",
      "fresh": 8
    }
  },
  {
    "_id": {
      "$oid": "573a1390f29313caabcd42e8"
    },
    "plot": "A group of bandits st",
    "genres": [
      "Short",
      "Western"
    ],
    "runtime": 11,
    "cast": [
      "A.C. Abadie",
      "Gilbert M. 'Broncho B",
      "George Barnes",
      "Justus D. Barnes"
    ],
    "poster": "https://m.media-amazo",
    "title": "The Great Train Robbe",
    "fullplot": "Among the earliest ex",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-2085523200000"
      }
    },
    "directors": [
      "Edwin S. Porter"
    ],
    "rated": "TV-G",
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-08-13 00:27:59.1",
    "year": 1903,
    "imdb": {
      "rating": 7.4,
      "votes": 9847,
      "id": 439
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 2559,
        "meter": 75
      },
      "fresh": 6,
      "critic": {
        "rating": 7.6,
        "numReviews": 6,
        "meter": 100
      },
      "rotten": 0,
      "lastUpdated": {
        "$date": "2015-08-08T19:16:10Z"
      }
    },
    "num_mflix_comments": 0
  }
]
*/
================================== Ai Message ==================================
Tool Calls:
  mongodb_query (call_HpeGRq9l7scuzoMq0SWGXoKT)
 Call ID: call_HpeGRq9l7scuzoMq0SWGXoKT
  Args:
    query: db.movies.aggregate([ { "$group": { "_id": null, "averageRating": { "$avg": "$imdb.rating" } } } ])
================================== Ai Message ==================================
Tool Calls:
  mongodb_query (call_wpK4lnKymMLjYt8YoypSWoNJ)
 Call ID: call_wpK4lnKymMLjYt8YoypSWoNJ
  Args:
    query: db.movies.aggregate([ { "$group": { "_id": null, "averageRating": { "$avg": "$imdb.rating" } } } ])
================================= Tool Message =================================
Name: mongodb_query

[
  {
    "_id": null,
    "averageRating": 6.662852311161217
  }
]
================================== Ai Message ==================================

**Answer to:** "How many movies are in the database?"

1. None
✅ Test 2 complete

📌 Test 3: Show me the top 5 directors by movie count
🧵 Thread: enhanced_test_f4288e1b
❓ Query: Show me the top 5 directors by movie count
📊 Agent: Custom LangGraph
==================================================
================================ Human Message =================================

Show me the top 5 directors by movie count
================================== Ai Message ==================================

Available collections: comments, embedded_movies, movies, sessions, theaters, users
================================== Ai Message ==================================
Tool Calls:
  mongodb_schema (call_ochl0Dj7JzLdWDBDMKEsAY5h)
 Call ID: call_ochl0Dj7JzLdWDBDMKEsAY5h
  Args:
    collection_names: movies
================================= Tool Message =================================
Name: mongodb_schema

Database name: sample_mflix
Collection name: movies
Schema from a sample of documents from the collection:
_id: ObjectId
plot: String
genres: Array<String>
runtime: Number
cast: Array<String>
num_mflix_comments: Number
poster: String
title: String
fullplot: String
languages: Array<String>
released: Timestamp
directors: Array<String>
writers: Array<String>
awards.wins: Number
awards.nominations: Number
awards.text: String
lastupdated: String
year: Number
imdb.rating: Number
imdb.votes: Number
imdb.id: Number
countries: Array<String>
type: String
tomatoes.viewer.rating: Number
tomatoes.viewer.numReviews: Number
tomatoes.viewer.meter: Number
tomatoes.dvd: Timestamp
tomatoes.lastUpdated: Timestamp

/*
3 documents from movies collection:
[
  {
    "_id": {
      "$oid": "573a1390f29313caabcd63d6"
    },
    "plot": "Two peasant children,",
    "genres": [
      "Fantasy"
    ],
    "runtime": 75,
    "cast": [
      "Tula Belle",
      "Robin Macdougall",
      "Edwin E. Reed",
      "Emma Lowry"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "The Blue Bird",
    "fullplot": "Two peasant children,",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1633305600000"
      }
    },
    "directors": [
      "Maurice Tourneur"
    ],
    "writers": [
      "Maurice Maeterlinck (",
      "Charles Maigne"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-07-20 00:32:04.8",
    "year": 1918,
    "imdb": {
      "rating": 6.6,
      "votes": 446,
      "id": 8891
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.6,
        "numReviews": 607,
        "meter": 60
      },
      "dvd": {
        "$date": "2005-09-06T00:00:00Z"
      },
      "lastUpdated": {
        "$date": "2015-08-21T18:10:22Z"
      }
    }
  },
  {
    "_id": {
      "$oid": "573a1391f29313caabcd7472"
    },
    "plot": "A con artist masquera",
    "genres": [
      "Drama"
    ],
    "runtime": 117,
    "cast": [
      "Rudolph Christians",
      "Miss DuPont",
      "Maude George",
      "Mae Busch"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "Foolish Wives",
    "fullplot": "\"Count\" Karanzim, a D",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1513900800000"
      }
    },
    "directors": [
      "Erich von Stroheim"
    ],
    "writers": [
      "Erich von Stroheim (s",
      "Marian Ainslee (title",
      "Walter Anthony (title"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-09-05 00:00:37.8",
    "year": 1922,
    "imdb": {
      "rating": 7.3,
      "votes": 1777,
      "id": 13140
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 1079,
        "meter": 77
      },
      "dvd": {
        "$date": "2000-09-19T00:00:00Z"
      },
      "critic": {
        "rating": 9.0,
        "numReviews": 9,
        "meter": 89
      },
      "lastUpdated": {
        "$date": "2015-09-15T17:02:32Z"
      },
      "rotten": 1,
      "production": "Universal Pictures",
      "fresh": 8
    }
  },
  {
    "_id": {
      "$oid": "573a1390f29313caabcd42e8"
    },
    "plot": "A group of bandits st",
    "genres": [
      "Short",
      "Western"
    ],
    "runtime": 11,
    "cast": [
      "A.C. Abadie",
      "Gilbert M. 'Broncho B",
      "George Barnes",
      "Justus D. Barnes"
    ],
    "poster": "https://m.media-amazo",
    "title": "The Great Train Robbe",
    "fullplot": "Among the earliest ex",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-2085523200000"
      }
    },
    "directors": [
      "Edwin S. Porter"
    ],
    "rated": "TV-G",
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-08-13 00:27:59.1",
    "year": 1903,
    "imdb": {
      "rating": 7.4,
      "votes": 9847,
      "id": 439
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 2559,
        "meter": 75
      },
      "fresh": 6,
      "critic": {
        "rating": 7.6,
        "numReviews": 6,
        "meter": 100
      },
      "rotten": 0,
      "lastUpdated": {
        "$date": "2015-08-08T19:16:10Z"
      }
    },
    "num_mflix_comments": 0
  }
]
*/
================================== Ai Message ==================================
Tool Calls:
  mongodb_query (call_x2uQmDgCP7QWnSemzDPcbOng)
 Call ID: call_x2uQmDgCP7QWnSemzDPcbOng
  Args:
    query: db.movies.aggregate([ { "$unwind": "$directors" }, { "$group": { "_id": "$directors", "movieCount": { "$sum": 1 } } }, { "$sort": { "movieCount": -1 } }, { "$limit": 5 } ])
================================== Ai Message ==================================
Tool Calls:
  mongodb_query (call_on1FxSEyj2F2eD2pg7e9TWFb)
 Call ID: call_on1FxSEyj2F2eD2pg7e9TWFb
  Args:
    query: db.movies.aggregate([ { "$unwind": "$directors" }, { "$group": { "_id": "$directors", "movieCount": { "$sum": 1 } } }, { "$sort": { "movieCount": -1 } }, { "$limit": 5 } ])
================================= Tool Message =================================
Name: mongodb_query

[
  {
    "_id": "Woody Allen",
    "movieCount": 40
  },
  {
    "_id": "Martin Scorsese",
    "movieCount": 32
  },
  {
    "_id": "Takashi Miike",
    "movieCount": 31
  },
  {
    "_id": "Steven Spielberg",
    "movieCount": 29
  },
  {
    "_id": "John Ford",
    "movieCount": 29
  }
]
================================== Ai Message ==================================

**Answer to:** "How many movies are in the database?"

1. Woody Allen: 40 movies
2. Martin Scorsese: 32 movies
3. Takashi Miike: 31 movies
4. Steven Spielberg: 29 movies
5. John Ford: 29 movies
✅ Test 3 complete

🔍 Enhanced Thread Analysis:
==================================================

🔍 Thread History: enhanced_test_f4288e1b
📊 Total steps: 10
================================================================================

📍 Step 1 [19:34:16]
   "🔄 Initial state"

📍 Step 2 [19:34:17]
   "📊 Movie count inquiry"

📍 Step 3 [19:34:18]
   "🔧 Available collections list"

📍 Step 4 [19:34:20]
   "🔧 Schema lookup: movies"

📍 Step 5 [19:34:22]
   "🔧 Schema details: movies"

📍 Step 6 [19:34:22]
   "🔧 Schema lookup: movies"
   └─ (repeated 1 more times)

📍 Step 8 [19:34:22]
   "❌ Count documents error"

📍 Step 9 [19:34:23]
   "📊 Large dataset warning"
   └─ (repeated 1 more times)

================================================================================

Demos

Demo 1: Run Basic Queries w/ `demo_basic_queries()`

[38]

🎬 DEMO: Basic Text-to-MQL Queries
==================================================

--- Demo Query 1 ---
Query: List the top 5 movies with highest IMDb ratings

🧵 Thread: demo_basic_1
❓ Query: List the top 5 movies with highest IMDb ratings
📊 Agent: Custom LangGraph
==================================================
================================ Human Message =================================

List the top 5 movies with highest IMDb ratings
================================== Ai Message ==================================

Available collections: comments, embedded_movies, movies, sessions, theaters, users
================================== Ai Message ==================================
Tool Calls:
  mongodb_schema (call_SlDBh65YW0pI1rnnaF8tuHX5)
 Call ID: call_SlDBh65YW0pI1rnnaF8tuHX5
  Args:
    collection_names: movies
================================= Tool Message =================================
Name: mongodb_schema

Database name: sample_mflix
Collection name: movies
Schema from a sample of documents from the collection:
_id: ObjectId
plot: String
genres: Array<String>
runtime: Number
cast: Array<String>
num_mflix_comments: Number
poster: String
title: String
fullplot: String
languages: Array<String>
released: Timestamp
directors: Array<String>
writers: Array<String>
awards.wins: Number
awards.nominations: Number
awards.text: String
lastupdated: String
year: Number
imdb.rating: Number
imdb.votes: Number
imdb.id: Number
countries: Array<String>
type: String
tomatoes.viewer.rating: Number
tomatoes.viewer.numReviews: Number
tomatoes.viewer.meter: Number
tomatoes.dvd: Timestamp
tomatoes.lastUpdated: Timestamp

/*
3 documents from movies collection:
[
  {
    "_id": {
      "$oid": "573a1390f29313caabcd63d6"
    },
    "plot": "Two peasant children,",
    "genres": [
      "Fantasy"
    ],
    "runtime": 75,
    "cast": [
      "Tula Belle",
      "Robin Macdougall",
      "Edwin E. Reed",
      "Emma Lowry"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "The Blue Bird",
    "fullplot": "Two peasant children,",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1633305600000"
      }
    },
    "directors": [
      "Maurice Tourneur"
    ],
    "writers": [
      "Maurice Maeterlinck (",
      "Charles Maigne"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-07-20 00:32:04.8",
    "year": 1918,
    "imdb": {
      "rating": 6.6,
      "votes": 446,
      "id": 8891
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.6,
        "numReviews": 607,
        "meter": 60
      },
      "dvd": {
        "$date": "2005-09-06T00:00:00Z"
      },
      "lastUpdated": {
        "$date": "2015-08-21T18:10:22Z"
      }
    }
  },
  {
    "_id": {
      "$oid": "573a1391f29313caabcd7472"
    },
    "plot": "A con artist masquera",
    "genres": [
      "Drama"
    ],
    "runtime": 117,
    "cast": [
      "Rudolph Christians",
      "Miss DuPont",
      "Maude George",
      "Mae Busch"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "Foolish Wives",
    "fullplot": "\"Count\" Karanzim, a D",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1513900800000"
      }
    },
    "directors": [
      "Erich von Stroheim"
    ],
    "writers": [
      "Erich von Stroheim (s",
      "Marian Ainslee (title",
      "Walter Anthony (title"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-09-05 00:00:37.8",
    "year": 1922,
    "imdb": {
      "rating": 7.3,
      "votes": 1777,
      "id": 13140
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 1079,
        "meter": 77
      },
      "dvd": {
        "$date": "2000-09-19T00:00:00Z"
      },
      "critic": {
        "rating": 9.0,
        "numReviews": 9,
        "meter": 89
      },
      "lastUpdated": {
        "$date": "2015-09-15T17:02:32Z"
      },
      "rotten": 1,
      "production": "Universal Pictures",
      "fresh": 8
    }
  },
  {
    "_id": {
      "$oid": "573a1390f29313caabcd42e8"
    },
    "plot": "A group of bandits st",
    "genres": [
      "Short",
      "Western"
    ],
    "runtime": 11,
    "cast": [
      "A.C. Abadie",
      "Gilbert M. 'Broncho B",
      "George Barnes",
      "Justus D. Barnes"
    ],
    "poster": "https://m.media-amazo",
    "title": "The Great Train Robbe",
    "fullplot": "Among the earliest ex",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-2085523200000"
      }
    },
    "directors": [
      "Edwin S. Porter"
    ],
    "rated": "TV-G",
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-08-13 00:27:59.1",
    "year": 1903,
    "imdb": {
      "rating": 7.4,
      "votes": 9847,
      "id": 439
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 2559,
        "meter": 75
      },
      "fresh": 6,
      "critic": {
        "rating": 7.6,
        "numReviews": 6,
        "meter": 100
      },
      "rotten": 0,
      "lastUpdated": {
        "$date": "2015-08-08T19:16:10Z"
      }
    },
    "num_mflix_comments": 0
  }
]
*/
================================== Ai Message ==================================
Tool Calls:
  mongodb_query (call_QzRaQ6RyJNvGIXQ3E0Ku96vO)
 Call ID: call_QzRaQ6RyJNvGIXQ3E0Ku96vO
  Args:
    query: db.movies.aggregate([ { "$sort": { "imdb.rating": -1 } }, { "$limit": 5 }, { "$project": { "title": 1, "imdb.rating": 1 } } ])
================================== Ai Message ==================================
Tool Calls:
  mongodb_query (call_3ORxwe3o4kXSrOQEj30EyIEs)
 Call ID: call_3ORxwe3o4kXSrOQEj30EyIEs
  Args:
    query: db.movies.aggregate([ { "$sort": { "imdb.rating": -1 } }, { "$limit": 5 }, { "$project": { "title": 1, "imdb.rating": 1 } } ])
================================= Tool Message =================================
Name: mongodb_query

[
  {
    "_id": {
      "$oid": "573a13b8f29313caabd4d540"
    },
    "title": "The Danish Girl",
    "imdb": {
      "rating": ""
    }
  },
  {
    "_id": {
      "$oid": "573a13b3f29313caabd3c7ac"
    },
    "title": "Landet som icke \u00e8r",
    "imdb": {
      "rating": ""
    }
  },
  {
    "_id": {
      "$oid": "573a13cff29313caabd88f5b"
    },
    "title": "Scouts Guide to the Zombie Apocalypse",
    "imdb": {
      "rating": ""
    }
  },
  {
    "_id": {
      "$oid": "573a13cef29313caabd86ddc"
    },
    "title": "Catching the Sun",
    "imdb": {
      "rating": ""
    }
  },
  {
    "_id": {
      "$oid": "573a1393f29313caabcddbed"
    },
    "title": "La nao capitana",
    "imdb": {
      "rating": ""
    }
  }
]
================================== Ai Message ==================================

**Answer to:** "List the top 5 movies with highest IMDb ratings"

1. {'$oid': '573a13b8f29313caabd4d540'}
2. {'$oid': '573a13b3f29313caabd3c7ac'}
3. {'$oid': '573a13cff29313caabd88f5b'}
4. {'$oid': '573a13cef29313caabd86ddc'}
5. {'$oid': '573a1393f29313caabcddbed'}

==================================================

--- Demo Query 2 ---
Query: Who are the top 10 most active commenters?

🧵 Thread: demo_basic_2
❓ Query: Who are the top 10 most active commenters?
📊 Agent: Custom LangGraph
==================================================
================================ Human Message =================================

Who are the top 10 most active commenters?
================================== Ai Message ==================================

Available collections: comments, embedded_movies, movies, sessions, theaters, users
================================== Ai Message ==================================
Tool Calls:
  mongodb_schema (call_E0G6xxsRv7Jn1BL0g9II1SU9)
 Call ID: call_E0G6xxsRv7Jn1BL0g9II1SU9
  Args:
    collection_names: comments, users
================================= Tool Message =================================
Name: mongodb_schema

Database name: sample_mflix
Collection name: comments
Schema from a sample of documents from the collection:
_id: ObjectId
name: String
email: String
movie_id: ObjectId
text: String
date: Timestamp

/*
3 documents from comments collection:
[
  {
    "_id": {
      "$oid": "5a9427648b0beebeb6957b89"
    },
    "name": "Lisa Rasmussen",
    "email": "lisa_rasmussen@fakegm",
    "movie_id": {
      "$oid": "573a1391f29313caabcd82da"
    },
    "text": "Illo nihil occaecati ",
    "date": {
      "$date": "1976-12-18T08:14:46Z"
    }
  },
  {
    "_id": {
      "$oid": "5a9427648b0beebeb6957bb6"
    },
    "name": "Ellaria Sand",
    "email": "indira_varma@gameofth",
    "movie_id": {
      "$oid": "573a1391f29313caabcd8780"
    },
    "text": "Quidem nesciunt quam ",
    "date": {
      "$date": "1985-02-24T20:04:25Z"
    }
  },
  {
    "_id": {
      "$oid": "5a9427648b0beebeb69579e7"
    },
    "name": "Mercedes Tyler",
    "email": "mercedes_tyler@fakegm",
    "movie_id": {
      "$oid": "573a1390f29313caabcd4323"
    },
    "text": "Eius veritatis vero f",
    "date": {
      "$date": "2002-08-18T04:56:07Z"
    }
  }
]
*/

Database name: sample_mflix
Collection name: users
Schema from a sample of documents from the collection:
_id: ObjectId
name: String
email: String
password: String

/*
3 documents from users collection:
[
  {
    "_id": {
      "$oid": "59b99db4cfa9a34dcd7885b6"
    },
    "name": "Ned Stark",
    "email": "sean_bean@gameofthron",
    "password": "$2b$12$UREFwsRUoyF0CR"
  },
  {
    "_id": {
      "$oid": "59b99db6cfa9a34dcd7885bb"
    },
    "name": "Daenerys Targaryen",
    "email": "emilia_clarke@gameoft",
    "password": "$2b$12$NzpbWHdMytemLt"
  },
  {
    "_id": {
      "$oid": "59b99dbfcfa9a34dcd7885cc"
    },
    "name": "Stannis Baratheon",
    "email": "stephen_dillane@gameo",
    "password": "$2b$12$vbPwOM9QkSOsOX"
  }
]
*/
================================== Ai Message ==================================
Tool Calls:
  mongodb_query (call_BnXwhKUYqwksZRYpYWc6Rs0A)
 Call ID: call_BnXwhKUYqwksZRYpYWc6Rs0A
  Args:
    query: db.comments.aggregate([ { "$group": { "_id": "$name", "commentCount": { "$sum": 1 } } }, { "$sort": { "commentCount": -1 } }, { "$limit": 10 } ])
================================== Ai Message ==================================
Tool Calls:
  mongodb_query (call_w4Fh5hnFJVD8anLQZeD2jsHw)
 Call ID: call_w4Fh5hnFJVD8anLQZeD2jsHw
  Args:
    query: db.comments.aggregate([ { "$group": { "_id": "$name", "commentCount": { "$sum": 1 } } }, { "$sort": { "commentCount": -1 } }, { "$limit": 10 } ])
================================= Tool Message =================================
Name: mongodb_query

[
  {
    "_id": "Mace Tyrell",
    "commentCount": 277
  },
  {
    "_id": "The High Sparrow",
    "commentCount": 260
  },
  {
    "_id": "Rodrik Cassel",
    "commentCount": 260
  },
  {
    "_id": "Missandei",
    "commentCount": 258
  },
  {
    "_id": "Robert Jordan",
    "commentCount": 257
  },
  {
    "_id": "Sansa Stark",
    "commentCount": 251
  },
  {
    "_id": "Thoros of Myr",
    "commentCount": 251
  },
  {
    "_id": "Donna Smith",
    "commentCount": 248
  },
  {
    "_id": "Nicholas Johnson",
    "commentCount": 248
  },
  {
    "_id": "Beric Dondarrion",
    "commentCount": 247
  }
]
================================== Ai Message ==================================

**Answer to:** "Who are the top 10 most active commenters?"

1. Mace Tyrell
2. The High Sparrow
3. Rodrik Cassel
4. Missandei
5. Robert Jordan
6. Sansa Stark
7. Thoros of Myr
8. Donna Smith
9. Nicholas Johnson
10. Beric Dondarrion

==================================================

--- Demo Query 3 ---
Query: Which states have the most theaters?

