Agent frameworks · Decision brief
Alfe vs Mastra
A framework you host, or an OS that hosts the agent for you.
Mastra is a polished open-source TypeScript framework for building agents — you write the code and run the infrastructure, storage, and ops yourself. Alfe is the managed substrate underneath a live agent: a dedicated per-agent server, one USD credit pool across 9 model providers, managed vector + knowledge-graph memory, channels, and voice — running the moment the agent boots.
Alfe
Managed agent OS
Best when you need
A persistent agent with compute, models, memory, identity and channels managed together.
Mastra
Agent frameworks
Best when you need
TypeScript-native agent applications.
The short answer
Which one fits the job?
These products often sit at different layers of the stack. The useful question is not which has more ticks—it is whether you want to assemble the system or operate a finished agent.
Choose Alfe when
Mastra gives you primitives; Alfe gives you a substrate
Mastra is a genuinely good TypeScript framework — agents, workflows, memory types, and a model router with fallback. But it's a library: you still stand up the servers, wire a storage backend for memory, and own the ops. Alfe is the managed layer that runs a live agent — dedicated per-agent compute, managed memory, channels, and voice — so there's no infrastructure to build before the agent is answering messages.
Memory with no backend to run
Mastra bundles working memory, semantic recall, and observational memory — but every one of them needs an external store you provision and maintain (PostgreSQL, LibSQL, or Redis). Alfe ships a managed semantic vector store plus a knowledge graph out of the box, persistent from the first message, with an interactive memory-map view in the dashboard. Nothing to provision.
One credit pool, not a router you feed keys
Mastra's model router reaches 90+ providers — but you hold every provider's API key and pay each of them directly. Alfe routes 9 providers through one proxy and meters everything into a single tenant-wide prepaid USD credit pool: one bill, bounded spend, no key rotation. Bring your own key per tenant if you prefer.
Choose Mastra when
- Open source & free to self-host: Core is Apache-2.0 and free to self-host
- Language: TypeScript-only — excludes teams standardized on Python
- Pricing predictability: Self-host is free; Cloud is usage-metered (Teams $250/mo, memory $10/1M tokens) so cost is harder to predict
Capability matrix
Alfe and Mastra, side by side.
A practical comparison of product shape, operations and the capabilities a team receives without additional assembly.
| Capability | Alfe | Mastra |
|---|---|---|
| What it is | AlfeA managed agent OS — a hosted platform that runs a live agent for you | MastraAn open-source TypeScript framework (with an optional Mastra Cloud) you build agents with |
| Open source & free to self-host | AlfeNot open source — a paid managed platform | MastraCore is Apache-2.0 and free to self-host |
| Hosting & lifecycle | AlfeDedicated per-agent server (Hetzner VM or ECS), crash recovery, systemd auto-restart, reconciliation loop | MastraSelf-host the library on your own infra, or deploy to managed Mastra Cloud |
| Language | AlfeRuntime-agnostic managed platform (OpenClaw + Hermes runtimes) | MastraTypeScript-only — excludes teams standardized on Python |
| Model access & billing | AlfePooled proxy across 9 providers on one prepaid USD credit pool; BYOK + approved-model policy | MastraModel router to 90+ providers with fallback; you hold each provider's keys and pay each directly |
| Managed memory | AlfeSemantic vector store + knowledge graph, managed and persistent — no storage to run | MastraWorking memory, semantic recall & observational memory — but requires a BYO backend (PostgreSQL / LibSQL / Redis) |
| MCP support | AlfeNative — plus agents self-bootstrap over mcp.alfe.ai (proof-of-work → claim own compute) | MastraNative MCP client and tools |
| Channels & voice | AlfeSlack, Discord, Teams, Google Chat, web, mobile — plus streaming voice, SMS & WhatsApp on a real number | MastraFramework adapters (Next.js, React, Astro, Express, SvelteKit, Hono) — messaging channels are yours to build |
| Teams, projects & fleets | AlfeFull org hierarchy — orgs, teams, projects, roles, scoped sharing of memory/files/integrations | MastraLibrary-level; org structure and fleet management are yours to assemble |
| Pricing predictability | AlfeOne prepaid USD credit pool funds compute, models, memory, voice & channels — bounded spend | MastraSelf-host is free; Cloud is usage-metered (Teams $250/mo, memory $10/1M tokens) so cost is harder to predict |
Where Alfe differs
The operating layer is the product.
