Which is more popular, LangGraph or Maestro?

LangGraph has more GitHub stars (42,656 vs 4,357).

Which is more actively developed, LangGraph or Maestro?

LangGraph had more commits in the last 90 days (132 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; LangGraph is actively maintained (132 commits in the last 90 days).
LangGraph is growing faster: +2,365 GitHub stars in the last 30 days vs +5 for Maestro.
Pick LangGraph for: build resilient language agents as graphs. Pick Maestro for: a framework for Claude Opus to intelligently orchestrate subagents.

From GitHub data refreshed daily.

LangGraphopen-source

Build resilient language agents as graphs.

A framework for Claude Opus to intelligently orchestrate subagents.

+Durable execution ensures agents automatically resume from exactly where they left off after failures or interruptions
+Comprehensive memory system with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions
+Seamless human-in-the-loop capabilities allow for inspection and modification of agent state at any point during execution

+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

-Low-level framework requires more technical expertise and setup compared to high-level agent builders
-Graph-based agent design paradigm may have a steeper learning curve for developers new to agent orchestration
-Production deployment complexity may be overkill for simple chatbot or single-turn use cases

-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

•Long-running autonomous agents that need to persist through system failures and operate over days or weeks
•Complex multi-step workflows requiring human oversight, approval, or intervention at specific decision points
•Stateful agents that must maintain context and memory across multiple sessions and interactions

•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, LangGraph or Maestro?: LangGraph has more GitHub stars (42,656 vs 4,357).
Which is more actively developed, LangGraph or Maestro?: LangGraph had more commits in the last 90 days (132 vs 0).
Should I use LangGraph or Maestro?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.