🧵 Thread: demo_basic_3
❓ Query: Which states have the most theaters?
📊 Agent: Custom LangGraph
==================================================
================================ Human Message =================================

Which states have the most theaters?
================================== Ai Message ==================================

Available collections: comments, embedded_movies, movies, sessions, theaters, users
================================== Ai Message ==================================
Tool Calls:
  mongodb_schema (call_N45yYn03A4N4C4fpoSebWoAP)
 Call ID: call_N45yYn03A4N4C4fpoSebWoAP
  Args:
    collection_names: theaters
================================= Tool Message =================================
Name: mongodb_schema

Database name: sample_mflix
Collection name: theaters
Schema from a sample of documents from the collection:
_id: ObjectId
theaterId: Number
location.address.street1: String
location.address.city: String
location.address.state: String
location.address.zipcode: String
location.geo.type: String
location.geo.coordinates: Array<Number>

/*
3 documents from theaters collection:
[
  {
    "_id": {
      "$oid": "59a47286cfa9a3a73e51e72e"
    },
    "theaterId": 1008,
    "location": {
      "address": {
        "street1": "1621 E Monte Vista Av",
        "city": "Vacaville",
        "state": "CA",
        "zipcode": "95688"
      },
      "geo": {
        "type": "Point",
        "coordinates": [
          -121.96328,
          38.367649
        ]
      }
    }
  },
  {
    "_id": {
      "$oid": "59a47286cfa9a3a73e51e735"
    },
    "theaterId": 1013,
    "location": {
      "address": {
        "street1": "9901 Brook Rd",
        "city": "Glen Allen",
        "state": "VA",
        "zipcode": "23059"
      },
      "geo": {
        "type": "Point",
        "coordinates": [
          -77.459908,
          37.667957
        ]
      }
    }
  },
  {
    "_id": {
      "$oid": "59a47286cfa9a3a73e51e738"
    },
    "theaterId": 1015,
    "location": {
      "address": {
        "street1": "1721 Osgood Dr",
        "city": "Altoona",
        "state": "PA",
        "zipcode": "16602"
      },
      "geo": {
        "type": "Point",
        "coordinates": [
          -78.382912,
          40.490524
        ]
      }
    }
  }
]
*/
================================== Ai Message ==================================
Tool Calls:
  mongodb_query (call_UMbfO1f7ZSzz06aKODMWeaeF)
 Call ID: call_UMbfO1f7ZSzz06aKODMWeaeF
  Args:
    query: db.theaters.aggregate([ { "$group": { "_id": "$location.address.state", "theaterCount": { "$sum": 1 } } }, { "$sort": { "theaterCount": -1 } }, { "$limit": 5 } ])
================================== Ai Message ==================================
Tool Calls:
  mongodb_query (call_mZgWpTXtqKot5uDLfPSXxv4c)
 Call ID: call_mZgWpTXtqKot5uDLfPSXxv4c
  Args:
    query: db.theaters.aggregate([ { "$group": { "_id": "$location.address.state", "theaterCount": { "$sum": 1 } } }, { "$sort": { "theaterCount": -1 } }, { "$limit": 5 } ])
================================= Tool Message =================================
Name: mongodb_query

[
  {
    "_id": "CA",
    "theaterCount": 169
  },
  {
    "_id": "TX",
    "theaterCount": 160
  },
  {
    "_id": "FL",
    "theaterCount": 111
  },
  {
    "_id": "NY",
    "theaterCount": 81
  },
  {
    "_id": "IL",
    "theaterCount": 70
  }
]
================================== Ai Message ==================================

**Answer to:** "Which states have the most theaters?"

1. CA
2. TX
3. FL
4. NY
5. IL

==================================================

--- Demo Query 4 ---
Query: Which theaters are furthest west?

🧵 Thread: demo_basic_4
❓ Query: Which theaters are furthest west?
📊 Agent: Custom LangGraph
==================================================
================================ Human Message =================================

Which theaters are furthest west?
================================== Ai Message ==================================

Available collections: comments, embedded_movies, movies, sessions, theaters, users
================================== Ai Message ==================================
Tool Calls:
  mongodb_schema (call_cPjzVTGIZ4GMv7OY4oD5x1gL)
 Call ID: call_cPjzVTGIZ4GMv7OY4oD5x1gL
  Args:
    collection_names: theaters
================================= Tool Message =================================
Name: mongodb_schema

Database name: sample_mflix
Collection name: theaters
Schema from a sample of documents from the collection:
_id: ObjectId
theaterId: Number
location.address.street1: String
location.address.city: String
location.address.state: String
location.address.zipcode: String
location.geo.type: String
location.geo.coordinates: Array<Number>

/*
3 documents from theaters collection:
[
  {
    "_id": {
      "$oid": "59a47286cfa9a3a73e51e72e"
    },
    "theaterId": 1008,
    "location": {
      "address": {
        "street1": "1621 E Monte Vista Av",
        "city": "Vacaville",
        "state": "CA",
        "zipcode": "95688"
      },
      "geo": {
        "type": "Point",
        "coordinates": [
          -121.96328,
          38.367649
        ]
      }
    }
  },
  {
    "_id": {
      "$oid": "59a47286cfa9a3a73e51e735"
    },
    "theaterId": 1013,
    "location": {
      "address": {
        "street1": "9901 Brook Rd",
        "city": "Glen Allen",
        "state": "VA",
        "zipcode": "23059"
      },
      "geo": {
        "type": "Point",
        "coordinates": [
          -77.459908,
          37.667957
        ]
      }
    }
  },
  {
    "_id": {
      "$oid": "59a47286cfa9a3a73e51e738"
    },
    "theaterId": 1015,
    "location": {
      "address": {
        "street1": "1721 Osgood Dr",
        "city": "Altoona",
        "state": "PA",
        "zipcode": "16602"
      },
      "geo": {
        "type": "Point",
        "coordinates": [
          -78.382912,
          40.490524
        ]
      }
    }
  }
]
*/
================================== Ai Message ==================================
Tool Calls:
  mongodb_query (call_qAPs1MUPRLHbB4dcCtL0BR5u)
 Call ID: call_qAPs1MUPRLHbB4dcCtL0BR5u
  Args:
    query: db.theaters.aggregate([ { "$sort": { "location.geo.coordinates.0": 1 } }, { "$limit": 5 } ])
================================== Ai Message ==================================
Tool Calls:
  mongodb_query (call_4FYxHTmYsp9M4GgEeBHiEHgQ)
 Call ID: call_4FYxHTmYsp9M4GgEeBHiEHgQ
  Args:
    query: db.theaters.aggregate([ { "$sort": { "location.geo.coordinates.0": 1 } }, { "$limit": 5 } ])
================================= Tool Message =================================
Name: mongodb_query

[
  {
    "_id": {
      "$oid": "59a47287cfa9a3a73e51ece9"
    },
    "theaterId": 852,
    "location": {
      "address": {
        "street1": "98-051 Kamehameha Hwy",
        "city": "Aiea",
        "state": "HI",
        "zipcode": "96701"
      },
      "geo": {
        "type": "Point",
        "coordinates": [
          -157.9497,
          21.384672
        ]
      }
    }
  },
  {
    "_id": {
      "$oid": "59a47287cfa9a3a73e51ec98"
    },
    "theaterId": 8140,
    "location": {
      "address": {
        "street1": "300 Rodgers Boulevard",
        "street2": null,
        "city": "Honolulu",
        "state": "HI",
        "zipcode": "96819"
      },
      "geo": {
        "type": "Point",
        "coordinates": [
          -157.919795,
          21.332003
        ]
      }
    }
  },
  {
    "_id": {
      "$oid": "59a47287cfa9a3a73e51eca2"
    },
    "theaterId": 8153,
    "location": {
      "address": {
        "street1": "300 Rodgers Boulevard",
        "street2": null,
        "city": "Honolulu",
        "state": "HI",
        "zipcode": "96819"
      },
      "geo": {
        "type": "Point",
        "coordinates": [
          -157.919795,
          21.332003
        ]
      }
    }
  },
  {
    "_id": {
      "$oid": "59a47287cfa9a3a73e51ecb9"
    },
    "theaterId": 8183,
    "location": {
      "address": {
        "street1": "300 Rodgers Boulevard",
        "street2": null,
        "city": "Honolulu",
        "state": "HI",
        "zipcode": "96819"
      },
      "geo": {
        "type": "Point",
        "coordinates": [
          -157.919795,
          21.332003
        ]
      }
    }
  },
  {
    "_id": {
      "$oid": "59a47287cfa9a3a73e51eca3"
    },
    "theaterId": 8152,
    "location": {
      "address": {
        "street1": "300 Rodgers Boulevard",
        "street2": null,
        "city": "Honolulu",
        "state": "HI",
        "zipcode": "96819"
      },
      "geo": {
        "type": "Point",
        "coordinates": [
          -157.919795,
          21.332003
        ]
      }
    }
  }
]
================================== Ai Message ==================================

**Answer to:** "Which theaters are furthest west?"

1. {'$oid': '59a47287cfa9a3a73e51ece9'}
2. {'$oid': '59a47287cfa9a3a73e51ec98'}
3. {'$oid': '59a47287cfa9a3a73e51eca2'}
4. {'$oid': '59a47287cfa9a3a73e51ecb9'}
5. {'$oid': '59a47287cfa9a3a73e51eca3'}

==================================================

--- Demo Query 5 ---
Query: Find directors with ≥20 films, highest avg IMDb rating (top-5)

🧵 Thread: demo_basic_5
❓ Query: Find directors with ≥20 films, highest avg IMDb rating (top-5)
📊 Agent: Custom LangGraph
==================================================
================================ Human Message =================================

Find directors with ≥20 films, highest avg IMDb rating (top-5)
================================== Ai Message ==================================

Available collections: comments, embedded_movies, movies, sessions, theaters, users
================================== Ai Message ==================================
Tool Calls:
  mongodb_schema (call_Uwp5BdXJAf5qgtJbf8U3dMh6)
 Call ID: call_Uwp5BdXJAf5qgtJbf8U3dMh6
  Args:
    collection_names: movies
================================= Tool Message =================================
Name: mongodb_schema

Database name: sample_mflix
Collection name: movies
Schema from a sample of documents from the collection:
_id: ObjectId
plot: String
genres: Array<String>
runtime: Number
cast: Array<String>
num_mflix_comments: Number
poster: String
title: String
fullplot: String
languages: Array<String>
released: Timestamp
directors: Array<String>
writers: Array<String>
awards.wins: Number
awards.nominations: Number
awards.text: String
lastupdated: String
year: Number
imdb.rating: Number
imdb.votes: Number
imdb.id: Number
countries: Array<String>
type: String
tomatoes.viewer.rating: Number
tomatoes.viewer.numReviews: Number
tomatoes.viewer.meter: Number
tomatoes.dvd: Timestamp
tomatoes.lastUpdated: Timestamp

/*
3 documents from movies collection:
[
  {
    "_id": {
      "$oid": "573a1390f29313caabcd63d6"
    },
    "plot": "Two peasant children,",
    "genres": [
      "Fantasy"
    ],
    "runtime": 75,
    "cast": [
      "Tula Belle",
      "Robin Macdougall",
      "Edwin E. Reed",
      "Emma Lowry"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "The Blue Bird",
    "fullplot": "Two peasant children,",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1633305600000"
      }
    },
    "directors": [
      "Maurice Tourneur"
    ],
    "writers": [
      "Maurice Maeterlinck (",
      "Charles Maigne"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-07-20 00:32:04.8",
    "year": 1918,
    "imdb": {
      "rating": 6.6,
      "votes": 446,
      "id": 8891
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.6,
        "numReviews": 607,
        "meter": 60
      },
      "dvd": {
        "$date": "2005-09-06T00:00:00Z"
      },
      "lastUpdated": {
        "$date": "2015-08-21T18:10:22Z"
      }
    }
  },
  {
    "_id": {
      "$oid": "573a1391f29313caabcd7472"
    },
    "plot": "A con artist masquera",
    "genres": [
      "Drama"
    ],
    "runtime": 117,
    "cast": [
      "Rudolph Christians",
      "Miss DuPont",
      "Maude George",
      "Mae Busch"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "Foolish Wives",
    "fullplot": "\"Count\" Karanzim, a D",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1513900800000"
      }
    },
    "directors": [
      "Erich von Stroheim"
    ],
    "writers": [
      "Erich von Stroheim (s",
      "Marian Ainslee (title",
      "Walter Anthony (title"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-09-05 00:00:37.8",
    "year": 1922,
    "imdb": {
      "rating": 7.3,
      "votes": 1777,
      "id": 13140
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 1079,
        "meter": 77
      },
      "dvd": {
        "$date": "2000-09-19T00:00:00Z"
      },
      "critic": {
        "rating": 9.0,
        "numReviews": 9,
        "meter": 89
      },
      "lastUpdated": {
        "$date": "2015-09-15T17:02:32Z"
      },
      "rotten": 1,
      "production": "Universal Pictures",
      "fresh": 8
    }
  },
  {
    "_id": {
      "$oid": "573a1390f29313caabcd42e8"
    },
    "plot": "A group of bandits st",
    "genres": [
      "Short",
      "Western"
    ],
    "runtime": 11,
    "cast": [
      "A.C. Abadie",
      "Gilbert M. 'Broncho B",
      "George Barnes",
      "Justus D. Barnes"
    ],
    "poster": "https://m.media-amazo",
    "title": "The Great Train Robbe",
    "fullplot": "Among the earliest ex",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-2085523200000"
      }
    },
    "directors": [
      "Edwin S. Porter"
    ],
    "rated": "TV-G",
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-08-13 00:27:59.1",
    "year": 1903,
    "imdb": {
      "rating": 7.4,
      "votes": 9847,
      "id": 439
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 2559,
        "meter": 75
      },
      "fresh": 6,
      "critic": {
        "rating": 7.6,
        "numReviews": 6,
        "meter": 100
      },
      "rotten": 0,
      "lastUpdated": {
        "$date": "2015-08-08T19:16:10Z"
      }
    },
    "num_mflix_comments": 0
  }
]
*/
================================== Ai Message ==================================
Tool Calls:
  mongodb_query (call_ghfO5T3gfo1y1YAWaIauclsh)
 Call ID: call_ghfO5T3gfo1y1YAWaIauclsh
  Args:
    query: db.movies.aggregate([ { "$unwind": "$directors" }, { "$group": { "_id": "$directors", "filmCount": { "$sum": 1 }, "avgRating": { "$avg": "$imdb.rating" } } }, { "$match": { "filmCount": { "$gte": 20 } } }, { "$sort": { "avgRating": -1 } }, { "$limit": 5 } ])
================================== Ai Message ==================================
Tool Calls:
  mongodb_query (call_QDDEEeLbt8VDeBKNBjLP5Pin)
 Call ID: call_QDDEEeLbt8VDeBKNBjLP5Pin
  Args:
    query: db.movies.aggregate([ { "$unwind": "$directors" }, { "$group": { "_id": "$directors", "filmCount": { "$sum": 1 }, "avgRating": { "$avg": "$imdb.rating" } } }, { "$match": { "filmCount": { "$gte": 20 } } }, { "$sort": { "avgRating": -1 } }, { "$limit": 5 } ])
================================= Tool Message =================================
Name: mongodb_query

[
  {
    "_id": "William Wyler",
    "filmCount": 21,
    "avgRating": 7.676190476190476
  },
  {
    "_id": "Martin Scorsese",
    "filmCount": 32,
    "avgRating": 7.640625
  },
  {
    "_id": "Alfred Hitchcock",
    "filmCount": 24,
    "avgRating": 7.5874999999999995
  },
  {
    "_id": "Steven Spielberg",
    "filmCount": 29,
    "avgRating": 7.479310344827587
  },
  {
    "_id": "Woody Allen",
    "filmCount": 40,
    "avgRating": 7.215000000000001
  }
]
================================== Ai Message ==================================

**Answer to:** "Find directors with ≥20 films, highest avg IMDb rating (top-5)"

1. William Wyler
2. Martin Scorsese
3. Alfred Hitchcock
4. Steven Spielberg
5. Woody Allen

Demo 2: Multi-turn conversations - `demo_conversation_memory()`

[39]

🎬 DEMO: Conversation Memory with Text-to-MQL
==================================================

--- Conversation Step 1 ---
🧵 Thread: conversation_demo_7e08f130
❓ Query: List the top 3 directors by movie count
📊 Agent: Custom LangGraph
==================================================
================================ Human Message =================================

List the top 3 directors by movie count
================================== Ai Message ==================================

Available collections: comments, embedded_movies, movies, sessions, theaters, users
================================== Ai Message ==================================
Tool Calls:
  mongodb_schema (call_TavHIcPtXu3JRSue5UnpSDbi)
 Call ID: call_TavHIcPtXu3JRSue5UnpSDbi
  Args:
    collection_names: movies
================================= Tool Message =================================
Name: mongodb_schema

Database name: sample_mflix
Collection name: movies
Schema from a sample of documents from the collection:
_id: ObjectId
plot: String
genres: Array<String>
runtime: Number
cast: Array<String>
num_mflix_comments: Number
poster: String
title: String
fullplot: String
languages: Array<String>
released: Timestamp
directors: Array<String>
writers: Array<String>
awards.wins: Number
awards.nominations: Number
awards.text: String
lastupdated: String
year: Number
imdb.rating: Number
imdb.votes: Number
imdb.id: Number
countries: Array<String>
type: String
tomatoes.viewer.rating: Number
tomatoes.viewer.numReviews: Number
tomatoes.viewer.meter: Number
tomatoes.dvd: Timestamp
tomatoes.lastUpdated: Timestamp

/*
3 documents from movies collection:
[
  {
    "_id": {
      "$oid": "573a1390f29313caabcd63d6"
    },
    "plot": "Two peasant children,",
    "genres": [
      "Fantasy"
    ],
    "runtime": 75,
    "cast": [
      "Tula Belle",
      "Robin Macdougall",
      "Edwin E. Reed",
      "Emma Lowry"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "The Blue Bird",
    "fullplot": "Two peasant children,",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1633305600000"
      }
    },
    "directors": [
      "Maurice Tourneur"
    ],
    "writers": [
      "Maurice Maeterlinck (",
      "Charles Maigne"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-07-20 00:32:04.8",
    "year": 1918,
    "imdb": {
      "rating": 6.6,
      "votes": 446,
      "id": 8891
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.6,
        "numReviews": 607,
        "meter": 60
      },
      "dvd": {
        "$date": "2005-09-06T00:00:00Z"
      },
      "lastUpdated": {
        "$date": "2015-08-21T18:10:22Z"
      }
    }
  },
  {
    "_id": {
      "$oid": "573a1391f29313caabcd7472"
    },
    "plot": "A con artist masquera",
    "genres": [
      "Drama"
    ],
    "runtime": 117,
    "cast": [
      "Rudolph Christians",
      "Miss DuPont",
      "Maude George",
      "Mae Busch"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "Foolish Wives",
    "fullplot": "\"Count\" Karanzim, a D",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1513900800000"
      }
    },
    "directors": [
      "Erich von Stroheim"
    ],
    "writers": [
      "Erich von Stroheim (s",
      "Marian Ainslee (title",
      "Walter Anthony (title"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-09-05 00:00:37.8",
    "year": 1922,
    "imdb": {
      "rating": 7.3,
      "votes": 1777,
      "id": 13140
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 1079,
        "meter": 77
      },
      "dvd": {
        "$date": "2000-09-19T00:00:00Z"
      },
      "critic": {
        "rating": 9.0,
        "numReviews": 9,
        "meter": 89
      },
      "lastUpdated": {
        "$date": "2015-09-15T17:02:32Z"
      },
      "rotten": 1,
      "production": "Universal Pictures",
      "fresh": 8
    }
  },
  {
    "_id": {
      "$oid": "573a1390f29313caabcd42e8"
    },
    "plot": "A group of bandits st",
    "genres": [
      "Short",
      "Western"
    ],
    "runtime": 11,
    "cast": [
      "A.C. Abadie",
      "Gilbert M. 'Broncho B",
      "George Barnes",
      "Justus D. Barnes"
    ],
    "poster": "https://m.media-amazo",
    "title": "The Great Train Robbe",
    "fullplot": "Among the earliest ex",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-2085523200000"
      }
    },
    "directors": [
      "Edwin S. Porter"
    ],
    "rated": "TV-G",
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-08-13 00:27:59.1",
    "year": 1903,
    "imdb": {
      "rating": 7.4,
      "votes": 9847,
      "id": 439
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 2559,
        "meter": 75
      },
      "fresh": 6,
      "critic": {
        "rating": 7.6,
        "numReviews": 6,
        "meter": 100
      },
      "rotten": 0,
      "lastUpdated": {
        "$date": "2015-08-08T19:16:10Z"
      }
    },
    "num_mflix_comments": 0
  }
]
*/
================================== Ai Message ==================================
Tool Calls:
  mongodb_query (call_4e8lfVyWrJyjRBZh8jKH1vfD)
 Call ID: call_4e8lfVyWrJyjRBZh8jKH1vfD
  Args:
    query: db.movies.aggregate([ { "$unwind": "$directors" }, { "$group": { "_id": "$directors", "movieCount": { "$sum": 1 } } }, { "$sort": { "movieCount": -1 } }, { "$limit": 3 } ])
================================== Ai Message ==================================
Tool Calls:
  mongodb_query (call_VV7SxdQi3WYXZBWEp3lP87BE)
 Call ID: call_VV7SxdQi3WYXZBWEp3lP87BE
  Args:
    query: db.movies.aggregate([ { "$unwind": "$directors" }, { "$group": { "_id": "$directors", "movieCount": { "$sum": 1 } } }, { "$sort": { "movieCount": -1 } }, { "$limit": 3 } ])
================================= Tool Message =================================
Name: mongodb_query

