memory-bench-pioneerBe one of the first to benchmark your agent's memory — and help shape how AI remembers. Peer-review-grade evaluation (LLM-as-judge, nDCG/MAP/MRR with 95% CIs, ablations) against your live memory system. Runs entirely LOCALLY by default — no memory content leaves your machine, and excerpts are redacted even on the local path. The optional OpenAI judge is opt-in, prints the exact request body it would send, redacts secrets first, requires typed consent, and cannot be switched on by an unattended run. Submitting results is a separate confirmed step that validates the report against the full schema and previews every field in it, and identifies you only if you pass --contributor. Built for the TinkerClaw fork — github.com/globalcaos/tinkerclaw. See Permissions, Data Flow & Consent.
Install via ClawdBot CLI:
clawdbot install globalcaos/memory-bench-pioneerGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Calls external URL not in known-safe list
http://127.0.0.1:8900/embedUses known external API (expected, informational)
api.openai.comAudited Apr 17, 2026 · audit v1.0
Generated Mar 22, 2026
Research teams use this skill to rigorously benchmark memory systems in AI agents, providing anonymized data for academic papers like ENGRAM/CORTEX. It enables peer-review-grade evaluations with metrics like nDCG and MRR, helping validate memory algorithms in controlled experiments.
Companies deploying AI agents for customer service or internal tools utilize this skill to measure memory retrieval quality over time. By running longitudinal assessments, they ensure consistent performance and identify improvements in episodic or procedural memory handling.
Developers and engineers optimize memory systems by using ablation studies to isolate the impact of features like spreading activation. This helps fine-tune retrieval accuracy and efficiency in real-world applications, such as chatbots or virtual assistants.
Open source contributors run benchmarks to submit anonymized statistics, aiding collective research efforts. This fosters transparency and standardization in AI memory evaluation, allowing community-driven improvements and comparisons across different systems.
Offer benchmarking services to AI companies and research institutions, charging for detailed memory evaluation reports and integration support. Revenue comes from subscription fees or per-assessment charges, leveraging the skill's peer-review-grade metrics.
Provide consulting to businesses implementing AI memory systems, using this skill to assess and optimize retrieval performance. Revenue is generated through project-based contracts or ongoing maintenance fees, focusing on improving agent efficiency.
License the benchmarking tools and scripts to enterprises for internal use, with premium support for customization and validation. Revenue streams include licensing fees and technical support packages, ensuring clients meet validation protocols.
💬 Integration Tip
Ensure you have an OpenAI API key for optimal judge accuracy, and run assessments over at least 14 days to capture longitudinal data for reliable results.
Scored Aug 20, 2026
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