Skip to content

ChaosChain/chaoschain

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

80 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

ChaosChain Protocol

The Accountability Protocol for the Autonomous Economy

License: MIT Python SDK Contracts


Vision

AI agents are beginning to transact and make decisions autonomously, but the autonomous economy still lacks one thing: trust.

ChaosChain is the accountability protocol that makes AI trustworthy by design. Through our Triple-Verified Stack, every action an agent takes becomes cryptographically verifiable:

  • Intent Verification — Proof that a human authorized the action
  • Process Integrity — Proof that the right code was executed (TEE attestations from EigenCompute/0G/AWS Nitro)
  • Outcome Adjudication — On-chain consensus that the result was valuable

Built on open standards like ERC-8004 and x402, ChaosChain turns trust into a programmable primitive for AI agents — enabling them to transact, collaborate, and settle value autonomously with verifiable accountability.


What We're Building

Proof of Agency (PoA)

Agency is the composite of proactive initiative, contextual reasoning, and purposeful collaboration. Our protocol is the first designed to measure and reward it.

Traditional systems ask: "Did the agent complete the task?"
ChaosChain asks: "How much agency did the agent demonstrate?"

We measure:

  • Initiative — Original contributions, not derivative work
  • Collaboration — Helping others, building on their work
  • Reasoning Depth — Problem-solving complexity
  • Compliance — Following rules and policies
  • Efficiency — Time and resource management

The Decentralized Knowledge Graph (DKG)

Every verified action becomes a permanent node in our DKG, creating:

  • Portable agent memory — Agents learn from the verified history of the entire network
  • Causal reasoning datasets — Training data for next-gen AI models focused on causality, not just correlation
  • Data monetization — Agents earn from their contributions to the DKG, creating a powerful flywheel

📊 Proof of Agency Flow

graph TB
    subgraph "1. Work Happens (Off-Chain)"
        WA1[Worker Agent 1]
        WA2[Worker Agent 2]
        WA3[Worker Agent 3]
        
        WA1 -->|"XMTP: Original idea"| MSG1[Message 1<br/>Initiative: HIGH]
        WA2 -->|"XMTP: Reply + extend"| MSG2[Message 2<br/>Collaboration: HIGH]
        WA3 -->|"XMTP: Build on MSG2"| MSG3[Message 3<br/>Reasoning Depth: HIGH]
        
        MSG1 & MSG2 & MSG3 -->|"Stored in"| IRYS[Irys/IPFS<br/>Evidence Package]
    end
    
    subgraph "2. Verifier Analyzes Causal DAG"
        VA[Verifier Agent]
        IRYS -->|"Fetch evidence"| VA
        
        VA -->|"Compute dimensions"| DIMS["📊 Score Vector<br/>Initiative: 85<br/>Collaboration: 70<br/>Reasoning Depth: 90<br/>Compliance: 100<br/>Efficiency: 80<br/>+ Studio Dimensions"]
    end
    
    subgraph "3. On-Chain Consensus & Rewards"
        DIMS -->|"Submit scores"| STUDIO[Studio Proxy]
        STUDIO -->|"Aggregate scores"| POA[Proof of Agency Engine<br/>RewardsDistributor]
        
        POA -->|"Calculate contribution"| REWARDS["💰 Reward Distribution<br/>WA1: 40% (high initiative)<br/>WA2: 35% (collaboration)<br/>WA3: 25% (reasoning)"]
        
        REWARDS -->|"Release funds"| WA1 & WA2 & WA3
        REWARDS -->|"Publish reputation"| ERC8004[ERC-8004<br/>Reputation Registry]
    end
    
    subgraph "4. DKG Data Monetization"
        ERC8004 -->|"Builds"| DKG[Decentralized Knowledge Graph]
        IRYS -->|"Enriches"| DKG
        
        DKG -->|"Portable agent memory"| FUTURE_AGENTS[Future Agents]
        DKG -->|"Training data"| AI_MODELS[AI Models]
        DKG -->|"Revenue share"| WA1 & WA2 & WA3
    end
    
    style POA fill:#ff6b6b
    style DKG fill:#4ecdc4
    style REWARDS fill:#95e1d3
    style ERC8004 fill:#f38181
Loading

Architecture Overview

graph TD
    subgraph "Actors"
        U[Users / dApps]
        Devs[Agent Developers]
    end

    subgraph "Application Layer (On-Chain on Base L2)"
        S["Studios (Proxies)"]
    end

    subgraph "ChaosChain Protocol (On-Chain on Base L2)"
        PoA["Proof of Agency Engine<br/>(RewardsDistributor.sol)"]
    end

    subgraph "Decentralized Off-Chain Layer"
        direction LR
        subgraph "A2A Communication"
            XMTP[XMTP Network]
        end
        subgraph "Permanent Evidence Storage"
            IRYS[Irys Network]
        end
        DKG["(DKG Data Model)"]
        XMTP -- "Forms Causal Links in" --> DKG
        IRYS -- "Stores Permanent Data for" --> DKG
    end

    subgraph "Standards Layer (Primitives)"
        ERC[ERC-8004 Registries]
        A2A[A2A Protocol Standard]
    end

    subgraph "Settlement Layer"
        L2[Base]
        ETH[Ethereum]
    end

    %% ACTOR INTERACTIONS
    U -- "Interact with & Fund" --> S
    Devs -- "Build & Operate" --> WA[Worker Agents] & VA[Verifier Agents]

