Anona Memory

Set up with your agent

Copy the line below and paste it into your coding agent: Claude Code, Codex, OpenCode, Goose, Gemini CLI, Cursor, Windsurf, Cline, or any agent that can run shell commands. Your agent reads the page, installs or updates the anona CLI, and connects itself. It starts on a temporary profile, so it works at once with nothing to sign in to; one browser approval later claims everything it stored. If you have no Anona account, that approval is also how you get one - it opens the sign-up page, and creating the account creates your organization.

Paste this into your coding agent

Fetch the full text of https://docs.anonalabs.com/agents/onboarding-prompt.md with curl (not a tool that summarizes it) and follow it to set up and use Anona Memory.

Want to try it first? You do not need an account: anona start gives you a temporary profile (1,000 credits, 72 hours) that you can claim later. See Temporary profiles.

Set up with your agent

One paste, one browser approval. What the flow does, and what to do if it stalls.

Set up by hand

Mint an API key and call the REST API or an SDK directly.

The same paste works in every agent above; only the last step differs by client. Claude Code can also connect through its native OAuth flow, while the others use anona login + anona mcp, which needs no API key either. See The four ways to connect for the one table of client against path, and where each client stands. A key from the dashboard is needed only for the REST API and the SDKs.

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. 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

For Agents

Anona CLI

API Essentials

Memory (data plane)

LLM Proxy (drop-in)

Account (control plane)

SDKs & MCP

Integrations

Guides

Use Cases

Legal

Changelog

Set up with your agent

Set up with your agent

Set up by hand

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

​Set up with your agent

Set up with your agent

Set up by hand

​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

Set up with your agent

The three operations

What you get

Who it is for

Docs for AI agents

Next steps