What is Acontext?
Agent Skills as a Memory Layer — learns from runs, writes Markdown skill files, reuses them on the next run
Acontext is an open-source Agent Skills as a Memory Layer: skill memory that automatically captures learnings from agent runs and stores knowledge as Markdown files. You can read, edit, and share those files across agents, LLMs and frameworks.
Why Skill Memory?
Other memory (e.g. Mem0, Zep, vector stores, RAG) stores or retrieves from conversations or static docs. If you need a layer that learns from what the agent did (task outcomes) and turns that into reusable SOPs and warnings, that's Agent Skills as a memory layer.
| Skill memory (Agent Skills layer) | Other memory (e.g. Mem0 / Zep) | Vector store / RAG | |
|---|---|---|---|
| Captures | What the agent did + outcome → procedures, preferences, warnings | What was said → facts, preferences | What you ingested → chunks |
| Stored as | Markdown files (human-readable, portable) | Embeddings / graph | Embeddings |
| Retrieval | Agent calls tools, gets full units (e.g. whole files) | Semantic or chat recall | Similarity search over chunks |
In short: Other memory stores and retrieves chat or ingested docs as-is. Acontext also takes session messages (and execution) as input, but distills from them what the agent did and how it turned out — then writes that as structured skill files that grow with every run.
Session Storage
Session-scoped storage for messages, files, artifacts, and sandboxes — with context engineering built in.
Task Tracking
Task status, progress tracking, and automatic summaries.
Skill Memory
Persistent skills learned across sessions — Markdown-native, configurable, and human-readable.
Product Overview
Long-term Skill
Agent memory stored as skills — filesystem-compatible, configurable, human-readable.
Short-term Memory
Multi-provider messages, S3-backed disk storage, and reusable skill packages.
Mid-term State
Track agent tasks, traces, token usage, and session analytics.
Sandbox
Secure code execution with mountable skills.
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