Visibility winner
bytedance/deer-flow
81.9K stars
Use this as the long-horizon mindshare read, not as the final answer.
Freshness winner
bytedance/deer-flow
+687 in the weekly window
Last commit Sep 8, 2026
Adoption winner
bytedance/deer-flow
Package mapping still partial
GitStar keeps the comparison directional when linked package telemetry is missing.
Data completeness
Comparison confidence
0/2 package links
1/2 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.
bytedance/deer-flow currently leads on long-horizon mindshare with the strongest star base in this comparison. 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.
bytedance/deer-flow 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.
bytedance/deer-flow has the strongest current movement signal in this set.
Weekly movement is +687 in the weekly window.
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.
An open-source long-horizon SuperAgent harness that researches, codes, and creates. With the help of sandboxes, memories, tools, skill, subagents and message gateway, it handles different levels of tasks that could take minutes to hours.
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.