How to Connect Hermes Agent to Elicit AI

According to a 2026 system administration survey, 43% of developers deploying autonomous tools local to their workstations experience installation blockages due to missing binary libraries rather than application code errors [TechJack 2026 Guide]. The gap between a running test script and a production deployment is where this guide operates. Specifically, only 9% of open-source agent frameworks feature persistent memory architectures or structured methods to save learned routines as reusable skills [Nous Research System Review 2026]. Connecting the Nous Research Hermes Agent to Elicit AI bridges this gap. It combines persistent memory with direct access to academic data.

Elicit indexes over 200 million academic papers, allowing researchers to run semantic searches and retrieve structured metadata [Elicit API Documentation]. The Hermes Agent can parse and summarize literature search outputs dynamically, storing the results in its persistent memory to guide future task cycles. Because Elicit AI is not officially supported or mentioned in Hermes Agent documentation, building this connection requires developers to configure a custom integration path.

This guide provides the instructions to build this connection. We will cover how to write a custom cURL skill using the agentskills.io standard and how to configure Elicit as a remote HTTP server using the Model Context Protocol. Finally, we will show how to direct the agent's outputs to a shared, persistent workspace where humans and agents collaborate. This setup creates a unified environment where search results are immediately available to the entire team, reducing the manual steps required to move from research to written documentation.

Developers can connect the Nous Research Hermes Agent to Elicit AI using two primary architectures: a custom skill or the Model Context Protocol. Each method has distinct characteristics for workflow control, security, and integration speed.

The custom skill approach relies on the agentskills.io open standard, which Hermes Agent supports natively. A skill is a local folder containing instructions and scripts that the agent loads dynamically. The agent decides when to run the skill based on the task description. This method is suitable when you need to parse search results using custom Python scripts or filter payloads before storing them. It allows developers to write custom wrappers for Elicit's REST API and run them as local CLI processes.

The Model Context Protocol approach uses a standardized connection to expose Elicit's tools directly to the agent. Because Elicit provides an official Model Context Protocol server, developers can configure it as a remote HTTP server in the agent's settings. Once configured, all Elicit tools become native capabilities within the agent's thought loop. This method is faster to set up and requires no custom code, but it provides less control over how queries are structured and how errors are handled. Both methods enable the agent to find papers and extract data without manual browser sessions. Choosing between them depends on whether your workflow requires custom preprocessing of academic metadata.

To build a custom skill, you must create a structured directory in the agent's local skills path. The folder path is ~/.hermes/skills/elicit_search. Inside this directory, you will write a SKILL.md file containing instructions and metadata, along with a script to make the API requests.

The featured snippet strategy for this integration is to provide a clean bash command template showing how to write a custom cURL wrapper skill for the Elicit API. You can create the directory and write the instruction file using the following command sequence:

mkdir -p ~/.hermes/skills/elicit_search

cat << 'EOF' > ~/.hermes/skills/elicit_search/SKILL.md
---
name: elicit_search
description: Search academic literature via Elicit API and summarize results
version: 1.0.0
author: Developer
---

### Elicit Search Skill

Use this skill when the user asks to find academic papers or summarize research findings.

#### Instructions
1. Retrieve the search query from the user prompt.
2. Call the Elicit search endpoint using the local python script.
3. Parse the JSON response to extract paper titles, authors, and abstracts.
4. Write a formatted summary to a collaborative note in the workspace.
EOF

Next, write the supporting Python script to manage the API communication. The script handles the Bearer token authorization and queries Elicit's v2 paper search endpoint. Create a file named search.py in the same directory:

import os
import sys
import json
import urllib.request

def query_elicit(query_text, limit=5):
    api_key = os.environ.get("ELICIT_API_KEY")
    if not api_key:
        print("Error: ELICIT_API_KEY environment variable is not set.")
        sys.exit(1)
        
    url = "https://api.elicit.com/v2/search/papers"
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }
    
    payload = {
        "query": query_text,
        "limit": limit
    }
    
    req = urllib.request.Request(
        url,
        data=json.dumps(payload).encode("utf-8"),
        headers=headers,
        method="POST"
    )
    
    try:
        with urllib.request.urlopen(req) as response:
            return json.loads(response.read().decode("utf-8"))
    except Exception as e:
        print(f"API Request failed: {e}")
        sys.exit(1)

if __name__ == "__main__":
    if len(sys.argv) < 2:
        print("Usage: python search.py '<search query>'")
        sys.exit(1)
    results = query_elicit(sys.argv[1])
    print(json.dumps(results, indent=2))

To run this skill, save your Elicit API key to your environment variables using export ELICIT_API_KEY="your_key". In your chat session, type /skills load elicit_search or direct the agent to run the search. The agent executes the script, parses the literature search outputs dynamically, and returns the summarized results. The persistent memory loop ensures that the findings from this execution are retained for future research cycles, preventing duplicate API calls for identical topics. For more information on writing custom skills, check the Nous Research Hermes Agent skill docs.

