{
  "session_id": "20260330_154721_92675c",
  "model": "claude-opus-4.6",
  "base_url": "https://api.anthropic.com",
  "platform": "telegram",
  "session_start": "2026-03-30T15:47:21.261300",
  "last_updated": "2026-03-30T15:50:02.819126",
  "system_prompt": "You are Hermes Agent, an intelligent AI assistant created by Nous Research. You are helpful, knowledgeable, and direct. You assist users with a wide range of tasks including answering questions, writing and editing code, analyzing information, creative work, and executing actions via your tools. You communicate clearly, admit uncertainty when appropriate, and prioritize being genuinely useful over being verbose unless otherwise directed below. Be targeted and efficient in your exploration and investigations.\n\nConversation started: Monday, March 30, 2026 03:47 PM\nModel: claude-opus-4.6\nProvider: anthropic\n\nYou are on a text messaging communication platform, Telegram. Please do not use markdown as it does not render. You can send media files natively: to deliver a file to the user, include MEDIA:/absolute/path/to/file in your response. Images (.png, .jpg, .webp) appear as photos, audio (.ogg) sends as voice bubbles, and videos (.mp4) play inline. You can also include image URLs in markdown format ![alt](url) and they will be sent as native photos.",
  "tools": [],
  "message_count": 1,
  "messages": [
    {
      "role": "user",
      "content": "Research comprehensive caching strategies for local LLM inference, specifically for llama.cpp and similar servers. Cover: 1) KV cache optimization (paged attention, prefix caching, cache reuse between requests) 2) Prompt caching (reusing computed KV states for common prefixes like system prompts) 3) Semantic caching (caching responses for similar queries) 4) Disk-based KV cache spilling for large contexts 5) llama.cpp specific: --cache-reuse, --slot-save-path, prompt caching features in current builds 6) vLLM prefix caching and how it works 7) Any 'cache everywhere' philosophy applied to inference pipelines 8) HTTP-level response caching for repeated identical queries. Focus on what's implementable TODAY with llama.cpp on CPU/Apple Silicon."
    }
  ]
}