Acontext: The Intelligent Evolution Platform Giving AI Agents Memory and Experience

Have you ever noticed how a powerful AI assistant, after completing a complex task, seems to “reset its memory,” forcing it to start from scratch the next time it faces a similar problem? It’s like having a brilliant but perpetually forgetful employee—full of potential but incapable of learning from experience. This is the core “context amnesia” challenge plaguing many AI Agents today.

Let’s explore an open-source project designed to solve this fundamental issue: Acontext. It is more than just a storage tool; it’s an AI Agent’s performance coach and knowledge base, dedicated to helping AI learn from every interaction and achieve genuine “self-evolution.”

The AI Agent Pain Point: Why Do They Need “Memory”?

Imagine you ask an AI assistant to analyze a market report. It performs excellently: sourcing data, performing cross-comparisons, and generating visual charts. A week later, you need another report analyzed. Ideally, the AI should recall the efficient method from last time, perhaps executing it even faster.

Often, the reality is that the AI assistant approaches the task as if for the first time, starting from zero. It lacks long-term memory and experience accumulation. This leads to several problems:

Inefficiency: Solving similar problems repeatedly wastes computational resources and time.
Unstable Performance: Success sometimes depends on “luck,” making consistent optimal results difficult.
Inability to Continuously Optimize: Without learning from success or failure, progress stalls.

Acontext was born to address these pain points. Its core mission is clear and powerful: One Place for Agents to Store, Observe, and Learn.

The Acontext Core: Three Stages of Empowerment

Acontext systematically enhances an AI Agent’s reliability and intelligence through a tightly integrated three-stage cycle.

Stage One: 🧠 Store — Building a “Full-Sensory Memory Bank”

If we compare an AI Agent to a person, its “short-term working memory” is limited. Acontext first acts as an external hippocampus, providing the Agent with persistent memory storage.

More Than Just Chat Logs: It stores not only the text dialogue between the user and the Agent but also various Artifacts produced during task execution.
Introducing the “Disk” Concept: Acontext creates a virtual “hard drive” for the Agent. The Agent can write and read files here, such as generated code snippets, organized data tables, or downloaded images. This ensures the integrity of the task context, making every step “verifiable.”

The Value: The Agent is no longer “forgetful.” Complete task histories and outputs are preserved, laying a solid data foundation for subsequent analysis and learning.

Stage Two: 👁️ Observe — Activating the “Invisible Task Supervisor”

While your primary Agent works on the user’s problem in the foreground, Acontext quietly launches a background Task Agent, acting as an “invisible supervisor.”

Real-Time Tracking & Analysis: This background Agent monitors the conversation flow in real time, automatically extracting key information: what task is being executed? What is its status (Pending/Success/Failed)? What is the progress? What user preferences have been expressed?
Visual Insights: All this information is presented in Acontext’s built-in local dashboard. Developers are no longer “blind.” They can clearly see the Agent’s execution flow, success rate bottlenecks, and user feedback hotspots.

The Value: Achieves transparency in monitoring the Agent’s workflow. Developers can precisely identify where the Agent gets stuck, quantitatively evaluate its performance, and provide data-driven optimization.

Stage Three: 📘 Learn — Enabling “SOP Experience Precipitation and Reuse”

This is Acontext’s most revolutionary capability, moving AI Agents from “repetitive labor” to “experience reuse.”

Distilling SOPs from Success: After a complex task is successfully completed, Acontext automatically evaluates it. If the task is sufficiently complex and representative, the system distills the successful operational path, tool call sequence, and decision logic into a Standard Operating Procedure (SOP).
Building a “Skill Space”: These distilled SOPs are stored in a structured, Notion-like Space. Think of it as the Agent team’s “internal Wiki” or “best practices knowledge base.”
Intelligent Skill Retrieval & Application: When the Agent encounters a similar task again, it first queries the Space: “How did I (or other Agent peers) handle this before?” It can then directly apply the proven SOP to execute, rather than starting from scratch through “trial and error.”

The Value: This marks the Agent’s entry into a self-evolution virtuous cycle. The more it’s used, the more SOPs are accumulated, and the more proficient, successful, and efficient the Agent becomes at handling specific tasks. Experience is inherited and reused across the team (multiple Agent instances).

