truememory 0.6.7

SOTA-level agent memory at zero infrastructure cost.

A living memory system for AI agents. Long-horizon recall on commodity hardware.

What is TrueMemory? · Quick Start · Edge / Base / Pro · Architecture · Benchmarks · API · Docs · FAQ

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💡 What is TrueMemory?

• Remembers everything across sessions. Facts, preferences, decisions, corrections. Your AI finally knows who you are.
• 93.0% on LoCoMo, 92.0% on LongMemEval, SOTA on BEAM-1M. Beats every live-retrieval memory system across three major benchmarks. Independently reproducible.
• Runs locally on a single SQLite file. Zero cloud, zero API keys for Edge and Base tiers. Your memories never leave your machine.
• Neuroscience-inspired architecture. Six retrieval layers plus an encoding gate that filters noise from signal before anything gets stored.
• Works with Claude Code and Claude Desktop. Four lifecycle hooks capture conversations automatically. No manual work needed.

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🚀 Quick Start

${\color{#1a73e8}\textbf{\textsf{Step 1.}}}$ Open a terminal.

• Mac: Cmd + Space, type Terminal
• Linux: Ctrl + Alt + T
• Windows: open PowerShell

 

${\color{#1a73e8}\textbf{\textsf{Step 2.}}}$ Paste this and press Enter:

Mac / Linux:

curl -LsSf https://raw.githubusercontent.com/buildingjoshbetter/TrueMemory/main/install.sh | sh

Windows (PowerShell):

irm https://raw.githubusercontent.com/buildingjoshbetter/TrueMemory/main/install.ps1 | iex

 

${\color{#1a73e8}\textbf{\textsf{Step 3.}}}$ Wait 3-5 minutes for installation.

 

${\color{#1a73e8}\textbf{\textsf{Step 4.}}}$ Quit Claude completely and reopen it (Mac: Cmd+Q, Windows: close all Claude windows).

 

${\color{#1a73e8}\textbf{\textsf{Step 5.}}}$ Type "Set up TrueMemory" and pick Edge, Base, or Pro.

 

That's it. TrueMemory remembers your conversations automatically from here.

• Switch tiers: tell Claude "switch to Pro" or "switch to Base"
• Update: run uv tool upgrade truememory in Terminal, then restart Claude
• Uninstall: run uv tool uninstall truememory

⭐ Tip: Quit your Claude sessions when you're done prompting. TrueMemory's memory hook runs when the session ends, so it can capture and store the full conversation.

Click here for the advanced setup guide ▸

What the installer does

Installs uv (Astral's Python tool manager) if needed, fetches a managed Python 3.12, installs TrueMemory with all tier models into an isolated tool environment, registers the MCP server, wires up lifecycle hooks, and merges instructions into ~/.claude/CLAUDE.md. Your system Python is never touched. No sudo, no venvs, no pip struggle.

Audit the script

It's ~200 lines of shell, no sudo, stays entirely under $HOME:

curl -LsSf https://raw.githubusercontent.com/buildingjoshbetter/TrueMemory/main/install.sh -o install.sh && less install.sh && sh install.sh

Python library (for developers)

If you're embedding TrueMemory in your own Python project (requires Python 3.10+):

pip install truememory

pip install installs the Python library only. It does NOT register the MCP server, install hooks, or configure Claude. For Claude Code / Claude Desktop, always use the curl | sh installer above.

from truememory import Memory

m = Memory()
m.add("Prefers dark mode and TypeScript", user_id="alex")
m.add("Allergic to peanuts", user_id="alex")

results = m.search("What are Alex's preferences?", user_id="alex")
print(results[0]["content"])
# "Prefers dark mode and TypeScript"

The database is created automatically at ~/.truememory/memories.db.

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🏗️ Edge / Base / Pro

Same architecture, three tiers. All included in a single install — switch anytime.

	Edge	Base	Pro
LoCoMo (3-run mean)	89.6%	92.0%	93.0%
LongMemEval (3-run mean)			92.0%
BEAM-1M (3-run mean)			76.6% (SOTA)
BEAM-10M (single run)			65.0%
Embedder	Model2Vec potion-base-8M (8M params, 256d)	Qwen3-Embedding-0.6B (600M params, 256d)	Qwen3-Embedding-0.6B (600M params, 256d)
Reranker	MiniLM-L-6-v2 (22M)	gte-reranker-modernbert (149M)	gte-reranker-modernbert (149M)
HyDE	off	off	on (requires LLM API key)
Runs on	Any machine, CPU only	4GB+ RAM, CPU or GPU	4GB+ RAM + LLM API key
Model size	~30MB	~1.5GB	~1.5GB

Edge works everywhere. Base is the strongest fully-offline tier. Pro adds HyDE query expansion for the highest scores.

