Mastra vs Langfuse: Which Fits Your Apps Best (An honest take)?

Mastra and Langfuse both help you understand and improve your AI application. Their runtime roles differ, while their observability and evaluation capabilities overlap.

We built Mastra, the TypeScript framework for building AI agents, so the agent runtime and the development loop around it stay connected. You can build the agent, run workflows, manage memory, inspect traces, and evaluate behavior without stitching those pieces together yourself.

Langfuse specializes in observability, evaluation, and prompt management across applications and frameworks. If you build with Mastra, you can inspect traces in Studio or Mastra Platform, export them to Langfuse, or configure both destinations. Langfuse itself built Halo, its in-product assistant, with Mastra. This comparison explains where the products overlap and when each setup makes sense.

Mastra vs Langfuse at a glance

There are two decisions here: how to build and run the application, and how to observe and improve it. Mastra provides an agent runtime as well as tracing, evaluation, and configuration tools. Langfuse can work with Mastra or another runtime. Its observability and evaluation features overlap with Mastra's own tools, so the choice depends on your existing stack and how your team works.

1. Runtime control

Runtime control is the logic that can change what an agent is allowed to do while a run is in progress. It covers things like tool access, request-specific policy, output checks, and authorization.

Think of an infrastructure agent that can restart services. An engineer using the agent may only have permission to restart staging, not production. Whether the agent allows that operation depends on the authenticated user, the target environment, and the resource being changed.

Mastra can help you set these decisions inside the runtime. Tool hooks can intercept execution before and after a tool runs. Fine-grained authorization can also apply permission checks to agents, workflows, tools, memory, routes, and MCP servers at supported entry points. This authorization feature requires a Mastra Enterprise Edition license in production.

Because the runtime already has the request context, you can apply a policy where the operation happens instead of maintaining a separate layer around each tool.

Langfuse does not enforce execution policy. It instead records what happened around the decision. You can capture the user, tool arguments, result, metadata, and other trace attributes, while your application or framework remains responsible for authorization.

That is a good fit if your runtime already has a mature policy layer.

Verdict: Use Mastra's runtime controls when your agents need policy checks during execution. You can inspect those decisions in Mastra or export traces to Langfuse; your application remains responsible for enforcing its policies.

2. Observability and tracing

Tracing records the steps inside a run so you can explain how the system reached an output.

For an agent, the trace may include retrieval, model calls, tools, memory access, workflow steps, retries, and application code.

Suppose an agent cites an outdated API. You need to find out whether retrieval returned the wrong document, the correct document was ranked poorly, or the model ignored the right context.

The debugging path looks something like:

Mastra creates traces from the same runtime that executes the application. Our observability stack understands agents, model calls, tools, workflows, memory operations, logs, and metrics as Mastra primitives.

In Studio, you can inspect those traces alongside the agents and workflows you are developing. If you export to Langfuse, Mastra also sends agent and workflow identities, span types, and metadata. The distinction is how that context fits into your team's debugging workflow.

Langfuse is broader. Its tracing system can collect telemetry from Python, JavaScript, TypeScript, OpenTelemetry, and a wide range of frameworks. If a request starts in a TypeScript agent, calls a Python retrieval service, and then goes through a separate inference service, Langfuse can give you one trace layer across all three when those services are instrumented and propagate trace context.

Verdict: Mastra keeps tracing close to its agent and workflow development tools. Langfuse can provide a shared tracing workspace across different application stacks, including Mastra.

Finding patterns across runs

Individual traces explain a run. Patterns across traces help you decide what to improve.

Mastra's Trace Intelligence analyzes completed traces for user goals, outcomes, agent behaviors, and sentiment, then clusters similar signals into themes. You can follow those themes over time, inspect example traces, and explore which behaviors and outcomes appear together. The relationships show association, not causation. This gives you an ongoing view of how people use an agent and where it struggles. Trace Intelligence is currently a private beta for enrolled platform projects using deployed Studio.

