Build a Self-Improving AI Agent Like Hermes with This Guide

Hermes Agent stands out as a breakthrough in autonomous AI systems by merging model-agnostic flexibility with a closed-loop learning process. Unlike conventional chatbots that rely on static prompts or predefined functions, Hermes actively refines its capabilities by documenting its own progress, storing persistent memory, and dynamically expanding its toolset. The result is an agent that doesn’t just answer questions—it grows smarter with every interaction.

This architecture isn’t built on complex prompt engineering; it’s rooted in procedural memory and modular design. Hermes treats skills, memory, and persona as living documents that it writes, reads, and evolves over time. By owning its learning artifacts, the agent transforms routine tasks into reusable expertise, making it uniquely adaptable for both personal and enterprise use cases.

How Hermes Agent Operates: Core Architecture Explained

At its heart, Hermes operates through a threaded, surface-agnostic design where a single AIAgent class powers all user interfaces. Whether accessed via CLI, messaging platforms like Discord, or a cron scheduler, the agent remains consistent in behavior while adapting to the interface’s constraints. This modularity ensures that platform-specific logic stays isolated, preventing code bloat and maintaining performance.

The architecture relies on three foundational pillars:

• Platform-agnostic core: Adapters translate platform events into standardized agent calls, ensuring the core logic remains unchanged regardless of the interface. For example, a Telegram message triggers the same agent.run_conversation() call as a CLI input.

• Prompt stability: The system prompt is assembled once per session and remains immutable during the conversation. This stability is critical for leveraging prompt caching in services like Anthropic or OpenAI, where mid-session updates can inflate costs by 10x.

• Progressive disclosure: Skills and tools are introduced incrementally. The agent first loads descriptions (Level 0), then full content (Level 1) only when needed, and finally referenced files (Level 2) as required. This approach allows Hermes to manage dozens of tools and skills without exceeding context limits.

The Agent Loop: Where Continuous Learning Begins

The agent loop is the engine of Hermes’ self-improvement. Every conversation follows a structured sequence:

1. Input reception: The agent receives a user query or platform event, which is normalized into a conversation object.
2. System prompt assembly: The prompt is generated by combining a base template with dynamic sections like skills, memory, and persona.
3. Skill selection: The agent identifies relevant skills from its registry, prioritizing those with the highest relevance scores.
4. Tool execution: The agent calls registered tools (e.g., web search, file operations) to gather information or perform actions.
5. Response generation: The agent synthesizes results into a coherent answer or action.
6. Memory update: The agent appends new insights to its persistent memory, documenting what it learned or solved.
7. Skill generation: If the task required novel problem-solving, the agent writes a new skill document, which becomes part of its toolkit for future use.

This loop ensures that every interaction contributes to the agent’s long-term competence, effectively turning routine tasks into institutional knowledge.

Skills System: The Secret to Autonomous Growth

Hermes’ skills system is its most transformative feature, enabling the agent to document and reuse its problem-solving prowess. A skill is essentially a markdown document that outlines:

• The problem it solves
• The steps taken to resolve it
• The tools used
• The outcome achieved

Skills are dynamically triggered based on user queries or contextual relevance. For example, if a user asks, "How do I back up my database?," Hermes may invoke a pre-written skill that guides the user through the process, or it may generate a new skill if the query is novel.

Key features of the skills system include:

• Conditional activation: Skills can include conditions (e.g., "only activate if the user mentions PostgreSQL") to avoid irrelevant suggestions.

• Self-improvement loop: The skill_manage tool allows the agent to edit, archive, or delete skills based on performance or user feedback. This ensures the skills repository remains high-quality and up-to-date.

• Sharing and collaboration: Skills can be exported and shared via a central hub, enabling communities to contribute to and benefit from a collective knowledge base.

Memory Management: Building Long-Term Intelligence

Hermes employs a multi-layered memory system to ensure continuity across sessions and interactions. Its design balances persistence with flexibility:

• Frozen-snapshot memory: Critical learnings are archived into immutable markdown files, preserving key insights for future reference.

• SessionDB for recall: Short-term memory is stored in a SQLite database, allowing the agent to reference recent interactions and user preferences without cluttering the system prompt.

