name: superlocalmemory description: "AI agent memory with mathematical foundations. Store, recall, search, and manage memories locally with zero cloud dependency." version: "3.4.22" author: "Varun Pratap Bhardwaj" license: Elastic-2.0 homepage: https://superlocalmemory.com repository: https://github.com/qualixar/superlocalmemory triggers:

• remember something
• recall memory
• search memories
• memory status
• store fact
• agent memory
• local memory
• memory health

SuperLocalMemory

AI agent memory that runs 100% locally. Four-channel retrieval (semantic, graph, BM25, temporal) with mathematical similarity scoring. No cloud, no API keys, EU AI Act compliant.

Installation

pip install superlocalmemory
# or
npm install -g superlocalmemory

Quick Start

slm remember "Alice works at Google as a Staff Engineer" --json
slm recall "Who is Alice?" --json
slm status --json

Commands

All data-returning commands support --json for structured agent-native output.

Memory Operations

slm remember "<content>" --json           # Store a memory
slm remember "<content>" --tags "a,b" --json
slm recall "<query>" --json               # Semantic search
slm recall "<query>" --limit 5 --json
slm list --json -n 20                     # List recent memories
slm forget "<query>" --json               # Preview matches (add --yes to delete)
slm forget "<query>" --json --yes         # Delete matching memories
slm delete <fact_id> --json --yes         # Delete specific memory by ID
slm update <fact_id> "<content>" --json   # Update a memory

Diagnostics

slm status --json                         # System status (mode, profile, DB)
slm health --json                         # Math layer health
slm trace "<query>" --json                # Recall with per-channel breakdown

Configuration

slm mode --json                           # Get current mode
slm mode a --json                         # Set mode (a=local, b=ollama, c=cloud)
slm profile list --json                   # List profiles
slm profile switch <name> --json          # Switch profile
slm profile create <name> --json          # Create profile
slm connect --json                        # Auto-configure IDEs
slm connect --list --json                 # List supported IDEs

Services (no --json)

slm setup                                 # Interactive setup wizard
slm mcp                                   # Start MCP server (for IDE integration)
slm dashboard                             # Open web dashboard
slm warmup                                # Pre-download embedding model

JSON Envelope

Every --json response follows a consistent envelope:

{
  "success": true,
  "command": "recall",
  "version": "3.0.22",
  "data": {
    "results": [
      {"fact_id": "abc123", "score": 0.87, "content": "Alice works at Google"}
    ],
    "count": 1,
    "query_type": "semantic"
  },
  "next_actions": [
    {"command": "slm list --json", "description": "List recent memories"}
  ]
}

Error responses:

{
  "success": false,
  "command": "recall",
  "version": "3.0.22",
  "error": {"code": "ENGINE_ERROR", "message": "Description of what went wrong"}
}

Operating Modes

| Mode | Description | Cloud Required | |------|-------------|----------------| | A | Local Guardian -- zero cloud, zero LLM, EU AI Act compliant | None | | B | Smart Local -- local Ollama LLM, data stays on your machine | Local only | | C | Full Power -- cloud LLM for maximum accuracy | Yes |

Dual Interface

SuperLocalMemory works via both MCP and CLI:

• MCP: 24 tools for IDE integration (Claude Code, Cursor, Windsurf, VS Code, JetBrains, Zed)
• CLI: 18 commands with --json for scripts, CI/CD, agent frameworks (OpenClaw, Codex, Goose)

Part of Qualixar | Author: Varun Pratap Bhardwaj (qualixar.com | varunpratap.com)

Why SuperLocalMemory?

Every hosted AI memory platform — Mem0 Cloud, Zep Cloud, Letta Cloud, EverMemOS Cloud — sends your data to cloud LLMs by default. Their self-hosted variants exist (Mem0 OpenMemory, Letta self-hosted, Graphiti) but require Docker + a separate graph DB or Ollama config, and most still default to OpenAI until you flip env vars. After August 2, 2026, any of those cloud paths becomes a compliance problem under the EU AI Act.

SuperLocalMemory V3 takes a different approach: mathematics instead of cloud compute. Three techniques from differential geometry, algebraic topology, and stochastic analysis replace the work that other systems need LLMs to do — similarity scoring, contradiction detection, and lifecycle management. The result is an agent memory that ships local-first out of the box — no Docker, no graph DB, no API keys — on CPU.

