Best Practices for Scaling OpenManus in Enterprise Environments

Launched in March 2025, OpenManus is an open-source AI agent framework that provides businesses with a scalable automation solution. With more than 16,000 GitHub stars, it is ready for widespread use. This document, which is pertinent to the Indian IT industry, describes best practices for scaling OpenManus in enterprise settings as of March 12, 2025.

• Base Setup: Ubuntu 22.04, 32GB RAM, Python 3.10+.

• Cloud: AWS, GCP or Azure account.

Best Practices

1. Cloud Deployment

• AWS Example:

• Use EC2 t3.large (8GB RAM) clusters.

• Configure load balancer for API calls.

• Steps:

2. Load Balancing

• Strategy: Distribute tasks across multiple instances using AWS ELB or GCP Load Balancer.

• Config: Set max_concurrent_tasks in config.toml.

3. Team Collaboration

• GitHub Workflow: Use branches for feature development, PRs for integration.

• API Keys: Centralize in a secure vault (e.g., AWS Secrets Manager).

4. Monitoring

• Tools: Integrate Prometheus for metrics on task throughput.

• Command: Run python3 main.py with logging enabled.

For a Small setup (10 users):

• Instances: 1 t3.medium
• RAM: 4GB
• Tasks per Hour: 50

For a Medium setup (50 users):

• Instances: 3 t3.large
• RAM: 24GB
• Tasks per Hour: 300

For a Large setup (200+ users):

• Instances: 10 t3.xlarge
• RAM: 160GB
• Tasks per Hour: 1,000+

• Cost: Free core, scales with cloud costs.
• Flexibility: Adapts to enterprise needs via open-source mods.

As demonstrated with AWS, scaling OpenManus for enterprise automation requires load balancing, deliberate cloud deployment, and teamwork. Its open-source nature allows for robust automation and cost savings for Indian businesses; best practices guarantee that it transforms from a tool to a pillar of IT efficiency.

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