Dropstone vs Mastra: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Dropstone and Mastra — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Dropstone
Blankline
Self-hosted AI agent with long-term memory that spans CLI, chat, SDK and real-world actions, running on open-weight models you host.
Key features
- Persistent Cross-Surface Memory: Teach the agent something once in the CLI and it already knows it in chat, in the SDK and on a phone call — memory persists per user across sessions and surfaces instead of dying with one login.
- Self-Hosted Open-Weight Stack: Run the entire agent inside your own walls on your keys, machines and network, using open weights the company hosts or local models through Ollama, so source code never leaves your infrastructure.
- Proactive Background Operation: The agent is already running rather than waiting to be opened — it monitors what you asked it to watch and hands back only the decision that was actually yours.
- Approval-Gated Real-World Actions: Control smart-home devices, monitor an inbox around the clock, place phone calls and look up half-remembered contacts, with every action gated behind an explicit approval.
- 1M-Token Context on Every Tier: A one-million-token context window is included even on the free plan, letting the agent hold an entire repository in mind at once.
- Model-Agnostic Tiering: Dropstone Fast, Pro and Heavy each run whatever tops the open-weight leaderboards that month rather than being tied to a single lab.
- Learned Skills: The agent picks up skills it does not yet have, retains them and reuses them without being asked twice, with the skill list growing month over month.
- Multi-Surface Access: Reach the same agent through the Dropstone CLI, a web dashboard, VS Code / Cursor / Windsurf extensions and Remote MCP connectors, with sandboxed code execution and plan mode before changes apply.
Best for
- Air-Gapped Engineering Teams: Ship real code with an AI agent while keeping the models, the repository and the network entirely inside company infrastructure.
- Always-On Inbox Triage: Let the agent watch an inbox around the clock and surface or act on the messages that matter instead of checking it yourself.
- Terminal-Native Development: Use the CLI agent to generate code, run it in a sandbox and open diffs, with plan mode and approval gates before anything is applied.
- Personal Operations Automation: Hand off recurring real-world tasks — smart-home control, placing a call, chasing a contact — to an agent that already has your context.
- Cost-Sensitive Heavy Usage: Get several times more weekly coding usage per dollar than subscription coding CLIs by running on self-hosted open-weight models.
- Custom Agent Integration: Embed the same memory-backed agent into your own stack through the SDK and Remote MCP connectors.
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