MemCtrl

Stop paying for context window upgrades. MemCtrl extends your LLM conversations 5.7x longer with 88% recall accuracy — for free.

MemCtrl is a context budget optimizer SDK that sits between your app and any LLM API. It manages a 3-tier memory hierarchy, compresses old messages, preserves critical facts, and returns an optimized message list that fits your token budget.

No API calls on your behalf. No hosted service. No fees. Your keys stay in your memory — never logged, never stored.

Why MemCtrl?

Problem	Without MemCtrl	With MemCtrl
Long conversations	Context window fills up, old messages lost	5.7x longer conversations, same budget
Token costs	Pay for full context every call	8-13% token savings via compression
Critical facts	Passwords, dosages, IDs vanish after compression	Auto-pinned, 100% preserved
Medical/code context	Treated same as casual chat	Task-aware retention (medical kept 7 days, code 3 days)

Eval Results

Tested with Ollama (llama3) across 3 real-world scenarios:

Scenario	Messages	Recall	Token Savings
Flask API Debugging	40	100%	11%
ML Tutoring Session	30	80%	13%
Medical Consultation	24	80%	—
Overall	94	88%	5.7x endurance

Install

pip install memctrl-llm            # Lightweight (~2MB), uses extractive compression

With neural compression (distilbart + embeddings):

pip install memctrl-llm[ml]          # Adds torch, transformers, sentence-transformers

With LLM backends:

pip install memctrl-llm[anthropic]   # For Claude
pip install memctrl-llm[openai]      # For GPT
pip install memctrl-llm[all]         # Everything

Quick Start

SDK API (recommended)

import memctrl
from openai import OpenAI

client = OpenAI(api_key="sk-...")
mc = memctrl.MemoryController(user_id="dev1")

# Record messages as they happen
mc.add_message("user", "My DB connection is postgresql://admin:pass@localhost:5432/mydb")
mc.add_message("assistant", "Got it. I'll remember your connection string.")
mc.add_message("user", "Now help me write a migration script")

# Get optimized context for your next API call
messages = mc.optimize(max_tokens=4096)

# Pass directly to any LLM
response = client.chat.completions.create(model="gpt-4o", messages=messages)

Zero-Code Wrapper

import memctrl
from openai import OpenAI

client = OpenAI(api_key="sk-...")
wrapped = memctrl.wrap(client, max_tokens=4096)

# Use normally — memctrl manages memory automatically
response = wrapped.chat("Help me debug my Flask app")
response = wrapped.chat("The error is on line 42")
response = wrapped.chat("What was my DB connection string?")  # Still remembers

Pin Critical Facts

mc.pin("Patient is allergic to penicillin")
mc.pin("API key: sk-abc123", note="Production key")

# Pinned facts are always included in optimize() output
messages = mc.optimize(max_tokens=4096)

Auto-Pin Suggestions

MemCtrl detects critical information and suggests pinning — it never auto-pins user-facing data without asking.

mc.add_message("user", "The password is hunter2")

suggestions = mc.suggest_pins()
# [{"chunk_id": "...", "category": "credential",
#   "reason": "Detected credential — this looks important. Pin it?"}]

# User decides
mc.accept_pin_suggestion(suggestions[0]["chunk_id"])

During compression, critical values (credentials, medical data, connection strings) are automatically preserved so they survive summarization.

Stale Memory Cleanup

suggestions = mc.suggest_cleanup(stale_hours=24.0)
# [{"chunk_id": "...", "tokens": 45, "hours_stale": 36.2,
#   "reason": "Not accessed in 36.2 hours. Deleting saves 45 tokens."}]

# User decides what to delete
mc.accept_cleanup([s["chunk_id"] for s in suggestions])

Context Budget Debugger

report = mc.budget_report(max_tokens=4096)
# {
#   "total_used": 2847, "remaining": 1249, "usage_pct": 69.5,
#   "breakdown": {
#     "system_prompt": {"tokens": 12},
#     "pinned": {"tokens": 89, "count": 3},
#     "active": {"tokens": 1820, "count": 14},
#     "compressed": {"tokens": 926, "count": 8},
#   },
#   "recommendations": ["Plenty of budget remaining."]
# }

