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alchemyst-mcp

Use this skill whenever you need to store, retrieve, search, or view persistent context using the Alchemyst AI MCP server at mcp.getalchemystai.com/mcp/sse. Triggers include: requests to "remember" or "recall" information across sessions, storing documents/notes/decisions for later retrieval, searching project knowledge, or any task that involves reading from or writing to Alchemyst's context store.

Why use this skill?

Enhance your OpenClaw agent with persistent memory. Use Alchemyst AI to store, search, and recall project knowledge and notes across sessions effortlessly.

skill-install — Terminal

Install via CLI (Recommended)

clawhub install openclaw/skills/skills/anuran-roy/alchemyst-mcp
Or

What This Skill Does

The Alchemyst AI MCP skill acts as a persistent memory and cognitive layer for your OpenClaw agents. By connecting to the Alchemyst context engine via Server-Sent Events (SSE), this tool allows your agent to store, index, and retrieve information across different sessions and project environments. Instead of relying solely on transient chat history, the agent can write important decisions, project requirements, or technical documentation to an external store and perform semantic searches to recall that data later, ensuring your workflow remains consistent and highly informed.

Installation

To integrate this capability into your OpenClaw environment, use the provided installer: clawhub install openclaw/skills/skills/anuran-roy/alchemyst-mcp

Ensure you have your Alchemyst API key generated from the platform portal. For secure deployments, configure your environment variables to include the ALCHEMYST_API_KEY rather than hardcoding credentials into your agent configuration files.

Use Cases

  • Project Continuity: Maintain complex project specs and architectural decisions over long timeframes.
  • Knowledge Retrieval: Query past meeting notes or documentation without manual searching through static files.
  • Unified Context: Synchronize memory across different tools and agent instances that share the same Alchemyst store.
  • Decision Tracking: Log key trade-offs during development for future reference when refactoring or debugging.

Example Prompts

  1. "Store these API design decisions in Alchemyst so I can reference them during the implementation phase later."
  2. "Search the Alchemyst context for any previous notes on the authentication flow requirements for this project."
  3. "Recall the architectural constraints we established last week and check if our current code changes comply with them."

Tips & Limitations

  • Threshold Tuning: When performing semantic searches, carefully adjust the similarity_threshold and minimum_similarity_threshold. High thresholds provide precise results, while lower thresholds offer broader context.
  • Metadata Accuracy: Providing accurate metadata (like groupName or fileType) significantly improves search performance when your knowledge base grows large.
  • Environment Safety: Never commit your API key to public repositories. Always utilize local environment variables or secure vault services in your production pipeline.
Read Full Documentation on GitHub

Metadata

Author@anuran-roy
Stars1696
Views1
Updated2026-03-01
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Add to Configuration

Paste this into your clawhub.json to enable this plugin.

{
  "plugins": {
    "official-anuran-roy-alchemyst-mcp": {
      "enabled": true,
      "auto_update": true
    }
  }
}

Tags(AI)

#memory#knowledge-management#context#persistence#search
Safety Score: 4/5

Flags: external-api, file-read, file-write