GoodMemory

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GoodMemory is a memory layer for AI products and coding agents.

It gives chat apps, copilots, and agent hosts a durable user/project memory loop: write selected facts, retrieve the right context, inject it into the next turn, audit what happened, and delete it when it is wrong.

GoodMemory is not an LLM, agent framework, vector database, or generic RAG system. It is the product memory layer between your app or installed agent host and the model runtime.

What You Get

• Durable memory API: remember, recall, buildContext, feedback, forget, exportMemory, and deleteAllMemory.
• Installed agent memory for Codex and Claude Code through goodmemory setup, managed hooks, installed Codex pre-action, goodmemory status, read-only MCP, and opt-in writeback.
• Public write customization with GoodMemoryConfig.remember, RememberProfile, rememberRules, RememberInput.annotations, and named extractor ids.
• Package exports for goodmemory, goodmemory/ai-sdk, goodmemory/host, and goodmemory/http through compiled dist artifacts and TypeScript declarations.
• Local-first storage: Bun gets durable SQLite by default; explicit Postgres, injected adapters, and embedding providers can be added when needed.
• Evaluation and release evidence paths for deterministic tests, live evals, provider-backed evals, package smoke tests, and quality gates.

Benchmark Results

GoodMemory separates gate-verified public claims from internal research evidence. A number may appear in the public-claims table only after gate:public-benchmark-claim --strict passes for its committed declaration: complete coverage, executionFailures: 0, a no-memory baseline, deterministic scoring or an independent judge, verified dataset source and license, and a reproducible run (commit + command + package version).

Public claims (gate-verified)

Every row reports two tracks. The strict track is deterministic or judge-free — a hard lower bound no LLM judge can inflate. The comparable track re-judges the same stored answers (not regenerated) under each benchmark's official or industry-standard judge protocol, verbatim, so the number sits on the same scale as published competitor results. The gap between the tracks is quantified judge leniency, disclosed instead of hidden. Comparable-track judging uses gpt-5.4 — a different model from the gpt-5.5 answerer but the same family; every per-protocol detail is recorded in the linked claim declarations.

The LongMemEval claim is judge-free, replacing an earlier internal with-judge number (0.908) that is superseded and not claimable. A case counts as correct only when a deterministic method scores it (abstention / exact / contains / expected_alternative / numeric_count); the eval pipeline's same-model semantic judge (gpt-5.5 judging gpt-5.5) is excluded by construction — with it, the diagnostic overall accuracy is 0.896, reported for transparency but not claimed. The claimed 0.720 (360/500, executionFailures: 0, v0.3.5) uses the embedding-free goodmemory-rules-only profile; abstention contributes only 28 of the 360 correct answers, while the no-memory baseline's 0.068 is mostly bare abstention (30 of its 34 correct), so the +65.2-point lift is the memory system's contribution. Judge-free refers to scoring — answers are still generated by gpt-5.5. Full provenance is in the claim declaration. The MemoryAgentBench claim is GoodMemory's first public benchmark claim, and it is deliberately scoped. Only Conflict Resolution (CR 0.959) and Test-Time Learning (TTL 0.767) are claimed: a no-memory ablation scores both 0.000 (the questions are unanswerable without GoodMemory's retrieved consolidated fact / in-context demos), so these are genuine memory contributions, scored deterministically with no LLM judge (executionFailures: 0, 259 questions). Accurate Retrieval and Long-Range Understanding are EXCLUDED: the no-memory ablation scores them higher (AR 0.926 vs 0.890; LRU 0.632 vs 0.518), so they are multiple-choice leaks where the model answers from the candidates in the question, not memory wins. CR/TTL measure answer-time current-value resolution and in-context retrieval, not general retrieval recall.

The LoCoMo claim is scored by deterministic token-F1 (judge-free, executionFailures: 0 across all 1986 questions of the full 10-conversation set, v0.3.5). The profile is disclosed and opt-in — provider-embedding semantic candidate union (retrieval.semanticCandidates, topK 16) plus conversational write-time extraction plus an abstention-format answer prompt; the embedding-free default scores 0.020 on the representative conv-1 slice (the banked retrieval boundary), so this claim is specifically about the embedding+extraction profile, not the zero-dependency default. Read the memory lift on the non-adversarial split (0.6117 vs 0.0045 — 942 vs 7 correct of 1540): the adversarial category (446 questions whose gold answer is the literal abstention string) is trivially aced by a no-memory arm that always abstains (0.998 vs 0.648 with memory), so the overall-vs-overall comparison (0.6198 vs 0.2276) understates the memory contribution on answerable questions. Answers are generated by gpt-5.5 — judge-free refers to scoring. The LoCoMo dataset is CC BY-NC 4.0 (non-commercial scope) and is fetched at eval time, never vendored. Full provenance is in the claim declaration.

The BEAM claim is scored under the benchmark's official unified rubric judge: each of the 1,051 rubric items is scored 1.0/0.5/0.0 and a question's score is the mean over its items (all 400 questions, judgeFailures: 0). The only public end-to-end BEAM 100K number scored the same way is 0.49; GoodMemory scores 0.802 (+31 points), with per-category detail in the declaration — including the one category below that reference (instruction_following 0.394 vs 0.66), disclosed rather than averaged away. The strict internal binary-judge track is 0.7225 vs a 0.5725 no-evidence-pack ablation (the answer-time evidence pack contributes +15 points). Recall is dual-metric per ADR-005: rules-only fitted 0.9621 vs generalization floor 0.6822 with all 148 scenario-fitted gates disabled (the shipped opt-in semantic-candidate union lifts that floor to 0.8529). One protocol deviation is disclosed: the paper pipeline scores event_ordering with a rank-correlation metric; both this run and the public reference rubric-judge it. Dataset CC BY-SA 4.0, fetched at eval time, never vendored.

