GOATCLAW 🚀

Greatest Of All Time - Coordinated Large-scale Autonomous Workflows

An advanced multi-agent orchestration system with production-grade features for autonomous task execution, self-healing workflows, and intelligent automation.

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🌟 Unique Selling Points (USPs)

1. Multi-Model LLM Support

• Seamless switching between Claude, GPT-4, Gemini, Llama, and local models
• Automatic fallback to backup providers
• Cost optimization through model selection

2. Advanced Event-Driven Architecture

• Asynchronous publish/subscribe event bus
• Event replay for debugging
• Dead letter queue for failed events
• Priority-based event processing

3. Zero-Trust Security

• Multi-factor authentication support
• Sliding window rate limiting
• Real-time threat detection
• Comprehensive audit logging
• IP blocking and threat scoring

4. AI-Powered Validation with Auto-Fix

• Semantic validation using LLMs
• Automatic error correction
• Schema validation
• Custom validation rules

5. Semantic Memory & Learning

• Vector embeddings for semantic search
• Pattern recognition from past executions
• Learning from failures
• Knowledge graph construction

6. Multi-Mode Execution

• Sequential: Step-by-step execution
• Parallel: Concurrent task processing
• Distributed: Cross-worker execution (coming soon)
• Streaming: Real-time progress updates

7. Circuit Breaker & Self-Healing

• Automatic failure detection
• Graceful degradation
• Configurable retry strategies (exponential, fibonacci, adaptive)
• Self-healing workflows

8. Plugin Architecture

• Extensible agent system
• Lifecycle hooks (before/after execution, on success/failure)
• Custom validator registration
• Easy integration of new capabilities

9. Real-Time Monitoring

• Performance metrics per agent
• Health checks
• Execution tracing
• Resource usage tracking

10. Production-Grade Design

• Comprehensive error handling
• Extensive logging
• Configurable timeouts
• Memory consolidation
• Cache management

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📋 Table of Contents

• Architecture
• Installation
• Quick Start
• Core Components
• Agent Types
• Configuration
• Examples
• API Reference
• Deployment
• Contributing

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🏗️ Architecture

┌─────────────────────────────────────────────────────────┐
│                     GOATCLAW                            │
├─────────────────────────────────────────────────────────┤
│                                                          │
│  ┌──────────────┐         ┌──────────────┐             │
│  │ Orchestrator │◄────────┤  Event Bus   │             │
│  └──────┬───────┘         └──────────────┘             │
│         │                                                │
│         ├───► Security Agent (Zero-Trust)               │
│         ├───► Validation Agent (AI-Powered)             │
│         ├───► Memory Agent (Semantic Search)            │
│         │                                                │
│         └───► Specialist Agents:                        │
│               • Research Agent                          │
│               • Code Agent                              │
│               • DevOps Agent                            │
│               • API Agent                               │
│               • Data Processing Agent                   │
│               • FileSystem Agent                        │
│                                                          │
└─────────────────────────────────────────────────────────┘

Key Design Principles

1. Modularity: Each agent is independent and composable
2. Scalability: Event-driven architecture supports horizontal scaling
3. Reliability: Circuit breakers and retries ensure fault tolerance
4. Observability: Comprehensive logging and metrics
5. Security: Zero-trust model with fine-grained permissions

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� Install

The recommended way to install GOATCLAW is via Pip:

pip install goatclaw

Or for development:

git clone https://github.com/Shivay00001/goatclaw.git
cd goatclaw
pip install -e .

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🎮 Getting Started (CLI)

GOATCLAW features a fully interactive guided terminal. Once installed, simply run:

goatclaw

What happens next?

1. Guided Setup: It checks for available LLMs (Ollama, OpenAI, NVIDIA, etc.).
2. Auto-Detection: If Ollama is running, it will use it as the default local backend.
3. Secure Key Entry: Use goatclaw config set-key <provider> <key> to add your own keys.
4. Execute Goals: Type any high-level goal in the REPL (e.g., goatclaw ▸ Build a landing page).