[
  {
    "_id": "Woody Allen",
    "movieCount": 40
  },
  {
    "_id": "Martin Scorsese",
    "movieCount": 32
  },
  {
    "_id": "Takashi Miike",
    "movieCount": 31
  }
]
================================== Ai Message ==================================

**Answer to:** "List the top 3 directors by movie count"

1. Woody Allen: 40 movies
2. Martin Scorsese: 32 movies
3. Takashi Miike: 31 movies

🔄 Building context for next query...
========================================

--- Conversation Step 2 ---
🧵 Thread: conversation_demo_7e08f130
❓ Query: What was the movie count for the first director?
📊 Agent: Custom LangGraph
==================================================
================================ Human Message =================================

What was the movie count for the first director?
================================== Ai Message ==================================

Available collections: comments, embedded_movies, movies, sessions, theaters, users
================================== Ai Message ==================================
Tool Calls:
  mongodb_schema (call_CrmuM4DXbeIGXyisNJh09NZ1)
 Call ID: call_CrmuM4DXbeIGXyisNJh09NZ1
  Args:
    collection_names: movies
================================= Tool Message =================================
Name: mongodb_schema

Database name: sample_mflix
Collection name: movies
Schema from a sample of documents from the collection:
_id: ObjectId
plot: String
genres: Array<String>
runtime: Number
cast: Array<String>
num_mflix_comments: Number
poster: String
title: String
fullplot: String
languages: Array<String>
released: Timestamp
directors: Array<String>
writers: Array<String>
awards.wins: Number
awards.nominations: Number
awards.text: String
lastupdated: String
year: Number
imdb.rating: Number
imdb.votes: Number
imdb.id: Number
countries: Array<String>
type: String
tomatoes.viewer.rating: Number
tomatoes.viewer.numReviews: Number
tomatoes.viewer.meter: Number
tomatoes.dvd: Timestamp
tomatoes.lastUpdated: Timestamp

/*
3 documents from movies collection:
[
  {
    "_id": {
      "$oid": "573a1390f29313caabcd63d6"
    },
    "plot": "Two peasant children,",
    "genres": [
      "Fantasy"
    ],
    "runtime": 75,
    "cast": [
      "Tula Belle",
      "Robin Macdougall",
      "Edwin E. Reed",
      "Emma Lowry"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "The Blue Bird",
    "fullplot": "Two peasant children,",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1633305600000"
      }
    },
    "directors": [
      "Maurice Tourneur"
    ],
    "writers": [
      "Maurice Maeterlinck (",
      "Charles Maigne"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-07-20 00:32:04.8",
    "year": 1918,
    "imdb": {
      "rating": 6.6,
      "votes": 446,
      "id": 8891
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.6,
        "numReviews": 607,
        "meter": 60
      },
      "dvd": {
        "$date": "2005-09-06T00:00:00Z"
      },
      "lastUpdated": {
        "$date": "2015-08-21T18:10:22Z"
      }
    }
  },
  {
    "_id": {
      "$oid": "573a1391f29313caabcd7472"
    },
    "plot": "A con artist masquera",
    "genres": [
      "Drama"
    ],
    "runtime": 117,
    "cast": [
      "Rudolph Christians",
      "Miss DuPont",
      "Maude George",
      "Mae Busch"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "Foolish Wives",
    "fullplot": "\"Count\" Karanzim, a D",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1513900800000"
      }
    },
    "directors": [
      "Erich von Stroheim"
    ],
    "writers": [
      "Erich von Stroheim (s",
      "Marian Ainslee (title",
      "Walter Anthony (title"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-09-05 00:00:37.8",
    "year": 1922,
    "imdb": {
      "rating": 7.3,
      "votes": 1777,
      "id": 13140
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 1079,
        "meter": 77
      },
      "dvd": {
        "$date": "2000-09-19T00:00:00Z"
      },
      "critic": {
        "rating": 9.0,
        "numReviews": 9,
        "meter": 89
      },
      "lastUpdated": {
        "$date": "2015-09-15T17:02:32Z"
      },
      "rotten": 1,
      "production": "Universal Pictures",
      "fresh": 8
    }
  },
  {
    "_id": {
      "$oid": "573a1390f29313caabcd42e8"
    },
    "plot": "A group of bandits st",
    "genres": [
      "Short",
      "Western"
    ],
    "runtime": 11,
    "cast": [
      "A.C. Abadie",
      "Gilbert M. 'Broncho B",
      "George Barnes",
      "Justus D. Barnes"
    ],
    "poster": "https://m.media-amazo",
    "title": "The Great Train Robbe",
    "fullplot": "Among the earliest ex",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-2085523200000"
      }
    },
    "directors": [
      "Edwin S. Porter"
    ],
    "rated": "TV-G",
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-08-13 00:27:59.1",
    "year": 1903,
    "imdb": {
      "rating": 7.4,
      "votes": 9847,
      "id": 439
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 2559,
        "meter": 75
      },
      "fresh": 6,
      "critic": {
        "rating": 7.6,
        "numReviews": 6,
        "meter": 100
      },
      "rotten": 0,
      "lastUpdated": {
        "$date": "2015-08-08T19:16:10Z"
      }
    },
    "num_mflix_comments": 0
  }
]
*/
================================== Ai Message ==================================

The movie count for the first director, Woody Allen, is 40 movies.

🔄 Building context for next query...
========================================

--- Conversation Step 3 ---
🧵 Thread: conversation_demo_7e08f130
❓ Query: Show me movies by that director with highest ratings
📊 Agent: Custom LangGraph
==================================================
================================ Human Message =================================

Show me movies by that director with highest ratings
================================== Ai Message ==================================

Available collections: comments, embedded_movies, movies, sessions, theaters, users
================================== Ai Message ==================================
Tool Calls:
  mongodb_schema (call_tVfyYdTFQYg1WSwKvyZuWjFp)
 Call ID: call_tVfyYdTFQYg1WSwKvyZuWjFp
  Args:
    collection_names: movies
================================= Tool Message =================================
Name: mongodb_schema

Database name: sample_mflix
Collection name: movies
Schema from a sample of documents from the collection:
_id: ObjectId
plot: String
genres: Array<String>
runtime: Number
cast: Array<String>
num_mflix_comments: Number
poster: String
title: String
fullplot: String
languages: Array<String>
released: Timestamp
directors: Array<String>
writers: Array<String>
awards.wins: Number
awards.nominations: Number
awards.text: String
lastupdated: String
year: Number
imdb.rating: Number
imdb.votes: Number
imdb.id: Number
countries: Array<String>
type: String
tomatoes.viewer.rating: Number
tomatoes.viewer.numReviews: Number
tomatoes.viewer.meter: Number
tomatoes.dvd: Timestamp
tomatoes.lastUpdated: Timestamp

/*
3 documents from movies collection:
[
  {
    "_id": {
      "$oid": "573a1390f29313caabcd63d6"
    },
    "plot": "Two peasant children,",
    "genres": [
      "Fantasy"
    ],
    "runtime": 75,
    "cast": [
      "Tula Belle",
      "Robin Macdougall",
      "Edwin E. Reed",
      "Emma Lowry"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "The Blue Bird",
    "fullplot": "Two peasant children,",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1633305600000"
      }
    },
    "directors": [
      "Maurice Tourneur"
    ],
    "writers": [
      "Maurice Maeterlinck (",
      "Charles Maigne"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-07-20 00:32:04.8",
    "year": 1918,
    "imdb": {
      "rating": 6.6,
      "votes": 446,
      "id": 8891
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.6,
        "numReviews": 607,
        "meter": 60
      },
      "dvd": {
        "$date": "2005-09-06T00:00:00Z"
      },
      "lastUpdated": {
        "$date": "2015-08-21T18:10:22Z"
      }
    }
  },
  {
    "_id": {
      "$oid": "573a1391f29313caabcd7472"
    },
    "plot": "A con artist masquera",
    "genres": [
      "Drama"
    ],
    "runtime": 117,
    "cast": [
      "Rudolph Christians",
      "Miss DuPont",
      "Maude George",
      "Mae Busch"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "Foolish Wives",
    "fullplot": "\"Count\" Karanzim, a D",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1513900800000"
      }
    },
    "directors": [
      "Erich von Stroheim"
    ],
    "writers": [
      "Erich von Stroheim (s",
      "Marian Ainslee (title",
      "Walter Anthony (title"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-09-05 00:00:37.8",
    "year": 1922,
    "imdb": {
      "rating": 7.3,
      "votes": 1777,
      "id": 13140
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 1079,
        "meter": 77
      },
      "dvd": {
        "$date": "2000-09-19T00:00:00Z"
      },
      "critic": {
        "rating": 9.0,
        "numReviews": 9,
        "meter": 89
      },
      "lastUpdated": {
        "$date": "2015-09-15T17:02:32Z"
      },
      "rotten": 1,
      "production": "Universal Pictures",
      "fresh": 8
    }
  },
  {
    "_id": {
      "$oid": "573a1390f29313caabcd42e8"
    },
    "plot": "A group of bandits st",
    "genres": [
      "Short",
      "Western"
    ],
    "runtime": 11,
    "cast": [
      "A.C. Abadie",
      "Gilbert M. 'Broncho B",
      "George Barnes",
      "Justus D. Barnes"
    ],
    "poster": "https://m.media-amazo",
    "title": "The Great Train Robbe",
    "fullplot": "Among the earliest ex",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-2085523200000"
      }
    },
    "directors": [
      "Edwin S. Porter"
    ],
    "rated": "TV-G",
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-08-13 00:27:59.1",
    "year": 1903,
    "imdb": {
      "rating": 7.4,
      "votes": 9847,
      "id": 439
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 2559,
        "meter": 75
      },
      "fresh": 6,
      "critic": {
        "rating": 7.6,
        "numReviews": 6,
        "meter": 100
      },
      "rotten": 0,
      "lastUpdated": {
        "$date": "2015-08-08T19:16:10Z"
      }
    },
    "num_mflix_comments": 0
  }
]
*/
================================== Ai Message ==================================
Tool Calls:
  mongodb_query (call_Ft6xllxobUsnisbxR1xm8JAh)
 Call ID: call_Ft6xllxobUsnisbxR1xm8JAh
  Args:
    query: db.movies.aggregate([ { "$match": { "directors": "Woody Allen" } }, { "$sort": { "imdb.rating": -1 } }, { "$project": { "title": 1, "imdb.rating": 1 } }, { "$limit": 5 } ])
================================== Ai Message ==================================
Tool Calls:
  mongodb_query (call_hV9m8OOwPoMvYc6Mchmoucts)
 Call ID: call_hV9m8OOwPoMvYc6Mchmoucts
  Args:
    query: db.movies.aggregate([ { "$match": { "directors": "Woody Allen" } }, { "$sort": { "imdb.rating": -1 } }, { "$project": { "title": 1, "imdb.rating": 1 } }, { "$limit": 5 } ])
================================= Tool Message =================================
Name: mongodb_query

[
  {
    "_id": {
      "$oid": "573a1397f29313caabce64fa"
    },
    "title": "Annie Hall",
    "imdb": {
      "rating": 8.1
    }
  },
  {
    "_id": {
      "$oid": "573a1398f29313caabceb5fc"
    },
    "title": "Crimes and Misdemeanors",
    "imdb": {
      "rating": 8.0
    }
  },
  {
    "_id": {
      "$oid": "573a1398f29313caabce9f96"
    },
    "title": "Hannah and Her Sisters",
    "imdb": {
      "rating": 8.0
    }
  },
  {
    "_id": {
      "$oid": "573a1397f29313caabce7388"
    },
    "title": "Manhattan",
    "imdb": {
      "rating": 8.0
    }
  },
  {
    "_id": {
      "$oid": "573a1398f29313caabce9a9a"
    },
    "title": "The Purple Rose of Cairo",
    "imdb": {
      "rating": 7.8
    }
  }
]
================================== Ai Message ==================================

**Answer to:** "List the top 3 directors by movie count"

1. {'$oid': '573a1397f29313caabce64fa'}
2. {'$oid': '573a1398f29313caabceb5fc'}
3. {'$oid': '573a1398f29313caabce9f96'}
4. {'$oid': '573a1397f29313caabce7388'}
5. {'$oid': '573a1398f29313caabce9a9a'}

🔍 Complete Conversation Analysis:
========================================

🔍 Thread History: conversation_demo_7e08f130
📊 Total steps: 10
================================================================================

📍 Step 1 [19:35:02]
   "🔄 Initial state"

📍 Step 2 [19:35:03]
   "📊 List top directors"

📍 Step 3 [19:35:03]
   "🔧 Available collections list"

📍 Step 4 [19:35:03]
   "🔧 Schema lookup: movies"

📍 Step 5 [19:35:03]
   "🔧 Schema details: movies"

📍 Step 6 [19:35:05]
   "🔧 Schema lookup: movies"
   └─ (repeated 1 more times)

📍 Step 8 [19:35:07]
   "📊 Director movie counts"

📍 Step 9 [19:35:08]
   "✨ Top directors by count"
   └─ (repeated 1 more times)

================================================================================

Demo 3: Enhanced Agent Comparison with Different Query Complexities"""

[40]

📊 Demo 3a: Simple Query Comparison
==================================================
Agent Comparison: ReAct vs LangGraph
============================================================
Query: Count all movies in the database
Max Retries: 2
Recursion Limit: 50
============================================================

ReAct Agent Execution:
----------------------------------------

Attempt 1/2
Thread: compare_d39279d2_react_attempt_1
Execution steps:
  Step 1: Response: Count all movies in the database

Final ReAct Response:
================================ Human Message =================================

Count all movies in the database
  Step 2: Tool call: mongodb_list_collections
  Step 3: Response: comments, embedded_movies, movies, sessions, theat...

Final ReAct Response:
================================= Tool Message =================================
Name: mongodb_list_collections

comments, embedded_movies, movies, sessions, theaters, users
  Step 4: Tool call: mongodb_query_checker
  Step 5: Response: content='```javascript\ndb.movies.aggregate([{ "$c...

Final ReAct Response:
================================= Tool Message =================================
Name: mongodb_query_checker

content='```javascript\ndb.movies.aggregate([{ "$count": "totalMovies" }])\n```' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 110, 'total_tokens': 127, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_34a54ae93c', 'id': 'chatcmpl-Bhhzi2ikqZSpf32gVoiRTpThzY6e3', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None} id='run--d2b6ba02-e5bb-4f9a-99a6-554cf7771a15-0' usage_metadata={'input_tokens': 110, 'output_tokens': 17, 'total_tokens': 127, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}
  Step 6: Tool call: mongodb_query
  Step 7: Response: [
  {
    "totalMovies": 21349
  }
]

Final ReAct Response:
================================= Tool Message =================================
Name: mongodb_query

[
  {
    "totalMovies": 21349
  }
]
  Step 8: Response: There are a total of 21,349 movies in the database...

Final ReAct Response:
================================== Ai Message ==================================

There are a total of 21,349 movies in the database.

ReAct agent succeeded in 8 steps

LangGraph Agent Execution:
----------------------------------------

Attempt 1/2
Thread: compare_d39279d2_graph_attempt_1
Execution steps:
  Step 1: Response: Count all movies in the database

Final LangGraph Response:
================================ Human Message =================================

Count all movies in the database
  Step 2: Response: Available collections: comments, embedded_movies, ...

Final LangGraph Response:
================================== Ai Message ==================================

Available collections: comments, embedded_movies, movies, sessions, theaters, users
  Step 3: Tool call: mongodb_schema
  Step 4: Response: Database name: sample_mflix
Collection name: movie...

Final LangGraph Response:
================================= Tool Message =================================
Name: mongodb_schema

Database name: sample_mflix
Collection name: movies
Schema from a sample of documents from the collection:
_id: ObjectId
plot: String
genres: Array<String>
runtime: Number
cast: Array<String>
num_mflix_comments: Number
poster: String
title: String
fullplot: String
languages: Array<String>
released: Timestamp
directors: Array<String>
writers: Array<String>
awards.wins: Number
awards.nominations: Number
awards.text: String
lastupdated: String
year: Number
imdb.rating: Number
imdb.votes: Number
imdb.id: Number
countries: Array<String>
type: String
tomatoes.viewer.rating: Number
tomatoes.viewer.numReviews: Number
tomatoes.viewer.meter: Number
tomatoes.dvd: Timestamp
tomatoes.lastUpdated: Timestamp

/*
3 documents from movies collection:
[
  {
    "_id": {
      "$oid": "573a1390f29313caabcd63d6"
    },
    "plot": "Two peasant children,",
    "genres": [
      "Fantasy"
    ],
    "runtime": 75,
    "cast": [
      "Tula Belle",
      "Robin Macdougall",
      "Edwin E. Reed",
      "Emma Lowry"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "The Blue Bird",
    "fullplot": "Two peasant children,",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1633305600000"
      }
    },
    "directors": [
      "Maurice Tourneur"
    ],
    "writers": [
      "Maurice Maeterlinck (",
      "Charles Maigne"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-07-20 00:32:04.8",
    "year": 1918,
    "imdb": {
      "rating": 6.6,
      "votes": 446,
      "id": 8891
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.6,
        "numReviews": 607,
        "meter": 60
      },
      "dvd": {
        "$date": "2005-09-06T00:00:00Z"
      },
      "lastUpdated": {
        "$date": "2015-08-21T18:10:22Z"
      }
    }
  },
  {
    "_id": {
      "$oid": "573a1391f29313caabcd7472"
    },
    "plot": "A con artist masquera",
    "genres": [
      "Drama"
    ],
    "runtime": 117,
    "cast": [
      "Rudolph Christians",
      "Miss DuPont",
      "Maude George",
      "Mae Busch"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "Foolish Wives",
    "fullplot": "\"Count\" Karanzim, a D",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1513900800000"
      }
    },
    "directors": [
      "Erich von Stroheim"
    ],
    "writers": [
      "Erich von Stroheim (s",
      "Marian Ainslee (title",
      "Walter Anthony (title"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-09-05 00:00:37.8",
    "year": 1922,
    "imdb": {
      "rating": 7.3,
      "votes": 1777,
      "id": 13140
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 1079,
        "meter": 77
      },
      "dvd": {
        "$date": "2000-09-19T00:00:00Z"
      },
      "critic": {
        "rating": 9.0,
        "numReviews": 9,
        "meter": 89
      },
      "lastUpdated": {
        "$date": "2015-09-15T17:02:32Z"
      },
      "rotten": 1,
      "production": "Universal Pictures",
      "fresh": 8
    }
  },
  {
    "_id": {
      "$oid": "573a1390f29313caabcd42e8"
    },
    "plot": "A group of bandits st",
    "genres": [
      "Short",
      "Western"
    ],
    "runtime": 11,
    "cast": [
      "A.C. Abadie",
      "Gilbert M. 'Broncho B",
      "George Barnes",
      "Justus D. Barnes"
    ],
    "poster": "https://m.media-amazo",
    "title": "The Great Train Robbe",
    "fullplot": "Among the earliest ex",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-2085523200000"
      }
    },
    "directors": [
      "Edwin S. Porter"
    ],
    "rated": "TV-G",
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-08-13 00:27:59.1",
    "year": 1903,
    "imdb": {
      "rating": 7.4,
      "votes": 9847,
      "id": 439
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 2559,
        "meter": 75
      },
      "fresh": 6,
      "critic": {
        "rating": 7.6,
        "numReviews": 6,
        "meter": 100
      },
      "rotten": 0,
      "lastUpdated": {
        "$date": "2015-08-08T19:16:10Z"
      }
    },
    "num_mflix_comments": 0
  }
]
*/
  Step 5: Tool call: mongodb_query
  Step 6: Tool call: mongodb_query
  Step 7: Response: [
  {
    "totalMovies": 21349
  }
]

Final LangGraph Response:
================================= Tool Message =================================
Name: mongodb_query

[
  {
    "totalMovies": 21349
  }
]
  Step 8: Response: **Answer to:** "Count all movies in the database"


Final LangGraph Response:
================================== Ai Message ==================================

**Answer to:** "Count all movies in the database"

LangGraph agent succeeded in 8 steps

Comparison Summary:
============================================================

ReAct Agent Results:
  Success: ✅
  Attempts: 1/2
  Execution Time: 4.40s

LangGraph Agent Results:
  Success: ✅
  Attempts: 1/2
  Execution Time: 3.05s

Execution Style Analysis:
  ReAct Agent:
    - Autonomous reasoning and tool selection
    - Dynamic decision making based on previous results
    - Can get stuck in reasoning loops with complex queries
    - More flexible but less predictable workflow
  LangGraph Agent:
    - Structured, deterministic workflow
    - Predefined step sequence with conditional branches
    - Better error isolation and recovery
    - More predictable but less flexible execution

Memory Pattern Analysis:
  ReAct Agent Memory:

🔍 Thread History: compare_d39279d2_react_attempt_1
📊 Total steps: 3
================================================================================

📍 Step 1 [19:35:15]
   "🔄 Initial state"

📍 Step 2 [19:35:16]
   "📊 Count all movies"

📍 Step 3 [19:35:17]
   "🔧 List MongoDB collections"

================================================================================
  LangGraph Agent Memory:

🔍 Thread History: compare_d39279d2_graph_attempt_1
📊 Total steps: 3
================================================================================

📍 Step 1 [19:35:20]
   "🔄 Initial state"

📍 Step 2 [19:35:20]
   "📊 Count all movies"

📍 Step 3 [19:35:20]
   "🔧 Available collections list"

================================================================================

Recommendations:
  - LangGraph agent was more efficient for this query
  - Both agents handled the query successfully

================================================================================

[41]

📊 Demo 3b: Moderate Complexity Comparison
==================================================
Agent Comparison: ReAct vs LangGraph
============================================================
Query: List the top 5 directors by movie count
Max Retries: 2
Recursion Limit: 40
============================================================

ReAct Agent Execution:
----------------------------------------

Attempt 1/2
Thread: compare_260fd616_react_attempt_1
Execution steps:
  Step 1: Response: List the top 5 directors by movie count

Final ReAct Response:
================================ Human Message =================================

List the top 5 directors by movie count
  Step 2: Tool call: mongodb_list_collections
  Step 3: Response: comments, embedded_movies, movies, sessions, theat...

