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LongMemory

Memory server

Created by CaviraOSS

Published in CaviraOSS/LongMemory. Publisher page

4,573 GitHub stars

What it is

LongMemory Hydrograph TypeScript package: library, CLI, self-hosted API server, and benchmark harness.
— the description by CaviraOSS

AutoGineer overview, from the brain’s own files

LongMemory is a cognitive memory engine designed to provide AI agents with durable, governed, and temporal recall. Unlike standard RAG pipelines that rely on simple vector similarity, LongMemory utilizes a Hydrograph substrate to manage immutable nodes, executable edges, and temporal truth. It supports complex recall modes including strict, historical, associative, and world-grounded retrieval. The system is built for local-first deployment using SQLite, offering a self-hosted API server, CLI, and native integrations for various agent frameworks, IDEs, and automation tools. It enforces governance policies across projects, users, and teams, ensuring that memory remains grounded, explainable, and context-aware.

How it works

Memory model: LLM wiki

AutoGineer overview, from the brain’s own files

LongMemory operates as a memory server that stores data in a local SQLite database. It ingests events, documents, and session data into a Hydrograph structure. When an agent needs information, the system performs recall based on specific modes—such as strict, historical, or associative—to retrieve relevant, token-bounded context. The engine supports governance by scoping memory to specific projects, tenants, or users. It provides an API for agents to interact with this memory, and includes tools for maintenance, such as decay and reinforcement, to manage the lifecycle of stored information.

Folders
FolderRoleFiles
.githubagent-config13
docsdocs34
packagessource-code7
srcsource-code340
toolsscripts5
.agentsfolder1
.dofolder1
appsfolder19
benchmarksfolder32
dashboardfolder34
integrationsfolder47

Requirements

Apps: Docker

Runtimes: Node.js >=20

Databases: SQLite

API keys (by name): AWS_SECRET_ACCESS_KEY, AWS_SESSION_TOKEN, AZURE_DEVOPS_TOKEN, BITBUCKET_TOKEN, GEMINI_API_KEY, GITHUB_TOKEN, GITLAB_TOKEN, GOOGLE_ACCESS_TOKEN, LINEAR_API_KEY, LONGMEMORY_API_KEY, MICROSOFT_GRAPH_TOKEN, NOTION_API_KEY, NVIDIA_API_KEY, OM_API_KEY, OM_GEMINI_API_KEY, OM_OPENAI_API_KEY, OM_SIRAY_API_TOKEN, OPENAI_API_KEY, SIRAY_API_TOKEN, SLACK_BOT_TOKEN

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Google Antigravity, Kiro, OpenCode, Amp, Roo Code, Cline, JetBrains Junie, Devin (ex-Windsurf)

Set it up

  1. This brain runs code automatically: 1 script. Review Makefile before opening the folder in Claude Code. No step below turns a hook on for you.

    • Makefile: corepack enable; pnpm install --frozen-lockfile
  2. Not scanned by AutoGineerThe author’s README also describes installing it with Docker Compose. Read its install section: AutoGineer did not scan that path. https://github.com/CaviraOSS/LongMemory/blob/aec98ced33a7586a2fcd198a04bc38b5590750c4/README.md#2-run-as-a-service
  3. Not scanned by AutoGineerThe author’s README also describes installing it from a clone of its repository. Read its install section: AutoGineer did not scan that path. https://github.com/CaviraOSS/LongMemory/blob/aec98ced33a7586a2fcd198a04bc38b5590750c4/README.md#2-run-as-a-service
  4. Created by CaviraOSS. Published in CaviraOSS/LongMemory. Licence: Apache License 2.0.

Usage

AutoGineer overview, from the brain’s own files

  • Install as a TypeScript library for in-memory or SQLite-backed agent memory.
  • Deploy as a self-hosted API server using Docker or Docker Compose.
  • Use the CLI to ingest data, manage project context, or perform maintenance tasks.
  • Connect to agent hosts via MCP (Model Context Protocol) for seamless integration.
  • Use the Python SDK to interact with the memory server from Python-based agents.
  • Visualize and manage memory through the provided Next.js dashboard or VS Code extension.
“What was the deployment region in January?”
The agent uses historical recall mode with a specified valid_time to retrieve the correct configuration from that period.
“Prepare the next release.”
The agent retrieves project context, including architecture notes, open tasks, and relevant code facts, to assist with release planning.

