{"slug": "a-markdown-wiki-outscored-every-ai-agent-memory-product-we-benchmarked", "title": "A Markdown wiki outscored every AI agent memory product we benchmarked", "summary": "A benchmark by an independent testing group found that a Markdown wiki outperformed every AI agent memory product tested, with Claude Code's built-in memory scoring 67.7% accuracy and costing $426.90 per 1,000 successful answers. The Agentic Memory Index v0.1, published in August 2026, measures accuracy, cost, and speed for each tool using the same agent model.", "body_md": "# See how much smarter your AI could be by adding tools\n\nWe independently measure the tools AI agents use and publish what each one actually adds.\n\n## Agentic Memory Indexi\n\n[Methodology](/methodology#memory)\n\nAgentic Memory Index v0.1 · Aug 2026\n\n## Highlights\n\nAccuracy, cost, and speed for every tool, measured the same way.\n\n### Agentic Memory Indexi\n\nThe overall score: how often each tool finds the right answer.\n\nClaude Code built-in memory 67.7\n\n### Costi\n\nUSD per 1,000 successful answers\n\nClaude Code built-in memory $426.90\n\n### Answer speedi\n\nHow long an answer takes end to end, same agent model on every tool.", "url": "https://wpnews.pro/news/a-markdown-wiki-outscored-every-ai-agent-memory-product-we-benchmarked", "canonical_source": "https://verginglabs.com/", "published_at": "2026-08-03 14:12:03+00:00", "updated_at": "2026-08-03 14:23:00.189437+00:00", "lang": "en", "topics": ["ai-agents", "ai-tools", "ai-research"], "entities": ["Claude Code", "Agentic Memory Index"], "alternates": {"html": "https://wpnews.pro/news/a-markdown-wiki-outscored-every-ai-agent-memory-product-we-benchmarked", "markdown": "https://wpnews.pro/news/a-markdown-wiki-outscored-every-ai-agent-memory-product-we-benchmarked.md", "text": "https://wpnews.pro/news/a-markdown-wiki-outscored-every-ai-agent-memory-product-we-benchmarked.txt", "jsonld": "https://wpnews.pro/news/a-markdown-wiki-outscored-every-ai-agent-memory-product-we-benchmarked.jsonld"}}