PREDIBAG: Revolutionizing Agent Creation with Browser-Based Intelligence

Executive Summary

PREDIBAG, powered by Naver’s innovative LispE WebAssembly (WASM) platform, is a transformative agent framework that enables the creation of intelligent, privacy-focused agents running entirely in web browsers. Unlike traditional frameworks that rely on server infrastructure or cloud APIs, PREDIBAG delivers unmatched flexibility, security, and efficiency through local processing, dynamic code generation, thread-safe asynchronous execution, in-memory data exchange, and real-time assessment of intermediate generations. Built on Lisp’s inherently compositional nature and enhanced by seamless JavaScript integration, PREDIBAG allows developers to craft complex agent behaviors with minimal code. A planned graphical user interface (GUI) will make it accessible to non-technical users, democratizing agent creation. This document outlines PREDIBAG’s potential to streamline agent creation, its competitive edge, and its strategic value for Naver’s innovation roadmap.

What is PREDIBAG?

PREDIBAG, or Predicate-Based Agent, is an advanced framework designed to develop highly adaptable and reliable artificial intelligence agents through a function-based system rooted in logic programming. PREDIBAG employs predicates—functions defined in LispE’s defpred constructs—that evaluate outputs from Large Language Models (LLMs) by returning either true (valid) or false (invalid). When a function returns false, PREDIBAG employs backtracking, a process whereby it systematically evaluates subsequent functions in sequence until a function returns true or all functions are exhausted. This structured evaluation ensures precise and flexible handling of diverse outputs.

Consider a document reviewer tasked with verifying submissions against a set of criteria. Each criterion is a function that assesses whether the submission meets specific requirements, yielding true if it does or false if it does not. If a function yields false, the reviewer proceeds to the next function, backtracking through the sequence until a valid match is identified or no functions remain applicable. PREDIBAG operates in a comparable manner, using predicate functions to process outputs, as demonstrated by the following examples:

• A function verifies if the output contains JavaScript code; if true, it executes the code: (defpred ([in result "javascript"]) ... execute JavaScript).
• A function confirms if the output is a JSON structure; if true, it extracts the structure: (defpred ([in result "json"]) ... extract JSON).
• A function checks if the output is Python code; if true, it executes the code: (defpred ([in result "python"]) ... execute Python).
• A function identifies invalid outputs; if true, it signals an error: (defpred (result) ... signal error).

This function-based approach enables PREDIBAG to manage complex agent behaviors with exceptional flexibility. To introduce a new requirement, such as ensuring outputs adhere to regulatory standards or maintain a specific tone, a new defpred function can be defined without necessitating architectural modifications—a significant advantage over conventional frameworks that require extensive refactoring to accommodate new behaviors. Furthermore, PREDIBAG’s asynchronous execution permits concurrent processing of multiple queries, evaluating each output against predicate functions in real time to retain valid results or discard those that fail to meet criteria. Operating entirely within the browser, PREDIBAG combines this sophisticated functionality with robust privacy and operational efficiency, positioning it as an ideal solution for developing tailored, high-quality AI agents.

The Problem: Limitations of Current Agent Frameworks

Existing agent frameworks, such as LangChain, AutoGPT, and OpenAI Assistants, face significant challenges that hinder their scalability, privacy, and adaptability:

• Server Dependency: Most frameworks require cloud or server infrastructure, increasing costs, latency, and operational complexity.
• Privacy Risks: Cloud-based processing (e.g., OpenAI, Anthropic) sends user data to remote servers, posing compliance risks in regulated industries like healthcare and finance.
• Rigid Customization: Frameworks like AgentGPT and BabyAGI rely on predefined components, limiting flexibility for complex or domain-specific use cases.
• Inefficient Prompt Handling: Many frameworks use synchronous or rigid prompt processing, leading to delays or failures when integrating with Large Language Models (LLMs).
• Limited Output Validation: Current frameworks often lack mechanisms to assess intermediate outputs in real time, risking errors or non-compliant responses.
• Limited State Management: Frameworks rely on external databases or file systems, complicating real-time data sharing and introducing concurrency challenges.
• Accessibility Barriers: Complex programming models exclude non-technical users and slow development for diverse teams.

These limitations prevent the creation of scalable, secure, and reliable agents that meet Naver’s diverse customer and partner needs.

PREDIBAG’s Solution: A Browser-Based Revolution

PREDIBAG addresses these challenges with a novel approach, leveraging LispE’s compositional power and WebAssembly’s lightweight, secure execution environment. Its core features deliver immediate value for agent creation:

1. Fully Browser-Based Execution:

   • PREDIBAG runs entirely in the browser’s sandbox, eliminating server infrastructure.
   • Benefit: Reduces deployment costs, simplifies scaling, and ensures low-latency performance, even on low-resource devices.

