zenith MCP Server
Intelligent-Internet/zenith
[](https://agentrank-ai.com/tool/Intelligent-Internet--zenith/?utm_source=badge&utm_medium=readme&utm_campaign=agentrank_badge) Zenith: a continuous-improvement harness for long-running agent tasks. Turns Claude Code, Codex, or Hermes into a multi-agent mission orchestrator via MCP/ACP.
claude mcp add agentrank -- npx -y agentrank-mcp-server Overview
Intelligent-Internet/zenith is a Python tool licensed under Apache-2.0. Zenith: a continuous-improvement harness for long-running agent tasks. Turns Claude Code, Codex, or Hermes into a multi-agent mission orchestrator via MCP/ACP. Topics: agent-client-protocol, agent-harness, ai-agents, claude-code, codex, llm, long-horizon-tasks, mcp, model-context-protocol, multi-agent, orchestration.
Ranked #5628 out of 24846 indexed tools.
Ecosystem
Score Breakdown
346 stars → growing community
Last commit 2mo ago → moderate activity
7/22 issues closed → many open issues
9 contributors → small team
No dependents → no downstream usage
Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →
How to Improve
Matched Queries
From the README
# Zenith: A Continuous-Improvement Harness for Long-Running Tasks <p> <a href="https://github.com/Intelligent-Internet/zenith/actions/workflows/ci.yml"></a> <a href="LICENSE"></a> <a href="technical_report/Technical_Report.pdf"></a> </p> Zenith is an agent harness for work that may run for days or weeks, where the dominant failure mode is *premature completion* rather than inability to make progress. It runs a coding agent (Claude Code, Codex, or Hermes) as a multi-agent orchestrator over MCP/ACP: one orchestrator session reads task state each turn and decides whether to spawn workers and testers, register reusable skills, replan, or stop. This repository contains the Zenith harness ([`zenith/`](zenith/)) and the Intelligent Internet technical report (2026) behind it. > **[Read the report (PDF)](technical_report/Technical_Report.pdf)** ## Abstract Long-running agents often fail not because they cannot make progress, but because they stop before the task is truly completeRead full README on GitHub →
Get the weekly AgentRank digest
Top movers, new tools, ecosystem insights — straight to your inbox.