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The traditional approach to deploying AI agents is painful. RunAgent eliminates the infrastructure complexity so you can focus on what matters: building great AI agents.

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The Problem Every AI Developer Faces

You have built an amazing AI agent in Python. It works perfectly. Now your team wants to use it:
  • Frontend team needs it in JavaScript
  • Mobile team wants it in Kotlin
  • Systems team requires it in Rust
  • Unity team needs it in C#

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The Traditional Approach

  • Create FastAPI/Flask endpoints
  • Handle request/response serialization
  • Write API documentation
  • Manage versioning and backward compatibility
  • Implement WebSocket handlers
  • Manage connection lifecycle
  • Handle reconnection logic
  • Deal with message ordering
  • Implement authentication
  • Add rate limiting
  • Manage API keys and scopes
  • Secure against common attacks
  • Set up load balancers
  • Configure auto-scaling
  • Monitor resource usage
  • Handle cold starts and warm-up
  • Create client libraries for each language
  • Handle async/await patterns
  • Manage connection pooling
  • Provide streaming interfaces
Result: Weeks or months of infrastructure work before your agent is usable.

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The RunAgent Solution

⚡ Deploy in Minutes

One command: runagent serve . and your agent is live with full API and SDK support.

🌐 Multi-Language Ready

Automatically generates native-feeling APIs for Python, JavaScript, Go, and Rust.

📡 Streaming Built-in

Real-time token streaming works out of the box across all languages.

🔒 Production Security

Sandboxed execution, API authentication, and rate limiting included.

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Cost Comparison

Time Investment:
  • 2-4 weeks for basic REST API
  • 1-2 weeks for streaming support
  • 2-3 weeks for security implementation
  • 1-2 weeks per language SDK
  • Ongoing maintenance and updates
Total: 2-3 months of development timeOngoing Costs:
  • Infrastructure management
  • Security updates
  • SDK maintenance
  • Monitoring and debugging
Time Investment:
  • 5 minutes to deploy
  • Focus on agent logic only
Ongoing Costs:
  • Zero infrastructure management
  • Automatic updates and security patches
  • Built-in monitoring and scaling

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Real-World Example

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Before RunAgent

# Your agent logic
def analyze_document(content: str) -> str:
    return llm.analyze(content)

# You need to build:
# - FastAPI server
# - WebSocket handlers  
# - Authentication middleware
# - Rate limiting
# - Error handling
# - SDKs for JS, Go, Rust
# - Deployment configuration
# - Monitoring setup
Time to production: 6-8 weeks

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With RunAgent

# Your agent logic (same as before)
def analyze_document(content: str) -> str:
    return llm.analyze(content)

# Configuration
# runagent.config.json
{
  "entrypoints": [{"file": "agent.py", "module": "analyze_document", "tag": "analyze"}]
}

# Deploy
runagent serve .
Time to production: 5 minutes

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What You Get vs What You Build

✅ What RunAgent Provides

  • Sandboxed execution (Firecracker microVMs)
  • Auto-scaling (0 to thousands of requests)
  • Multi-language SDKs (Python, JS, Go, Rust)
  • Streaming support (WebSocket + HTTP)
  • Authentication (API keys, scopes)
  • Rate limiting (per-agent, per-user)
  • Monitoring (logs, metrics, traces)
  • Error handling (retries, timeouts)
  • Security (isolation, input validation)

❌ What You Would Build

  • Custom API framework
  • WebSocket infrastructure
  • Language-specific clients
  • Authentication system
  • Rate limiting logic
  • Monitoring dashboard
  • Error handling
  • Security measures
  • Deployment automation

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The Developer Experience Difference

1

Traditional Approach

# Week 1-2: Set up FastAPI
pip install fastapi uvicorn
# Write API endpoints, middleware, etc.

# Week 3-4: Add streaming
pip install websockets
# Implement WebSocket handlers

# Week 5-6: Security
pip install python-jose
# Add auth, rate limiting

# Week 7-8: SDKs
# Write clients for each language

# Week 9-10: Deployment
# Docker, Kubernetes, CI/CD
2

RunAgent Approach

# Write your agent
def my_agent(query: str) -> str:
    return process(query)

# Deploy
runagent serve .

# Use from any language
# Python, JS, Go, Rust - all work immediately

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When to Choose RunAgent

✅ Perfect For

  • Rapid prototyping of AI agents
  • Multi-language teams needing shared agents
  • Startups wanting to focus on AI, not infrastructure
  • Production deployments requiring reliability
  • Streaming applications (chat, real-time analysis)
  • Teams without dedicated DevOps resources

❌ Not Ideal For

  • Simple scripts that only run locally
  • Legacy systems with complex integration requirements
  • Teams with extensive existing infrastructure
  • Applications requiring custom protocols
  • On-premise only deployments (currently)

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The Bottom Line

RunAgent is not just another deployment tool — it is a complete platform designed specifically for AI agents. It eliminates the 80% of infrastructure work that is common to all agent deployments, letting you focus on the 20% that makes your agent unique.
Time saved: 2-3 months of development Focus gained: 100% on AI agent logic Languages supported: 4+ out of the box Production ready: From day one

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Ready to Get Started?