GitHub - janaraj/volnix: A living world where agents exist as participants alongside NPCs, internal actors, real service APIs, budgets, policies, and consequences.

Programmable worlds for AI agents.

Volnix creates living, governed realities for AI agents. Not mock servers. Not test harnesses. Complete worlds with stateful services, policies that push back, budgets that run out, NPCs that follow up and escalate, and consequences that cascade. Worlds are defined in YAML, run on their own timelines, and score every agent that interacts with them.

Watch the 1-minute demo

Quick Start

Requirements: Python 3.12+, uv (recommended), and at least one LLM API key (GOOGLE_API_KEY, OPENAI_API_KEY, or ANTHROPIC_API_KEY). See docs/llm-providers.md for supported providers.

Option 1: pip install

pip install volnix
export GOOGLE_API_KEY=...    # or OPENAI_API_KEY / ANTHROPIC_API_KEY
volnix check                 # verify setup
volnix run dynamic_support_center --internal agents_dynamic_support  # compile + run + report

Option 2: From source (includes dashboard)

git clone https://github.com/janaraj/volnix.git && cd volnix
uv sync --all-extras
export GOOGLE_API_KEY=...
uv run volnix run dynamic_support_center --internal agents_dynamic_support  # compile + run + report

# Dashboard (separate terminal — adds live event feed while running)
cd volnix-dashboard && npm install && npm run dev    # http://localhost:3000

With venv activated (source .venv/bin/activate), you can run volnix directly instead of uv run volnix.

Note: The React dashboard is only available when installed from source. The pip package includes the full backend and CLI.

How It Works

Volnix supports two modes — connect your own agents to a governed world, or deploy internal agent teams that collaborate autonomously.

  Mode 1: Connect Your Own Agent           Mode 2: Deploy Internal Agent Teams
  ────────────────────────                ──────────────────────────

  Your Agent (any framework)              Mission + Team YAML
       │                                       │
       ▼                                       ▼
  Gateway (MCP/REST/SDK)                  Lead Agent ──▶ Slack ◀── Agent N
       │                                       │            ▲
       ▼                                       ▼            │
  ┌──────────────────────┐               ┌──────────────────────┐
  │   Volnix World       │               │   Volnix World       │
  │   7-Step Pipeline    │               │   7-Step Pipeline    │
  │   Simulated Services │               │   Simulated Services │
  │   Policies + Budget  │               │   Policies + Budget  │
  │   Static world       │               │   Living world (NPCs)│
  └──────────┬───────────┘               └──────────┬───────────┘
             │                                      │
             ▼                                      ▼
  Scorecard + Event Log                   Deliverable + Scorecard

Every action flows through a 7-step governance pipeline — permission, policy, budget, capability, responder, validation, commit — before it touches the world. Nothing bypasses it.

Internal Agents

Deploy agent teams that coordinate through the world itself — posting in Slack, updating tickets, processing payments. A lead agent manages a 4-phase lifecycle (delegate → monitor → buffer → synthesize) to produce a deliverable.

mission: >
  Investigate each open ticket. Process refunds where appropriate.
  Senior-agent handles refunds under $100. Supervisor approves over $100.
deliverable: synthesis

agents:
  - role: supervisor
    lead: true
    permissions: { read: [zendesk, stripe, slack], write: [zendesk, stripe, slack] }
    budget: { api_calls: 50, spend_usd: 500 }
  - role: senior-agent
    permissions: { read: [zendesk, stripe, slack], write: [zendesk, stripe, slack] }
    budget: { api_calls: 40, spend_usd: 100 }

See docs/internal-agents.md for the complete guide.

External Agents

Connect any agent framework — CrewAI, PydanticAI, LangGraph, AutoGen, or plain HTTP. Your agent interacts with simulated services as if they were real. It doesn't know it's in a simulation.