The ImplicitMemBench Full-300 claim uses the canonical zero-failure run-phase61-full300-rerun-20260706-codex-current answers, then re-scores the same stored answers with gpt-5.4 (sourceAnswersUnchanged: true). The judge is cross-version but the same GPT family as the gpt-5.5 answer model, not a cross-family judge. The public score is 0.691 (207.35/300) versus an upstream-chat baseline of 0.400 (120/300), with 530 judge-required row decisions across the baseline and GoodMemory arms; deterministic structured_first_action rows are carried forward rather than judged. The older same-model diagnostic score was 0.708 and is not the public claim. The freshest clean answer-regeneration drift check after recent code changes scored 0.6895 with executionFailures: 0; it shows current checkout drift, not a replacement for the stored-answer comparability artifact. Dataset CC BY 4.0, fetched at eval time, never vendored.

Internal diagnostics (not public claims)

Blocked benchmark numbers stay out of the public-claims table until their declaration says they are claimable and gate:public-benchmark-claim --strict passes. The underlying run reports live under gitignored reports/ and are reproducible from the run commands recorded in the declarations.

Use task-board/00-README.txt for execution order and docs/GoodMemory-Current-Status-and-Evidence.md for claim boundaries.

Choose Your Integration Path

GoodMemory has three primary product entry points. They are not the only APIs: lower-level surfaces such as goodmemory/host, custom stores, eval tooling, and runtime helpers support these paths. They are the README-level ways to decide how to start.

Autonomous agent? Start here

If you are an agent that wants to give yourself durable memory, match one path and run it. Machine-readable versions of this tree live in llms.txt and .well-known/goodmemory.json (a deployed bridge also serves the descriptor at /.well-known/goodmemory.json).

• You are, or run inside, Claude Code or Codex → npm install -g goodmemory@0.5.1 && goodmemory setup. Unsure what is already wired? Run goodmemory adopt (add --json for a machine-readable plan): it inspects .claude/, .codex/, and existing MCP config, then prints the exact next command for your environment.
• You speak MCP (Cursor, Windsurf, Cline, Claude Desktop, Gemini CLI, OpenCode, or a custom client) → add the standalone MCP server; the two tools you need are goodmemory_get_context (recall) and goodmemory_remember (opt-in write).
• You are a framework agent or a backend → call the HTTP bridge: hosted at goodmemory.vibenest.net or self-hosted with goodmemory-http-bridge --recommended (or GOODMEMORY_PROFILE=agent-recommended goodmemory-http-bridge); Python callers use pip install goodmemory-client.

The prose paths below expand each option.

1. Build Memory Into An Agent, Chatbox, Or Copilot

Use this when you own the product server and the model call. Install goodmemory in your Node/Bun service, create one memory instance, and pass a stable scope such as userId, workspaceId, sessionId, and optionally agentId.

The request flow is:

1. Before the model call, run recall() for the current scope and query.
2. Run buildContext() to turn recall hits into a prompt fragment.
3. Call your model with that memory context.
4. After the response, write selected signals with memory.jobs.enqueueRemember() or remember().
5. Use feedback(), targeted reviseMemory(), forget(), and exportMemory() for correction, deletion, and user audit.

If your server already uses Vercel AI SDK, use goodmemory/ai-sdk to wrap generateText() or streamText() instead of hand-wiring the whole loop. Start with App Quickstart, then read AI SDK Adapter if you use AI SDK.

2. Add Memory To Codex Or Claude Code

Use this when you want an installed coding agent to remember project and user context without changing the agent itself. Install the global CLI and run goodmemory setup.

The installed-host flow is:

1. session-start injects a session brief; user-prompt-submit injects per-prompt context (relevance-gated on fresh installs so low-signal prompts stay clean).
2. The Claude Code Stop hook captures each turn from the session transcript (transcript_path) into governed writeback candidates — bounded, redacted, never raw transcripts; for Codex, goodmemory codex writeback --from-rollout feeds the newest session rollout through the same pipeline.
3. Codex pre-tool-use can deny or redirect risky Bash through goodmemory codex action on the same installed config and storage path.
4. MCP gives trace, context, stats, and artifact inspection; the goodmemory_remember write tool is opt-in via mcp.allowWrite (or goodmemory enable <host> --mcp-allow-write).
5. Writeback stays off for scripted installs; interactive install and goodmemory setup --recommended (one consent prompt) enable selective durable writes — auditable via writeback inspect, reversible via writeback forget --event-id.
6. Fresh installs start on the measured BM25 hybrid retrieval tier with a 1024-token session brief and 512-token gated prompt injection; goodmemory status shows the retrieval tier, capture proof-of-life, and injection telemetry. Optional sharedAgents config lets one host read the other host's records (writes stay attributed).

Start with Quickstart: Codex Or Claude Code Memory. Use Installed Host Writeback when you are ready to review or enable writes.

3. Deploy GoodMemory As A Backend Memory-Layer Service

Use this when another backend should call GoodMemory as a service, especially when the product backend is Python/FastAPI or when a product such as OneLife should keep memory server-side instead of bundling GoodMemory into a mobile or browser client.

Deploy the packaged goodmemory-http-bridge in a Node/Bun sidecar. Your backend then calls:

• /memory/recall-context before its own model call
• /memory/remember after a user-confirmed or product-approved signal
• /memory/feedback for procedural corrections
• /memory/export and /memory/forget for audit and deletion
• /memory/revise for targeted correction by explicit memory id

Your service still owns auth, product policy, UI, and model orchestration. GoodMemory owns memory storage, recall, context assembly, write governance, and audit/export/delete behavior. Start with Python/FastAPI HTTP Bridge — the official Python client (pip install goodmemory-client) and a hosted bridge instance at goodmemory.vibenest.net are documented there — then check Runtime And Storage for SQLite/Postgres choices.

During a model turn, GoodMemory does four jobs:

1. Resolve memory for the current scope.
2. Build a prompt-ready context fragment.
3. Record selected post-response signals when your app or host allows it.
4. Provide audit, correction, export, and deletion paths for user control.

Your app or installed agent still owns auth, UI, model calls, and product policy. GoodMemory owns the memory loop and storage boundary.

