Five real MCP tool call failure modes for MemNexus — server not appearing, auth errors, timeouts, stale schemas after upgrades, empty search results — with the exact commands to diagnose and fix each one.
A hands-on tutorial for wiring MemNexus into a Claude Code lead agent and its subagents — named state memories, codeContext tagging, conversation grouping, and the concurrent-write gotcha nobody tells you about.
build_context is a single MCP call that gives your coding agent a structured briefing — active work, key facts, gotchas, recent activity — before it touches your codebase.
A technical deep-dive into how AI agents store and retrieve memory — covering memory types, storage approaches, retrieval strategies, and multi-agent patterns.
A technical deep-dive into MCP memory servers: what they are, how they differ from other MCP servers, and how MemNexus implements the full extraction-graph-retrieval pipeline.
A technical comparison of how LangChain, CrewAI, AutoGen, Semantic Kernel, and LlamaIndex handle agent memory — and the cross-tool gap none of them fill.
Multiple agent teams contradict each other and rediscover the same bugs without shared memory. A shared knowledge base keeps every team aligned and coherent.
Most AI agents forget everything between sessions. Three patterns — session, preference, and knowledge memory — make agents genuinely useful over time.
Build-context delivers a structured briefing — active work, key facts, gotchas, recent activity — before your agent starts. One command. No cold starts.
We benchmarked MCP vs CLI across three AI agents. GPT-based agents were 2x faster with MCP. Claude-based Kiro performed equally well with CLI. Choose wisely.
We built and tested an agent-help feature for our CLI. AI agents ignored it. Here's what actually helps agents use CLI tools effectively.
Memory Digest assembles complete project briefings in one command. Gathers up to 100 memories, expands via topic and entity graphs, and synthesizes with LLM.