Mastra gives you primitives; Alfe gives you a substrate
Mastra is a genuinely good TypeScript framework — agents, workflows, memory types, and a model router with fallback. But it's a library: you still stand up the servers, wire a storage backend for memory, and own the ops. Alfe is the managed layer that runs a live agent — dedicated per-agent compute, managed memory, channels, and voice — so there's no infrastructure to build before the agent is answering messages.
Memory with no backend to run
Mastra bundles working memory, semantic recall, and observational memory — but every one of them needs an external store you provision and maintain (PostgreSQL, LibSQL, or Redis). Alfe ships a managed semantic vector store plus a knowledge graph out of the box, persistent from the first message, with an interactive memory-map view in the dashboard. Nothing to provision.
One credit pool, not a router you feed keys
Mastra's model router reaches 90+ providers — but you hold every provider's API key and pay each of them directly. Alfe routes 9 providers through one proxy and meters everything into a single tenant-wide prepaid USD credit pool: one bill, bounded spend, no key rotation. Bring your own key per tenant if you prefer.
Not just TypeScript
Mastra is TypeScript-only, which shuts out teams standardized on Python. Alfe is a managed platform, not a library you import — it runs OpenClaw or Hermes runtimes on your agent's server regardless of your own stack, and you talk to it over MCP, channels, and the agent API.
Where Mastra genuinely wins: it's open and free
Mastra's core is Apache-2.0 and free to self-host — if you want full code-level control and no vendor in the loop, that's a real strength Alfe doesn't match. Alfe is a paid managed platform. The trade is ops: you own the servers, storage, and scaling with Mastra; Alfe owns them for you.
How this comparison is made
Transparent by design.
This is a first-party Alfe comparison, not an independent review. We assess product positioning, hosting, model billing, memory, MCP, team controls and channels against publicly available product information. Products change; use the official source below for the latest detail.
Read Mastra documentation Compared with Alfe · MastraQuestions teams ask
Alfe vs Mastra FAQ.
Is Alfe a Mastra alternative?
For a different buyer. Mastra is an open-source TypeScript framework you build with and host yourself; Alfe is a managed platform that runs a live agent for you. If you want full code-level control and don't mind operating infrastructure, Mastra is excellent. If you want an always-on hosted agent with managed compute, memory, channels, and billing, Alfe covers the ops layer Mastra leaves to you.
Is Mastra free? Isn't that cheaper than Alfe?
Mastra's core is Apache-2.0 and free to self-host, and that's a real advantage — but 'free' is the license, not the running cost. You still pay for your own servers, a storage backend for memory, each model provider directly, and your team's ops time. Alfe is a paid platform that folds compute, models, memory, voice, and channels into one prepaid USD credit pool. Which is cheaper depends on whether you'd rather run infrastructure or buy it managed.
Do I have to bring a storage backend for memory like I do with Mastra?
No. Mastra's memory types require you to provision PostgreSQL, LibSQL, or Redis. Alfe ships a managed semantic vector store and a knowledge graph out of the box — persistent from the first message, with nothing to provision or maintain.
Can Alfe reach as many model providers as Mastra's router?
Mastra routes to 90+ providers; Alfe pools 9 (OpenAI, Anthropic, DeepSeek, Google Gemini, MiniMax, Mistral, xAI/Grok, OpenRouter, Zhipu/GLM) — and via OpenRouter reaches many more. The difference is billing: Mastra's router expects you to hold and pay each provider's key, while Alfe meters all 9 into one credit pool with an approved-model policy and per-tenant BYOK override.
Ready to run
Skip the infrastructure, keep the agent.
Mastra hands you the framework and the ops. Alfe runs the agent — dedicated compute, one credit pool across 9 providers, managed memory, channels, and voice — managed for you.