AI Infra

AI Infra is Cerebe's cognitive substrate: the memory, knowledge, retrieval, and meta-learning services that give an AI application real state. It is the layer the Cerebe Services team builds on, and it is available to you directly through the published SDKs and API.

This section is reference. The API is live, the SDKs (cerebe on PyPI, @cerebe/sdk on npm) are published, and every request authenticates through the X-API-Key header (a ck_live_ production key in these examples; see Authentication for the full key taxonomy). There is no self-serve funnel here — if you want AI Infra wired into a product, talk to us.

What it provides

• Memory Fabric — hybrid vector + graph memory that persists across sessions.
• Knowledge Graph — temporal knowledge that evolves with full provenance.
• Retrieval (RAG) — hybrid embedding + retrieval over your own corpus.
• Meta-Learning — the PLRE framework that models how a user learns.
• LLM Router — OpenAI-compatible chat enriched with cognitive context.
• Storage — S3-compatible object storage, bridged into the Memory Fabric.

Quick example

from cerebe import AsyncCerebe

client = AsyncCerebe(api_key="ck_live_...")

# Store a memory, tied to a durable entity
await client.memory.add(
    content="User prefers visual explanations",
    session_id="session_abc",
    entity_id="user_123",
)

# Later, in a different session, retrieve it by the same entity
results = await client.memory.search(
    query="What does the user prefer?",
    session_id="*",         # "*" searches across every session
    entity_id="user_123",   # scoped to this entity
)
# → results.data["memories"][0]["content"] == "User prefers visual explanations"

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