Multi-Model Consensus Engine Via Claude Code Plugin

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Multi-Model Consensus Engine via Claude Code Plugin

Hey guys! Let's dive into an exciting project that brings the power of multi-model consensus right into your IDE, making your development workflow smarter and more efficient. Inspired by the original llm-council project, this implementation uses a Claude Code plugin to adapt the three-phase deliberation protocol for real-time IDE integration. Think of it as having a council of intelligent agents ready to assist you at every step of your coding journey!

Repository

Check out the code here: https://github.com/xrf9268-hue/llm-council-plugin

Implementation Approach

The core of this plugin revolves around a three-phase deliberation protocol, ensuring a well-rounded and robust decision-making process. Let’s break it down:

Stage 1: Parallel opinion collection
Stage 2: Cross-examination peer reviews
Stage 3: Chairman synthesis → final verdict

This protocol is designed to gather diverse opinions, rigorously review them, and then synthesize a final, well-considered verdict. Now, let's highlight the key differences between this Claude Code plugin and the original project.

  • Integration: Instead of a standalone script, this implementation is a Claude Code plugin, meaning it leverages slash commands and skills directly within your IDE.
  • Model Access: Rather than direct API calls, it uses CLI orchestration (Claude CLI, Codex CLI, Gemini CLI) to interact with the models.
  • Execution: It relies on bash scripts with progressive disclosure to maintain context efficiency.
  • Use Case: It's designed for real-time IDE queries during development sessions, offering immediate assistance as you code.

Deep Dive into the Three-Phase Deliberation Protocol

Let's get into the nitty-gritty of each stage to truly understand how this multi-model consensus engine works. In Stage 1, parallel opinion collection is where the magic begins. The plugin sends out the same query to multiple language models (LLMs) simultaneously. These models, such as Claude, Codex, and Gemini, independently analyze the query and generate their initial responses or opinions. The beauty of this parallel approach is that it allows for a broad spectrum of perspectives to be gathered quickly. Think of it as brainstorming with a team of experts, each bringing their unique knowledge and expertise to the table. The plugin efficiently captures these diverse viewpoints, setting the stage for the next critical phase.

Stage 2 is all about cross-examination peer reviews. Once the initial opinions are collected, they are then presented to the other models for review. Each model examines the responses from its peers, identifying strengths, weaknesses, inconsistencies, or potential errors. This cross-examination process is vital for ensuring the quality and accuracy of the final verdict. It's like having a team of editors meticulously scrutinizing each other's work, catching mistakes, and suggesting improvements. The models might challenge assumptions, provide alternative interpretations, or offer additional insights, leading to a more refined and robust understanding of the problem at hand. This peer review stage fosters a collaborative environment, where the models learn from each other and collectively improve the overall outcome. The rigorousness of this phase is a cornerstone of the multi-model consensus engine's reliability.

Finally, we arrive at Stage 3: Chairman synthesis and the final verdict. In this stage, a designated model, acting as the "chairman," synthesizes all the collected opinions and peer reviews to arrive at a final verdict. The chairman model analyzes the various responses, weighs the evidence, and resolves any conflicting viewpoints. It then formulates a comprehensive and well-supported conclusion, which serves as the final answer to the initial query. This synthesis process requires a high level of reasoning and judgment, as the chairman model must effectively integrate diverse perspectives and make informed decisions. The final verdict represents the collective intelligence of the entire council of models, providing a reliable and trustworthy solution to the problem at hand. This three-phase deliberation protocol ensures that the multi-model consensus engine delivers high-quality results, making it an invaluable tool for developers.

Features

This plugin comes packed with features designed to enhance its usability and reliability:

  • Graceful degradation: It works seamlessly with 1-3 models, requiring a minimum quorum of 2 for consensus.
  • Security hooks: It includes pre/post-execution validation to ensure the integrity of the process.
  • Configurable settings: You can easily adjust settings like enabled members, timeout, and quorum to suit your specific needs.
  • Comprehensive test coverage: Thorough testing, including failure scenarios, ensures the plugin's robustness.

Diving Deeper into the Core Features

Let's elaborate on these features to fully appreciate their significance. Graceful degradation is a critical aspect of the plugin, ensuring that it remains functional even when not all models are available. In practical scenarios, some models might be temporarily unavailable due to maintenance, network issues, or API limitations. Instead of crashing or producing unreliable results, the plugin gracefully adapts to the situation. It can operate with as few as one model, although a minimum quorum of two is required to achieve a consensus. This ensures that the final verdict is based on at least two independent opinions, enhancing its reliability and trustworthiness. The graceful degradation feature makes the plugin highly resilient and suitable for real-world development environments, where unforeseen issues can arise.

