Acontext

What does this agent do, and when should you use it?

Acontext is an open-source skill-memory layer for AI agents that stores learned knowledge as Markdown skill files. It exposes Python and TypeScript SDKs, a REST API, a web dashboard, and a self-hosted backend. Its learning flow extracts tasks from session messages and optional tool calls or artifacts, then distills outcomes after a task completes or fails and updates skills according to a SKILL.md schema. On later runs, an agent retrieves content through list_skills, get_skill, and get_skill_file rather than vector search. Skill files can be exported as ZIP archives for reuse with other agents, LLMs, and runtimes.

An application creates a learning space and session, then connects them with client.learning_spaces.learn(space.id, session_id=session.id). During an agent run, it records user and assistant messages with client.sessions.store_message(session.id, blob=...); after a task completes or fails, Acontext extracts tasks, distills successful and failed approaches plus user preferences, and has its Skill Agent create or update Markdown skill files. After client.learning_spaces.wait_for_learning, the application can enumerate learned files with client.learning_spaces.list_skills(space.id) and download them with client.skills.download(skill_id=..., path=...). On a later run, the agent calls get_skill and get_skill_file to retrieve needed content. For local deployment, acontext server up starts the local API, dashboard, and persistent data directory.

1. An OpenAI Agent SDK developer building a support or task agent who wants successful task practices captured as reviewable Markdown skills.
2. A team using Claude Agent SDK that wants failures as well as completed tasks to contribute reusable operational knowledge.
3. An engineering team maintaining LangGraph, Claude, or AI SDK agents that needs to share skill files across frameworks instead of migrating a vector index.
4. A developer evaluating agent memory locally who can start the backend and dashboard with Docker and acontext server up.
5. A team building agents with code execution that wants skills mounted in a sandbox alongside disk, sandbox, and skill tools.

What are this agent's strengths and limitations?

How do you install or deploy this agent?

Cloud setup: complete onboarding at Acontext.io to obtain an API key beginning with sk-ac, then run:

pip install acontext

For a self-hosted proof of concept, install Docker and have an OpenAI API Key, then run:

curl -fsSL https://install.acontext.io | sh
mkdir acontext_server && cd acontext_server
acontext server up

The command creates or uses .env and config.yaml and persists data in db. The local API is http://localhost:8029/api/v1 and the dashboard is http://localhost:3000/.

How do you use this agent?

A first Python flow is:

from acontext import AcontextClient
import os

client = AcontextClient(api_key=os.getenv("ACONTEXT_API_KEY"))
space = client.learning_spaces.create()
session = client.sessions.create()
client.learning_spaces.learn(space.id, session_id=session.id)
client.sessions.store_message(session.id, blob={"role": "user", "content": "My name is Gus"})
client.sessions.store_message(session.id, blob={"role": "assistant", "content": "Hi Gus! How can I help you today?"})
client.learning_spaces.wait_for_learning(space.id, session_id=session.id)
skills = client.learning_spaces.list_skills(space.id)

For the self-hosted API, initialize instead with:

client = AcontextClient(base_url="http://localhost:8029/api/v1", api_key="sk-ac-your-root-api-bearer-token")

FAQ