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Avenlo: Infrastructure for Building Persistent Shared Memory for AI Agents

Avenlo is an open-source MCP-native memory server designed to provide cross-session persistent context sharing capabilities for multiple AI tools, enabling AI Agents to truly have "memory".

AI AgentMCP记忆基础设施持久化存储多Agent协作开源项目开发者工具
Published 2026-04-28 08:15Recent activity 2026-04-28 08:19Estimated read 6 min
Avenlo: Infrastructure for Building Persistent Shared Memory for AI Agents
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Section 01

Introduction: Avenlo—Infrastructure for Building Persistent Shared Memory for AI Agents

Avenlo is an open-source MCP-native memory server aimed at solving the "amnesia" problem of AI Agents, providing cross-session persistent context sharing capabilities so that AI Agents can truly have "memory". It fills the gap in the current AI tool ecosystem, serving as a dedicated "shared brain" for AI Agents to enhance multi-agent collaboration efficiency.

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Section 02

Background: Pain Points and Needs of AI Agent Memory Loss

Most current AI tools use a stateless request-response model, where each conversation starts fresh. Users have to repeatedly provide background information, and AI cannot accumulate learning over the long term. In multi-agent collaboration scenarios, each agent becomes an information silo, leading to low collaboration efficiency. Avenlo was created to fill this gap.

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Section 03

Methodology: Project Positioning and Technical Architecture Design of Avenlo

Avenlo is positioned as an "MCP-native memory server", based on Anthropic's MCP protocol to achieve seamless integration with MCP-supported AI tools. It adopts a CLI tool + lightweight web dashboard architecture: the CLI provides deployment and management capabilities for developers, while the web dashboard is used for visual monitoring and search. The tech stack follows minimalism: dark minimalist style, indigo accent color, JetBrains Mono/Geist Sans fonts, and single-column vertical layout.

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Section 04

Evidence: Core Function Modules of the Avenlo Dashboard

According to the design document, the dashboard includes five core modules:

1. Status Bar

Displays system health status, uptime, and version information to ensure the service is online.

2. MCP Endpoint

Provides the MCP server URL and access token; one-click copy allows connection to AI tools.

3. Quick Statistics

Shows the number of stored pages, last update time, and number of active Agents.

4. Recent Pages

Lists the 10 most recently stored pages, supporting detailed viewing.

5. Search

A single input box supports full-text search across all pages for quick location of historical information.

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Section 05

Design Decisions: Minimalism and User-Centric Thinking Behind Avenlo

Key design decisions for Avenlo include:

  • Dark mode first: Focused on developers' expectations; no light mode provided in version v0.
  • Indigo accent color: Avoids the cliché AI purple, choosing #6366F1 to maintain recognizability.
  • No animation design: Only retains micro-transition effects, focusing on tool practicality.
  • Single-column layout: Reduces cognitive load and maintenance costs.
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Section 06

Current Status: Development Progress and Open Source License of Avenlo

As of the document update, Avenlo is in the planning phase (🟡 Planning phase), and the team is developing core functions. The project is fully open source under the CC0 license; anyone can freely use, modify, and distribute it, aiming to become a public memory infrastructure for the AI ecosystem.

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Section 07

Conclusion: Potential Impact of Avenlo on the AI Agent Ecosystem

Avenlo represents an important direction in the evolution of AI infrastructure, focusing on the "connection layer" beyond models. Its value lies in promoting the formation of a collaborative, shared, and continuously learning agent network. If successful, it may become the "memory standard" for AI Agents, similar to how HTTP unifies information transmission and MCP unifies model-tool connections.

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Section 08

Epilogue: Future Outlook of Avenlo and Insights for Developers

Although Avenlo is in the early stage, its design philosophy and technical direction are worth paying attention to. It chooses to build a real infrastructure rather than a quick demo, providing developers with ideas for adding persistent memory capabilities, and bringing the industry the possibility of AI Agents getting rid of "goldfish memory". Memory is the cornerstone of intelligence, and Avenlo is contributing to this foundation.