Sim offers an open‑source, node‑based visual canvas that lets engineers design, test, and deploy AI agent workflows using drag‑and‑drop blocks for LLM agents, conditionals, loops, parallel execution, and custom Python code. The platform provides native integrations with 100+ SaaS and data sources, built‑in scheduling, rate‑limiting, hosted or self‑hosted runtimes, and a CLI/SDK for CI/CD, along with enterprise‑grade logging and role‑based access controls.
Funding
$500K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Developers and product teams must write extensive code to orchestrate large language models (LLMs) with external APIs, schedule recurring tasks, and handle looping or conditional logic, which slows prototyping and raises maintenance overhead. Existing solutions lack a unified visual interface that can represent complex agent architectures without deep engineering effort.
Solution
Sim delivers an open‑source, node‑based canvas that lets users design, test, and deploy AI agent workflows visually. Users can drag and drop blocks representing LLM agents, conditionals, loops, parallel execution, and custom Python functions, then wire them to over 100 native integrations such as Slack, Salesforce, GitHub, and vector databases. The platform handles scheduling, rate‑limiting, and asynchronous execution out of the box, while a CLI/SDK enables CI/CD pipelines and version control. Workflows are exported as portable definitions that can run on Sim’s hosted runtime or on self‑hosted infrastructure, providing flexibility for both startups and enterprise deployments. Built‑in logging, execution tracing, and role‑based access controls ensure observability and security without additional tooling.
Target Audience
The primary users are software engineers, data scientists, and product teams building LLM‑powered automation, as well as enterprises that need scalable, auditable AI agent pipelines.
Features
- Visual drag‑and‑drop canvas with modular blocks (Agent, Loop, Condition, Parallel, Webhook, etc.) for rapid workflow assembly
- Native connectors to 100+ SaaS and data sources (e.g., Slack, Salesforce, GitHub, Pinecone, MongoDB) with OAuth and API key management
- Embedded Python function block allowing custom code execution within the workflow graph
- Scheduler and loop engine supporting cron‑style triggers, time‑zone aware execution, and conditional branching
- Hosted execution environment with configurable sync/async rate limits (e.g., 150 runs/min sync, 1,000 runs/min async) and self‑hosted option via Docker
- CLI and SDK for programmatic workflow deployment, versioning, and integration into CI/CD pipelines
- Comprehensive logging, execution tracing, and role‑based access control compliant with SOC2 standards for enterprise use