Final ReAct Response:
================================= Tool Message =================================
Name: mongodb_list_collections

comments, embedded_movies, movies, sessions, theaters, users
  Step 4: Tool call: mongodb_schema
  Step 5: Response: Database name: sample_mflix
Collection name: movie...

Final ReAct Response:
================================= Tool Message =================================
Name: mongodb_schema

Database name: sample_mflix
Collection name: movies
Schema from a sample of documents from the collection:
_id: ObjectId
plot: String
genres: Array<String>
runtime: Number
cast: Array<String>
num_mflix_comments: Number
poster: String
title: String
fullplot: String
languages: Array<String>
released: Timestamp
directors: Array<String>
writers: Array<String>
awards.wins: Number
awards.nominations: Number
awards.text: String
lastupdated: String
year: Number
imdb.rating: Number
imdb.votes: Number
imdb.id: Number
countries: Array<String>
type: String
tomatoes.viewer.rating: Number
tomatoes.viewer.numReviews: Number
tomatoes.viewer.meter: Number
tomatoes.dvd: Timestamp
tomatoes.lastUpdated: Timestamp

/*
3 documents from movies collection:
[
  {
    "_id": {
      "$oid": "573a1390f29313caabcd63d6"
    },
    "plot": "Two peasant children,",
    "genres": [
      "Fantasy"
    ],
    "runtime": 75,
    "cast": [
      "Tula Belle",
      "Robin Macdougall",
      "Edwin E. Reed",
      "Emma Lowry"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "The Blue Bird",
    "fullplot": "Two peasant children,",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1633305600000"
      }
    },
    "directors": [
      "Maurice Tourneur"
    ],
    "writers": [
      "Maurice Maeterlinck (",
      "Charles Maigne"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-07-20 00:32:04.8",
    "year": 1918,
    "imdb": {
      "rating": 6.6,
      "votes": 446,
      "id": 8891
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.6,
        "numReviews": 607,
        "meter": 60
      },
      "dvd": {
        "$date": "2005-09-06T00:00:00Z"
      },
      "lastUpdated": {
        "$date": "2015-08-21T18:10:22Z"
      }
    }
  },
  {
    "_id": {
      "$oid": "573a1391f29313caabcd7472"
    },
    "plot": "A con artist masquera",
    "genres": [
      "Drama"
    ],
    "runtime": 117,
    "cast": [
      "Rudolph Christians",
      "Miss DuPont",
      "Maude George",
      "Mae Busch"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "Foolish Wives",
    "fullplot": "\"Count\" Karanzim, a D",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1513900800000"
      }
    },
    "directors": [
      "Erich von Stroheim"
    ],
    "writers": [
      "Erich von Stroheim (s",
      "Marian Ainslee (title",
      "Walter Anthony (title"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-09-05 00:00:37.8",
    "year": 1922,
    "imdb": {
      "rating": 7.3,
      "votes": 1777,
      "id": 13140
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 1079,
        "meter": 77
      },
      "dvd": {
        "$date": "2000-09-19T00:00:00Z"
      },
      "critic": {
        "rating": 9.0,
        "numReviews": 9,
        "meter": 89
      },
      "lastUpdated": {
        "$date": "2015-09-15T17:02:32Z"
      },
      "rotten": 1,
      "production": "Universal Pictures",
      "fresh": 8
    }
  },
  {
    "_id": {
      "$oid": "573a1390f29313caabcd42e8"
    },
    "plot": "A group of bandits st",
    "genres": [
      "Short",
      "Western"
    ],
    "runtime": 11,
    "cast": [
      "A.C. Abadie",
      "Gilbert M. 'Broncho B",
      "George Barnes",
      "Justus D. Barnes"
    ],
    "poster": "https://m.media-amazo",
    "title": "The Great Train Robbe",
    "fullplot": "Among the earliest ex",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-2085523200000"
      }
    },
    "directors": [
      "Edwin S. Porter"
    ],
    "rated": "TV-G",
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-08-13 00:27:59.1",
    "year": 1903,
    "imdb": {
      "rating": 7.4,
      "votes": 9847,
      "id": 439
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 2559,
        "meter": 75
      },
      "fresh": 6,
      "critic": {
        "rating": 7.6,
        "numReviews": 6,
        "meter": 100
      },
      "rotten": 0,
      "lastUpdated": {
        "$date": "2015-08-08T19:16:10Z"
      }
    },
    "num_mflix_comments": 0
  }
]
*/
  Step 6: Tool call: mongodb_query_checker
  Step 7: Response: content='```javascript\ndb.movies.aggregate([\n   ...

Final ReAct Response:
================================= Tool Message =================================
Name: mongodb_query_checker

content='```javascript\ndb.movies.aggregate([\n    { "$unwind": "$directors" },\n    { "$group": { "_id": "$directors", "movieCount": { "$sum": 1 } } },\n    { "$sort": { "movieCount": -1 } },\n    { "$limit": 5 }\n])\n```' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 68, 'prompt_tokens': 156, 'total_tokens': 224, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_34a54ae93c', 'id': 'chatcmpl-BhhzpJznhSUbadHnAAVeL71mfizbo', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None} id='run--60aa7549-fb46-4335-83f7-c8a820e92569-0' usage_metadata={'input_tokens': 156, 'output_tokens': 68, 'total_tokens': 224, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}
  Step 8: Tool call: mongodb_query
  Step 9: Response: [
  {
    "_id": "Woody Allen",
    "movieCount": ...

Final ReAct Response:
================================= Tool Message =================================
Name: mongodb_query

[
  {
    "_id": "Woody Allen",
    "movieCount": 40
  },
  {
    "_id": "Martin Scorsese",
    "movieCount": 32
  },
  {
    "_id": "Takashi Miike",
    "movieCount": 31
  },
  {
    "_id": "Sidney Lumet",
    "movieCount": 29
  },
  {
    "_id": "Steven Spielberg",
    "movieCount": 29
  }
]
  Step 10: Response: The top 5 directors by movie count are:

1. **Wood...

Final ReAct Response:
================================== Ai Message ==================================

The top 5 directors by movie count are:

1. **Woody Allen** - 40 movies
2. **Martin Scorsese** - 32 movies
3. **Takashi Miike** - 31 movies
4. **Sidney Lumet** - 29 movies
5. **Steven Spielberg** - 29 movies

ReAct agent succeeded in 10 steps

LangGraph Agent Execution:
----------------------------------------

Attempt 1/2
Thread: compare_260fd616_graph_attempt_1
Execution steps:
  Step 1: Response: List the top 5 directors by movie count

Final LangGraph Response:
================================ Human Message =================================

List the top 5 directors by movie count
  Step 2: Response: Available collections: comments, embedded_movies, ...

Final LangGraph Response:
================================== Ai Message ==================================

Available collections: comments, embedded_movies, movies, sessions, theaters, users
  Step 3: Tool call: mongodb_schema
  Step 4: Response: Database name: sample_mflix
Collection name: movie...

Final LangGraph Response:
================================= Tool Message =================================
Name: mongodb_schema

Database name: sample_mflix
Collection name: movies
Schema from a sample of documents from the collection:
_id: ObjectId
plot: String
genres: Array<String>
runtime: Number
cast: Array<String>
num_mflix_comments: Number
poster: String
title: String
fullplot: String
languages: Array<String>
released: Timestamp
directors: Array<String>
writers: Array<String>
awards.wins: Number
awards.nominations: Number
awards.text: String
lastupdated: String
year: Number
imdb.rating: Number
imdb.votes: Number
imdb.id: Number
countries: Array<String>
type: String
tomatoes.viewer.rating: Number
tomatoes.viewer.numReviews: Number
tomatoes.viewer.meter: Number
tomatoes.dvd: Timestamp
tomatoes.lastUpdated: Timestamp

/*
3 documents from movies collection:
[
  {
    "_id": {
      "$oid": "573a1390f29313caabcd63d6"
    },
    "plot": "Two peasant children,",
    "genres": [
      "Fantasy"
    ],
    "runtime": 75,
    "cast": [
      "Tula Belle",
      "Robin Macdougall",
      "Edwin E. Reed",
      "Emma Lowry"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "The Blue Bird",
    "fullplot": "Two peasant children,",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1633305600000"
      }
    },
    "directors": [
      "Maurice Tourneur"
    ],
    "writers": [
      "Maurice Maeterlinck (",
      "Charles Maigne"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-07-20 00:32:04.8",
    "year": 1918,
    "imdb": {
      "rating": 6.6,
      "votes": 446,
      "id": 8891
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.6,
        "numReviews": 607,
        "meter": 60
      },
      "dvd": {
        "$date": "2005-09-06T00:00:00Z"
      },
      "lastUpdated": {
        "$date": "2015-08-21T18:10:22Z"
      }
    }
  },
  {
    "_id": {
      "$oid": "573a1391f29313caabcd7472"
    },
    "plot": "A con artist masquera",
    "genres": [
      "Drama"
    ],
    "runtime": 117,
    "cast": [
      "Rudolph Christians",
      "Miss DuPont",
      "Maude George",
      "Mae Busch"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "Foolish Wives",
    "fullplot": "\"Count\" Karanzim, a D",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1513900800000"
      }
    },
    "directors": [
      "Erich von Stroheim"
    ],
    "writers": [
      "Erich von Stroheim (s",
      "Marian Ainslee (title",
      "Walter Anthony (title"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-09-05 00:00:37.8",
    "year": 1922,
    "imdb": {
      "rating": 7.3,
      "votes": 1777,
      "id": 13140
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 1079,
        "meter": 77
      },
      "dvd": {
        "$date": "2000-09-19T00:00:00Z"
      },
      "critic": {
        "rating": 9.0,
        "numReviews": 9,
        "meter": 89
      },
      "lastUpdated": {
        "$date": "2015-09-15T17:02:32Z"
      },
      "rotten": 1,
      "production": "Universal Pictures",
      "fresh": 8
    }
  },
  {
    "_id": {
      "$oid": "573a1390f29313caabcd42e8"
    },
    "plot": "A group of bandits st",
    "genres": [
      "Short",
      "Western"
    ],
    "runtime": 11,
    "cast": [
      "A.C. Abadie",
      "Gilbert M. 'Broncho B",
      "George Barnes",
      "Justus D. Barnes"
    ],
    "poster": "https://m.media-amazo",
    "title": "The Great Train Robbe",
    "fullplot": "Among the earliest ex",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-2085523200000"
      }
    },
    "directors": [
      "Edwin S. Porter"
    ],
    "rated": "TV-G",
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-08-13 00:27:59.1",
    "year": 1903,
    "imdb": {
      "rating": 7.4,
      "votes": 9847,
      "id": 439
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 2559,
        "meter": 75
      },
      "fresh": 6,
      "critic": {
        "rating": 7.6,
        "numReviews": 6,
        "meter": 100
      },
      "rotten": 0,
      "lastUpdated": {
        "$date": "2015-08-08T19:16:10Z"
      }
    },
    "num_mflix_comments": 0
  }
]
*/
  Step 5: Tool call: mongodb_query
  Step 6: Tool call: mongodb_query
  Step 7: Response: [
  {
    "_id": "Woody Allen",
    "movieCount": ...

Final LangGraph Response:
================================= Tool Message =================================
Name: mongodb_query

[
  {
    "_id": "Woody Allen",
    "movieCount": 40
  },
  {
    "_id": "Martin Scorsese",
    "movieCount": 32
  },
  {
    "_id": "Takashi Miike",
    "movieCount": 31
  },
  {
    "_id": "Steven Spielberg",
    "movieCount": 29
  },
  {
    "_id": "Sidney Lumet",
    "movieCount": 29
  }
]
  Step 8: Response: **Answer to:** "List the top 5 directors by movie ...

Final LangGraph Response:
================================== Ai Message ==================================

**Answer to:** "List the top 5 directors by movie count"

1. Woody Allen: 40 movies
2. Martin Scorsese: 32 movies
3. Takashi Miike: 31 movies
4. Steven Spielberg: 29 movies
5. Sidney Lumet: 29 movies

LangGraph agent succeeded in 8 steps

Comparison Summary:
============================================================

ReAct Agent Results:
  Success: ✅
  Attempts: 1/2
  Execution Time: 7.72s

LangGraph Agent Results:
  Success: ✅
  Attempts: 1/2
  Execution Time: 3.79s

Execution Style Analysis:
  ReAct Agent:
    - Autonomous reasoning and tool selection
    - Dynamic decision making based on previous results
    - Can get stuck in reasoning loops with complex queries
    - More flexible but less predictable workflow
  LangGraph Agent:
    - Structured, deterministic workflow
    - Predefined step sequence with conditional branches
    - Better error isolation and recovery
    - More predictable but less flexible execution

Memory Pattern Analysis:
  ReAct Agent Memory:

🔍 Thread History: compare_260fd616_react_attempt_1
📊 Total steps: 3
================================================================================

📍 Step 1 [19:35:23]
   "🔄 Initial state"

📍 Step 2 [19:35:23]
   "📊 List top directors"

📍 Step 3 [19:35:23]
   "🔧 List MongoDB collections"

================================================================================
  LangGraph Agent Memory:

🔍 Thread History: compare_260fd616_graph_attempt_1
📊 Total steps: 3
================================================================================

📍 Step 1 [19:35:31]
   "🔄 Initial state"

📍 Step 2 [19:35:31]
   "📊 List top directors by movies"

📍 Step 3 [19:35:31]
   "🔧 Available collections list"

================================================================================

Recommendations:
  - LangGraph agent was more efficient for this query
  - Both agents handled the query successfully

================================================================================

[42]

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  }
]
*/
  Step 6: Tool call: mongodb_query_checker
  Step 7: Response: content='```javascript\ndb.movies.aggregate([\n   ...

Final ReAct Response:
================================= Tool Message =================================
Name: mongodb_query_checker

content='```javascript\ndb.movies.aggregate([\n    { "$match": { "$expr": { "$gte": [ "$awards.wins", 1 ] } } },\n    { "$group": { "_id": "$directors", "totalWins": { "$sum": "$awards.wins" }, "movieCount": { "$sum": 1 } } },\n    { "$match": { "movieCount": { "$gte": 5 } } },\n    { "$sort": { "totalWins": -1 } },\n    { "$limit": 5 }\n])\n```' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 117, 'prompt_tokens': 204, 'total_tokens': 321, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_34a54ae93c', 'id': 'chatcmpl-Bhi04rPKCP7Y76UWVAptxY2we8PEm', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None} id='run--f6eb4227-9693-4a00-a1e3-ab244d223e4e-0' usage_metadata={'input_tokens': 204, 'output_tokens': 117, 'total_tokens': 321, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}
  Step 8: Tool call: mongodb_query
  Step 9: Response: Error: ValueError('Cannot execute command db.movie...

Final ReAct Response:
================================= Tool Message =================================
Name: mongodb_query

Error: ValueError('Cannot execute command db.movies.aggregate([ { "$match": { "$expr": { "$gte": [ "$awards.wins", 1 ] } } }, { "$group": { _id: "$directors", "totalWins": { "$sum": "$awards.wins" }, "movieCount": { "$sum": 1 } } }, { "$match": { "movieCount": { "$gte": 5 } } }, { "$sort": { "totalWins": -1 } }, { "$limit": 5 } ])')
 Please fix your mistakes.
  Step 10: Tool call: mongodb_query_checker
  Step 11: Response: content='```json\ndb.movies.aggregate([\n    { "$m...

Final ReAct Response:
================================= Tool Message =================================
Name: mongodb_query_checker

content='```json\ndb.movies.aggregate([\n    { "$match": { "awards.wins": { "$gte": 1 } } },\n    { "$group": { "_id": "$directors", "totalWins": { "$sum": "$awards.wins" }, "movieCount": { "$sum": 1 } } },\n    { "$match": { "movieCount": { "$gte": 5 } } },\n    { "$sort": { "totalWins": -1 } },\n    { "$limit": 5 }\n])\n```' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 112, 'prompt_tokens': 199, 'total_tokens': 311, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_34a54ae93c', 'id': 'chatcmpl-Bhi0EkQAnKosnhA5KNZyvP8LfoDKB', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None} id='run--45a1e727-84e9-4288-8f6c-ef7b82299077-0' usage_metadata={'input_tokens': 199, 'output_tokens': 112, 'total_tokens': 311, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}
  Step 12: Tool call: mongodb_query
  Step 13: Response: [
  {
    "_id": null,
    "totalWins": 1250,
    ...

Final ReAct Response:
================================= Tool Message =================================
Name: mongodb_query

[
  {
    "_id": null,
    "totalWins": 1250,
    "movieCount": 181
  },
  {
    "_id": [
      "Steven Spielberg"
    ],
    "totalWins": 696,
    "movieCount": 27
  },
  {
    "_id": [
      "Martin Scorsese"
    ],
    "totalWins": 582,
    "movieCount": 26
  },
  {
    "_id": [
      "Alfonso Cuar\u00e8n"
    ],
    "totalWins": 575,
    "movieCount": 7
  },
  {
    "_id": [
      "Peter Jackson"
    ],
    "totalWins": 524,
    "movieCount": 12
  }
]
  Step 14: Response: Here are the top 5 directors with the most award w...

Final ReAct Response:
================================== Ai Message ==================================

Here are the top 5 directors with the most award wins, each having directed at least 5 movies:

1. **Steven Spielberg**
   - Total Wins: 696
   - Movie Count: 27

2. **Martin Scorsese**
   - Total Wins: 582
   - Movie Count: 26

3. **Alfonso Cuarón**
   - Total Wins: 575
   - Movie Count: 7

4. **Peter Jackson**
   - Total Wins: 524
   - Movie Count: 12

5. **(Aggregate Total)**
   - Total Wins: 1250
   - Movie Count: 181

(Note: The aggregate total represents the combined wins across all directors, not a specific individual.)

ReAct agent succeeded in 14 steps

LangGraph Agent Execution:
----------------------------------------

Attempt 1/3
Thread: compare_69c47d7a_graph_attempt_1
Execution steps:
  Step 1: Response: Find the top 5 directors with most award wins and ...

Final LangGraph Response:
================================ Human Message =================================

Find the top 5 directors with most award wins and at least 5 movies
  Step 2: Response: Available collections: comments, embedded_movies, ...

Final LangGraph Response:
================================== Ai Message ==================================

Available collections: comments, embedded_movies, movies, sessions, theaters, users
  Step 3: Tool call: mongodb_schema
  Step 4: Response: Database name: sample_mflix
Collection name: movie...

