Visibility winner
deepset-ai/haystack
26.2K stars
Use this as the long-horizon mindshare read, not as the final answer.
Freshness winner
deepset-ai/haystack
Momentum not captured in this snapshot
Last commit Aug 15, 2026
Adoption winner
humanlayer/agentcontrolplane
Package mapping still partial
GitStar keeps the comparison directional when linked package telemetry is missing.
Data completeness
Comparison confidence
0/3 package links
0/3 repos have weekly momentum in the current snapshot.
Compare up to four repositories with comma-separated owner/repo names. GitStar keeps missing package or momentum data explicit instead of flattening it into a false zero.
deepset-ai/haystack has the strongest current movement signal in this set. Package mapping is still partial, so this comparison cannot declare a clean adoption winner yet.
GitStar keeps cross-repository gaps visible. Missing package mappings or partial momentum data lower confidence, but they do not stop the page from showing a directional read.
deepset-ai/haystack currently leads on long-horizon mindshare with the strongest star base in this comparison.
Stars are strongest at showing durable visibility, not direct production fit.
deepset-ai/haystack has the strongest current movement signal in this set.
Recent movement is missing from the current cache, so freshness falls back to commit recency.
Package mapping is still partial, so this comparison cannot declare a clean adoption winner yet.
Treat this as a directional evaluation and verify ecosystem usage directly from package registries.
This comparison lives in the same frameworks lane, so package usage and repo detail should carry more weight than raw stars alone.
Shared category overlap makes the comparison more decision-useful than a random popularity matchup.
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.
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?