This project introduces Agentic Authoring Assistant, a multi-agent system designed to autonomously generate, refine, and reference project metadata. Leveraging modular agents orchestrated via LangGraph, the system demonstrates a structured workflow for AI-driven content creation and retrieval.
The system is composed of independent agents, each with a clear role:
Metadata Agent – Generates raw project metadata including titles, summaries, and tags using NLP models.
Refiner Agent – Cleans and standardizes metadata, ensuring consistency and readability.
Reference Agent – Searches the web for relevant references and organizes results for contextual support.
Agents communicate through a shared state graph, allowing seamless orchestration and data flow.
Each node in the graph corresponds to an agent’s function, executed in sequence to produce the final enriched metadata.
| Agent | Role | Output |
|---|---|---|
| Metadata Agent | Generates raw metadata (titles, summaries, tags) using NLP models | {"titles": [...], "summary": "...", "tags": [...]} |
| Refiner Agent | Cleans and standardizes metadata for readability | Refined metadata object |
| Reference Agent | Searches the web for relevant references | {"references": [{"title": "...", "url": "..."}]} |
The orchestration leverages LangGraph, enabling:
Modular agent execution and easy integration of new agents.
Sequential and conditional data processing between agents.
Aggregation of outputs into a unified metadata object ready for use or display.
The Metadata Agent initiates the workflow, generating raw data.
The Refiner Agent optionally processes metadata to improve quality.
The Reference Agent supplements metadata with web-sourced references.
The final output is a structured dictionary of titles, summaries, tags, and references, facilitating automated project documentation and knowledge management.
Input a project description.
Metadata agent generates raw titles, summary, and tags.
Refiner agent improves clarity and formatting.
Reference agent adds relevant external sources.
Final aggregated metadata is ready for display or storage.
Extend agents by adding new nodes to StateGraph.
Integrate additional tools (image generation, sentiment analysis, etc.).
Improve orchestration logic for more complex workflows.
This multi-agent architecture showcases an extensible approach to AI-assisted content generation. By separating concerns across specialized agents and orchestrating them via a state graph, the system provides a reproducible, maintainable, and scalable framework for automated project authorship.