Acontext Architecture Explained: How Core Concepts Work Together

To understand Acontext, you first need to know its core building blocks:

Session: A dialogue thread, the basic unit of interaction between the Agent and user, supporting multi-modal message storage.
Task Agent: The background observer mentioned above, responsible for extracting task status, progress, and preferences from a Session.
Disk: The file storage system for Agent Artifacts, like the Agent’s dedicated “work hard drive.”
Space: The structured knowledge base storing learned skills, organized in a tree-like structure of folders, pages, and blocks.
Experience Agent: Another background Agent, specifically tasked with distilling SOPs from successful tasks and saving/indexing them into the Space.

How do they work together? The following diagram clearly shows the data flow:

In simpler terms, this workflow is:

The User interacts with your AI Agent.
All interaction information (messages and files) is recorded in the Session and Disk.
The Task Agent observes the Session in real time, extracting task information.
After task success, the Experience Agent intervenes, evaluates, and distills valuable operational paths into SOPs.
SOPs are stored in the structured Space.
When your Agent faces a new task, it can search and apply relevant SOPs from the Space to guide its actions, forming a closed loop.

A skill (SOP) that has been learned and stored in the Space might exist as structured data like this:

{
    "use_when": "star a repo on github.com",
    "preferences": "use personal account. star but not fork",
    "tool_sops": [
        {"tool_name": "goto", "action": "goto the user given github repo url"},
        {"tool_name": "click", "action": "find login button if any, and start to login first"},
        // ... subsequent steps
    ]
}

The organization of the entire Skill Space is clear, facilitating management and retrieval:

/
└── github/ (folder)
    └── GTM (page)
        ├── find_trending_repos (sop block)
        └── find_contributor_emails (sop block)
    └── basic_ops (page)
        ├── create_repo (sop block)
        └── delete_repo (sop block)

Getting Started with Acontext: A Practical Guide from Zero to One

Acontext offers an excellent developer experience, making integration straightforward through its command-line tool and rich SDKs.

Step One: Quick Deployment of the Backend Service

Acontext recommends using Docker for one-click local deployment. Ensure Docker is installed on your machine and that you have an OpenAI API key ready.

# 1. Install the acontext command-line tool
curl -fsSL https://install.acontext.io | sh

# 2. Create a project directory and start the service
mkdir acontext_server && cd acontext_server
acontext docker up

After running acontext docker up, the tool handles environment configuration automatically. Once started, you can access:

API Service: http://localhost:8029/api/v1
Local Dashboard: http://localhost:3000/

Step Two: Choose a Template That Suits Your Tech Stack

Acontext provides ready-to-use example templates for different technology stacks to help you get started quickly.

For Python Developers:

acontext create my-proj --template-path "python/openai-basic"

Additionally, there are more advanced templates for openai-agent, the agno framework, and handling Artifacts.

For TypeScript Developers:

acontext create my-proj --template-path "typescript/openai-basic"

Templates for integrating with @vercel/ai-sdk are also supported.

All example code can be found in the Acontext-Examples repository.

Step Three: Integrate Acontext into Your Agent via SDK

Acontext provides comprehensive Python and TypeScript SDKs that can be easily integrated into your existing projects based on OpenAI, LangChain, or Vercel AI SDK.

1. Install the SDK

# Python
pip install acontext
# TypeScript
npm i @acontext/acontext

2. Initialize the Client

from acontext import AcontextClient

client = AcontextClient(
    base_url="http://localhost:8029/api/v1",
    api_key="sk-ac-your-root-api-bearer-token" # default key
)
client.ping() # test connection

3. Store Sessions and Messages
One of Acontext’s core functions is providing persistent storage for your Agent conversations.

# Create a new session
session = client.sessions.create()

# Save conversation messages
messages = [
    {"role": "user", "content": "I need to write a landing page of iPhone 15 pro max"},
    {"role": "assistant", "content": "Sure, my plan is below:\n1. Search for the latest news about iPhone 15 pro max\n2. Init Next.js project for the landing page\n3. Deploy the landing page to the website"},
]
for msg in messages:
    client.sessions.send_message(session_id=session.id, blob=msg, format="openai")

# You can later load these messages to use as context for the LLM
historical_messages = client.sessions.get_messages(session.id).items

4. Manage Task Artifacts
Files generated by the Agent during task execution can be centrally stored in Acontext’s Disk.

from acontext import FileUpload

# Create a disk
disk = client.disks.create()

# Upload a file (e.g., a to-do list)
file = FileUpload(
    filename="sprint_plan.md",
    content=b"# Sprint Plan\n\n## Goals\n- Complete user authentication\n- Fix critical bugs"
)
artifact = client.disks.artifacts.upsert(
    disk.id,
    file=file,
    file_path="/plans/" # specify storage path
)

# List or retrieve files
file_list = client.disks.artifacts.list(disk.id, path="/plans/")

5. Observe Task Execution Status
Acontext automatically extracts task information from sessions. You can retrieve this information via the SDK for context management or status display.

