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GPT All Star

Generate web application code from scratch with a collaborating team of AI agents.

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Source repo
kyaukyuai/gpt-all-star
Stars
★ 238
Last updated
1y ago
License
MIT
Primary language
Python
FA score
47/100 · Major gaps

At a glance

How it runs
CLISelf-hosted serviceLibrary / SDK
Works with
Universal · cross-platformOpenAI APIClaude API (Partial support)
Cost
Free software; you pay for model usage
Setup effort
Medium · a few setup steps
You'll need
PythonDockerOpenAI API key, Azure OpenAI credentials, or Anthropic API keyShell / CLINetwork accessLocal filesystem
Typical use
Developers exploring multi-agent collaboration can use it to try a from-scratch client web application workflow.
Not a fit if
  • Teams needing a mature, production-ready code-generation workflow
  • Users unwilling to configure model API credentials
Source review
47/100 · Major gaps 1 safety controls not found

What does this agent do, and when should you use it?

GPT All Star is a research project for generating web applications from scratch through collaboration among AI agents. For each step, a team selects a leader; the leader plans the work, and team members carry out the tasks. Users can install and run the CLI, or follow the developer setup with Docker Compose before launching the project. The README says the current focus is validating client web applications built with JavaScript, React, and Chakra UI. It documents configuration for OpenAI, Azure OpenAI, or Anthropic endpoints, and GitHub variables for managing generated code; it does not specify the generated files’ exact location or a deployment workflow.

Users can install the package with pip install gpt-all-star and run gpt-all-star, or clone the repository, configure .env, start the Docker environment with make build and make up, install dependencies with Poetry, and launch the command. The CLI's --step option selects none, default, build, specification, system design, development, entrypoint, UI design, or healing. Other options include --project_name, --japanese_mode, --review_mode, --debug_mode, and --plan_and_solve. Team members can be edited in gpt_all_star/agents.yml. The configured model endpoint is used by agents that plan and collaborate on web application generation; the README does not specify the exact output path or delivery process.

  1. Developers exploring multi-agent collaboration can use it to try a from-scratch client web application workflow.
  2. Teams working with JavaScript, React, and Chakra UI can use it to validate generation of a new web application.
  3. Developers who want to prototype by stages can select specification, system design, or development with --step.
  4. Users who want to change the run mode can try the Japanese, review, or debug CLI options.
  5. Researchers who want a custom agent team can edit gpt_all_star/agents.yml and run the project.

How do you install or deploy this agent?

User installation:

How do you use this agent?

pip install gpt-all-star
export OPENAI_API_MODEL=gpt-4o
export OPENAI_API_KEY=<your-openai-api-key>
gpt-all-star

For developer setup, configure .env for an OpenAI, Azure OpenAI, or Anthropic endpoint; GitHub and LangSmith variables are also documented. To start the Docker development environment:

mv .env.sample .env
make build
make up

Open the web terminal at http://localhost:7681, then run:

poetry install
poetry run gpt-all-star --step development --project_name <project-name>

What are this agent's strengths and limitations?

Pros
  • Organizes agents as a collaborating team, with a step leader planning work for members to execute.
  • The CLI exposes stages including specification, system design, development, UI design, and healing.
  • Documents configurable model endpoints for OpenAI, Azure OpenAI, and Anthropic.
  • Provides a Docker Compose developer path and lets users edit team members in agents.yml.
Limitations
  • The project identifies itself as research-focused, currently validating React, Chakra UI, and JavaScript client applications.
  • Running it requires model provider credentials, and API usage may incur costs.
  • The Docker developer path involves environment configuration, container startup, and Poetry dependency installation.
  • The README does not document the exact generated-code location, deployment boundary, or production guarantees.

How does this agent compare with similar options?

Key facts side by side with the most closely related agents.

Agent Source review Form / cost Stars Updated Language Full support on
GPT All Star This agent 47 · Major gaps CLIFree + model costs ★ 238 1y ago Python OpenAI API
Lang-Agent Visual Agent Configuration Platform 45 · Major gaps Web appFree + model costs ★ 331 10mo ago TypeScript —
Dive into LangGraph 44 · Major gaps Library / SDKFree + model costs ★ 469 1mo ago Jupyter Notebook Claude Code
Generative AI with LangChain 41 · Major gaps Library / SDKFree + model costs ★ 1.5k 2mo ago Jupyter Notebook OpenAI API · Claude API

How does FollowAgents rate this agent?

FollowAgents source review · FARS-2.1
Major gaps
47/ 100 5-point scale 2.4 / 5
Trust 8/29
Reliability 6/14
Adaptability 12/18
Convention 10/18
Effectiveness 7/13
Verifiability 4/8
Why each dimension lost points
Trust8 / 29 · 1.4/5

The Agent receives file read, write, and delete tools plus a ShellTool scoped to a working directory; the README also mentions using a GitHub token to manage generated code. This shows some scope control, but the evidence does not show granular permissions or confirmation before risky actions, so least privilege and user confirmation are only partly supported. The README lists OpenAI, Azure, Anthropic, LangSmith, and GitHub environment variables, but does not explain where prompts, generated code, or telemetry go, or how credentials are protected. The dependency list is broad, with no lock details or vulnerability response policy in this material. No rollback mechanism is shown. The LICENSE and package metadata name Yuya Kakui, but fuller provenance verification and maintenance responsibility are not documented.