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🧠 Architecture

TrueMemory uses a 6-layer retrieval pipeline inspired by how the brain encodes and recalls memory, plus an encoding gate that decides what gets stored.

Ingest (what gets stored)

Every conversation flows through three stages before anything is stored:

Stage	What it does
LLM Extractor	Pulls atomic facts from raw conversation text. Classifies each as personal, preference, decision, correction, temporal, technical, or relationship.
Encoding Gate	Three-signal filter: compression novelty + speech-act salience + embedding pair-diff prediction error. Rejects noise, keeps signal.
Dedup	ADD / UPDATE / SKIP against existing memories using vector similarity + word-overlap Jaccard matching. Prevents duplicates and catches rephrased versions of the same fact.

Encoding Gate + Storage

Three signals decide whether a fact gets stored or skipped:

Signal	What it measures
Compression Novelty	gzip-based information gain. How much new information does this fact add compared to what's already stored?
Speech-Act Salience	Rule-based scorer for short messages, learned scorer for longer text. Filters out greetings, reactions, and filler.
Embedding Pair-Diff	Embedding divergence between the message and existing memories on the same topic. Detects when someone says something different about a known subject.

The weighted sum of all three must exceed a threshold (default 0.30) to be stored. Everything below gets skipped. One SQLite file at ~/.truememory/memories.db. Portable. Backupable. cp it anywhere.

Retrieve (how it answers)

When you ask a question, six layers work together:

Layer	Name	What it does
L0	Personality	Char-n-gram style vectors + entity profiles. Answers "what kind of person is X?" questions that keyword search can't touch.
L2	Episodic	FTS5 full-text keyword search with temporal filtering. Fast, broad recall.
L3	Semantic	Dense vector search (Model2Vec or Qwen3 by tier) + RRF fusion + cross-encoder reranking. The heavy lifter.
L4	Salience	Noise filtering + entity boosting. Learned 13-feature logistic regression scorer trained on retrieval-utility labels.
L5	Consolidation	Structured fact summaries, contradiction resolution, and surprise-weighted reranking (alpha=0.2).
+	Reranker	Cross-encoder reranking (MiniLM or gte-modernbert by tier) for final precision.

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🔬 Benchmarks

Tested on LoCoMo (1,540 questions, 10 conversations), LongMemEval (500 questions, multi-session), BEAM-1M (700 questions, 35 conversations at 1M+ tokens), and BEAM-10M (200 questions, 10 conversations at 10M tokens). All systems share the same answer model (GPT-4.1-mini), judge (GPT-4o-mini, 3x majority vote), and scoring pipeline.

LoCoMo (3-run validated means)

Tier	Overall	Single-hop	Multi-hop	Temporal	Open-domain
Edge	89.6%	88.7%	88.5%	79.2%	91.4%
Base	92.0%	91.5%	91.3%	82.3%	93.9%
Pro	93.0%	92.6%	90.0%	86.5%	95.4%

BEAM-1M (Pro tier, 3-run mean)

Category	Score
Preference following	97.1%
Contradiction resolution	91.4%
Information extraction	91.4%
Summarization	89.5%
Instruction following	84.8%
Abstention	82.4%
Knowledge update	77.6%
Multi-session reasoning	67.1%
Temporal reasoning	64.8%
Event ordering	19.5%
Overall	76.6%

LongMemEval (Pro tier, 3-run mean)

Variant	Accuracy	Correct/500
Oracle	92.0%	460
Strict (_s)	87.8%	439

BEAM-10M (Pro tier, single run)

Overall	65.0% (130/200)
GPU	A100 80GB
Conversations	10 at 10M tokens (~20K messages each)

Reproduce any result yourself

Every benchmark script is self-contained and runs on Modal.

• LoCoMo Scripts — 8 systems (TrueMemory, Mem0, Zep, Engram, etc.)
• LoCoMo Results — per-category breakdowns, latency, cost
• LoCoMo Eval Config — exact models, prompts, parameters
• LongMemEval Scripts — oracle + strict variants
• LongMemEval Results — 6 TM Pro runs + 5 competitor results
• BEAM-1M Script — 35 conversations at 1M+ tokens
• BEAM-10M Script — 10 conversations at 10M tokens
• BEAM Results — 3 runs (1M) + 1 run (10M)

Evaluation config

All benchmarks use the same eval pipeline. Nothing is hidden.

