Memory

Memory is one half of the cross-session state thematic island — paired with plans. Memory spans sessions; plans span turns. Both outlive a single session arc; neither fits the runtime-lifecycle cluster.

The memory layer is the variable surface for what the runtime has learned about a user across sessions: typed facts with categories and confidence scores, the per-agent vs global namespace they live in, the exponential-decay curve that erodes unverified confidence over time, and the conflict groups that surface when two agents disagree. The substrate ships the extraction pipeline and the auditor; the application picks the LLM, the policy, and what gets stored. See Agents for the per-agent namespace this shares, and Audit ledger for the LLM-call row that records the extraction.

Ships from @pleach/core/memory.

import {
  memoryExtractionQueue,
  memoryExtractionHook,
  extractAndStoreFacts,
  loadExtractedFacts,
  setFactExtractorStore,
  consolidateAgentFindings,
  LearningAuditor,
  type ExtractedFact,
  type ExtractionPayload,
  type LearnedFact,
  type ConflictGroup,
  type PruneResult,
} from "@pleach/core/memory";

Pipeline

Production extraction runs as a graph node. When a turn terminates (shouldContinue: false), the memoryExtraction node calls the configured executor — extractAndStoreFacts — directly. No queue, no debounce.

turn terminates (shouldContinue: false)
                 │
                 ▼
       memoryExtraction graph node
                 │
                 ▼
        extractAndStoreFacts
                 │
   ┌─────────────┴─────────────┐
   ▼                           ▼
per-user / per-agent      LearningAuditor
fact store                (correct, prune,
                           detect conflicts)
                 ▲
                 │
      consolidateAgentFindings
      (cross-agent rollup after subagent fan-in)

Every stage is fire-and-forget — extraction never blocks a turn, consolidation never blocks a response, and a failed write logs and moves on. The point of memory is to be cheap and recoverable, not to be transactional.

The queue path — memoryExtractionHook (a PostModelHook) feeding memoryExtractionQueue with a 5s debounce — ships in @pleach/core/memory as a standalone option for hosts that drive extraction off post-model hooks instead of the graph. It is not registered by default; wire it yourself with memoryExtractionQueue.setExtractFn.

memoryExtractionHook

A PostModelHook that queues the last 20 messages of the turn for background extraction. Runs server-side only, skips guests, and waits for at least three user turns of conversation before firing.

The hook never returns retry, interrupt, or a modified response. It pushes a payload into the queue and returns {}.

memoryExtractionQueue

The queue is a process-wide singleton — the same instance is shared across every runtime in the process. Hook up the extractor once at startup with memoryExtractionQueue.setExtractFn(fn) and the queue routes drained batches into it.

ExtractionPayload carries messages, userId, orgId, sessionId, and optional agentName. agentName decides which namespace the extracted facts land in.

extractAndStoreFacts

The default extractor calls a lightweight model via OpenRouter (google/gemini-2.0-flash-001, temperature: 0.1, max_tokens: 1024) with a prompt that includes existing facts for deduplication context. Returns parsed ExtractedFact[].

setFactExtractorStore(store) binds the store the extractor writes to. Without it, the extractor logs and returns [].

Fact namespace:

loadExtractedFacts

Reads facts back for system-prompt injection. Runs decay in the same pass:

Returns the top 20 facts at confidence >= 0.7, sorted descending.

consolidateAgentFindings

Runs after a multi-subagent task. Pulls existing global facts and per-agent facts, asks the lightweight model for cross-agent insights (max 5), and writes them to the global namespace with source: "consolidation" and sourceAgents attribution.

await consolidateAgentFindings({
  parentSessionId,
  results,                                  // SubAgentResult[]
  store,
  orgId,
  userId,
}).catch((err) => log.warn("consolidation failed", err));

Returns { factsExtracted, factsUpdated }. Existing keys get the same +0.1 confidence bump as single-agent extraction.

LearningAuditor

The post-batch surface for inspecting, correcting, and pruning the fact store.

Confidence decay is exponential: c * e^(-λt) with λ = 0.003 (roughly 10% per 30 days). userVerified facts skip the decay entirely.

Auditor in practice

The block shows a dryRun prune followed by a targeted correction.

import { LearningAuditor } from "@pleach/core/memory";

const auditor = new LearningAuditor(store, orgId, userId);

const preview = await auditor.prune({
  maxAgeDays:    180,
  minConfidence: 0.4,
  dryRun:        true,
});

for (const row of preview.details) {
  console.log(row.key, row.reason, "—", row.content);
}

// Pin a fact the model keeps drifting on. confidence flips to 1.0,
// userVerified becomes true, the prior text lands in `priorContent`.
await auditor.correctFact(
  "fact_2a9k",
  "Default citation style is numbered footnotes, not parenthetical.",
);

prune skips userVerified facts unconditionally — a correction is the way to say "this one is durable, stop decaying it."

Loading facts into the prompt

The reader sees how high-confidence facts get pulled in at turn start and rendered for system-prompt injection.

import { loadExtractedFacts } from "@pleach/core/memory";

const facts = await loadExtractedFacts(store, orgId, userId);

const memoryBlock = facts.length
  ? "## What we know about this user\n" +
    facts.map((f) => `- ${f.content}`).join("\n")
  : "";

The call also runs decay in the same pass — stale low-confidence rows are deleted, 30-day-old rows get their confidence multiplied by 0.9. The returned list is already filtered to confidence >= 0.7 and capped at 20.

LearnedFact shape

The substrate does not opinionate on what's safe to extract. Consent gating, PII stripping, and tenant-specific policy belong in the extractor implementation or the storage adapter.

Where to go next