Federate LangGraph long-term memory

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Prerequisites

• a backend-owned provider client and explicit tenant-subject namespace resolver;
• synthetic records for at least two tenants and two subjects per tenant;
• the exact @praxa/sdk memory package version and supported provider contract;
• a provider cleanup procedure plus degraded-state and timeout fixtures;
• an acceptance assertion that proves the adapter reads long-term store records, excludes checkpoints, and passes 2x2 namespace isolation.

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1. Keep a tenant-subject namespace

import { createLangGraphMemorySource } from "@praxa/sdk/memory";

const source = createLangGraphMemorySource({
  store,
  mapNamespace: ({ tenantId, subjectId }) => [
    tenantId,
    subjectId,
    "memory",
  ],
  kind: "document",
});

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2. Recall through Praxa

import { MemoryFederation } from "@praxa/sdk/memory";

const memory = new MemoryFederation({ sources: [source] });
const result = await memory.recall({
  query: "What has this user told us about response style?",
  namespace: {
    tenantId: "tenant-a",
    subjectId: "user-42",
    scope: ["support"],
  },
  limit: 10,
});

if (result.status === "failed") {
  throw new Error(JSON.stringify(result.sources));
}

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3. Prove isolation and compatibility

1. The intended tenant and subject retrieve it.
2. A second subject in the same tenant does not.
3. The same subject ID in a second tenant does not.
4. A checkpointer record is never returned through the adapter.
5. A store outage returns an explicit source status.
6. Deleting the provider record removes it from later recalls.

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Troubleshooting

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Best practices

• Keep provider writes and lifecycle under the provider’s documented API.
• Resolve tenant and subject in trusted backend code and test 2x2 isolation.
• Preserve provenance, source matches, contradictions, and per-source status.
• Bound concurrency, result count, context bytes, and source timeout.
• Never promote checkpoint, hidden, or unverified content into portable recall.

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Optimize for production

• Query only providers and record kinds relevant to the current purpose.
• Use bounded parallel recall and give each source an explicit timeout budget.
• Deduplicate normalized content while retaining every source match and contradiction.
• Measure source p50/p95 latency, partial/failed recalls, result precision, context bytes, and isolation failures.

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Cleanup and next steps

1. Delete synthetic provider records using the provider’s normal API.
2. Hard-erase disposable hosted candidates and verify content-free receipts when used.
3. Revoke test credentials and remove namespace fixtures for every tenant and subject.
4. Retain only non-content source statuses, identifiers, and test results required for audit.

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Frequently asked questions

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What proves this tutorial works?

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Can a browser, mobile app, or model prompt hold the credential?

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How should an ambiguous mutation be retried?

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What should we monitor after release?