name	bench-compare
description	Compare multiple agent memory systems by analyzing their code, benchmarks, and architecture
user_invocable	true
arguments	[{"name":"projects","description":"Comma-separated list of projects to compare (e.g. mem0,zep,letta)","required":true},{"name":"criteria","description":"Comparison criteria (e.g. performance,api-design,scalability)","required":false}]

Benchmark & Compare

Compare multiple agent memory projects across key dimensions.

Instructions

For each project in ${projects}:
- Search for benchmarks, performance reports, and comparisons
- Analyze the architecture and API design
- Check community feedback (GitHub issues, discussions, Reddit, HN)
Compare across these default criteria (override with ${criteria} if provided):
- Memory Types: What types of memory are supported
- Retrieval Strategy: How memories are retrieved (vector, graph, hybrid)
- Scalability: How it handles large memory stores
- API Design: Ease of integration
- Ecosystem: Integrations with LLM frameworks
- Performance: Latency, throughput if data available
- Community: Stars, contributors, activity
Output a comparison matrix (markdown table)
Provide a recommendation summary with trade-offs

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name	bench-compare
description	Compare multiple agent memory systems by analyzing their code, benchmarks, and architecture
user_invocable	true
arguments	[{"name":"projects","description":"Comma-separated list of projects to compare (e.g. mem0,zep,letta)","required":true},{"name":"criteria","description":"Comparison criteria (e.g. performance,api-design,scalability)","required":false}]

Benchmark & Compare

Compare multiple agent memory projects across key dimensions.

Instructions

For each project in ${projects}:
- Search for benchmarks, performance reports, and comparisons
- Analyze the architecture and API design
- Check community feedback (GitHub issues, discussions, Reddit, HN)
Compare across these default criteria (override with ${criteria} if provided):
- Memory Types: What types of memory are supported
- Retrieval Strategy: How memories are retrieved (vector, graph, hybrid)
- Scalability: How it handles large memory stores
- API Design: Ease of integration
- Ecosystem: Integrations with LLM frameworks
- Performance: Latency, throughput if data available
- Community: Stars, contributors, activity
Output a comparison matrix (markdown table)
Provide a recommendation summary with trade-offs