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INDEPENDENT SIDE-BY-SIDE

omnigent-ai/omnigent vs 777genius/agent-teams-ai

Compare current project strength and the unmet needs people are actually discussing on GitHub. No paid placement influences this comparison.

RADAR VERDICT

777genius/agent-teams-ai has the stronger current discovery signal, driven by the score components shown below.

omnigent-ai/omnigent is categorized as Coding; 777genius/agent-teams-ai is categorized as Coding. Compare their product descriptions and source repositories before choosing.

Signalomnigent-ai/omnigent777genius/agent-teams-ai
Radar Score83/10091/100
Adoption23/2519/25
Maintenance20/2020/20
Project quality20/2020/20
Agent relevance20/2020/20
Demand evidence0/1010/10
Observed momentum0/52/5
GitHub stars10,9932,292
CategoryCodingCoding
Qualified unmet needs03
Leading problem patternsNo qualified pattern yetProduct Capability workflows (2), Multi-user & team routing (1)
TRACEABLE GITHUB DEMAND

omnigent-ai/omnigent

0 qualified needs

No qualified pattern yet

No current Issue clears the demand threshold.

Full project evidence

omnigent-ai/omnigent

Omnigent is an open-source AI agent framework and meta-harness: orchestrate Claude Code, Codex, Cursor, Pi, and custom agents — swap harnesses without rewriting, enforce policies and sandboxing, and collaborate in real time from any device.

Full analysis

777genius/agent-teams-ai

You're the boss, agents are your team. They handle tasks on their own, message each other, and review each other's work. You just watch the kanban board and give high-level commands. Codex/Claude/OpenCode/Cursor/Grok/GitHub/Kiro/Z.AI/Xiaomi/MiniMax/Kimi(300+ models, 200+ LLM providers, free models no auth) Build your AI company with multiple teams

Full analysis
FROM COMPARISON TO ACTION

Test the need you understand best

A stronger repository is not automatically a stronger business. Open the leading GitHub need, speak to affected users and let behavioral evidence decide.

Transparent scoring

Adoption (25 points) uses stars and forks, maintenance (20) uses code activity, quality (20) checks license and metadata, relevance (20) checks agent focus, demand (10) uses open Issues, and momentum (5) uses star growth.