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agentmemory vs memanto

Persistent memory for coding agents on the iii engine: MCP server with 53 tools, 12 auto-capture hooks, hybrid search + knowledge graph, zero external DBs. Claims 95% R@5 and 92% token cuts. — versus — Companion memory agent for 20+ coding agents, built on Moorcheh — its own information-theoretic engine, no third-party vector DB to manage. Runs local (Docker + Ollama, keyless) or on their cloud.

The curated verdict

Both give many coding agents persistent memory without a third-party DB: Memanto is a companion memory agent — three primitives (remember/recall/answer) on its own Moorcheh engine; agentmemory is a 53-tool MCP toolbelt with auto-capture hooks and a knowledge graph.

agentmemorymemanto
Stars26k1.7k
Forks2.2k560
LanguageTypeScriptPython
LicenseApache-2.0MIT
Last activityyesterday4 days ago
Topicsmemory, codingmemory, local
Curated connections23

agentmemory — the curator's take

The benchmark-forward entry in a crowded field — '#1 on real-world benchmarks' is self-run, so weigh it accordingly; what's independently real: 1,428 tests, the viral design gist it implements (Karpathy's LLM-wiki pattern plus confidence scoring and lifecycle), and the smoothest onboarding in the category — hand your agent one URL and it installs itself. The iii engine is your infrastructure bet: a server on :3111, not a library. A 53-tool MCP surface is the opposite of the plain-files philosophy — richer, but agents need guidance to use it well (they ship 15 skills for exactly that reason). Same prolific author as pro-workflow and tailclaude — expect fast movement, budget for churn.

memanto — the curator's take

The no-third-party-infrastructure entry in the agent-memory category: retrieval runs on Moorcheh, the team's own information-theoretic engine, so there is no separate vector DB, embedding pipeline or reranker to manage — one CLI wires up 20+ agent integrations. Two deployments: fully local (Docker + Ollama, keyless, nothing leaves the machine) or their managed cloud (free tier, Moorcheh API key), which is also how shared team memory scales. The novel engine is the differentiator AND the risk: benchmark recall on YOUR corpus before trusting it over boring embeddings, and betting on Moorcheh is betting on one vendor's engine — memanto.ai signals the company behind it.