Install

GoodMemory 0.5.1 has two normal install paths.

Use the global CLI when you want memory enhancement inside installed coding agents:

npm install -g goodmemory@0.5.1
goodmemory setup
goodmemory status

Use the package dependency when you are building an application:

npm install goodmemory@0.5.1

If you want to type goodmemory directly, install the global CLI. A project-local npm install goodmemory@0.5.1 does not put goodmemory on your shell PATH. Use npx goodmemory, npm exec -- goodmemory, or ./node_modules/.bin/goodmemory from that project instead.

npx goodmemory -V

Bun consumers can install it directly:

bun add goodmemory@0.5.1

Tarball verification for release rehearsal:

npm install ./goodmemory-0.5.1.tgz

The installed CLI is Bun-backed for non-version commands. The package bin is Node-safe for goodmemory -V and goodmemory --version; other commands delegate to Bun.

Quickstart: Codex Or Claude Code Memory

For most users, the first useful path is installed-host memory.

npm install -g goodmemory@0.5.1
goodmemory setup
goodmemory status

goodmemory setup detects Codex and Claude Code, installs managed host wiring, and asks for:

• host: codex, claude, or both detected hosts
• activation: global, current workspace, or manual opt-in
• GoodMemory user id
• optional Postgres storage
• optional embedding provider
• optional LLM extraction provider
• writeback mode: off, observe, or selective

Interactive setup defaults to global activation with workspace-derived isolation and recommends observe for new host configs so users can review writeback candidates before enabling durable writes. Existing host configs keep their current writeback mode when the interactive prompt default is accepted. Scripted installs stay safe with --json or --no-interactive. Skipping provider setup is valid: GoodMemory still works with local SQLite and rules-only extraction.

Useful commands:

goodmemory setup --host codex
goodmemory status codex --workspace-root .
goodmemory enable codex --workspace-root . --writeback observe
goodmemory enable codex --workspace-root . --writeback selective
goodmemory disable codex --workspace-root .
goodmemory uninstall codex

The installed host path has four pieces:

• Managed pre-action for Codex: pre-tool-use can deny or redirect risky Bash and goodmemory codex action executes the vetted first step on the same installed config, storage, provider, and scope path used by recall and writeback.
• Recall injection: session-start and user-prompt-submit hooks call recall() plus buildContext() and fail open if config, parsing, or storage is unavailable.
• Deep inspection: goodmemory mcp serve --host codex and goodmemory-mcp --host codex expose read-only context, trace, stats, and artifact tools.
• Optional writeback: session-stop and explicit writeback commands can turn selected after-response signals into durable memory.

Standalone MCP For Any Client

Hosts without a managed install path (Cursor, Windsurf, Cline, Claude Desktop, Gemini CLI, OpenCode, or your own MCP client) can run the same MCP server in standalone mode — no goodmemory setup, no host config files. Scope and storage come from flags/env; the served surface is the same 8 read-only tools, plus an opt-in governed write tool:

{
  "mcpServers": {
    "goodmemory": {
      "command": "goodmemory-mcp",
      "args": ["--standalone", "--user-id", "YOUR_USER_ID"]
    }
  }
}

Equivalent invocation: goodmemory-mcp --standalone --user-id <id> (requires Bun on PATH; GOODMEMORY_USER_ID works as the flag's env fallback). --allow-write (or GOODMEMORY_MCP_ALLOW_WRITE=1) registers goodmemory_remember, which writes through the normal governed remember pipeline. Agent-tagged memories written by installed hosts stay private to their agent; add --agent-id codex plus the shared --storage-url to opt into reading an installed host's store. Full flag/env matrix, scope notes, and per-host recipes: docs/GoodMemory-Standalone-MCP-Setup-Guide.md (Cursor · Gemini CLI · OpenCode).

Installed Host Writeback

Installed Host Writeback is opt-in. Runtime config defaults and new scripted installs remain off unless the user explicitly chooses a writeback mode. Existing configs keep their current writeback mode when no explicit override is provided. New interactive installs recommend observe so candidates are visible before durable writes are enabled.

Use observe before selective:

goodmemory enable codex --writeback observe
goodmemory codex writeback --json

goodmemory enable codex --writeback selective
goodmemory codex writeback --json

Writeback rules:

• off: no after-response memory extraction.
• observe: store local bounded/redacted candidate previews for review without raw transcripts or durable memory writes.
• selective: write selected candidates through the public remember surface.
• Raw transcripts are not persisted as memory.
• Assistant-originated durable memory is blocked unless the host confirms or verifies it and the active profile allows it.
• remember: "never" masks annotated content before deterministic, custom, or assisted extraction.

Audit and undo:

goodmemory codex writeback inspect --json
goodmemory codex writeback forget --event-id <event-id> --review-outcome false_write

The audit ledger stores bounded redacted candidate previews, candidate keys, typed linked record ids, status, reasons, host, mode, timestamps, scope/session digests, and optional manual review metadata. It does not store raw host payloads. forget --event-id deletes linked memory/evidence records through public forget() before marking durable audit events forgotten; for observe-only events it marks the candidate dismissed without calling forget().

Claude Code has deterministic CLI parity for hook and writeback commands; Codex is the canonical live-evidence path.

Scripted Host Install

Use goodmemory install <host> when you want a fully non-interactive setup:

goodmemory install codex \
  --user-id <user-id> \
  --activation-mode global \
  --writeback observe \
  --storage-provider postgres \
  --storage-url "postgres://user:pass@host:5432/goodmemory" \
  --embedding-provider openai \
  --embedding-model text-embedding-3-small \
  --embedding-api-key <key> \
  --llm-provider openai \
  --llm-model gpt-4o-mini \
  --llm-api-key <key> \
  --no-interactive

Managed config lives under ~/.goodmemory/<host>.json. Re-running install with provider flags updates the same config and keeps MCP/hook registration idempotent. Package uninstall does not delete ~/.goodmemory, repo-local .goodmemory, local SQLite files, or remote Postgres data. Use goodmemory uninstall <host> to remove managed host wiring, and use goodmemory forget ... or explicit storage deletion to remove memory data.