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🚀 Usage Examples

1. One-Shot Execution

Execute a single goal directly from your shell:

# Using auto-detected local Ollama
goatclaw run "Summarize my recent git commits"

# Overriding provider and model
goatclaw run "Write a technical blog post" --provider nvidia --model moonshotai/kimi-k2.5

2. Python SDK Example

Integrate multi-agent orchestration into your own Python applications:

import asyncio
from goatclaw.runner import create_orchestrator, create_default_security_context
from goatclaw.core.structs import TaskGraph, TaskNode, AgentType, PermissionScope

async def main():
    # Create orchestrator (auto-resolves LLM from config/.env)
    orch = create_orchestrator()
    await orch.start()
    
    ctx = create_default_security_context()
    graph = TaskGraph(goal_summary="Deep research on AI trends")
    
    node = TaskNode(
        node_id="research_1",
        name="Market Research",
        agent_type=AgentType.RESEARCH,
        input_data={"query": "AI Agents 2025", "action": "synthesize"}
    )
    graph.add_node(node)
    
    result = await orch.process_goal(graph, ctx)
    print(f"Goal Status: {result['status']}")
    
    await orch.stop()

asyncio.run(main())

Run Demo

# Sequential execution demo
python -m goatclaw.runner

# Parallel execution demo
python -m goatclaw.runner parallel

---

🧩 Core Components

1. Event Bus

Asynchronous publish/subscribe system for agent communication.

from goatclaw.core.event_bus import EventBus, Event

bus = EventBus()
await bus.start()

# Subscribe
async def handler(event: Event):
    print(f"Received: {event.event_type}")

bus.subscribe("task.started", handler)

# Publish
await bus.publish(Event(
    event_type="task.started",
    payload={"node_id": "task_1"}
))

Features:

• Priority queues
• Event replay
• Dead letter queue
• Wildcard subscriptions
• Request-response pattern

2. Security Agent

Zero-trust security enforcement.

from goatclaw.agents.security_agent import SecurityAgent

security = SecurityAgent(event_bus, config={
    "max_requests_per_hour": 100,
    "threat_threshold": 0.8
})

# Rate limiting
result = await security.execute(
    TaskNode(input_data={"action": "check_rate_limit"}),
    context
)

# Risk assessment
risk = await security.execute(
    TaskNode(input_data={"action": "assess_risk"}),
    context
)

Features:

• Sliding window rate limiting
• Threat scoring
• IP blocking
• Audit logging
• Session management

3. Validation Agent

AI-powered output validation.

from goatclaw.agents.validation_agent import ValidationAgent

validator = ValidationAgent(event_bus, config={
    "auto_fix_enabled": True
})

# Validate output
node.validation_rule = "output.confidence > 0.8"
result = await validator.execute(node, context)

Validation Types:

• Schema validation
• Type checking
• Range validation
• Format validation (email, URL, UUID)
• Custom expressions
• Semantic validation (AI-powered)

4. Memory Agent

Semantic memory with pattern learning.

from goatclaw.agents.memory_agent import MemoryAgent

memory = MemoryAgent(event_bus, config={
    "max_memories": 10000,
    "similarity_threshold": 0.85
})

# Store memory
await memory.execute(
    TaskNode(input_data={
        "action": "store",
        "goal_summary": "Task completed",
        "task_graph": {...}
    }),
    context
)

# Semantic search
results = await memory.execute(
    TaskNode(input_data={
        "action": "search",
        "query": "similar task"
    }),
    context
)

Features:

• Vector embeddings
• Pattern recognition
• Failure analysis
• Knowledge graph
• Memory consolidation

---

🤖 Agent Types

Research Agent

• Web search
• Document analysis
• Information synthesis

Code Agent

• Code generation
• Code review
• Refactoring
• Test generation

DevOps Agent

• Deployment
• Infrastructure provisioning
• System monitoring

API Agent

• REST API calls
• GraphQL queries
• Rate limiting

Data Processing Agent

• ETL operations
• Data cleaning
• Format conversion

FileSystem Agent

• File operations (sandboxed)
• Directory management

---

⚙️ Configuration

Orchestrator Configuration

config = {
    "max_event_history": 10000,
    "security": {
        "max_requests_per_hour": 100,
        "threat_threshold": 0.8,
        "session_timeout": 3600
    },
    "validation": {
        "auto_fix_enabled": True
    },
    "memory": {
        "max_memories": 10000,
        "similarity_threshold": 0.85,
        "consolidation_threshold": 100
    }
}

orch = create_orchestrator(config)

Security Context

from goatclaw.core.types import SecurityContext, PermissionScope

context = SecurityContext(
    user_id="user123",
    auth_token="token",
    origin_ip="192.168.1.1",
    allowed_scopes=[
        PermissionScope.READ,
        PermissionScope.WRITE,
        PermissionScope.EXECUTE
    ],
    is_authenticated=True,
    mfa_verified=True
)

Retry Configuration

from goatclaw.core.types import RetryConfig, RetryStrategy

retry_config = RetryConfig(
    max_retries=3,
    strategy=RetryStrategy.EXPONENTIAL_BACKOFF,
    initial_delay_seconds=1.0,
    max_delay_seconds=60.0,
    backoff_multiplier=2.0,
    jitter=True
)

node.retry_config = retry_config

---

📚 Examples

Example 1: Multi-Step Workflow

graph = TaskGraph(
    goal_summary="Research, code, and deploy",
    execution_mode=ExecutionMode.SEQUENTIAL
)

