coremem 0.1.0

Zero-LLM memory retrieval for AI agents — semantic search and deterministic heuristics

CoreMem

Zero-LLM memory retrieval for AI agents. CoreMem gives agents instant access to conversation history — semantic search plus deterministic retrieval heuristics, all without a single API call. Scores 98.0% R@5 on LongMemEval (500 questions) in the Executive Assistant retrieval stack — no LLM, no tuning, no cloud.

Embedded. Local. Open source. No external APIs, no vector DB services, no internet connection required. Runs entirely on-device with ChromaDB or HybridDB + sentence-transformers. Ships as a single Python package with zero infrastructure dependencies.

Dual-backend architecture. Drop-in backends (ChromaDB baseline, HybridDB enhanced) with the same API. Ranking pipeline: backend retrieval → deterministic heuristics → recency-aware rescoring → session-aware retrieval.

from coremem import MemoryCore
from coremem.backends.chroma import ChromaBackend

core = MemoryCore(backend=ChromaBackend(path="./memory"))

# Ingest conversation turns
core.ingest("user", "I visited the Museum of Modern Art today")
core.ingest("assistant", "That sounds wonderful! How was it?")
core.ingest("user", "I went to an Ancient Civilizations exhibition at the Natural History Museum")

# Search with deterministic heuristic reranking
results = core.search("When did I visit art museums?")

for r in results:
    print(f"[{r.memory.ts}] [{r.memory.role}] {r.memory.content}")

Why CoreMem?

Every AI agent needs memory. But cloud-based vector search is expensive, slow, and doesn't work offline. Pure embedding similarity misses keyword matches and temporal context. LLM-based memory systems cost tokens per query.

CoreMem solves all three:

Component	What it does
Semantic search	Embedding similarity via ChromaDB or HybridDB
Deterministic heuristics	Keyword overlap, temporal recency, person-name boost, quoted-phrase matching
Session deduplication	One result per conversation, with full context retrieval

LongMemEval Results (500 questions, no LLM, no tuning)

Metric	Score
R@5	98.0%
R@10	98.4%
MRR	0.944
P@5	0.592
F1@5	0.684
Selectivity	11.5% haystack scanned
Rank distribution	#1: 91.8%, #2-3: 5.0%, #4-5: 1.2%, #6-10: 0.4%, >10: 1.6%

Outperforms MemPalace raw (96.6%) and matches their hybrid v4 held-out (98.4%) — with zero tuning, zero dev-set peeking.

Installation

pip install coremem

With HybridDB backend for enhanced FTS5 + vector hybrid search:

pip install coremem[hybrid]

Core Concepts

Backends

# ChromaDB baseline — pure vector search
from coremem.backends.chroma import ChromaBackend
core = MemoryCore(backend=ChromaBackend(path="./data"))

# HybridDB enhanced — FTS5 + vector hybrid search
from coremem.backends.hybrid import HybridBackend
core = MemoryCore(backend=HybridBackend(path="./data"))

Ingestion

# Simple ingestion
core.ingest("user", "I built a Spitfire model kit", session_id="conv_001")

# Batch ingestion
from coremem import ingest_batch
ingest_batch(core, [
    ("user", "What's the weather today?"),
    ("assistant", "Sunny with a high of 72°F"),
], session_id="conv_001")

Search

# Basic search
results = core.search("How many model kits?", limit=10)

# Limit results
results = core.search("model building projects", limit=5)

Heuristics

Deterministic, zero-LLM scoring boosts applied to every result:

Heuristic	What it catches
keyword_overlap	Exact word matches between query and content
temporal_boost	Queries with "latest", "current", "recently"
recency_decay	Unconditional exponential decay (30-day half-life)
person_name_boost	Proper name mentions in content
quoted_phrase_boost	Exact phrase matches in quotes

from coremem import SearchHeuristics

# Apply all heuristics to a single result
score = SearchHeuristics.apply_all(
    query="latest project",
    content="Just finished the Q3 project report",
    score=0.75,
    ts="2026-05-28T10:00:00Z",
)

Wake-Up Context

Give the agent instant situational awareness:

context = core.wake_up(user_id="alice")
# Returns a compact string with L0 identity and L1 recent context.

License

MIT — see LICENSE.

Author

Eddy Vinck

CoreMem is the retrieval engine behind the Executive Assistant agent system. Pairs with HybridDB for storage and ConnectKit for real-time sync.