voltmem 0.1.0

Current-truth memory for LLM agents — protect stable facts, update volatile ones.

VoltMem

Current-truth memory for LLM agents.

Most memory layers treat every fact the same — your hometown and today's mood get equal weight. That forces a bad tradeoff: go stale on fast-changing facts, or get corrupted when a confident-but-wrong update overwrites something durable.

VoltMem scales protection and retrieval freshness by how fast each kind of fact actually changes. Volatile facts update; stable facts resist corruption; stale volatile memories rank lower at search time.

Mem0 remembers relevant facts. VoltMem remembers current truth.

Research & benchmarks: docs/RESEARCH.md

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Install

pip install voltmem[embeddings]
# from source:
# pip install -e ".[embeddings]"

Core library has zero required dependencies. Embeddings extras pull in sentence-transformers (recommended). LangChain: pip install -e ".[langchain]".

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Quickstart

from voltmem import create_memory

mem = create_memory("app.db", user_id="alice")

mem.add("I live in Berlin")
mem.add("I prefer concise, direct answers")
mem.add("Actually I moved to Paris last month")   # updates location, not prefs

hits = mem.search("where does the user live?", limit=3)
print(hits[0]["memory"])   # Actually I moved to Paris last month

Message pairs (auto fact extraction)

mem.add([
    {"role": "user", "content": "I moved to Paris. I'm working on a DB migration."},
], extract=True)   # default for message lists — splits into atomic facts

Optional: create_memory(..., llm_extract=True) for Ollama-powered extraction.

Inject into a prompt

memories = mem.search(user_message, limit=5)
context = "\n".join(f"- {m['memory']}" for m in memories)
system = f"What you know about this user:\n{context}"

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API

Method	Description
create_memory(db, user_id)	Factory with auto-detected embeddings
Memory.add(text | messages)	Store a fact; slot-aware linking updates related memories
Memory.search(query, limit=5)	Ranked memories (relevance + freshness)
Memory.get_all()	All active memories for this user
Memory.delete(id)	Remove one memory
Memory.clear()	Wipe user namespace

Advanced: mem.layer exposes MemoryLayer for low-level observe() / write().

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Why VoltMem

Problem	ADD-only memory	VoltMem
User moves cities	Berlin and Paris both stored	Updates to current city
Old project name in haystack	Ranks by similarity	Down-ranks stale volatile facts
Confident wrong blip on stable pref	Often accepted	Resists corruption

Example results (reproducible)

Run locally with pip install -e ".[embeddings]". Embeddings: sentence-transformers (all-MiniLM-L6-v2).

examples/contradiction_demo.py — 5-turn script vs naive always-add:

After scenario	always-add	VoltMem
User moves Berlin → Paris	2 location facts (stale + current)	1 current fact
Paraphrase blip on stable pref	adopts blip ("really like short replies")	keeps original ("concise, direct answers")

experiments/mem0_comparison.py — 3 scenarios, top-1 search (always-add baseline):

Scenario	always-add	VoltMem
location_update	WIN (2 facts stored)	WIN (1 fact)
stable_pref_blip	LOSE	WIN
volatile_mood	LOSE (stale "great")	WIN (current "stressed")

VoltMem clearer wins: 2/3 (always-add also finds Paris on location, but keeps stale facts).

experiments/mem0_side_by_side.py — same 3 scenarios vs real Mem0 (open-source, gpt-4o-mini + text-embedding-3-small):

Scenario	Mem0	VoltMem
location_update	LOSE (stale "Berlin", 2 facts)	WIN ("Paris", 1 fact)
stable_pref_blip	PARTIAL (adopts blip)	WIN (keeps "concise")
volatile_mood	LOSE (stale "great", 2 facts)	WIN ("stressed", 1 fact)

VoltMem clearer wins: 3/3. Mem0 keeps contradictory facts; VoltMem updates volatile slots and protects stable prefs via domain volatility + slot-aware linking.

experiments/memory_demo.py — 3 final Q&A checks vs ground truth:

Policy	Score
VoltMem	3/3
never-overwrite	2/3
always-overwrite	1/3
reliability-threshold	1/3

VoltMem is the only policy that both rejects confident false blips on stable facts and tracks weak-but-true updates on volatile ones. Full distributions: docs/RESEARCH.md (llm_memory_bench.py).

python examples/contradiction_demo.py
python experiments/mem0_comparison.py
python experiments/mem0_side_by_side.py   # pip install mem0ai; OPENAI_API_KEY or MEM0_BACKEND=ollama
python experiments/memory_demo.py

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Integrations

LangChain

pip install -e ".[langchain]"
python examples/langchain_agent.py

from voltmem.integrations.langchain import VoltMemMemory

memory = VoltMemMemory(session_id="user-42", db_path="app.db")
memory.load_memory_variables({"input": "Where do I live?"})
memory.save_context({"input": "I moved to Paris"}, {"output": "Noted."})

Multi-tenant

One SQLite file, many users — user_id maps to an isolated namespace:

alice = create_memory("app.db", user_id="alice")
bob   = create_memory("app.db", user_id="bob")

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Examples

Script	What it shows
examples/contradiction_demo.py	VoltMem vs always-add on contradictions
experiments/mem0_comparison.py	3-scenario head-to-head vs always-add
experiments/mem0_side_by_side.py	3-scenario head-to-head vs real Mem0 (3/3 wedge)
examples/quickstart_batteries.py	remember() / recall() low-level API
examples/multi_tenant.py	One DB, many users
examples/langchain_agent.py	LangChain adapter

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Domain volatility priors

Domain	Volatility	Behavior
personality_trait	0.05	Very protected
core_preference	0.08	Very protected
biographical	0.10	High protection
current_project	0.55	Updates readily
emotional_context	0.80	Fast-moving
current_task	0.90	Minimal protection

Custom domains: voltmem/domains.py.

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Development

pip install -e ".[all]"
python tests/test_voltmem.py
python tests/test_client.py

Experiments and benchmarks live in experiments/ — see docs/RESEARCH.md.

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License

MIT