2. Uncompromising Privacy:

   • All processing occurs locally, ensuring sensitive data (e.g., customer queries, medical records) never leaves the client device.
   • Benefit: Enables compliance with strict regulations (e.g., GDPR, HIPAA) and builds trust in privacy-sensitive sectors like healthcare, finance, and legal services.

3. Real-Time Assessment of Intermediate Generations:

   • PREDIBAG enables on-the-fly evaluation of intermediate outputs using defpred functions, allowing LLMs or custom judges to assess text against constraints (e.g., tone, accuracy, compliance) before issuance.
   • Benefit: Enhances output quality and reliability, reducing errors and ensuring compliance in critical applications (e.g., customer support, healthcare), with seamless integration into asynchronous workflows.

4. Thread-Safe Asynchronous Prompt Resolution:

   • PREDIBAG associates each LLM prompt with an asynchronous callback function, enabling flexible, non-blocking interactions with local or cloud-based LLMs. JavaScript’s single-threaded model ensures thread-safe execution, eliminating concurrency issues.
   • Benefit: Improves agent responsiveness and reliability by handling LLM responses dynamically, reducing delays and enabling robust error recovery (e.g., retrying failed prompts) without synchronization risks.

5. Compositional Logic via Lisp:

   • Built on LispE’s inherently compositional nature, PREDIBAG uses pattern matching, macros, and backtracking predicates to create modular, reusable agent behaviors with minimal code.
   • Benefit: Accelerates development by allowing developers to compose complex logic declaratively, unlike the imperative, verbose code required by Python or JavaScript-based frameworks.

6. Seamless JavaScript Integration:

   • PREDIBAG leverages JavaScript for dynamic code generation and execution, ensuring compatibility with web ecosystems and browser APIs in a thread-safe manner.
   • Benefit: Enhances developer accessibility and enables integration with Naver’s web-based products (e.g., LINE), making PREDIBAG versatile for web developers.

7. Thread-Safe In-Memory Persistence and Data Exchange:

   • LispE’s persistent in-memory state allows multiple agents or processes to share data in real time within the browser, with JavaScript’s single-threaded model ensuring thread-safe access without concurrency issues.
   • Benefit: Enables collaborative agent workflows (e.g., shared context in customer support) without external databases, enhancing efficiency, privacy, and reliability.

8. Dynamic Code Generation:

   • PREDIBAG generates and executes JavaScript code on-the-fly within the browser’s sandbox, enabling agents to adapt to new tasks or inputs in real time.
   • Benefit: Speeds up prototyping and enhances agent adaptability, surpassing rigid frameworks like LangChain or AutoGPT.

9. Flexible LLM Integration:

   • Seamlessly connects to local LLMs (e.g., LM Studio) or cloud APIs (e.g., OpenAI, Anthropic) via JavaScript, with declarative logic for retries and fallbacks.
   • Benefit: Supports diverse AI models, reducing dependency on specific providers and enhancing reliability.

10. Lightweight and Efficient:

    • WebAssembly ensures minimal memory and CPU usage, making PREDIBAG suitable for mobile devices and resource-constrained environments.
    • Benefit: Expands market reach to users with low-end hardware, enhancing accessibility.

Competitive Advantage

PREDIBAG stands out in a crowded field of agent frameworks, as shown below:

Framework	Execution	Privacy	Customization	Prompt Handling	Output Validation	State Management	Cost	Key Limitation
PREDIBAG	Browser (WASM)	High (fully local)	High (compositional Lisp, macros, JS integration)	Thread-safe async callbacks	Real-time defpred assessment	Thread-safe in-memory persistence	Free (open source)	Steep learning curve (mitigated by GUI)
LangChain	Server/Browser	Medium (cloud risks)	Medium (predefined)	Synchronous	Limited	External databases	Free	Server dependency, concurrency issues
AutoGPT	Server	Low-Medium	Medium	Synchronous	Limited	File system	Free	Privacy concerns, concurrency risks
OpenAI Assistants	Cloud API	Low	Medium	API-driven	Limited	API-managed	Pay-per-use	Data exposure, cost
CrewAI	Server	Medium	High (roles)	Synchronous	Limited	External databases	Free	Complex setup, concurrency challenges

Key Differentiators:

• Real-Time Output Validation: On-the-fly assessment of intermediate generations using defpred functions ensures high-quality, compliant outputs, unlike frameworks with limited validation like LangChain or CrewAI.
• Privacy-First: Fully local processing ensures data security, unlocking opportunities in regulated industries, unlike cloud-based OpenAI or Anthropic.
• Predicate-Based Logic: Declarative defpred functions with backtracking enable flexible constraint addition without architectural refactoring, unlike rigid frameworks requiring extensive modifications.
• Thread-Safe Async Prompt Handling: Async callbacks with JavaScript’s single-threaded execution enable responsive, reliable agents, surpassing synchronous frameworks like LangChain or AutoGPT.
• Thread-Safe Collaborative Workflows: In-memory persistence with no concurrency issues enables real-time data exchange, eliminating database dependencies unlike LangChain or CrewAI.
• Zero Server Overhead: Browser-based execution eliminates infrastructure costs, unlike LangChain or CrewAI.

