widemem.ai

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Goldfish memory? ¬_¬ Fixed.

Because your AI deserves better than amnesia. ¬_¬

An open-source AI memory layer that actually remembers what matters. Local-first, batteries-included, and opinionated about not forgetting your user's blood type.

Look, AI memory has come a long way. Context windows are bigger, RAG pipelines are everywhere, and most frameworks have some form of "remember this for later." It's not terrible anymore. But it's not great either. Most memory systems treat every fact the same — your user's blood type sits next to what they had for lunch, decaying at the same rate, with the same priority. Contradictions pile up silently. There's no sense of "this matters more than that." And when you need to remember something from three months ago that actually matters? Good luck.

widemem is for when "good enough" isn't good enough.

widemem gives your AI a real memory — one that scores what matters, forgets what doesn't, and absolutely refuses to lose track of someone's prescritpion medication just because 72 hours passed and the decay function got bored. Think of it as long-term memory for LLMs, except it actually works and doesn't require a PhD to set up.

• Memories that know their place — Importance scoring (1-10) + time decay means "has a peanut allergy" always outranks "had pizza on Tuesday". As it should. Not all memories are created equal, and your retrieval system should know the difference between a life-threatening allergy and a lunch preference.
• One brain, three layers — Facts roll up into summaries, summaries into themes. Ask "where does Alice live" and get the fact. Ask "tell me about Alice" and get the big picture. Your AI can zoom in and zoom out without breaking a sweat or making a second API call.
• YMYL or GTFO — Health, legal, and financial facts get VIP treatment: higher importance floors, immunity from decay, and forced contradiction detectoin. Because forgetting someone's medication is not a "minor regression". It's a lawsuit waiting to happen. ¬_¬
• Conflict resolution that isn't stupid — Add "I live in Paris" after "I live in Berlin" and the system doesn't just blindly append both. It detects the contradiction, resolves it in a single LLM call, and updates the memory. Like a reasonable adult would.
• Local by default, cloud if you want — SQLite + FAISS out of the box. No accounts, no API keys for storage, no "please sign up for our enterprise plan to store more than 100 memories". Plug in Qdrant or any cloud provider when you're ready. Or don't. We won't guilt-trip you.

---

Architecture

<p align="center"> <img src="docs/architecture.png" alt="widemem architecture diagram" width="100%"> </p>

---

TL;DR

Six features, one library. Here's what widemem does that most memory systems don't:

#	Feature	What it does	Why it matters
1	Batch conflict resolution	Single LLM call for all facts vs. existing memories	N facts = 1 API call, not N. Your wallet will thank you.
2	Importance + decay	Facts rated 1-10, with exponential/linear/step decay	Old trivia fades. Critical facts don't.
3	Hierarchical memory	Facts -> summaries -> themes, auto-routed	Broad questions get themes, specfic ones get facts.
4	Active retrieval	Contradiction detection + clarifying questions	"Wait, you said you live in Berlin AND Paris?"
5	Self-supervised extraction	Collect training data, distill to a small model	LLM extraction quality, local model costs. Eventually.
6	YMYL prioritization	Health/legal/financial facts are untouchable	Some things you just don't forget.

140 tests. Zero external services required. SQLite + FAISS by default. Plug in OpenAI, Anthropic, Ollama, Qdrant, or sentence-transformers as needed.

---

Table of Contents

• Install
• Quick Start
• Configuration
• Scoring & Decay
• LLM Providers
• Embedding Providers
• Vector Store Providers
• YMYL (Your Money or Your Life)
• Topic Weights
• Hierarchical Memory
• Active Retrieval
• Temporal Search
• Self-Supervised Extraction
• History & Audit Trail
• Batch Conflict Resolution
• API Reference
• Development
• Terms & Conditions
• Contact
• License

---

Install

pip install widemem-ai

Trouble installing?

1. pip not found? Use pip3:

pip3 install widemem-ai

2. pip too old? Upgrade it first:

python3 -m pip install --upgrade pip

3. Python 3.9 or older? widemem requires Python 3.10+. Install via Homebrew (macOS):

brew install [email protected]
/opt/homebrew/bin/python3.10 -m pip install widemem-ai

No Homebrew? Install it first:

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

Verify installation:

python3 -c "import widemem; print(widemem.__version__)"

Optional providers

pip install widemem-ai[anthropic]             # Claude LLM provider
pip install widemem-ai[ollama]                # Local LLM via Ollama
pip install widemem-ai[sentence-transformers] # Local embeddings (no API key needed, imagine that)
pip install widemem-ai[qdrant]                # Qdrant vector store
pip install widemem-ai[all]                   # Everything. You want it all? You got it.