Documentation by CaviraOSS

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LongMemory

Durable, temporal, governed memory for AI agents. Not just RAG. Not just a vector database. Local-first and self-hosted.

Image: npm Image: PyPI Image: VS Code Image: Container Image: License

Image: LongMemory dashboard

LongMemory is a cognitive memory engine for LLM applications and autonomous agents.

  • Durable local-first storage with SQLite

  • Immutable content, provenance, and temporal truth

  • Strict, historical, associative, grounded, and multilingual recall

  • Explainable evidence selection and token-bounded context

  • Governed project memory, Skills, Chat Memory, LLM-Wiki, and CodeGraph

  • One TypeScript engine across npm, CLI, HTTP, MCP, dashboard, and VS Code

  • Native integrations for agent hosts, automation tools, and Python frameworks

Your model stays stateless. Your application stops being amnesiac.


1. Use It in 10 Seconds

Install as a library
npm install longmemory
import { createMemory } from 'longmemory';

const memory = await createMemory();
await memory.ingest({
    user_id: 'alice',
    text: 'I prefer TypeScript for backend services',
});

const result = await memory.recall({
    text: 'What language does Alice prefer?',
    mode: 'strict',
});

console.log(result);
await memory.close();

No service or external database is required for in-memory use.

For optional evidence-grounded answers with your own model, use the exported answer_from_evidence adapter. It accepts authorized evidence, validates cited excerpts, and makes at most one model call; ordinary recall is unchanged. See docs/answering.md for policies, limits, and integration.

Persist with SQLite
const memory = await createMemory({
    store: 'sqlite',
    db_path: './longmemory.db',
    tenant_id: 'acme',
    user_id: 'alice',
});

Reopening the same database restores nodes, worlds, entities, edges, temporal history, grounding, and lifecycle state.

Install the CLI
npm install --global longmemory
longmemory init
longmemory recall "current project priorities" --mode associative
Call a self-hosted server from Python
pip install longmemory-sdk
from longmemory import LongMemory

memory = LongMemory(
    "http://127.0.0.1:7331",
    api_key="change-me",
    user_id="alice",
)

memory.ingest("I prefer TypeScript")
result = memory.recall("What language do I prefer?", mode="strict")

The Python package is a zero-dependency HTTP client. The Hydrograph engine remains in the self-hosted TypeScript service. See docs/python-sdk.md.


2. Run as a Service

From source
git clone https://github.com/CaviraOSS/LongMemory.git
cd LongMemory
corepack enable
pnpm install --frozen-lockfile
pnpm build
pnpm start

The API listens on http://127.0.0.1:7331 by default.

Docker
docker run --rm \
  -p 7331:7331 \
  -v longmemory-data:/data \
  -e LONGMEMORY_API_KEY=change-me \
  ghcr.io/caviraoss/longmemory:latest
Docker Compose
cp .env.example .env
docker compose up --build -d longmemory

Include the dashboard:

docker compose --profile ui up --build -d
  • API and MCP: http://127.0.0.1:7331

  • Dashboard: http://127.0.0.1:3000

  • Health: http://127.0.0.1:7331/health


3. Why LongMemory

Most systems called memory are retrieval pipelines:

  1. Split text into chunks.

  2. Embed the chunks.

  3. Return the nearest vectors.

That does not establish what was true at a particular time, whether a new fact superseded an old one, which source is authoritative, who may see it, or why a result belongs in context.

LongMemory models those concerns directly:

  • Temporal truth: recorded time and valid time are separate.

  • Immutable memory: content, vectors, hashes, and provenance are not rewritten by recall or decay.

  • Executable graph: typed relationships participate in recall and explanation.

  • Governance: project, tenant, user, team, role, agent, task, and framework scope are enforced.

  • Lifecycle: deterministic decay, explicit reinforcement, consolidation, compression, and reconsolidation.

  • Evidence: recall is bounded by relevance, contradictions, grounding, permissions, and token cost.

See Why.md for the design rationale.


4. Recall Modes

const strict = await memory.recall({
    text: 'What is the current deployment region?',
    mode: 'strict',
});

const historical = await memory.recall({
    text: 'What was the deployment region in January?',
    mode: 'historical',
    valid_time: Date.UTC(2026, 0, 15),
});

const associative = await memory.recall({
    text: 'Incidents related to the payment migration',
    mode: 'associative',
});

const grounded = await memory.recall({
    text: 'Which production endpoint is currently live?',
    mode: 'world_grounded',
});

Strict recall applies temporal, contradiction, contract, confidence, and grounding gates. Historical recall preserves superseded truth. Associative recall follows semantic, lexical, entity, activation, and graph signals. World-grounded recall requires current external evidence.