Reliability6 / 14 · 2.1/5

The README, CLI, and package metadata are broadly consistent about the product and how to start it. The material lists a Python version range and major dependencies but shows no availability guarantees or recovery plan. AgentExecutor enables parsing-error handling and console input handles EOF, but evidence about general error messages and external service failures is limited.

Adaptability12 / 18 · 3.3/5

The README identifies the project as research, says its current focus is validating React, Chakra UI, and JavaScript, and welcomes other technologies; this makes the audience and current scope fairly clear. CLI options cover steps, Japanese, review, debug, and Plan-and-Solve modes, and team members can be changed in configuration. More detailed scenario boundaries and an environment compatibility matrix are not shown, so scores are partial to adequate.

Convention10 / 18 · 2.8/5

The README provides a contents list, user and developer setup, environment variables, startup commands, and a Pomodoro example; organization is clear, but examples and FAQs are sparse. Package, CLI, and role names are broadly stable. The research status and current technical focus are stated. The LICENSE and package metadata both identify MIT, and the license text is complete. A package version is present, but no changelog appears in the sample; contribution instructions exist without a clear maintenance owner or update commitment.

Effectiveness7 / 13 · 2.7/5

The product focuses on turning requirements into web applications through multi-agent collaboration and can display output files, giving users understandable artifacts and a clear use. The README describes a research project, and provides no cost, latency, or output-quality measurements, so cost-benefit support is limited.

Verifiability4 / 8 · 2.5/5

README setup instructions, options, and technical scope partly correspond to the CLI and package metadata; the test sample also provides source-level corroboration for some console, LLM construction, and configuration-loading behavior. The packet is a sample, so cross-source coverage is limited. The README clearly states the research positioning and current focus; this review treats unexecuted tests and unshown files as unknown rather than evidence of passing or failing.

Risks and how to mitigate them
  • Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
  • Before running it, confirm the working directory and ShellTool boundaries, and put generated code under version control or backup; no automatic rollback is shown.
  • Before configuring API keys, a GitHub token, or LangSmith tracing, verify which services and workflows use those credentials; the sample does not explain data flows or credential protection.
Evidence confidence: Low Reviewed Nov 07, 2026 Reviewed revision cc9336f763bf
Review evidence README.mdgpt_all_star/main.pyagents/agent.pyLICENSEpyproject.tomlworkflows/ai-pr-reviewer.ymlworkflows/ci.ymlcli/test_console_terminal.pycore/test_llm.pyhelper/test_config_loader.py
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Recent releases

Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.

  1. v0.0.65
    Release notes excerpt
    What's Changed * chore(deps): update dependency mypy to v1.14.1 by @renovate in https://github.com/kyaukyuai/gpt-all-star/pull/785 * fix(deps): update dependency llama-index to v0.12.9 by @renovate in https://github.com/kyaukyuai/gpt-all-star/pull/786 * chore(deps): update dependency ruff to v0.8.5 by @renovate in https://github.com/kyaukyuai/gpt-all-star/pull/788 * fix(deps): update dependency gitpython to v3.1.44 by @renovate in https://github.com/kyaukyuai/gpt-all-star/pull/787 …
    View on GitHub ↗
  2. v0.0.64 v0.0.64 🌈
    Release notes excerpt
    Changes - fix(deps): update dependency langchain to v0.3.13 @renovate (#775) - fix(deps): update dependency langchain-anthropic to v0.3.1 @renovate (#778) - fix(deps): update dependency llama-index to v0.12.8 @renovate (#772) - fix(deps): update dependency langgraph to v0.2.60 @renovate (#774) - fix(deps): update dependency langchain-openai to v0.2.14 @renovate (#776) - fix(deps): update dependency langchain-experimental to v0.3.4 - autoclosed @renovate (#779) - fix(deps): update dependency jinja2 to v3.1.5 @renovate (#782) …
    View on GitHub ↗
  3. v0.0.63 v0.0.63 🌈
    Release notes excerpt
    Changes - fix(deps): update dependency uvicorn to ^0.34.0 @renovate (#767) - chore(deps): update dependency python-semantic-release to v9.15.2 @renovate (#768) - chore(deps): update dependency mkdocs-material to v9.5.49 @renovate (#769) - chore(deps): update dependency ruff to v0.8.3 @renovate (#763) - fix(deps): update dependency openai to v1.57.4 @renovate (#764) - fix(deps): update dependency uvicorn to ^0.33.0 @renovate (#765) - fix(deps): update dependency langchain to v0.3.12 @renovate (#766) - fix(deps): update dependency langchain to v0.3.11 @renovate (#754) …
    View on GitHub ↗

FAQ

Which technologies are currently in scope?
The README says the current focus is client web applications using JavaScript, React, and Chakra UI, while inviting experiments with other languages and libraries.
Which model providers can I configure?
The developer configuration lists OpenAI, Azure OpenAI, and Anthropic endpoints; the user installation example uses OpenAI gpt-4o.
Does running it cost money?
The repository is MIT-licensed, but running it requires model API credentials. The README does not state provider pricing.
Does it deploy the generated application?
The README describes code generation but does not document automatic deployment or the exact location of generated files.
Can I change the agent team?
Yes. The README directs users to edit gpt_all_star/agents.yml to change team members.
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