App Quickstart

Use the root package when you are building a chatbox, copilot, or product agent. The recommended Node service path is the same thin loop used by the Express and Fastify examples. A longer walkthrough lives in docs/GoodMemory-15-Minute-App-Integration.md.

import type { GoodMemoryTraceSpan } from "goodmemory";
import { createGoodMemory } from "goodmemory";

const traceSpans: GoodMemoryTraceSpan[] = [];

const memory = createGoodMemory({
  observability: {
    traceSink: {
      emit(span) {
        traceSpans.push(span);
      },
    },
  },
});

const scope = {
  userId: "u-1",
  workspaceId: "workspace-a",
  sessionId: "s-1",
};
const userMessage = "Remember that the migration rollout is blocked on QA signoff.";

// Call startSession once when the product opens a new session. For later turns
// with the same sessionId, append to the existing runtime state instead.
await memory.runtime.startSession({ scope });
await memory.runtime.appendMessage({
  scope,
  message: {
    role: "user",
    content: userMessage,
  },
});

const recall = await memory.recall({
  scope,
  query: "What should the assistant know before replying?",
  retrievalProfile: "general_chat",
});
const context = await memory.buildContext({
  recall,
  output: "system_prompt_fragment",
});

const assistantText = await callYourModel({
  memoryContext: context.content,
  userMessage,
});

await memory.runtime.appendMessage({
  scope,
  message: {
    role: "assistant",
    content: assistantText,
  },
});

const writeJob = await memory.jobs.enqueueRemember({
  scope,
  messages: [
    {
      role: "user",
      content: userMessage,
    },
    {
      role: "assistant",
      content: assistantText,
    },
  ],
  idempotencyKey: "turn-1",
  reason: "post_response_memory_write",
});
const drained = await memory.jobs.drain({ maxJobs: 1 });
const committedJob =
  drained.jobs.find((job) => job.jobId === writeJob.jobId) ?? writeJob;

console.log({
  traceCount: traceSpans.length,
  writeJobId: writeJob.jobId,
  writeJobStatus: committedJob.status,
});

async function callYourModel(input: {
  memoryContext: string;
  userMessage: string;
}): Promise<string> {
  void input.memoryContext;
  return `Got it. I will keep that in mind: ${input.userMessage}`;
}

The core memory loop is intentionally small:

• remember() writes selected user, app, or host signals.
• recall() retrieves scoped memory for a query.
• buildContext() turns recall hits into a prompt fragment or JSON payload.
• feedback() records explicit corrections and procedural preferences.
• forget() deletes wrong or obsolete memory.

For production app integrations, the recommended turn loop adds the governed runtime layer around that core:

• memory.runtime.startSession() and memory.runtime.appendMessage() track current-session state without making raw transcripts durable memory.
• memory.jobs.enqueueRemember() schedules after-response memory writes with idempotency and visible job status.
• memory.jobs.drain() commits queued writes in this in-memory scheduler. In a production service, run draining in your worker or request-adjacent job loop.
• GoodMemoryConfig.observability.traceSink receives redaction-safe traces for remember, recall, context, revise, forget, export, and job events.
• memory.reviseMemory({ target: { memoryId } }) corrects a known memory by explicit id, not by fuzzy text selection.
• exportMemory() gives the user an audit/export path.

Runtime archive persistence is off by default. If you call memory.runtime.endSession({ scope, archive: "off" }), session state is cleared without writing an archive. If you opt into archive persistence, keep it summary-only and never treat raw transcripts as the default memory source.

For server integrations, start with the thin examples: examples/express-chat-server.ts or examples/fastify-chat-server.ts. For Python/FastAPI backends, use the packaged goodmemory-http-bridge path described below.

Opt-In Recall Tuning: Recommended Preset, Multi-Hop, Local Embeddings, And Conversational Extraction

The knobs below are optional and conservative by design. Default recall is single-pass and rules-only, and default extraction is unchanged; nothing happens unless you opt in. The recommended preset is the one knob with a hard requirement: it refuses to construct without a neural embedding endpoint instead of silently degrading.

One-flag recommended retrieval preset

retrieval.preset: "recommended" enables the retrieval+extraction side of the profile behind the public LoCoMo claim with one flag:

const memory = createGoodMemory({
  retrieval: { preset: "recommended" },
});

When active it (a) enables the semantic candidate-generation union at topK: 16 (the engine derives the noise budget from it), (b) biases auto recall routing to hybrid so the union fires without a per-call strategy: "hybrid" (an explicit per-call strategy still wins), and (c) flips assisted extraction to mode: "conversational" — only when an extraction model already resolves (provider config or GOODMEMORY_ASSISTED_EXTRACTOR_* env) and mode was not set explicitly; it never injects a provider. Explicit config fields always win over the preset, and leaving preset unset keeps the zero-dependency default byte-identical.

Requirements and boundaries:

• A neural embedding endpoint must resolve (GOODMEMORY_EMBEDDING_*, providers.embedding, or adapters.embeddingAdapter) — otherwise createGoodMemory throws with setup instructions. The zero-egress local path is the Ollama recipe below. createLocalEmbeddingAdapter() is rejected: hashed-lexical vectors are not semantic.
• Check inspectGoodMemoryRuntime(memory).retrievalPreset — its extraction field reports whether the write-time half engaged ("conversational") or an extractor was unavailable/kept as-is.
• The preset covers library configuration only. The LoCoMo claim additionally used an answer-side abstention-format prompt, which belongs to the application's answer step, and the LongMemEval public claim uses the rules-only profile — unchanged by this preset.
• Pairing it with bm25Ranking: true swaps the additive ranking slot from the neural score to BM25 and deviates from the claims profile; the preset never sets it.
• If you use env-resolved extraction and adopt the preset, write-time output becomes conversational atomic claims; the escape hatch is an explicit providers.extraction object with mode: "default".

