Tgms

TGMS — Agent-Native Bi-Temporal Graph Management System

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A temporal graph database whose query surface is built for LLM agents — and whose answers can be audited claim by claim.

Project page & blog: https://zxf-work.github.io/tgms/ · Paper: paper/main.pdf

LLM agents are unreliable at exactly the things temporal graph analytics requires: arithmetic, identifiers, and asserting only what the evidence shows. TGMS's answer is architectural — give the model no opportunity to do any of them:

• a bi-temporal property graph (valid time × transaction time) that distinguishes *evolution* ("the edge ended") from *correction* ("we were wrong"), so agents can answer *"what did we believe on March 1?"* — a question latest-state snapshots and the RAG configurations we evaluated cannot express. Bi-temporality itself is inherited, not invented here — it has a four-decade literature, a place in SQL:2011, and production databases built around it. We measure against the clearest of those, XTDB: fed the same operation stream, the two systems agree on believed state at 400 of 400 probe points, with TGMS 3.9–4.7× faster at correction-heavy ingest on 23–27× less disk (the head-to-head);
• and because a belief can be corrected *after* you've already acted on an answer, TGMS now tells you when that happened: `tgms trace check` reads a saved answer's dependency scope against the event log — no recompute, no store lock required — and returns FRESH / POSSIBLY_STALE / UNDECIDABLE, sound in the direction that matters (it never calls a stale answer fresh). Measured across two injection campaigns, 6,978 trials: 0 false-fresh verdicts on 447 changed answers in the M4 campaign (run 2, the record of account) and 0 more on 1,852 changed answers in the M5 carve-2 campaign (28,044 trials), where the obvious cheap check — "did the correction touch a row in the stored result?" — is wrong on 47.4% of the M4 trials;
• and a saved result you want to *keep*, not just check, can now maintain itself: `tgms artifact register/check/refresh` turns it into a named, generation-numbered artifact — refresh recomputes only what you ask, the old generation stays byte-identical on disk, and a refresh propagates one hop to whatever else was built on top of it, even when that dependent's own scope was never touched. Measured across the M5 maintenance campaign: 0 false-fresh in 37,371 trials, 0 false-safe over 5,867 propagation decisions (99.0% resolved without recomputing anything), and a 600/600 pinned-answer exemption;
• 15 verified temporal operators (reachability over time-respecting paths, δ-motifs, snapshot diffs, burst detection, interval joins, grouped aggregation over edge events, and the belief log itself) — typed, deterministic, bounded, cost-guarded, exposed as tools (MCP or in-process); identifiers must come from a resolver, arithmetic from a compute operator;
• a Planner–Executor–Verifier loop: the LLM only plans and reports; plans are statically validated (including a grounding rule that makes fabricated identifiers impossible and output-field contracts that reject invented result paths), executed deterministically with content-addressed traces, and every claim in the written answer is machine-checked against the trace that produced it — including truncation taint, so "correct arithmetic over incomplete evidence" is caught too;
• a purpose-built native storage engine (Rust, PyO3): bi-temporal columnar segments, a temporal-CSR traversal index, group commit, and a single-writer / many-reader concurrency mode — 24.6 bytes per edge version, versus 78.4 on ClickHouse and 549.7 on PostgreSQL for the same 1M-event log.

Quickstart

pip install tgms

tgms demo

No GPU, no API key, no dataset download. tgms demo builds a small store of its own in a temp directory and runs the arc every TGMS answer follows: what the graph currently believes, what it believed *before* a correction landed, and the trace that backs both claims up. Clean environment to first temporal result: under 5 minutes.

Once you want your own graph data, the native test suite, the MCP server, or an agent wired to a real LLM, see Full setup below — this quickstart is deliberately the smallest possible first step, not a tour of the operator surface.

Next steps, in the order most people need them: bring your own temporal graph data · give TGMS to an agent over MCP · audit an answer · maintain derived results · what you can rely on across versions · what's coming

Does it work?

Three different questions, three different answers. All three are reported because the third is the least flattering.

