Skip to main content
📖 The AI Tool Bible

gpt-engineer vs LangGraph

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

 
gpt-engineer
Agents
LangGraph
Agents
TaglineDescribe software in natural language, watch an AI agent write, run, and improve it.Stateful, graph-based agent orchestration from LangChain.
CategoryAgentsAgents
PricingFree· Free and open source under MIT license. Users pay only for the underlying LLM API calls (OpenAI, Anthropic, Azure OpenAI) or run local models at zero token cost.Freemium· Developer: $0 / seat per month · Plus: $39 / seat per month · Enterprise: Custom pricing
ModelOpenAI GPT (default), Anthropic Claude, Azure OpenAI, and open-weights models like WizardCoder via configurationBYO (Claude / GPT / open)
Editorial score—8.8 / 10
Use cases
Greenfield script and prototype generationSmall single-file utility creation from a specIterative code improvement via improve modeCoding-agent research and benchmarking (APPS, MBPP)Teaching example for LLM agent loopsScriptable code generation in CI pipelinesLocal-model code generation with self-hosted LLMs
stateful agentshuman-in-loopproduction
Pros
  • Fully open source (MIT) with a small, readable codebase that's easy to fork or study.
  • Model-agnostic: swap OpenAI, Azure OpenAI, Anthropic, or local open-weights models.
  • Zero platform cost — you pay only for tokens, or nothing at all with local models.
  • Simple CLI-first workflow (`gpte <dir>`) that scripts and CI can call.
  • Built-in improve mode for iterating on existing code, not just greenfield generation.
  • Preprompt customization lets you retune agent behavior without patching source.
  • Ships with benchmarking against APPS and MBPP for coding-agent research.
  • Reliable, debuggable agent graphs
  • Built-in persistence + HITL
  • Production-grade
  • Tight LangSmith integration
Cons
  • Repository was archived in April 2026 — no active maintenance, bug fixes, or new model support.
  • Best suited to small greenfield projects; struggles on large multi-file codebases.
  • No IDE integration — lives entirely in a terminal with a text prompt file.
  • Requires bring-your-own API keys and manual configuration for non-OpenAI models.
  • Python 3.10-3.12 only; older environments are unsupported.
  • Newer agents (Aider, Cursor, Claude Code, Cline) have overtaken it on both quality and DX.
  • Steeper learning curve than CrewAI
  • Verbose to set up
Websitegithub.comwww.langchain.com
Pick gpt-engineer if
  • ✅ Fully open source (MIT) with a small, readable codebase that's easy to fork or study.
  • ✅ Model-agnostic: swap OpenAI, Azure OpenAI, Anthropic, or local open-weights models.
  • ✅ Zero platform cost — you pay only for tokens, or nothing at all with local models.
  • ✅ Simple CLI-first workflow (`gpte <dir>`) that scripts and CI can call.
Pick LangGraph if
  • ✅ Reliable, debuggable agent graphs
  • ✅ Built-in persistence + HITL
  • ✅ Production-grade
  • ✅ Tight LangSmith integration
Compare a different pair →