Tool
Hermes Python API Client — Programmatic Agent Control
Python client library for programmatic interaction with Hermes Agent. Send messages, manage memory, trigger skills, and build automations.
Quick answer
A Hermes Python API client lets you drive the agent programmatically: send messages, manage memory, and trigger skills from your own code with async support. It is how you build automations on top of Hermes rather than only chatting with it through the CLI or a messaging channel.
When you want Hermes inside your own software — not just a chat window — a Python client gives you programmatic access to messages, memory, and skills, with async support for real workloads.
Features
- ✓Python SDK
- ✓Async support
- ✓Memory API
- ✓Skill triggers
Why this tool matters
A client library moves Hermes from interactive tool to building block. You can wire it into a backend, a job runner, or another app, sending messages and triggering skills as part of a larger automation rather than typing in a terminal.
Async support matters because agent calls are slow and bursty. Non-blocking calls let you fan out work, handle webhooks, and keep a service responsive while the model and tools run.
Programmatic memory access pairs naturally with this: your code can seed context before a run and read back what the agent stored after, treating Hermes memory as part of your data flow instead of a black box.
If your goal is event-driven triggers rather than full SDK control, a webhook bridge is the lighter path. Reach for the API client when you need fine-grained, in-process control over the agent.
Best use cases
FAQ
Drive the agent from your own code: send messages, manage memory, and trigger skills programmatically with async support — so Hermes becomes a building block in a larger app rather than an interactive tool.
Use a webhook bridge for simple event-driven triggers. Reach for the API client when you need fine-grained, in-process control over messages, memory, and skills.
Yes — programmatic memory access lets your code seed context before a run and read back what the agent stored after, treating memory as part of your data flow.