1. Does the agent layer beat the alternatives? Dev-split campaign (CollegeMsg, open-source models served locally on one 24 GB GPU. "Answer accuracy" is normalized typed-answer accuracy — counts and values scored strictly, interval answers credited at IoU ≥ 0.5. Full receipts ship with the paper and the eval records in benchmarks/results-v1/):

• vs static-graph RAG: +36 points, paired-bootstrap 95% CI [0.18, 0.59]
• verifier fault injection: 500/500 injected false claims caught, 0 false positives; on the frozen campaign, 0 of 199 emitted answers contained an unsupported claim with gating (21 of 220 without it) — coverage is 199/282, so some of that is bought by declining to answer
• accuracy tracks planner capability where baselines stay flat: 13.8% / 34.0% / 62.8% at Qwen2.5 7B / 14B / 32B fp16, correction probes saturating at 100% at 32B

2. Is the engine competitive? Six systems answer one 13-query registry — TGMS native, TGMS-on-DuckDB, PostgreSQL, ClickHouse, Neo4j, Memgraph — with every cell hash-verified before it was timed:

The last row is the one we cannot close: ClickHouse keeps a factor of 2.2 on whole-window aggregation at both 1M and 10M, and it is a constant of the shape rather than something that grows with scale. Three rounds of profiling took that gap from 12× to 2.2× and each round found our own implementation rather than the workload. Single latency cells reproduce to about ±20% between days, which is stated everywhere they are quoted.

At 10M events the full query suite runs inside 1.76 GB of peak RSS, 16 concurrent readers get 10.2× the throughput of one, and a live writer costs those readers 0–3% of per-query latency.

3. Can it answer the questions people actually ask? This is the honest one, and it now has a sequel. 110 questions were written by people who saw a plain-language description of two public datasets and never saw the operator list. Of those, 94 were expressible under the fixed 15-operator catalog — 10 were expressible when the study was pre-registered. Of LDBC SNB's 41 read templates, 3 executed — the operator-execution axis (does a plan compile, load, admit and run at all), a lower bar than the stricter ECQR result-contract axis, which stood at 7 of 41 — and that number had not moved in eight sessions, because 35 of the 38 misses needed labelled multi-way pattern matching: a deliberately deferred design decision, not a missing operator.

That deferred decision shipped. TGIR, a 12-primitive compositional temporal-graph IR, now runs the entire 15-operator catalog as byte-identical leaves and additionally *compiles* some question shapes — including labelled multi-way pattern matching — into chains of those primitives. Both axes were forecast before TGIR was built, frozen before the first row was measured, and moved to *exactly* the predicted level: LDBC operator-execution coverage 3 → 24 of 41, independent-question coverage 94 → 102 of 110, delivered/predicted 29/29 on the full 52-row forecast (28/28 on the 51 scoreable rows — one row was excluded by name in the freeze because its canonical corpus carries no corrections to find), 0 over-deliveries, 0 misses.

The store is still good and the surface is still narrower than "24 of 41" sounds. There is no LDBC SNB dataset in this repository. The 21 LDBC rows execute against a hand-built fixture carrying LDBC's labels, relationship types and multi-hop topology at a size a reviewer can read on one screen (scripts/build_ldbc_fixture.py); it establishes that a plan compiles, loads, admits and executes, and nothing about scale. The independent-question axis is the one measured on real data (bitcoin-otc, CollegeMsg), and it's where the admission/cost-guard claim is meaningful. Both instruments live in the repo (scripts/independent_questions.py, scripts/ldbc_fit.py), they re-run in seconds, and each capability shipped has been scored against a forecast made *before* it was built — delivered/predicted has now run 14/30, 4/7, 10/13, 14/16, 15/15, 4/8, 5/5, 4/5, and 29/29. 5/5 and 4/5 were the first forecasts made per question rather than in aggregate, and TGIR's 29/29 is the first forecast — made per row, before any row was measured — with zero misses across the whole universe.

What the operators can express

Fifteen operators — fourteen of them unchanged since D-044, because most of the growth since v0.4.0 happened *inside* them, driven question by question by the study above. The newest growth happened *underneath* them instead: TGIR, a 12-primitive compositional temporal-graph IR, now runs the entire operator set as byte-identical leaves — same semantics, digest- receipted, switchable off with TGIR_PLAN_PATH=off — and additionally compiles some question shapes, chiefly labelled multi-way pattern matching, into chains of those primitives. TGIR plans are not yet a user-facing query language: there is no public syntax for writing one directly, and the fifteen operators below are still the whole interface an agent (or you) calls. What changed is what happens underneath a call, and it is measured in the byte-reproducible record at benchmarks/tgir-v1/measured.yaml.

The pre-existing fifteen: