Mastra vs Polyant (2026)

Mastra

by Mastra (ex-Gatsby team)

Polyant

by Exelab

Pros

• ✓TypeScript-native framework rather than a Python-first port. Designed from scratch for the TS/JS ecosystem with full IDE autocomplete, Zod schema validation, and deep integration with Next.js, React, Express, and Hono. Meaningful advantage for web developers who do not want to learn Python to ship production agents.
• ✓Model router connects to 40+ providers (OpenAI, Anthropic, Google, DeepSeek, Groq, Mistral, xAI, Bedrock, Azure, Ollama, OpenRouter, Vercel) through one standard interface with automatic fallbacks. Broader provider coverage than LangChain's JS port and dramatically simpler than wiring providers manually.
• ✓Strong adoption signals demonstrate production readiness. 28.7k GitHub stars, $13M YC seed funding, v1.0 in January 2026, and published case studies with Replit, Salesforce, Sanity, SoftBank, Factorial, WorkOS and Plaid confirm it is meaningfully more battle-tested than newer agent frameworks.

Limitations

• –TypeScript-only by design with no Python support. The team views this as a feature, but Python-first teams committed to LangChain, CrewAI, or AutoGen workflows cannot use Mastra without rewriting in TypeScript. Not appropriate for data science or ML teams already standardized on the Python ecosystem.
• –Regulated data is contractually out of scope on the standard terms. Mastra holds SOC 2 Type II but publishes no ISO 27001, and its agreement prohibits submitting protected health information under HIPAA, cardholder data under PCI DSS, or Gramm-Leach-Bliley financial data unless separately agreed in an order form.
• –Enterprise features (RBAC, SSO, ACL) require paid Mastra Cloud or a commercial license. The Teams plan at $250/month adds SSO and SOC 2 documentation, and the Enterprise tier is quote-only on both the hosted and self-hosted surfaces. Only monthly rates are published, with no annual option.

Pros

• ✓The runtime, admin panel, long-term memory, channels and audit logging are open source under AGPLv3 on GitHub, with no license fee for self-hosting.
• ✓Every runtime decision is written to PostgreSQL tables in the customer's own database, where compliance teams can query it with a standard SQL client.
• ✓Agents run on-premises, in the customer's AWS, Azure or GCP account, or on Exelab infrastructure in an EU region, with the model provider chosen per agent.

Limitations

• –The ten runtime governance controls and the regulatory audit registry come with the Advanced and Ultra managed plans, not with the self-hosted build.
• –Managed plans and Exelab build services are priced per case, and model usage is billed separately by the chosen provider.
• –Email, HubSpot Conversations and phone-call channels need managed services, and enterprise SSO over SAML or OIDC is on the Ultra plan.