Comparisons / DSPy vs Eve
DSPy vs Eve: Which Agent Framework to Use?
DSPy vs Eve, head to head
DSPy and Eve 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.
Eve is Vercel's open-source TypeScript agent framework, launched June 17 2026.
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; Eve will feel like translation if they don't.
Pick Eve if
Pick Eve if eve earns its keep when you want durable execution, sandboxed code exec, and multi-model routing without wiring three separate services. If you're already on Vercel, it composes; if not, the runtime pieces are the value and they don't travel. For a single-loop tool-using agent, plain TypeScript ships faster. 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)
Eve
3.5k
180
TypeScript
Apache-2.0
2026-06-17
Vercel
Vercel (public)
Runs on Vercel Sandbox + AI Gateway; deploys anywhere Node runs
Yes
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | DSPy | Eve |
|---|---|---|
| Agent | `dspy.ReAct` module with signature and tools | A directory with `agent.ts` + `instructions.md` + subfolders — the framework wires them together |
| 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 | Each file in `tools/` exports one tool; schema comes from a Zod export |
| Chaining | `dspy.ChainOfThought`, `dspy.Module` with `forward()` composition | — |
| Evaluation | `dspy.Evaluate` with metric functions and dev sets | — |
| Durability | — | Vercel Workflow SDK checkpoints every step so a crashed agent resumes where it left off |
| Sub-agents | — | Each `subagents/*.ts` becomes a callable sub-agent the parent can hand off to |
| Sandboxed exec | — | Vercel Sandbox runs untrusted code in isolated micro-VMs, one API call away |
| Schedules | — | `schedules/*.ts` exports a cron expression + handler; Vercel runs it |
Or build your own in 60 lines
Both DSPy and Eve 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 →