Add persistent memory to Claude Code in 2 minutes (MCP)
Paste a Claude Code MCP config that runs npx answer-engine-mcp, points at engine.answeragent.ai, and gives Claude persistent scoped memory.
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Pillar guides
These pages are written as durable references, not campaign blurbs. Each one is fully visible in server-rendered HTML and links to its source material.
Add Persistent Memory to Codex (MCP)
Configure Codex with Answer Engine memory through MCP so the coding agent can retrieve source-linked project facts instead of starting cold.
Add persistent memory to Cursor (MCP)
Paste a Cursor .cursor/mcp.json config that runs npx answer-engine-mcp, points at engine.answeragent.ai, and gives Cursor scoped persistent memory.
Add Persistent Memory to Gemini CLI (MCP)
Configure Gemini CLI with Answer Engine memory through MCP so terminal-first coding workflows can retrieve source-linked project facts.
Add Persistent Memory to Windsurf (MCP)
Configure Windsurf Cascade with Answer Engine memory through MCP so project facts and prior decisions survive across coding sessions.
My Agent Contradicts Earlier Answers: Diagnosing Memory Conflicts
Agent contradictions usually come from conflicting memories, stale sources, missing supersession, or prompt context that hides the current evidence.
Agent Memory Keeps Growing: Memory Bloat and Token Cost
Memory bloat happens when agents write too much, retrieve too broadly, and resend low-value context instead of curated source-linked evidence.
Agent Memory: The Complete Guide
A practical guide to agent memory: what it is, how it differs from context windows and RAG, and how to evaluate memory for production AI agents.
Mem0 vs Zep vs Letta vs Cognee vs Answer Engine
A five-way agent memory comparison using inspectability, supersession, forgetting, portability, source breadth, and tenant isolation.
My Agent Recommends Stale or Outdated Info: The Supersession Fix
Why agents confidently recommend stale information, how supersession-aware memory fixes old-to-new fact conflicts, and what config to copy.
Answer Engine vs Mem0
Compare Answer Engine and Mem0 for teams choosing between a hosted memory API and an inspectable MCP memory layer.
Answer Engine vs Zep
Compare Answer Engine and Zep for teams deciding between temporal graph memory and inspectable multi-source agent memory.
Best Agent Memory Framework (2026)
A practical 2026 shortlist for agent memory systems, with best-fit verdicts for Answer Engine, Mem0, Zep, Letta, and Cognee.
Best MCP Memory Server (2026): Honest Comparison
Compare MCP memory servers by inspectability, forgetting, portability, permission-aware retrieval, and public evidence without guessing hidden scores.
Claude Code Loses Context After Compaction: How to Persist It
Claude Code compaction helps a session continue, but durable project memory needs transcript sync, MCP retrieval, and source-linked writes.
CLAUDE.md is Not Memory
CLAUDE.md and other static context files are useful boot instructions, but they do not replace durable, source-aware, inspectable agent memory.
Combine Jira, Google Drive, and Confluence into One Agent Context
Build one agent memory context across Jira tickets, Google Drive documents, and Confluence pages using real Answer Engine adapters and MCP.
Connect Confluence to Your AI Agent via MCP
Connect Confluence pages to an AI agent through Answer Engine memory and MCP, using the real Confluence document adapter and engine.answeragent.ai config.
Connect Google Drive Documents to Your AI Agent via MCP
Connect Google Drive files to an AI agent by ingesting Drive documents into Answer Engine document memory and exposing them through MCP.
Connect Jira to Your AI Agent via MCP
Connect Jira issues to an AI agent through Answer Engine ticket memory and MCP, using the real Jira ticket adapter and engine.answeragent.ai config.
Context Engineering vs Memory
Context engineering decides what an agent sees right now. Agent memory decides what survives later. Production systems need both.
Context Rot: Why Agents Degrade Below the Token Limit
Context rot happens when a long prompt is technically within the token window but the useful signal is diluted by stale, conflicting, or irrelevant context.