The numbers (evaluated on LoCoMo, the standard long-conversation memory benchmark). Published numbers as of April 2026:

| System | Score | Config | Cloud LLM required? | Open Source | Source | |:-------|:-----:|:-------|:-------------------:|:-----------:|:-------| | EverMemOS | 93.05% | Cloud (proprietary) | Yes | Core only | evermind.ai (Feb 2026) | | Hindsight (LoComo10) | 92.0% | Cloud | Yes | No | benchmarks.hindsight.vectorize.io (Apr 2026) | | Mem0 (token-efficient) | 91.6% | Hybrid (Cohere/OpenAI) | Yes | Partial | mem0.ai blog (Apr 16 2026) | | SLM V3 Mode C | 87.7% | Local + optional LLM | Optional (Ollama OK) | Yes (AGPL-3.0) | In-house, repro script in docs/benchmarks/ | | Zep v3 Cloud | 85.2% | Cloud | Yes | Community deprecated | getzep.com | | SLM V3 Mode A | 74.8% | Local, CPU-only, zero-LLM | No | Yes (AGPL-3.0) | In-house, repro script in docs/benchmarks/ | | Mem0 (zero-retrieval-LLM) | 64.2% | Local baseline | No | Partial | Mem0 paper, zero-LLM row |

How to read this table. Scores from different papers use different LoCoMo splits, judge models, and prompt variants. We do NOT claim these numbers are apples-to-apples across rows. The rows we re-ran in-house are marked "In-house"; cited rows link to the vendor's public source and date. Mode A is the only zero-LLM configuration in the list, so the comparison that is apples-to-apples is Mode A 74.8% vs Mem0 zero-retrieval-LLM 64.2% (+10.6pp). Mem0's 91.6% and EverMemOS's 93.05% use cloud LLMs; Mode C uses a local LLM (Ollama). BEAM-10M, the emerging successor benchmark, will be added in a future release.

What Mode A is: CPU-only, SQLite-only, zero-LLM retrieval pipeline on published LoCoMo questions. To the best of our knowledge it is the only publicly-released local-first memory that clears Mem0's zero-LLM baseline on this benchmark. If another fully-local system hits similar numbers, please open an issue so we can update the table.

Mathematical layers contribute +12.7 percentage points on average across 6 conversations (n=832 questions), with up to +19.9pp on the most challenging dialogues. This isn't more compute — it's better math.

Upgrading from V2 (2.8.6)? V3 is a complete architectural reinvention — new mathematical engine, new retrieval pipeline, new storage schema. Your existing data is preserved but requires migration. After installing V3, run slm migrate to upgrade your data. Read the Migration Guide before upgrading. Backup is created automatically.

V3.3 gives your memory a lifecycle. Memories strengthen when used, fade when neglected, compress when idle, and consolidate into reusable patterns — all automatically, all locally. Your agent gets smarter the longer it runs.

Features at a Glance

• Adaptive Memory Lifecycle — memories naturally strengthen with use and fade when neglected. No manual cleanup, no hardcoded TTLs.
• Smart Compression — embedding precision adapts to memory importance. Low-priority memories compress up to 32x. High-value memories stay full-resolution.
• Cognitive Consolidation — the system automatically extracts patterns from clusters of related memories. One decision referenced 50 times becomes one reusable insight.
• Pattern Learning — auto-learned soft prompts injected into your agent's context at session start. The system teaches itself what matters to you.
• Hopfield Retrieval (6th Channel) — vague or partial queries now complete themselves. Ask half a question, get the whole answer.
• Process Health — orphaned SLM processes detected and cleaned automatically. No more zombie workers eating RAM.

New CLI Commands

# Run a memory lifecycle review — strengthens active memories, archives neglected ones
slm decay

# Run smart compression — adapts embedding precision to memory importance
slm quantize

# Extract reusable patterns from memory clusters
slm consolidate --cognitive

# View auto-learned patterns that get injected into agent context
slm soft-prompts

# Clean up orphaned SLM processes
slm reap

New MCP Tools

| Tool | Description | |:-----|:------------| | forget | Programmatic memory archival via lifecycle rules | | quantize | Trigger smart compression on demand | | consolidate_cognitive | Extract and store patterns from memory clusters | | get_soft_prompts | Retrieve auto-learned patterns for context injection | | reap_processes | Clean orphaned SLM processes | | get_retention_stats | Memory lifecycle analytics |

Mode A/B Memory Improvements

| Metric | V3.2 | V3.3 | Change | |:-------|:----:|:----:|:------:| | RAM usage (Mode A/B) | ~4GB | ~40MB | 100x reduction | | Retrieval channels | 5 | 6 | +Hopfield completion | | MCP tools (default) | 29 | 33 | +4 new (mesh set) | | CLI commands | 21 | 26 | +5 new | | Dashboard tabs | 17 | 17 | (H-22: Reward / Shadow / EvolutionCost tiles deferred to next cycle — data exposed via API today, see DASHBOARD-COVERAGE.md) | | API endpoints | 9 | 16 | +7 new |

Embedding migration happens automatically when you switch modes — no manual steps needed.

Dashboard

Three new tabs: Memory Lifecycle (retention curves, decay stats), Compression (storage savings, precision distribution), and Patterns (auto-learned soft prompts, consolidation history). Seven new API endpoints power the new views.

Enable V3.3 Features

All new features default OFF. Zero breaking changes. Opt in when ready:

# Turn on adaptive memory lifecycle
slm config set lifecycle.enabled true

# Turn on smart compression
slm config set quantization.enabled true

# Turn on cognitive consolidation
slm config set consolidation.cognitive.enabled true

# Turn on pattern learning (soft prompts)
slm config set soft_prompts.enabled true

# Turn on Hopfield retrieval (6th ch