prose

Execute structured multi-agent programs on a virtual machine with stateful sessions.

Updated Mar 11, 2026

Search Tags:#agents #orchestration #parallel #state-management #vm #bindings #openprose

Ready to run

npx skills add https://github.com/x402agent/NanoSolana --skill prose-x402agent

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: prose
Source: https://github.com/x402agent/NanoSolana/tree/main/extensions/open-prose/skills/prose
Command: npx skills add https://github.com/x402agent/NanoSolana --skill prose-x402agent

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

OpenProse provides a structured, self-describing framework to orchestrate multi-agent AI workflows, reducing ad-hoc scripting and enabling repeatable orchestration of tasks in a scalable VM environment.

Core Features & Use Cases

Agent orchestration: Define templates for agents, spawn sessions, and manage prompts and context across runs.
Parallel and block patterns: Coordinate concurrent tasks, nested calls, and reusable blocks to compose complex workflows.
Stateful runs: Persist execution state via file-based, in-context, or database-backed backends, enabling resumption and auditability.
Use Case: Build office automation flows where multiple agents collaborate to complete a multi-step research and reporting task.

Quick Start

Install prose.md and run a basic example to see the OpenProse VM orchestrate a single session.

Frequently Asked Questions about prose

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I orchestrate parallel AI agents with reliable state management?▼

Parallel AI agent orchestration with reliable state management is achieved by modeling a deterministic VM that executes structured programs, enabling concurrent task coordination, nested calls, and block reuse across file-based, in-context, or database-backed state backends for reproducible workflows.

What is deterministic multi-agent workflow execution and when do I need it?▼

Deterministic multi-agent workflow execution models a VM that runs structured programs with sessions, imports, and state backends. You need it when reliable multi-agent workflows require reproducibility, clear audit trails, and robust memory management with testable contracts.

Can I persist execution state for AI agent sessions across different backends?▼

Yes, AI agent execution state can be persisted via file-based, in-context, or database-backed state backends. This enables session resumption and auditability across runs, ensuring stateful runs maintain clear, testable contracts for modular agent definitions.

How do I build office automation flows where multiple AI agents collaborate?▼

Office automation flows with multiple collaborating AI agents are built by defining agent templates, spawning sessions, and managing prompts and context across runs. The VM coordinates concurrent tasks and reusable blocks to compose complex multi-step research and reporting workflows.

Does this agent orchestration framework require external dependencies?▼

No, this agent orchestration framework requires no external dependencies. It provides a self-describing structured framework to orchestrate multi-agent AI workflows, reducing ad-hoc scripting and enabling repeatable orchestration in a scalable VM environment.

What's the best way to audit and resume multi-agent AI workflows?▼

The best way to audit and resume multi-agent AI workflows is using a deterministic VM that persists execution state via file-based, in-context, or database-backed backends, providing clear audit trails and enabling robust memory management with reproducible results.