SmritiTantra
Memory for AI agents: ingest, recall, learn — one shared async core, served three ways: embedded in your process, over HTTP, or as an MCP server.
Install
A bare install is a lightweight remote SDK (smrititantra.connect(), models,
exceptions, the sync facade):
pip install smrititantra
Everything heavier lives behind an extra — mix in what you need:
pip install "smrititantra[local]" # embedded engine + Postgres store
pip install "smrititantra[server]" # HTTP + MCP service
pip install "smrititantra[all]" # local + server + every scale backend
| Extra | Adds |
|---|---|
local |
the embedded engine, Postgres store |
server |
the FastAPI HTTP service + MCP server |
s3 |
S3/MinIO artifact store |
qdrant |
Qdrant vector-index backend at scale |
neo4j |
Neo4j graph-serving tier at scale |
mongo |
MongoDB source connector |
otel |
OpenTelemetry tracing spans |
Quickstart
import asyncio
from smrititantra import Smrititantra
async def main() -> None:
async with Smrititantra() as smriti:
await smriti.concepts.author(
kind="metric", name="net_revenue",
metadata={"title": "Net Revenue", "description": "Gross minus refunds."},
body="Net revenue is gross order value minus refunds.",
)
await smriti.snapshots.compile()
pack = await smriti.recall("what is net revenue")
print(pack.confidence, [i.ref for i in pack.required])
asyncio.run(main())
Or try the self-contained demo (no database or config needed):
pip install "smrititantra[local]"
smrititantra demo
CLI
smrititantra --help covers the full surface: orgs, sources, sync,
import, mine-workload, mine, coverage, gaps, reviews, compile,
recall, search, get, graph, snapshots, config, agents, keys,
serve, mcp, demo, init. Details in the
CLI reference.
License
Apache-2.0 — see LICENSE.
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