If you prefer a direct connection without writing custom scripts, you can register Elicit's Model Context Protocol server. The Hermes Agent can connect to remote servers over Streamable HTTP. This protocol allows the agent to discover and invoke tools dynamically.

To configure the server, open the agent's main configuration file, located at ~/.hermes/config.yaml on macOS or Linux, or %LOCALAPPDATA%\hermes\config.yaml on Windows. Add the Elicit server details under the mcp_servers key:

mcp_servers:
  elicit:
    url: "https://elicit.com/api/mcp"
    headers:
      Authorization: "Bearer YOUR_ELICIT_API_KEY"
    enabled: true

Replace YOUR_ELICIT_API_KEY with your actual credential. If you prefer to manage configurations via the terminal, run the interactive command TUI:

hermes mcp

This TUI displays all configured tools and servers, allowing you to add, enable, or disable connections. Once configured, run hermes doctor to verify that the path is registered and that the client can communicate with the remote host. The agent will now display Elicit's research tools in its default tool palette, enabling it to execute literature searches during its reasoning cycles. Unlike standard local processes that use stdio streams, this connection runs over Streamable HTTP, allowing you to run the agent locally while accessing Elicit's server over the network securely.

When running an autonomous agent, managing the data output is a primary challenge. Local storage on a workstation or a virtual server works for individual tasks, but it leads to data isolation. Standard cloud storage options, such as S3 buckets or Google Drive, hold files but lack the intelligence required to index academic research. Fastio solves this issue by providing shared workspaces where humans and agents collaborate.

Fastio is an intelligent workspace platform. When you create a workspace, you can enable Intelligence Mode. This automatically indexes all uploaded files (including PDFs, notes, and datasets) for semantic search and RAG chat. The Hermes Agent can read and write files using Fastio's Model Context Protocol server, which exposes Streamable HTTP at /mcp and legacy SSE at /sse. This allows the agent to save its literature reviews directly to a shared space. More information on tool-surface integration is available in the Fastio MCP server guide.

Once the research papers are in the workspace, you can use Metadata Views to turn them into a queryable database. Metadata Views use AI-powered extraction to read documents and populate a spreadsheet. For example, you can define columns like "Publication Year", "Sample Size", "Methodology", and "Primary Finding" in plain English. The platform designs the typed schema and extracts the data from all PDF files automatically, without manual OCR rules. This differentiates it from Intelligence Mode, which is designed for conversational search and summarization. Learn more about document data extraction on the Fastio metadata extraction product page.

Humans and agents can co-edit notes in real time using Collaborative Notes. In these documents, both the researcher and the Hermes Agent are first-class editors with visible cursors. When the work is complete, you can transfer ownership of the organization from the agent account to a human manager via a claim link. Fastio has a credit-based billing system: the Starter plan costs $29/month, the Business plan is $99/month, and the Growth plan is $299/month. Every organization starts with a 14-day free trial, which requires a credit card. There is no permanent free plan. You can view plan structures on the Fastio pricing page. When your agent sets up a new workspace, it can run its initial flows during the trial, allowing you to verify the entire pipeline before committing to a plan.

Persist Hermes Agent research files across sessions

Deploy a shared workspace with a Model Context Protocol server endpoint for your agent's reads and writes, complete with version history and semantic search. Starts with a 14-day free trial.

Start 14-Day Trial

Deploying this pipeline in production requires managing API rate limits and handling asynchronous tasks. Elicit's search endpoints are subject to rate limits based on your subscription tier. If the agent makes too many requests, the API returns an HTTP 429 status code. Your wrapper scripts must check for this status and implement retry logic with exponential backoff.

In addition, Elicit's automated report generation is asynchronous. When you request a systematic review or a report, the API does not return the results immediately. Instead, you send a POST request to https://elicit.com/api/v1/reports and receive a report ID. The agent must then poll the status endpoint at https://elicit.com/api/v1/reports/{report_id} using a scheduled loop. The agent should write a placeholder task in the workspace, poll the API every 30 seconds, and update the task status once the report is complete.

Finally, clean the raw JSON payloads before passing them to the language model. Academic search outputs often contain verbose metadata, which can consume unnecessary tokens. Configure your custom skill to extract only the title, primary authors, publication year, and abstract from the Elicit response. Saving this clean data to a Collaborative Note keeps your token usage efficient while preserving the details needed for the final literature review. This practice reduces the risk of hitting model context limits during long research loops.