# Step 1: Research
research = TaskNode(
    node_id="research",
    agent_type=AgentType.RESEARCH,
    input_data={"query": "microservices patterns"}
)

# Step 2: Generate code (depends on research)
code = TaskNode(
    node_id="codegen",
    agent_type=AgentType.CODE,
    dependencies=["research"],
    input_data={"action": "generate"}
)

# Step 3: Deploy (depends on code)
deploy = TaskNode(
    node_id="deploy",
    agent_type=AgentType.DEVOPS,
    dependencies=["codegen"],
    input_data={"action": "deploy"}
)

graph.add_node(research)
graph.add_node(code)
graph.add_node(deploy)

result = await orch.process_goal(graph, ctx)

Example 2: Parallel Execution

graph = TaskGraph(
    goal_summary="Parallel research tasks",
    execution_mode=ExecutionMode.PARALLEL,
    max_parallel_tasks=5
)

topics = ["Python", "Rust", "Go", "JavaScript", "TypeScript"]

for topic in topics:
    node = TaskNode(
        node_id=f"research_{topic}",
        agent_type=AgentType.RESEARCH,
        input_data={"query": f"{topic} best practices"}
    )
    graph.add_node(node)

result = await orch.process_goal(graph, ctx)

Example 3: Custom Validation

# Register custom validator
def validate_price(config, output, task_node):
    price = output.get("price", 0)
    return {
        "valid": 0 < price < 1000,
        "message": f"Price ${price} is {'valid' if 0 < price < 1000 else 'invalid'}"
    }

validator.register_custom_validator("price_check", validate_price)

# Use in task
node.validation_rule = "price_check: {}"

---

📊 Monitoring & Metrics

Health Check

health = orch.get_health()
print(f"Active: {health.active_tasks}")
print(f"Completed: {health.completed_tasks}")
print(f"Failed: {health.failed_tasks}")
print(f"Error Rate: {health.error_rate:.1%}")
print(f"Uptime: {health.uptime_seconds}s")

Agent Metrics

metrics = agent.get_metrics()
print(f"Executions: {metrics['execution_count']}")
print(f"Success Rate: {metrics['success_rate']:.1%}")
print(f"Avg Time: {metrics['avg_execution_time_ms']}ms")
print(f"Circuit Breaker: {metrics['circuit_breaker_state']}")

Event Bus Metrics

metrics = event_bus.get_metrics()
print(f"Total Events: {metrics['total_events']}")
print(f"Error Rate: {metrics['error_rate']:.1%}")
print(f"Queue Size: {metrics['queue_size']}")

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🔒 Security Features

Rate Limiting

# Automatic rate limiting per user/IP
# Configurable limits and windows
# Sliding window algorithm
# Automatic blocking of abusive IPs

Audit Logging

# All security events are logged
logs = security_agent.get_audit_logs(
    user_id="user123",
    action="permission_check"
)

for log in logs:
    print(f"{log['timestamp']}: {log['action']} - {log['allowed']}")

Threat Detection

# Automatic threat scoring
# Pattern-based detection
# Configurable thresholds
# Real-time alerting

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🚢 Deployment

Docker (Coming Soon)

FROM python:3.11-slim
WORKDIR /app
COPY . .
RUN pip install -r requirements.txt
CMD ["python", "-m", "goatclaw.runner"]

Kubernetes (Coming Soon)

apiVersion: apps/v1
kind: Deployment
metadata:
  name: goatclaw
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: goatclaw
        image: goatclaw:latest

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🎯 Roadmap

• GraphQL API layer
• Web dashboard for monitoring
• Distributed execution across workers
• Integration with popular LLM APIs
• Plugin marketplace
• Advanced scheduling (cron-like)
• Multi-tenancy support
• Real-time collaboration features

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🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

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📄 License

MIT License - see LICENSE for details.

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💬 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: support@goatclaw.io

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🧪 Testing

GOATCLAW uses a comprehensive unified test suite powered by pytest.

Running Tests Locally

1. Install development dependencies:

   pip install -e .[dev]
   

2. Run the full test suite:

   pytest tests/
   

3. Run with coverage report:

   pytest --cov=goatclaw tests/
   

CI/CD

The project includes a GitHub Actions workflow that automatically runs tests across multiple Python versions (3.9, 3.10, 3.11, 3.12) on every push to the main branch.

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🙏 Acknowledgments

Built with ❤️ using modern Python async patterns and best practices from the distributed systems community.

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GOATCLAW - Autonomous workflows, intelligent execution, production-ready.