5. Features

  • Hydrograph memory substrate with immutable nodes, executable edges, worlds, entities, facets, and traces.

  • Temporal reasoning with point-in-time truth, event ordering, supersession, and stale-evidence controls.

  • Multilingual memory with script detection, code switching, transliteration, and cross-language embeddings.

  • Project memory for architecture, decisions, tasks, conventions, failures, handoffs, and code impact.

  • Governed assets for Chat Memory, Skills, LLM-Wiki, and CodeGraph with lifecycle and ACL policy.

  • Session porter for Claude Code, Codex, OpenCode, Gemini CLI, Copilot Chat, Cline, and raw harness logs.

  • Connectors for repositories, local files, Markdown, web content, feeds, cloud documents, and provider APIs.

  • Embeddings through OpenAI-compatible APIs, Gemini, AWS Bedrock, Ollama, Siray, and local HTTP models.

  • Operational surfaces through HTTP, MCP, dashboard, VS Code, n8n, and framework-native MCP clients.

  • Auditable benchmarks for LongMemEval, LoCoMo, BEAM, retrieval quality, temporal behavior, and latency.


6. MCP and Agent Integrations

Start local stdio MCP:

longmemory mcp --db .longmemory/project.db --project current

Expose authenticated Streamable HTTP MCP:

LONGMEMORY_API_KEY=change-me longmemory serve --mcp-http

LongMemory exposes 13 high-level governed tools plus readable resources and agent workflow prompts. Tool arguments cannot override server-bound runtime identity.

Installable integrations include:

  • Claude Code plugin

  • Codex and ChatGPT desktop plugin

  • Gemini CLI extension

  • Agent Plugins 1.0 bundle for OpenClaw and compatible hosts

  • n8n community node usable as an AI Agent tool

  • Cline, Continue, and LibreChat configuration packs

  • Dify and Flowise native MCP setup

  • CrewAI, AutoGen, LangGraph/LangChain, OpenAI Agents SDK, and PydanticAI examples

See integrations/README.md and docs/mcp.md.


7. Temporal and Project Memory

import { createProjectMemory } from 'longmemory';

const projects = await createProjectMemory({
    tenant_id: 'cavira',
    organization_id: 'CaviraOSS',
    project_id: 'longmemory',
    name: 'LongMemory',
    store: 'sqlite',
    db_path: './longmemory.db',
});

await projects.ingestProjectEvent('longmemory', {
    kind: 'decision',
    topic: 'persistence',
    text: 'Use SQLite for local-first persistence',
    source_type: 'architecture_note',
});

const context = await projects.getProjectContext('longmemory', 'prepare the next release');

Project context combines relevant architecture, current decisions, open tasks, failures, code facts, matched Skills, conflicts, and governed asset loadouts under one token budget.


8. CLI

longmemory init
longmemory tui
longmemory status --memories 20 --json
longmemory ingest "Remember the rollback procedure" --type procedure
longmemory recall "What is the rollback procedure?" --mode associative
longmemory memory list --limit 50
longmemory project context "prepare the next release"
longmemory maintenance decay --all
longmemory maintenance reinforce <memory-id>
longmemory skill match "run the release checklist" --agent reviewer
longmemory asset loadout "prepare the release" --agent reviewer --framework codex
longmemory code impact createMemory
longmemory detect
longmemory session discover --from claude-code
longmemory port --from claude-code --to longmemory --all
longmemory session wiki --from gemini-cli --all --name "Project knowledge"
longmemory serve --mcp-http

Finite commands emit stable JSON outside a TTY or when --json is supplied. The session porter reads supported coding-agent stores without modifying them. See docs/cli.md.


9. Dashboard and VS Code

The Next.js dashboard provides health, memory browsing, ingestion, search, project selection, activity, decay, settings, timelines, and memory-aware chat through a same-origin API proxy.

pnpm --dir dashboard build
pnpm --dir dashboard start

The VS Code extension provides an activity-bar browser, status bar, recall, project context, explanation, reinforcement, explicit decay, session import, and reviewed AI-change capture.

pnpm extension:package

The generated package is apps/vscode-extension/longmemory-vscode-0.2.0.vsix.


