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AI Frameworks · memodb-io

Acontext

Acontext is an open-source memory layer for AI agents that automatically captures learnings from agent runs and stores them as plain Markdown skill files. Agents can read, edit, and share these skills across different LLMs and frameworks without vendor lock-in or semantic embeddings.

Source: GitHub — github.com/memodb-io/Acontext
3.6k
GitHub stars
323
Forks
JavaScript
Primary language
Apache-2.0
License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

FieldValue
Repositorymemodb-io/Acontext
Ownermemodb-io
Primary languageJavaScript
LicenseApache-2.0 — OSI-approved
Stars3.6k
Forks323
Open issues35
Latest releasesdk-ts/v0.1.21 (2026-04-08)
Last updated2026-06-30
Sourcehttps://github.com/memodb-io/Acontext

What Acontext is

Acontext provides a skill memory system where agent learnings are distilled via LLM and stored as structured Markdown files organized by a schema you define. Agents retrieve skills via tool calls (get_skill, get_skill_file) rather than semantic search, enabling progressive disclosure and local file-based portability across frameworks.

Quickstart

Get the Acontext source

Clone the repository and explore it locally.

terminalbash
git clone https://github.com/memodb-io/Acontext.gitcd Acontext# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Multi-run Agent Learning

Build agents that capture and refine skills across multiple runs. Acontext automatically distills conversation history into persistent, human-readable skill files that improve agent performance over time.

Skill Reuse Across Teams and Frameworks

Export skill files as ZIP and run them locally or with different LLMs (Claude, GPT, etc.) and frameworks (LangGraph, AI SDK, etc.) without re-embedding or migration. Git-friendly format supports version control and team collaboration.

Transparent Agent Memory Inspection

Replace opaque vector stores with plain Markdown files that engineers and non-technical stakeholders can read, edit, and debug directly. Understand exactly what the agent has learned and why.

Implementation considerations

  • Define your SKILL.md schema upfront to specify the structure and naming of skill files. The system routes and updates skills based on this schema, so clear governance prevents skill fragmentation.
  • Integrate Skill Content Tools (get_skill, get_skill_file) into your agent's tool set. The agent must be able to call these tools to retrieve and reason about skills during execution.
  • Distillation is triggered on task completion or failure. Implement explicit outcome reporting in your agent logic or rely on automatic detection. Monitor learning latency; production agents do not block on learning.
  • Plan for skill versioning and conflict resolution. Acontext stores skills as files; use Git or a version control strategy to manage iterations across teams and agent versions.
  • Test skill portability early. Export a ZIP of learned skills and verify they work with target frameworks (LangGraph, Claude, etc.) to catch schema or tool incompatibilities.

When to avoid it — and what to weigh

  • High-frequency semantic retrieval needs — Acontext uses tool-based retrieval (agent reasoning) rather than embedding-based similarity search. If you need sub-millisecond top-k similarity queries at scale, vector databases are better suited.
  • Real-time knowledge graph requirements — Acontext optimizes for skill file simplicity and portability, not graph traversal or complex relational queries. If your use case requires ACID transactions or deep relationship queries, consider a graph or relational database.
  • Heavily regulated compliance with immutable audit trails — While skill files are versionable via Git, Acontext's architecture and security posture for compliance-grade audit logging are not clearly documented. Requires review before deployment in HIPAA, SOC2, or similar environments.
  • Extremely resource-constrained environments — Self-hosting requires Docker and an LLM API (default gpt-4.1). Cloud mode depends on Acontext's hosted infrastructure. Not suitable for highly resource-limited edge devices or air-gapped systems without careful planning.

License & commercial use

Apache License 2.0 (Apache-2.0) — a permissive OSI-approved license. Allows commercial use, modification, and distribution with minimal restrictions. Requires attribution and includes warranty disclaimers.

Apache-2.0 is a permissive license that explicitly permits commercial use. You may use Acontext in commercial products without license fees. However, the LICENSE file and any modifications must include proper attribution. Review the full Apache 2.0 license terms and consult legal counsel if compliance auditing is required for your use case.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

SignalAssessment
MaintenanceActive
DocumentationAdequate
License clarityClear
Deployment complexityModerate
DEV.co fitGood
Assessment confidenceMedium
Security considerations

Not clearly stated. Considerations to investigate: (1) API key storage and rotation for self-hosted instances; (2) encryption of skill files in transit and at rest; (3) authentication and authorization between agents and the Acontext backend; (4) data retention and deletion policies; (5) LLM API key handling during distillation. Self-hosting requires running local Docker containers; audit your network and image security accordingly. Cloud mode depends on Acontext's server-side security posture—requires review of their privacy policy and compliance certifications.

Alternatives to consider

LangChain Memory / LangGraph State

Built-in memory primitives for LangChain/LangGraph agents. Tightly coupled to framework; less portable and harder to inspect than plain skill files. No auto-distillation.

Pinecone / Weaviate vector databases

Semantic retrieval at scale via embeddings. Fast similarity search; opaque vector representations. Requires embedding re-training for portability. Better for high-throughput retrieval, worse for transparency.

Firebase / Firestore

Managed NoSQL with real-time sync and offline support. No built-in agent learning or skill distillation. Generic database; requires custom logic to implement memory layer and schema governance.

Software development agency

Build on Acontext with DEV.co software developers

Start with Acontext cloud (free credits at acontext.io) or self-host locally. Explore docs and join the Discord community to learn how to integrate skill memory into your agent workflow.

Talk to DEV.co

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Acontext FAQ

Can I use Acontext with frameworks other than LangGraph or Claude?
Yes. Acontext exports skills as plain Markdown files and provides REST APIs. Any agent framework that supports function calling can integrate Skill Content Tools. No framework lock-in.
What happens to my skills if I leave Acontext?
You can download all skill files as ZIP and run them locally or with any other LLM/framework. Skills are plain Markdown; no re-embedding or migration required. Full data portability by design.
Does Acontext use embeddings for memory?
No. Acontext uses tool-based retrieval (agent reasoning via get_skill, get_skill_file) for progressive disclosure. Skills are stored as structured Markdown, not vectors.
Can I self-host Acontext?
Yes. Use the `acontext-cli` to run `acontext server up` locally (requires Docker and OpenAI API key). Data persists in a local `db` folder. API and dashboard run on localhost:8029 and localhost:3000.

Work with a software development agency

Need help beyond evaluating Acontext? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and ai frameworks integrations — and maintain them long-term.

Ready to build transparent, learning agents?

Start with Acontext cloud (free credits at acontext.io) or self-host locally. Explore docs and join the Discord community to learn how to integrate skill memory into your agent workflow.