Goose by Block vs Letta
Detailed side-by-side comparison to help you choose the right tool
Goose by Block
🔴DeveloperAI Agent Framework
Open source, locally-running AI agent that automates engineering tasks using any LLM and connects to anything via MCP.
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CustomLetta
🔴DeveloperAI Agent Framework
Framework and cloud service for building stateful AI agents that remember, learn continuously, and improve over time — the successor to MemGPT.
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FreeFeature Comparison
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Goose by Block - Pros & Cons
Pros
- ✓Truly local — source code never leaves the workstation unless you choose a cloud LLM
- ✓BYO model lets you balance cost (local Ollama) vs capability (Claude/GPT)
- ✓Reference-quality MCP client; new MCP servers usually work out of the box
- ✓Backed by Block, which dogfoods it internally — active maintenance is realistic
Cons
- ✗Requires more setup than a hosted IDE like Cursor or Windsurf
- ✗Quality of agent reasoning is bound to whichever LLM you wire up
- ✗Desktop UI is less polished than commercial agent IDEs
- ✗MCP extension model means debugging failures often spans multiple processes
Letta - Pros & Cons
Pros
- ✓Persistent, editable memory is built into the agent runtime
- ✓ADE exposes agent state and tool calls for debugging
- ✓Python, TypeScript, REST, multiple model providers, and MCP offer flexible integration
- ✓Open-source self-hosting avoids mandatory platform lock-in
Cons
- ✗Stateful agents add privacy, retention, deletion, and migration responsibilities
- ✗Cloud plan and model usage can create separate cost layers
- ✗Memory quality still depends on prompts, models, and application-specific evaluation
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