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Use the Hermes adapter to run Nous Research Hermes Agent on the Charm platform. Hermes is a self-improving AI agent with built-in learning loops, cross-session memory (SQLite FTS5), and multi-provider LLM support.

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Manifest

For always-on agents with persistent memory:

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Best For

  • Agents that learn and improve across sessions.
  • Long-running daemon agents with persistent state.
  • Multi-provider LLM workflows (OpenRouter, OpenAI, Anthropic, local endpoints).
  • Agents needing built-in tools (terminal, browser, code search).

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How It Works

The adapter bridges Hermes’s AIAgent class to the Charm Runner:
  1. Persistence — HERMES_HOME is redirected to CHARM_WORKSPACE_DIR/.hermes, so SQLite session databases, FTS5 search indexes, auto-generated skills, and memory files all persist across daemon restarts.
  2. Streaming — Hermes’s stream_delta_callback is wired to Charm’s SSE emitter for real-time token output in the Store frontend.
  3. Headless mode — The agent runs with quiet_mode=True to suppress TUI spinners and terminal chrome.
  4. Tool tracking — Hermes’s tool_start_callback and tool_complete_callback feed into Charm’s tool usage analytics.

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Environment Variables

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Runtime Image

For production, use the pre-built Hermes runtime image:
This image pre-installs Hermes and its system dependencies (git, ripgrep, ffmpeg, sqlite3) on top of the Charm base image.

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Operational Notes

  • Use lifecycle: "daemon" for agents that need persistent memory across sessions.
  • Always set LLM API keys via charm.yaml environment_variables section, not hardcoded in source.
  • Test with charm validate before publishing.
  • The adapter is distributed as a separate pip package (charm-adapter-hermes). Install it in your runtime image or let the runner install it from requirements.txt.