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. The default recall(strategy="episodic") path scores 99.9% session recall@5 on LongMemEval Oracle (500 questions) and 95.0% on LongMemEval S (500 questions, ~48 sessions each) with zero LLM calls.

Embedded. Local. Open source. No external APIs, no vector DB services, no internet connection required. Runs entirely on-device with HybridDB (SQLite + FTS5 + ChromaDB) + sentence-transformers. Ships as a single Python package with zero infrastructure dependencies.

Single-backend architecture. HybridDB (SQLite + FTS5 + ChromaDB) is the only backend since v0.6.0. Retrieval pipeline: FTS5 + vector search → deterministic heuristics → query decomposition → cross-encoder reranking → MMR session diversity → session-deduplicated retrieval.

from coremem import MemoryCore

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

# Ingest conversation turns
core.ingest("user", "I visited the Museum of Modern Art today", session_id="conv_001")
core.ingest("assistant", "That sounds wonderful! How was it?", session_id="conv_001")
core.ingest("user", "I went to an Ancient Civilizations exhibition at the Natural History Museum", session_id="conv_001")

# Retrieve with the default episodic strategy (zero LLM)
results = core.recall("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:

LongMemEval Results

Oracle (500 questions, ~2 sessions each, k=5)

S (500 questions, ~48 sessions each, k=5)

Numbers are the pre-improvement episodic baseline; the validated improvements below add +0.034 session recall overall (temporal +0.037, preference +0.033) — see the next section.

Recommendation: use recall(strategy="episodic") (the default). It is the strongest zero-LLM mode on both evaluations — best session recall, competitive message recall, and no retrieval LLM calls. Use direct for single-session factual questions (best message precision), expanded when highest precision is needed (1 LLM call for query rephrasing), and fusion when session diversity is critical (2× compute).

All modes abstain correctly on unanswerable questions (0% false positive rate).

End-to-end answer accuracy (LLM answer → LLM judge, 500 S questions)

Measured with scripts/eval_answer_longmemeval.py (deepseek-v4-flash as answer model and judge, anonymous shuffled judging, evidence-first bundle formatting):

Abstention accuracy 0.867 for the top modes. Result: results/eval_answer_s500.json.

Results: eval_output/lme-oracle/results.json, eval_output/lme-s/results.json

Validated improvements (2026-08, all zero-LLM, folded into the default)

Measured on LongMemEval-S (500 questions) against the episodic baseline, with the resumable harness in scripts/:

The composition lesson: individually-positive improvements do not always sum — a combined 500/500 S-scale validation showed the L-12 reranker cancels the temporal decomposition's session gains. The default strategy ships only the validated combination (L-6 + temporal decomposition + preference routing), measured at +0.034 session recall overall with zero regressions.

Installation

pip install coremem

Optional extras:

pip install "coremem[mcp]"    # MCP server
pip install "coremem[all]"     # all extras

Note on model downloads. ChromaDB downloads a bundled MiniLM embedding model (~80MB) on first PersistentClient() init. The cross-encoder downloads cross-encoder/ms-marco-MiniLM-L-6-v2 (~500MB) on first recall(strategy="episodic") call. Both cache locally after download. Run one recall at startup to pre-load models predictably.

Core Concepts

Ingestion

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

# Batch ingestion (one turn = one turn_id)
core.ingest_turn([
    {"role": "user", "content": "What's the weather today?"},
    {"role": "assistant", "content": "Sunny with a high of 72°F"},
], session_id="conv_001")

Recall

recall() is the single retrieval entry point, with four strategies:

results = core.recall("How many model kits?", limit=10)
results = core.recall("What did I build recently?", strategy="direct")

# Session bundles — surrounding context around each hit
# (4k-char total budget, evidence-first ordering — the validated default)
bundles = core.recall("model kits", bundles=True)
for b in bundles:
    print(f"## Session {b.session_id} (complete={b.complete})")
    for m in b.messages:
        print(f"  [{m.role}] {m.content}")

# Filter params
results = core.recall("coffee", role="user", session_id="conv_001", ts_after="2024-01-01")

# Session-cap selection: up to 2 messages per session instead of the
# one-per-session MMR cap (recovers answers in a second message of an
# already-found session; eval mode memorycore_episodic_reranked_v3)
results = core.recall("model kits", session_cap=2)

Heuristics

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

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",
)

Memory lifecycle

core.fetch(session_id="conv_001")          # query with filters
core.fetch_all()                            # everything (limit 10k)
core.store([Memory(id="m1", content="...")])
core.count()
core.delete(session_id="conv_001")
core.clear()

AgentJournal

The AgentJournal subsystem compiles conversation turns into dense, retrieval-optimized daily journal pages (markdown + frontmatter), with deterministic validation of every claim against its source:

# Compile a turn into daily/YYYY-MM-DD.md (1 LLM call per turn)
await core.compile_turn(turn_id=tid)
await core.compile_latest_turn(session_id="conv_001")
await core.compile_uncompiled_turns()

# Dreaming consolidation — LLM analysis of daily pages, analysis and
# promoted facts appended to DREAMS.md (MEMORY.md is compiler-owned)
await core.dream()

# Rebuild weekly/monthly/index navigation files from daily pages
core.rebuild_index()

The LLM compiler (openai:gpt-4o-mini by default) produces a structured plan that the deterministic compiler validates — every claim is checked against source messages (exact quote substrings, role/evidence-type compatibility) before it is written. Set COREMEM_LLM_MODEL (e.g. ollama:llama3.2) to change the model.

CLI, MCP, and hooks

coremem recall "model kits" --strategy direct
coremem ingest user "I built a Spitfire model kit" --session-id conv_001
coremem compile <turn_id>
coremem rebuild
coremem sessions
coremem mcp   # MCP stdio server (also the default command)

• MCP server — 5 tools: recall, ingest, compile, rebuild_index, list_sessions
• Hooks — Claude Code and Codex: UserPromptSubmit (capture + retrieval injection), Stop (capture), PreCompact (no-op)
• Integration configs in integrations/ for Claude Code, Codex, and OpenCode

Environment variables

Agent Memory Leaderboard (AML) deployment

CoreMem participates in the Agent Memory Leaderboard — an open, reader-matched evaluation of long-term memory systems. The adapter lives in integrations/aml/:

Submission status: submitted via the academic route (public GitHub repo, Docker deployment — no leaderboard key). The platform runs the smoke suite (Top K 90) then the full evaluation across LongMemEval-S, PersonaMem, ScriptMem, BEAM, CLBench, and LoCoMo-Refined — an independent, reader-matched, multi-judge measurement of CoreMem's end-to-end QA accuracy.

License

MIT — see LICENSE.

Author

Eddy Xu

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