---

Quick Start

Five lines to a working memory system. Six if you count the import.

from widemem import WideMemory, MemoryConfig

memory = WideMemory()

# Add memories
result = memory.add("I live in Berlin and work as a software engineer", user_id="alice")

# Search
results = memory.search("where does alice live", user_id="alice")
for r in results:
    print(f"{r.memory.content} (score: {r.final_score:.2f})")

# Update happens automatically — add contradicting info and the resolver handles it
memory.add("I just moved to Paris", user_id="alice")

# Delete
memory.delete(results[0].memory.id)

# History audit trail
history = memory.get_history(results[0].memory.id)

That's it. No 47-step setup guide. No YAML files. No existential dread. Your AI just went from goldfish to elephant in six lines.

WideMemory also works as a context manager if you're the responsible type:

with WideMemory() as memory:
    memory.add("I live in Berlin", user_id="alice")
    results = memory.search("where does alice live", user_id="alice")
# Connection closed automatically. You're welcome.

---

Configuration

All settings live in MemoryConfig. Here's the full kitchen sink — most of these have sane defaults so you don't actualy need to touch them:

from widemem import WideMemory, MemoryConfig
from widemem.core.types import (
    LLMConfig,
    EmbeddingConfig,
    VectorStoreConfig,
    ScoringConfig,
    YMYLConfig,
    TopicConfig,
    DecayFunction,
)

config = MemoryConfig(
    llm=LLMConfig(
        provider="openai",          # "openai", "anthropic", or "ollama"
        model="gpt-4o-mini",
        api_key="sk-...",           # Or set OPENAI_API_KEY env var
        temperature=0.0,
        max_tokens=2000,
    ),
    embedding=EmbeddingConfig(
        provider="openai",          # "openai" or "sentence-transformers"
        model="text-embedding-3-small",
        dimensions=1536,
    ),
    vector_store=VectorStoreConfig(
        provider="faiss",           # "faiss" or "qdrant"
        path=None,                  # Optional path for persistent storage
    ),
    scoring=ScoringConfig(
        decay_function=DecayFunction.EXPONENTIAL,
        decay_rate=0.01,            # Higher = faster decay
        similarity_weight=0.5,      # Weight for vector similarity
        importance_weight=0.3,      # Weight for importance score
        recency_weight=0.2,         # Weight for recency score
    ),
    ymyl=YMYLConfig(
        enabled=False,              # Enable YMYL prioritization
    ),
    topics=TopicConfig(
        weights={},                 # Topic boost multipliers
        custom_topics=[],           # Hints for extraction
    ),
    history_db_path="~/.widemem/history.db",
    enable_hierarchy=False,
    enable_active_retrieval=False,
    active_retrieval_threshold=0.6,
    collect_extractions=False,
    extractions_db_path="~/.widemem/extractions.db",
)

memory = WideMemory(config)

---

Scoring & Decay

The Formula

Every search result gets a combined score. It's not rocket science, but it's close enough:

final_score = (similarity_weight * similarity) + (importance_weight * importance) + (recency_weight * recency)
final_score *= topic_boost   # if topic weights are set

• similarity — Cosine similarity from vector search (0-1)
• importance — Normalized from the 1-10 rating assigned at extraction (0-1)
• recency — Time decay score (0-1), computed by the decay function
• topic_boost — Multiplier from topic weights (default 1.0)

Decay Functions

Control how memories fade over time. Like real memories, but configurable. Unlike a goldfish, you can turn decay off entirely.

Function	Formula	Use Case
exponential	e^(-rate * days)	Smooth, natural decay (default)
linear	max(1 - rate * days, 0)	Predictable, linear drop-off
step	1.0 / 0.7 / 0.4 / 0.1 at 7/30/90 days	Discrete tiers
none	Always 1.0	Elephants never forget

# Fast decay — what happened last week? who cares
ScoringConfig(decay_function=DecayFunction.EXPONENTIAL, decay_rate=0.05)

# Slow decay — memories stay relevant longer
ScoringConfig(decay_function=DecayFunction.EXPONENTIAL, decay_rate=0.005)

# No decay — all memories equaly fresh forever
ScoringConfig(decay_function=DecayFunction.NONE)

---

LLM Providers

OpenAI (default)

config = MemoryConfig(
    llm=LLMConfig(provider="openai", model="gpt-4o-mini"),
)

