Comparisons / AutoGPT vs Eve

AutoGPT vs Eve: Which Agent Framework to Use?

AutoGPT vs Eve, head to head

AutoGPT 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.

AutoGPT was one of the first autonomous agent projects, spawning 165k+ GitHub stars.

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 AutoGPT if

Pick AutoGPT if autoGPT pioneered the autonomous agent pattern, but most of its complexity comes from managing an unbounded loop — not from the core agent logic. For bounded tasks, a plain while loop with tool dispatch gives you the same capability with full control over when to stop. The tradeoffs in its intro should match how your team already thinks about agents; Eve will feel like translation if they don't.

Full AutoGPTcomparison →

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; AutoGPT will feel like translation if they don't.

Full Evecomparison →

What both add

Whichever you pick, you're inheriting a dependency tree and a vocabulary your team has to learn before they ship anything. AutoGPT has its own class hierarchy and tool registration conventions; Eve has its. Either way, when something misbehaves you'll be reading framework source before you reach the actual HTTP call.

If the real workload is one model and a handful of tools, both can feel like a workbench for driving a nail. The lesson below builds the same pattern in plain Python — useful as a comparison point even if you ultimately keep the framework.

By the numbers

By the numbers

AutoGPT

GitHub Stars

183.1k

Forks

46.2k

Language

Python

License

MIT

Created

2023-03-16

Created by

Toran Bruce Richards

github.com/Significant-Gravitas/AutoGPT

Eve

GitHub Stars

3.5k

Forks

180

Language

TypeScript

License

Apache-2.0

Created

2026-06-17

Created by

Vercel

Backed by

Vercel (public)

Cloud/SaaS

Runs on Vercel Sandbox + AI Gateway; deploys anywhere Node runs

Production ready

Yes

github.com/vercel/eve

GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.

ConceptAutoGPTEve
AgentAutoGPT `Agent` class with goal decomposition and self-prompting loopA directory with `agent.ts` + `instructions.md` + subfolders — the framework wires them together
ToolsPlugin system with web browsing, file I/O, code execution, Google searchEach file in `tools/` exports one tool; schema comes from a Zod export
Agent LoopAutonomous loop: think → plan → act → observe → repeat until goal met
MemoryVector DB (Pinecone/local) for long-term memory, message history for short-term
PlanningGPT-4 generates multi-step plans, stores in task queue, revises on failure
Self-CritiqueBuilt-in self-evaluation prompt that critiques each action before executing
DurabilityVercel Workflow SDK checkpoints every step so a crashed agent resumes where it left off
Sub-agentsEach `subagents/*.ts` becomes a callable sub-agent the parent can hand off to
Sandboxed execVercel 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 AutoGPT 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 →