Acontext is an open skill memory layer for AI agents. It captures learnings from agent runs and turns them into editable skill files. Past executions become reusable across agents and frameworks. The goal is agents that learn from past runs without opaque context pollution.
What it does
Acontext captures task outcomes, observations, and repeatable patterns from agent executions. It stores them as files you can read, edit, and share. The system moves beyond ephemeral memory and gives agents a lightweight file-based skill layer. This approach works similarly to how PDFCraft keeps document processing private by handling everything locally.
Skill files are human-readable and derived from past runs. Observations and SOPs are captured automatically. Files can be shared across agents and LLMs. The setup requires only a quick start from the repository.
Project link:
https://github.com/memodb-io/Acontext
How it works
Acontext hooks into agent runs and extracts structured data about actions and results. It writes those as versioned skill files. Agents query these files during execution and update them after new runs. Proven procedures become reusable in future tasks. This creates a feedback loop where agents improve from past executions.
Run Acontext in a sandboxed workspace while evaluating it. Audit generated skill files before allowing agents to act on them automatically. Agents that self-write skills can amplify bad behavior if not reviewed. Always validate and version control generated skills before production use. You can also explore DOOM Coding for writing code on your phone using a similar minimal-setup approach.
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