Final LangGraph Response:
================================= Tool Message =================================
Name: mongodb_schema

Database name: sample_mflix
Collection name: movies
Schema from a sample of documents from the collection:
_id: ObjectId
plot: String
genres: Array<String>
runtime: Number
cast: Array<String>
num_mflix_comments: Number
poster: String
title: String
fullplot: String
languages: Array<String>
released: Timestamp
directors: Array<String>
writers: Array<String>
awards.wins: Number
awards.nominations: Number
awards.text: String
lastupdated: String
year: Number
imdb.rating: Number
imdb.votes: Number
imdb.id: Number
countries: Array<String>
type: String
tomatoes.viewer.rating: Number
tomatoes.viewer.numReviews: Number
tomatoes.viewer.meter: Number
tomatoes.dvd: Timestamp
tomatoes.lastUpdated: Timestamp

/*
3 documents from movies collection:
[
  {
    "_id": {
      "$oid": "573a1390f29313caabcd63d6"
    },
    "plot": "Two peasant children,",
    "genres": [
      "Fantasy"
    ],
    "runtime": 75,
    "cast": [
      "Tula Belle",
      "Robin Macdougall",
      "Edwin E. Reed",
      "Emma Lowry"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "The Blue Bird",
    "fullplot": "Two peasant children,",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1633305600000"
      }
    },
    "directors": [
      "Maurice Tourneur"
    ],
    "writers": [
      "Maurice Maeterlinck (",
      "Charles Maigne"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-07-20 00:32:04.8",
    "year": 1918,
    "imdb": {
      "rating": 6.6,
      "votes": 446,
      "id": 8891
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.6,
        "numReviews": 607,
        "meter": 60
      },
      "dvd": {
        "$date": "2005-09-06T00:00:00Z"
      },
      "lastUpdated": {
        "$date": "2015-08-21T18:10:22Z"
      }
    }
  },
  {
    "_id": {
      "$oid": "573a1391f29313caabcd7472"
    },
    "plot": "A con artist masquera",
    "genres": [
      "Drama"
    ],
    "runtime": 117,
    "cast": [
      "Rudolph Christians",
      "Miss DuPont",
      "Maude George",
      "Mae Busch"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "Foolish Wives",
    "fullplot": "\"Count\" Karanzim, a D",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1513900800000"
      }
    },
    "directors": [
      "Erich von Stroheim"
    ],
    "writers": [
      "Erich von Stroheim (s",
      "Marian Ainslee (title",
      "Walter Anthony (title"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-09-05 00:00:37.8",
    "year": 1922,
    "imdb": {
      "rating": 7.3,
      "votes": 1777,
      "id": 13140
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 1079,
        "meter": 77
      },
      "dvd": {
        "$date": "2000-09-19T00:00:00Z"
      },
      "critic": {
        "rating": 9.0,
        "numReviews": 9,
        "meter": 89
      },
      "lastUpdated": {
        "$date": "2015-09-15T17:02:32Z"
      },
      "rotten": 1,
      "production": "Universal Pictures",
      "fresh": 8
    }
  },
  {
    "_id": {
      "$oid": "573a1390f29313caabcd42e8"
    },
    "plot": "A group of bandits st",
    "genres": [
      "Short",
      "Western"
    ],
    "runtime": 11,
    "cast": [
      "A.C. Abadie",
      "Gilbert M. 'Broncho B",
      "George Barnes",
      "Justus D. Barnes"
    ],
    "poster": "https://m.media-amazo",
    "title": "The Great Train Robbe",
    "fullplot": "Among the earliest ex",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-2085523200000"
      }
    },
    "directors": [
      "Edwin S. Porter"
    ],
    "rated": "TV-G",
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-08-13 00:27:59.1",
    "year": 1903,
    "imdb": {
      "rating": 7.4,
      "votes": 9847,
      "id": 439
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 2559,
        "meter": 75
      },
      "fresh": 6,
      "critic": {
        "rating": 7.6,
        "numReviews": 6,
        "meter": 100
      },
      "rotten": 0,
      "lastUpdated": {
        "$date": "2015-08-08T19:16:10Z"
      }
    },
    "num_mflix_comments": 0
  }
]
*/
  Step 5: Tool call: mongodb_query
  Step 6: Tool call: mongodb_query
  Step 7: Response: [
  {
    "_id": null,
    "totalWins": 1250,
    ...

Final LangGraph Response:
================================= Tool Message =================================
Name: mongodb_query

[
  {
    "_id": null,
    "totalWins": 1250,
    "movieCount": 181
  },
  {
    "_id": [
      "Steven Spielberg"
    ],
    "totalWins": 696,
    "movieCount": 27
  },
  {
    "_id": [
      "Martin Scorsese"
    ],
    "totalWins": 582,
    "movieCount": 26
  },
  {
    "_id": [
      "Alfonso Cuar\u00e8n"
    ],
    "totalWins": 575,
    "movieCount": 7
  },
  {
    "_id": [
      "Peter Jackson"
    ],
    "totalWins": 524,
    "movieCount": 12
  }
]
  Step 8: Response: **Answer to:** "Find the top 5 directors with most...

Final LangGraph Response:
================================== Ai Message ==================================

**Answer to:** "Find the top 5 directors with most award wins and at least 5 movies"

1. None: 181 movies
2. ['Steven Spielberg']: 27 movies
3. ['Martin Scorsese']: 26 movies
4. ['Alfonso Cuarèn']: 7 movies
5. ['Peter Jackson']: 12 movies

LangGraph agent succeeded in 8 steps

Comparison Summary:
============================================================

ReAct Agent Results:
  Success: ✅
  Attempts: 1/3
  Execution Time: 25.42s

LangGraph Agent Results:
  Success: ✅
  Attempts: 1/3
  Execution Time: 5.50s

Execution Style Analysis:
  ReAct Agent:
    - Autonomous reasoning and tool selection
    - Dynamic decision making based on previous results
    - Can get stuck in reasoning loops with complex queries
    - More flexible but less predictable workflow
  LangGraph Agent:
    - Structured, deterministic workflow
    - Predefined step sequence with conditional branches
    - Better error isolation and recovery
    - More predictable but less flexible execution

Memory Pattern Analysis:
  ReAct Agent Memory:

🔍 Thread History: compare_69c47d7a_react_attempt_1
📊 Total steps: 3
================================================================================

📍 Step 1 [19:35:35]
   "🔄 Initial state"

📍 Step 2 [19:35:35]
   "📊 Top directors search"

📍 Step 3 [19:35:35]
   "🔧 List MongoDB collections"

================================================================================
  LangGraph Agent Memory:

🔍 Thread History: compare_69c47d7a_graph_attempt_1
📊 Total steps: 3
================================================================================

📍 Step 1 [19:36:00]
   "🔄 Initial state"

📍 Step 2 [19:36:00]
   "📊 Top directors query"

📍 Step 3 [19:36:00]
   "🔧 Available collections list"

================================================================================

Recommendations:
  - LangGraph agent was more efficient for this query
  - Both agents handled the query successfully

================================================================================

📊 Demo 3d: Comprehensive Agent Test Suite
==================================================
Running Comparison Test Suite
============================================================

==================== Simple Query ====================
Agent Comparison: ReAct vs LangGraph
============================================================
Query: Count the total number of movies in the database
Max Retries: 2
Recursion Limit: 30
============================================================

ReAct Agent Execution:
----------------------------------------

Attempt 1/2
Thread: compare_446205bd_react_attempt_1
Execution steps:
  Step 1: Response: Count the total number of movies in the database

Final ReAct Response:
================================ Human Message =================================

Count the total number of movies in the database
  Step 2: Tool call: mongodb_list_collections
  Step 3: Response: comments, embedded_movies, movies, sessions, theat...

Final ReAct Response:
================================= Tool Message =================================
Name: mongodb_list_collections

comments, embedded_movies, movies, sessions, theaters, users
  Step 4: Tool call: mongodb_schema
  Step 5: Response: Database name: sample_mflix
Collection name: movie...

Final ReAct Response:
================================= Tool Message =================================
Name: mongodb_schema

Database name: sample_mflix
Collection name: movies
Schema from a sample of documents from the collection:
_id: ObjectId
plot: String
genres: Array<String>
runtime: Number
cast: Array<String>
num_mflix_comments: Number
poster: String
title: String
fullplot: String
languages: Array<String>
released: Timestamp
directors: Array<String>
writers: Array<String>
awards.wins: Number
awards.nominations: Number
awards.text: String
lastupdated: String
year: Number
imdb.rating: Number
imdb.votes: Number
imdb.id: Number
countries: Array<String>
type: String
tomatoes.viewer.rating: Number
tomatoes.viewer.numReviews: Number
tomatoes.viewer.meter: Number
tomatoes.dvd: Timestamp
tomatoes.lastUpdated: Timestamp

/*
3 documents from movies collection:
[
  {
    "_id": {
      "$oid": "573a1390f29313caabcd63d6"
    },
    "plot": "Two peasant children,",
    "genres": [
      "Fantasy"
    ],
    "runtime": 75,
    "cast": [
      "Tula Belle",
      "Robin Macdougall",
      "Edwin E. Reed",
      "Emma Lowry"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "The Blue Bird",
    "fullplot": "Two peasant children,",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1633305600000"
      }
    },
    "directors": [
      "Maurice Tourneur"
    ],
    "writers": [
      "Maurice Maeterlinck (",
      "Charles Maigne"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-07-20 00:32:04.8",
    "year": 1918,
    "imdb": {
      "rating": 6.6,
      "votes": 446,
      "id": 8891
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.6,
        "numReviews": 607,
        "meter": 60
      },
      "dvd": {
        "$date": "2005-09-06T00:00:00Z"
      },
      "lastUpdated": {
        "$date": "2015-08-21T18:10:22Z"
      }
    }
  },
  {
    "_id": {
      "$oid": "573a1391f29313caabcd7472"
    },
    "plot": "A con artist masquera",
    "genres": [
      "Drama"
    ],
    "runtime": 117,
    "cast": [
      "Rudolph Christians",
      "Miss DuPont",
      "Maude George",
      "Mae Busch"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "Foolish Wives",
    "fullplot": "\"Count\" Karanzim, a D",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1513900800000"
      }
    },
    "directors": [
      "Erich von Stroheim"
    ],
    "writers": [
      "Erich von Stroheim (s",
      "Marian Ainslee (title",
      "Walter Anthony (title"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-09-05 00:00:37.8",
    "year": 1922,
    "imdb": {
      "rating": 7.3,
      "votes": 1777,
      "id": 13140
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 1079,
        "meter": 77
      },
      "dvd": {
        "$date": "2000-09-19T00:00:00Z"
      },
      "critic": {
        "rating": 9.0,
        "numReviews": 9,
        "meter": 89
      },
      "lastUpdated": {
        "$date": "2015-09-15T17:02:32Z"
      },
      "rotten": 1,
      "production": "Universal Pictures",
      "fresh": 8
    }
  },
  {
    "_id": {
      "$oid": "573a1390f29313caabcd42e8"
    },
    "plot": "A group of bandits st",
    "genres": [
      "Short",
      "Western"
    ],
    "runtime": 11,
    "cast": [
      "A.C. Abadie",
      "Gilbert M. 'Broncho B",
      "George Barnes",
      "Justus D. Barnes"
    ],
    "poster": "https://m.media-amazo",
    "title": "The Great Train Robbe",
    "fullplot": "Among the earliest ex",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-2085523200000"
      }
    },
    "directors": [
      "Edwin S. Porter"
    ],
    "rated": "TV-G",
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-08-13 00:27:59.1",
    "year": 1903,
    "imdb": {
      "rating": 7.4,
      "votes": 9847,
      "id": 439
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 2559,
        "meter": 75
      },
      "fresh": 6,
      "critic": {
        "rating": 7.6,
        "numReviews": 6,
        "meter": 100
      },
      "rotten": 0,
      "lastUpdated": {
        "$date": "2015-08-08T19:16:10Z"
      }
    },
    "num_mflix_comments": 0
  }
]
*/
  Step 6: Tool call: mongodb_query_checker
  Step 7: Response: content='```javascript\ndb.movies.aggregate([{ "$c...

Final ReAct Response:
================================= Tool Message =================================
Name: mongodb_query_checker

content='```javascript\ndb.movies.aggregate([{ "$count": "totalMovies" }])\n```' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 110, 'total_tokens': 127, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_34a54ae93c', 'id': 'chatcmpl-Bhi0Wl1tbOdBTaZmOb8HQIQOobOOe', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None} id='run--5f209ed1-50f6-4e09-8fda-2aadffbe3b3e-0' usage_metadata={'input_tokens': 110, 'output_tokens': 17, 'total_tokens': 127, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}
  Step 8: Tool call: mongodb_query
  Step 9: Response: [
  {
    "totalMovies": 21349
  }
]

Final ReAct Response:
================================= Tool Message =================================
Name: mongodb_query

[
  {
    "totalMovies": 21349
  }
]
  Step 10: Response: The total number of movies in the database is 21,3...

Final ReAct Response:
================================== Ai Message ==================================

The total number of movies in the database is 21,349.

ReAct agent succeeded in 10 steps

LangGraph Agent Execution:
----------------------------------------

Attempt 1/2
Thread: compare_446205bd_graph_attempt_1
Execution steps:
  Step 1: Response: Count the total number of movies in the database

Final LangGraph Response:
================================ Human Message =================================

Count the total number of movies in the database
  Step 2: Response: Available collections: comments, embedded_movies, ...

Final LangGraph Response:
================================== Ai Message ==================================

Available collections: comments, embedded_movies, movies, sessions, theaters, users
  Step 3: Tool call: mongodb_schema
  Step 4: Response: Database name: sample_mflix
Collection name: movie...

Final LangGraph Response:
================================= Tool Message =================================
Name: mongodb_schema

Database name: sample_mflix
Collection name: movies
Schema from a sample of documents from the collection:
_id: ObjectId
plot: String
genres: Array<String>
runtime: Number
cast: Array<String>
num_mflix_comments: Number
poster: String
title: String
fullplot: String
languages: Array<String>
released: Timestamp
directors: Array<String>
writers: Array<String>
awards.wins: Number
awards.nominations: Number
awards.text: String
lastupdated: String
year: Number
imdb.rating: Number
imdb.votes: Number
imdb.id: Number
countries: Array<String>
type: String
tomatoes.viewer.rating: Number
tomatoes.viewer.numReviews: Number
tomatoes.viewer.meter: Number
tomatoes.dvd: Timestamp
tomatoes.lastUpdated: Timestamp

/*
3 documents from movies collection:
[
  {
    "_id": {
      "$oid": "573a1390f29313caabcd63d6"
    },
    "plot": "Two peasant children,",
    "genres": [
      "Fantasy"
    ],
    "runtime": 75,
    "cast": [
      "Tula Belle",
      "Robin Macdougall",
      "Edwin E. Reed",
      "Emma Lowry"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "The Blue Bird",
    "fullplot": "Two peasant children,",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1633305600000"
      }
    },
    "directors": [
      "Maurice Tourneur"
    ],
    "writers": [
      "Maurice Maeterlinck (",
      "Charles Maigne"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-07-20 00:32:04.8",
    "year": 1918,
    "imdb": {
      "rating": 6.6,
      "votes": 446,
      "id": 8891
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.6,
        "numReviews": 607,
        "meter": 60
      },
      "dvd": {
        "$date": "2005-09-06T00:00:00Z"
      },
      "lastUpdated": {
        "$date": "2015-08-21T18:10:22Z"
      }
    }
  },
  {
    "_id": {
      "$oid": "573a1391f29313caabcd7472"
    },
    "plot": "A con artist masquera",
    "genres": [
      "Drama"
    ],
    "runtime": 117,
    "cast": [
      "Rudolph Christians",
      "Miss DuPont",
      "Maude George",
      "Mae Busch"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "Foolish Wives",
    "fullplot": "\"Count\" Karanzim, a D",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1513900800000"
      }
    },
    "directors": [
      "Erich von Stroheim"
    ],
    "writers": [
      "Erich von Stroheim (s",
      "Marian Ainslee (title",
      "Walter Anthony (title"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-09-05 00:00:37.8",
    "year": 1922,
    "imdb": {
      "rating": 7.3,
      "votes": 1777,
      "id": 13140
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 1079,
        "meter": 77
      },
      "dvd": {
        "$date": "2000-09-19T00:00:00Z"
      },
      "critic": {
        "rating": 9.0,
        "numReviews": 9,
        "meter": 89
      },
      "lastUpdated": {
        "$date": "2015-09-15T17:02:32Z"
      },
      "rotten": 1,
      "production": "Universal Pictures",
      "fresh": 8
    }
  },
  {
    "_id": {
      "$oid": "573a1390f29313caabcd42e8"
    },
    "plot": "A group of bandits st",
    "genres": [
      "Short",
      "Western"
    ],
    "runtime": 11,
    "cast": [
      "A.C. Abadie",
      "Gilbert M. 'Broncho B",
      "George Barnes",
      "Justus D. Barnes"
    ],
    "poster": "https://m.media-amazo",
    "title": "The Great Train Robbe",
    "fullplot": "Among the earliest ex",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-2085523200000"
      }
    },
    "directors": [
      "Edwin S. Porter"
    ],
    "rated": "TV-G",
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-08-13 00:27:59.1",
    "year": 1903,
    "imdb": {
      "rating": 7.4,
      "votes": 9847,
      "id": 439
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 2559,
        "meter": 75
      },
      "fresh": 6,
      "critic": {
        "rating": 7.6,
        "numReviews": 6,
        "meter": 100
      },
      "rotten": 0,
      "lastUpdated": {
        "$date": "2015-08-08T19:16:10Z"
      }
    },
    "num_mflix_comments": 0
  }
]
*/
  Step 5: Tool call: mongodb_query
  Step 6: Tool call: mongodb_query
  Step 7: Response: [
  {
    "totalMovies": 21349
  }
]

Final LangGraph Response:
================================= Tool Message =================================
Name: mongodb_query

[
  {
    "totalMovies": 21349
  }
]
  Step 8: Response: **Answer to:** "Count the total number of movies i...

Final LangGraph Response:
================================== Ai Message ==================================

**Answer to:** "Count the total number of movies in the database"

LangGraph agent succeeded in 8 steps

Comparison Summary:
============================================================

ReAct Agent Results:
  Success: ✅
  Attempts: 1/2
  Execution Time: 4.59s

LangGraph Agent Results:
  Success: ✅
  Attempts: 1/2
  Execution Time: 3.97s

Execution Style Analysis:
  ReAct Agent:
    - Autonomous reasoning and tool selection
    - Dynamic decision making based on previous results
    - Can get stuck in reasoning loops with complex queries
    - More flexible but less predictable workflow
  LangGraph Agent:
    - Structured, deterministic workflow
    - Predefined step sequence with conditional branches
    - Better error isolation and recovery
    - More predictable but less flexible execution

Memory Pattern Analysis:
  ReAct Agent Memory:

🔍 Thread History: compare_446205bd_react_attempt_1
📊 Total steps: 3
================================================================================

📍 Step 1 [19:36:05]
   "🔄 Initial state"

📍 Step 2 [19:36:06]
   "📊 Total movie count request"

📍 Step 3 [19:36:06]
   "🔧 List MongoDB collections"

================================================================================
  LangGraph Agent Memory:

🔍 Thread History: compare_446205bd_graph_attempt_1
📊 Total steps: 3
================================================================================

📍 Step 1 [19:36:10]
   "🔄 Initial state"

📍 Step 2 [19:36:11]
   "📊 Total movie count request"

📍 Step 3 [19:36:11]
   "🔧 Available collections list"

================================================================================

Recommendations:
  - LangGraph agent was more efficient for this query
  - Both agents handled the query successfully

==================== Moderate Query ====================
Agent Comparison: ReAct vs LangGraph
============================================================
Query: List the top 5 directors who have directed the most movies
Max Retries: 2
Recursion Limit: 40
============================================================

ReAct Agent Execution:
----------------------------------------

Attempt 1/2
Thread: compare_3879a4e0_react_attempt_1
Execution steps:
  Step 1: Response: List the top 5 directors who have directed the mos...

Final ReAct Response:
================================ Human Message =================================

List the top 5 directors who have directed the most movies
  Step 2: Tool call: mongodb_list_collections
  Step 3: Response: comments, embedded_movies, movies, sessions, theat...

Final ReAct Response:
================================= Tool Message =================================
Name: mongodb_list_collections

comments, embedded_movies, movies, sessions, theaters, users
  Step 4: Tool call: mongodb_schema
  Step 5: Response: Database name: sample_mflix
Collection name: movie...

Final ReAct Response:
================================= Tool Message =================================
Name: mongodb_schema

Database name: sample_mflix
Collection name: movies
Schema from a sample of documents from the collection:
_id: ObjectId
plot: String
genres: Array<String>
runtime: Number
cast: Array<String>
num_mflix_comments: Number
poster: String
title: String
fullplot: String
languages: Array<String>
released: Timestamp
directors: Array<String>
writers: Array<String>
awards.wins: Number
awards.nominations: Number
awards.text: String
lastupdated: String
year: Number
imdb.rating: Number
imdb.votes: Number
imdb.id: Number
countries: Array<String>
type: String
tomatoes.viewer.rating: Number
tomatoes.viewer.numReviews: Number
tomatoes.viewer.meter: Number
tomatoes.dvd: Timestamp
tomatoes.lastUpdated: Timestamp

/*
3 documents from movies collection:
[
  {
    "_id": {
      "$oid": "573a1390f29313caabcd63d6"
    },
    "plot": "Two peasant children,",
    "genres": [
      "Fantasy"
    ],
    "runtime": 75,
    "cast": [
      "Tula Belle",
      "Robin Macdougall",
      "Edwin E. Reed",
      "Emma Lowry"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "The Blue Bird",
    "fullplot": "Two peasant children,",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1633305600000"
      }
    },
    "directors": [
      "Maurice Tourneur"
    ],
    "writers": [
      "Maurice Maeterlinck (",
      "Charles Maigne"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-07-20 00:32:04.8",
    "year": 1918,
    "imdb": {
      "rating": 6.6,
      "votes": 446,
      "id": 8891
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.6,
        "numReviews": 607,
        "meter": 60
      },
      "dvd": {
        "$date": "2005-09-06T00:00:00Z"
      },
      "lastUpdated": {
        "$date": "2015-08-21T18:10:22Z"
      }
    }
  },
  {
    "_id": {
      "$oid": "573a1391f29313caabcd7472"
    },
    "plot": "A con artist masquera",
    "genres": [
      "Drama"
    ],
    "runtime": 117,
    "cast": [
      "Rudolph Christians",
      "Miss DuPont",
      "Maude George",
      "Mae Busch"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "Foolish Wives",
    "fullplot": "\"Count\" Karanzim, a D",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1513900800000"
      }
    },
    "directors": [
      "Erich von Stroheim"
    ],
    "writers": [
      "Erich von Stroheim (s",
      "Marian Ainslee (title",
      "Walter Anthony (title"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-09-05 00:00:37.8",
    "year": 1922,
    "imdb": {
      "rating": 7.3,
      "votes": 1777,
      "id": 13140
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 1079,
        "meter": 77
      },
      "dvd": {
        "$date": "2000-09-19T00:00:00Z"
      },
      "critic": {
        "rating": 9.0,
        "numReviews": 9,
        "meter": 89
      },
      "lastUpdated": {
        "$date": "2015-09-15T17:02:32Z"
      },
      "rotten": 1,
      "production": "Universal Pictures",
      "fresh": 8
    }
  },
  {
    "_id": {
      "$oid": "573a1390f29313caabcd42e8"
    },
    "plot": "A group of bandits st",
    "genres": [
      "Short",
      "Western"
    ],
    "runtime": 11,
    "cast": [
      "A.C. Abadie",
      "Gilbert M. 'Broncho B",
      "George Barnes",
      "Justus D. Barnes"
    ],
    "poster": "https://m.media-amazo",
    "title": "The Great Train Robbe",
    "fullplot": "Among the earliest ex",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-2085523200000"
      }
    },
    "directors": [
      "Edwin S. Porter"
    ],
    "rated": "TV-G",
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-08-13 00:27:59.1",
    "year": 1903,
    "imdb": {
      "rating": 7.4,
      "votes": 9847,
      "id": 439
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 2559,
        "meter": 75
      },
      "fresh": 6,
      "critic": {
        "rating": 7.6,
        "numReviews": 6,
        "meter": 100
      },
      "rotten": 0,
      "lastUpdated": {
        "$date": "2015-08-08T19:16:10Z"
      }
    },
    "num_mflix_comments": 0
  }
]
*/
  Step 6: Tool call: mongodb_query_checker
  Step 7: Response: content='```javascript\ndb.movies.aggregate([\n   ...

