---
title: "Acontext"
type: "tool"
slug: "memodb-io-acontext"
canonical_url: "https://www.graphcanon.com/tools/memodb-io-acontext"
github_url: "https://github.com/memodb-io/Acontext"
homepage_url: "https://acontext.io"
stars: 3571
forks: 323
primary_language: "JavaScript"
license: "Apache-2.0"
categories: ["ai-agents"]
tags: ["llmops", "self-evolving", "agent-development-kit", "self-learning", "llm-observability"]
updated_at: "2026-07-07T18:42:23.40606+00:00"
---

# Acontext

> Agent Skills as a Memory Layer

An open-source skill memory layer for AI agents that captures learnings and stores them as agent skill files, enabling learning from mistakes and reuse of effective strategies.

## Facts

- Repository: https://github.com/memodb-io/Acontext
- Homepage: https://acontext.io
- Stars: 3,571 · Forks: 323 · Open issues: 35 · Watchers: 27
- Primary language: JavaScript
- License: Apache-2.0
- Last pushed: 2026-06-30T18:35:29+00:00

## Categories

- [AI Agents](/categories/ai-agents.md)

## Tags

llmops, self-evolving, agent-development-kit, self-learning, llm-observability

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## README (excerpt)

```text
<div align="center">
  <a href="https://discord.acontext.io">
      <img alt="Acontext - Agent Skills as a Memory Layer" src="./assets/Acontext-header-banner.png">
  </a>
 	<p align="center">
 	  	<a href="https://acontext.io">🌐 Website</a>
      |
 	  	<a href="https://docs.acontext.io">📚 Document</a>
  </p>
  <p align="center">
    <a href="https://pypi.org/project/acontext/"><img src="https://img.shields.io/pypi/v/acontext.svg"></a>
    <a href="https://www.npmjs.com/package/@acontext/acontext"><img src="https://img.shields.io/npm/v/@acontext/acontext.svg?logo=npm&logoColor=fff&style=flat&labelColor=2C2C2C&color=28CF8D"></a>
    <a href="https://github.com/memodb-io/acontext/actions/workflows/core-test.yaml"><img src="https://github.com/memodb-io/acontext/actions/workflows/core-test.yaml/badge.svg"></a>
    <a href="https://github.com/memodb-io/acontext/actions/workflows/api-test.yaml"><img src="https://github.com/memodb-io/acontext/actions/workflows/api-test.yaml/badge.svg"></a>
    <a href="https://github.com/memodb-io/acontext/actions/workflows/cli-test.yaml"><img src="https://github.com/memodb-io/acontext/actions/workflows/cli-test.yaml/badge.svg"></a>
  </p>
<p align="center">
 	  	<a href="https://x.com/acontext_io"><img src="https://img.shields.io/twitter/follow/acontext_io?style=social" alt="Twitter Follow"></a>
    <a href="https://discord.acontext.io"><img src="https://img.shields.io/badge/dynamic/json?label=Acontext&style=flat&query=approximate_member_count&url=https%3A%2F%2Fdiscord.com%2Fapi%2Fv10%2Finvites%2FSG9xJcqVBu%3Fwith_counts%3Dtrue&logo=discord&logoColor=white&suffix=+members&color=36393f&labelColor=5765F2" alt="Acontext Discord"></a>
  </p>
</div>






## What is Acontext?

Acontext is an open-source skill memory layer for AI agents. It **automatically** captures learnings from agent runs and stores them as **agent skill files** — files you can read, edit, and share across agents, LLMs, and frameworks.

If you want the agent you build to **learn from its mistakes** and **reuse what worked** — without opaque memory polluting your context — give Acontext a try.



## Skill is All You Need

Agent memory is getting increasingly complicated🤢 — hard to understand, hard to debug, and hard for users to inspect or correct. Acontext takes a different approach: if agent skills can represent every piece of knowledge an agent needs as simple files, so can the memory.

- **Acontext builds memory in the agent skills format**, so everyone can see and understand what the memory actually contains.
- **Skill is Memory, Memory is Skill**. Whether a skill comes from one you downloaded from Clawhub or one you created yourself, Acontext can follow it and evolve it over time.



## The Philosophy of Acontext

- **Plain file, any framework** — Skill memories are Markdown files. Use them with LangGraph, Claude, AI SDK, or anything that reads files. No embeddings, no API lock-in. Git, grep, and mount to the sandbox.
- **You design the structure** — Attach more skills to define the schema, naming, and file layout of the memory. For example: one file per contact, one per project by uploading a working context skill.
- **Progressive disclosure, not search** — The agent can use  `get_skill` and `get_skill_file` to fetch what it needs. Retrieval is by tool use and reasoning, not semantic top-k.
- **Download as ZIP, reuse anywhere** — Export skill files as ZIP. Run locally, in another agent, or with another LLM. No vendor lock-in; no re-embedding or migration step.

## How It Works

### Store — How skills get memorized?

```mermaid
flowchart LR
  A[Session messages] --> C[Task complete/failed]
  C --> D[Distillation]
  D --> E[Skill Agent]
  E --> F[Update Skills]
```

- **Session messages** — Conversation (and optionally tool calls, artifacts) is the raw input. Tasks are extracted from the message stream automatically (or inferred from explicit outcome reporting).
- **Task complete or failed** — When a task is marked done or fail
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/memodb-io-acontext`](/api/graphcanon/tools/memodb-io-acontext)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_