engram-search 0.1.3

High-recall conversational memory retrieval. 98% R@5 on LongMemEval, 94% on LoCoMo — no LLM required. Local-first, cloud-ready.

Engram

High-recall conversational memory retrieval. Local-first, cloud-ready.

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Benchmark Results

Tested on two major benchmarks — no LLM required, zero cost per query.

LongMemEval (500 questions)

Metric	Score
R@5	98.4% (492/500)
R@10	99.4%
NDCG@5	0.934

Question Type	R@5
knowledge-update	98.7%
multi-session	99.2%
single-session-assistant	100.0%
single-session-user	100.0%
temporal-reasoning	97.0%
single-session-preference	93.3%

LoCoMo (1982 questions, 10 conversations)

Metric	Score
R@5	93.9% (1862/1982)
R@10	95.0%
NDCG@5	0.894

Category	R@5	R@10
Single-hop (factual)	90.4%	93.3%
Temporal (dates)	93.1%	94.7%
Multi-hop (inference)	75.0%	78.3%
Contextual (details)	97.1%	97.5%
Adversarial (speaker)	94.6%	94.8%

Reported with --mode rerank (chunking + cross-encoder reranker + speaker-name injection).

What It Does

Engram stores conversation history and retrieves it with state-of-the-art accuracy. It uses a three-stage retrieval pipeline — dense embeddings, sparse keyword matching, and cross-encoder reranking — to achieve higher recall than systems relying on LLM-based extraction or summarization.

Nothing is summarized. Nothing is paraphrased. Your exact words are stored and returned.

How It Compares

LoCoMo — Zero-LLM Memory Systems

System	LoCoMo Accuracy	LLM Required
Engram	93.9%	No
EverMemOS	92.3%	Yes (cloud)
Hindsight	89.6%	Yes (cloud)
Zep	~85%	Yes (cloud)
Letta / MemGPT	~83.2%	Yes (cloud)
SLM V3 (zero-cloud)	74.8%	No
Supermemory	~70%	Yes
Mem0 (independent)	~58%	Yes

Engram is the top-performing system on LoCoMo — beating paid cloud-LLM services at $0/query.

LongMemEval

	Engram	MemPalace	Mem0
R@5 (LongMemEval)	98.4%	96.6%	—
Embedding model	bge-large (1024d)	all-MiniLM (384d)	Varies
Sparse retrieval	BM25 + RRF fusion	Ad-hoc keyword overlap	N/A
Reranking	Cross-encoder (free)	LLM call ($0.001/q)	N/A
Indexing	User + assistant + preference docs	User turns only	LLM-extracted facts
Cloud deployment	Qdrant backend	No	Yes
LLM required	No	No (optional rerank)	Yes

Install

pip install engram-search

Optional extras:

# With cloud backend (Qdrant)
pip install engram-search[cloud]

# With cross-encoder reranker
pip install engram-search[rerank]

# Everything (dev + cloud + rerank)
pip install engram-search[all]

Quickstart — CLI

# Initialize a memory store
engram init ./my_memories

# Ingest conversations
engram ingest conversations.json --store ./my_memories

# Search
engram search "why did we switch to GraphQL" --store ./my_memories

Quickstart — Python API

from engram.backends.faiss_backend import FaissBackend
from engram.backends.base import Document
from engram.ingestion.parser import session_to_documents
from engram.retrieval.embedder import Embedder
from engram.retrieval.pipeline import RetrievalPipeline

# Initialize
embedder = Embedder("bge-large")
backend = FaissBackend(path="./my_memories", dimension=1024)
pipeline = RetrievalPipeline(embedder=embedder)

# Ingest a conversation
turns = [
    {"role": "user", "content": "I'm switching our API from REST to GraphQL."},
    {"role": "assistant", "content": "What's driving the switch?"},
    {"role": "user", "content": "Too many round trips. Our mobile app makes 12 calls per screen."},
]
docs = session_to_documents(turns, session_id="session_1", timestamp="2025-01-15")
texts = [d["text"] for d in docs]
embeddings = embedder.encode_documents(texts)

documents = [
    Document(id=d["id"], text=d["text"], embedding=e.tolist(), metadata=d["metadata"])
    for d, e in zip(docs, embeddings)
]
backend.add(documents)

# Search
results = pipeline.search("why did we switch to GraphQL", documents=documents, top_k=3)
for r in results:
    print(r.text)

Quickstart — Cloud Mode

# Set up Qdrant (managed or self-hosted)
export ENGRAM_BACKEND=qdrant
export ENGRAM_QDRANT_URL=https://your-cluster.qdrant.io:6333
export ENGRAM_QDRANT_API_KEY=your-api-key

# Start the API server
pip install fastapi uvicorn
uvicorn engram.server:app --host 0.0.0.0 --port 8000

API Endpoints

Method	Endpoint	Description
POST	/ingest	Add conversations
POST	/search	Search memories
GET	/health	Health check
GET	/stats	Store statistics

Examples

Check out the interactive notebooks in examples/:

Notebook	Description
Getting Started	Ingest conversations, search memories, understand hybrid retrieval
Customer Support	Build a support agent with full customer history recall
Personal Assistant	AI assistant with long-term memory across conversations

Docker

# Local mode
docker compose up

# Or build and run directly
docker build -t engram .
docker run -p 8000:8000 -v engram_data:/data engram

Architecture

┌─────────────────────────────────────────────────────────────┐
│                        Engram                               │
│                                                             │
│  ┌────────────┐  ┌─────────────┐  ┌───────────────────┐    │
│  │ Ingestion  │  │   Index     │  │    Retrieval      │    │
│  │            │→ │             │→ │                   │    │
│  │ user+asst  │  │ FAISS (local│  │ 1. Dense (bi-enc) │    │
│  │ turns      │  │  or Qdrant  │  │ 2. BM25 (sparse)  │    │
│  │ preference │  │ (cloud)     │  │ 3. RRF fusion     │    │
│  │ extraction │  │             │  │ 4. Cross-encoder   │    │
│  └────────────┘  └─────────────┘  └───────────────────┘    │
│                                                             │
│  Local: FAISS + SQLite    Cloud: Qdrant + REST API          │
└─────────────────────────────────────────────────────────────┘

Run Benchmarks

LongMemEval

# Download dataset
curl -fsSL -o data/longmemeval_s_cleaned.json \
  https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned/resolve/main/longmemeval_s_cleaned.json

pip install engram-search[all]

python benchmarks/longmemeval_bench.py data/longmemeval_s_cleaned.json --mode hybrid

LoCoMo

# Download dataset (from Snap Research)
curl -fsSL -o data/locomo10.json \
  https://raw.githubusercontent.com/snap-research/locomo/main/data/locomo10.json

python benchmarks/locomo_bench.py data/locomo10.json --mode rerank

Requirements

• Python 3.9+
• ~1.3 GB disk for bge-large embedding model (downloaded on first use)
• No API keys required for local mode

License

MIT