10. Architecture

graph TB
  INPUT[Events, documents, sessions] --> INGEST[Immutable ingest pipeline]
  INGEST --> GRAPH[(Hydrograph)]
  GRAPH --> STRICT[Strict and historical recall]
  GRAPH --> ASSOC[Associative recall]
  GRAPH --> GROUND[World-grounded recall]
  GRAPH --> PROJECT[Project memory and governed assets]
  GRAPH --> SQLITE[(SQLite)]
  STRICT --> CONTEXT[Explainable bounded context]
  ASSOC --> CONTEXT
  GROUND --> CONTEXT
  PROJECT --> MCP[MCP tools, resources, prompts]
  CONTEXT --> API[Library, CLI, HTTP]
  MCP --> AGENTS[Agents, IDEs, automation]
  API --> UI[Dashboard and VS Code]

Read ARCHITECTURE.md and docs/architecture.md for subsystem details.


11. Deployment Options

PlatformConfigurationWhat it deploys
DockerDockerfileAPI and Streamable HTTP MCP
Docker Composedocker-compose.ymlAPI/MCP plus optional dashboard
Herokuapp.jsonContainerized API/MCP
Railwayrailway.jsonContainerized API/MCP
Renderrender.yamlAPI/MCP with persistent disk
DigitalOcean.do/spec.yamlApp Platform API/MCP service
Vercelvercel.jsonDashboard; configure LONGMEMORY_API_URL
Windowsstart-longmemory.ps1Background local API/MCP process

For hosted API deployments, set LONGMEMORY_API_KEY, mount persistent storage at /data, and terminate TLS at the platform edge. Vercel hosts only the stateless dashboard and requires a separately deployed LongMemory API.


12. Benchmarks

pnpm bench
pnpm bench:ci
pnpm bench:full

The benchmark harness publishes explicit manifests, dataset completion, evidence metrics, answer judgments, temporal categories, latency percentiles, and N/A reasons. Official scorecards fail closed on incomplete datasets or semantic embedding fallback. See benchmarks/README.md.


13. Migration

Import supported SQLite, JSON, or JSONL memory:

longmemory migrate \
  --from ./legacy.db \
  --to ./longmemory.db \
  --report ./migration-report.json

Import coding-agent conversations as governed Chat Memory:

longmemory port --from codex --to longmemory --all

See MIGRATION.md and docs/migration.md.

Legacy package migration:

npm uninstall openmemory-js && npm install longmemory
pip uninstall openmemory-py && pip install longmemory-sdk

openmemory-js@2 and openmemory-py@2 are forwarding bridges for existing installations. New applications should use longmemory and longmemory-sdk directly. PyPI's unrelated longmemory name is owned by another project, so the official distribution is longmemory-sdk while the import remains longmemory.


14. Release and Operations

corepack enable
pnpm install --frozen-lockfile
pnpm release:check
pnpm pack
pnpm extension:package

release:check validates branding, types, integration manifests, the benchmark smoke gate, the root build, extension build, and dashboard production build.

Useful Make targets:

make install
make build
make check
make docker-up
make dashboard

15. Security

LongMemory is local-first, but network deployment still requires explicit controls:

  • Protect API and MCP routes with LONGMEMORY_API_KEY.

  • Restrict allowed origins and terminate TLS at the edge.

  • Keep connector and embedding credentials outside repository files.

  • Treat recalled content as untrusted evidence, not authorization.

  • Preserve server-bound user, project, agent, and framework identity.

Report vulnerabilities privately as described in SECURITY.md.


16. Contributing

Issues and pull requests are welcome. Read CONTRIBUTING.md, GOVERNANCE.md, and CODE_OF_CONDUCT.md before contributing.


17. License

LongMemory is licensed under the Apache License 2.0. The separately published n8n community node uses MIT as required by n8n's strict package validator.

