Hermes: The Self-Improving Agent That Gets Smarter Every Day

Highlights

• Hermes is an open-source AI agent that builds its own skills and automatically manages a long-term memory system to improve performance over time.

• The system stores persistent memory in external files like memory.md or services such as Mem0, pre-loading a compacted 700-token summary at the start of each session.

• Full session histories are archived in an SQLite database using FTS5 for efficient full-text searches of past interactions.

• Context window management triggers at 50% capacity by removing old tool outputs and compressing the middle of the conversation while preserving the head and tail.

• Users can swap LLM models mid-session using a simple slash command to balance speed, cost, and reasoning quality.

• The agent self-nudges every 10 turns to identify and extract important information for permanent storage or new skill creation.

Timeline

1. 00:00 - 01:29

   Core Capabilities and Comparison

   The system distinguishes itself through autonomous evolution where every interaction contributes to a permanent knowledge base. It targets developers who find existing agent SDKs too restrictive or feature-heavy for specialized tasks like content creation and writing style mimicry.

   • Hermes is a self-evolving AI agent developed by News Research that learns from user feedback and creates its own tools.
   • The agent serves as an alternative to OpenClaw and NanoClaw for users seeking local execution or independence from specific subscription rules.
2. 01:30 - 02:07

   Installation and Environment Setup

   The setup process mirrors the workflow of OpenClaw, allowing users to connect to local hardware or cloud-based model providers. Initial execution displays available tools and prompts the user to start a session that can either be fresh or a continuation of previous work.

   • Installation requires a single CLI command and supports various backend models including Gemma 2 or GLM-4 via OpenRouter.
   • The agent runs locally or on a Virtual Private Server (VPS) to ensure data privacy and environment control.
3. 02:08 - 03:22

   Style Adaptation and Model Swapping

   During a test run, the agent analyzed video scripts to accurately identify a transparent and relatable brand voice. When high context usage slowed the response time, switching to a faster model like GLM-4 Turbo allowed the task to proceed without losing the established stylistic parameters.

   • Hermes analyzes local file directories to identify specific writing patterns, such as a pragmatic or developer-centric tone.
   • The model slash command enables immediate switching between different LLMs within a single active session.
4. 03:23 - 04:18

   Skill Creation and Autonomous Memory

   After refining a tweet draft, the agent saved the final preferences to its memory and generated a reusable skill for future tweet threads. This eliminates the need for repetitive prompting, as the agent recalls specific vocabulary constraints and formatting rules across separate sessions.

   • The Skill Manager tool converts repeated feedback and instructions into permanent executable skills for future use.
   • New sessions retain specific preferences like emoji usage and character counts even after the original conversation ends.
5. 04:19 - 06:11

   Technical Architecture of Memory and Compression

   To prevent context bloat, Hermes employs a specialized compression logic that is more aggressive than competing tools like Claude Code. By pre-fetching a 700-token summary and utilizing an external database for deep retrieval, the agent maintains high relevance without hitting token limits.

   • Memory management utilizes a compacted version of the memory.md file, limited to roughly 3,500 characters to optimize token usage.
   • Context compression occurs at 50% window utilization by purging old tool call results while retaining the start and end of the dialogue.
   • An SQLite database provides the backbone for long-term storage, enabling the agent to retrieve facts from previous days through full-text search.