The Semantic Hyper-Tree.
A new database for AI memory.

You already know how this works.

KOSHA's labels are pure Sanskrit, but the structure maps cleanly to anything you've ever queried. If you've worked with row-based or graph-based storage, you can read a KOSHA tree on first sight.

Row. Graph. Tree. Hyper-Tree.

Four classes of structured storage have shipped over the last 50 years, each shaped by the workloads of its era. A vector index is not in this list — it's a similarity-search index that sits inside a storage layer as an implementation detail.

Two guarantees a vector index cannot make.

A vector index returns the top-K most cosine-similar chunks for a query. Two failure modes are baked in: chunks come from anywhere in the embedding space (boundary leak), and they arrive ranked by similarity, not by sequence (temporal scramble). The LLM has to re-derive context every time.

A storage tool. Not a chat box.

Personal memory. Local by definition.

Built on the original formal grammar.

Plug any AI agent into KOSHA.

KOSHA runs an MCP server at 127.0.0.1:4096/mcp. Two tools — kosha_ingest (write) and kosha_search (read). One question in, one answer out. Every call is a sealed Chakra.

curl -s -X POST http://127.0.0.1:4096/mcp \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list"}'

import json, urllib.request

def kosha_call(method, params=None):
    payload = {"jsonrpc": "2.0", "id": 1, "method": method}
    if params: payload["params"] = params
    req = urllib.request.Request(
        "http://127.0.0.1:4096/mcp",
        data=json.dumps(payload).encode(),
        headers={"Content-Type": "application/json"},
    )
    with urllib.request.urlopen(req) as r:
        return json.loads(r.read())

# Overview of the active Kosha
print(kosha_call("tools/call", {"name": "kosha_search", "arguments": {}}))

# Write a fact under "Work" (hint is required — KOSHA enforces structure on write)
kosha_call("tools/call", {
    "name": "kosha_ingest",
    "arguments": {"content": "Migration 057 ships Friday.", "hint": "Work"},
})

async function koshaCall(method, params) {
  const res = await fetch("http://127.0.0.1:4096/mcp", {
    method: "POST",
    headers: { "Content-Type": "application/json" },
    body: JSON.stringify({ jsonrpc: "2.0", id: 1, method, params }),
  });
  return res.json();
}

// Read mode — query inside a scope (4 search modes total: overview / scope / query / query+scope)
const hits = await koshaCall("tools/call", {
  name: "kosha_search",
  arguments: { query: "migration 057", scope: "Work" },
});
console.log(hits);

{
  "mcpServers": {
    "kosha": {
      "url": "http://127.0.0.1:4096/mcp"
    }
  }
}

A note on hint: kosha_ingest requires a hint (a SURYA or sub-branch name like "Work") — KOSHA enforces structure on write. If your agent doesn't know where the fact belongs, call kosha_search first to discover the right branch, OR pass auto_route: true and KOSHA picks via Gemma path embeddings (requires Ollama + gemma4:e2b).

Stay in the loop on V1.1.

We'll email when V1.1 ships — first-run auto-install of Ollama + Gemma 4, per-account encryption keys, and the pre-populated KOSHA tutorial Nabha.