5 Developer Projects to Build Using Hermes Agents

TL;DR

• Build practical software engineering tools using Hermes Agents to master autonomous coding workflows.
• Discover how to connect your intelligent agent to GitHub APIs, AST parsers, and vector databases for true codebase mastery.
• Learn how these application software projects perfectly align with the skills needed to build robust workflows using Hermes Agents.
• Understand the architecture behind multi-agent DevOps teams and self-improving memory systems.

Software Engineering with Intelligent Agents

If you want to master AI development, you need practical experience building real systems with Hermes Agents. Theoretical knowledge is a good starting point, but you must construct actual application software to understand how an intelligent agent interacts with APIs, memory states, and sandbox environments. The self-improving AI ecosystem built by Nous Research provides a perfect foundation for this, which means you can use Hermes Agent to handle complex multi-step workflows right out of the box.

Once you install the platform, you can initiate a baseline environment by running a simple setup command.

# Easiest path using the Nous Portal for instant API access
hermes setup --portal

The moment this setup completes, your system is ready to connect to external developer tools. The following list details five specific projects you can start building today. We will explore how each concept connects to modern open-source repositories, so you can leverage community knowledge while building advanced intelligent systems.

1. 🤖 Autonomous GitHub Developer

Build an agent that can actually work on a GitHub repository instead of just answering coding questions. You give it an issue like "Fix the authentication bug", and Hermes explores the codebase, finds the relevant files, writes the code, runs tests, fixes errors, and creates a PR.

Tech: Hermes Agents + GitHub API + Docker + test runner + LLM Cool part: It can work in a loop - Understand → Code → Test → Debug → PR.

When you build an autonomous GitHub developer, you are essentially deploying an automated software engineering team. You can take inspiration from the CodeForge Agent, which wraps the OpenHands runtime into an isolated Docker sandbox. Because executing unverified code on your local machine is dangerous, Docker isolation is an absolute necessity. You can also explore DOT AI, which utilizes a GitHub App integration to listen for repository webhooks and trigger a secure sandbox execution queue.

// Example configuration for a sandbox queue in your agent
export const sandboxConfig = {
  executionMode: process.env.OPEN_SWE_DOCKER_MODE === "true" ? "docker" : "local",
  concurrencyLimit: 5,
  image: "open-swe-sandbox:latest",
  githubAppId: process.env.GITHUB_APP_ID,
};

2. 🔍 AI Codebase Investigator

Give Hermes a large GitHub repository and let it understand the entire project. You could ask things like "Where is user authentication handled?" or "What will break if I change this database schema?"

The agent explores files, follows imports and dependencies, searches Git history, and builds a map of how different parts of the code connect.

Tech: Hermes Agents + AST parsing + Git + embeddings/RAG Cool part: Think of it as an AI engineer who knows the entire codebase.

Simple text-based retrieval often fails in complex application software, because traditional chunking splits functions right down the middle. That is why projects like Codebase Expert and RAGED utilize Abstract Syntax Tree parsing via Tree-sitter. By parsing the code into an AST before chunking, the intelligent agent preserves function and class boundaries. You can use the RAG Codebase Indexer pattern to create vector embeddings specific to code logic, and then store those vectors in a graph database for semantic lookup.

# Initialize an AST-aware graph database for your project
ast-rag init /path/to/your/project
# Query the intelligent agent for specific definitions
ast-rag query "API request handling logic"

3. 🧪 Autonomous Testing Agent

Build an agent that finds bugs before developers do. Hermes looks at the code, understands what the functions are supposed to do, generates test cases, runs them, analyzes failures, and creates additional tests for edge cases.

For example, it could notice that a function works for normal inputs but breaks when the input is empty, extremely large, or malformed.

Tech: Hermes Agents + pytest/Jest + Docker + GitHub Cool part: Make it continuously test every new commit and automatically report problems.

Testing is a repetitive task that developers frequently rush, so automating this layer provides massive value. You can configure your agent to monitor continuous integration pipelines, and then automatically generate fixes when jobs fail. The Claude Code GitHub Agent demonstrates this perfectly by hooking into over forty GitHub webhook events. The moment a CI job fails, the agent pulls the failure logs, identifies the root cause, and pushes a patch branch without any human intervention.

4. 🔄 Multi-Agent DevOps Engineer

Create a team of agents that manages the software deployment process.

One agent checks the code and PR, another checks dependencies and security, another analyzes CI/CD failures, and another monitors deployment logs. They share their findings and work together to figure out what went wrong.

Example: Code Agent → Security Agent → Test Agent → Deployment Agent → Monitoring Agent

Cool part: Instead of one huge agent, you build an actual AI DevOps team with different responsibilities.

Multi-agent orchestration is a core competency when building with Hermes Agents. Instead of relying on one massive language model to do everything, you separate concerns into specialized roles. The DevAutoPilot platform provides inspiration for implementing this exact architecture, which you can adapt using Hermes Agents. You can complement this with workflow generation patterns seen in CI-Copilot, which allows developers to build entire deployment pipelines through natural language conversations.

5. 🧠 Self-Improving Developer Agent

Build a coding agent that learns from its previous work. Every time Hermes solves an issue, it stores what happened - which files were changed, what errors occurred, which solution worked, and what tests were needed.

Later, when a similar issue appears, the agent retrieves those previous solutions before starting from scratch.

Tech: Hermes Agents + GitHub + vector database/RAG + code embeddings Cool part: Over time, your agent develops its own engineering memory instead of treating every task as completely new.

Continuous learning separates a basic script from a truly intelligent agent. When an agent solves a complex bug, it needs to distill that experience into a reusable format. The Tilsley Agents repository features a "distiller" agent that summarizes pull requests and failure resolutions into markdown-based memory files. Hermes natively supports procedural memory through its built-in learning loop, which means the agent will automatically nudge itself to persist knowledge as it assists you throughout the day.

Practical Application Software Takeaways

To successfully build these projects with Hermes Agents, you must understand how to architect these systems efficiently. Since these architectures involve many moving parts, I have provided a comparison table of the distinct intelligent agent strategies you will need to master.

How do these projects help you master Hermes Agents?

These projects test your ability to design scalable, secure, and intelligent software solutions. By building an autonomous GitHub developer or a multi-agent DevOps team with Hermes, you are forced to learn identity management, secure sandbox provisioning, and token optimization. The whole time you are troubleshooting an AST parser, you are actually learning how to preprocess data for large language models. These hands-on challenges ensure that you comprehend the underlying mechanics of AI applications rather than just memorizing API endpoints.

What are the best application software patterns for an intelligent agent?

The best patterns rely on strict separation of duties and asynchronous execution. You should always use an orchestration layer to validate webhooks before passing payloads to your LLM. Furthermore, your intelligent agent must rely on deterministic tooling for critical operations. It should generate the plan using probabilistic models, but it must execute tests using standard runners like Jest or Pytest to guarantee reliability.

Frequently Asked Questions