How to Delete or Forget a Specific Memory in an Agent
Delete a specific agent memory by targeting the source artifact, derived artifacts, retrieval paths, and audit record instead of relying on prompt instructions.
How to Evaluate RAG and Agent-Memory Accuracy
Build a golden set over your own data, inspect retrieval evidence, and score agent memory without relying on borrowed benchmark numbers.
Give Your Coding Agent Memory of Your Codebase and Past Sessions
Use MCP plus Answer Engine CLI transcript sync to give coding agents durable codebase memory across Claude Code, Cursor, Codex, Gemini CLI, and Windsurf.
Inspectable Agent Memory: See What Your Agent Remembered and Why
Inspectable agent memory lets teams audit what an agent recalled, which source supported it, and why that memory appeared in the answer.
Letta Alternatives for Agent Memory
Compare Letta alternatives for teams choosing between stateful agent runtimes and source-aware memory layers.
Mem0 Alternatives for Agent Memory
Compare Mem0 alternatives for teams that need inspectable source memory, temporal graph memory, stateful agents, or self-operated graph/vector stacks.
Memory Needs a Write Path, Not Just a Retriever
A retriever can find documents, but agent memory needs a write path that decides what to remember, how to scope it, and how to inspect or forget it later.
Memory is not RAG is not Fine-Tuning
Agent memory, RAG, and fine-tuning solve different problems: persistence, retrieval, and model behavior. Use this guide to choose the right layer.
My AI Agent Forgets Context Between Sessions: Here's Why and the Fix
Why AI agents forget context between sessions, why bigger context windows do not make memory durable, and how to fix it with a persistent MCP memory server.
Permission-Aware Retrieval: Enforce ACLs at the Vector Layer
Permission-aware retrieval applies tenant, library, visibility, and user scope before vector or hybrid search can return candidate memories.
Portable Agent Memory: Avoid Memory Lock-In
Portable agent memory keeps remembered facts outside one agent client so teams can move memory across Claude Code, Cursor, Codex, Gemini CLI, Windsurf, APIs, and future tools.
Provable Forgetting: Can You Prove Your Agent Forgot?
Provable forgetting is the ability to show which memory, source artifact, embedding, and derived summary were removed or disabled after a deletion request.
My RAG Leaks Docs to the Wrong Users
RAG leaks happen when retrieval ignores tenant, library, visibility, or user ACLs before candidate documents enter the model context.
RAG Returns Wrong or Irrelevant Results: 7 Retrieval Failure Modes to Inspect
A practical troubleshooting guide for wrong or irrelevant RAG results, with seven named retrieval failure modes and how to inspect each one.
REST vs MCP vs CLI for Agent Memory
Choose REST, MCP, or CLI for agent memory based on who calls memory, where auth lives, and whether humans need terminal-native workflows.
What Self-Improving Memory Means and What It Does Not Mean Yet
A present-vs-roadmap guide to self-improving memory claims, eval loops, correction workflows, and what Answer Engine should not overclaim.
Supersession-Aware Memory: How Agents Handle Facts That Change
Supersession-aware memory tracks when newer source evidence replaces older facts, so agents do not treat stale and current memories as equal.
The Types of Agent Memory
A practical taxonomy of agent memory types: working, short-term, long-term, episodic, semantic, procedural, source, and retrieval memory.
Vector Search Returns Noise or Fabricated Sources
Noisy vector search and fabricated citations come from weak candidates, missing source verification, citation generation, and retrieval traces users cannot inspect.
What Does Agent Memory Actually Cost?
Compare agent-memory spend in dollars per 1,000 queries against context-stuffing input-token cost, with a calculator and no fabricated savings claim.
What is an MCP Memory Server?
An MCP memory server exposes durable memory tools to AI agent clients so they can search, retrieve, write, inspect, and scope remembered facts.
Why AI Agents Forget and How to Fix It
AI agents forget because model calls are bounded, context is temporary, and most products lack a durable write path with source-aware recall.
Zep Alternatives for Agent Memory
Compare Zep alternatives when temporal graph memory is not the only requirement for your agent memory layer.