Final ReAct Response:
================================= Tool Message =================================
Name: mongodb_query_checker

content='```javascript\ndb.movies.aggregate([\n    { "$unwind": "$directors" },\n    { "$group": { "_id": "$directors", "movieCount": { "$sum": 1 } } },\n    { "$sort": { "movieCount": -1 } },\n    { "$limit": 5 }\n])\n```' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 68, 'prompt_tokens': 156, 'total_tokens': 224, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_34a54ae93c', 'id': 'chatcmpl-Bhi0fZrsWZwT2GGpClWhbJ1ZzXwxi', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None} id='run--b7cae6a8-a0fd-4586-94e9-76e2aa553387-0' usage_metadata={'input_tokens': 156, 'output_tokens': 68, 'total_tokens': 224, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}
  Step 8: Tool call: mongodb_query
  Step 9: Response: Error: ValueError('Cannot execute command db.movie...

Final ReAct Response:
================================= Tool Message =================================
Name: mongodb_query

Error: ValueError('Cannot execute command db.movies.aggregate([ { "$unwind": "$directors" }, { "$group": { _id: "$directors", "movieCount": { "$sum": 1 } } }, { "$sort": { "movieCount": -1 } }, { "$limit": 5 } ])')
 Please fix your mistakes.
  Step 10: Tool call: mongodb_query_checker
  Step 11: Response: content='```javascript\ndb.movies.aggregate([\n   ...

Final ReAct Response:
================================= Tool Message =================================
Name: mongodb_query_checker

content='```javascript\ndb.movies.aggregate([\n    { "$unwind": "$directors" },\n    { "$group": { "_id": "$directors", "movieCount": { "$sum": 1 } } },\n    { "$sort": { "movieCount": -1 } },\n    { "$limit": 5 }\n])\n```' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 68, 'prompt_tokens': 156, 'total_tokens': 224, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_34a54ae93c', 'id': 'chatcmpl-Bhi0jDJsZGTMUFAzm3b4mTnCTbjWS', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None} id='run--a1d6b934-7e74-440c-951a-07bfc6c2a23c-0' usage_metadata={'input_tokens': 156, 'output_tokens': 68, 'total_tokens': 224, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}
  Step 12: Tool call: mongodb_query
  Step 13: Response: [
  {
    "_id": "Woody Allen",
    "movieCount": ...

Final ReAct Response:
================================= Tool Message =================================
Name: mongodb_query

[
  {
    "_id": "Woody Allen",
    "movieCount": 40
  },
  {
    "_id": "Martin Scorsese",
    "movieCount": 32
  },
  {
    "_id": "Takashi Miike",
    "movieCount": 31
  },
  {
    "_id": "Steven Spielberg",
    "movieCount": 29
  },
  {
    "_id": "Sidney Lumet",
    "movieCount": 29
  }
]
  Step 14: Response: The top 5 directors who have directed the most mov...

Final ReAct Response:
================================== Ai Message ==================================

The top 5 directors who have directed the most movies are:

1. **Woody Allen** - 40 movies
2. **Martin Scorsese** - 32 movies
3. **Takashi Miike** - 31 movies
4. **Steven Spielberg** - 29 movies
5. **Sidney Lumet** - 29 movies

ReAct agent succeeded in 14 steps

LangGraph Agent Execution:
----------------------------------------

Attempt 1/2
Thread: compare_3879a4e0_graph_attempt_1
Execution steps:
  Step 1: Response: List the top 5 directors who have directed the mos...

Final LangGraph Response:
================================ Human Message =================================

List the top 5 directors who have directed the most movies
  Step 2: Response: Available collections: comments, embedded_movies, ...

Final LangGraph Response:
================================== Ai Message ==================================

Available collections: comments, embedded_movies, movies, sessions, theaters, users
  Step 3: Tool call: mongodb_schema
  Step 4: Response: Database name: sample_mflix
Collection name: movie...

Final LangGraph Response:
================================= Tool Message =================================
Name: mongodb_schema

Database name: sample_mflix
Collection name: movies
Schema from a sample of documents from the collection:
_id: ObjectId
plot: String
genres: Array<String>
runtime: Number
cast: Array<String>
num_mflix_comments: Number
poster: String
title: String
fullplot: String
languages: Array<String>
released: Timestamp
directors: Array<String>
writers: Array<String>
awards.wins: Number
awards.nominations: Number
awards.text: String
lastupdated: String
year: Number
imdb.rating: Number
imdb.votes: Number
imdb.id: Number
countries: Array<String>
type: String
tomatoes.viewer.rating: Number
tomatoes.viewer.numReviews: Number
tomatoes.viewer.meter: Number
tomatoes.dvd: Timestamp
tomatoes.lastUpdated: Timestamp

/*
3 documents from movies collection:
[
  {
    "_id": {
      "$oid": "573a1390f29313caabcd63d6"
    },
    "plot": "Two peasant children,",
    "genres": [
      "Fantasy"
    ],
    "runtime": 75,
    "cast": [
      "Tula Belle",
      "Robin Macdougall",
      "Edwin E. Reed",
      "Emma Lowry"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "The Blue Bird",
    "fullplot": "Two peasant children,",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1633305600000"
      }
    },
    "directors": [
      "Maurice Tourneur"
    ],
    "writers": [
      "Maurice Maeterlinck (",
      "Charles Maigne"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-07-20 00:32:04.8",
    "year": 1918,
    "imdb": {
      "rating": 6.6,
      "votes": 446,
      "id": 8891
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.6,
        "numReviews": 607,
        "meter": 60
      },
      "dvd": {
        "$date": "2005-09-06T00:00:00Z"
      },
      "lastUpdated": {
        "$date": "2015-08-21T18:10:22Z"
      }
    }
  },
  {
    "_id": {
      "$oid": "573a1391f29313caabcd7472"
    },
    "plot": "A con artist masquera",
    "genres": [
      "Drama"
    ],
    "runtime": 117,
    "cast": [
      "Rudolph Christians",
      "Miss DuPont",
      "Maude George",
      "Mae Busch"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "Foolish Wives",
    "fullplot": "\"Count\" Karanzim, a D",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1513900800000"
      }
    },
    "directors": [
      "Erich von Stroheim"
    ],
    "writers": [
      "Erich von Stroheim (s",
      "Marian Ainslee (title",
      "Walter Anthony (title"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-09-05 00:00:37.8",
    "year": 1922,
    "imdb": {
      "rating": 7.3,
      "votes": 1777,
      "id": 13140
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 1079,
        "meter": 77
      },
      "dvd": {
        "$date": "2000-09-19T00:00:00Z"
      },
      "critic": {
        "rating": 9.0,
        "numReviews": 9,
        "meter": 89
      },
      "lastUpdated": {
        "$date": "2015-09-15T17:02:32Z"
      },
      "rotten": 1,
      "production": "Universal Pictures",
      "fresh": 8
    }
  },
  {
    "_id": {
      "$oid": "573a1390f29313caabcd42e8"
    },
    "plot": "A group of bandits st",
    "genres": [
      "Short",
      "Western"
    ],
    "runtime": 11,
    "cast": [
      "A.C. Abadie",
      "Gilbert M. 'Broncho B",
      "George Barnes",
      "Justus D. Barnes"
    ],
    "poster": "https://m.media-amazo",
    "title": "The Great Train Robbe",
    "fullplot": "Among the earliest ex",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-2085523200000"
      }
    },
    "directors": [
      "Edwin S. Porter"
    ],
    "rated": "TV-G",
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-08-13 00:27:59.1",
    "year": 1903,
    "imdb": {
      "rating": 7.4,
      "votes": 9847,
      "id": 439
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 2559,
        "meter": 75
      },
      "fresh": 6,
      "critic": {
        "rating": 7.6,
        "numReviews": 6,
        "meter": 100
      },
      "rotten": 0,
      "lastUpdated": {
        "$date": "2015-08-08T19:16:10Z"
      }
    },
    "num_mflix_comments": 0
  }
]
*/
  Step 5: Tool call: mongodb_query
  Step 6: Tool call: mongodb_query
  Step 7: Response: [
  {
    "_id": "Woody Allen",
    "movieCount": ...

Final LangGraph Response:
================================= Tool Message =================================
Name: mongodb_query

[
  {
    "_id": "Woody Allen",
    "movieCount": 40
  },
  {
    "_id": "Martin Scorsese",
    "movieCount": 32
  },
  {
    "_id": "Takashi Miike",
    "movieCount": 31
  },
  {
    "_id": "Steven Spielberg",
    "movieCount": 29
  },
  {
    "_id": "John Ford",
    "movieCount": 29
  }
]
  Step 8: Response: **Answer to:** "List the top 5 directors who have ...

Final LangGraph Response:
================================== Ai Message ==================================

**Answer to:** "List the top 5 directors who have directed the most movies"

1. Woody Allen: 40 movies
2. Martin Scorsese: 32 movies
3. Takashi Miike: 31 movies
4. Steven Spielberg: 29 movies
5. John Ford: 29 movies

LangGraph agent succeeded in 8 steps

Comparison Summary:
============================================================

ReAct Agent Results:
  Success: ✅
  Attempts: 1/2
  Execution Time: 12.06s

LangGraph Agent Results:
  Success: ✅
  Attempts: 1/2
  Execution Time: 3.93s

Execution Style Analysis:
  ReAct Agent:
    - Autonomous reasoning and tool selection
    - Dynamic decision making based on previous results
    - Can get stuck in reasoning loops with complex queries
    - More flexible but less predictable workflow
  LangGraph Agent:
    - Structured, deterministic workflow
    - Predefined step sequence with conditional branches
    - Better error isolation and recovery
    - More predictable but less flexible execution

Memory Pattern Analysis:
  ReAct Agent Memory:

🔍 Thread History: compare_3879a4e0_react_attempt_1
📊 Total steps: 3
================================================================================

📍 Step 1 [19:36:14]
   "🔄 Initial state"

📍 Step 2 [19:36:15]
   "📊 List top directors"

📍 Step 3 [19:36:15]
   "🔧 List MongoDB collections"

================================================================================
  LangGraph Agent Memory:

🔍 Thread History: compare_3879a4e0_graph_attempt_1
📊 Total steps: 3
================================================================================

📍 Step 1 [19:36:26]
   "🔄 Initial state"

📍 Step 2 [19:36:27]
   "📊 List top directors"

📍 Step 3 [19:36:27]
   "🔧 Available collections list"

================================================================================

Recommendations:
  - LangGraph agent was more efficient for this query
  - Both agents handled the query successfully

==================== Complex Query ====================
Agent Comparison: ReAct vs LangGraph
============================================================
Query: Find the top 5 directors with most award wins and at least 5 movies
Max Retries: 3
Recursion Limit: 50
============================================================

ReAct Agent Execution:
----------------------------------------

Attempt 1/3
Thread: compare_8e075611_react_attempt_1
Execution steps:
  Step 1: Response: Find the top 5 directors with most award wins and ...

Final ReAct Response:
================================ Human Message =================================

Find the top 5 directors with most award wins and at least 5 movies
  Step 2: Tool call: mongodb_list_collections
  Step 3: Response: comments, embedded_movies, movies, sessions, theat...

Final ReAct Response:
================================= Tool Message =================================
Name: mongodb_list_collections

comments, embedded_movies, movies, sessions, theaters, users
  Step 4: Tool call: mongodb_schema
  Step 5: Response: Database name: sample_mflix
Collection name: comme...

Final ReAct Response:
================================= Tool Message =================================
Name: mongodb_schema

Database name: sample_mflix
Collection name: comments
Schema from a sample of documents from the collection:
_id: ObjectId
name: String
email: String
movie_id: ObjectId
text: String
date: Timestamp

/*
3 documents from comments collection:
[
  {
    "_id": {
      "$oid": "5a9427648b0beebeb6957b89"
    },
    "name": "Lisa Rasmussen",
    "email": "lisa_rasmussen@fakegm",
    "movie_id": {
      "$oid": "573a1391f29313caabcd82da"
    },
    "text": "Illo nihil occaecati ",
    "date": {
      "$date": "1976-12-18T08:14:46Z"
    }
  },
  {
    "_id": {
      "$oid": "5a9427648b0beebeb6957bb6"
    },
    "name": "Ellaria Sand",
    "email": "indira_varma@gameofth",
    "movie_id": {
      "$oid": "573a1391f29313caabcd8780"
    },
    "text": "Quidem nesciunt quam ",
    "date": {
      "$date": "1985-02-24T20:04:25Z"
    }
  },
  {
    "_id": {
      "$oid": "5a9427648b0beebeb69579e7"
    },
    "name": "Mercedes Tyler",
    "email": "mercedes_tyler@fakegm",
    "movie_id": {
      "$oid": "573a1390f29313caabcd4323"
    },
    "text": "Eius veritatis vero f",
    "date": {
      "$date": "2002-08-18T04:56:07Z"
    }
  }
]
*/
  Step 6: Tool call: mongodb_query_checker
  Step 7: Response: content='```json\ndb.movies.aggregate([\n    { "$m...

Final ReAct Response:
================================= Tool Message =================================
Name: mongodb_query_checker

content='```json\ndb.movies.aggregate([\n    { "$match": { "awards.wins": { "$gt": 0 } } },\n    { "$group": { "_id": "$directors", "totalWins": { "$sum": "$awards.wins" }, "movieCount": { "$sum": 1 } } },\n    { "$match": { "movieCount": { "$gte": 5 } } },\n    { "$sort": { "totalWins": -1 } },\n    { "$limit": 5 }\n])\n```' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 112, 'prompt_tokens': 199, 'total_tokens': 311, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_34a54ae93c', 'id': 'chatcmpl-Bhi0w3oih1OhY4ldVAAXmKQLEbuAU', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None} id='run--140549af-6ca2-46d0-b972-8ba6bc3c8002-0' usage_metadata={'input_tokens': 199, 'output_tokens': 112, 'total_tokens': 311, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}
  Step 8: Tool call: mongodb_query
  Step 9: Response: [
  {
    "_id": null,
    "totalWins": 1250,
    ...

Final ReAct Response:
================================= Tool Message =================================
Name: mongodb_query

[
  {
    "_id": null,
    "totalWins": 1250,
    "movieCount": 181
  },
  {
    "_id": [
      "Steven Spielberg"
    ],
    "totalWins": 696,
    "movieCount": 27
  },
  {
    "_id": [
      "Martin Scorsese"
    ],
    "totalWins": 582,
    "movieCount": 26
  },
  {
    "_id": [
      "Alfonso Cuar\u00e8n"
    ],
    "totalWins": 575,
    "movieCount": 7
  },
  {
    "_id": [
      "Peter Jackson"
    ],
    "totalWins": 524,
    "movieCount": 12
  }
]
  Step 10: Response: Here are the top 5 directors with the most award w...

Final ReAct Response:
================================== Ai Message ==================================

Here are the top 5 directors with the most award wins, each having directed at least 5 movies:

1. **Steven Spielberg**
   - Total Wins: 696
   - Movie Count: 27

2. **Martin Scorsese**
   - Total Wins: 582
   - Movie Count: 26

3. **Alfonso Cuarón**
   - Total Wins: 575
   - Movie Count: 7

4. **Peter Jackson**
   - Total Wins: 524
   - Movie Count: 12

5. **(Aggregate Total)**
   - Total Wins: 1250
   - Movie Count: 181 (This entry does not correspond to a specific director but represents the total wins across all directors.) 

If you need more specific details or additional directors, feel free to ask!

ReAct agent succeeded in 10 steps

LangGraph Agent Execution:
----------------------------------------

Attempt 1/3
Thread: compare_8e075611_graph_attempt_1
Execution steps:
  Step 1: Response: Find the top 5 directors with most award wins and ...

Final LangGraph Response:
================================ Human Message =================================

Find the top 5 directors with most award wins and at least 5 movies
  Step 2: Response: Available collections: comments, embedded_movies, ...

Final LangGraph Response:
================================== Ai Message ==================================

Available collections: comments, embedded_movies, movies, sessions, theaters, users
  Step 3: Tool call: mongodb_schema
  Step 4: Response: Database name: sample_mflix
Collection name: movie...

Final LangGraph Response:
================================= Tool Message =================================
Name: mongodb_schema

Database name: sample_mflix
Collection name: movies
Schema from a sample of documents from the collection:
_id: ObjectId
plot: String
genres: Array<String>
runtime: Number
cast: Array<String>
num_mflix_comments: Number
poster: String
title: String
fullplot: String
languages: Array<String>
released: Timestamp
directors: Array<String>
writers: Array<String>
awards.wins: Number
awards.nominations: Number
awards.text: String
lastupdated: String
year: Number
imdb.rating: Number
imdb.votes: Number
imdb.id: Number
countries: Array<String>
type: String
tomatoes.viewer.rating: Number
tomatoes.viewer.numReviews: Number
tomatoes.viewer.meter: Number
tomatoes.dvd: Timestamp
tomatoes.lastUpdated: Timestamp

/*
3 documents from movies collection:
[
  {
    "_id": {
      "$oid": "573a1390f29313caabcd63d6"
    },
    "plot": "Two peasant children,",
    "genres": [
      "Fantasy"
    ],
    "runtime": 75,
    "cast": [
      "Tula Belle",
      "Robin Macdougall",
      "Edwin E. Reed",
      "Emma Lowry"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "The Blue Bird",
    "fullplot": "Two peasant children,",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1633305600000"
      }
    },
    "directors": [
      "Maurice Tourneur"
    ],
    "writers": [
      "Maurice Maeterlinck (",
      "Charles Maigne"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-07-20 00:32:04.8",
    "year": 1918,
    "imdb": {
      "rating": 6.6,
      "votes": 446,
      "id": 8891
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.6,
        "numReviews": 607,
        "meter": 60
      },
      "dvd": {
        "$date": "2005-09-06T00:00:00Z"
      },
      "lastUpdated": {
        "$date": "2015-08-21T18:10:22Z"
      }
    }
  },
  {
    "_id": {
      "$oid": "573a1391f29313caabcd7472"
    },
    "plot": "A con artist masquera",
    "genres": [
      "Drama"
    ],
    "runtime": 117,
    "cast": [
      "Rudolph Christians",
      "Miss DuPont",
      "Maude George",
      "Mae Busch"
    ],
    "num_mflix_comments": 0,
    "poster": "https://m.media-amazo",
    "title": "Foolish Wives",
    "fullplot": "\"Count\" Karanzim, a D",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-1513900800000"
      }
    },
    "directors": [
      "Erich von Stroheim"
    ],
    "writers": [
      "Erich von Stroheim (s",
      "Marian Ainslee (title",
      "Walter Anthony (title"
    ],
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-09-05 00:00:37.8",
    "year": 1922,
    "imdb": {
      "rating": 7.3,
      "votes": 1777,
      "id": 13140
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 1079,
        "meter": 77
      },
      "dvd": {
        "$date": "2000-09-19T00:00:00Z"
      },
      "critic": {
        "rating": 9.0,
        "numReviews": 9,
        "meter": 89
      },
      "lastUpdated": {
        "$date": "2015-09-15T17:02:32Z"
      },
      "rotten": 1,
      "production": "Universal Pictures",
      "fresh": 8
    }
  },
  {
    "_id": {
      "$oid": "573a1390f29313caabcd42e8"
    },
    "plot": "A group of bandits st",
    "genres": [
      "Short",
      "Western"
    ],
    "runtime": 11,
    "cast": [
      "A.C. Abadie",
      "Gilbert M. 'Broncho B",
      "George Barnes",
      "Justus D. Barnes"
    ],
    "poster": "https://m.media-amazo",
    "title": "The Great Train Robbe",
    "fullplot": "Among the earliest ex",
    "languages": [
      "English"
    ],
    "released": {
      "$date": {
        "$numberLong": "-2085523200000"
      }
    },
    "directors": [
      "Edwin S. Porter"
    ],
    "rated": "TV-G",
    "awards": {
      "wins": 1,
      "nominations": 0,
      "text": "1 win."
    },
    "lastupdated": "2015-08-13 00:27:59.1",
    "year": 1903,
    "imdb": {
      "rating": 7.4,
      "votes": 9847,
      "id": 439
    },
    "countries": [
      "USA"
    ],
    "type": "movie",
    "tomatoes": {
      "viewer": {
        "rating": 3.7,
        "numReviews": 2559,
        "meter": 75
      },
      "fresh": 6,
      "critic": {
        "rating": 7.6,
        "numReviews": 6,
        "meter": 100
      },
      "rotten": 0,
      "lastUpdated": {
        "$date": "2015-08-08T19:16:10Z"
      }
    },
    "num_mflix_comments": 0
  }
]
*/
  Step 5: Tool call: mongodb_query
  Step 6: Tool call: mongodb_query
  Step 7: Response: [
  {
    "_id": null,
    "totalWins": 1250,
    ...

