# Actx0

> Managed memory infrastructure for AI agents and apps that stores session memories, extracts key facts, and retrieves them in milliseconds before the next reply.

Actx0 is a managed memory infrastructure service for AI agents and applications, designed to give agents persistent, searchable memory across sessions. It handles the full memory lifecycle — storing user messages, extracting short facts from them, and returning relevant hits before a model generates its next reply — all with sub-10ms P99 retrieval latency according to the product page.

## What It Is

Actx0 sits between your application and your language model, acting as a dedicated memory layer rather than a general-purpose vector store. Developers POST user messages into an agent session; Actx0 indexes them, runs background extraction to distill useful content into compact facts (skipping low-signal turns like greetings), deduplicates and consolidates those facts within the session, and exposes a semantic search API so the right context can be injected into the prompt before each reply. The product page claims this approach can reduce token usage by up to 90% compared to replaying full transcripts.

## How the Two-Call Model Works

The core workflow wraps around the model without replacing it:

- **Add** — POST user messages into a named agent session; Actx0 stores and indexes the turn.
- **Learn** — Background extraction converts useful message content into short, searchable facts and consolidates them within the session.
- **Retrieve** — Before generating a reply, search memories (and optionally workspace knowledge documents) and inject the hits into the prompt.

This keeps the developer in control of the model while Actx0 handles the memory plumbing.

## Developer Experience and SDK Support

Actx0 ships official clients for Python (`pctx0`), Node.js, Go, and a REST API, so teams can integrate without operating their own vector store or search infrastructure. The homepage code sample shows creating an agent, opening a session, posting messages, and running a semantic memory search in under 20 lines of Python. Documentation is available at docs.actx0.com.

## Target Use Cases

The product page highlights six verticals where persistent agent memory adds clear value:

- **Customer Support** — pick up ticket context without replaying the full thread
- **Sales & CRM** — store objections and commitments on a deal session
- **Healthcare** — remember appointment preferences across intake sessions
- **Education** — adaptive tutors that recall what worked in prior sessions
- **DevTools** — coding agents that remember project conventions
- **E-Commerce** — personal shopper agents with size, style, and purchase history

## Enterprise and Multi-Tenant Architecture

For teams operating at scale, Actx0 provides workspaces, role-based access keys, audit logs, and per-team usage tracking. Memory namespaces are isolated per workspace while sharing managed infrastructure, removing the need to provision, patch, or scale vector store servers. The product is fully cloud-hosted with monthly self-serve billing and a separate Enterprise tier for custom limits and dedicated support.

## Features
- Persistent session memory for AI agents
- Background fact extraction from user messages
- Semantic memory search with <10ms P99 retrieval latency
- Memory deduplication and consolidation within sessions
- Workspace knowledge document search
- Multi-tenant workspaces with isolated memory namespaces
- Role-based access keys
- Audit logs and usage tracking
- Python, Node.js, Go, and REST API clients
- Managed cloud infrastructure — no vector store to operate
- Token reduction via compact fact retrieval instead of full transcript replay

## Integrations
Python, Node.js, Go, REST API

## Platforms
API, DEVELOPER_SDK

## Pricing
Freemium — Free tier available with paid upgrades

## Links
- Website: https://actx0.com
- Documentation: https://docs.actx0.com/
- EveryDev.ai: https://www.everydev.ai/tools/actx0