> For the complete documentation index, see [llms.txt](https://docs.bio.xyz/bio/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.bio.xyz/bio/introduction/bioagents/about-bioagents.md).

# How BioAgents Work

BioAgents are autonomous AI agents designed to automate and accelerate the scientific discovery process. They are built on a robust, plugin-based framework that allows them to perform complex tasks like processing scientific literature, generating novel hypotheses, and interacting with decentralized knowledge graphs.

While the [core concept](/bio/introduction/bioagents/bioagents-conceptual-overview.md) page explains the "why" behind BioAgents, this page offers a closer look at the "how."

## The BioAgents Framework

At its core, the BioAgents system is built on top of **Eliza v2**, an open-source agentic framework. This provides a modular and extensible architecture where specific functionalities are encapsulated into plugins.

This framework allows a BioAgent to:

* **Ingest Data:** Connect to various data sources like Google Drive to automatically pull in new scientific papers and documents.
* **Process Information:** Use a pipeline of tools like Grobid and custom scripts to parse documents, extract structured data (JSON-LD), and store it in a local knowledge graph (Oxigraph).
* **Generate Insights:** Leverage large language models (like Anthropic's Claude) to query the knowledge graph, synthesize information, and generate novel, testable hypotheses.
* **Ensure Provenance:** Store generated hypotheses and their supporting evidence in a structured database (PostgreSQL) and publish key assets to a decentralized knowledge graph (OriginTrail DKG) for permanent, verifiable record-keeping.

## Core Workflow: From Data to Discovery

The primary workflow of a BioAgent can be simplified into a three-stage pipeline:

1. **Ingestion & Processing:** A new scientific paper (e.g., a PDF) is detected. The agent retrieves the paper and uses specialized services to parse its contents, breaking it down into structured, machine-readable data.
2. **Knowledge Synthesis:** The structured data is added to a knowledge graph. The agent then uses AI models to analyze the new information in the context of the existing knowledge, looking for novel connections and insights.
3. **Hypothesis Generation:** Based on its analysis, the agent formulates a new, structured hypothesis. This hypothesis, along with its evidence trail and an AI-generated evaluation score, is stored and can be published to the community for further review and validation.

This automated pipeline creates a powerful flywheel for scientific discovery, allowing for the rapid, continuous, and transparent generation of new ideas. For a more detailed technical breakdown, see the [BioAgents Technical Deep Dive](/bio/introduction/bioagents/bioagents-technical-deepdive.md).


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