Anthropic Claude

pip install widemem-ai[anthropic]

config = MemoryConfig(
    llm=LLMConfig(provider="anthropic", model="claude-sonnet-4-20250514"),
)

Ollama (fully local)

For those of you who don't trust the cloud. We respect that.

pip install widemem-ai[ollama]
ollama pull llama3

config = MemoryConfig(
    llm=LLMConfig(provider="ollama", model="llama3"),
    embedding=EmbeddingConfig(provider="sentence-transformers", model="all-MiniLM-L6-v2", dimensions=384),
)

---

Embedding Providers

OpenAI (default)

EmbeddingConfig(provider="openai", model="text-embedding-3-small", dimensions=1536)

Sentence Transformers (local)

pip install widemem-ai[sentence-transformers]

EmbeddingConfig(provider="sentence-transformers", model="all-MiniLM-L6-v2", dimensions=384)

---

Vector Store Providers

FAISS (default, local)

VectorStoreConfig(provider="faiss")

Zero setup. It just works. Like it should.

Qdrant

pip install widemem-ai[qdrant]

# Local Qdrant (file-based)
VectorStoreConfig(provider="qdrant", path="./qdrant_data")

# Remote Qdrant
VectorStoreConfig(provider="qdrant")  # Configure via QDRANT_URL env var

---

YMYL (Your Money or Your Life)

Some facts are more equal than others. YMYL prioritization ensures that critical facts about health, finances, legal matters, and safety are never lost, never deprioritized, and never quietly forgotten because the decay function decided Tuesday was a good day to forget someone's insulin dosage.

For the full deep dive on how YMYL works, edge cases, and limitations, see YMYL.md.

config = MemoryConfig(
    ymyl=YMYLConfig(
        enabled=True,
        categories=["health", "medical", "financial", "legal", "safety", "insurance", "tax", "pharmaceutical"],
        min_importance=8.0,          # Floor importance for strong YMYL facts
        decay_immune=True,           # Strong YMYL facts don't decay over time
        force_active_retrieval=True, # Force contradiction detection for strong YMYL facts
    ),
)

Two-Tier Confidence System

Not every mention of "bank" means someone's talking about their finances. Could be a river bank. Could be a piggy bank. widemem uses a two-tier confidence system to avoid crying wolf:

Confidence	How it triggers	Importance	Decay immunity	Active retrieval
Strong	Multi-word match ("bank account", "blood type") OR 2+ weak keywords	Floor at 8.0	Yes	Forced
Weak	Single ambiguous keyword ("doctor", "bank")	Nudged to 6.0	No	No
None	No YMYL keywords	Unchanged	No	No

So "walked by the bank" gets a gentle 6.0 nudge. "Opened a savings account at the bank" gets the full 8.0 treatment. "Watching Doctor Who" gets a minor bump, not a medical emergency. As it should be.

YMYL Categories

8 categories, each with strong (unambiguous) and weak (context-dependent) patterns:

Category	Strong Patterns	Weak Patterns
health	blood pressure, diabetes diagnosis, mental health	doctor, hospital, medication, anxiety
medical	lab results, medical condition, treatment plan	clinic, vaccine, MRI, scan
financial	bank account, savings account, credit score, 401k	bank, loan, debt, salary
legal	power of attorney, child custody, court order	lawyer, contract, divorce
safety	emergency contact, blood type, epipen, DNR order	evacuation, flood
insurance	insurance policy, insurance premium	insurance, coverage, claim
tax	tax return, W-2, 1099, IRS audit	deduction, filing
pharmaceutical	side effect, drug interaction	drug, dosage, prescription

You can enable a subset if you only care about some categories:

YMYLConfig(enabled=True, categories=["health", "medical", "financial"])

---

Topic Weights

Boost or suppress specific topics during retrieval. Think of it as putting your thumb on the scale, but in a socially acceptable way.

config = MemoryConfig(
    topics=TopicConfig(
        weights={
            "python": 2.0,      # 2x boost for Python-related memories
            "machine learning": 1.8,
            "cooking": 0.5,     # Sorry, cooking. Not today.
        },
        custom_topics=["python", "machine learning"],  # Extraction hints
    ),
)

• weights — Applied as a multiplier to final_score during search. Values > 1.0 boost, < 1.0 suppress. Matching is case-insensitive substring.
• custom_topics — Passed to the LLM during extraction as a hint to pay extra attention to these topics. The LLM listens. Mostly.