Langfuse's Halo assistant can investigate and cluster errors across large observation sets, then help create datasets or dashboards from the findings. For recurring monitoring, configured evaluators can automatically classify incoming traffic into categories for charts and analysis. Halo is available on Langfuse Cloud, with self-hosting in public beta.

Compare the workflows: Mastra organizes recurring signals into evolving themes; Langfuse combines assistant-led investigations with configured evaluation and reporting.

3. Workflow state and memory

State in an agent application usually falls into two different categories. Workflow state tells you where execution should continue while memory tells you what information should influence a future model call.

Consider a deployment workflow that builds, deploys a canary, checks health, requests approval, and then deploys globally.

If approval arrives hours later, the system should resume at the suspended approval step and continue once approval is validated. It shouldn’t require inferring from the full conversation. In Mastra, the suspended step runs again with the supplied resume data, so it can process the approval before global deployment continues.

Mastra workflows persist execution snapshots to configured storage and can suspend while waiting for external input. Memory carries useful information across interactions and controls what enters future context. Observational Memory is one example of that. It condenses long histories into observations so the agent does not need the full transcript on every run.

Langfuse can record both types of state in traces. You can inspect the workflow execution and see the context that reached the model. It does not own the state itself. Your runtime still decides where execution resumes and what memory enters the next request.

Verdict: I’d keep workflow state and memory in Mastra when Mastra runs the application. Langfuse is useful for inspecting that state across runs and services.

4. Evals and experiments

Evals help you determine whether an agent follows its instructions and performs its assigned role.

A model change may leave the final answer intact while making the system worse in another way. An agent may call an expensive tool twice. A workflow may take the wrong branch. Retrieval may use a weaker source.

If those behaviors matter in production, the eval needs to cover them.

Here, “runtime score” evaluates execution behavior after a run. The evaluator defines when a repeated search counts as a failure; this is not a policy check that blocks execution.

Mastra experiments can run datasets against agents and workflows, then score the results. They target the same runtime primitives you use in the application, keeping evaluation close to your agent and workflow code.

For instance, for an agent, you may score answer quality. For a workflow, you may care about the path taken or final state. For a tool-heavy system, you may care about whether a particular tool was called at all.

Langfuse approaches evaluation from the shared platform side. Production traces can become dataset items, and its experiment SDK can run your application logic against those datasets. It also supports manual scoring, code-based checks, and model-based evaluators.

That works well when one evaluation system needs to cover applications built on different runtimes.

Verdict: If you use Mastra, its experiments and scorers let you evaluate agents and workflows in the same development environment. Langfuse is useful when your team wants to manage datasets and evaluation results in a shared platform across applications.

5. Prompt and agent versioning

Versioning gives you a controlled way to change agent behavior and roll it back. In an agent workflow, a prompt assumes certain tools exist. Those tools have schemas and permissions. The agent may also depend on memory, model settings, scorers, and runtime conditions.

We built Agent Editor around the wider agent configuration. You can work with draft and published versions, compare changes, roll back, and edit more than the instruction string. Studio edits instructions, tools, MCP clients, and variables on code-defined agents. The editor API covers a wider configuration surface, including memory and scorers. Fields such as the model on a code-defined agent still come from code.

Suppose you change an agent so it can issue refunds below $100. That change only works if the tool permissions and surrounding runtime rules agree with the new instructions.

Langfuse versions prompts and their configuration. Each prompt has immutable versions and environment labels such as production or staging. Its optional JSON configuration can include model parameters, output schemas, and tool definitions. Your application remains responsible for executing the tools and enforcing permissions.

Applications fetch the active version at runtime, and a label change can promote or roll back a prompt without redeploying the application. SDK caching can delay when a running application picks up that change.

That is useful when teams want to manage prompt releases across applications. Mastra's editor also lets teams publish supported agent configuration changes without redeploying. With either approach, you need to check that the published configuration remains compatible with the application's tools and runtime rules.