Local embedding endpoint (Ollama)

The recommended preset needs a neural embedding endpoint, but that does not require data egress: GOODMEMORY_EMBEDDING_BASE_URL accepts any OpenAI-compatible /v1/embeddings endpoint, including a local Ollama server.

ollama pull nomic-embed-text        # or bge-m3 for stronger multilingual recall

export GOODMEMORY_EMBEDDING_PROVIDER=openai
export GOODMEMORY_EMBEDDING_BASE_URL=http://localhost:11434/v1
export GOODMEMORY_EMBEDDING_MODEL=nomic-embed-text
export GOODMEMORY_EMBEDDING_API_KEY=ollama   # any placeholder; Ollama ignores it, the variable stays required

# smoke-check the endpoint before starting your app
curl http://localhost:11434/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{"model": "nomic-embed-text", "input": "hello"}'

• provider stays openai: it selects the OpenAI-compatible wire protocol, not the vendor.
• Keep one embedding model per store: vectors from different models or dimensions are not comparable, and switching models means re-remembering (re-embedding) the corpus.
• Local embedding quality differs from text-embedding-3-small; the public LoCoMo numbers were measured with the OpenAI endpoint. This recipe reproduces the mechanism with zero egress, not the exact number.
• This is not createLocalEmbeddingAdapter() (below), which is hashed-lexical, not semantic, and is rejected by the recommended preset.

Opt-in multi-hop recall

recall() is single-pass by default. Pass multiHop: true for an opt-in two-pass retrieval: GoodMemory runs the query, extracts bridge entities named in the first-pass evidence, expands the query with them, and runs a second pass.

const recall = await memory.recall({
  scope,
  query: "Who manages the project Alice started?",
  multiHop: true,
});

Use it when the answer needs an entity that only the first hop names (hop 1 finds "Alice started Project Atlas"; hop 2 needs "who manages Project Atlas").

• It is opt-in. Default recall stays single-pass; leaving multiHop unset changes nothing.
• It is not a general semantic retriever. It bridges named entities lexically; it does not rank by meaning.
• It can add noise when first-pass recall is weak: if hop 1 surfaces the wrong evidence, the extracted bridge entities are wrong and the expanded query dilutes recall. Measured on LoCoMo (where base retrieval is very low) multiHop hurt recall, so do not reach for it to fix conversational / phrasing-gap retrieval — that needs real semantic retrieval, not multi-hop bridging.

Offline local embedding adapter

createLocalEmbeddingAdapter() is a deterministic, offline, dependency-free embedding adapter (hashed character-n-gram vectors). Inject it for lexical/morphological tie-breaking without configuring an embedding provider:

import { createGoodMemory, createLocalEmbeddingAdapter } from "goodmemory";

const memory = createGoodMemory({
  adapters: { embeddingAdapter: createLocalEmbeddingAdapter() },
});

• It is not neural semantic retrieval. The vectors are hashed lexical features, so they break ties between lexically similar candidates; they do not understand meaning.
• Do not use it to claim a semantic benchmark improvement. It cannot bridge a question-to-text phrasing gap that surface lexical overlap already misses.
• For real semantic ranking, configure a neural embedding provider via GOODMEMORY_EMBEDDING_* instead.

Opt-in conversational fact extraction

By default, assisted extraction (when a providers.extraction model is configured) pulls durable product memory — profiles, preferences, references, and facts. Set providers.extraction.mode: "conversational" to instead decompose dialogue into self-contained, coreference-resolved, entity- and date-normalized atomic claims at write time, so later retrieval matches a normalized fact instead of a raw conversational turn.

const memory = createGoodMemory({
  providers: {
    extraction: {
      provider: "openai",
      model: "gpt-5.5",
      apiKey: process.env.GOODMEMORY_ASSISTED_EXTRACTOR_API_KEY!,
      baseURL: process.env.GOODMEMORY_ASSISTED_EXTRACTOR_BASE_URL,
      mode: "conversational",
    },
  },
});

Use it for chat/agent products where memory comes from multi-turn conversation and questions are phrased differently from how things were said ("Who is the user's manager?" vs. "yeah my boss Dana signed off").

• It is opt-in. Leaving mode unset (or omitting providers.extraction) keeps the default extraction behavior; the recall ranking path is untouched.
• It is a write-time LLM pass: it uses your configured chat model, so it adds extraction latency and token cost, and like any LLM step it can drop or misphrase a claim. Raw turns remain the ground truth.
• It is not semantic retrieval. It normalizes the stored text so lexical retrieval has a better surface to match; it does not rank by meaning. It is the embedding-free lever for the conversational phrasing gap, not a replacement for a neural embedding provider.
• Do not quote a benchmark number from it without held-out validation, and do not tune the extraction prompt to a specific benchmark's phrasing.

Runtime And Storage

createGoodMemory({}) follows a local-first auto-storage contract:

• Explicit storage.provider wins when supplied.
• Without explicit storage, GoodMemory uses Postgres only when a configured target can bootstrap the GoodMemory backend.
• On Bun, zero-config durable storage is local SQLite at ./.goodmemory/memory.sqlite.
• On Node runtimes without the built-in local SQLite adapter, zero-config storage falls back to in-memory.
• Unsupported explicit built-in sqlite or postgres selections are reported as unavailable rather than mislabeled durable.
• Injected documentStore, sessionStore, or vectorStore adapters are reported as adapter-defined storage.
• Without GOODMEMORY_EMBEDDING_*, runtime behavior remains rules-only.
• Supported local runtimes can use sqlite-vss for SQLite semantic indexing; unsupported runtimes keep durable non-accelerated fallback behavior.

