How does huml prevent my AI agents from losing context between different chat sessions?

Huml solves the problem of "agent amnesia" by providing a persistent Markdown-based storage layer that allows AI agents to remember project status, user preferences, and past decisions across different sessions and chat interfaces.

Which AI agents and IDEs can I use with the huml MCP server?

This skill is compatible with any AI client or IDE that supports the Model Context Protocol (MCP), including Claude Desktop, Cursor, and various CLI tools, enabling a unified memory across all your development environments.

What happens if two different agents try to update the same memory file at the same time?

Huml uses a sophisticated compare-and-swap logic with version IDs to manage concurrent writes, ensuring that data is never overwritten or lost even if multiple agents are updating the same memory file simultaneously.

What are the technical requirements to get huml up and running?

Setup requires a running huml MCP server; once active, you simply connect the skill to your host, allowing the agent to immediately begin using the memory_list, search, and write tools to manage your data.

Does this skill support semantic search for retrieving my stored memories?

Huml currently utilizes lexical search for precise keyword and alias matching within your Markdown files, though it does not yet support vector-based semantic search.

Are there any specific limitations on the size or format of the data stored?

Memory files are stored as individual Markdown documents with YAML frontmatter, with a size limit of 100KB per file to ensure optimal performance and context window management for your agents.

huml

Persistent cross-session memory for AI agents using the huml MCP server.

Free

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Works with the AI Agents you already use

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+18 agents

huml

Example session with this skill installed

Set up huml so Claude Code and Cursor share one memory.

Read your context and instructions
Compiled the huml

huml is deployed to your Cloudflare account and connected to both Claude Code and Cursor. They now read and write the same memory.

What I did

Step	Command / change	Result
Clone and install	`git clone https://github.com/CodeBlastr/huml && cd huml && npm install`	Done
Env file	Copied `.env.example` to `.env`	Required by the deploy scripts
Cloudflare auth	`npx wrangler login`	You approved in the browser
Database	`npx wrangler d1 create huml`	ID copied into `wrangler.toml`
OAuth store	`npx wrangler kv namespace create OAUTH_KV`	ID copied into `wrangler.toml`
URL	`workers_dev = true`, `routes` removed, `PUBLIC_URL` set	`https://huml.<your-subdomain>.workers.dev`
Tables and deploy	`npm run migrate:remote` then `npm run deploy`	Live
Root token	`openssl rand -hex 32 \| npx wrangler secret put AUTH_TOKEN`	Set, never displayed
Client tokens	You ran `npm run tokens -- issue claude-code`, `issue cursor` and `issue scripts`	Tokens stayed out of this session

Client config

You pasted your tokens into these yourself.

Claude Code

claude mcp add --transport http --scope user huml https://huml.<your-subdomain>.workers.dev/mcp \
  --header "Authorization: Bearer <claude-code token>"

Cursor (~/.cursor/mcp.json):

{
  "mcpServers": {
    "huml": {
      "url": "https://huml.<your-subdomain>.workers.dev/mcp",
      "headers": { "Authorization": "Bearer <cursor token>" }
    }
  }
}

Verified

npm run smoke passed: created, read and deleted a test doc under /_smoke/
Wrote /preferences/setup.md from Claude Code and read it back from Cursor

Next steps

Restart Cursor so it picks up the new MCP server
Import existing notes with npm run seed -- ./notes
Add this line to CLAUDE.md and Cursor rules: "Check huml memory for context about people, projects and my preferences before answering, and save anything worth remembering."
Revoke a client anytime with npm run tokens -- revoke <id>

Connects securely to your tools. The creator never sees your data.

About this skill

Standard LLM sessions are ephemeral, losing context the moment you close the tab. This skill provides a persistent memory layer for your AI agents using the huml MCP protocol, allowing them to store and retrieve long-term data across different sessions, IDEs, and chat interfaces.

What it does

Persistent Storage manages long-term facts, project statuses, and user preferences in Markdown files.
Cross-Client Sync enables shared context between Claude, ChatGPT, Cursor, and CLI tools via a central MCP server.
Conflict Resolution uses compare-and-swap logic to prevent data loss during concurrent writes from multiple agents.
Lexical Search provides full-text search capabilities to find specific documents by name, alias, or content snippets.
Context Management automatically reads relevant project history or person-specific notes before starting new tasks.

How it works

Discovery involves using memory_list or memory_search to find existing records related to the current task.
Retrieval fetches the full content and version ID of relevant files using memory_read.
Modification updates data using memory_append for logs or memory_str_replace for specific edits, passing the version ID to ensure consistency.
Creation initializes new memory entries with YAML frontmatter using memory_write and a "new" version flag.

Frameworks & tools

This skill requires a running huml server acting as an MCP (Model Context Protocol) host. It interacts with the server via standard MCP tool calls and manages Markdown files with YAML frontmatter stored in a SQLite-backed environment.

Why this beats prompting it yourself

Manual prompting cannot bridge the gap between separate chat sessions or different AI tools. This skill provides the logic for safe concurrent writes and structured data management that prevents agents from hallucinating past interactions.

Use cases

Tracking project status and blockers across multiple developer tools.
Storing user-specific coding standards and architectural preferences.
Maintaining dossiers on clients, teammates, or stakeholders.
Logging architectural decisions and their underlying rationales.

Known limitations

Requires an active huml MCP server deployment. Does not support semantic search, only lexical word matching. Files are capped at 100KB each.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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