Which is more popular, Letta or Maestro?

Letta has more GitHub stars (25,005 vs 4,357).

Which is more actively developed, Letta or Maestro?

Letta had more commits in the last 90 days (7 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; Letta is actively maintained (7 commits in the last 90 days).
Letta is growing faster: +513 GitHub stars in the last 30 days vs +5 for Maestro.
Pick Letta for: letta is the platform for building stateful agents: AI with advanced memory that can learn and self-improve. Pick Maestro for: a framework for Claude Opus to intelligently orchestrate subagents.

From GitHub data refreshed daily.

Lettaopen-source

Letta is the platform for building stateful agents: AI with advanced memory that can learn and self-improve over time.

A framework for Claude Opus to intelligently orchestrate subagents.

+Advanced persistent memory system that allows agents to learn and improve over time across sessions
+Dual deployment options with both local CLI tool and cloud API for different use cases and security requirements
+Model-agnostic architecture supporting multiple LLM providers with extensive SDK support for TypeScript and Python

+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

-Requires Node.js 18+ for CLI usage, which may limit adoption in some environments
-API-based functionality requires API keys and cloud dependency for full feature access
-As a relatively new platform for stateful agents, may have a learning curve for developers new to persistent memory concepts

-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

•Building coding assistants that remember project context and learn from previous debugging sessions
•Creating customer support agents that maintain conversation history and learn customer preferences over time
•Developing personal AI assistants that evolve their responses based on user behavior patterns and feedback

•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, Letta or Maestro?: Letta has more GitHub stars (25,005 vs 4,357).
Which is more actively developed, Letta or Maestro?: Letta had more commits in the last 90 days (7 vs 0).
Should I use Letta or Maestro?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.