AI Agents & Memory

AI agents are only as good as their context. Without memory, every interaction starts from zero — no knowledge of your preferences, your projects, or what happened yesterday. MemNexus changes this.

The memory problem

Today's AI agents are powerful reasoners but terrible rememberers. They can write code, analyze data, and answer complex questions — but they forget everything the moment a conversation ends.

This creates three problems:

1. Repeated context — You explain the same things every session
2. Lost decisions — Important choices made during a conversation disappear
3. No learning — The agent can't improve from past interactions

How MemNexus solves it

MemNexus gives your agents persistent memory through three interfaces:

Interface	How it works
MCP	Agents call MemNexus tools natively via Model Context Protocol
SDK	Your application manages memory programmatically
CLI	You manage memory from the terminal alongside your agent

With MemNexus, an agent can:

• Store what it learns during a session
• Search past knowledge before answering questions
• Recall your preferences and project context
• Build a knowledge graph of facts and relationships
• Learn patterns from your behavior over time

What makes this different

Cross-platform memory

Most AI platforms have some form of memory — but it's siloed. ChatGPT Memory only works in ChatGPT. Claude Projects only work in Claude. MemNexus works across all of them.

What Claude learns in one conversation, Cursor can recall in the next. What you discuss in ChatGPT, your custom agent can access via the SDK.

Structured knowledge

MemNexus doesn't just store raw text. It builds a graph of:

• Memories — Episodic records of what happened
• Facts — Structured knowledge (subject-predicate-object triples)
• Topics — Categorization and clustering
• Entities — People, tools, and concepts that appear across memories

This structure means agents can traverse relationships, not just search keywords.

Semantic search

When an agent searches memory, it searches by meaning. "That deployment issue from last week" finds relevant memories even without exact keyword matches. The hybrid search pipeline combines vector similarity with keyword matching for the best results.

Agent design patterns

MemNexus enables several powerful patterns for building memory-aware agents:

• Store-and-Recall — Save important context, retrieve it later
• Search-Before-Ask — Check memory before asking the user
• Progressive Learning — Build knowledge over multiple sessions

See Agent Patterns for implementation details.

Getting started

The fastest way to give your agent memory:

1. MCP integration — If you use Claude Desktop, Claude Code, or Cursor, see MCP Integration
2. SDK integration — If you're building a custom agent, see SDK Installation
3. Prompt engineering — Copy-paste prompts that teach agents to use memory, see Prompt Library

Next steps

• MCP Integration — Connect via Model Context Protocol
• Prompt Library — Ready-to-use prompts for memory-aware agents
• Agent Patterns — Design patterns for memory-powered agents
• Platform Examples — Working examples for Claude, Cursor, and more