Security hooks are another essential component, providing pre and post-execution validation to safeguard the integrity of the process. Before executing any code or running any queries, the pre-execution hook verifies that all necessary conditions are met. This might include checking for valid API keys, verifying the input data format, or ensuring that the required models are available. If any of these checks fail, the plugin will halt execution and alert the user, preventing potentially harmful actions. After the execution is complete, the post-execution hook validates the output to ensure that it is consistent with expectations. This might involve checking for syntax errors, verifying the accuracy of the results, or ensuring that the output conforms to a predefined schema. If any inconsistencies are detected, the plugin will flag them for further investigation. The security hooks act as a safety net, protecting against errors, vulnerabilities, and malicious attacks.

The configurable settings provide a high degree of flexibility, allowing you to tailor the plugin to your specific needs and preferences. You can easily adjust various parameters, such as the enabled members, timeout, and quorum, to optimize the plugin's performance in different scenarios. The enabled members setting allows you to specify which models should be included in the consensus process. This is useful if you want to experiment with different combinations of models or if you have specific models that you trust more than others. The timeout setting determines how long the plugin will wait for each model to respond before timing out. This is important for preventing the plugin from getting stuck indefinitely if a model becomes unresponsive. The quorum setting specifies the minimum number of models that must agree on a verdict for it to be considered valid. This allows you to adjust the level of consensus required, depending on the criticality of the task at hand. The configurable settings empower you to fine-tune the plugin's behavior to achieve the best possible results in your development environment.

Comprehensive test coverage, including failure scenarios, is paramount to ensuring the plugin's reliability and robustness. The test suite includes a wide range of test cases, covering various input conditions, edge cases, and potential failure scenarios. These tests are designed to thoroughly evaluate the plugin's functionality and identify any bugs or vulnerabilities. The failure scenarios tests simulate real-world problems that might occur, such as network outages, API errors, or invalid input data. By testing the plugin under these adverse conditions, the developers can ensure that it can handle unexpected situations gracefully and continue to provide reliable results. The comprehensive test coverage provides confidence in the plugin's quality and makes it a dependable tool for developers.

Technical Stack

Under the hood, this plugin is built using a combination of technologies:

  • Plugin manifest following the Claude Code plugin schema.
  • Bash orchestration scripts for parallel execution.
  • Structured JSON output for hook integration.
  • Progressive disclosure pattern (65% context reduction).

Diving Deeper into the Technical Aspects

Let's break down these technical components to give you a clearer understanding of how the plugin works behind the scenes. The plugin manifest is a crucial file that adheres to the Claude Code plugin schema. This manifest provides all the necessary information for the Claude platform to understand and execute the plugin correctly. It includes details such as the plugin's name, description, version, entry points, and dependencies. The manifest acts as a blueprint, guiding the Claude platform on how to interact with the plugin and leverage its functionalities. By following the Claude Code plugin schema, the manifest ensures seamless integration and compatibility with the Claude ecosystem.

Bash orchestration scripts play a central role in managing the parallel execution of queries across multiple language models. These scripts are responsible for sending the same query to each model simultaneously and collecting their responses. They also handle the coordination of the peer review process, ensuring that each model has access to the responses from its peers. The bash scripts use command-line interfaces (CLIs) to interact with the language models, such as Claude CLI, Codex CLI, and Gemini CLI. This approach allows for efficient and scalable execution, as bash scripts are well-suited for managing parallel processes. The scripts are carefully designed to minimize overhead and maximize performance, ensuring that the plugin can deliver timely results.

Structured JSON output is used for seamless integration with security hooks and other components of the development environment. JSON (JavaScript Object Notation) is a lightweight data-interchange format that is easy to parse and generate. The plugin uses JSON to represent the results of the deliberation process, including the initial opinions from each model, the peer reviews, and the final verdict. This structured output makes it easy for other tools and applications to consume and process the data. For example, security hooks can use the JSON output to validate the results and ensure that they meet certain criteria. Similarly, other parts of the development environment can use the JSON output to display the results to the user in a user-friendly format. The use of structured JSON output enhances the interoperability and flexibility of the plugin.

The progressive disclosure pattern is a technique used to reduce the amount of context that needs to be passed to the language models, resulting in a significant reduction in context size (approximately 65%). Language models have a limited context window, which is the amount of text that they can process at one time. By using progressive disclosure, the plugin only provides the models with the information that they need at each stage of the deliberation process. For example, in the initial opinion collection stage, the models only receive the original query. In the peer review stage, they receive the original query and the responses from their peers. This approach avoids overwhelming the models with unnecessary information and allows them to focus on the task at hand. The progressive disclosure pattern improves the efficiency and performance of the plugin, especially when dealing with complex queries that require a lot of context.

This project is MIT licensed and credits the original repository in its acknowledgments.