Zeroshot — AI Agent Framework: Live Stats & TrendScore

Live GitHub stats, community sentiment, and trend data for Zeroshot. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: Apr 27, 2026 • Sentiment updated: May 8, 2026

GitHub Statistics

Community Sentiment

Community Buzz: I've been using Zeroshot - how would you say it compares?, Reddit

Pros & Cons

What People Love

Fast output, Zeroshot's capabilities, Reddit users praise Zeroshot

Common Complaints

Weak engineering, Overpriced models

Biggest Positive: Fast output

Biggest Negative: Weak output

Why Zeroshot Stands Out

Zeroshot is different from alternatives because it takes a multi-agent approach to code generation and validation, ensuring that code changes are thoroughly tested and validated before being accepted. This approach, combined with its support for multiple AI providers and isolated agents, makes Zeroshot a powerful tool for automating code review and generation. By using Zeroshot, users can ensure that their code changes are correct, reliable, and efficient. Additionally, Zeroshot's crash recovery feature and cost control mechanisms make it a robust and reliable choice for large-scale code generation tasks.

Built With

Build autonomous code review workflows — Zeroshot enables this by providing a CLI that runs multi-agent workflows to autonomously implement, review, test, and verify code changes, Build AI-powered pull request automation — Zeroshot supports this by allowing users to point the tool at an issue and have it generate production-grade code, Build automated bug fixing systems with validation — Zeroshot's multi-agent approach ensures that code changes are thoroughly validated before being accepted, Build multi-agent code generation systems for software engineering — Zeroshot's support for multiple AI providers and isolated agents makes it an ideal choice for this task, Build agentic coding workflows with blind validation — Zeroshot's unique approach to validation ensures that validators never see the worker's context or code history, preventing bias

Getting Started

  1. Install Zeroshot using the command `npm install -g @covibes/zeroshot`
  2. Install one or more AI provider CLIs, such as `npm i -g @anthropic-ai/claude-code` or `npm i -g @openai/codex`
  3. Authenticate with the provider CLI using commands like `claude login` or `codex login`
  4. Configure Zeroshot to use the installed provider CLI by running `zeroshot providers set-default codex`
  5. Try running Zeroshot with a sample issue, such as `zeroshot run 123`, to verify that it works

About

Your autonomous engineering team in a CLI. Point Zeroshot at an issue, walk away, and return to production-grade code. Supports Claude Code, OpenAI Codex, OpenCode, and Gemini CLI.

Official site: https://covibes.ai

Category & Tags

Category: multi-agent

Tags: agent-orchestration, agentic-workflow, ai-agent, ai-agents, autonomous-agents, claude, cli, codex, coding-assistant, developer-tools, gemini, generative-ai, github-automation, llm, llm-ops, llm-tools, multi-agent, vibecoding

Market Context

Competitive AI market