Inspect the resolved runtime instead of guessing:

import { createGoodMemory, inspectGoodMemoryRuntime } from "goodmemory";

const memory = createGoodMemory({});
const runtime = inspectGoodMemoryRuntime(memory);

console.log(runtime.storage);

SQLite vector controls:

• GOODMEMORY_SQLITE_VECTOR_MODE=off|prefer|require
• GOODMEMORY_SQLITE_CUSTOM_LIBRARY_PATH
• GOODMEMORY_SQLITE_VECTOR_EXTENSION_PATH
• GOODMEMORY_SQLITE_VECTOR_EXTENSION_ENTRYPOINT
• GOODMEMORY_SQLITE_VECTOR_SEARCH_FUNCTION

Public Remember Customization

Product integrations should customize writes through the public remember surface. Do not use test-only extractor seams for product behavior.

import { createGoodMemory, rememberRules } from "goodmemory";

const memory = createGoodMemory({
  remember: {
    preset: "default",
    profiles: [
      {
        id: "life-coach",
        when: { agentId: "life-coach" },
        rules: [
          rememberRules.fact(/my top priority this quarter is (.+)/i, {
            id: "life-goal-priority",
            category: "goal",
            tags: ["life_coach", "long_term_goal"],
            attributes: { horizon: "quarter" },
            content: ({ match }) => match[1] ?? "",
          }),
          rememberRules.preference(/please coach me with (.+)/i, {
            id: "life-coaching-style",
            category: "coaching_style",
            value: ({ match }) => match[1] ?? "",
          }),
        ],
        assistantOutputs: { mode: "confirmed_or_verified_only" },
      },
    ],
  },
});

await memory.remember({
  scope: { userId: "u-1", agentId: "life-coach" },
  messages: [
    {
      role: "user",
      content: "My top priority this quarter is rebuilding my sleep routine.",
    },
  ],
  annotations: [
    {
      messageIndex: 0,
      remember: "always",
      metadataPatch: { tags: ["confirmed_by_host"] },
    },
  ],
});

Profile extractors can be raw MemoryExtractor objects or named { id, extractor } entries. Use named extractors for real integrations so remember events and eval reports carry stable extractorIds even if profile composition changes. Remember events also carry resolved profileId and presetId metadata.

AI SDK Adapter

GoodMemory's Node-compatible AI SDK path is a plain Request -> Response server handler built from createGoodMemory() and createGoodMemoryAISDK().

import { createGoodMemory } from "goodmemory";
import type { GoodMemoryStreamTextInput } from "goodmemory/ai-sdk";
import { createGoodMemoryAISDK } from "goodmemory/ai-sdk";

const memory = createGoodMemory({});

const aiSDK = createGoodMemoryAISDK({
  memory,
});

type MemoryChatRequest = Pick<
  GoodMemoryStreamTextInput,
  "messages" | "query" | "scope" | "system"
>;

function isMemoryChatRequest(value: unknown): value is MemoryChatRequest {
  if (!value || typeof value !== "object" || Array.isArray(value)) {
    return false;
  }

  const candidate = value as Record<string, unknown>;
  const scope = candidate.scope;
  return Array.isArray(candidate.messages)
    && !!scope
    && typeof scope === "object"
    && !Array.isArray(scope)
    && typeof (scope as { userId?: unknown }).userId === "string"
    && (scope as { userId: string }).userId.trim().length > 0;
}

export async function handleMemoryChat(request: Request): Promise<Response> {
  const body: unknown = await request.json();
  if (!isMemoryChatRequest(body)) {
    return new Response(
      JSON.stringify({
        error: "Expected a request body with a messages array and scope.userId.",
      }),
      {
        headers: { "content-type": "application/json; charset=utf-8" },
        status: 400,
      },
    );
  }

  const result = aiSDK.streamText({
    messages: body.messages,
    query: body.query,
    scope: body.scope,
    system: body.system,
    model: {} as never,
  });

  return result.toTextStreamResponse();
}

Notes:

• The canonical server example is examples/plain-ai-sdk-server.ts.
• Thin Express and Fastify examples are examples/express-chat-server.ts and examples/fastify-chat-server.ts.
• examples/vercel-ai-chat.ts remains a lower-level wrapper/API example.
• Next.js App Router can map export async function POST(request: Request) to the same handler body.
• The first public server path is ModelMessage-first.
• The wrapper augments system through recall() and buildContext() and soft-fails if the memory layer errors.

Python/FastAPI HTTP Bridge

Use the packaged HTTP bridge when a Python backend should call GoodMemory as a server-side memory service.

GOODMEMORY_HTTP_BRIDGE_TOKEN="replace-with-service-token" \
GOODMEMORY_STORAGE_PROVIDER=postgres \
GOODMEMORY_STORAGE_URL="postgres://user:pass@host:5432/goodmemory" \
./node_modules/.bin/goodmemory-http-bridge --profile life-coach

Python callers send Authorization: Bearer <token> plus the x-goodmemory-* scope headers to POST /memory/recall-context, /memory/remember, /memory/feedback, /memory/export, /memory/forget, and targeted /memory/revise. The TypeScript bridge API is available from goodmemory/http.

To serve the recommended retrieval preset (semantic candidate union + BM25) over the bridge, start it with the one switch --recommended (or GOODMEMORY_PROFILE=agent-recommended or GOODMEMORY_HTTP_BRIDGE_RECOMMENDED=1); it requires an embedding endpoint (GOODMEMORY_EMBEDDING_*) or the bridge refuses to start (fail-loud, not a silent downgrade). GET /healthz then reports retrievalTier and embeddingEnabled so the active tier is visible at a glance; recall requests default to strategy: "auto", which the preset routes to hybrid — degraded recalls carry a routing.warnings code (semantic_recall_inactive) plus routing.warningMessages with semantic recall inactive — set strategy:hybrid + RETRIEVAL_PRESET, instead of silently returning the lexical floor.

Or deploy it with Docker in one command (SQLite volume included; add the compose postgres profile for pgvector):

GOODMEMORY_HTTP_BRIDGE_TOKEN="replace-with-service-token" docker compose up -d
curl -fsS http://127.0.0.1:8739/healthz

GET /healthz is the auth-free liveness endpoint for containers, load balancers, and client ready-waits. Python backends should use the official client — pip install goodmemory-client (PyPI) — which derives the caller headers from one Scope object, mirrors the per-endpoint idempotency rules, and surfaces recall routing (so silent strategy downgrades are visible). Details: docs/GoodMemory-Python-HTTP-Integration-Bridge.md.

