Temporal Reasoning

Temporal Reasoning

**Perfect memory. Exact reasoning. Complete history.**

Temporal Reasoning gives AI coding agents bi-temporal graph memory: query any past state, traverse live dependency graphs, and correlate architectural decisions with structural change — all with deterministic Datalog, no fuzzy retrieval.

Questions Only Temporal Reasoning Can Answer

These queries are impossible with git log, vector search, or key-value memory:

; What did the dependency graph look like before the auth refactor?
[:find ?caller ?callee
 :as-of 30
 :where [?caller :calls ?callee]]

; When did this coupling first appear — and what decision caused it?
[:find ?reason
 :where [:project/service-a :depends-on :project/service-b]
        [?d :motivated-by ?c]
        [?c :description ?reason]]

; Which modules were coupled to the payment service when we made the DB decision?
[:find ?module
 :as-of 15
 :where [?module :depends-on :service/payment]]

This is the only tool where both the decision and the structural change live as datoms in the same graph and can be joined in a single query. See [Phase 5](ROADMAP.md) for code structure evolution from git history.

Why Temporal Reasoning?

Most memory tools for agents are key-value stores or vector databases. They answer "what do you know now?" Temporal Reasoning answers a harder question: **"what did you know then, and what changed?"**

**Time travel.** Every write is stamped with a transaction number. You can query the graph as it existed at any past transaction:

# Decision made in session 1, transaction 3
transact('[[:project/db :name "PostgreSQL"]]', reason="Initial choice")

# Changed in session 4, transaction 11
retract('[[:project/db :name "PostgreSQL"]]', reason="Switching to CockroachDB for geo-distribution")
transact('[[:project/db :name "CockroachDB"]]', reason="Switching to CockroachDB for geo-distribution")

# Later: what did we think the database was before session 4?
query("[:find ?name :as