OpenExpertise vs Mastra

OpenExpertise is a YAML-defined SOP runtime with a self-improvement loop; Mastra is a TypeScript framework for building full-stack AI applications with agents, workflows, and RAG.

What Mastra is

Mastra is an open-source TypeScript framework aimed at developers building AI-powered applications — think production backends that need agents, retrieval-augmented generation, memory, and workflow orchestration all in one coherent package. It integrates with a broad set of LLM providers via the AI SDK and ships first-class support for tool calling, voice, and multi-step workflow graphs defined in TypeScript code.

Mastra's workflow system lets you define directed graphs as TypeScript objects: nodes are typed steps, edges carry typed context, and the runtime handles execution, retries, and state. Unlike pure orchestration layers, Mastra is designed to be the application framework itself — you build your AI features on top of it, not alongside it. The Mastra team also provides managed cloud deployment for workflows, agents, and knowledge bases.

For a TypeScript team building a new AI product — customer support bots, internal knowledge assistants, code generation pipelines — Mastra is a compelling all-in-one choice. It reduces the number of libraries a team needs to integrate and provides consistent abstractions across agents, memory, and retrieval.

Where they overlap

• TypeScript / Node.js runtime.
• Workflow graphs with typed state flowing between nodes.
• LLM agent abstractions with structured output.
• Support for multiple LLM providers.
• Tool/function calling integration.

Where OpenExpertise differs

Dimension	OpenExpertise	Mastra
Graph definition	Declarative YAML — the graph is data, not code	TypeScript code — Mastra Step / Workflow objects
Schema validation	AJV validates state.schema and graph structure before execution	TypeScript types + Zod schemas at runtime
Target use case	Codifying a team's expert SOP into a persistent, evolvable artifact	Building a production AI application backend
Evolution loop	oe evolve proposes graph patches from runtime traces	No equivalent
CLI integration	cli-agent node calls Claude Code / Codex / Gemini subprocesses	No native CLI delegation
Self-improvement	First-class: author → run → evolve is a closed loop	Not present
MCP server	Exposes OE as 6 MCP tools; can be called from Claude Code	Supports MCP as a consumer
Authoring	oe ultra "<task>" auto-generates YAML	Code-first; no LLM authoring of the workflow graph
Node heterogeneity	6 kinds: tool, agent, skill, dataset, experience, cli-agent	Steps are TypeScript functions; heterogeneity modeled in code

When to pick Mastra over OpenExpertise

• You're building a production AI application (not codifying an existing SOP) and want an all-in-one framework.
• You need built-in RAG, vector search, and memory alongside workflows.
• Your team wants workflow logic expressed in TypeScript rather than YAML.
• You need Mastra Cloud for managed deployment, scaling, and observability.
• You're integrating voice or multi-modal capabilities into your application.

When to pick OpenExpertise over Mastra

• The problem is a recurring, human-defined SOP — a process your team already follows that you want to codify, not a new application you're building.
• Non-engineer stakeholders need to read and review the workflow definition.
• You want the evolution loop: runtime behavior drives graph improvement proposals.
• You want to call Claude Code, Codex, or Gemini as first-class subprocess workers from within the graph.
• You want the graph to live as a YAML file alongside your code, tracked in git and deployed without a cloud service.

Specific positioning

Mastra and OpenExpertise are both TypeScript-first and both model work as graphs, but they serve different ambitions. Mastra is for building AI products; OpenExpertise is for automating the recurring expert processes that run those products — code review, incident triage, release gates, compliance checks.