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zeroshot
by covibes
CLI-driven autonomous engineering with multi-LLM agent orchestration
JavaScript
Updated Jul 18, 2026
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How It Works
Automates end-to-end developer workflows by running an autonomous engineering team from the CLI. Point Zeroshot at a GitHub issue and it spawns agents that plan, code, test, and deploy using supported LLMs like Claude, OpenAI Codex, OpenCode, and Gemini. Notable for its CLI-driven, hands-off approach that targets production-grade code delivery and GitHub automation, leveraging patterns like the Event-Driven Agent Pattern and the Planning Pattern.
The Value Proposition
As teams adopt autonomous agents for coding tasks, being able to run, audit, and reproduce agent work is essential for trust. Zeroshot makes agent delegation and pipeline execution visible and repeatable, which helps capture agent track records and failure modes. For anyone building agent-to-agent evaluation or reputation systems, the repo provides a real-world workflow generator to test continuous evaluation and traceability ideas, drawing on Reputation concepts.
When to Use
Developers and teams who want to automate GitHub issue-to-production workflows and evaluate agent-generated code in real repositories, supported by alignment with the Model Context Protocol (MCP).
How It's Used
- Automate issue triage and PR creation from GitHub issues using LLM agents
- Validate agent delegation patterns by running full plan→implement→test cycles
- Capture agent interaction logs and outcomes for pre-production evaluation
Works With
openaianthropic
Topics
agent-orchestrationagentic-workflowai-agentai-agentsautonomous-agentsclaudeclicodexcoding-assistantdeveloper-tools+8 more
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Keywords
multi-agent orchestrationagent delegationautonomous-agentsgithub-automation