Which is more popular, DeerFlow or Maestro?

DeerFlow has more GitHub stars (83,349 vs 4,357).

Which is more actively developed, DeerFlow or Maestro?

DeerFlow had more commits in the last 90 days (1,274 vs 0).

Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.

Side-by-side comparison of two AI agent tools

Maestro has had no commit in 27 months; DeerFlow is actively maintained (1,274 commits in the last 90 days).
DeerFlow is growing faster: +5,271 GitHub stars in the last 30 days vs +5 for Maestro.
Pick DeerFlow for: open-source agent harness for long-horizon research, coding, and content creation. Pick Maestro for: a framework for Claude Opus to intelligently orchestrate subagents.

From GitHub data refreshed daily.

DeerFlowopen-source

Open-source agent harness for long-horizon research, coding, and content creation

A framework for Claude Opus to intelligently orchestrate subagents.

+Comprehensive agent orchestration system that coordinates sub-agents, memory, and sandboxes for complex multi-step tasks
+Extensible skills framework allows customization and expansion of agent capabilities beyond basic functionality
+Active development with a complete 2.0 rewrite showing commitment to architectural improvements and long-term maintenance

+Multi-provider support allows switching between Anthropic, OpenAI, Google, and local models seamlessly
+Intelligent task decomposition automatically breaks complex objectives into executable sub-tasks
+Local execution capabilities through Ollama and LMStudio reduce API costs and increase privacy

-Version 2.0 is a complete rewrite with no backward compatibility, requiring migration effort for existing users
-Complex architecture with multiple components may require significant setup and configuration effort
-Limited documentation visible in the provided materials, potentially creating a steep learning curve

-Requires multiple API keys and setup for different providers, adding configuration complexity
-Python-only implementation limits accessibility for non-Python developers
-Performance depends heavily on the quality of the chosen orchestrator model

•Automated research workflows that require gathering information from multiple sources and synthesizing findings
•Software development projects requiring coordination between planning, coding, testing, and deployment phases
•Content creation tasks that involve research, writing, editing, and publication across multiple platforms

•Complex research projects requiring multiple specialized AI agents for different aspects
•Content creation workflows where tasks need to be broken down and executed systematically
•Local AI orchestration for privacy-sensitive tasks using Ollama or LMStudio

Which is more popular, DeerFlow or Maestro?: DeerFlow has more GitHub stars (83,349 vs 4,357).
Which is more actively developed, DeerFlow or Maestro?: DeerFlow had more commits in the last 90 days (1,274 vs 0).
Should I use DeerFlow or Maestro?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.