Hosted instance. A live GoodMemory bridge runs at https://goodmemory.vibenest.net (liveness: /healthz). Point any client at it via GOODMEMORY_BRIDGE_HOST / --goodmemory-host (or the GoodMemoryClient host argument) instead of a local URL; it enforces bearer-token auth, so bring your own service token. It is a single-process, write-capable API — before exposing one publicly, add rate limiting and disposable-scope data, and never publish a shared write token.

Host Adapter API

Use goodmemory/host when an external host wants artifacts or host-specific contracts without importing internals.

import { createGoodMemory } from "goodmemory";
import { createHostAdapter } from "goodmemory/host";

const memory = createGoodMemory({});

const adapter = createHostAdapter({
  id: "codex-handoff",
  hostKind: "codex",
  memory,
  readableArtifactTypes: ["session_memory"],
});

const result = await adapter.readArtifacts({
  scope: {
    userId: "u-1",
    workspaceId: "workspace-a",
    sessionId: "s-1",
  },
  includeRuntime: true,
});

Modes:

• file-assisted: read compiled artifacts such as MEMORY.md, user.md, session-memory/<sessionId>.md, and playbooks/*.md without treating files as canonical storage.
• file-authoritative: available for the minimal writable subset. Today that subset is the canonical playbooks/*.md file shape, writing structured deltas back into active validated-pattern feedback records.

Writable guardrails:

• Prompt and skill snippet files remain derived read-only outputs.
• Risky guidance edits require explicit verifyWrite approval.
• Low-risk metadata edits such as appliesTo and Why can write back without the extra approval step.
• Failed writable operations return diagnostics with rollback guidance.

Current Claude/Codex examples stay in file-assisted mode by default.

CLI Reference

The goodmemory command on your shell PATH is the global CLI installed with npm install -g goodmemory@0.5.1. In a local dependency install, invoke the package bin as npx goodmemory, npm exec -- goodmemory, or ./node_modules/.bin/goodmemory. The repo-local bun run goodmemory script is for development only.

Memory-first commands:

./node_modules/.bin/goodmemory inspect --user-id <user-id> --workspace-id <workspace-id>
./node_modules/.bin/goodmemory trace --user-id <user-id> --workspace-id <workspace-id> --query "Which runbook is the source of truth?"
./node_modules/.bin/goodmemory export-memory --user-id <user-id> --workspace-id <workspace-id> --output ./tmp/export
./node_modules/.bin/goodmemory stats --user-id <user-id> --workspace-id <workspace-id>
./node_modules/.bin/goodmemory remember --user-id <user-id> --workspace-id <workspace-id> --session-id <session-id> --message "Remember that the deploy is blocked on smoke verification."
./node_modules/.bin/goodmemory feedback --host codex --workspace-root . --session-id <session-id> --signal "Keep coding summaries short and list explicit next steps."
./node_modules/.bin/goodmemory forget --host codex --workspace-root . --session-id <session-id> --memory-id <memory-id>

Installed-host commands:

goodmemory -V
goodmemory --version
goodmemory setup --host codex
goodmemory status codex --workspace-root .
goodmemory install codex --activation-mode global --writeback observe --user-id <user-id>
goodmemory enable codex --workspace-root . --writeback selective
goodmemory mcp serve --host codex
goodmemory-mcp --host codex
goodmemory codex bootstrap --user-id <user-id> --workspace-id <workspace-id>
goodmemory claude bootstrap --user-id <user-id> --workspace-id <workspace-id>

Hook and writeback examples:

printf '%s' '{"cwd":".","session_id":"s-1","hook_event_name":"SessionStart","source":"startup"}' \
  | goodmemory codex hook session-start

printf '%s' '{"cwd":".","session_id":"s-1","tool_name":"Bash","tool_input":{"command":"./tools/DeepAnalyzer --detailed"}}' \
  | goodmemory codex hook pre-tool-use

goodmemory codex action -- ./tools/DeepAnalyzer --detailed

printf '%s' '{"cwd":".","session_id":"s-1","messages":[{"role":"user","content":"Next step is to finish the release smoke."}]}' \
  | goodmemory codex writeback --json

printf '%s' '{"cwd":".","session_id":"s-1","event_id":"stop-1","summary":"Keep coding summaries short."}' \
  | goodmemory codex hook session-stop

Eval artifact inspection:

./node_modules/.bin/goodmemory eval inspect --run-dir reports/eval/live/<run-id> --case-id <case-id>
./node_modules/.bin/goodmemory eval trace --run-dir reports/eval/live/<run-id> --case-id <case-id>
./node_modules/.bin/goodmemory eval export-case --run-dir reports/eval/live/<run-id> --case-id <case-id> --output /tmp/case.json

CLI surface:

• goodmemory -V
• goodmemory --version
• goodmemory setup
• goodmemory status
• goodmemory install
• goodmemory uninstall
• goodmemory enable
• goodmemory disable
• goodmemory inspect
• goodmemory trace
• goodmemory export-memory
• goodmemory stats
• goodmemory remember
• goodmemory feedback
• goodmemory forget
• goodmemory mcp serve
• goodmemory-mcp
• goodmemory codex hook
• goodmemory codex writeback
• goodmemory claude hook
• goodmemory claude writeback
• goodmemory codex bootstrap
• goodmemory claude bootstrap
• goodmemory eval inspect
• goodmemory eval trace
• goodmemory eval export-case

Examples

Installed-package guides:

• 15-minute app integration guide: docs/GoodMemory-15-Minute-App-Integration.md
• Reference integration guide: docs/GoodMemory-Reference-Integration-Guide.md
• Codex handoff setup guide: docs/GoodMemory-Codex-Handoff-Setup-Guide.md
• Claude Code setup guide: docs/GoodMemory-Claude-Code-Setup-Guide.md

Repo-local examples:

