Apache Maka vs Mastra: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Apache Maka and Mastra — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Apache Maka
The Apache Software Foundation
Apache-licensed local-first agent workspace that runs tools in a sandbox and records every model message and tool call as a recoverable execution log.
Key features
- Append-Only Execution Record: Model messages, tool calls, tool results, permission decisions, and turn termination events are written down durably, so the transcript is evidence rather than a disposable chat buffer.
- Context Trimming Without Data Loss: Old tool output can be omitted from the next prompt to shorten context while the full saved history remains intact and inspectable.
- Single Runtime Host: Desktop, terminal, and evaluation all execute through one runtime, so behavior does not diverge between how you develop and how you benchmark.
- Sandboxed Tool Boundary: Built-in Read, Write, Edit, Bash, Glob, and Grep tools run under a sandbox; anything leaving that boundary requires approval, and Computer Use and catalog skills are opt-in.
- Crash Recovery and Resume: Runs can be aborted, failures are classified, and an interrupted turn can optionally be resumed rather than restarted from scratch.
- Session Branching and Search: The desktop workspace supports creating, archiving, searching, renaming, retrying, regenerating, and branching sessions from any turn.
- Bring Your Own Model: Connect a cloud API, a locally hosted model, or a compatible gateway, with streaming output, thinking, usage reporting, and clearer provider errors.
- Declarative Evaluation Harness: maka eval expands multi-arm experiments into task by repetition by subject cells with immutable per-cell attempts and a result kernel covering score, normalized usage, attributable cost, duration, and failure reason.
- Local-First Storage: Sessions, settings, artifacts, and run records stay on the machine by default, with local memory and optional web search when configured.
Best for
- Auditable Agent Runs: Keeping a defensible record of exactly what an agent did and which permissions were granted during a task.
- Long Coding Sessions: Working through a multi-turn refactor with branching and resume instead of losing state when a turn fails.
- Agent Benchmarking: Running reproducible multi-arm experiments comparing models, prompts, or external agent subjects on the same task set.
- Air-Gapped or Regulated Work: Running an agent workspace where sessions and artifacts must remain on local infrastructure.
- Cost and Usage Analysis: Attributing token usage, cost, and duration per experiment cell to decide which model configuration to ship.
- Terminal Workflows: Driving an agent from the current project directory or scripting a single non-interactive turn from CI or a shell.
- Open-Source Agent Research: Building on a permissively licensed runtime whose execution semantics and architecture are fully documented.
Mastra
Mastra (team behind Gatsby)
A TypeScript-first agent framework with workflows, memory, streaming, playground, evals, and tracing for building AI apps.
Key features
- Unified Model Interface: Provides a single API to access hundreds of models from dozens of providers (documented access to 1113 models from 53 providers) so developers can switch or compare models without changing application logic.
- Workflows and Orchestration: First-class workflow primitives to compose multi-step agent behaviors and pipelines, enabling complex task decomposition, tool invocation, and sequential processing.
- Long-term Memory: Built-in memory abstractions to persist and recall conversational or agent state across sessions, improving continuity and personalized behavior.
- Streaming Outputs: Support for streaming model responses to enable low-latency progressive output and responsive UX in interactive applications.
- Interactive Playground: A development playground for iterating on prompts, agent strategies, and tool integrations with live testing and debugging.
- Evals and Tracing: Integrated evaluation tooling and tracing to measure agent performance, run automated evaluations, and inspect decision traces for observability and improvement.
- Templates and Example Agents: Ready-made templates (e.g., an AI web search assistant) and sample projects to accelerate building real-world applications.
- Multi-provider Tooling: Facilities to equip agents with external tools, connectors, and integrations while managing provider-specific details through Mastra abstractions.
- TypeScript-first agent framework optimized for modern TypeScript stacks
- Workflow orchestration for multi-step agent behaviors
- Persistent memory management for agents
- Streaming response support for real-time output
- Interactive playground for developing and testing agents
- Evaluation tooling (evals) for measuring agent performance
- Tracing and observability for agent executions
- Unified model interface providing access to 1,113 models from 53 providers via a single API
- Templates and example applications (including a web search assistant)
- Open-source repository and community resources (mastra-ai/mastra on GitHub)
- Course and learning materials for building and deploying agents
Best for
- Building autonomous TypeScript agents that coordinate tools, perform multi-step reasoning, and maintain state with memory across interactions.
- Creating an AI-powered web search assistant that crawls, extracts, and sources open-web information using Mastra templates and connectors.
- Comparing and switching LLM providers easily during development by leveraging Mastra's unified model interface to test dozens of models without rewriting code.
- Developing production workflows that stream partial model outputs to users for real-time feedback while tracing and evaluating agent decisions.
- Prototyping and evaluating agent strategies using the interactive playground and built-in evals to iterate on prompts and measure performance.
- Teaching and onboarding teams through the Mastra course to learn how to equip agents with tools, memory, and MCP patterns in a TypeScript environment.
- Packaging TypeScript-based AI applications with reproducible workflows, templates, and observability for deployment and maintenance.
- Building tool-enabled conversational agents with memory and multi-step workflows
- Creating web search and information retrieval assistants with sourced answers
- Rapidly prototyping and testing agent behavior in an interactive playground
- Integrating many LLM providers through a single unified API for model experimentation
- Deploying production agents with tracing, evals, and observability