# Acontext — Memory Layer SDK + Backend for Agents

> Memory layer for agents: run the backend with Docker, then use the SDK to persist, retrieve, and share context across workflows.

## Install

Save the content below to `.claude/skills/` or append to your `CLAUDE.md`:

## Quick Use

1. (Optional) Install the self-host helper CLI:
   ```bash
   curl -fsSL https://install.acontext.io | sh
   ```
2. Start a local backend:
   ```bash
   mkdir acontext_server && cd acontext_server
   acontext server up
   ```
3. Install the Python SDK:
   ```bash
   pip install acontext
   ```

## Intro

Memory layer for agents: run the backend with Docker, then use the SDK to persist, retrieve, and share context across workflows.

- **Best for:** agent builders who want a dedicated memory backend + SDK instead of ad-hoc files, notes, or one-off vector stores
- **Works with:** Docker for self-hosting; Python/TS SDKs; your agent runtime (tool calling recommended)
- **Setup time:** 30–60 minutes

## Practical Notes

- Quant: use the published local endpoints (API + dashboard) to verify your environment; treat “backend up” as a deploy check.
- Quant: define retention and a maximum memory size per workspace; without limits, memory layers become unmaintainable.

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## Memory layers need product thinking

A memory backend isn’t useful unless it’s predictable:

- What schema do you store?
- How do you scope memories (project/user/agent)?
- How do you prune and consolidate?

## A pragmatic adoption plan

1. Stand up the backend locally.
2. Pick one workflow that benefits from persistence (onboarding, incident response, long refactors).
3. Define write rules: only store verified facts + decisions.

## Operational note

Treat memory infra like any service: backup, upgrade path, and a clear “reset” procedure when experiments go wrong.

### FAQ

**Q: Do I need the cloud service?**
A: No. The README includes a self-host path; you can prototype locally first.

**Q: What should I store first?**
A: Short decisions and constraints that are reused across tasks, not raw conversation dumps.

**Q: How do I prevent memory rot?**
A: Retention + consolidation + strict write rules; measure reuse, delete the rest.

## Source & Thanks

> Source: https://github.com/memodb-io/Acontext
> License: Apache-2.0
> GitHub stars: 3,370 · forks: 313

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## 快速使用

1. （可选）安装自托管辅助 CLI：
   ```bash
   curl -fsSL https://install.acontext.io | sh
   ```
2. 启动本地后端：
   ```bash
   mkdir acontext_server && cd acontext_server
   acontext server up
   ```
3. 安装 Python SDK：
   ```bash
   pip install acontext
   ```

## 简介

Agent 记忆层：通过 Docker 启动后端，再用 SDK 持久化/检索/共享上下文，把‘技能+记忆’从单次对话升级为可跨项目复用的长期资产，并支持把记忆写入你的 Agent 工作流与工具链。

- **适合谁：** 想要“专用记忆后端 + SDK”，而不是零散文件/笔记/一次性向量库拼装的 agent 开发者
- **可搭配：** Docker 自托管；Python/TS SDK；你的 agent 运行时（建议支持工具调用）
- **准备时间：** 30–60 分钟

## 实战建议

- 量化建议：用文档里的本地 endpoints（API 与 dashboard）验证环境；把“后端启动成功”当作部署检查项。
- 量化建议：为每个 workspace 定义保留策略与最大容量；没有上限的记忆层会迅速失控。

## 记忆层需要“产品化思维”

记忆后端只有在可预测时才有价值：

- 存什么 schema？
- 如何做作用域（项目/用户/agent）？
- 如何做清理、归并与过期？

## 一个务实的落地步骤

1. 先把后端在本地跑起来。
2. 选一条最需要持久化的工作流（入职、故障排查、长期重构）。
3. 规定写入规则：只存已验证事实与决策。

## 运维提示

把记忆层当作正式服务来管理：备份、升级路径、以及实验翻车时的“重置流程”。

### FAQ

**必须用云服务吗？**
答：不必须。README 提供自托管路径；建议先在本地做 PoC。

**最先存什么？**
答：跨任务复用的短决策与约束，不要把整段对话原样塞进去。

**怎么避免记忆腐烂？**
答：保留策略 + 归并 + 严格写入规则；量化复用率，不复用就清理。

## 来源与感谢

> Source: https://github.com/memodb-io/Acontext
> License: Apache-2.0
> GitHub stars: 3,370 · forks: 313


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Source: https://tokrepo.com/en/workflows/acontext-memory-layer-sdk-backend-for-agents
Author: Script Depot