Overview

In this Module 2 project for the Ready Tensor AAIDC course, I built a multi-agent system called Cross Publication Insight Assistant. The goal was to support exploratory insight across multiple GitHub repositories by automatically detecting patterns and trends, comparing implementations, and responding to user queries. The project is rooted in the need to derive deeper, grounded insights from disparate sources of implementation, whether for research, technical audit, or strategy.

This system aligns with the course’s emphasis on agent coordination, orchestration frameworks (LangGraph), and tool-augmented intelligence. It demonstrates practical utility in analyzing large sets of AI/ML projects by generating grounded, useful insights for developers, researchers, or technical managers. By leveraging LangGraph orchestration, custom agents, and integrated tools, the system automates trend discovery, comparison, fact-checking, and summarization across GitHub repositories. It provides useful answers to technical queries while allowing optional human-in-the-loop (HITL) participation.

Motivation

Manual repository analysis is often laborious, inconsistent, and error-prone. Developers and researchers frequently need to:

• Compare implementations

• Spot trends in tooling and techniques

• Identify gaps or innovations

• Get summarized, grounded insight

This system automates these high-effort processes, answering questions like:

• “How does Repo A differ from Repo B?”

• “Which of these projects uses LangGraph?”

• “What dominant trends emerge from these LLM projects?”

Architecture: How the Assistant Works

The system uses a set of modular, cooperative agents, each with a defined role. These agents communicate via a state machine built with LangGraph. The architecture promotes robustness, traceability, and easy extension.

Agents and Their Specializations

Agents can operate asynchronously and allow HITL input before summarization if desired.

Tooling and Integrations

• LLMs (OpenAI / Local): Used for summarization, comparison, trend analysis

• SentenceTransformers: For semantic fallback in trend detection

• LangGraph: Manages agent state transitions and flow

• Custom Tools: Including a repo parser and comparison utilities

• HITL Module: Optional user intervention for fine-tuning

Project Structure

.
├── agents/ # All agent definitions
│ ├── project_analyzer.py
│ ├── llm_trend_agent.py
│ ├── trend_aggregator.py
│ ├── comparison_agent.py
│ ├── fact_checker.py
│ └── summarize_agent.py
│
├── tools/ # Supporting tools/utilities
│ ├── semantic_trend_detector.py
│ ├── repo_parser.py
│ ├── comparison_tool.py
│ └── hitl_intervention.py
│
├── orchestrator/
│ └── orchestrator.py # LangGraph state orchestrator
│
├── config/
│ ├── config.yaml # LLM and model settings
│ └── prompts/ # Custom prompt templates
│
├── utils/ # Logging, config loader, HITL logic, etc.
├── main.py # CLI entrypoint
├── README.md
├── LICENSE.md
├── requirements.in
├── requirements.txt
├── .env # Local environment variables
└── .env-example # Sample .env template

Query Capabilities

System Diagram

Performance Evaluation

While the system is functional and accurate in its current form, the next phase of development will focus on:

• Evaluating LLM outputs for consistency and hallucination rate

• Measuring summarization quality using ROUGE/BLEU metrics

• Benchmarking trend detection accuracy across multiple repo sets

Comparisons with baseline single-agent approaches (e.g. one-shot summarization) will also be explored to demonstrate improved utility through modularity.

Code Snippets

Orchestrator

from langgraph.graph import StateGraph, END
from agents.project_analyzer import run as analyze_project
from agents.aggregate_query_agent import run as aggregate_query_run
...

class CrossPublicationInsightOrchestrator:
    def __init__(self, user_query: str = ""):
        self.graph = StateGraph(dict)
        self.graph.add_node("analyze", analyze_project)
        ...
        self.graph.set_entry_point("analyze")
        self.executor = self.graph.compile()

    def run(self, input_data: dict):
        return self.executor.invoke(input_data)

        

Status and Maintenance

The project is actively maintained and built with extensibility in mind. Future versions will include:

• API-first interface with session state

• Evaluation dashboards

• Modular RAG enhancements for project retrieval

Installation Instructions

Detailed information can be found in the repository readme here: Cross Publication Insight Assistant

Highlights

• Modular and extensible agent design
• Semantic fallback strategy ensures robustness
• Built-in Human-in-the-loop (HITL) review before final summary
• Jinja-based prompts and configuration support easy customization
• LangGraph-powered orchestration makes the system traceable and explainable

License

This project is provided under the MIT License. You are free to use, modify, and distribute the codebase for both commercial and non-commercial purposes, provided that the original license terms are included. Refer to the LICENSE file in the Github Repository
Cross Publication Insight Assistant for more information.

Thank you for reading!