AgentIndex icon
AgentIndex
hmem logo
hmem
★ 24
vs
ogham-mcp logo
ogham-mcp
★ 116

hmem vs ogham-mcp

hmem: hmem is a memory system for AI agents that persists project context across sessions, devices, and AI providers. It uses a 5-level lazy loading architecture to minimize token usage while retaining full details. It supports auto-logging, background checkpoints, and cross-platform sync.; ogham-mcp: Ogham MCP is a persistent, searchable shared memory system for AI coding agents that works across different clients and sessions. It stores memories as vector embeddings in PostgreSQL, enabling hybrid search combining semantic and full-text retrieval with high accuracy. Ogham supports multiple embedding providers, knowledge graph traversal, wiki compilation, and automatic memory management features like scoring, condensing, and novelty detection.

01

TL;DR

hmem logo
hmemMemory & Context · ★ 24

AI coding assistants that need to maintain project context across sessions

Read hmem full review →
ogham-mcp logo
ogham-mcpMemory & Context · ★ 116

Store and retrieve architectural decisions and coding context across sessions and different AI coding agents

Read ogham-mcp full review →
02

Side-by-Side Comparison

Field
hmem logohmem
ogham-mcp logoogham-mcp
Category
Memory & Context
Memory & Context
Stars
★ 24
★ 116
License
MIT
MIT
Install Command
npm install -g hmem-mcp && npx hmem init
uvx --from ogham-mcp ogham init
Updated
3mo ago
1w ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
ai-agents, ai-tools, claude
agent-memory, ai-memory, claude
03

Features

hmem logohmem
015-level lazy loading to minimize token usage
02Automatic session logging and background checkpoints via Haiku
03Cross-provider and cross-tool support
04Project-based memory organization
05Encrypted sync across devices
ogham-mcp logoogham-mcp
01Persistent shared memory across sessions and clients
02Hybrid search with Reciprocal Rank Fusion (semantic + full-text)
03Knowledge graph with entity linking, traversal, and contradiction detection
04Multi-provider embeddings (OpenAI, Ollama, Mistral, Voyage, Gemini, ONNX)
05Automatic condensing, scoring, novelty detection, and memory lifecycle management
04

Use Cases

hmem logohmem
↳AI coding assistants that need to maintain project context across sessions
↳Multi-device workflow where different agents continue the same project
↳Knowledge management for personal AI assistants
ogham-mcp logoogham-mcp
↳Store and retrieve architectural decisions and coding context across sessions and different AI coding agents
↳Provide consistent, context-aware assistance to AI coding agents by maintaining a shared knowledge base
↳Preserve project-specific information, preferences, and facts for long-term reference without repetition
FAQ

FAQ

What is the difference between hmem and ogham-mcp?
Both hmem and ogham-mcp are in the Memory & Context category. hmem has 24 stars, while ogham-mcp has 116 stars.
Which is better, hmem or ogham-mcp?
The best choice depends on your use case. Choose hmem if AI coding assistants that need to maintain project context across sessions, and ogham-mcp if Store and retrieve architectural decisions and coding context across sessions and different AI coding agents.
Is hmem free or open source?
Yes, hmem is open source on GitHub (MIT).
Is ogham-mcp free or open source?
Yes, ogham-mcp is open source on GitHub (MIT).
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Related

Alternatives to hmem →Alternatives to ogham-mcp →hmem details →ogham-mcp details →