memoryhub-cli 0.1.1

CLI client for MemoryHub — centralized, governed memory for AI agents

memoryhub-cli

Command-line client for MemoryHub — centralized, governed memory for AI agents.

Install

pip install memoryhub-cli

Usage

# Authenticate to a MemoryHub instance
memoryhub login

# Search for memories
memoryhub search "deployment patterns"

# Read a specific memory
memoryhub read <memory-id>

# Write a new memory
memoryhub write "Use Podman, not Docker" --scope user --weight 0.9

# Set up project-level memory loading
memoryhub config init
memoryhub config regenerate

Project configuration

memoryhub config generates a project-local .memoryhub.yaml and a companion .claude/rules/memoryhub-loading.md rule file. Both files are meant to be committed so every contributor's agent inherits the same loading policy.

memoryhub config init is an interactive wizard that asks about session shape, loading pattern, focus source, and retrieval defaults, then writes both files at the project root. If a legacy .claude/rules/memoryhub-integration.md already exists, it is backed up to .bak before the new rule file is written.

memoryhub config regenerate re-renders the rule file from .memoryhub.yaml after you hand-edit the YAML. It reads the YAML and rewrites the Markdown rule file only; it does not modify .memoryhub.yaml.

Per-developer connection params (url, auth_url, client_id, client_secret) live separately at ~/.config/memoryhub/config.json and are managed by memoryhub login. They are not stored in .memoryhub.yaml and are not committed.

Further documentation

The CLI is one surface of the memory-hub monorepo. For deeper context:

• Architecture overview — System design, deployment topology
• MCP server tool reference — The 15 tools the CLI wraps
• Agent memory ergonomics design — Full .memoryhub.yaml schema, rule file templates, and session-loading patterns
• Python SDK — if you'd rather call the tools from Python

Links

• GitHub repository
• Issue tracker
• License (Apache 2.0)