Memori

Engineering

Written by Memori Team

Memori Labs releases its integration with Hermes Agent, giving agents long-term persistent memory

We released our integration with the Hermes Agent framework by Nous Research. The new Memori plugin gives Hermes agents long-term persistent memory that captures not only conversation, but also agent trace and execution context. As Hermes completes work, Memori can structure memory from the agent's actions: tool calls, workflow steps, assistant decisions, outcomes, and more.

This means Hermes can remember what happened during prior executions, not just what was said in the transcript. Instead of stuffing old conversation history into every prompt, Hermes can retrieve the structured context it needs to continue work, avoid repeated mistakes, preserve project knowledge, and improve across sessions.

The new Hermes plugin introduces these features

  • Structured, persistent memory for AI agents — Memori replaces flat markdown memory files with a structured knowledge graph that captures facts, decisions, outcomes, and patterns across every session — without bloating the prompt.
  • Grounded in what agents actually do, not just what they say — Memori captures tool calls, execution traces, and real-time agent decisions alongside conversation, giving agents a fuller picture of prior task execution.
  • Agent-controlled intelligent recall — Agents decide when and what to retrieve, scoped precisely by project, session, entity, or time range — eliminating irrelevant context and cross-project noise.
  • Automatic memory building, zero latency impact — Memory is structured and updated asynchronously after each interaction, so it never slows the agent's response.
  • Smarter daily briefs — Memori generates structured daily briefings built from execution traces and structured memory — covering priorities, risks, active goals, open loops, and known failure patterns — far beyond a simple conversation recap.
  • Built for multi-user, multi-project environments — Memory is fully scoped and isolated by project, process, session, and entity, preventing data bleed across users and contexts.
  • Production-ready observability — Full visibility into memory creation, recall activity, retrieval performance, and quota usage via Memori Cloud.

Set up takes 2 minutes for existing Hermes users

It requires Python 3.10 or later. Developers can sign up for a free API key at https://app.memorilabs.ai.

1. Install the plugin

pip install hermes-memori

2. Configure your Memori API key and Entity ID

Option A: Via CLI (Recommended)

hermes memory setup

Select memori, then enter your Memori API key and Entity ID.

Option B: Manual configuration

hermes config set memory.provider memori
HERMES_HOME="${HERMES_HOME:-$HOME/.hermes}"
mkdir -p "$HERMES_HOME"
echo "MEMORI_API_KEY=your-key" >> "$HERMES_HOME/.env"
echo "MEMORI_ENTITY_ID=your-user-or-workspace-id" >> "$HERMES_HOME/.env"

Environment variables override file config:

  • MEMORI_API_KEY
  • MEMORI_ENTITY_ID
  • MEMORI_PROJECT_ID
  • MEMORI_PROCESS_ID
  • MEMORI_API_URL_BASE

MEMORI_PROJECT_ID is optional. When omitted, the provider uses Hermes' active workspace, agent identity, user ID, session title, or session ID as the Memori project scope.

3. Verify

After configuring, restart the gateway and verify your API connectivity:

hermes memory status

You should see:

Memori Plugin Status
────────────────────────────────────
  API Key:    ****...A3xQ
  Entity ID:  your-app-user-id
  Project ID: my-project

Checking API connectivity... OK
Status: Ready

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