{
  "name": "DriftOS",
  "tagline": "Graph-native context management for AI agents",
  "description": "DriftOS transforms flat conversation logs into navigable semantic graphs. It detects topic shifts, routes messages into semantic branches, and assembles focused context windows instead of dumping entire chat history into every LLM call.",
  "website": "https://driftos.dev",
  "docs": "https://driftos.dev/docs",
  "llm_txt": "https://driftos.dev/llm.txt",
  "github": "https://github.com/DriftOS",
  "license": "MIT",
  "patent_pending": true,
  "problem_solved": "AI applications dump entire conversation history into every LLM call, causing unfocused context, token waste (~$5 per call for 1000 messages), and silent context corruption in multi-topic conversations.",
  "value_proposition": {
    "cost_reduction": "Up to 50x reduction in token costs",
    "context_quality": "20 relevant messages instead of 1000",
    "auditability": "Every routing decision logged with reasoning",
    "latency": "Sub-100ms NLP analysis with zero external API calls"
  },
  "routing_actions": {
    "STAY": "Same topic, continue in current branch",
    "BRANCH": "Topic drift detected, create new branch",
    "ROUTE": "Return to a previous topic"
  },
  "key_concepts": [
    "conversational branches",
    "semantic routing",
    "context isolation",
    "decision provenance",
    "fact extraction",
    "graph-native context assembly"
  ],
  "technical_features": {
    "dual_axis_drift_detection": {
      "semantic": "Embedding similarity via paraphrase-MiniLM-L6-v2",
      "functional": "Conversational mode analysis"
    },
    "nlp_pipeline": {
      "anaphora_detection": "6 categories of implicit references",
      "oov_detection": "Out-of-vocabulary compound reference detection",
      "entity_overlap": "Cross-message entity analysis",
      "local_processing": "spaCy + transformers.js, no external calls"
    },
    "explainability": {
      "audit_trail": "Every decision logged",
      "similarity_scores": "Numeric confidence values",
      "boost_multipliers": "Adjustments with reasons",
      "compliance_ready": true
    }
  },
  "routing_engines": {
    "driftos-embed": {
      "approach": "Embedding-based",
      "latency": "<200ms",
      "cost": "$0 for routing",
      "accuracy": "Good",
      "model": "paraphrase-MiniLM-L6-v2",
      "best_for": "Real-time applications, clear topic shifts"
    },
    "driftos-core": {
      "approach": "LLM-based",
      "latency": "500-1000ms",
      "cost": "~$0.001 per decision",
      "accuracy": "High",
      "model": "Llama 3.1 via Groq",
      "best_for": "Nuanced understanding, subtle topic shifts"
    }
  },
  "architecture": {
    "pipeline": "Message → NLP Pipeline → Drift Detection → Router → Storage → Context",
    "stack": {
      "nlp": "spaCy + transformers.js",
      "embeddings": "paraphrase-MiniLM-L6-v2 (local)",
      "storage": "PostgreSQL + Prisma",
      "framework": "Fastify + TypeScript"
    }
  },
  "api_endpoints": [
    { "method": "POST", "path": "/api/v1/drift/route", "description": "Route a message to a branch" },
    { "method": "GET", "path": "/api/v1/drift/branches/:conversationId", "description": "List all branches" },
    { "method": "GET", "path": "/api/v1/context/:branchId", "description": "Get optimized LLM context" },
    { "method": "POST", "path": "/api/v1/facts/:branchId/extract", "description": "Extract facts from branch" },
    { "method": "GET", "path": "/api/v1/facts/:branchId", "description": "Get existing facts" }
  ],
  "sdk": {
    "package": "@driftos/client",
    "install": "npm install @driftos/client",
    "methods": ["route", "getBranches", "getContext", "extractFacts", "getFacts", "buildPrompt"]
  },
  "integrations": {
    "mcp": {
      "name": "driftos-mcp-server",
      "description": "MCP server for Claude Desktop integration",
      "github": "https://github.com/DriftOS/driftos-mcp-server"
    }
  },
  "comparisons": {
    "vs_rag": "RAG retrieves documents; DriftOS controls conversational structure and routing",
    "vs_long_context": "Long context increases capacity; DriftOS provides control, auditability, and cost reduction",
    "vs_summarization": "Summarization loses detail; DriftOS preserves full messages in scoped branches",
    "vs_vector_search": "Vector search finds similar content; DriftOS understands conversational flow and topic boundaries"
  },
  "use_cases": [
    "Multi-topic AI assistants",
    "Customer support agents with conversation threading",
    "AI copilots requiring context isolation",
    "Compliance-sensitive applications needing audit trails",
    "Cost-optimized LLM applications"
  ],
  "ecosystem": [
    { "name": "driftos-core", "description": "LLM-based routing engine" },
    { "name": "driftos-embed", "description": "Embedding-based routing engine" },
    { "name": "drift-sdk", "description": "TypeScript/JavaScript SDK" },
    { "name": "driftos-mcp-server", "description": "MCP server for Claude Desktop" }
  ]
}