Parameter	LoCoMo	LongMemEval	BEAM-1M	BEAM-10M
Dataset	10 convs, 1,540 Qs	500 Qs, multi-session	35 convs at 1M tokens, 700 Qs	10 convs at 10M tokens, 200 Qs
Answer model	gpt-4.1-mini	gpt-4.1-mini	gpt-4.1-mini	gpt-4.1-mini
Answer temp	0	0	0	0
Judge model	gpt-4o-mini	gpt-4o-mini	gpt-4o-mini	gpt-4o-mini
Judge voting	3x majority	3x majority	3x majority	3x majority
Retrieval top-k	100	100	100	100
Compute	Modal T4	Modal A10G	Modal T4	Modal A100 80GB

Full details: LoCoMo | LongMemEval | BEAM

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🐍 Python API

from truememory import Memory

m = Memory()

# Store
m.add("Prefers dark mode and TypeScript", user_id="alex")
m.add("Allergic to peanuts", user_id="alex")
m.add("Works at Anthropic as a senior engineer", user_id="alex")

# Search
results = m.search("What are Alex's preferences?", user_id="alex")

# Deep search (multi-round, higher accuracy, slower)
results = m.search_deep("What do we know about Alex's career?", user_id="alex")

Method	Description
m.add(content, user_id, metadata)	Store a memory
m.search(query, user_id, limit)	Search memories (6-layer pipeline + reranker)
m.search_deep(query, user_id, limit, llm_fn)	Multi-round agentic search (slower, higher accuracy)
m.search_vectors(query, limit)	Pure vector cosine similarity search
m.get(id)	Get a specific memory by ID
m.get_all(user_id, limit, offset)	Get all memories with pagination
m.update(id, content)	Update a memory
m.delete(id)	Delete a memory
m.delete_all(user_id)	Delete all memories and associated data
m.stats()	Memory system statistics

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📖 Docs

Document	Description
Python API Reference	Full Memory class reference with examples
MCP Tool Reference	All 8 MCP tools with parameters and usage
CLI Reference	truememory-mcp and truememory-ingest commands
Environment Variables	All TRUEMEMORY_* configuration options
Getting Started	Install to first memory, step by step
Tier Selection	Edge vs Base vs Pro comparison
Debugging	Logs, traces, common issues, file locations

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❓ FAQ

My session doesn't seem to know anything about me. What's wrong?

On your first session, TrueMemory runs setup. It won't recall memories until setup is complete. After that, every new session automatically searches your memory and injects up to 25 relevant facts as context. If memories still aren't loading, check that the MCP server is connected (truememory_stats) and that you have memories stored (truememory_search with a broad query).

Where is my data stored? Is anything sent to the cloud?

Everything lives locally in a single SQLite file at ~/.truememory/memories.db. Edge and Base tiers make zero external network calls. Pro tier sends only your search query text to an LLM API for HyDE expansion. Your memories themselves are never transmitted. Back up anytime with cp ~/.truememory/memories.db backup.db.

How do I switch tiers (Edge → Base → Pro)?

Call truememory_configure(tier="base") (or "pro") in any session, or run truememory-ingest upgrade-tier base from the terminal. All tier models are included in the standard install — switching just re-embeds your existing memories with the new model. Pro also needs an API key for HyDE query expansion.

I switched tiers and search results seem off. How do I fix it?

After a tier switch, TrueMemory re-embeds all memories with the new model. If this was interrupted, run truememory_configure(tier="...") again to retry. If results are still degraded, you can delete ~/.truememory/memories.db and start fresh. Your conversations are still in your chat history and will be re-extracted.

Do I need Python installed?

No. The recommended install (curl -LsSf .../install.sh | sh) uses uv to manage a sandboxed Python 3.12. Your system Python is never touched. To uninstall cleanly: uv tool uninstall truememory.

Does TrueMemory collect telemetry?

TrueMemory collects anonymous usage telemetry to help us understand how the product is used and improve it. We track which tools are called and how often, session counts, and basic platform info (OS, tier, version). We never track your memory content, search queries, file paths, or API keys. Telemetry is on by default. To opt out:

export TRUEMEMORY_TELEMETRY=off

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Find me on X @Building_Josh · Follow us @Sauron_Labs

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📝 Citation

@software{truememory,
  title = {TrueMemory: Neuroscience-Inspired Persistent Memory for AI Agents},
  author = {Building\_Josh},
  organization = {Sauron},
  year = {2026},
  url = {https://github.com/buildingjoshbetter/TrueMemory},
  version = {0.6.0}
}

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⚖️ License

Licensed under AGPL-3.0. Free for personal and research use. Commercial use requires a separate license. Contact [e-mail].

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TrueMemory, a sauron company · sauronlabs.ai