Comparisons / DSPy vs Mastra
DSPy vs Mastra: Which Agent Framework to Use?
DSPy vs Mastra, head to head
DSPy and Mastra both let you build an agent, but they sit in different parts of the stack and they assume different things about who's writing the code.
DSPy replaces hand-written prompts with compiled modules.
Mastra is a TypeScript-first framework for building AI agents, from the team behind Gatsby.
Underneath, both wrap the same thing: a model call, a tool dispatch, a loop. The decision is about which abstraction your team wants to think in day to day, and which ecosystem you're willing to inherit along with it. There's an honest, framework-free version of the same pattern in about 60 lines of Python in the lesson at the bottom of this page — useful as a baseline regardless of which framework wins.
Pick DSPy if
Pick DSPy if dSPy's real innovation is automated prompt optimization — replacing manual prompt engineering with algorithmic tuning. This is genuinely novel and valuable for production systems where prompt quality matters at scale. For simple agents or learning, hand-written prompts are easier to understand and modify. The tradeoffs in its intro should match how your team already thinks about agents; Mastra will feel like translation if they don't.
Pick Mastra if
Pick Mastra if mastra is the best option for TypeScript teams that want a batteries-included agent framework without leaving the Node.js ecosystem. The workflow engine and Studio are genuinely productive. For simple agents or Python teams, the plain approach avoids an unnecessary dependency. The tradeoffs in its intro should match how your team already thinks about agents; DSPy will feel like translation if they don't.
By the numbers
By the numbers
DSPy
33.4k
2.8k
Python
MIT
2023-01-09
Stanford NLP (Omar Khattab)
Mastra
22.7k
1.8k
TypeScript
Apache-2.0
2024-08-06
Mastra AI
Spark Capital, Y Combinator
Series A ($22M, Apr 2026 — $35M total)
244.0k
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | DSPy | Mastra |
|---|---|---|
| Agent | `dspy.ReAct` module with signature and tools | `new Agent({ model, instructions, tools })` with automatic tool dispatch |
| Prompts | `dspy.Signature` defines input/output fields, compiled to optimized prompts | — |
| Optimization | `dspy.BootstrapFewShot`, `MIPROv2` auto-tune prompts against a metric | — |
| Tools | Tools passed to `ReAct` module as callable list | `createTool({ name, schema, execute })` with Zod validation |
| Chaining | `dspy.ChainOfThought`, `dspy.Module` with `forward()` composition | — |
| Evaluation | `dspy.Evaluate` with metric functions and dev sets | — |
| Workflows | — | `Workflow` class with `.step()`, `.then()`, `.branch()` for orchestration |
| RAG | — | Built-in document syncing, chunking, embedding, and vector search |
| Memory | — | Short-term thread memory + long-term vector memory across sessions |
| Studio | — | Mastra Studio: local GUI for testing agents, viewing traces, debugging |
Or build your own in 60 lines
Both DSPy and Mastra implement the same 8 patterns. An agent is a function. Tools are a dict. The loop is a while loop. The whole thing composes in ~60 lines of Python.
No framework. No dependencies. No opinions. Just the code.
Build it from scratch →