Democratizing Agent Creation with a GUI

PREDIBAG’s LispE foundation, while powerful, may present a learning curve for non-technical users or teams unfamiliar with Lisp’s compositional paradigm. To address this, we propose a graphical user interface (GUI) that abstracts LispE’s complexity, enabling rapid agent creation for all skill levels. The GUI will include:

• Drag-and-Drop Workflow Builder: Allows users to define agent behaviors (e.g., thread-safe async prompt submission, real-time output validation, data sharing) using visual components, hiding LispE’s syntax.
• Prebuilt Templates: Offers ready-to-use agent templates for common use cases (e.g., customer support, tutoring), leveraging JS integration, thread-safe in-memory persistence, and defpred-based validation.
• Live Preview: Enables real-time testing of agents in the browser, streamlined by thread-safe design and on-the-fly assessment.
• Code Export: Allows advanced users to access and modify the underlying LispE or JavaScript code for customizations.

Impact: The GUI will empower non-developers (e.g., product managers, domain experts) to create agents, while preserving LispE’s compositional power and JS integration for technical teams. For example, a marketing team could build a customer support agent with real-time output validation and data sharing without coding, while developers can leverage Lisp’s modularity, predicate-based functions, and thread-safe async workflows for complex logic.

Strategic Value for Naver

PREDIBAG aligns with Naver’s mission to deliver innovative, user-centric technology by offering:

• Market Differentiation: Positions Naver as a leader in privacy-focused, browser-based AI, distinguishing it from cloud-reliant competitors.
• Cost Efficiency: Eliminates server costs, enabling scalable deployment across Naver’s products (e.g., LINE, Naver Japan).
• Versatile Applications: Supports diverse use cases, including:
  • Customer Support: Local chatbots with thread-safe async prompt handling, real-time output validation, and in-memory data sharing for reliable query resolution.
  • Healthcare: Secure agents for patient data analysis, leveraging local processing, JS integration, and defpred validation for compliant outputs.
  • Education: Personalized tutoring agents running on student devices, using compositional logic and predicate-based functions for adaptive responses.
  • E-Commerce: Dynamic recommendation agents with real-time adaptability, validation, and collaborative workflows.
• Open-Source Potential: Releasing PREDIBAG as open source could drive community adoption, enhance Naver’s developer ecosystem, and accelerate innovation.
• Internal Productivity: Equips Naver’s teams with a flexible tool for rapid agent prototyping, reducing development cycles through compositional design, JS compatibility, thread-safe execution, and predicate-based validation.

Implementation Roadmap

To maximize PREDIBAG’s impact, we propose:

1. GUI Development (3-6 months):
   • Build a browser-based drag-and-drop interface for agent creation, with templates for thread-safe async prompt handling, JS integration, real-time output validation via defpred, and collaborative workflows leveraging in-memory persistence.
2. Use Case Pilots (6-9 months):
   • Deploy PREDIBAG in internal projects (e.g., customer support for Naver Japan) to demonstrate thread-safe async prompts, predicate-based validation, data sharing, and privacy benefits.
   • Partner with clients in healthcare or finance to validate value in regulated sectors.
3. Performance Optimization (Ongoing):
   • Optimize LispE’s WASM runtime for thread-safe async callback execution, in-memory persistence, JS code generation, and defpred-based validation, ensuring robust performance.
   • Ensure compatibility across major browsers (Chrome, Firefox, Safari).
4. Community Engagement (9-12 months):
   • Release PREDIBAG as open source with tutorials showcasing predicate-based functions, thread-safe async prompts, compositional design, JS integration, and real-time output validation.
   • Host hackathons to encourage developer contributions.

Conclusion

PREDIBAG redefines agent creation by combining LispE’s predicate-based logic, thread-safe asynchronous execution, real-time output validation, in-memory data exchange, and seamless JavaScript integration in a browser-based, privacy-first framework. Its ability to add functions without refactoring, handle multiple async queries, and ensure high-quality outputs addresses the limitations of existing frameworks, positioning Naver as a pioneer in AI-driven innovation. The proposed GUI will democratize access, enabling both developers and non-technical users to create sophisticated agents. We recommend approving funding for GUI development and initiating pilot projects to showcase PREDIBAG’s transformative potential across Naver’s ecosystem.

Recommendation: Invest in GUI development and launch pilot projects to demonstrate PREDIBAG’s value, leveraging its predicate-based functions, thread-safe async prompt handling, real-time output validation, compositional design, JS integration, and in-memory persistence to drive Naver’s leadership in privacy-focused AI.