# Build Your Own "Longevity Scientist": A Paper-to-Action Agent using LangGraph & Mistral-7B

> Source: <https://dev.to/beck_moulton/build-your-own-longevity-scientist-a-paper-to-action-agent-using-langgraph-mistral-7b-20a4>
> Published: 2026-06-06 00:09:00+00:00

We live in an era where scientific breakthroughs are published faster than we can read them. For the **biohacking** community, the gap between a new PubMed study on NAD+ precursors and actually knowing what dose to take is a chasm of manual research. What if you could build an **LLM Agent** that monitors research papers, processes them through a **RAG (Retrieval-Augmented Generation)** pipeline, and maps findings to your specific health profile?

In this tutorial, we are building **Paper-to-Action**, a state-of-the-art agentic workflow using **LangGraph**, **ChromaDB**, and **Mistral-7B**. This isn't just a simple bot; it's a multi-stage reasoning engine designed to turn raw academic data into actionable health interventions. If you've been looking to master **AI agents** and personalized medicine automation, you’re in the right place. 🚀

Traditional RAG pipelines are linear. To handle the nuance of medical research, we need a "looping" logic. We use **LangGraph** to manage the state of our agent, allowing it to decide if a paper is relevant before attempting to extract a protocol.

``` php
graph TD
    A[Start: Keyword Trigger] --> B[Search PubMed/Arxiv API]
    B --> C{Relevance Filter}
    C -- No --> B
    C -- Yes --> D[Store in ChromaDB]
    D --> E[RAG: Extract Intervention Protocol]
    E --> F[Cross-Reference with User Profile]
    F --> G[Generate Personalized Action Plan]
    G --> H[End: Push to Health Checklist]
```

To follow this advanced guide, you'll need:

In LangGraph, everything revolves around the `State`

. We need to track the fetched papers, the extracted data, and the final recommendation.

``` python
from typing import Annotated, List, TypedDict
from langgraph.graph import StateGraph, END

class AgentState(TypedDict):
    keywords: List[str]
    user_profile: dict
    raw_papers: List[dict]
    extracted_protocols: List[dict]
    final_recommendation: str
```

We use the Arxiv API to fetch the latest papers. We want to find studies that mention human-ready interventions.

``` python
import arxiv

def fetch_research(state: AgentState):
    query = " AND ".join(state['keywords'])
    search = arxiv.Search(
        query=query,
        max_results=5,
        sort_by=arxiv.SortCriterion.SubmittedDate
    )

    papers = []
    for result in search.results():
        papers.append({
            "title": result.title,
            "summary": result.summary,
            "url": result.entry_id
        })

    return {"raw_papers": papers}
```

Once we have the papers, we chunk them and store them in **ChromaDB**. When the agent needs to find "Dosage" or "Contraindications," it queries this local vector store.

``` python
from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import HuggingFaceEmbeddings

def extract_protocols(state: AgentState):
    # Initialize Vector Store
    vectorstore = Chroma(
        collection_name="research_papers",
        embedding_function=HuggingFaceEmbeddings()
    )

    # Logic to add state['raw_papers'] to vectorstore...
    # Then query Mistral-7B

    prompt = f"""
    Based on the following research snippets, extract the specific intervention:
    1. Substance/Activity
    2. Recommended Dosage
    3. Duration
    Context: {state['raw_papers']}
    """

    # Assume 'llm' is our Mistral-7B instance
    response = llm.invoke(prompt)
    return {"extracted_protocols": response}
```

This is where the magic happens. We connect our nodes into a circular, intelligent workflow.

```
workflow = StateGraph(AgentState)

# Add Nodes
workflow.add_node("fetcher", fetch_research)
workflow.add_node("extractor", extract_protocols)

# Define Edges
workflow.set_entry_point("fetcher")
workflow.add_edge("fetcher", "extractor")
workflow.add_edge("extractor", END)

# Compile
app = workflow.compile()
```

While this tutorial covers the core logic of a biohacking agent, moving from a script to a production-grade health platform requires deeper considerations like HIPAA compliance, complex data persistence, and agent memory.

For more production-ready examples and advanced AI architecture designs, I highly recommend checking out the ** WellAlly Tech Blog**. It was a massive source of inspiration for the state-management patterns used in this build, especially regarding how to handle "Human-in-the-loop" nodes for medical validation.

The final step is mapping the research to the **User Profile**. If a paper suggests "High-Intensity Interval Training" but the user profile says "History of Knee Injury," the agent must flag this.

``` python
def personalize_report(state: AgentState):
    profile = state['user_profile']
    protocol = state['extracted_protocols']

    analysis = llm.invoke(f"Compare {protocol} with User Profile {profile}. Output a safe, actionable 7-day plan.")
    return {"final_recommendation": analysis}
```

The "Paper-to-Action" agent transforms the way we consume scientific knowledge. By combining **LangGraph's** stateful orchestration with **Mistral-7B's** reasoning, we turn a mountain of PDFs into a personalized health dashboard.

**Next Steps:**

What's the first health keyword you're going to track? Let me know in the comments! 👇