Building a Self-Improving AI Agent with Hermes and Obsidian

Overview

This course explores how to create an AI agent that continuously improves itself by integrating with a personal knowledge base. By leveraging Hermes (a large language model) and Obsidian (a knowledge management tool), you can build an agent that not only reasons using your existing notes but also writes new insights back into your vault. This creates a feedback loop where the AI agent becomes more effective over time as it accumulates more knowledge and context. This approach is particularly valuable for researchers, writers, and professionals who want to augment their workflow with AI while maintaining control over their data.

Background & Context

The concept of self-improving AI agents has gained traction as large language models (LLMs) become more capable. Traditional AI agents operate in isolation, but modern approaches emphasize integration with external knowledge bases to enhance reasoning and context awareness. Obsidian, a popular markdown-based knowledge management tool, is well-suited for this purpose due to its flexibility and plugin ecosystem. The Filesystem MCP (Message Control Protocol) allows Hermes to interact with Obsidian's vault as if it were a filesystem, enabling seamless read and write operations. This setup is inspired by the broader trend of "AI-augmented cognition," where AI systems work alongside humans to enhance productivity and creativity.

Core Concepts

Large Language Models (LLMs) and AI Agents

Large language models like Hermes are trained on vast amounts of text data and can generate human-like responses to prompts. When used as AI agents, they can perform tasks such as reasoning, decision-making, and content generation. However, their effectiveness is limited by the context they have access to during inference. By integrating an LLM with a personal knowledge base, you can provide it with domain-specific information, improving its ability to generate relevant and accurate outputs.

Obsidian and Knowledge Management

Obsidian is a knowledge management tool that allows users to create and link notes in a markdown-based environment. Its graph view and backlinking features make it ideal for building interconnected knowledge bases. By using Obsidian as the external memory for an AI agent, you can leverage its organizational capabilities to structure and retrieve information efficiently. This integration turns the AI agent into a dynamic extension of your personal knowledge base.

Filesystem MCP (Message Control Protocol)

The Filesystem MCP is a protocol that enables communication between an AI agent and a filesystem. In this context, it allows Hermes to read from and write to your Obsidian vault as if it were a local filesystem. This means the AI agent can pull relevant notes during reasoning and write new insights back as markdown files. The MCP handles the serialization and deserialization of data, ensuring smooth interaction between the AI agent and the knowledge base.

Self-Improving Feedback Loop

The core innovation in this setup is the self-improving feedback loop. Every time the AI agent reasons about a problem, it pulls relevant information from the Obsidian vault. After generating an output, it writes the result back as a new note. Over time, this process accumulates more knowledge in the vault, making the AI agent more effective in subsequent interactions. This loop is similar to how humans learn and improve through experience, but it operates at the speed of an AI system.

How It Works / Step-by-Step

Step 1: Setting Up the VPS

To host Hermes and the Filesystem MCP, you need a Virtual Private Server (VPS). A VPS provides the necessary computing resources and network connectivity to run the AI agent continuously. Popular VPS providers include DigitalOcean, Linode, and AWS. Choose a plan that meets the computational requirements of your LLM and the storage needs of your Obsidian vault.

Step 2: Installing and Configuring Hermes

Hermes is a large language model that can be deployed on your VPS. You will need to install the necessary dependencies and configure the model to run as a service. This typically involves setting up a Python environment, installing the required libraries, and downloading the model weights. Hermes can be fine-tuned or used as-is, depending on your specific needs.

Step 3: Integrating Obsidian with Filesystem MCP

To enable communication between Hermes and your Obsidian vault, you need to set up the Filesystem MCP. This involves installing the MCP client on your VPS and configuring it to connect to your Obsidian vault. The vault can be stored locally on the VPS or accessed via a network share. The MCP client will handle the read and write operations, allowing Hermes to interact with the vault seamlessly.

Step 4: Configuring the AI Agent

Once Hermes and the Filesystem MCP are set up, you need to configure the AI agent to use the vault as its external memory. This involves defining the prompts and instructions that guide the agent's reasoning process. For example, you might instruct the agent to pull relevant notes from the vault before generating a response and to write the output back as a new note. The configuration can be adjusted based on the specific tasks you want the agent to perform.

Step 5: Testing and Iterating

After setting up the AI agent, it's important to test its performance and iterate on the configuration. Start with simple tasks and gradually increase the complexity as you refine the agent's behavior. Monitor the notes generated by the agent and provide feedback to improve its reasoning and output quality. Over time, the agent will become more effective as it accumulates more knowledge in the vault.

Real-World Examples & Use Cases

Research Assistance

Imagine you are conducting research on a complex topic. Your AI agent can pull relevant notes from your Obsidian vault, analyze the information, and generate insights or summaries. As you continue your research, the agent writes new notes back into the vault, creating a growing body of knowledge that informs future reasoning. This can significantly speed up the research process and help you uncover new connections between ideas.

Content Creation

For writers and content creators, the AI agent can serve as a collaborative partner. It can pull relevant notes and drafts from your vault, generate new content based on your instructions, and write the output back as a new note. Over time, the agent becomes more attuned to your writing style and the topics you cover, making it an invaluable tool for content creation.

Personal Knowledge Management

Even outside of specific tasks, the AI agent can help you manage and organize your personal knowledge. It can pull notes from your vault, identify patterns and connections, and generate new insights or summaries. By writing these insights back into the vault, the agent helps you build a more comprehensive and interconnected knowledge base.

Key Insights & Takeaways

Integration is Key: By integrating an AI agent with a personal knowledge base, you can enhance its reasoning and context awareness.
Continuous Improvement: The self-improving feedback loop allows the AI agent to become more effective over time as it accumulates more knowledge.
Flexibility and Control: Using a VPS and a local knowledge base gives you control over your data and the ability to customize the AI agent to your needs.
Practical Applications: The setup can be applied to various domains, including research, content creation, and personal knowledge management.
Iterative Development: Testing and iterating on the AI agent's configuration is crucial for optimizing its performance and ensuring it meets your specific requirements.

Common Pitfalls / What to Watch Out For

Data Privacy: Ensure that your Obsidian vault and the AI agent are properly secured to protect your personal knowledge.
Model Limitations: Be aware of the limitations of the large language model and the potential for biases or inaccuracies in its outputs.
Configuration Complexity: Setting up the AI agent and integrating it with the knowledge base can be complex, requiring a good understanding of the tools and protocols involved.
Resource Requirements: Running an LLM on a VPS can be resource-intensive, so choose a plan that meets your computational needs.
Feedback Loop Management: Monitor the notes generated by the AI agent to ensure they are accurate and relevant, and provide feedback to improve its performance.

Review Questions

Core Concepts: Explain the role of the Filesystem MCP in integrating an AI agent with a personal knowledge base.
Process Understanding: Describe the steps involved in setting up an AI agent with Hermes and Obsidian, and how they interact to create a self-improving feedback loop.
Application: How can an AI agent integrated with a personal knowledge base assist in research tasks, and what are the potential benefits and challenges of this approach?

Further Learning

Advanced LLM Techniques: Explore fine-tuning and prompt engineering to enhance the performance of your AI agent.
Knowledge Graphs: Learn about knowledge graphs and how they can be used to structure and retrieve information in a knowledge base.
AI-Augmented Cognition: Study the broader field of AI-augmented cognition and its applications in various domains.
Security and Privacy: Delve into best practices for securing your personal knowledge base and protecting your data.
VPS Management: Gain a deeper understanding of VPS management and optimization to ensure your AI agent runs efficiently.

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