Sim

What is Sim?

Sim is an open-source workspace where teams build, deploy, and govern AI agents in one place. Design agents on a visual canvas, describe them in chat, or write code, then connect them to hundreds of apps and every major model. Built-in logs, knowledge bases, and spend controls keep everything manageable.

Top Features:

• Flexible building: design agent workflows visually, through chat, or directly in code.
• Knowledge bases: ground agents in docs synced from Notion, Google Drive, and Confluence.
• Governance: set spend limits, permission groups, SSO, and audit records organization-wide.

Use Cases:

• Support routing: an agent reads tickets, checks the knowledge base, and routes them.
• Lead enrichment: a webhook triggers research on new leads and updates a table.
• Knowledge upkeep: scheduled jobs sync the knowledge base from Slack, GitHub, and SharePoint.

Who Can Use Sim?

• Developers: write agents in code, then deploy them as APIs or webhooks.
• Operations teams: automate routine processes with agents using company data and apps.
• IT leaders: control access, spending, and data for every agent in the company.

Pricing

• Free ($0): 1,000 one-time credits, one workspace, and 30 days of logs.
• Pro ($25 per user monthly): 6,000 monthly credits, three workspaces, 50 GB storage, and unlimited logs.
• Max ($100 per user monthly): 25,000 monthly credits, unlimited workspaces, sandboxes, and live knowledge sync.
• Enterprise (contact sales): custom credits, SSO, access control, SOC2 compliance, self-hosting, and dedicated support.

Pros and Cons

Pros:

• Open source: the Apache 2.0 code on GitHub can be inspected and self-hosted.
• Detailed logs: every run is traced block by block with costs and failures.
• Wide model choice: connect OpenAI, Anthropic, Google, DeepSeek, xAI, or local models via Ollama.

Cons:

• Credit system: credit billing makes monthly costs harder to predict in advance.
• Small free tier: the free plan gives one-time credits with no monthly refill.
• Learning curve: governance, tables, and deployment options take newcomers time to master.

FAQs:

1) Is it open source?

Yes, the Apache 2.0 code on GitHub has nearly 30,000 stars.

2) Can I host it myself?

Yes, it runs on Docker or Kubernetes on your own servers.

3) Which models does it support?

OpenAI, Anthropic, Google, DeepSeek, xAI, Cerebras, and local models all work.

4) How can agents be triggered?

Agents run from API calls, webhooks, schedules, or a chat window.

5) Is there a free plan?

Yes, it includes 1,000 one-time credits and one workspace to start.