AgenticMemory Overview

AgenticMemory stores an agent's knowledge as a navigable graph in a single portable .amem file.

What you can do

• Store facts, decisions, inferences, corrections, skills, and episodes as connected graph nodes.
• Query by traversal, similarity, temporal range, causal chains, and quality diagnostics.
• Expose the graph through MCP with agentic-memory-mcp.

Why teams adopt AgenticMemory

Teams adopt AgenticMemory because it closes both the original and current memory gaps:

• Foundational problems already solved: sessions start from zero, vector search returns similar text but no reasoning trails, corrections overwrite truth, memory degrades silently, and long-term memory is not portable.
• New high-scale problems now solved: multi-session context continuity, decision lineage across conversations, memory quality and drift diagnostics at runtime, cross-project knowledge comparison via workspaces, and auto-session lifecycle management.
• Practical outcome for teams: agents remember decisions, correct themselves over time, and carry portable evidence across models, clients, and deployments.

For a detailed before-and-after view, see Experience With vs Without.

Artifact

• Primary artifact: .amem
• Cross-sister server workflows can pair .amem with .acb and .avis

Start here

• Installation
• Quickstart
• Command Surface
• Runtime and Sync
• Integration Guide
• Experience With vs Without
• V3 Architecture

Works with

• AgenticCodebase — link code-graph nodes to memory decisions for traceable reasoning across refactors.
• AgenticVision — link visual captures to memory nodes with vision_link for cross-modal evidence trails.