Your voice agent forgets every caller. Cosmictron gives it a brain — real-time media, persistent memory, and database-grade security in a single binary.
Your voice agent handles the call. Then forgets the caller. Building production agent memory today means wiring together Postgres, Redis, a vector DB, and hundreds of lines of retrieval code. It shouldn't.
Agent memory is automatic. Every conversation persists. Cross-session recall is a SQL query, not a RAG pipeline.
Only changes are transmitted. 10-100x less bandwidth than data channels. Your 50-agent dashboard gets deltas, not full state dumps.
Agent logic runs inside the database. Data access in microseconds, not the 30ms network hop to PostgreSQL.
CRDT sync keeps agents working during network failures. The only AI platform that works when the internet doesn't.
Declarative row-level security on every table. One BAA instead of six. Patient data is filtered before it leaves the database -- structural isolation, not application-level metadata filters.
#[table(name = "conversations")]
pub struct ConversationTurn {
#[primary_key] #[auto_inc]
pub id: u64,
pub user_id: Identity,
pub role: String,
pub content: String,
pub timestamp: u64,
}#[reducer]
fn on_user_message(ctx: &ReducerContext, text: String) {
// Auto-persisted. Queryable. Protected by RLS.
db::insert(ConversationTurn {
user_id: ctx.sender,
role: "user".into(),
content: text,
timestamp: ctx.timestamp,
});
}# Next time this user calls, the agent remembers
history = await db.query(
"SELECT content FROM conversations "
"WHERE user_id = $1 ORDER BY timestamp DESC LIMIT 20",
[session.user_id]
)Every code example on the left is real. It's what voice AI builders write today with LiveKit + Postgres + Redis + Pinecone. The right side is Cosmictron.
# You manage all of this yourself
from livekit.agents import AgentSession, Agent
import asyncpg, os
pool = None
async def entrypoint(ctx):
global pool
if not pool:
pool = await asyncpg.create_pool(
os.environ["DATABASE_URL"]
)
await ctx.connect()
session = AgentSession(
llm=openai.realtime.RealtimeModel()
)
@session.on("conversation_item_added")
async def on_message(ev):
async with pool.acquire() as conn:
await conn.execute(
"INSERT INTO conversations "
"(session_id, user_id, role, "
"content, created_at) "
"VALUES ($1,$2,$3,$4,NOW())",
str(ctx.room.name),
get_user_id(ctx),
ev.item.role,
ev.item.text_content
)
await session.start(
room=ctx.room,
agent=Agent(instructions="...")
)Plus: migration file, index creation, RLS policies, connection pool config, deploy error handling...
#[reducer]
fn on_user_message(
ctx: &ReducerContext,
text: String,
) {
db::insert(ConversationTurn {
user_id: ctx.sender,
role: "user".into(),
content: text,
timestamp: ctx.timestamp,
});
}
// Auto-persisted. Queryable.
// RLS-protected. No pool.
// No migration file.# Option A: Postgres
# Deterministic, but 30ms network hop
history = await conn.fetch(
"SELECT content FROM conversations "
"WHERE user_id = $1 "
"ORDER BY created_at DESC LIMIT 20",
user_id
)
# Option B: Pinecone RAG
# Fast, but probabilistic
results = index.query(
vector=embed("water damage claim"),
top_k=5,
filter={"user_id": user_id}
)
# Maybe returns the right conversation.
# Maybe returns a dental claim.
# Option C: Redis cache
# Fast, but volatile
cached = await redis.get(
f"session:{user_id}"
)
# Redis restarted? Gone.
# No audit trail. No RLS.# Next time this user calls,
# the agent remembers everything.
history = await db.query(
"SELECT content "
"FROM conversations "
"WHERE user_id = $1 "
"ORDER BY timestamp "
"DESC LIMIT 20",
[session.user_id]
)
# Microseconds. Deterministic.
# RLS-protected. Persistent.
# Same process. No network hop.# You need to configure ALL of
# these separately:
#
# 1. Postgres RLS policies
# (if you remembered to enable them)
# 2. Pinecone namespace isolation
# (metadata filters != security)
# 3. Redis key prefixing
# (any bug can read any key)
# 4. Auth0 tenant config
# 5. Audit logging across 4 services
# (correlating timestamps: good luck)
# 6. BAAs with Pinecone, Redis Cloud,
# Auth0, and your Postgres host
#
# The "secure but leaky" problem:
# Infrastructure passes audit, but a
# retrieval bug shows Patient A's
# medication to Patient B.
# Pinecone metadata filters are
# application-level, not database-level.// Row-level security is a SQL
// predicate. Enforced on every query.
// Structural, not optional.
#[policy(Read, table = "conversations")]
fn patient_isolation(
row: &ConversationTurn,
caller: Identity,
) -> bool {
row.user_id == caller
}
// One BAA. One audit log.
// One system. Data is filtered
// before it leaves the database.Real-time voice, video, persistent storage, and pub/sub in a single binary. No inter-service latency. No deployment choreography.
Every conversation auto-persists. Cross-session recall is SELECT, not a vector similarity pipeline. Deterministic, debuggable, fast.
Row-level security policies are SQL predicates. Passkey auth, RBAC, and full audit logging ship with the binary. HIPAA compliance is configuration, not code.
One binary. Zero glue code.
Customer service, sales, support. Your agent recalls every prior conversation, every preference, every unresolved issue -- with a SQL query, not a RAG pipeline.
HIPAA-compliant triage agents with patient history, medication tracking, and row-level data isolation.
Game state sync + AI characters with persistent memory. One binary, no separate game server.
Field service, rural healthcare, disaster response. AI assistants that work without connectivity.
LiveKit handles media beautifully. We add the brain: persistent state, agent memory, row-level security, offline sync. One binary instead of five services.
Telephony is Twilio's strength. We add real-time state management, embedded compute, and DBSP incremental subscriptions. Your agent doesn't need a separate database.
Great databases. We add WebRTC media transport and an AI agent framework. Your real-time backend and your agent platform are the same system.
Benchmarked on a single node. No caching tricks. Reproducible.
Managed hosting, agent deployment, built-in observability. Join the waitlist.
No spam. Just one email when we launch.