ACP is the Agent Control Plane - a distributed agent scheduler optimized for simplicity, clarity, and control. It is designed for outer-loop agents that run without supervision, and make asynchronous tool calls like requesting human feedback on key operations. Full MCP support.
First read
humanlayer/agentcontrolplane is better read as a directional signal than a clean recommendation. Keep the snapshot conservative and validate source activity, package reality, and close alternatives before committing to it.
462 public stars in the current GitStar snapshot.
Needs a narrower context read
Last commit Jul 2, 2025.
Stale activity
Treat stars as discovery context until a linked package appears.
No linked package mapping
One or more key signals are partial, so GitStar keeps the interpretation conservative.
Partial snapshot
Snapshot facts
Compare lens
deepset-ai/haystack and humanlayer/12-factor-agents are the closest comparison targets GitStar found. A side-by-side comparison usually tells you more than a single raw rank.
Signal trail
Read the recent motion first. This block is for deciding whether the repository still looks alive, compounding, or flattening before you trust stars alone.
Package reality
No linked npm or PyPI package is mapped for this repository yet, so the page leans more heavily on GitHub-visible popularity and should be read more conservatively.
No linked package signal is expected for this project type, so the read leans more heavily on repository-level public signals.
Validation note
GitStar can summarize public signals for humanlayer/agentcontrolplane, but the GitHub repository is still the primary place to confirm release cadence, issue activity, and maintainer intent.
GitStar surfaces public popularity and package signals. These rankings are not endorsements, security reviews, or investment advice.
Why this rank
This repo is here because it still carries strong GitHub attention.
Reconstructed from current stars and cached daily/weekly/monthly deltas.
GitStar can see repository momentum, but it does not have a reliable linked package signal yet.
Treat stars and recent movement as discovery context only until npm or PyPI usage is available.
Shares the frameworks category footprint with humanlayer/agentcontrolplane, so the comparison is closer to a same-problem decision than a same-language coincidence.
Shares the frameworks category footprint with humanlayer/agentcontrolplane, so the comparison is closer to a same-problem decision than a same-language coincidence.
Shares the frameworks category footprint with humanlayer/agentcontrolplane, so the comparison is closer to a same-problem decision than a same-language coincidence.
Shares the frameworks category footprint with humanlayer/agentcontrolplane, so the comparison is closer to a same-problem decision than a same-language coincidence.
GitStar picked haystack + 12-factor-agents as the closest next comparison from the related repository set.
[](https://gitstar.space/repo/humanlayer/agentcontrolplane)<a href="https://gitstar.space/repo/humanlayer/agentcontrolplane"><img src="https://gitstar.space/api/badge/humanlayer/agentcontrolplane" alt="GitStar"></a>Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.
What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?
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Your ultimate Go microservices framework for the cloud-native era.
Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or self-hosted, so teams move from prototype to production without rebuilding the stack.
The agent engineering platform.
This page provides a quick overview of humanlayer/agentcontrolplane based on GitStar's cached data. The signal chart reconstructs approximate checkpoints from current stars plus cached daily, weekly, and monthly star deltas, so it is best read as directional context rather than as a precise historical audit log.
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