Pillar 03 · Cerebe · live at cerebe.ai

Cognitive services
through one API.

The cognitive infrastructure that production agentic AI demands — persistent memory, capability-based LLM routing, versioned prompts, multi-agent coordination, and meta-learning. Stop building plumbing; ship product.

Visit cerebe.ai → Explore Services
cerebe-sdk — python
>>> from cerebe import Cerebe >>> cb = Cerebe(api_key=...) >>> cb.memory.add("patient prefers morning workouts", ... session="sess_123", type="semantic") { "id": "mem_a4b...", "stored_at": "2026-05-04T16:10:42Z" } >>> cb.llm.complete(prompt="What does Maria prefer?", ... session="sess_123", route="capability:reasoning-fast") { "model": "claude-haiku-4.5", # router picked "answer": "Maria prefers morning workouts.", "memory_hits": 1, "trace_id": "otel_a89..." } >>> cb.evaluate(suite="coaching-tone") ✓ 42/42 evals passed · grafana dashboard →

Two SDKs

One API. Two languages. Cloud or on-prem.

Python and TypeScript SDKs auto-generated from the OpenAPI spec. Contract-tested. Stable across language boundaries. Memory, LLM routing, prompt management, evaluation — all behind the same surface.

Privacy-first deployment: cloud-managed, VPC-deployed, or fully on-prem. Doppler-managed secrets, External Secrets Operator, data residency controls. Your data stays where your contracts require.

SDK docs on cerebe.ai →

The six services

The cognitive infrastructure
that production agentic AI demands.

Cerebe decomposes into six service surfaces, exposed through one API. Full reference docs and self-signup live at cerebe.ai.

01

Memory Fabric

Working, episodic, long-term. Qdrant (vector) + Neo4j (graph). Cross-session, multi-tenant, with COPPA-grade entity controls.
02

PLRE Meta-Learning

Plan → Learn → Reflect → Evolve. Domain-agnostic adaptive learning loops. Agents that improve every week without manual tuning.
03

LLM Router

Route by capability, not model name. Switch providers without touching app code. Per-tenant overrides, vendor-portfolio resilient.
04

Prompt Versioning

YAML-managed prompts with semver. A/B evaluation. LangSmith integration. Auto-rollback on quality regression.
05

Multi-Agent Coordination

Shared scratchpad in memory fabric. Deliberation primitives. Multi-agent reasoning grounded in shared context.
06

Python + TypeScript SDKs

Auto-generated from OpenAPI. Contract-tested. Stable across languages. Cloud, VPC, or fully on-prem.

Why Cerebe

What stateless LLM calls leave on the table.

vs. Stateless LLM APIs

Persistent memory across sessions

Plain LLM APIs forget every turn. Cerebe's hybrid memory fabric (vector + graph + temporal) gives agents continuity — they remember the user, the domain, what's been discussed, what worked.

vs. Vendor-Locked Stacks

Model-agnostic by design

OpenAI shops. Anthropic shops. Mistral shops. Open-weights shops. Cerebe routes by capability, not model name. Switch providers without touching app code; switch mid-conversation if a vendor degrades.

vs. DIY Memory + Routing

Months of infrastructure, zero days

Memory fabric, capability router, prompt versioning, eval harness, multi-agent coordination — each is a quarter of engineering work. Cerebe ships all of them behind one API.

vs. Cloud-Only AI Services

VPC + on-prem from day 1

Regulated industries can't ship customer data to a third-party SaaS. Cerebe deploys to your VPC or fully on-prem with the same SDK. Data residency by config.

Get Started

The cognitive backend for production agentic AI.

Persistent memory. Capability routing. Meta-learning. One API. Visit cerebe.ai for full platform docs and self-signup, or reach out for a workshop on integrating Cerebe into your stack.

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