Verdict: Use Mastra's editor when you want to manage supported configuration for Mastra agents. Use Langfuse when you want a centralized prompt system across applications. You can also use Langfuse-managed prompts with Mastra agents.

6. Cross-framework telemetry

Cross-framework telemetry lets several applications send execution data into one observability system. This becomes relevant once the AI stack stops being uniform.

You may have a Mastra service in TypeScript, a Python retrieval service, direct model calls in another application, and a separate system built on LangChain.

Mastra's observability automatically instruments its runtime. Agents, workflows, tools, and memory operations can carry framework-specific context.

Langfuse is designed for the broader case. Its SDKs and OpenTelemetry support let unrelated applications send telemetry into the same project.

Langfuse's own assistant shows how the products fit together. The team built Halo with Mastra, using Mastra agents and skills to work with its existing MCP tools. Mastra powers the agent that helps Langfuse users investigate traces, create datasets, and build dashboards. Langfuse observes Halo with its own platform, using opt-in telemetry to improve its tools and guidance.

Mastra's Langfuse integration lets Mastra remain the application runtime while Langfuse receives its traces. You can also keep Mastra's storage or platform exporter configured to inspect traces in Studio or Mastra Platform.

Verdict: Choose where your team needs to inspect traces. Mastra integrates that work with its agent development tools; Langfuse brings traces from different application stacks into a shared workspace. You can configure both destinations.

7. Application stack coverage

The final difference between Mastra and Langfuse is how much infrastructure each product replaces.

These are capability categories, not a feature-parity chart. Permissions here means authorization within the running agent application.

Langfuse assumes you already have an application runtime. You still need a place to execute agents, run workflows, store memory, enforce permissions, handle retries, and manage state.

Langfuse then takes responsibility for a large part of the engineering layer around that application, including traces, datasets, experiments, scores, and prompt management.

That is a strong setup if those runtime pieces already exist.

Mastra covers more of the application layer directly. Agents can use tools and memory, participate in workflows, emit traces, and run through experiments without your team defining separate integration contracts between each subsystem. Studio gives you one development surface across those pieces.

The tradeoff is that you adopt more of Mastra's architecture.

If you already have Temporal, an internal authorization system, a memory service, and an agent runtime your team trusts, evaluate which Mastra components would fit alongside them. Check those integration points before deciding whether any existing system needs to change.

If you are starting a new agent product, those same systems are still decisions you need to make, integrate, test, and maintain.

Verdict: I’d start with Mastra when I need its agent runtime, workflows, and integrated development tools. For a Mastra application, evaluate whether its own observability meets your needs or whether Langfuse adds a useful shared workspace. An established runtime alone does not determine that choice.

If you are considering moving observability from Langfuse to Mastra Platform, trace imports let you bring supported historical trace observations with you. Scores, feedback, datasets, attachments, and logs are not included.

Getting started with Mastra

If you're evaluating Mastra for an agent product, try it against a piece of architecture you will need in production.

Follow the quickstart to create a project with your model provider. This example uses OpenAI:

npm create mastra@latest my-agent-app -- --llm openai
cd my-agent-app
npm run dev

Complete the setup prompts and configure your model provider credentials before running the agent. Studio runs at http://localhost:4111.

Build one agent around a narrow but real workflow. Give it the same kind of read-only tools your production agent will need.

If you're building developer infrastructure, that might mean repository search, documentation lookup, and CI status. If you're building an internal operations product, it might mean querying a database and an internal API with tenant-aware credentials.

Then exercise the boundaries you care about.

Make tool access depend on runtime state. Give the agent information that has to survive another interaction.

Put one deterministic step around the agent with a workflow. Save a few representative inputs as a dataset and run them again after changing the model or instructions.

You are not trying to prove that Mastra can call an LLM. You can do that with a few lines of code anywhere.

You are testing whether the pieces that appear after the first successful model call still form one system when you start building the product you intend to maintain.

Ready to test Mastra? Sign up to try Mastra.