---

Hierarchical Memory

Three-tier memory system. Facts are great, but sometimes you need the big picture.

config = MemoryConfig(enable_hierarchy=True)
memory = WideMemory(config)

# Add many facts
for msg in conversation_history:
    memory.add(msg, user_id="alice")

# Trigger summarization (groups related facts, creates summaries and themes)
memory.summarize(user_id="alice")

# Broad queries return themes, specific queries return facts
results = memory.search("tell me about alice")        # Returns themes
results = memory.search("where does alice live")      # Returns facts

# Filter by tier
from widemem.core.types import MemoryTier
results = memory.search("alice", tier=MemoryTier.SUMMARY)

Tiers

Tier	Description	Query Type
fact	Individual extracted facts	Specific questions ("what is X?")
summary	Groups of related facts summarized	Moderate scope ("alice's work")
theme	High-level themes across summaries	Broad questions ("tell me about alice")

Query routing uses keyword heuristics (no extra LLM call) with a fallback chain — if the preferred tier has no results, it falls back to the next tier. No results left behind.

---

Active Retrieval

Your AI shouldn't silently overwrite "lives in Berlin" with "lives in Paris" without at least raising an eyebrow. Active retrieval detects contradictions and ambiguities, then asks clarifying questions via callbacks.

config = MemoryConfig(
    enable_active_retrieval=True,
    active_retrieval_threshold=0.6,  # Similarity threshold for conflict detection
)
memory = WideMemory(config)

def handle_clarification(clarifications):
    for c in clarifications:
        print(f"Conflict: {c.question}")
        print(f"  Old: {c.existing_memory}")
        print(f"  New: {c.new_fact}")
    # Return None to abort the add, or a list of answers to proceed
    return ["User moved to Paris"]

result = memory.add(
    "I just moved to Paris",
    user_id="alice",
    on_clarification=handle_clarification,
)

if result.has_clarifications:
    print(f"Resolved {len(result.clarifications)} conflicts")

Callback behavior

• on_clarification receives a list of Clarification objects
• Return None to abort the add entirely (the nuclear option)
• Return a list of strings (answers) to proceed with the add
• If no callback is provided, the add proceeds and clarifications are returned in AddResult.clarifications for you to deal with later. Or never. We won't judge.

---

Temporal Search

Filter and rank memories by time. Because sometimes you only care about what happend recently.

from datetime import datetime, timedelta

now = datetime.utcnow()

# Only memories from the last week
results = memory.search(
    "what happened recently",
    user_id="alice",
    time_after=now - timedelta(days=7),
)

# Only memories before January 2026
results = memory.search(
    "old preferences",
    user_id="alice",
    time_before=datetime(2026, 1, 1),
)

# Combined range
results = memory.search(
    "december events",
    user_id="alice",
    time_after=datetime(2025, 12, 1),
    time_before=datetime(2025, 12, 31),
)

---

Self-Supervised Extraction

LLM extraction is great but expensive. widemem can collect extraction training data and optionally use a small local model for cost-efficient extraction. Think of it as teaching a smaller, cheaper brain to do the expensive brain's job.

# Enable data collection
config = MemoryConfig(
    collect_extractions=True,
    extractions_db_path="~/.widemem/extractions.db",
)
memory = WideMemory(config)

# Use the memory normally — all LLM extractions are logged as training pairs
memory.add("I live in Berlin", user_id="alice")

Training a small model

python scripts/train_extractor.py export --db ~/.widemem/extractions.db --output training_data.json
python scripts/train_extractor.py train --data training_data.json --model distilbert-base-uncased --output ./my_extractor

The self-supervised extractor uses a fallback chain: try the small model first, fall back to the LLM if confidence is low. Best of both worlds.

---

History & Audit Trail

Every add, update, and delete is logged to SQLite. Full audit trail. Because "who changed this and when" is a question you'll eventually ask.

history = memory.get_history(memory_id)
for entry in history:
    print(f"{entry.timestamp}: {entry.action.value}")
    if entry.old_content:
        print(f"  From: {entry.old_content}")
    if entry.new_content:
        print(f"  To: {entry.new_content}")

---

Batch Conflict Resolution

When new facts are added, widemem finds related existing memories and sends everything to the LLM in a single call. The LLM decides for each fact whether to ADD (new), UPDATE (modify existing), DELETE (contradicted), or NONE (duplicate).

This is the main architectural improvement over mem0's per-fact approach. One call instead of N. The LLM sees the full context and can make better decisions. Your API bill sees fewer line items.

