AgentIndex icon
AgentIndex
AgentRecall-MCP logo
AgentRecall-MCP
★ 371
vs
ogham-mcp logo
ogham-mcp
★ 116

AgentRecall-MCP vs ogham-mcp

AgentRecall-MCP: AgentRecall is a learning loop for AI agents that provides persistent, compounding memory. It captures corrections automatically, surfaces past insights across projects, and uses a five-layer memory pyramid with Ebbinghaus decay and Bayesian feedback. Zero cloud, all local markdown files.; 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

AgentRecall-MCP logo
AgentRecall-MCPMemory & Context · ★ 371

Maintain context across AI agent sessions (Claude Code, Cursor, etc.)

Read AgentRecall-MCP 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
AgentRecall-MCP logoAgentRecall-MCP
ogham-mcp logoogham-mcp
Category
Memory & Context
Memory & Context
Stars
★ 371
★ 116
License
MIT
MIT
Install Command
claude mcp add --scope user agent-recall -- npx -y agent-recall-mcp
uvx --from ogham-mcp ogham init
Updated
1w ago
1w ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
agent-memory, ai-agents, claude-code
agent-memory, ai-memory, claude
03

Features

AgentRecall-MCP logoAgentRecall-MCP
01Persistent, compounding memory with 200-line awareness cap
02Automatic correction capture and alignment checking
03Cross-project insight recall via keyword and semantic (pgvector) search
04Zero cloud, all local markdown files, Obsidian-compatible
0510 MCP tools for agents, plus SDK and CLI
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

AgentRecall-MCP logoAgentRecall-MCP
↳Maintain context across AI agent sessions (Claude Code, Cursor, etc.)
↳Capture and learn from user corrections in software development
↳Coordinate memory across multiple parallel agents
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 AgentRecall-MCP and ogham-mcp?
Both AgentRecall-MCP and ogham-mcp are in the Memory & Context category. AgentRecall-MCP has 371 stars, while ogham-mcp has 116 stars.
Which is better, AgentRecall-MCP or ogham-mcp?
The best choice depends on your use case. Choose AgentRecall-MCP if Maintain context across AI agent sessions (Claude Code, Cursor, etc.), and ogham-mcp if Store and retrieve architectural decisions and coding context across sessions and different AI coding agents.
Is AgentRecall-MCP free or open source?
Yes, AgentRecall-MCP 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

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