Anona Memory

Public beta. The data plane (record, retrieve, reason) is stable and we run it in production. Breaking changes are announced in advance, and the old shape stays live through a deprecation window. Newer surfaces are marked Beta on their own pages. Questions: support@anonalabs.com.

Large language models are stateless. Every conversation starts from scratch unless your application explicitly retrieves and injects context. Building a reliable memory layer for that means operating vector databases, building retrieval pipelines, managing embedding models, and handling multi-tenancy: substantial infrastructure work that is not core to most AI products. Anona Memory abstracts all of it behind one REST API.

The three operations

Operation	Endpoint	What it does
Record	`POST /v1/record`	Stores a memory. Anona extracts the durable facts and indexes them for semantic search.
Retrieve	`POST /v1/retrieve`	Ranks memories by meaning, not keyword overlap, and returns the most relevant context.
Reason	`POST /v1/reason`	Runs an agentic pass across the space and returns a synthesized answer instead of a ranked list.

from anona import AnonaClient

client = AnonaClient(api_key="anona_live_YOUR_KEY")

client.record(space_id="customer-support-bot", content="Alice is a senior engineer at Google")
client.retrieve(space_id="customer-support-bot", query="Where does Alice work?")
client.reason(space_id="customer-support-bot", query="How has Alice's career progressed?")

What you get

Capability	Detail
Semantic search	Retrieval ranks by meaning, with entity extraction and event-time awareness built in.
Multi-tenant isolation	Each organization’s data lives in its own database schema. No shared tables.
Managed embeddings	Anona generates and indexes every embedding. There is no model to choose or host.
Documents and RAG	Upload PDFs, Office files, web pages, text, images, audio and video. Their content becomes searchable memory.
Webhooks	Get notified when memories are stored or consolidated, instead of polling.
Usage metering	Credit quotas and rate limits are enforced per organization and reported through the API. See plans and billing.
REST plus MCP	Call the API directly, use the Python SDK, or connect an MCP client with no integration code.

Who it is for

Audience	Use case
AI product developers	User-facing agents that need to remember context across sessions.
Platform teams	Internal assistants that accumulate organizational knowledge over time.
ML engineers	A production memory store without operating a vector database directly.

Docs for AI agents

Every page here is machine readable. Point a coding agent at one of these instead of pasting screenshots into a prompt.

Source	URL	What it is
Whole docs	`https://docs.anonalabs.com/llms-full.txt`	Every page concatenated into one Markdown file.
Docs index	`https://docs.anonalabs.com/llms.txt`	A link map of every page, for agents that fetch on demand.
One page	Append `.md` to any docs URL	`https://docs.anonalabs.com/api-reference/retrieve.md`
Docs MCP server	`https://docs.anonalabs.com/mcp`	Search these docs from any MCP client, with no key.

The menu at the top of every page also copies that page as Markdown, or opens it directly in ChatGPT, Claude, Cursor, or VS Code.

claude mcp add --transport http anona-docs https://docs.anonalabs.com/mcp

That MCP server serves the documentation. The memory API has its own MCP server at https://memory.anonalabs.com/mcp, which needs an API key. See MCP integration.

Next steps

Quickstart

Record and retrieve your first memory in five minutes.

Core concepts

Spaces, memory types, and tenant isolation.

Plans and billing

What a credit is, what each plan includes, and when credits renew.

API reference

Every endpoint, grouped by data plane and control plane.

Framework adapters

LangChain, CrewAI, LlamaIndex, Google ADK, Agent Framework, Strands.

SDKs

Official clients for Python and TypeScript.

Getting Started

API Essentials

Memory (data plane)

LLM Proxy (drop-in)

Account (control plane)

SDKs & MCP

Integrations

Guides

Changelog

The three operations

What you get

Who it is for

Docs for AI agents

Next steps

Quickstart

Core concepts

Plans and billing

API reference

Framework adapters

SDKs

​The three operations

​What you get

​Who it is for

​Docs for AI agents

​Next steps

Quickstart

Core concepts

Plans and billing

API reference

Framework adapters

SDKs

The three operations

What you get

Who it is for

Docs for AI agents

Next steps