• Basic chat integration: examples/basic-chat.ts
• Coding-agent flavored integration: examples/coding-agent.ts
• Plain AI SDK server integration: examples/plain-ai-sdk-server.ts
• Express chat server integration: examples/express-chat-server.ts
• Fastify chat server integration: examples/fastify-chat-server.ts
• AI SDK wrapper integration: examples/vercel-ai-chat.ts
• Life-coach public remember profile: examples/life-coach-remember-profile.ts
• Claude-style host artifact consumption: examples/host-claude-artifacts.ts
• Codex-style session handoff consumption: examples/host-codex-handoff.ts

Run examples from this repo:

bun run example:chat
bun run example:coding-agent
bun run example:ai-sdk-server
bun run example:express-chat
bun run example:fastify-chat
bun run example:vercel-ai
bun run example:life-coach-profile
bun run example:host-claude
bun run example:host-codex

Testing And Eval

Default local gates:

bun test
bun run typecheck
bun run test:coverage

Use bun run test:all only when you intentionally want the broader sweep through vendored or third-party test trees.

Eval commands:

bun run eval:smoke
bun run eval:fallback
bun run eval:live
bun run eval:live-memory
bun run eval:live-auto-memory
bun run eval:live-provider-memory
bun run eval:summary

Meanings:

• eval:smoke: harness self-check.
• eval:fallback: deterministic validation without live model calls.
• eval:live: live generator plus live judge with an in-memory backend.
• eval:live-memory: live generator plus live judge using auto-storage semantics; default storage is local SQLite unless provider storage resolves.
• eval:live-auto-memory: alias for eval:live-memory when scripts need to make auto-storage explicit.
• eval:live-provider-memory: provider-backed evidence path requiring Postgres, embeddings, and assisted extraction; it does not silently fall back to SQLite.
• eval:summary: summarize existing eval output directories.

Live eval environment:

• GOODMEMORY_EVAL_PROVIDER
• GOODMEMORY_EVAL_BASE_URL for OpenAI-compatible gateways
• GOODMEMORY_EVAL_MODEL
• GOODMEMORY_EVAL_API_KEY
• GOODMEMORY_EVAL_MAX_CONCURRENCY optional parallelism cap
• GOODMEMORY_JUDGE_PROVIDER
• GOODMEMORY_JUDGE_BASE_URL for OpenAI-compatible gateways
• GOODMEMORY_JUDGE_MODEL
• GOODMEMORY_JUDGE_API_KEY

eval:live-memory and eval:live-auto-memory also need embedding and assisted extractor configuration:

• GOODMEMORY_EMBEDDING_PROVIDER
• GOODMEMORY_EMBEDDING_BASE_URL for OpenAI-compatible gateways
• GOODMEMORY_EMBEDDING_MODEL
• GOODMEMORY_EMBEDDING_API_KEY
• GOODMEMORY_ASSISTED_EXTRACTOR_PROVIDER
• GOODMEMORY_ASSISTED_EXTRACTOR_BASE_URL for OpenAI-compatible gateways
• GOODMEMORY_ASSISTED_EXTRACTOR_MODEL
• GOODMEMORY_ASSISTED_EXTRACTOR_API_KEY

eval:live-provider-memory additionally requires:

• GOODMEMORY_TEST_POSTGRES_URL

Output directories:

• live runs: reports/eval/live/run-*
• auto-storage live memory runs: reports/eval/live-memory/run-*
• provider-backed live memory runs: reports/eval/live-provider-memory/run-*
• fallback runs: reports/eval/fallback/run-*

Strategy Rollout

GoodMemory keeps rules-only as the supported baseline. New retrieval behavior moves through observe -> assist -> promote.

Operator guidance:

• observe: collect isolated shadow evidence without changing the executed path.
• assist: allow candidate execution in controlled eval runs.
• promote: require strategy-promotion-gate.json, a clean regression-dashboard.json, and strategy-promotion-authorization.json.
• Stay rules-only when eval evidence is incomplete, provider-backed dependencies are unavailable, or rollback conditions are present.

Current Status

Current stable public surface:

• root memory API through goodmemory
• AI SDK adapter through goodmemory/ai-sdk
• host adapter and host contracts through goodmemory/host
• HTTP bridge API through goodmemory/http and packaged goodmemory-http-bridge
• installed CLI and managed host setup through goodmemory setup
• Codex and Claude Code hooks for recall
• read-only MCP for inspection and debugging
• opt-in installed-host writeback with audit and undo
• local SQLite durable fallback on Bun
• Postgres, embeddings, assisted extraction, and provider-backed evals when configured

Still outside the accepted public claim:

• default-on automatic writeback
• raw transcript archive
• dashboard or managed cloud
• treating exported artifact files as canonical storage
• broadening root exports with internal proposal or promotion internals

For the detailed current-state and evidence map, use docs/GoodMemory-Current-Status-and-Evidence.md.

Documentation

• Documentation map and archive policy: docs/README.md
• Current status and evidence: docs/GoodMemory-Current-Status-and-Evidence.md
• Product comparison: docs/GoodMemory-Product-Comparison.md
• Canonical design: docs/GoodMemory-First-Principles-and-Reference-Architecture.md
• v1 implementation architecture: docs/GoodMemory-OSS-Architecture-v1.md
• PRD: docs/GoodMemory-PRD.md
• TDD and evaluation strategy: docs/GoodMemory-TDD-and-Evaluation-Strategy.md
• Strategy rollout guide: docs/GoodMemory-Strategy-Rollout-Guide.md
• Release checklist: docs/GoodMemory-v1-Release-Checklist.md
• Historical quality-gate archive: docs/archive/quality-gates/README.md
• Historical v1 snapshot: docs/GoodMemory-v1-Quality-Gate.md
• Framework cookbooks — durable memory in an agent framework: LangGraph · CrewAI · OpenAI Agents SDK

Use task-board/00-README.txt for execution order, open follow-up work, and phase-specific acceptance boundaries. Archived design inputs are not current truth and are routed through docs/README.md.