🧠 Mnemos

Stop burning $10/day on AI context. Give your AI coding agents long-term memory for free.

Mnemos is a blazing-fast, persistent memory engine for AI coding agents (Kiro, Claude Code, Cursor, Windsurf). It acts as a "second brain" via the Model Context Protocol (MCP), saving you money and frustration.

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💸 The Reality of AI Coding

❌ Without Mnemos: Your session grows to 50k tokens. The AI gets confused and forgets earlier instructions. You clear the chat to save money. Next session, you have to re-explain your project structure, CSS conventions, and old bugs all over again. Result: Wasted time and high API bills.

✅ With Mnemos: Your AI learns architecture decisions, bug root causes, and project conventions once. Mnemos automatically deduplicates and stores it. Next session, Mnemos precisely injects only the relevant 2k tokens of context. Result: Infinite memory continuity across sessions for pennies.

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🚀 Why it's a Must-Have

• Zero Bullshit Stack: Single Go binary. Embedded pure-Go SQLite (FTS5). Zero runtime dependencies. No Docker. No Python. No Node.
• MCP-Native: Designed specifically for the Model Context Protocol. Plugs straight into your favorite agents.
• 1-Click Autopilot: Instantly wires hooks, .cursorrules, and MCP configs for Claude Code, Cursor, and Kiro.
• Smart Lifecycle: Built-in 3-tier deduplication, relevance decay, and garbage collection. It only remembers what actually matters.
• Hybrid Search: Fast local FTS5 keyword search + optional Semantic Vector search (Ollama/OpenAI) using Reciprocal Rank Fusion (RRF).

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⚡ Quick Start

Get up and running in 30 seconds:

# 1. Install via curl (macOS/Linux)
curl -fsSL https://raw.githubusercontent.com/mnemos-dev/mnemos/main/install.sh | bash

# Or via Homebrew (macOS)
brew install s60yucca/tap/mnemos

# 2. First-time setup
mnemos init

# 3. Start the MCP server (runs in background/stdio)
mnemos serve

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🔌 1-Click Autopilot Integrations

Mnemos isn't just a dumb database. It actively injects memory into your workflow. Run one of these to wire it up instantly:

# For Kiro (Fully Tested & Highly Recommended)
mnemos setup kiro

# For Gemini CLI / Antigravity (Google)
mnemos setup gemini-cli

# For Cursor (Experimental - Community Testing)
mnemos setup cursor

# For Claude Code (Experimental - Community Testing)
mnemos setup claude

Boom. Your agent now remembers everything automatically. From now on:

1. Session start: Mnemos loads relevant context.
2. During work: Mnemos searches memory when the topic changes.
3. Session end: Mnemos safely stores the durable learnings.

(Use --global to install for all projects, or --force to overwrite existing config files).

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🆚 Why Mnemos?

Feature	claude-mem	engram	neural-memory	mnemos 🧠
MCP native	✅	✅	✅	✅
Zero BS Stack	❌	✅	❌ (pip)	✅ (Single Go Binary)
1-Click Autopilot Setup	❌	❌	❌	✅
Hybrid Search (FTS + Vector)	❌	❌	❌	✅
Memory Decay / GC	❌	❌	✅	✅
Smart Deduplication	❌	❌	❌	✅ (3-tier)
Token-budget Context	❌	partial	❌	✅
Works w/ Gemini & Cursor	❌	✅	✅	✅

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🛠️ CLI Mastery

Mnemos comes with a powerful CLI for when you want to get your hands dirty:

mnemos store "JWT uses RS256, tokens expire in 1h"    # store a memory manually
mnemos search "authentication"                        # hybrid search
mnemos search "auth" --mode text                      # text-only search
mnemos list --project myapp                           # list memories
mnemos get <id>                                       # fetch by id
mnemos update <id> --content "updated text"           # update
mnemos delete <id>                                    # soft delete
mnemos delete <id> --hard                             # permanent delete
mnemos relate <src-id> <tgt-id> --type depends_on     # create relation
mnemos stats --project myapp                          # storage stats
mnemos maintain                                       # force decay + GC

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⚙️ Advanced Configuration & Embeddings

Mnemos works fully offline with zero configuration using FTS5 text search. If you want Semantic Search (finding memories by meaning, not just keywords), you can easily hook up Ollama or OpenAI.

Edit ~/.mnemos/config.yaml:

Local & Free (Ollama):

embeddings:
  provider: ollama
  base_url: http://localhost:11434
  model: nomic-embed-text
  dims: 768

OpenAI:

embeddings:
  provider: openai
  model: text-embedding-3-small
  dims: 1536
  api_key: sk-...

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⚡ Performance

Benchmarked on macOS (Apple Silicon), SQLite WAL mode, cold process start per operation:

• store (new): 24-57 ms (includes dedup check)
• search hybrid (RRF): ~40 ms
• Context assembly (token budget packing): < 1 ms
• Binary size: ~12 MB
• Runtime dependencies: 0

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🌐 REST API

Need to connect Mnemos to something else? Run it as a REST API:

mnemos serve --rest --port 8080

(Supports standard GET, POST, PATCH, DELETE on /memories, /search, /stats, etc.)

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🏗️ Build from Source

git clone https://github.com/mnemos-dev/mnemos
cd mnemos
make build    # → bin/mnemos

📜 License

MIT