Contributors

<!-- readme: contributors -start -->

<table> <tbody> <tr> <td align="center"> <a href="https://github.com/nullure"> <img src="https://avatars.githubusercontent.com/u/81895400?v=4" width="100;" alt="nullure"/> <br /> <sub><b>Morven</b></sub> </a> </td> <td align="center"> <a href="https://github.com/dontbanmeplz"> <img src="https://avatars.githubusercontent.com/u/59851616?v=4" width="100;" alt="dontbanmeplz"/> <br /> <sub><b>Chis</b></sub> </a> </td> <td align="center"> <a href="https://github.com/amihos"> <img src="https://avatars.githubusercontent.com/u/35190548?v=4" width="100;" alt="amihos"/> <br /> <sub><b>Hossein Amirkhalili</b></sub> </a> </td> <td align="center"> <a href="https://github.com/DKB0512"> <img src="https://avatars.githubusercontent.com/u/23116307?v=4" width="100;" alt="DKB0512"/> <br /> <sub><b>DKB</b></sub> </a> </td> <td align="center"> <a href="https://github.com/stevo1403"> <img src="https://avatars.githubusercontent.com/u/34807365?v=4" width="100;" alt="stevo1403"/> <br /> <sub><b>Stephen Adebayo</b></sub> </a> </td> <td align="center"> <a href="https://github.com/mameikagou"> <img src="https://avatars.githubusercontent.com/u/116348059?v=4" width="100;" alt="mameikagou"/> <br /> <sub><b>mrlonely</b></sub> </a> </td> </tr> <tr> <td align="center"> <a href="https://github.com/haosenwang1018"> <img src="https://avatars.githubusercontent.com/u/167664334?v=4" width="100;" alt="haosenwang1018"/> <br /> <sub><b>Sense_wang</b></sub> </a> </td> <td align="center"> <a href="https://github.com/recabasic"> <img src="https://avatars.githubusercontent.com/u/102372274?v=4" width="100;" alt="recabasic"/> <br /> <sub><b>Elvoro</b></sub> </a> </td> <td align="center"> <a href="https://github.com/zfaustk"> <img src="https://avatars.githubusercontent.com/u/4340287?v=4" width="100;" alt="zfaustk"/> <br /> <sub><b>Clio</b></sub> </a> </td> <td align="center"> <a href="https://github.com/suyua9"> <img src="https://avatars.githubusercontent.com/u/273297082?v=4" width="100;" alt="suyua9"/> <br /> <sub><b>suyua9</b></sub> </a> </td> <td align="center"> <a href="https://github.com/vincenzopalazzo"> <img src="https://avatars.githubusercontent.com/u/17150045?v=4" width="100;" alt="vincenzopalazzo"/> <br /> <sub><b>Vincenzo Palazzo</b></sub> </a> </td> <td align="center"> <a href="https://github.com/msris108"> <img src="https://avatars.githubusercontent.com/u/43115330?v=4" width="100;" alt="msris108"/> <br /> <sub><b>Sriram M</b></sub> </a> </td> </tr> <tr> <td align="center"> <a href="https://github.com/fparrav"> <img src="https://avatars.githubusercontent.com/u/9319430?v=4" width="100;" alt="fparrav"/> <br /> <sub><b>Felipe Parra</b></sub> </a> </td> <td align="center"> <a href="https://github.com/DoKoB0512"> <img src="https://avatars.githubusercontent.com/u/123281216?v=4" width="100;" alt="DoKoB0512"/> <br /> <sub><b>DoKoB0512</b></sub> </a> </td> <td align="center"> <a href="https://github.com/whiterabb17"> <img src="https://avatars.githubusercontent.com/u/90134688?v=4" width="100;" alt="whiterabb17"/> <br /> <sub><b>MistrHyde</b></sub> </a> </td> <td align="center"> <a href="https://github.com/octo-patch"> <img src="https://avatars.githubusercontent.com/u/266937838?v=4" width="100;" alt="octo-patch"/> <br /> <sub><b>Octopus</b></sub> </a> </td> <td align="center"> <a href="https://github.com/therexone"> <img src="https://avatars.githubusercontent.com/u/27387245?v=4" width="100;" alt="therexone"/> <br /> <sub><b>Ayush Singh</b></sub> </a> </td> <td align="center"> <a href="https://github.com/kishan0725"> <img src="https://avatars.githubusercontent.com/u/36665975?v=4" width="100;" alt="kishan0725"/> <br /> <sub><b>Kishan Lal</b></sub> </a> </td> </tr> <tr> <td align="center"> <a