# PydanticAI via MCP — zero Volnix imports
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

server = MCPServerStreamableHTTP("http://localhost:8080/mcp/")
agent = Agent("openai:gpt-4.1-mini", toolsets=[server])

async with agent:
    result = await agent.run("Check the support queue and handle urgent tickets.")

See docs/agent-integration.md for the full guide.

Games

Games are a run mode where agents compete through structured tool calls, are scored move-by-move, and a winner is declared (or a behavioral report is produced). Players call structured tools the LLM provider validates natively (no regex parsing of chat messages); an event-driven orchestrator scores each committed move, evaluates win conditions, and activates the next player — no rounds, no turn manager.

Different players can run on different LLM providers — head-to-head Claude vs. Gemini vs. OpenAI in the same game, with per-agent model selection and Claude extended thinking opt-in.

agents:
  - role: buyer
    llm:
      model: claude-sonnet-4-6
      provider: anthropic
      thinking: { enabled: true, budget_tokens: 4096 }   # extended thinking
    permissions: { read: [slack], write: [slack, game] }
    budget: { api_calls: 30, spend_usd: 3 }

  - role: supplier
    llm:
      model: gemini-3-flash-preview
      provider: gemini
    permissions: { read: [slack], write: [slack, game] }
    budget: { api_calls: 30, spend_usd: 3 }

Adding a new game type (auction, debate, …) is a single-file plug-in: declare your structured tools and implement a GameScorer — the framework handles tool dispatch, multi-turn conversation, event-driven activation, scoring, win conditions, and the deliverable.

volnix serve negotiation_competition --internal agents_negotiation --port 8080

Today, players must be internal agents. The GameOrchestrator activates each player through the agency engine when a game event commits — external (gateway) agents push actions asynchronously and don't have an activation entry point yet. The structured tools, scorer, and governance pipeline are all caller-agnostic, so adding external players is a future enhancement, not an architectural rework.

See docs/games.md for the complete guide.

Key Features

• 7-step governance pipeline on every action (permission → policy → budget → capability → responder → validation → commit)
• Policy engine with block, hold, escalate, and log enforcement modes
• Budget tracking per agent (API calls, LLM spend, time)
• Reality dimensions — tune information quality, reliability, social friction, complexity, and boundaries
• 11 verified service packs — Stripe, Zendesk, Slack, Gmail, GitHub, Calendar, Twitter, Reddit, Notion, Alpaca, Browser
• BYOSP — bring any service; the compiler auto-resolves from API docs
• Multi-provider LLM — Gemini, OpenAI, Anthropic, Ollama, vLLM, CLI tools, with per-agent model + provider selection and Claude extended thinking opt-in
• Game framework — event-driven agent contests (negotiation, …) with structured move tools, pluggable scorers, and win conditions; head-to-head across LLM providers
• Decision trace — activation-level artifact answering "what did the agent do, why did governance intervene, and did the agent actually use the information it read?" (decision_trace.json saved alongside scorecard after every run)
• Real-time dashboard with event feed, scorecards, and agent timeline
• Causal graph — every event traces back to its causes
• 13 built-in blueprints across support, finance, DevOps, research, security, marketing, and games

Use Cases

Some of the things you can do with Volnix:

Built-in Blueprints

volnix blueprints                        # list all
volnix serve market_prediction_analysis \
  --internal agents_market_analysts --port 8080

See docs/blueprints-reference.md for the full catalog.

Dashboard

cd volnix-dashboard && npm install && npm run dev    # http://localhost:3000

Live event streaming, governance scorecards, policy trigger logs, deliverable inspection, agent activity timeline, entity browser.

Documentation

Development

uv sync --all-extras          # install
uv run pytest                 # test (3400+ tests)
uv run ruff check volnix/     # lint
uv run ruff format --check volnix/  # format

See CONTRIBUTING.md for development setup and PR process.

Acknowledgments

• Context Hub by Andrew Ng — curated, versioned documentation for coding agents. Volnix uses Context Hub for dynamic API schema extraction during service profile resolution.

License

MIT License. See LICENSE for details.