How to Build a Hermes Agent Elicit AI Workflow for Literature Reviews

According to Elicit research on evidence synthesis, autonomous agents can reduce the time required for screening academic literature by up to 80%, providing a scalable solution to the manual review of large document sets. In a world where Elicit indexes over 200 million academic papers, manual screening has become a significant bottleneck for scientific discovery. The primary keyword hermes agent elicit ai workflow represents a modern architecture designed to resolve this bottleneck. It combines the autonomous task execution of Nous Research's Hermes Agent with the scientific literature analysis of Elicit AI to summarize academic papers.

For research organizations, managing literature reviews has traditionally been a human-only endeavor. Researchers spend days downloading PDFs, reading abstracts, and pasting metadata into spreadsheets. This process does not scale as the volume of scientific publications increases. When developer teams try to solve this with simple scripts, they run into limitations. Basic API wrappers do not maintain session memory, and they lack the autonomous logic required to handle complex research queries. Connecting a persistent agent like Hermes Agent to Elicit AI creates a pipeline that can search databases, filter results based on relevance, and save structured markdown summaries to a persistent shared folder.

Nous Research Hermes Agent is an open-source, MIT-licensed agentic framework designed to run continuously on user-controlled infrastructure, such as local machines, remote servers, or Docker containers. It is model-agnostic and discovers custom capabilities using the agentskills.io standard. By pairing it with Elicit AI's extensive research database, teams can build autonomous research assistants that grow with the project. To see how these tools fit into developer workspaces, refer to the Nous Research Hermes Agent GitHub repository page.

To construct a hermes agent elicit ai workflow, you must establish a protocol for querying the Elicit API, processing the returned JSON array, and writing the final markdown reports to a shared directory. The pipeline executes in five distinct steps:

1. Authentication and Session Initialization: The Hermes Agent starts a task session and reads the environment variables to load the Elicit API key and Fastio credentials.

2. Triggering Elicit Search: The agent sends a POST request containing the user's research question to the Elicit Search endpoint (https://elicit.com/api/v1/search), passing a Bearer token in the authorization header.

3. Parsing JSON Results: The agent parses the JSON response, iterating over the papers array to extract key details including title, authors, year, abstract, and DOI.

4. Generating Markdown Summary: The agent structures the extracted metadata and abstracts into a cleanly formatted markdown document with clear headings, bullets, and citations.

5. Writing Output to Fastio Workspace: The agent uploads the markdown file to a shared Fastio workspace folder through the Fastio MCP server, making it immediately available for team collaboration and RAG search.

Developers can implement the Elicit query in a Python script. Hermes Agent then persists the parsed markdown through the Fastio MCP upload tool:

import os
import requests

ELICIT_API_KEY = os.environ.get("ELICIT_API_KEY")

def run_research_workflow(query):
    url = "https://elicit.com/api/v1/search"
    headers = {"Authorization": f"Bearer {ELICIT_API_KEY}"}
    payload = {"query": query, "maxResults": 5}
    
    response = requests.post(url, headers=headers, json=payload)
    if response.status_code != 200:
        raise Exception(f"Elicit Search failed: {response.text}")
        
    data = response.json()
    papers = data.get("papers", [])
    
    summary_md = f"# Literature Review: {query}" + chr(10) + chr(10)
    for paper in papers:
        title = paper.get("title")
        authors = ", ".join(paper.get("authors", []))
        year = paper.get("year", "N/A")
        abstract = paper.get("abstract", "")
        doi = paper.get("doi", "N/A")
        
        summary_md += f"## {title} ({year})" + chr(10)
        summary_md += f"**Authors:** {authors}" + chr(10)
        summary_md += f"**DOI:** {doi}" + chr(10) + chr(10)
        summary_md += f"### Abstract" + chr(10) + abstract + chr(10) + chr(10)
        
    temp_filename = "elicit-summary.md"
    with open(temp_filename, "w") as f:
        f.write(summary_md)

print("Research summary written to elicit-summary.md")

if __name__ == "__main__":
    run_research_workflow("Impact of machine learning on systematic literature reviews")

Point Hermes Agent at Streamable HTTP on https://mcp.fast.io/mcp, or https://mcp.fast.io/mcp/key when the client sends a Bearer token. This tools/call imports a source paper from a URL into the workspace:

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "upload",
    "arguments": {
      "action": "web-import",
      "url": "https://example.com/report.pdf",
      "profile_type": "workspace",
      "profile_id": "1234567890123456789"
    }
  }
}

Hermes uses the same MCP upload tool to write the generated markdown summary into that workspace so the team can open it in the web UI.

In the Hermes Agent ecosystem, capabilities are packaged as skills using the agentskills.io specification. You can create a custom skill by placing a SKILL.md file in a dedicated directory. The agent will discover this skill and run it automatically:

#~/.hermes/skills/elicit_research/SKILL.md
---
name: Elicit Research Summary
description: Automates scientific literature reviews by querying Elicit AI and uploading structured summaries to a Fastio workspace.
version: 1.0.0
---

Elicit Research Summary Skill

Instructions:
1. Read the research question from the user prompt.
2. Trigger the local Python execution script elicit_workflow.py with the research question.
3. Verify that the script successfully queries Elicit AI and parses the JSON response.
4. Save the output Markdown summary file to the configured Fastio workspace.
5. Generate a sharing link from the Fastio workspace and report the link to the user.

