fx vs Sim: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of fx and Sim — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

fx

Vercel Labs

Free

Vercel Labs' tiny open-source coding agent — a ~6 MB native CLI written in Zig, built for speed, embeddability and Unix-style ergonomics.

Tiny Native Binary: The whole agent ships as a roughly 6 MB executable designed for instant installation and for embedding in resource-constrained environments and agent sandboxes.
Instant Time to Prompt: fx cold starts in about 10 microseconds and performs no unnecessary work or I/O before accepting user input, which matters for programmatic invocation.
Minimal Memory Footprint: A single-digit-megabyte memory baseline lets you pack many concurrent instances onto one machine.
Shell-Like Ergonomics: Scroll history is preserved by default and output is deliberately sparse, so the CLI composes like a Unix tool instead of taking over the terminal.
Context Efficiency: A minimal system prompt and tool surface reduce token spend and improve time-to-first-token performance.
WebAssembly Builds: Optimal fx.wasm builds from the Zig toolchain shrink the binary further and make the network stack pluggable, enabling the in-browser demo.
Model and Provider Agnostic: Works with local models, LLM gateways, direct provider API access or existing subscriptions rather than locking you to one vendor.
Extensible Small Core: Capabilities are added through skills, plugins and MCPs, following a Unix-like philosophy of a small core with composable extensions.

Sandboxed Agent Execution: Ship a full coding agent inside a container or sandbox where a large runtime would not fit.
Embedding in Larger Systems: Use fx as the agent harness inside your own product or internal platform rather than building a loop from scratch.
CI and Scripted Automation: Invoke a coding agent from pipelines and scripts where fast cold starts and quiet output matter more than an interactive UI.
Agent Harness Research: Experiment with system prompt and tool design on a deliberately minimal, readable Apache-2.0 codebase.
Local-Model Coding: Run agentic coding against a locally hosted model without any dependency on a specific cloud provider.
Browser-Based Demos and Playgrounds: Compile to WebAssembly and run the agent client-side with networking delegated to browser fetch.

Freemium

Open-source AI workspace to build, deploy and manage AI agents across 1,000+ integrations from one platform.

Visual Agent Builder: Drag-and-drop canvas for wiring prompts, tools, memory and LLM calls into deployable agents.
1,000+ Integrations: Ready-made connectors to CRM, sales, support, dev and productivity tools so agents can act across a company's stack.
Multi-model Support: Route each step to the best model from every major LLM provider (OpenAI, Anthropic, Google, open-weights).
Runtime & Scheduling: Deploy agents as scheduled jobs, webhooks or API endpoints with retries and error handling.
Team Workspace: Shared library of prompts, tools and agents with role-based access so teams reuse rather than rebuild.
Open Source & Self-Hostable: Full source available so security-sensitive teams can run Sim in their own cloud.
Observability: Traces every agent run with inputs, tool calls and outputs so builders can debug and improve prompts.

Sales operations: Build agents that enrich inbound leads, sync them into the CRM and hand them to reps.
Customer support: Deploy triage agents that read tickets, pull knowledge-base answers, and draft or send replies.
Internal automations: Wire agents into Slack, Google Workspace or Notion to automate reports, briefs and reminders.
Data workflows: Chain LLM steps with API tools to summarize dashboards, clean CSVs or extract structured data.
Compliance-sensitive teams: Self-host Sim to keep prompts, tool credentials and traces inside the company's own cloud.