Open-Source AI Agent Platforms: What to Compare Before Self-Hosting
Evaluate open-source AI agent platforms by model support, workflow control, observability, security, deployment and maintenance cost.
Open source gives access to code, not freedom from operations. The right platform depends on who will build agents, where data may travel and how failures are reviewed.
Selection checklist
- Model and provider flexibility, including local-model support.
- Workflow control, retries, human approval and state management.
- Tracing, evaluations, prompt/version history and cost visibility.
- Authentication, secrets, tenant isolation and tool permissions.
Recommended products
Relevant products from the RoboFacet database, with facts to verify before choosing.
Browse related productsPrototype the failure path
Test tool timeouts, malformed model output, rate limits and human escalation. Agent demos are easy; predictable recovery and auditability determine production readiness.
Price the operating model
Include inference, vector storage, observability, backups, upgrades and on-call ownership. A free license can still produce an expensive production system.
Frequently asked questions
Is self-hosting automatically more private?
No. Data may still leave through model APIs, tools or telemetry. Map every data flow and configure logging, retention and secrets deliberately.
Framework or visual platform—which is better?
Visual platforms suit cross-functional teams and fast workflows; frameworks offer deeper control. Many teams prototype visually and code the critical path later.