• Pluggable memory providers: Hermes supports integrations with external memory services like Honcho, mem0, or supermemory, enabling scalability for enterprise deployments.

Tools and Plugins: Extending Functionality Safely

Hermes’ tools system is designed for extensibility without sacrificing safety. Tools are self-registering modules that integrate seamlessly with the agent’s workflow. The system includes:

• A registry pattern: New tools are added by simply placing a Python file in the tools directory, where they automatically register themselves via registry.register().

• Execution environments: Tools can run in isolated environments (e.g., Docker containers) to prevent security risks.

• Layered defense: Approval workflows ensure tools are vetted before activation, with user confirmation required for sensitive operations.

• MCP integration: Hermes supports Model Context Protocol for interoperability with other AI-native tools and services.

Building Your Own Hermes-Style Agent: A Practical Checklist

Creating a self-improving agent like Hermes requires a methodical approach. Follow this phased roadmap to avoid common pitfalls:

Phase 1: Core Loop Setup (Days 1–2)

• Define the agent loop structure, including input normalization and response generation.
• Implement a basic conversation handler in Python or your preferred language.
• Test the loop with static prompts to ensure stability.

Phase 2: CLI Interface (Day 3)

• Build a minimal CLI using libraries like click or argparse.
• Integrate the agent loop into the CLI’s main loop.
• Validate user input parsing and response formatting.

Phase 3: Tools Registry (Days 4–5)

• Design a plugin system for tools, using a registry pattern.
• Implement a self-registering mechanism for new tools.
• Add 3–5 basic tools (e.g., file I/O, web search) to test the workflow.

Phase 4: Memory and Persona (Day 6)

• Create a memory system using markdown files or a SQLite database.
• Implement persona management to customize the agent’s tone and expertise.
• Test memory persistence across sessions.

Phase 5: Skills System (Days 7–10)

• Develop a skills registry and trigger mechanism.
• Write 5–10 skills manually to validate the system.
• Implement the skill_manage tool for dynamic updates.

Phase 6: Prompt Caching Optimization (Day 11)

• Ensure system prompts are assembled once per session.
• Test caching with services like Anthropic or OpenAI to measure cost savings.

Phase 7: Multi-Surface Integration (Days 12+)

• Extend the agent to support messaging platforms (e.g., Discord, Slack).
• Implement a cron scheduler for automated tasks.
• Design a web UI for broader accessibility.

Phase 8: Advanced Integrations (Day 14+)

• Add MCP support for interoperability.
• Implement multimodal capabilities (e.g., image or audio processing).
• Explore RL/Atropos training for fine-tuning.

Recommended Tech Stack for Your Build

Hermes is framework-agnostic, but certain technologies align well with its design:

• Language: Python (for ease of integration and library support).
• Memory: SQLite for lightweight persistence, with optional integrations for Honcho or mem0.
• Tools: Requests for HTTP calls, BeautifulSoup for web scraping, and Docker for safe execution environments.
• UI: CLI with click, TUI with textual or rich, and web UIs with FastAPI.
• Testing: Pytest for unit tests and promptfoo for prompt evaluation.

Key Pitfalls to Avoid

Even with a robust architecture, common mistakes can derail your build:

• Overloading the system prompt: Avoid including all tools and skills in the prompt. Use progressive disclosure to manage context limits.
• Ignoring prompt stability: Mid-session prompt changes can invalidate caches and spike costs. Batch updates for the next session.
• Hardcoding paths: Always use get_hermes_home() to avoid breaking multi-instance setups.
• Skipping tool approval: Implement layered defenses to prevent unauthorized or risky tool executions.
• Neglecting memory management: Poor memory design leads to bloated prompts or lost context. Plan for scalability early.

The Future of Self-Improving Agents

Hermes Agent represents a paradigm shift in AI autonomy, proving that agents can evolve beyond static functionality. As tools like MCP and pluggable memory systems mature, the potential for these agents to integrate seamlessly into workflows—whether for personal productivity, enterprise automation, or collaborative problem-solving—will only grow. The next frontier lies in collaborative learning, where agents share skills and memory across organizational boundaries, creating a network of ever-improving intelligence.

For developers, the opportunity is clear: build systems that don’t just assist but learn alongside their users. The tools and principles behind Hermes provide a blueprint for that future.