Empower your agents with deterministic logic trees—at 3.5x compression and a fraction of the cost.
5x faster searches. Zero hallucinations. Infinite context.
Integrate Sanskrit-powered semantic memory into your AI agents in minutes. Production-ready SDKs for every language.
Zero dependencies
curl -X POST https://api.mantr.net/v1/walk \
-H "Authorization: Bearer vak_live_..." \
-H "Content-Type: application/json" \
-d '{
"phonemes": ["dharma", "karma"]
}'pip install mantr
from mantr import MantrClient
mantr = MantrClient(
api_key='vak_live_...'
)
paths = mantr.walk(['dharma', 'karma'])npm install @mantr/sdk
import { MantrClient } from '@mantr/sdk';
const mantr = new MantrClient({
apiKey: 'vak_live_...'
});
const paths = await mantr.walk([
'dharma', 'karma'
]);go get github.com/Mantrnet/go-sdk
import "github.com/Mantrnet/go-sdk"
client := mantr.NewClient("vak_live_...")
paths, err := client.Walk(
[]string{"dharma", "karma"}
)Start with 5,000 free walks/month. No credit card required.
No infrastructure. No orchestration. No wiring. Just one API call to retrieve perfect context every time.
Weeks of engineering
Ship in minutes
# Customer support pod
mantr.create_pod("support")
# Product docs pod
mantr.create_pod("docs")
# Legal/compliance pod
mantr.create_pod("legal")# Get relevant context
context = mantr.walk(
query="refund policy",
pod="support"
)
# Pass to your LLM
response = openai.chat({
context: context
})Sanskrit's compound words eliminate "glue words" (the, and, of). A 22-word English sentence becomes 6 Sanskrit semantic units.
Cause, instrument, and location are mathematically fused into word structure. Vectors are "laser points" not "scattershots."
Based on Maheshwar Sutra—a 2,500-year-old ontology that models reality deterministically. No ambiguity, no conflicts.
Sanskrit's 8-case grammatical system preserves exact relationships, plurality, tense, causality, and perspective—enabling precise walks across billions of documents.
"John gave Mary a book at the library yesterday" → [0.23, 0.45, 0.12, ...] // Loses structure! // Cannot query: // - WHO received? // - FROM WHERE? // - WHEN?
कर्ता: John (agent) कर्म: book (object) सम्प्रदान: Mary (recipient) अधिकरण: library, yesterday // Structure preserved! // O(1) query on ANY dimension
देवःone godदेवौtwo gods (dual!)देवाःmany godsकरोतिdoes (present)अकरोत्did (past)करिष्यतिwill do (future)कुर्यात्would do (potential)पचतिhe cooksपाचयतिhe causes to cookपच्यतेit is cooked| Operation | Traditional RAG | Karak-Indexed |
|---|---|---|
| Semantic query | O(n) scan | O(1) index + O(k) |
| Entity lookup | O(n) or missing | O(1) |
| Relational query | Impossible | O(1) per relation |
| Complex query (100M docs) | ~2 seconds | <10ms |
Phoneme graph traversal faster than traditional vector search. Production-tested at scale.
State machine that updates facts, not just appends. 'User loves Python' overwrites 'User loves Java'.
Hybrid architecture: tree logic + semantic search. One database, infinite complexity.
Connect facts across time. 'Payment Gateway → Nexus → Shutdown' resolved deterministically.
30k vocab SentencePiece model trained on Sanskrit. 70% smaller index, 2x faster scans.
Your agent's memory survives crashes. No data loss, ever. Built on Postgres.
Start free. Scale with confidence.
For exploration
For production
Enterprise
Production-grade security, compliance, and reliability from day one. No compromises.
Full transparency. Your data, your rights.