Final LangGraph Response:
================================= Tool Message =================================
Name: mongodb_query

[
  {
    "_id": null,
    "totalWins": 1250,
    "movieCount": 181
  },
  {
    "_id": [
      "Steven Spielberg"
    ],
    "totalWins": 696,
    "movieCount": 27
  },
  {
    "_id": [
      "Martin Scorsese"
    ],
    "totalWins": 582,
    "movieCount": 26
  },
  {
    "_id": [
      "Alfonso Cuar\u00e8n"
    ],
    "totalWins": 575,
    "movieCount": 7
  },
  {
    "_id": [
      "Peter Jackson"
    ],
    "totalWins": 524,
    "movieCount": 12
  }
]
  Step 8: Response: **Answer to:** "Find the top 5 directors with most...

Final LangGraph Response:
================================== Ai Message ==================================

**Answer to:** "Find the top 5 directors with most award wins and at least 5 movies"

1. None: 181 movies
2. ['Steven Spielberg']: 27 movies
3. ['Martin Scorsese']: 26 movies
4. ['Alfonso Cuarèn']: 7 movies
5. ['Peter Jackson']: 12 movies

LangGraph agent succeeded in 8 steps

Comparison Summary:
============================================================

ReAct Agent Results:
  Success: ✅
  Attempts: 1/3
  Execution Time: 11.22s

LangGraph Agent Results:
  Success: ✅
  Attempts: 1/3
  Execution Time: 5.96s

Execution Style Analysis:
  ReAct Agent:
    - Autonomous reasoning and tool selection
    - Dynamic decision making based on previous results
    - Can get stuck in reasoning loops with complex queries
    - More flexible but less predictable workflow
  LangGraph Agent:
    - Structured, deterministic workflow
    - Predefined step sequence with conditional branches
    - Better error isolation and recovery
    - More predictable but less flexible execution

Memory Pattern Analysis:
  ReAct Agent Memory:

🔍 Thread History: compare_8e075611_react_attempt_1
📊 Total steps: 3
================================================================================

📍 Step 1 [19:36:30]
   "🔄 Initial state"

📍 Step 2 [19:36:30]
   "📊 Top directors search"

📍 Step 3 [19:36:31]
   "🔧 List MongoDB collections"

================================================================================
  LangGraph Agent Memory:

🔍 Thread History: compare_8e075611_graph_attempt_1
📊 Total steps: 3
================================================================================

📍 Step 1 [19:36:41]
   "🔄 Initial state"

📍 Step 2 [19:36:41]
   "📊 Top directors query"

📍 Step 3 [19:36:41]
   "🔧 Available collections list"

================================================================================

Recommendations:
  - LangGraph agent was more efficient for this query
  - Both agents handled the query successfully

Test Suite Summary:
==============================
Simple Query: ReAct ✅ | LangGraph ✅
Moderate Query: ReAct ✅ | LangGraph ✅
Complex Query: ReAct ✅ | LangGraph ✅

Demo 4: List all threads - `list_conversation_threads()`

[43]

📋 Available Conversation Threads:
📊 Total checkpoints: 222
==================================================
  1. Thread: compare_260fd616_graph_attempt_1
     └─ 9 checkpoints
  2. Thread: compare_260fd616_react_attempt_1
     └─ 11 checkpoints
  3. Thread: compare_3879a4e0_graph_attempt_1
     └─ 9 checkpoints
  4. Thread: compare_3879a4e0_react_attempt_1
     └─ 15 checkpoints
  5. Thread: compare_446205bd_graph_attempt_1
     └─ 9 checkpoints
  6. Thread: compare_446205bd_react_attempt_1
     └─ 11 checkpoints
  7. Thread: compare_69c47d7a_graph_attempt_1
     └─ 9 checkpoints
  8. Thread: compare_69c47d7a_react_attempt_1
     └─ 15 checkpoints
  9. Thread: compare_8e075611_graph_attempt_1
     └─ 9 checkpoints
  10. Thread: compare_8e075611_react_attempt_1
     └─ 11 checkpoints
  11. Thread: compare_d39279d2_graph_attempt_1
     └─ 9 checkpoints
  12. Thread: compare_d39279d2_react_attempt_1
     └─ 9 checkpoints
  13. Thread: conversation_demo_7e08f130
     └─ 24 checkpoints
  14. Thread: demo_basic_1
     └─ 9 checkpoints
  15. Thread: demo_basic_2
     └─ 9 checkpoints
  16. Thread: demo_basic_3
     └─ 9 checkpoints
  17. Thread: demo_basic_4
     └─ 9 checkpoints
  18. Thread: demo_basic_5
     └─ 9 checkpoints
  19. Thread: enhanced_test_f4288e1b
     └─ 27 checkpoints

['compare_260fd616_graph_attempt_1',
, 'compare_260fd616_react_attempt_1',
, 'compare_3879a4e0_graph_attempt_1',
, 'compare_3879a4e0_react_attempt_1',
, 'compare_446205bd_graph_attempt_1',
, 'compare_446205bd_react_attempt_1',
, 'compare_69c47d7a_graph_attempt_1',
, 'compare_69c47d7a_react_attempt_1',
, 'compare_8e075611_graph_attempt_1',
, 'compare_8e075611_react_attempt_1',
, 'compare_d39279d2_graph_attempt_1',
, 'compare_d39279d2_react_attempt_1',
, 'conversation_demo_7e08f130',
, 'demo_basic_1',
, 'demo_basic_2',
, 'demo_basic_3',
, 'demo_basic_4',
, 'demo_basic_5',
, 'enhanced_test_f4288e1b']

Demo 5: Enhanced inspection - `inspect_thread_with_summaries_enhanced(thread_id)`

[ ]

❌ No checkpoints found for thread: conversation_demo_42dffc93

[]

Demo 6: Interactive Query Interface

[45]

