Introducing Acontext: Context Data Platform for Self-learning AI Agents

Every AI agent developer has felt the same pain: your agent works beautifully in one run, and fails mysteriously in the next.

It doesn't remember what worked before. It can't explain why it failed. And even when it succeeds, you can't capture why.

We built Acontext to change that. It started with a simple question:

“What if an agent could observe, remember, and learn from every interaction it has, just like a human learning skills from its own past?”

Acontext's mission is to give agent developers the missing foundation for self-evolving agents: a context data platform that makes agents' execution observable, reusable, and learnable.

Why Do We Need Something like Acontext?

Today, an agent's context is scattered across memory stores, RAG pipelines, logs, and user feedback.

The result? Fragmented, transient, and nearly impossible to analyze over time.

This creates critical challenges for agent developers:

• Integration Overhead and Local-to-Cloud Complexity: Agent developers spend excessive time merging data to maintain a consistent state. Local setups work fine, but when you move to production, your memory, files, and context systems don't scale seamlessly.

• Complexity in Context Engineering: Reduction, compression, offloading, and "Claude Skills"-style capability building often become ad-hoc, brittle layers scattered across your stack.

• Agent Stability: Getting an agent to work once is easy. Maintaining reliability over time is challenging. Developers struggle to track what the agent promised, whether users were satisfied, and why performance drifts.

• Limited Experience Learning: Agents today don't learn from their successes effectively. "Memory" solutions only store text summaries, but true experience learning requires capturing the full task context: actions, tool calls, and solid SOPs, etc.

All these challenges stem from one root cause: context data is not treated as a first-class infrastructure.

We don't need another framework or database; we need a unified layer that makes context observable, persistent, and reusable.

That's what Acontext does.

What Acontext Brings to Your Agent Stack

Acontext is a context data platform that gives your AI agents context management, observability, and experience learning. Here's how it works:

1. Multi-Modal Context Storage

Acontext acts as your storage backend, offering a simple API for unified storage and persistent management of multimodal conversation data.

It is designed like a filesystem for agents' artifact storage, enabling easy context offloading and cross-task collaboration, especially ideal for scenarios where a sandbox environment is not required.

2. Real-Time Context Observability

Acontext provides a built-in dashboard, giving you a clear view of your agent's execution process:

• Track all context sessions, monitor each task's objectives, execution process, and success or failure status.

Unlike LangSmith or Langfuse, which focus on latency or token usage, Acontext focuses on real-time, context-aware, task-level tracking. Every dynamic aspect of agent running is evident and actionable.

3. Memory Layer for Experience Learning

Acontext is the memory layer your AI agent actually needs. It captures what your agent does well and turns those wins into reusable skills:

Organized Workspace: Each agent has its own Notion-like workspace where skills are automatically organized and managed.

Personalized Skill Library: Imagine you have 100,000 users, each with their own autonomous "Claude Skills" that collect past successful complex tasks. Every time your agent exceeds expectations and achieves a breakthrough, that success is captured and stored in Acontext. This means your agent's success rate is no longer a random occurrence, and the experience-based learning continues to grow and improve.

Customized Control: Use the full CRUD API to add custom experiences or manage workspace content manually, whenever you need.

Flexible and Precise Skill Retrieval: Acontext offers two search modes for quickly retrieving the skills and experiences you need:

• Semantic Search: Fast and accurate retrieval of relevant skills.

• Agentic Search: A progressive, multi-step skill organization and retrieval system, suitable for both auto-learned and manually customized content.

Built to Collaborate, not Compete

Acontext doesn't replace existing frameworks, databases, or observability tools. It's here to work alongside them, providing your agents with a shared layer where all their context data lives and evolves.

Acontext vs. Frameworks

Acontext is not an agent framework. Think of it as the place where your agent's data lives. Use OpenAI, Anthropic, LangChain, or any other stack. Acontext simply ensures agents' messages and artifacts are stored and reusable.

Acontext vs. Databases

Acontext is not a new type of database either. It builds on Postgres, S3, Redis, and other stack to store all the data your agent needs — text, code, PDFs, or images — in one place.

Acontext vs. Observability Tools

Acontext doesn't replace existing AI observability tools, but it reveals what they can't. Traditional tools can show errors, latency, and token usage, but they can't tell you whether your agent actually satisfied the user.

Acontext tracks the full context: capturing what really happened, whether it worked, and why.

What Acontext Means for Agent Developers

Imagine having a Supabase-like data platform, but purpose-built for AI agents.

That's what we want to create with Acontext.

Instead of juggling storage, logging, and context engineering, you get a clean API that tailors to your needs. No more wiring multiple systems, no more tedious bits.

You are the person to build agents to solve real problems, not to babysit context loops.

Let Acontext handle the memory, context, and skill learning underneath. You focus on delivering real value.

How to Use Acontext

Acontext is still in its early stage, and what you see today is just the first version of what it will become.

Currently, Acontext supports storing agents' context data using Postgres and S3, offers an intuitive local dashboard, and delivers one of the most effective agent self-learning experiences available today.

But there's much more on the roadmap, and we'd love for you to try Acontext in your POC and help shape what comes next.

So, how to get started?

Open-Source Release

We're currently in open-source mode, moving fast and gathering feedback from the community. Download our acontext-cli for a quick test drive:

curl -fsSL https://install.acontext.io | sh

With acontext docker up, you can quickly launch an Acontext instance on your local machine. We provide Python and TypeScript SDKs, so you can easily push data in.

Acontext also supports storing OpenAI and Anthropic message formats directly.

You can explore a few examples to get a feel for how to use Acontext:

If you'd like to look through more examples first, check out our example repo.

Join the Journey

We're building the Context Data Platform for AI agents together, through open source.

This is a new territory. No one exactly knows what a Context Data Platform should look like yet. But that's what makes it exciting: we get to figure it out together with the developers who are building the next generation of AI Agents.

Here's how you can get involved:

• Join our Discord Community to connect with other builders

• Try Acontext locally and share what you learn

• Open issues, submit PRs, or tell us what's working (and what's not)

• GitHub: http://github.com/memodb-io/Acontext

We're at the beginning of something big: building a Data Platform for AI Agents.

Let's build it together!