Agrus Agents Project (a.k.a. Process-GPT)

A BPMN-based Multi-Agent Orchestration Framework for the Enterprise

Process GPT is an open-source platform that combines the 30-year-proven BPMN (Business Process Model and Notation) international standard with the autonomy of modern AI agents. It lets non-technical users design business processes in natural language, lets multiple specialized agents collaborate to execute them, and continuously learns from feedback to make every future run better.

Where classic BPM requires code and specialists, and pure agent frameworks (CrewAI, LangGraph, AutoGen, Swarm/OpenAI SDK) leave you without visual processes, audit trails, or compensation semantics — Process GPT sits at the intersection: visual + code + standards-based, engineered for production.

Online Service 🌐 www.process-gpt.io

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Why Process GPT

• Flexible and Robust Collaboration via Multi-Agent Systems — Process GPT configures multiple AI agents to collaborate within a single BPMN-based business process, so that multiple agent frameworks can professionally handle complex tasks and share intermediate results. This reliably automates high-complexity work that would be difficult for a single agent. Each agent leverages specialized domain knowledge and tools, and can automatically call upon other specialized agents to delegate tasks when necessary.

• Automated Business Process Generation — Process GPT is designed to let AI agents automatically define business processes, producing results without constant human instruction or manual execution of every step. This minimizes human intervention, embodying the ambient agent philosophy, and ensures that human involvement does not become a bottleneck.

• Natural-Language-Based Continuous Process Learning and Optimization — Non-expert users can define business processes using natural language, which automatically generates initial process models. An automatic optimization cycle analyzes user feedback and agent execution logs, continuously improving processes and augmenting training data for workflows that need correction.

• Deterministic Regularization of AI Decisions — When agents repeatedly make the same kind of judgment, Process GPT automatically converts it into a DMN decision table or Python rule, guaranteeing that "same input = same output" for enterprise-critical paths while keeping AI flexibility for exceptions.

• Enterprise-Grade Reliability — BPMN Compensation Events provide automatic rollback and compensating transactions on failure. Human-in-the-Loop is a native BPMN pattern, not a workaround. Every step is auditable against an ISO/IEC 19510 process model.

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What's Under the Hood

• Framework-agnostic Multi-Agent System — powered by LangChain Deepagents, CrewAI, and more; pick the best runtime per task
• BPMN-based Hybrid Process Execution — deterministic (DMN/code) and stochastic (LLM reasoning) modes in one diagram
• Collaborative Work via the Agent-to-Agent (A2A) Protocol — agents discover and negotiate with each other through Agent Cards
• Isolated Agent/Tool Execution — each MCP and A2A server runs in its own container, orchestrated on Kubernetes with KEDA queue-based autoscaling and a Sidecar isolation pattern
• Skill Self-Learning & Feedback Loops — agents improve their own Skills through a Think → Execute → Reflect cycle driven by user feedback
• Context Engineering — Mem0 + Neo4j knowledge graph + Memento RAG service give agents deep organizational context
• Voice & Realtime Channels — GPT-4 Realtime API + Twilio PSTN integration for voice-driven process triggers
• Process Marketplace — share and reuse verified process templates across teams and organizations
• Integrations — Browser-use, OpenAI Deep Research, Supabase (Postgres, Realtime, Storage, Auth), ERP/CRM via MCP, N8n (coming soon)

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Core Architecture (5 Layers)

Layer	Role	Key Components
UI & Gateway	User entry & routing	Vue 3 Frontend, React Voice Agent, Nginx / Spring Cloud Gateway
Core Process	BPMN definition, instance lifecycle, polling	Execution Engine (FastAPI), Polling Service
Knowledge & RAG	Document parsing, embedding, retrieval	Memento (Supabase vector DB, Google Drive ingestion)
AI Agents	Task execution	CrewAI Action, CrewAI Deep Research, OpenAI Deep Research, Browser-Use, BPMN Extractor
Infrastructure	State, events, storage, auth	Supabase (Postgres, Realtime, Storage, Auth), Docker Compose / Kubernetes

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Where Process GPT Sits in the Market

Process GPT is the only player in the "BPMN + AI Hybrid" category — purpose-built for enterprises that need the governance of BPM and the autonomy of modern agents at the same time.