# After sending some messages containing task steps...
client.sessions.flush(session.id) # Wait for task extraction to complete (for development/debugging)

# Get the extracted tasks
tasks = client.sessions.get_tasks(session.id)
for task in tasks.items:
    print(f"Task: {task.data['task_description']}, Status: {task.status}")
    # Possible output: Task: Search for iPhone latest news and report, Status: success

In the dashboard, you can see a more intuitive overview of the task flow:

6. Enable and Utilize Self-Learning Capabilities
To enable Agent learning, you need to associate its session with a Space.

# Create a skill learning space
space = client.spaces.create()

# Create a session attached to this space
session = client.sessions.create(space_id=space.id)

# ... Later, the Agent successfully completes a complex task within this session
# Acontext will automatically evaluate it in the background (usually with a 10-30 second delay) and distill/store the successful SOP into the Space

The learning process happens automatically in the background, following this logic:

graph LR
    A[Task Completed] --> B[Task Extraction]
    B --> C{Space Connected?}
    C -->|Yes| D[Queue for Learning]
    C -->|No| E[Skip Learning]
    D --> F[Extract SOP]
    F --> G{Hard Enough?}
    G -->|No - Too Simple| H[Skip Learning]
    G -->|Yes - Complex| I[Store as Skill Block]
    I --> J[Available for Future Sessions]

You can browse all skills accumulated in a Space via the dashboard:

When an Agent encounters a new task, it can proactively search for relevant experience from the Space:

# Search for relevant skills from the Space
search_results = client.spaces.experience_search(
    space_id=space.id,
    query="I need to implement user login authentication",
    mode="fast" # or use the more in-depth "agentic" mode
)
# The returned results are SOP lists that can directly guide Agent actions

Frequently Asked Questions

Q: How is Acontext different from a regular vector database or memory cache?
A: They are fundamentally different. Vector databases are primarily used for similarity-based information retrieval, while Acontext is a full-process platform. It not only stores conversations (memory) but also actively observes task status through background Agents and possesses the advanced learning ability to distill and structure experience (SOPs), with the ultimate goal of continuous optimization of Agent behavior.

Q: Do I need to modify my existing Agent code?
A: Integration is required, but changes are incremental and non-invasive. You can start by calling Acontext’s SDK to store sessions and files, which provides baseline value. Subsequently, you can gradually incorporate observation and learning functionalities without refactoring your core Agent logic.

Q: Are the learned SOPs secure? Could they contain sensitive information?
A: The Acontext learning process occurs within your private deployment environment. All data (sessions, files, SOPs) is stored locally. The system distills SOPs based on task execution traces. You can review all learned content through the dashboard to ensure it meets expectations.

Q: Does it support multi-modal interactions?
A: Yes. Acontext’s session storage natively supports multi-modal messages. This means that besides text, images, documents, and other materials related to Agent interactions can also be associated, stored, and used for contextual analysis.

Q: Is it production-ready?
A: Acontext is built with Go, focusing on performance. It offers Docker deployment, a visual dashboard, and a clear API, designed from the outset to serve production-level AI applications. You can start with a single service and scale as needed.

Conclusion: Why Acontext is Worth Your Attention

At the critical juncture where AI Agents move from conceptual demos to scalable applications, reliable repeatability and continuous evolution become core challenges. Acontext precisely targets this pain point, offering a systematic solution:

It Makes Agents Reliable: Through complete context storage and real-time task monitoring, it transforms the Agent’s “black box” operations into an observable, analyzable process.
It Makes Agents Smart: By turning one-time successes into reusable, structured experience, it enables knowledge precipitation and inheritance, breaking the cycle of Agents “having to relearn to walk every time.”
It Gives Developers Control: The local dashboard provides unprecedented transparency, making debugging, optimization, and trust-building significantly easier.

Whether you’re building customer service assistants, coding partners, data analysis Agents, or complex automation workflows, giving your AI “memory” and “learning” capabilities is a crucial step toward next-generation intelligent applications. As an open-source project, Acontext provides a powerful toolkit for all developers to realize this vision.

If you are exploring the deep application of AI Agents, perhaps start by helping them overcome “forgetfulness” and begin “accumulating experience.” Acontext might just be the core piece you’ve been looking for.

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