---

API Reference

WideMemory

Method	Description
add(text, user_id, agent_id, run_id, on_clarification)	Extract and store memories. Returns AddResult.
add_batch(texts, user_id, agent_id, run_id)	Process multiple texts. Returns List[AddResult].
search(query, user_id, agent_id, top_k, time_after, time_before, tier)	Search memories. Returns List[MemorySearchResult].
get(memory_id)	Get a single memory by ID. Returns Memory or None.
delete(memory_id)	Delete a memory by ID.
get_history(memory_id)	Get audit trail for a memory. Returns List[HistoryEntry].
summarize(user_id, agent_id, force)	Trigger hierarchical summarization. Returns List[Memory].
count(user_id, agent_id, tier)	Count memories with optional filters. Returns int.
export_json(user_id, agent_id)	Export memories as JSON string.
import_json(data)	Import memories from JSON string. Returns count imported.

AddResult

Field	Type	Description
memories	List[Memory]	Newly created/updated memories
clarifications	List[Clarification]	Any detected conflicts
has_clarifications	bool	Whether conflicts were detected

MemorySearchResult

Field	Type	Description
memory	Memory	The memory object
similarity_score	float	Vector similarity (0-1)
temporal_score	float	Recency score after decay (0-1)
importance_score	float	Normalized importance (0-1)
final_score	float	Combined weighted score

---

MCP Server (Model Context Protocol)

widemem ships with an MCP server so you can plug it directly into Claude Desktop, Cursor, or any MCP-compatible client. Memory as a tool — add, search, delete, and count memories without writing a single line of glue code.

pip install widemem-ai[mcp]

Run it

python -m widemem.mcp_server

This starts a stdio-based MCP server. Configure it in your MCP client (e.g. Claude Desktop claude_desktop_config.json):

{
  "mcpServers": {
    "widemem": {
      "command": "python",
      "args": ["-m", "widemem.mcp_server"],
      "env": {
        "WIDEMEM_LLM_PROVIDER": "ollama",
        "WIDEMEM_LLM_MODEL": "llama3.2",
        "WIDEMEM_EMBEDDING_PROVIDER": "sentence-transformers"
      }
    }
  }
}

Available tools

Tool	Description
widemem_add	Add memories (extracts facts, resolves conflicts)
widemem_search	Semantic search over memories
widemem_delete	Delete a memory by ID
widemem_count	Count stored memories
widemem_health	Health check

Environment variables

Variable	Default	Description
WIDEMEM_DATA_PATH	~/.widemem/data	Storage directory
WIDEMEM_LLM_PROVIDER	ollama	LLM provider (openai, anthropic, ollama)
WIDEMEM_LLM_MODEL	llama3.2	LLM model name
WIDEMEM_LLM_BASE_URL	http://localhost:11434	LLM API base URL
WIDEMEM_EMBEDDING_PROVIDER	sentence-transformers	Embedding provider

---

Development

git clone https://github.com/remete618/widemem-ai
cd widemem
pip install -e ".[dev]"
pytest

140 tests. They all pass. We checked. Even a goldfish could run them. Well, maybe not.

---

Terms & Conditions

By using widemem, you agree to the following completely reasonable terms:

1. No warranty. This software is provided "as is". If your AI forgets something important, that's between you and your AI. We tried our best with YMYL but we're not lawyers, doctors, or financial advisors — and neither is your vector database.

2. Use responsibly. widemem stores user data locally by default. If you point it at a cloud provider, that's your responsibility. We don't collect, transmit, or even look at your data. We have better things to do.

3. YMYL is a best-effort safety net, not a medical device. It uses keyword matching to flag health/legal/financial content. It will catch "diabetes diagnosis" but it won't catch subtle medical references buried in metaphors. Don't rely on it for life-critical decisions. Use it as one layer of many.

4. LLM providers have their own terms. If you use OpenAI, Anthropic, or any other provider through widemem, their terms of service apply to those API calls. Read them. Or don't. But they exist.

5. Apache 2.0. You can use this commercially, modify it, distribute it, and even pretend you wrote it (though we'd appreciate a star on GitHub instead).

---

Contact

Radu Cioplea

• Email: [email protected]
• Project: widemem.ai
• Repository: github.com/remete618/widemem-ai

Bug reports, feature requests, and unsolicited opinions are all welcome at the GitHub issues page.

---

License

Apache 2.0 — See LICENSE for the full text that nobody reads.

---

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