Hermes Memory
A memory layer for agent-powered teams: searchable context, relationship graphs, workflow status, and an operational view of what an AI system knows.
See what the agent remembers.
Persistent agent memory becomes difficult to inspect when conclusions, relationships, and background processing are scattered across an API. Hermes Memory brings those views into the existing agent dashboard.
04 / Technologies & context
- Python
- FastAPI
- React
- PostgreSQL
Inside the project
Implementation
A FastAPI router connects the dashboard to Honcho. The implementation can aggregate directly from PostgreSQL, with a paginated HTTP fallback. The frontend uses the host's React and UI primitives, while peers and relationship counts come from current memory rather than fixed examples.
The workflow
- Inspect the memory pipeline and relationship graph
- Filter facts by peer, level, and search query
- Ask memory questions or request consolidation
Scope & status
The plugin depends on a configured Hermes/Honcho environment. Chat and dream scheduling are explicit write actions. Repository documentation and source differ on caching details, so no latency or scale guarantees are claimed here.