	ProcessGPT	CrewAI / LangGraph / AutoGen	Dify.ai / n8n	Google ADK / AWS Bedrock
Orchestration	BPMN visual modeling	Code-based roles/graphs	Visual low-code	Console / blueprint
Determinism	DMN + Python auto-conversion	None	Conditional nodes	Guardrails only
Agent-to-Agent	A2A + event-driven	Sequential / group chat	N/A	A2A (Google) / internal
Self-learning	Skills + feedback loop	Memory only	None	None
Compensation	BPMN Compensation Events	None	Basic error branches	None
Autoscaling	KEDA + Sidecar	Manual	Manual	Managed (vendor-locked)
Non-developer access	High (NL + visual)	Low (code)	High	Medium
Deployment	Open source, multi-cloud, on-prem	Library-level	SaaS / self-host	Cloud-locked

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Who It's For

• Enterprises modernizing legacy BPM — keep your BPMN assets, add AI autonomy
• Regulated industries — finance, healthcare, public sector where audit trails and compliance are non-negotiable
• Citizen developers — business users automating their own work in natural language, no coding required
• AI teams building production agents — skip the infrastructure rebuild; get K8s-native isolation, scaling, and observability out of the box

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Get Started

• Website: process-gpt.io
• Documentation: docs.process-gpt.io
• SaaS: try it instantly at process-gpt.io
• Self-host: docker compose up deploys the full stack; Kubernetes manifests included for production

Process GPT creates a new category — "the BPMN of AI agents" — and sets the standard for enterprise multi-agent orchestration.

Maintained by uEngine Solutions · learning@uengine.org

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Subprojects

• execution (Execution Engine): GitHub
• memento (Document Memory Storage): GitHub
• crewai-action (MCP / Multi-Agent Task Execution Agent): GitHub
• crewai-deep-research (Multi-Agent Deep Research Agent): GitHub
• openai-deep-research (OpenAI-based Deep Research Agent): GitHub
• react-voice-agent (Voice Interaction Agent): GitHub
• API gateway: GitHub
• frontend: GitHub
• completion: GitHub
• autonomous-execution: GitHub
• agents.github.io: GitHub
• generic-agent: GitHub
• agent-feedback: GitHub
• mcp-validator: GitHub
• agent-sdk: GitHub
• langchain-react: GitHub
• a2a-orch: GitHub
• agent-utils: GitHub
• bpmn-extractor (ProcessGPT BPMN extractor from PDFs): GitHub
• computer-use: GitHub
• claude-skills (MCP server for searching and retrieving Claude Agent Skills using vector search): GitHub
• deep-research: GitHub
• office-mcp: GitHub

Syster(Related) Projects

• Robo Architect: GitHub

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Design Principles

Design Principles

Users should be able to declare and modify processes, rules, system integration mechanisms, etc. in natural language, and the system should automatically improve with minimal feedback provided during use.

All such changes must be logged for tracking and recovery, while users should simultaneously be able to directly control automation results and regulations through a generalized UI at any time.

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Principle 1. Natural Language-Centric Definition and Training-Free Operation

• All process definitions, rules, system integrations, and business interfaces should be writable in natural language without requiring programming knowledge or complex logical/mathematical thinking.
• Users should be able to design automation with business objective or strategic-level descriptions alone, without undergoing separate training processes.
• The system should be progressively refined and managed through minimal feedback (approval, modification, rejection) provided during actual use.

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Principle 2. Human-in-the-Loop and Learning by Example

• Automated agents must provide human interfaces that allow people to substitute and perform tasks at any time.
• Each task should provide necessary context (related data, previous step outputs, similar cases) in a clear and organized manner to facilitate human processing.
• Agents learn from actual performance examples where humans directly handle tasks, correcting and improving their execution knowledge. In other words, human exemplars become the agent's training data.

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Principle 3. Automatic Compensation and Separation of Recovery Responsibility

• When errors or failures occur in automated processes performed by agents, recovery should be automatically implemented through compensating transactions (rollback).
• Operators should not need to track and correct agent details individually; the system itself should take responsibility for failure recovery and processing.
• This liberates users from system imperfections and ensures overall business continuity.

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Process-GPT Local Installation Guide (Kind)

Please refer to the Local Installation Guide.

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User Manual

📖 Process-GPT User Manual