Hermes + ROSTR.
Production Intelligence.

Self-improving multi-agent framework combining the complete Hermes Agent runtime with ROSTR's structured intelligence layer. PAL, NPAO, RAG DAL, Hub. Proven in production. Backed by research.

41
Skills
+82pp
Memory
-33%
Tokens
MIT
License

Published Framework.
Production Proven.

ROSTR is grounded in peer-reviewed research and deployed in production systems processing millions of tasks daily.

Published on Zenodo

ROSTR: A Unified Architecture for Production-Grade Multi-Agent Systems with Phase-Aware Orchestration and Persistent Knowledge Compounding

Patrick Diamitani · April 2026 · 22,000 words · 27 references

A comprehensive framework for production multi-agent AI systems. Covers PAL (Prompt Abstraction), NPAO (orchestration), RAG DAL (retrieval), and Hub (persistent memory).

Read Paper on Zenodo →

Performance Gains vs Hermes

+23pp
Task Completion
+28pp
First-Attempt Accuracy
+48pp
Context Utilization
+25pp
Coherence
+82pp
Knowledge Retention
8.4/10
Expert Rating
-33%
Token Cost
+87pp
Multi-Step
6.2→8.4
Decision Quality

Evaluated across 200 tasks, 8 domains. Same model (Sonnet 4-6), same temperature, same tools.

Four Pillars of Intelligence

A complete framework that transforms raw prompts into structured, accountable, continuously-improving agent systems.

PAL — Prompt Abstraction Layer

Transforms natural language into strictly-typed AgentManifests. Intent extraction → context injection → semantic enhancement → compilation → routing. The first step toward intelligence.

NPAO — Orchestration Engine

Navigate, Prioritize, Allocate, Orchestrate. Classifies tasks into 5D phases, computes priority via formula, allocates agents, executes task graphs. Deterministic execution with human-in-the-loop.

RAG DAL — Retrieval Pipeline

Dynamic Acquisition Layer. Multi-pass retrieval with 3-tier source credibility, gap analysis, contradiction detection. Converges at 0.8 confidence. Real retrieval, not simulation.

Rostr Hub — Persistent Memory

Multi-level state management (session → project → org → agent). Stores decisions, learnings, execution history. Knowledge compounds across sessions. The agent OS.

41 Skills.
Ready to Deploy.

Production-grade skills across GTM, development, content, data, automation, and operations. Generalized, LLM-agnostic, MIT licensed.

GTM & Sales

Clay Prospecting, Agent Factory, GTM Architect, JTBD Builder, Use Case Builder, Asana Organizer, Video Builder, GTM Insider Report.

Developer Tools

PAL Compiler, Context Engine, ROSTR Builder, Prompt Rewriter, n8n Engineer, Workflow Architect, Execution Analyst, CSV Router.

Content & Video

Video Editor (HyperFrames), Video Studio, Case Study Builder, Project Instructions, Session Recap, Project Handoff.

Data & Analytics

HubSpot Analyst, Dashboard Builder, Token Spend Analyzer, Clay Credit Estimator, CSV Enricher, Credit Calculator.

Automation

Workflow Builder, n8n Architect, Execution Analyst, CSV Router, Executive Assistant, File Organizer.

Product & Ops

SaaS Architect, Product Builder, Project Closeout, Handoff Builder, New Project System, Instructions Builder.

Proven Results.
Real Data.

Evaluated against vanilla Hermes across 200 diverse tasks. Same model, temperature, and tools. Only variable: ROSTR structure.

Metric Hermes (baseline) ROSTR Agent Delta
Task Completion Rate 68% 91% +23pp
First-Attempt Accuracy 54% 82% +28pp
Context Utilization 41% 89% +48pp
Multi-Step Coherence 62% 87% +25pp
Knowledge Retention (cross-session) 12% 94% +82pp
Decision Quality (expert rated) 6.2/10 8.4/10 +2.2
Avg Tokens per Completed Task 4,200 2,800 -33%

Evaluation: 200 tasks across 8 domains (code, research, ops, design, sales, content, deploy, debug). Same model (claude-sonnet-4-6), same temperature (0.7), same tools. Only variable: ROSTR intelligence layer.

Ready to Build?

Deploy ROSTR locally, to AWS, or use Vercel. Free and open source. MIT licensed. No vendor lock-in.