Forget and Temporary

# Soft-delete with confirmation
result = mc.forget("old project notes")
mc.forget_confirmed(result["matches"])

# Session-only memory (auto-deleted on close)
mc.temporary("Meeting at 3pm today")

# Restore from trash
mc.restore_from_trash(chunk_id)

Cross-Session Persistence

Pins, trash, and audit logs persist to SQLite across sessions:

# Session 1
mc = memctrl.MemoryController(user_id="dev1")
mc.pin("Deploy to us-east-1")
mc.close_session()

# Session 2 — pin is still there
mc = memctrl.MemoryController(user_id="dev1")
messages = mc.optimize(max_tokens=4096)  # Includes "Deploy to us-east-1"

How It Works

User App ──► add_message() ──► TierManager ──► optimize() ──► LLM API
                                    │
                 ┌──────────────────┼──────────────────┐
                 │                  │                  │
          ┌──────┴──────┐   ┌──────┴──────┐   ┌──────┴──────┐
          │   Tier 0    │   │   Tier 1    │   │   Tier 2    │
          │   Active    │   │  Compressed │   │  Persistent │
          │  LRU evict  │   │  distilbart │   │  SQLite+FTS │
          │  OrderedDict│   │  + entities │   │  Embeddings │
          └─────────────┘   └─────────────┘   └─────────────┘

Tier 0 (Active) — Recent messages in full. LRU eviction, pinned chunks protected.

Tier 1 (Compressed) — Older messages summarized by local distilbart model (free, no API calls). Named entities (numbers, URLs, field names, medical values) are extracted and appended to summaries so they survive compression.

Tier 2 (Persistent) — All chunks stored in SQLite with FTS5 full-text search and sentence-transformer embeddings for semantic retrieval.

Task-Aware Retention

MemCtrl classifies each message by domain and adjusts retention:

Task Type	Retention Weight	Decay Period	Promotion Threshold
Medical	1.5x	7 days	30 (easiest)
Code	1.3x	3 days	35
Tutoring	1.2x	2 days	40
Writing	1.0x	1 day	45
General	0.8x	12 hours	50 (hardest)

Medical data is kept 14x longer than general chat before eviction.

Entity-Preserving Compression

When messages are compressed, MemCtrl extracts and preserves:

• Version numbers, formatted numbers, percentages
• URLs, connection strings, API keys
• Medical measurements, lab results, vitals
• Code identifiers (snake_case, CamelCase, function calls)
• Math notation, X-Headers

These are appended to summaries in brackets so the LLM can still reference them.

BYOK (Bring Your Own Key)

MemCtrl supports multiple LLM backends. Keys are passed in memory only — never logged, never persisted.

# Anthropic
mc = memctrl.MemoryController(provider="anthropic", api_key="sk-...")

# OpenAI
mc = memctrl.MemoryController(provider="openai", api_key="sk-...")

# Ollama (local, no key needed)
mc = memctrl.MemoryController(provider="ollama")

# Auto-detect (tries Anthropic → OpenAI → Ollama → Echo)
mc = memctrl.MemoryController(provider="auto")

CLI

memctrl chat "Hello" --user dev1
memctrl pin "Allergic to penicillin" --user dev1
memctrl forget "old notes" --user dev1
memctrl show --user dev1 --category pinned
memctrl stats --user dev1
memctrl export --user dev1 --format json

Configuration

Override defaults with a YAML file:

# config.yaml
tier0_budget_gb: 4.0
tier1_budget_gb: 4.0
llm_provider: "auto"
embedding_model: "sentence-transformers/all-MiniLM-L6-v2"
tokenizer_model: "distilbert-base-uncased"
control_mode: "hybrid"
compression_ratio: 4.0
max_tokens_per_chunk: 512
max_context_tokens: 4096

export MEMCTRL_CONFIG=path/to/config.yaml

Testing

pip install memctrl-llm[dev]
pytest tests/ -o addopts="" -q

160+ tests covering models, storage, tiers, controller, SDK, features, tokenizer, LLM backends, CLI, and web UI.

License

MIT License