Streaming output truncated to the last 5000 lines.
    "_id": "Gary Hardwick",
    "movieCount": 2
  },
  {
    "_id": "Gary Hustwit",
    "movieCount": 2
  },
  {
    "_id": "Gary Lundgren",
    "movieCount": 2
  },
  {
    "_id": "Gary Yates",
    "movieCount": 2
  },
  {
    "_id": "Gaston Kabor\u00e8",
    "movieCount": 2
  },
  {
    "_id": "Gast\u00e8n Duprat",
    "movieCount": 2
  },
  {
    "_id": "Gene Wilder",
    "movieCount": 2
  },
  {
    "_id": "Genndy Tartakovsky",
    "movieCount": 2
  },
  {
    "_id": "Geoff Marslett",
    "movieCount": 2
  },
  {
    "_id": "Geoffrey Smith",
    "movieCount": 2
  },
  {
    "_id": "Georg Fenady",
    "movieCount": 2
  },
  {
    "_id": "George Abbott",
    "movieCount": 2
  },
  {
    "_id": "George Armitage",
    "movieCount": 2
  },
  {
    "_id": "George Casey",
    "movieCount": 2
  },
  {
    "_id": "George Fitzmaurice",
    "movieCount": 2
  },
  {
    "_id": "George Huang",
    "movieCount": 2
  },
  {
    "_id": "George Ratliff",
    "movieCount": 2
  },
  {
    "_id": "George Sluizer",
    "movieCount": 2
  },
  {
    "_id": "Gerald Potterton",
    "movieCount": 2
  },
  {
    "_id": "Gerardo Olivares",
    "movieCount": 2
  },
  {
    "_id": "Gerrard Verhage",
    "movieCount": 2
  },
  {
    "_id": "Giacomo Battiato",
    "movieCount": 2
  },
  {
    "_id": "Giacomo Campiotti",
    "movieCount": 2
  },
  {
    "_id": "Giacomo Ciarrapico",
    "movieCount": 2
  },
  {
    "_id": "Gianfranco Mingozzi",
    "movieCount": 2
  },
  {
    "_id": "Gianfranco Rosi",
    "movieCount": 2
  },
  {
    "_id": "Gil Cates Jr.",
    "movieCount": 2
  },
  {
    "_id": "Gil Kenan",
    "movieCount": 2
  },
  {
    "_id": "Gilles Bourdos",
    "movieCount": 2
  },
  {
    "_id": "Gilles Paquet-Brenner",
    "movieCount": 2
  },
  {
    "_id": "Giorgia Farina",
    "movieCount": 2
  },
  {
    "_id": "Gisaburo Sugii",
    "movieCount": 2
  },
  {
    "_id": "Giulio Base",
    "movieCount": 2
  },
  {
    "_id": "Giulio Manfredonia",
    "movieCount": 2
  },
  {
    "_id": "Giuseppe Colizzi",
    "movieCount": 2
  },
  {
    "_id": "Giuseppe Moccia",
    "movieCount": 2
  },
  {
    "_id": "Giuseppe Piccioni",
    "movieCount": 2
  },
  {
    "_id": "Glen Goei",
    "movieCount": 2
  },
  {
    "_id": "Glenn Ficarra",
    "movieCount": 2
  },
  {
    "_id": "Glenn Gordon Caron",
    "movieCount": 2
  },
  {
    "_id": "Glenn Leyburn",
    "movieCount": 2
  },
  {
    "_id": "Gonzalo L\u00e8pez-Gallego",
    "movieCount": 2
  },
  {
    "_id": "Gonzalo Su\u00e8rez",
    "movieCount": 2
  },
  {
    "_id": "Gordon Parks",
    "movieCount": 2
  },
  {
    "_id": "Gottfried Reinhardt",
    "movieCount": 2
  },
  {
    "_id": "Govind Nihalani",
    "movieCount": 2
  },
  {
    "_id": "Graham Baker",
    "movieCount": 2
  },
  {
    "_id": "Grant Harvey",
    "movieCount": 2
  },
  {
    "_id": "Granz Henman",
    "movieCount": 2
  },
  {
    "_id": "Greg Berlanti",
    "movieCount": 2
  },
  {
    "_id": "Greg Harrison",
    "movieCount": 2
  },
  {
    "_id": "Greg MacGillivray",
    "movieCount": 2
  },
  {
    "_id": "Greg Manwaring",
    "movieCount": 2
  },
  {
    "_id": "Greg McLean",
    "movieCount": 2
  },
  {
    "_id": "Greg Olliver",
    "movieCount": 2
  },
  {
    "_id": "Greg Spence",
    "movieCount": 2
  },
  {
    "_id": "Greg Whiteley",
    "movieCount": 2
  },
  {
    "_id": "Grigori Kozintsev",
    "movieCount": 2
  },
  {
    "_id": "Grzegorz Kr\u00e8likiewicz",
    "movieCount": 2
  },
  {
    "_id": "Gr\u00e8mur H\u00e8konarson",
    "movieCount": 2
  },
  {
    "_id": "Gr\u00e8ta Olafsd\u00e8ttir",
    "movieCount": 2
  },
  {
    "_id": "Gualtiero Jacopetti",
    "movieCount": 2
  },
  {
    "_id": "Guillaume Ivernel",
    "movieCount": 2
  },
  {
    "_id": "Gustav Hofer",
    "movieCount": 2
  },
  {
    "_id": "Gustavo Loza",
    "movieCount": 2
  },
  {
    "_id": "Guy Jenkin",
    "movieCount": 2
  },
  {
    "_id": "G\u00e8la Babluani",
    "movieCount": 2
  },
  {
    "_id": "G\u00e8rard Bitton",
    "movieCount": 2
  },
  {
    "_id": "G\u00e8rard Corbiau",
    "movieCount": 2
  },
  {
    "_id": "G\u00e8rard Depardieu",
    "movieCount": 2
  },
  {
    "_id": "G\u00e8rard Oury",
    "movieCount": 2
  },
  {
    "_id": "G\u00e8tz Spielmann",
    "movieCount": 2
  },
  {
    "_id": "H. Bruce Humberstone",
    "movieCount": 2
  },
  {
    "_id": "Haile Gerima",
    "movieCount": 2
  },
  {
    "_id": "Hannes Holm",
    "movieCount": 2
  },
  {
    "_id": "Hans Fjellestad",
    "movieCount": 2
  },
  {
    "_id": "Hans-J\u00e8rgen Syberberg",
    "movieCount": 2
  },
  {
    "_id": "Hansal Mehta",
    "movieCount": 2
  },
  {
    "_id": "Hany Abu-Assad",
    "movieCount": 2
  },
  {
    "_id": "Harald Sicheritz",
    "movieCount": 2
  },
  {
    "_id": "Hari",
    "movieCount": 2
  },
  {
    "_id": "Harold Prince",
    "movieCount": 2
  },
  {
    "_id": "Harry Baweja",
    "movieCount": 2
  },
  {
    "_id": "Harry Bromley Davenport",
    "movieCount": 2
  },
  {
    "_id": "Harry Elfont",
    "movieCount": 2
  },
  {
    "_id": "Harry Harris",
    "movieCount": 2
  },
  {
    "_id": "Harry Winer",
    "movieCount": 2
  },
  {
    "_id": "Harry d'Abbadie d'Arrast",
    "movieCount": 2
  },
  {
    "_id": "Hayden Schlossberg",
    "movieCount": 2
  },
  {
    "_id": "Heddy Honigmann",
    "movieCount": 2
  },
  {
    "_id": "Heitor Dhalia",
    "movieCount": 2
  },
  {
    "_id": "Helmut K\u00e8utner",
    "movieCount": 2
  },
  {
    "_id": "Helvecio Ratton",
    "movieCount": 2
  },
  {
    "_id": "Hendel Butoy",
    "movieCount": 2
  },
  {
    "_id": "Henrique Goldman",
    "movieCount": 2
  },
  {
    "_id": "Henry Barrial",
    "movieCount": 2
  },
  {
    "_id": "Henry Cornelius",
    "movieCount": 2
  },
  {
    "_id": "Herbert Wise",
    "movieCount": 2
  },
  {
    "_id": "Hideo Gosha",
    "movieCount": 2
  },
  {
    "_id": "Hiroshi Shimizu",
    "movieCount": 2
  },
  {
    "_id": "Hiroshi Teshigahara",
    "movieCount": 2
  },
  {
    "_id": "Ho Choi",
    "movieCount": 2
  },
  {
    "_id": "Holger Tappe",
    "movieCount": 2
  },
  {
    "_id": "Holly Dale",
    "movieCount": 2
  },
  {
    "_id": "Homi Adajania",
    "movieCount": 2
  },
  {
    "_id": "Howard Hall",
    "movieCount": 2
  },
  {
    "_id": "Howard J. Ford",
    "movieCount": 2
  },
  {
    "_id": "Hrishikesh Mukherjee",
    "movieCount": 2
  },
  {
    "_id": "Hubert Sauper",
    "movieCount": 2
  },
  {
    "_id": "Hugo Latulippe",
    "movieCount": 2
  },
  {
    "_id": "Hunter Weeks",
    "movieCount": 2
  },
  {
    "_id": "Hwi Kim",
    "movieCount": 2
  },
  {
    "_id": "Hyeong-Cheol Kang",
    "movieCount": 2
  },
  {
    "_id": "Hype Williams",
    "movieCount": 2
  },
  {
    "_id": "Ian Fitzgibbon",
    "movieCount": 2
  },
  {
    "_id": "Ian Iqbal Rashid",
    "movieCount": 2
  },
  {
    "_id": "Ian McCrudden",
    "movieCount": 2
  },
  {
    "_id": "Iara Lee",
    "movieCount": 2
  },
  {
    "_id": "Ice Cube",
    "movieCount": 2
  },
  {
    "_id": "Igor Kovalyov",
    "movieCount": 2
  },
  {
    "_id": "Igor Voloshin",
    "movieCount": 2
  },
  {
    "_id": "Ilmar Raag",
    "movieCount": 2
  },
  {
    "_id": "Ilya Maksimov",
    "movieCount": 2
  },
  {
    "_id": "Ira Sachs",
    "movieCount": 2
  },
  {
    "_id": "Irakli Kvirikadze",
    "movieCount": 2
  },
  {
    "_id": "Irving Pichel",
    "movieCount": 2
  },
  {
    "_id": "Isaac Julien",
    "movieCount": 2
  },
  {
    "_id": "Ishai Setton",
    "movieCount": 2
  },
  {
    "_id": "Ishir\u00e8 Honda",
    "movieCount": 2
  },
  {
    "_id": "Israel C\u00e8rdenas",
    "movieCount": 2
  },
  {
    "_id": "Issa L\u00e8pez",
    "movieCount": 2
  },
  {
    "_id": "Isshin Inud\u00e8",
    "movieCount": 2
  },
  {
    "_id": "Ivan Sen",
    "movieCount": 2
  },
  {
    "_id": "Ivars Seleckis",
    "movieCount": 2
  },
  {
    "_id": "J. Stuart Blackton",
    "movieCount": 2
  },
  {
    "_id": "J.-P. Valkeap\u00e8\u00e8",
    "movieCount": 2
  },
  {
    "_id": "J.A. Bayona",
    "movieCount": 2
  },
  {
    "_id": "J.B. Rogers",
    "movieCount": 2
  },
  {
    "_id": "J.P. Sniadecki",
    "movieCount": 2
  },
  {
    "_id": "J.S. Cardone",
    "movieCount": 2
  },
  {
    "_id": "Jacek Borcuch",
    "movieCount": 2
  },
  {
    "_id": "Jacek Bromski",
    "movieCount": 2
  },
  {
    "_id": "Jack Cardiff",
    "movieCount": 2
  },
  {
    "_id": "Jack Couffer",
    "movieCount": 2
  },
  {
    "_id": "Jack Hofsiss",
    "movieCount": 2
  },
  {
    "_id": "Jack Perez",
    "movieCount": 2
  },
  {
    "_id": "Jacob Gentry",
    "movieCount": 2
  },
  {
    "_id": "Jacob Thuesen",
    "movieCount": 2
  },
  {
    "_id": "Jacques Becker",
    "movieCount": 2
  },
  {
    "_id": "Jacques Cluzaud",
    "movieCount": 2
  },
  {
    "_id": "Jacques Dorfmann",
    "movieCount": 2
  },
  {
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    "_id": "Seth MacFarlane",
    "movieCount": 2
  },
  {
    "_id": "Seth Rogen",
    "movieCount": 2
  },
  {
    "_id": "Shaad Ali",
    "movieCount": 2
  },
  {
    "_id": "Shamim Sarif",
    "movieCount": 2
  },
  {
    "_id": "Shana Feste",
    "movieCount": 2
  },
  {
    "_id": "Shane Acker",
    "movieCount": 2
  },
  {
    "_id": "Sharmeen Obaid-Chinoy",
    "movieCount": 2
  },
  {
    "_id": "Sharon Lockhart",
    "movieCount": 2
  },
  {
    "_id": "Sharon Maguire",
    "movieCount": 2
  },
  {
    "_id": "Sharon Maymon",
    "movieCount": 2
  },
  {
    "_id": "Shawn Christensen",
    "movieCount": 2
  },
  {
    "_id": "Shawn Ku",
    "movieCount": 2
  },
  {
    "_id": "Sheldon Wilson",
    "movieCount": 2
  },
  {
    "_id": "Sheree Folkson",
    "movieCount": 2
  },
  {
    "_id": "Sherry Hormann",
    "movieCount": 2
  },
  {
    "_id": "Shimako Sato",
    "movieCount": 2
  },
  {
    "_id": "Shimit Amin",
    "movieCount": 2
  },
  {
    "_id": "Shin-yeon Won",
    "movieCount": 2
  },
  {
    "_id": "Shinichir\u00e8 Watanabe",
    "movieCount": 2
  },
  {
    "_id": "Shinji Aoyama",
    "movieCount": 2
  },
  {
    "_id": "Shinji Higuchi",
    "movieCount": 2
  },
  {
    "_id": "Shinobu Yaguchi",
    "movieCount": 2
  },
  {
    "_id": "Shinsuke Sato",
    "movieCount": 2
  },
  {
    "_id": "Shonali Bose",
    "movieCount": 2
  },
  {
    "_id": "Shun Nakahara",
    "movieCount": 2
  },
  {
    "_id": "Sh\u00e8hei Morita",
    "movieCount": 2
  },
  {
    "_id": "Sh\u00e8ichi Okita",
    "movieCount": 2
  },
  {
    "_id": "Sh\u00e8suke Kaneko",
    "movieCount": 2
  },
  {
    "_id": "Siddique",
    "movieCount": 2
  },
  {
    "_id": "Sijie Dai",
    "movieCount": 2
  },
  {
    "_id": "Silvio Narizzano",
    "movieCount": 2
  },
  {
    "_id": "Simo Halinen",
    "movieCount": 2
  },
  {
    "_id": "Simon Rumley",
    "movieCount": 2
  },
  {
    "_id": "Simon Verhoeven",
    "movieCount": 2
  },
  {
    "_id": "Sirri S\u00e8reyya \u00e8nder",
    "movieCount": 2
  },
  {
    "_id": "Slawomir Fabicki",
    "movieCount": 2
  },
  {
    "_id": "Slobodan Sijan",
    "movieCount": 2
  },
  {
    "_id": "So Yong Kim",
    "movieCount": 2
  },
  {
    "_id": "Sophie Barthes",
    "movieCount": 2
  },
  {
    "_id": "Sophie Letourneur",
    "movieCount": 2
  },
  {
    "_id": "Spencer Susser",
    "movieCount": 2
  },
  {
    "_id": "Srdan Golubovic",
    "movieCount": 2
  },
  {
    "_id": "Stan Winston",
    "movieCount": 2
  },
  {
    "_id": "Stanislav Rostotskiy",
    "movieCount": 2
  },
  {
    "_id": "Stavros Kazantzidis",
    "movieCount": 2
  },
  {
    "_id": "Stefan Prehn",
    "movieCount": 2
  },
  {
    "_id": "Stephan Komandarev",
    "movieCount": 2
  },
  {
    "_id": "Stephen Bradley",
    "movieCount": 2
  },
  {
    "_id": "Stephen J. Anderson",
    "movieCount": 2
  },
  {
    "_id": "Stephen Kay",
    "movieCount": 2
  },
  {
    "_id": "Stephen Kijak",
    "movieCount": 2
  },
  {
    "_id": "Stephen Low",
    "movieCount": 2
  },
  {
    "_id": "Stephen St. Leger",
    "movieCount": 2
  },
  {
    "_id": "Stephen Surjik",
    "movieCount": 2
  },
  {
    "_id": "Steve Beck",
    "movieCount": 2
  },
  {
    "_id": "Steve Bendelack",
    "movieCount": 2
  },
  {
    "_id": "Steve De Jarnatt",
    "movieCount": 2
  },
  {
    "_id": "Steve Hickner",
    "movieCount": 2
  },
  {
    "_id": "Steve Kloves",
    "movieCount": 2
  },
  {
    "_id": "Steve Martino",
    "movieCount": 2
  },
  {
    "_id": "Steve Wang",
    "movieCount": 2
  },
  {
    "_id": "Steve Yeager",
    "movieCount": 2
  },
  {
    "_id": "Steven Cantor",
    "movieCount": 2
  },
  {
    "_id": "Steven Quale",
    "movieCount": 2
  },
  {
    "_id": "Steven Shainberg",
    "movieCount": 2
  },
  {
    "_id": "Steven de Jong",
    "movieCount": 2
  },
  {
    "_id": "Stu Pollard",
    "movieCount": 2
  },
  {
    "_id": "Stuart Beattie",
    "movieCount": 2
  },
  {
    "_id": "Stuart Orme",
    "movieCount": 2
  },
  {
    "_id": "St\u00e8phane Aubier",
    "movieCount": 2
  },
  {
    "_id": "St\u00e8phane Lafleur",
    "movieCount": 2
  },
  {
    "_id": "Sue Brooks",
    "movieCount": 2
  },
  {
    "_id": "Sujoy Ghosh",
    "movieCount": 2
  },
  {
    "_id": "Sukumar",
    "movieCount": 2
  },
  {
    "_id": "Suresh Krishna",
    "movieCount": 2
  },
  {
    "_id": "Susan Froemke",
    "movieCount": 2
  },
  {
    "_id": "Susan Jacobson",
    "movieCount": 2
  },
  {
    "_id": "Susan Muska",
    "movieCount": 2
  },
  {
    "_id": "Susumu Kudo",
    "movieCount": 2
  },
  {
    "_id": "Suzanne Chisholm",
    "movieCount": 2
  },
  {
    "_id": "Suzie Templeton",
    "movieCount": 2
  },
  {
    "_id": "Sven Taddicken",
    "movieCount": 2
  },
  {
    "_id": "Sven Unterwaldt Jr.",
    "movieCount": 2
  },
  {
    "_id": "Sylvain White",
    "movieCount": 2
  },
  {
    "_id": "Sylvia Soska",
    "movieCount": 2
  },
  {
    "_id": "Sylvie Verheyde",
    "movieCount": 2
  },
  {
    "_id": "S\u00e8bastien Lifshitz",
    "movieCount": 2
  },
  {
    "_id": "T. Hee",
    "movieCount": 2
  },
  {
    "_id": "Tae-yong Kim",
    "movieCount": 2
  },
  {
    "_id": "Taika Waititi",
    "movieCount": 2
  },
  {
    "_id": "Takahisa Zeze",
    "movieCount": 2
  },
  {
    "_id": "Takao Okawara",
    "movieCount": 2
  },
  {
    "_id": "Takashi Koizumi",
    "movieCount": 2
  },
  {
    "_id": "Takeshi Koike",
    "movieCount": 2
  },
  {
    "_id": "Takuya Fukushima",
    "movieCount": 2
  },
  {
    "_id": "Tamara Jenkins",
    "movieCount": 2
  },
  {
    "_id": "Taru M\u00e8kel\u00e8",
    "movieCount": 2
  },
  {
    "_id": "Tar\u00e8 Ohtani",
    "movieCount": 2
  },
  {
    "_id": "Tassos Boulmetis",
    "movieCount": 2
  },
  {
    "_id": "Taweewat Wantha",
    "movieCount": 2
  },
  {
    "_id": "Ted Nicolaou",
    "movieCount": 2
  },
  {
    "_id": "Terry Green",
    "movieCount": 2
  },
  {
    "_id": "Terry Sanders",
    "movieCount": 2
  },
  {
    "_id": "Thilo Rothkirch",
    "movieCount": 2
  },
  {
    "_id": "Thomas Balm\u00e8s",
    "movieCount": 2
  },
  {
    "_id": "Thomas Gilou",
    "movieCount": 2
  },
  {
    "_id": "Thomas Riedelsheimer",
    "movieCount": 2
  },
  {
    "_id": "Tigmanshu Dhulia",
    "movieCount": 2
  },
  {
    "_id": "Tiller Russell",
    "movieCount": 2
  },
  {
    "_id": "Tim Kirkman",
    "movieCount": 2
  },
  {
    "_id": "Tim Reid",
    "movieCount": 2
  },
  {
    "_id": "Timo Tjahjanto",
    "movieCount": 2
  },
  {
    "_id": "Tjebbo Penning",
    "movieCount": 2
  },
  {
    "_id": "Toby Shelton",
    "movieCount": 2
  },
  {
    "_id": "Todd Berger",
    "movieCount": 2
  },
  {
    "_id": "Todd Field",
    "movieCount": 2
  },
  {
    "_id": "Todd Graff",
    "movieCount": 2
  },
  {
    "_id": "Todd Holland",
    "movieCount": 2
  },
  {
    "_id": "Todd Louiso",
    "movieCount": 2
  },
  {
    "_id": "Todd Strauss-Schulson",
    "movieCount": 2
  },
  {
    "_id": "Tom Hanks",
    "movieCount": 2
  },
  {
    "_id": "Tom Noonan",
    "movieCount": 2
  },
  {
    "_id": "Tom Stern",
    "movieCount": 2
  },
  {
    "_id": "Tom Vaughan",
    "movieCount": 2
  },
  {
    "_id": "Tomm Moore",
    "movieCount": 2
  },
  {
    "_id": "Tommy Chong",
    "movieCount": 2
  },
  {
    "_id": "Tommy Lee Wallace",
    "movieCount": 2
  },
  {
    "_id": "Tommy Wirkola",
    "movieCount": 2
  },
  {
    "_id": "Tomoyuki Takimoto",
    "movieCount": 2
  },
  {
    "_id": "Toni Myers",
    "movieCount": 2
  },
  {
    "_id": "Tony Ayres",
    "movieCount": 2
  },
  {
    "_id": "Tony Cervone",
    "movieCount": 2
  },
  {
    "_id": "Tony Craig",
    "movieCount": 2
  },
  {
    "_id": "Tony Jaa",
    "movieCount": 2
  },
  {
    "_id": "Tony McNamara",
    "movieCount": 2
  },
  {
    "_id": "Tony Mitchell",
    "movieCount": 2
  },
  {
    "_id": "Tony Randel",
    "movieCount": 2
  },
  {
    "_id": "Torsten K\u00e8nstler",
    "movieCount": 2
  },
  {
    "_id": "Trent Harris",
    "movieCount": 2
  },
  {
    "_id": "Troy Byer",
    "movieCount": 2
  },
  {
    "_id": "Tudor Giurgiu",
    "movieCount": 2
  },
  {
    "_id": "Turner Ross",
    "movieCount": 2
  },
  {
    "_id": "Tuukka Tiensuu",
    "movieCount": 2
  },
  {
    "_id": "Tyler Gillett",
    "movieCount": 2
  },
  {
    "_id": "Tyler Measom",
    "movieCount": 2
  },
  {
    "_id": "Udayan Prasad",
    "movieCount": 2
  },
  {
    "_id": "Ulrik Imtiaz Rolfsen",
    "movieCount": 2
  },
  {
    "_id": "Ulrike Ottinger",
    "movieCount": 2
  },
  {
    "_id": "Umesh Shukla",
    "movieCount": 2
  },
  {
    "_id": "Ute Wieland",
    "movieCount": 2
  },
  {
    "_id": "Vadim Jean",
    "movieCount": 2
  },
  {
    "_id": "Vadim Perelman",
    "movieCount": 2
  },
  {
    "_id": "Valeria Bruni Tedeschi",
    "movieCount": 2
  },
  {
    "_id": "Veit Harlan",
    "movieCount": 2
  },
  {
    "_id": "Vera Storozheva",
    "movieCount": 2
  },
  {
    "_id": "Ver\u00e8nica Chen",
    "movieCount": 2
  },
  {
    "_id": "Vicco von B\u00e8low",
    "movieCount": 2
  },
  {
    "_id": "Vicente Ferraz",
    "movieCount": 2
  },
  {
    "_id": "Victor Cook",
    "movieCount": 2
  },
  {
    "_id": "Victor Mignatti",
    "movieCount": 2
  },
  {
    "_id": "Victor Schertzinger",
    "movieCount": 2
  },
  {
    "_id": "Vidhu Vinod Chopra",
    "movieCount": 2
  },
  {
    "_id": "Viktor Shamirov",
    "movieCount": 2
  },
  {
    "_id": "Vince Offer",
    "movieCount": 2
  },
  {
    "_id": "Vincent J. Donehue",
    "movieCount": 2
  },
  {
    "_id": "Vincent Paronnaud",
    "movieCount": 2
  },
  {
    "_id": "Vincent Patar",
    "movieCount": 2
  },
  {
    "_id": "Vincenzo Salemme",
    "movieCount": 2
  },
  {
    "_id": "Vinko Bresan",
    "movieCount": 2
  },
  {
    "_id": "Vishnuvardhan",
    "movieCount": 2
  },
  {
    "_id": "Vladimir Menshov",
    "movieCount": 2
  },
  {
    "_id": "Vladimir Naumov",
    "movieCount": 2
  },
  {
    "_id": "Vladim\u00e8r Mich\u00e8lek",
    "movieCount": 2
  },
  {
    "_id": "Vlasta Posp\u00e8silov\u00e8",
    "movieCount": 2
  },
  {
    "_id": "Vyacheslav Krishtofovich",
    "movieCount": 2
  },
  {
    "_id": "V\u00e8ctor Garc\u00e8a",
    "movieCount": 2
  },
  {
    "_id": "V\u00e8ctor Gaviria",
    "movieCount": 2
  },
  {
    "_id": "Wai Man Yip",
    "movieCount": 2
  },
  {
    "_id": "Walerian Borowczyk",
    "movieCount": 2
  },
  {
    "_id": "Walon Green",
    "movieCount": 2
  },
  {
    "_id": "Walter Carvalho",
    "movieCount": 2
  },
  {
    "_id": "Wayne Kramer",
    "movieCount": 2
  },
  {
    "_id": "Weikai Huang",
    "movieCount": 2
  },
  {
    "_id": "Wes Ball",
    "movieCount": 2
  },
  {
    "_id": "Wesley Ruggles",
    "movieCount": 2
  },
  {
    "_id": "Will Finn",
    "movieCount": 2
  },
  {
    "_id": "Will Koopman",
    "movieCount": 2
  },
  {
    "_id": "Will Speck",
    "movieCount": 2
  },
  {
    "_id": "Willard Huyck",
    "movieCount": 2
  },
  {
    "_id": "Willem van de Sande Bakhuyzen",
    "movieCount": 2
  },
  {
    "_id": "William A. Seiter",
    "movieCount": 2
  },
  {
    "_id": "William Boyd",
    "movieCount": 2
  },
  {
    "_id": "William Brent Bell",
    "movieCount": 2
  },
  {
    "_id": "William C. de Mille",
    "movieCount": 2
  },
  {
    "_id": "William Hanna",
    "movieCount": 2
  },
  {
    "_id": "William Heise",
    "movieCount": 2
  },
  {
    "_id": "William K. Howard",
    "movieCount": 2
  },
  {
    "_id": "William Mesa",
    "movieCount": 2
  },
  {
    "_id": "William Peter Blatty",
    "movieCount": 2
  },
  {
    "_id": "William Phillips",
    "movieCount": 2
  },
  {
    "_id": "William Sachs",
    "movieCount": 2
  },
  {
    "_id": "Witold Leszczynski",
    "movieCount": 2
  },
  {
    "_id": "Wojciech Marczewski",
    "movieCount": 2
  },
  {
    "_id": "Wolfgang Becker",
    "movieCount": 2
  },
  {
    "_id": "Wolfgang Lauenstein",
    "movieCount": 2
  },
  {
    "_id": "Woo-Suk Kang",
    "movieCount": 2
  },
  {
    "_id": "Xan Cassavetes",
    "movieCount": 2
  },
  {
    "_id": "Xaver Schwarzenberger",
    "movieCount": 2
  },
  {
    "_id": "Xavier Dolan",
    "movieCount": 2
  },
  {
    "_id": "Xavier Gens",
    "movieCount": 2
  },
  {
    "_id": "Xavier Palud",
    "movieCount": 2
  },
  {
    "_id": "Xiao Lu Xue",
    "movieCount": 2
  },
  {
    "_id": "Yann Samuell",
    "movieCount": 2
  },
  {
    "_id": "Yasuhiro Yoshiura",
    "movieCount": 2
  },
  {
    "_id": "Yen-Ping Chu",
    "movieCount": 2
  },
  {
    "_id": "Yi'nan Diao",
    "movieCount": 2
  },
  {
    "_id": "Yi-kwan Kang",
    "movieCount": 2
  },
  {
    "_id": "Yibai Zhang",
    "movieCount": 2
  },
  {
    "_id": "Yilmaz Erdogan",
    "movieCount": 2
  },
  {
    "_id": "Yilmaz G\u00e8ney",
    "movieCount": 2
  },
  {
    "_id": "Yorgos Lanthimos",
    "movieCount": 2
  },
  {
    "_id": "Yorgos Tsemberopoulos",
    "movieCount": 2
  },
  {
    "_id": "Yoshihiro Nakamura",
    "movieCount": 2
  },
  {
    "_id": "Yoshimitsu Morita",
    "movieCount": 2
  },
  {
    "_id": "Yoshitar\u00e8 Nomura",
    "movieCount": 2
  },
  {
    "_id": "Youssef Delara",
    "movieCount": 2
  },
  {
    "_id": "Yung Chang",
    "movieCount": 2
  },
  {
    "_id": "Yurek Bogayevicz",
    "movieCount": 2
  },
  {
    "_id": "Yuriy Bykov",
    "movieCount": 2
  },
  {
    "_id": "Yvan Attal",
    "movieCount": 2
  },
  {
    "_id": "Yves All\u00e8gret",
    "movieCount": 2
  },
  {
    "_id": "Yvette Kaplan",
    "movieCount": 2
  },
  {
    "_id": "Zach Braff",
    "movieCount": 2
  },
  {
    "_id": "Zackary Adler",
    "movieCount": 2
  },
  {
    "_id": "Zaida Bergroth",
    "movieCount": 2
  },
  {
    "_id": "Zal Batmanglij",
    "movieCount": 2
  },
  {
    "_id": "Zalman King",
    "movieCount": 2
  },
  {
    "_id": "Zeki \u00e8kten",
    "movieCount": 2
  },
  {
    "_id": "Zev Berman",
    "movieCount": 2
  },
  {
    "_id": "Zhuangzhuang Tian",
    "movieCount": 2
  },
  {
    "_id": "Zolt\u00e8n F\u00e8bri",
    "movieCount": 2
  },
  {
    "_id": "\u00e8lvaro Brechner",
    "movieCount": 2
  },
  {
    "_id": "\u00e8mile Gaudreault",
    "movieCount": 2
  }
]
================================== Ai Message ==================================

**Answer to:** "Directors with 2 movies"
Found **1811** results. Showing first 10:

1. Aaron J. Wiederspahn: 2 movies
2. Aaron Lipstadt: 2 movies
3. Aarèn Fernèndez Lesur: 2 movies
4. Abbas Fahdel: 2 movies
5. Abhishek Chaubey: 2 movies
6. Abraham Polonsky: 2 movies
7. Achero Maèas: 2 movies
8. Adam Bernstein: 2 movies
9. Adam Bhala Lough: 2 movies
10. Adam Brooks: 2 movies

... and 1801 more results.
💡 **Tip**: Try 'Show me the top 10...' for more manageable results

[interactive_94e95ca1] Enter your query: exit

Mongodb Building A Text To Mql Agent

Build a Production-Ready Text-to-MQL Agent for MongoDB

Overview

Use Cases

Implementation Approaches

ReAct Agent

Custom LangGraph Agent

Memory System

Business Applications

Technical Components

Prerequisites

🌐 Network Setup: Connect to Your Atlas Cluster

System Setup and Configuration

Step 1: Install Dependencies

Configure Credentials

Initialize Core Components

MongoDB Toolkit Overview

Data Discovery

Persisting Agent Outputs

Overview

Features

Readable Step Summaries

Enhanced Thread Inspection

Enhanced Metadata

Implementation

Usage

Benefits

Thread Inspection and Debugging

inspect_thread_with_summaries_enhanced(thread_id: str, limit: int = 20, show_details: bool = False)

ReAct Agent Creation Functions

create_react_agent_with_enhanced_memory()

Core LangGraph Components

Workflow Design

Tool Nodes

Workflow Node Functions

list_collections(state: MessagesState)

call_get_schema(state: MessagesState)

generate_query(state: MessagesState)

check_query(state: MessagesState)

format_answer(state: MessagesState)

Control Flow

need_checker(state: MessagesState) -> Literal[END, "check_query"]

Custom LangGraph Agent Creation

create_langgraph_agent_with_enhanced_memory()

Agent Initialization

Creating Both Agent Types

System Capabilities

Usage Options

Agent Execution Functions

execute_react_with_memory(thread_id: str, user_input: str)

execute_graph_with_memory(thread_id: str, user_input: str)

Memory Management Functions

list_conversation_threads()

inspect_thread_history(thread_id: str, limit: int = 10)

clear_thread_history(thread_id: str)

memory_system_stats()

Demonstration Functions

Running Demos

demo_basic_queries()

demo_conversation_memory()

compare_agents_with_memory()

test_memory_functionality()

test_enhanced_summarization()

Supporting Test Functions

Interactive Query Interface

interactive_query()

Available Commands

Interactive Session Example

Session Management

Usage

System Initialization and Quick Reference

System Status Display

Available Functions Reference

Quick Start Recommendations

System Capabilities Summary

Initial Test Execution

Automatic Startup Test

Demos

Demo 1: Run Basic Queries w/ demo_basic_queries()

Demo 2: Multi-turn conversations - demo_conversation_memory()

`inspect_thread_with_summaries_enhanced(thread_id: str, limit: int = 20, show_details: bool = False)`

`create_react_agent_with_enhanced_memory()`

`list_collections(state: MessagesState)`

`call_get_schema(state: MessagesState)`

`generate_query(state: MessagesState)`

`check_query(state: MessagesState)`

`format_answer(state: MessagesState)`

`need_checker(state: MessagesState) -> Literal[END, "check_query"]`

`create_langgraph_agent_with_enhanced_memory()`

`execute_react_with_memory(thread_id: str, user_input: str)`

`execute_graph_with_memory(thread_id: str, user_input: str)`

`list_conversation_threads()`

`inspect_thread_history(thread_id: str, limit: int = 10)`

`clear_thread_history(thread_id: str)`

`memory_system_stats()`

`demo_basic_queries()`

`demo_conversation_memory()`

`compare_agents_with_memory()`

`test_memory_functionality()`

`test_enhanced_summarization()`

`interactive_query()`

Demo 1: Run Basic Queries w/ `demo_basic_queries()`

Demo 2: Multi-turn conversations - `demo_conversation_memory()`

Demo 4: List all threads - `list_conversation_threads()`

Demo 5: Enhanced inspection - `inspect_thread_with_summaries_enhanced(thread_id)`