elo-memory 0.2.0

Bio-inspired episodic memory system for AI agents

Elo Memory

The memory brain for AI agents. 3 lines to give any LLM persistent memory that knows what to remember, what to forget, what changed, and what's missing.

from elo_memory import EloBrain

brain = EloBrain("user_123")
response = brain.think("I switched from Django to FastAPI", your_llm)
# Brain auto: recalled context, stored the fact, updated KB to "backend: FastAPI",
# superseded old Django memory, extracted causal link if reason given,
# detected knowledge gaps, suggested follow-up questions

---

Why Elo Memory?

Every other memory system stores text and searches it. Elo Memory understands it.

"Switched from Django to FastAPI because it was too slow"

System	What it does
ChromaDB	Stores the text. Retrieves it when query is similar. Returns old Django AND new FastAPI results with no way to know which is current.
Mem0	Calls GPT to extract facts ($0.005/store). Stores "backend: FastAPI". Slow (500ms), needs API key, sends your data to OpenAI.
Elo Memory	Stores the episode. Updates KB to backend: FastAPI. Supersedes Django memory. Extracts causal link: "too slow → switched to FastAPI". Detects knowledge gaps. All locally, in 30ms, for $0.

---

Quick Start

pip install "elo-memory[ml]"

For AI agents (recommended)

from elo_memory import EloBrain

brain = EloBrain("sarah")

# Full agent loop: recall → prompt → LLM → store
response = brain.think(
    "I'm Sarah, senior engineer at Shopify. We use PostgreSQL.",
    llm_fn=lambda prompt: your_llm(prompt),
)

# What does the brain know?
state = brain.what_i_know()
# {
#   knowledge: {name: "Sarah", role: "senior engineer", company: "Shopify", database: "PostgreSQL"},
#   knowledge_gaps: [{topic: "infrastructure", missing: ["hosting", "ci/cd", "monitoring"]}],
#   suggestions: ["Consider asking about: hosting, ci/cd, monitoring"],
#   facts: [...], entities: {...}, causal_links: 0, decisions_tracked: 0
# }

What happens automatically on every turn

1. Recalls relevant memories + KB facts (30ms)
2. Stores the message (skips filler: "thanks", "ok", "what time?")
3. Updates KB with structured facts extracted from text
4. Detects conflicts — "switched from X to Y" supersedes old X memories
5. Extracts entities — names, emails, dates, amounts
6. Tracks causality — "because X" links cause to effect
7. Detects knowledge gaps — knows what it DOESN'T know
8. Suggests follow-ups — unresolved decisions, missing context
9. Generates derived facts — "Switched to FastAPI" → also indexes "Currently using FastAPI"
10. Filters noise — near-duplicates (>0.92 cosine) silently skipped

Direct memory access

from elo_memory import UserMemory

memory = UserMemory("sarah", persistence_path="./memories")

# Store
result = memory.store("My email is [e-mail]")
# {stored: True, entities: {emails: ["[e-mail]"], names: ["Sarah"]}}

# Recall
results = memory.recall("contact info?", k=7)
# [("My email is [e-mail]", 0.47)]

# Profile
memory.get_profile()
# {user_id: "sarah", total_memories: 42, entities: {emails: [...], names: [...]}}

# Current facts only (superseded removed)
memory.get_facts()

---

What No Competitor Has

1. Knowledge Gap Detection

brain.think("I'm building a payment system with Stripe", llm)
state = brain.what_i_know()
state["knowledge_gaps"]
# [{topic: "payment", missing: ["currency", "volume", "compliance", "region"],
#   suggestion: "Consider asking about: currency, volume, compliance, region"}]

The brain knows what it doesn't know and tells the agent to ask.

2. Causal Reasoning

brain.think("Switched to FastAPI because Django was too slow for websockets", llm)
brain.prepare("Why did we change the backend?")
# System prompt includes:
# ## Reasons (causal links)
# - Django was too slow for websockets → Switched to FastAPI

3. Conflict Resolution (no LLM needed)

brain.think("I drive a BMW", llm)
brain.think("Just picked up my new Tesla yesterday", llm)
# BMW memory automatically superseded. "What car?" → Tesla only.

Works for explicit ("switched from X to Y") and implicit ("got a new X") contradictions.

4. Structured Knowledge Base

Every message automatically updates a structured KB. Queries hit the KB first (0ms), episodes as fallback.

brain._kb.get_all()
# {name: "Sarah", role: "senior engineer", company: "Shopify",
#  backend: "FastAPI", database: "PostgreSQL", team_size: "12"}

---

vs Alternatives

	Elo Memory	Mem0	ChromaDB	Zep
Recall accuracy (24q test)	100%	N/A ($)	83%	—
Store latency	30ms	500-2000ms	30ms	~100ms
Cost per 1000 ops	$0	$0.50-20	$0	$99/mo
Works offline	Yes	No	Yes	No
Needs API key	No	Yes	No	Yes
Structured KB	Yes	Yes (LLM)	No	No
Conflict detection	Yes	Yes (LLM)	No	No
Knowledge gaps	Yes	No	No	No
Causal reasoning	Yes	No	No	No
Decision tracking	Yes	No	No	No

---

Installation

# Recommended (includes sentence-transformers)
pip install "elo-memory[ml]"

# Minimal (provide your own embeddings)
pip install elo-memory

# From source
git clone https://github.com/server-elo/elo-memory.git
cd elo-memory && pip install -e ".[dev]"

Running Tests

pytest tests/ -v

---

Architecture

User message
  │
  ├─→ Knowledge Base (instant structured facts)
  │     "backend: FastAPI", "team_size: 12"
  │
  ├─→ Episodic Store (narrative context)
  │     ChromaDB + temporal expansion
  │
  ├─→ Intelligence Layer
  │     Knowledge gaps, causal chains, decision tracking
  │
  ├─→ Conflict Detection
  │     Supersede old facts, implicit contradictions
  │
  └─→ Entity Extraction
        Names, emails, dates, amounts (regex, no LLM)

---

API Levels

Level	Entry Point	For
Newbie	EloBrain	3 lines, automatic everything
Intermediate	UserMemory	Per-user isolation, sessions, profiles
Advanced	EpisodicMemoryStore	Full config control, raw components
Expert	Individual engines	Surprise, segmentation, forgetting, consolidation

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Documentation

• Usage Guide — Integration patterns
• MCP Setup — Claude Code integration
• Changelog — What's new

License

MIT — see LICENSE.

Acknowledgments

• EM-LLM (ICLR 2025) — Research foundation
• Itti & Baldi (2009) — Bayesian Surprise
• Squire & Alvarez (1995) — Systems Consolidation

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GitHub: https://github.com/server-elo/elo-memory PyPI: https://pypi.org/project/elo-memory/