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Existence Synchronization System Entity

Existence Synchronization System Entity

Local
github.com/VeniceEsseAI & Agents1Updated 21w ago

About Existence Synchronization System Entity

ESSE Agent is an MCP server and orchestration framework that coordinates multiple AI agents as a unified system. It supports shared memory, task delegation, and agent fusion for teams building collaborative agent workflows.

Key features

  1. 01Fuse 2–5 agents
  2. 02Share memory and context
  3. 03Delegate tasks across agents
  4. 04Combine agent capabilities
  5. 05Support three fusion modes

Use cases

  1. 01Combine research, writing, and critique
  2. 02Coordinate parallel agent workloads
  3. 03Run plan, code, test, and review pipelines

MCP support

The MCP server exposes seven tools for fusing, executing, and managing agents and sessions. Setup documentation lists Venice AI, OpenAI, and Anthropic support.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"VVV-Esse": {
"command": "npx",
"args": [
"-y",
"vvvesse-mcp"
],
"env": {
"ESSE_API_KEY": "<ESSE_API_KEY>",
"ESSE_PROVIDER": "<ESSE_PROVIDER>"
}
}
}
}
Run commandnpx -y vvvesse-mcp
Environment variables to set
ESSE_API_KEYSecret
ESSE_PROVIDER

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npmvvvesse-mcp

Tools

7

Saved tool list · updated 3w ago

ParameterTypeRequiredDescription
agentsarrayYesList of agent names to fuse. Use built-in names (research, writer, critic, coder, planner, tester) or custom names.
modeenumNoFusion mode. Default: capability-assimilation.capability-assimilation | synchronization-link | perfect-convergence

Details

From the project README
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ESSE Agent

Existence Synchronization System Entity

A powerful meta-agent that unifies, coordinates, and fuses multiple AI agents into one smarter, more efficient system.

Version License TypeScript Tests X Website

Documentation · Quick Start · Examples · Contributing


What is ESSE?

ESSE is a meta-agent orchestration framework designed to unify, coordinate, and fuse multiple AI agents into a single, more intelligent system. Instead of running agents in isolation, ESSE creates a real-time synchronization layer — shared memory, collaborative reasoning, and automatic task delegation — so your agents work as a single unified mind.

┌─────────────────────────────────────────────────┐
│                   ESSE CORE                     │
│                                                 │
│   [Research] ──┐                                │
│   [Writer]  ───┼──► Fusion Engine ──► Output   │
│   [Critic]  ───┘         │                      │
│                     Memory Hub                  │
│               (shared context & state)          │
└─────────────────────────────────────────────────┘

Core Capabilities

⚡ Agent Fusion

Merge 2–5 AI agents into one temporary unified entity. The merged agents continue to exist as sub-processes inside ESSE, combining their strengths for better reasoning and smoother workflow.

🧠 Capability Assimilation

Borrow and intelligently combine tools, knowledge, reasoning styles, and specialties from other agents — without disabling them.

Example: ResearchAgent + WriterAgent + CriticAgent = one highly effective content creation agent.

🔗 Synchronization Link

Creates real-time connections between agents:

  • Shared memory and context
  • Real-time communication
  • Automatic task delegation
  • Collaborative reasoning
🌀 Perfect Convergence (Ultimate Mode)

Merges all connected agents into one singular consciousness. Massive boost in performance — ideal for complex, creative, or high-stakes tasks.


Quick Start

Installation
npx vvvesse-mcp

MCP Setup (Claude Desktop / Cline / Cursor)

{
  "mcpServers": {
    "esse": {
      "command": "npx",
      "args": ["-y", "vvvesse-mcp"],
      "env": {
        "ESSE_API_KEY": "your-venice-api-key",
        "ESSE_PROVIDER": "venice"
      }
    }
  }
}

📦 npm → npmjs.com/package/vvvesse-mcp

Basic Usage
import { ESSE, ResearchAgent, WriterAgent, CriticAgent } from 'esse-agent'

const esse = new ESSE({
  mode: 'capability-assimilation',
  maxAgents: 5,
  sharedMemory: true,
})

// Fuse agents into one unified entity
await esse.fuse([
  new ResearchAgent(),
  new WriterAgent(),
  new CriticAgent(),
])

// Execute task as a unified mind
const result = await esse.execute('Write a research-backed article on AGI timelines')
console.log(result)
Perfect Convergence Mode
import { ESSE, PlannerAgent, CoderAgent, TesterAgent, ReviewerAgent } from 'esse-agent'

const esse = new ESSE({ mode: 'perfect-convergence' })

await esse.fuse([
  new PlannerAgent(),
  new CoderAgent(),
  new TesterAgent(),
  new ReviewerAgent(),
])

// All agents think as one — maximum performance
const software = await esse.execute('Build a REST API for user authentication')

Architecture

ComponentFunction
Core BrainMain LLM orchestrator (GPT-4o, Claude, Grok)
Agent RegistryDatabase of all connectable + custom agents
Fusion EngineHandles merging and capability blending
Sync ProtocolReal-time communication between agents
Memory HubShared long-term memory across all agents
Conflict ResolverResolves disagreements between agent outputs

Fusion Modes

ModeDescriptionBest For
capability-assimilationBorrows tools from each agentGeneral tasks
synchronization-linkShared memory + real-time commsParallel workloads
perfect-convergenceSingular unified consciousnessComplex/creative tasks

Built-in Agents

  • ResearchAgent — Knowledge retrieval and web search
  • WriterAgent — Content generation and editing
  • CriticAgent — Quality analysis and fact-checking
  • CoderAgent — Software engineering and code review
  • PlannerAgent — Strategy, roadmaps, and task breakdown
  • TesterAgent — QA, validation, and edge case detection

Custom Agents

Implement the IAgent interface to create your own:

import { IAgent, AgentContext, AgentResult } from 'esse-agent'

export class MyCustomAgent implements IAgent {
  name = 'MyCustomAgent'
  role = 'Custom task specialist'

  async execute(task: string, context: AgentContext): Promise<AgentResult> {
    // your logic here
    return { output: '...', confidence: 0.95 }
  }
}

Examples

See the examples/ directory for:

  • content-creation.ts — Research + Write + Critique pipeline
  • software-dev.ts — Plan + Code + Test + Review pipeline
  • decision-making.ts — Multi-perspective analysis
  • brainstorming.ts — Creative divergence with convergence

Documentation

Full docs at vvvesse.xyz


Contributing

PRs are welcome! Please read CONTRIBUTING.md before submitting.

All custom agents must:

  1. Implement the IAgent interface
  2. Pass the fusion compatibility test (pnpm test:compat)
  3. Include unit tests with >80% coverage

Community


Built with ⚡ by @VeniceEsse · vvvesse.xyz

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.