Letta vs Goose (by Block)
Detailed side-by-side comparison to help you choose the right tool
Letta
π΄DeveloperAI Agents
Stateful AI agent platform from the MemGPT team, providing long-term memory, tools, and a managed runtime for production agents.
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FreeGoose (by Block)
π΄DeveloperAI Agents
Open-source, extensible AI agent from Block that runs on your machine and orchestrates LLMs, MCP tools, and developer workflows.
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CustomFeature Comparison
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Letta - Pros & Cons
Pros
- βBuilt by the team that invented MemGPT-style stateful memory
- βMemory blocks are inspectable and editable β no black-box embeddings vault
- βModel-agnostic: switch between Claude, GPT, Gemini, and local Ollama freely
- βMCP support layers Letta's memory on top of the broader tool ecosystem
- βGenerous Free tier for prototyping stateful agents
Cons
- βMemory editing adds tokens to every turn β costs grow on long sessions
- βDashboard debugging is less mature than dedicated tracing tools
- βHosted runtime locks you into Letta's data model unless you self-host
- βMemory tuning still benefits from periodic human-curated summaries
Goose (by Block) - Pros & Cons
Pros
- βOpen source and free under Apache 2.0 β no vendor lock-in
- βMCP-native: the entire MCP server ecosystem is available out of the box
- βBring-your-own LLM, including local models like Ollama for cost control
- βApproval flows let you inspect every tool call before it runs
- βBacked by a real product company (Block) rather than a one-person side project
Cons
- βSetup is config-driven β not a one-click consumer experience
- βDesktop UI is less polished than Cursor or Claude Desktop
- βNo managed cloud option for non-developer teammates
- βYou're responsible for picking model + tool combos and managing safety
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