Mastra vs OpenLIT: Which is Better in 2026?
A comprehensive comparison of Mastra and OpenLIT covering features, pricing, use cases, and which tool is the right choice for your needs.
⚡ Quick Verdict
Choose Mastra if:
- →You need a broader feature set (6 features vs 5)
- →You need agents, tools, and workflows as typescript primitives or built-in observability: traces, metrics, and logs
Choose OpenLIT if:
- →You need opentelemetry-native tracing — spans go to your existing collector and backend or instruments gpus, llms, mcp servers, vector dbs and coding agents
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Mastra vs OpenLIT: At a Glance
Pricing Comparison: Mastra vs OpenLIT
Understanding the pricing differences between Mastra and OpenLIT is crucial for making the right choice. Here's how their plans compare side by side.
Mastra Pricing
OpenLIT Pricing
💡 Pricing takeaway: Both Mastra and OpenLIT offer free tiers, making it easy to try before you buy. Compare the specific plans to find the best value for your use case.
Feature-by-Feature Comparison
Here's how every feature from Mastra and OpenLIT stacks up.
What Makes Each Tool Unique
🔵 Unique to Mastra
Features available in Mastra but not in OpenLIT:
- ✓Agents, tools, and workflows as TypeScript primitives
- ✓Built-in observability: traces, metrics, and logs
- ✓Evals, experiments, scorers, and datasets
- ✓Studio for collaborative iteration on agents
- ✓Server product for cloud deployment
- ✓Extensive learning material — course, books, templates, workshops
🟣 Unique to OpenLIT
Features available in OpenLIT but not in Mastra:
- ✓OpenTelemetry-native tracing — spans go to your existing collector and backend
- ✓Instruments GPUs, LLMs, MCP servers, vector DBs and coding agents
- ✓Built-in LLM evaluations and model comparison
- ✓Prompt management and an API key vault
- ✓Apache 2.0, self-hosted via Helm chart or Docker with OAuth sign-in
Use Case Recommendations
Best for: Mastra
Mastra is a TypeScript framework for building AI agents and the applications around them, built by the team that previously created Gatsby. The core framework covers agents, tools, and workflows as first-class primitives, so a long-running agent is expressed in ordinary TypeScript rather than assembled from prompt strings and glue code. Around that framework sits a platform: observability with metrics, logs, and traces so agent runs are inspectable after the fact; evals, experiments, scorers, and datasets for measuring whether a change actually improved behavior; a Studio for collaborating on and iterating over agents; an Agent Builder; and a Server product that handles cloud deployment for agents. The company emphasizes agents that run for days rather than single-turn calls, which is what pushes the observability and scoring surface to the center of the product instead of leaving it as an add-on. Mastra is open source with roughly 26.7k GitHub stars and maintains a substantial learning surface — a quickstart, project templates, a video course, two books (Principles of Building AI Agents and Patterns of Building AI Agents), live workshops, and a weekly podcast — which makes it one of the more approachable entry points for JavaScript developers moving into agent work.
Ideal use cases:
- •Teams or individuals who need agents, tools, and workflows as typescript primitives
- •Teams or individuals who need built-in observability: traces, metrics, and logs
- •Teams or individuals who need evals, experiments, scorers, and datasets
- •Teams or individuals who need studio for collaborative iteration on agents
- •Anyone focused on typescript workflows
- •Anyone focused on agent framework workflows
Best for: OpenLIT
OpenLIT is an Apache 2.0 open-source observability and engineering platform for LLM and agent workloads, built on OpenTelemetry rather than on a proprietary tracing format. That choice is the substance of the product: because instrumentation emits standard OTel spans, traces can go to your existing collector and backend instead of being locked into a vendor's storage, and the same pipeline that carries your service traces carries your agent traces. Coverage runs wider than the usual LLM-call span — it instruments GPUs, LLMs, MCP servers, vector databases and coding agents, which means an agent's slow step can be attributed to the retrieval layer or the GPU rather than assumed to be the model. Around tracing it adds the adjacent pieces teams otherwise assemble separately: running LLM evaluations, managing prompts, comparing models against one another, and storing API keys in a built-in vault rather than in environment variables scattered across services. The lifecycle framing on the site runs instrument, develop, manage, observe, improve, covering both development and production stages. Deployment is self-hosted via Helm chart or Docker with OAuth sign-in, documented for both. A fully hosted OpenLIT Cloud is announced for teams that would rather not operate it, but is not yet available.
Ideal use cases:
- •Teams or individuals who need opentelemetry-native tracing — spans go to your existing collector and backend
- •Teams or individuals who need instruments gpus, llms, mcp servers, vector dbs and coding agents
- •Teams or individuals who need built-in llm evaluations and model comparison
- •Teams or individuals who need prompt management and an api key vault
- •Anyone focused on opentelemetry workflows
- •Anyone focused on observability workflows
🤖 Other AI Agent Infrastructure Tools to Consider
Mastra and OpenLIT aren't the only options. Here are other popular tools in the same space:
SuperAGI
Open-source autonomous AI agent framework with visual dashboard — 14K GitHub stars
MetaGPT
Multi-agent AI framework simulating software teams — 45K GitHub stars, builds full apps from prompts
Cerebras
Fastest LLM inference powered by the Wafer Scale Engine.
Scale AI
AI data platform for training data and model evaluation.
Roboflow
End-to-end computer vision platform for developers.
Labelbox
Enterprise data labeling platform for ML training datasets.
Is one of these your tool?
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Frequently Asked Questions
Is Mastra better than OpenLIT?
It depends on your needs. Mastra offers 6 key features including Agents, tools, and workflows as TypeScript primitives and Built-in observability: traces, metrics, and logs, while OpenLIT provides 5 features including OpenTelemetry-native tracing — spans go to your existing collector and backend and Instruments GPUs, LLMs, MCP servers, vector DBs and coding agents. Mastra uses a freemium model with a free tier, while OpenLIT is free with free access available. Choose based on which features and pricing model align with your requirements.
Is Mastra cheaper than OpenLIT?
OpenLIT doesn't have standard paid plans, while Mastra starts at The framework is open source and free. Hosted platform tiers exist on the pricing page but the tier table is rendered client-side and not readable from a plain fetch, so no figure is quoted here.. Both tools offer free tiers, so you can try each before committing. Always check the official websites for the most current pricing.
Can I use Mastra and OpenLIT together?
Yes, many users combine Mastra and OpenLIT in their workflow. Mastra excels at agents, tools, and workflows as typescript primitives, while OpenLIT shines with opentelemetry-native tracing — spans go to your existing collector and backend. Using both allows you to leverage the strengths of each tool, though this means managing two subscriptions — though free tiers can help manage costs.
What's the main difference between Mastra and OpenLIT?
While both are ai agent infrastructure tools, Mastra emphasizes agents, tools, and workflows as typescript primitives, whereas OpenLIT is known for opentelemetry-native tracing — spans go to your existing collector and backend. The best choice depends on your specific workflow and feature priorities.
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