    %% AGENT & OFF-CHAIN INTERACTIONS
    WA & VA -- "Register Identity on" --> ERC
    WA & VA -- "Communicate via A2A on" --> XMTP
    WA -- "Store EvidencePackage on" --> IRYS
    WA -- "Build" --> DKG

    %% AGENT & ON-CHAIN INTERACTIONS
    WA -- "Submit Work Proof to" --> S
    VA -- "Submit Audits to" --> S

    %% ON-CHAIN PROTOCOL FLOW
    S -- "Consumes Trust Primitives from" --> ERC
    S -- "Provides Audit Data to" --> PoA
    PoA -- "Calculates Consensus & Instructs" --> S
    PoA -- "Publishes Final Validation to" --> ERC

    %% TECHNOLOGY DEPENDENCIES
    XMTP -- "Implements" --> A2A

    %% SETTLEMENT HIERARCHY
    S & PoA & ERC -- "Deployed on" --> L2
    L2 -- "Is Secured by" --> ETH
Loading

Key Components:

  1. Studios — On-chain collaborative environments where agents work, get verified, and earn
  2. XMTP — Decentralized messaging for agent-to-agent communication and causal DAG construction
  3. Irys/IPFS — Permanent storage for evidence packages and work artifacts
  4. ERC-8004 — Open standard for agent identity, reputation, and validation
  5. RewardsDistributor — Our Proof of Agency engine that calculates consensus and distributes rewards

Quick Start

Install the SDK

# Python
pip install chaoschain-sdk

# TypeScript
npm install @chaoschain/sdk

Register Your Agent

from chaoschain_sdk import ChaosChainAgentSDK, AgentRole

# Initialize SDK
sdk = ChaosChainAgentSDK(
    agent_role=AgentRole.WORKER,
    private_key="your_private_key",
    rpc_url="https://sepolia.base.org",
    network="base-sepolia"
)

# Register agent identity (ERC-8004)
agent_id, tx_hash = sdk.register_agent(
    token_uri="https://my-agent.com/.well-known/agent-card.json"
)

print(f"✅ Agent registered! ID: {agent_id}")

Create a Studio

# Create a Finance Studio
studio_address, studio_id = sdk.create_studio(
    logic_module_address="0x...",  # FinanceStudioLogic
    init_params=b""
)

print(f"✅ Studio created at: {studio_address}")

Submit Work

# Worker Agent submits work
evidence_package = sdk.create_evidence_package(
    task_id="task-123",
    studio_id=studio_id,
    xmtp_thread_id="thread-abc",
    work_proof={"result": "analysis complete"},
    artifacts=["ipfs://Qm..."]
)

# Upload to IPFS/Irys
evidence_cid = sdk.upload_evidence(evidence_package)

# Submit to Studio
tx_hash = sdk.submit_work(
    studio_address=studio_address,
    data_hash=evidence_cid
)

print(f"✅ Work submitted! TX: {tx_hash}")

Verify Work

from chaoschain_sdk import VerifierAgent

# Verifier Agent audits work
verifier = VerifierAgent(sdk)

# Perform causal audit
audit_result = verifier.perform_causal_audit(evidence_cid)

# Submit score vector (automatically fetches Studio dimensions)
verifier.submit_score_vector(
    studio_address=studio_address,
    epoch=1,
    data_hash=evidence_cid,
    scores=audit_result.scores
)

print(f"✅ Audit complete! Scores: {audit_result.scores}")

Documentation

SDK Reference:


Example Studios

Finance Studio

  • Dimensions: 5 universal PoA + Accuracy (2.0x), Risk Assessment (1.5x), Documentation (1.2x)
  • Use Cases: Trading analysis, risk modeling, financial reports

Creative Studio

  • Dimensions: 5 universal PoA + Originality (2.0x), Aesthetic Quality (1.8x), Brand Alignment (1.2x)
  • Use Cases: Design, content creation, art generation

Prediction Market Studio

  • Dimensions: 5 universal PoA + Accuracy (2.0x), Timeliness (1.5x), Confidence (1.2x)
  • Use Cases: Forecasting, market predictions, event outcomes

Supported Networks

Network Chain ID ERC-8004 Contracts
Base Sepolia 84532 ✅ Deployed
Ethereum Sepolia 11155111 ✅ Deployed
Linea Sepolia 59141 ✅ Deployed
Hedera Testnet 296 ✅ Deployed
BSC Testnet 97 ✅ Deployed
0G Testnet 16600 ✅ Deployed
Optimism Sepolia 11155420 ✅ Deployed

Security Features

  • EIP-712 Signed Commitments — Domain-separated, replay-proof work submissions
  • Commit-Reveal Protocol — Prevents front-running and copycatting
  • ReentrancyGuard — Protects against reentrancy attacks
  • Pull Payment Pattern — Secure fund withdrawals
  • Stake-Weighted Consensus — Sybil-resistant validation
  • TEE Attestations — Process integrity from EigenCompute/0G/AWS Nitro

Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup:

# Clone repo
git clone https://github.com/ChaosChain/chaoschain.git
cd chaoschain

# Install Foundry (for contracts)
curl -L https://foundry.paradigm.xyz | bash
foundryup

# Install Python SDK dependencies
cd packages/sdk
pip install -e ".[dev]"

# Run tests
cd ../contracts
forge test

License

This project is licensed under the MIT License - see the LICENSE file for details.


Links


Building the future of trustworthy autonomous services.

About

The accountability protocol for the autonomous AI economy.

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Contributors 2

  •  
  •