Memori Labs announced today that it has been featured by Nous Research's Hermes Agent as a core memory provider. Unique among memory providers, Memori gives Hermes Agents long-term persistent memory that captures not only conversation, but also agent trace and execution.
SAN FRANCISCO, June 8, 2026 /PRNewswire-PRWeb/ -- Memori Labs announced today that it has been featured by Nous Research's Hermes Agent as a core memory provider. Unique among memory providers, Memori gives Hermes Agents long-term persistent memory that captures not only conversation, but also agent trace and execution. As Hermes completes tasks, 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.
As a result, Hermes Agents can now achieve industry-leading accuracy-to-token-cost performance powered by Memori: 81.95% accuracy on just 4.97% of token cost as shown in peer-reviewed benchmarks.
"For AI agents to remember context across different sessions, the memory layer must evolve from flat .md files into structured infrastructure," said Adam B. Struck, CEO and Co-Founder of Memori Labs. "By bringing Memori to the Hermes ecosystem, we are giving developers the ability to build agents that don't just remember what was said, but also understand what was done."
The agent-native version of Memori is now available within the Hermes harness of the Memori Labs Hermes plugin.
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.
- 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.
Memori Hermes plugin is now available to install. It requires Python 3.10 or later.
Availability
The Memori Hermes plugin is available now via `hermes plugins install @memorilabs/hermes-memori`. Developers can sign up for a free API key at app.memorilabs.ai and view setup documentation at https://memorilabs.ai/docs/memori-cloud/hermes/quickstart/.
Industry Leading Benchmarks
Memori Labs' peer-reviewed benchmark results can be downloaded and verified at https://memorilabs.ai/benchmark/.
About Memori Labs
Memori Labs is an agent-native memory platform built for production AI systems. Unlike conversational memory wrappers or vector retrieval layers, Memori structures memory from both conversation and agent execution — turning tool calls, decisions, and workflow traces into persistent, queryable state. Memori's benchmark results reflect the approach: 81.95% accuracy on LoCoMo using only 1,294 tokens per query, roughly 5% of full-context cost, saving users more than 95% on inference costs. The open-source project has grown to more than 15,200 GitHub stars, signaling strong developer pull. Bessemer Venture Partners has identified memory and context management as a key part of the emerging AI infrastructure harness layer, citing Memori as one of the category leaders.
Media Contact
Adam B. Struck, Memori Labs, 1 5612890486, [email protected], Memori Labs
SOURCE Memori Labs
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