This skill configuration allows the Hermes Agent to recognize when a user asks for a literature search. It locates the Python script, passes the research query, and executes the code. The agent monitors the execution logs and handles errors such as rate limits or network timeouts before returning the final file path to the user.

When deploying autonomous research agents, selecting the storage backend is critical for team accessibility. Developers often write agent outputs to local storage, AWS S3, or Google Drive, but these alternatives have limitations. Local directories cannot easily span multiple server instances, making files inaccessible to other team members and vulnerable to container destruction. Storing research papers in AWS S3 buckets resolves the persistence issue, but S3 requires custom development to build a file explorer UI and does not support real-time co-editing. Google Drive provides a user-friendly interface but lacks native MCP tool integration, making it difficult for autonomous agents to read, write, or version documents without complex OAuth configuration.

Fastio provides a collaborative workspace platform designed for agentic teams. Instead of separating files into isolated databases, Fastio allows human team members and autonomous agents to work in the same shared workspaces. When your Hermes Agent saves an Elicit research summary to a Fastio folder, the file is immediately available in the web UI for human review.

The workspace offers three primary advantages for research workflows:

Per-File Version History: Every file uploaded to the workspace has a complete version history. If the Hermes Agent updates a summary with new Elicit results, the previous versions are preserved. Humans can inspect edits, compare changes, and restore older versions on demand.

Branded Shares for Distribution: Once the agent compiles a research report, it can generate a branded share link to send to clients or external colleagues. These shares can be durable or set to expire, and they support per-recipient access controls. This allows team members to revoke access on an individual basis if project requirements change.

Automated activity log: Fastio maintains an append-only audit log that records all file operations, including uploads, downloads, and co-editing events. This immutable audit log ensures a transparent chain of custody for all agentic and human modifications.

Automate your literature screening in one workspace

Store Elicit reports, run Hermes Agent skills, and co-edit summaries in a shared workspace with automatic versioning, full semantic search, and structured Metadata Views. Starts with a 14-day free trial.

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Downloading academic papers is only the first step in a research workflow. To conduct a systematic review, research teams must extract specific data points, such as study size, patient demographics, drug dosages, or statistical outcomes. Traditional document processing relies on manual data entry or rigid OCR rules that fail when paper layouts change.

Fastio simplifies this extraction through Metadata Views, turning a folder of unstructured academic PDFs into a live, queryable database. While Intelligence Mode handles semantic search and RAG-powered chat, Metadata Views serve as the structured extraction layer for your workspace. To configure schemas and automate document processing, developers can consult the Metadata Views product page for detailed guidelines.

To use Metadata Views in a Hermes Agent Elicit AI workflow:

1. Define the Schema in Natural Language: You describe the columns you want to extract in plain English. For example, you can write: "Extract the study methodology, the size of the participant sample, and the key findings."

2. AI-Designed Typed Schema: The workspace's AI analyzes your instructions and designs a typed schema. It maps the columns to seven supported field types: Text, Integer, Decimal, Boolean, URL, JSON, and Date & Time.

3. Smart Document Matching: The system scans the folder, identifies which files match the criteria, and extracts the values to populate a spreadsheet-like grid.

For a medical research project, a Hermes Agent can create a Metadata View over a collection of clinical trial papers downloaded from Elicit. The view can extract the drug name, the dosage administered (Decimal), whether a control group was used (Boolean), and the publication date (Date & Time). The resulting queryable data grid can be filtered or sorted, allowing agents to query results programmatically via the Fastio MCP server, and humans to review the structured findings in the browser.

A Hermes Agent Elicit AI workflow can be deployed to a remote Linux VPS, a Docker container, or serverless compute environments like Modal. Because the Hermes Agent runs as a persistent daemon, it requires a secure API key to authenticate with both Elicit and Fastio.

To set up the workspace for a client or research team, developers can follow the Fastio agent ownership transfer flow. Under this model, the agent starts by creating an account. The agent builds the required workspaces, configures the directories, and sets up the Metadata Views and shares. Once the setup is complete, the agent generates a claim link to transfer ownership of the organization to the human team leader.

When the human co-worker accepts the ownership transfer, they create or join an organization to start a 14-day free trial, which requires a credit card. The platform offers three pricing plans to accommodate different research scales:

Starter Plan: Designed for individual researchers at $29 per month, which includes 1 TB of storage and 300,000 monthly usage credits.

Business Plan: Designed for collaborative teams at $99 per month, supporting up to 20 seats, 10 TB of storage, and 1,200,000 monthly usage credits.

Growth Plan: Designed for large research institutions at $299 per month, supporting up to 50 seats, 50 TB of storage, and 4,500,000 monthly usage credits.

Once ownership is transferred, the human team has full administrative control over billing and membership, while the Hermes Agent retains its programmatic access through a scoped API key. If the agent encounters a connection failure, developers should check the local environment configurations. Ensure that the Elicit API key is correctly exported in the agent's shell, and verify that the Fastio MCP server connection is active by running /reload-mcp in the agent's chat interface.