href="https://github.com/pc-quiknode"> <img src="https://avatars.githubusercontent.com/u/126496711?v=4" width="100;" alt="pc-quiknode"/> <br /> <sub><b>Peter Chung</b></sub> </a> </td> <td align="center"> <a href="https://github.com/muhammad-fiaz"> <img src="https://avatars.githubusercontent.com/u/75434191?v=4" width="100;" alt="muhammad-fiaz"/> <br /> <sub><b>Muhammad Fiaz</b></sub> </a> </td> <td align="center"> <a href="https://github.com/jasonkneen"> <img src="https://avatars.githubusercontent.com/u/502002?v=4" width="100;" alt="jasonkneen"/> <br /> <sub><b>Jason Kneen</b></sub> </a> </td> <td align="center"> <a href="https://github.com/Hchunjun"> <img src="https://avatars.githubusercontent.com/u/11238835?v=4" width="100;" alt="Hchunjun"/> <br /> <sub><b>shan</b></sub> </a> </td> <td align="center"> <a href="https://github.com/naabakkcrypto"> <img src="https://avatars.githubusercontent.com/u/258882739?v=4" width="100;" alt="naabakkcrypto"/> <br /> <sub><b>Naabakk</b></sub> </a> </td> <td align="center"> <a href="https://github.com/mikemikimike"> <img src="https://avatars.githubusercontent.com/u/186855910?v=4" width="100;" alt="mikemikimike"/> <br /> <sub><b>mikemikimike</b></sub> </a> </td> </tr> <tr> <td align="center"> <a href="https://github.com/mgajewskik"> <img src="https://avatars.githubusercontent.com/u/47600161?v=4" width="100;" alt="mgajewskik"/> <br /> <sub><b>Maciej Gajewski</b></sub> </a> </td> <td align="center"> <a href="https://github.com/ajitam"> <img src="https://avatars.githubusercontent.com/u/672661?v=4" width="100;" alt="ajitam"/> <br /> <sub><b>Matija Urh</b></sub> </a> </td> <td align="center"> <a href="https://github.com/machj8968-lab"> <img src="https://avatars.githubusercontent.com/u/274563644?v=4" width="100;" alt="machj8968-lab"/> <br /> <sub><b>machj8968-lab</b></sub> </a> </td> <td align="center"> <a href="https://github.com/buyua9"> <img src="https://avatars.githubusercontent.com/u/274236111?v=4" width="100;" alt="buyua9"/> <br /> <sub><b>buyua9</b></sub> </a> </td> <td align="center"> <a href="https://github.com/aziham"> <img src="https://avatars.githubusercontent.com/u/94932043?v=4" width="100;" alt="aziham"/> <br /> <sub><b>Hamza</b></sub> </a> </td> <td align="center"> <a href="https://github.com/GPT-AGI"> <img src="https://avatars.githubusercontent.com/u/181050363?v=4" width="100;" alt="GPT-AGI"/> <br /> <sub><b>Ting Zhang</b></sub> </a> </td> </tr> <tr> <td align="center"> <a href="https://github.com/RockinPaul"> <img src="https://avatars.githubusercontent.com/u/3741448?v=4" width="100;" alt="RockinPaul"/> <br /> <sub><b>Paul Zarudnev</b></sub> </a> </td> <td align="center"> <a href="https://github.com/oantoshchenko"> <img src="https://avatars.githubusercontent.com/u/1749531?v=4" width="100;" alt="oantoshchenko"/> <br /> <sub><b>Oleksandr Antoshchenko</b></sub> </a> </td> <td align="center"> <a href="https://github.com/lwsinclair"> <img src="https://avatars.githubusercontent.com/u/2829939?v=4" width="100;" alt="lwsinclair"/> <br /> <sub><b>Lawrence Sinclair</b></sub> </a> </td> <td align="center"> <a href="https://github.com/jungdaesuh"> <img src="https://avatars.githubusercontent.com/u/78460559?v=4" width="100;" alt="jungdaesuh"/> <br /> <sub><b>jungdaesuh</b></sub> </a> </td> <td align="center"> <a href="https://github.com/josephgoksu"> <img src="https://avatars.githubusercontent.com/u/6523823?v=4" width="100;" alt="josephgoksu"/> <br /> <sub><b>Joseph Goksu</b></sub> </a> </td> <td align="center"> <a href="https://github.com/EikoocS"> <img src="https://avatars.githubusercontent.com/u/80829508?v=4" width="100;" alt="EikoocS"/> <br /> <sub><b>EikoocS</b></sub> </a> </td> </tr> <tr> <td align="center"> <a href="https://github.com/Dhravya"> <img src="https://avatars.githubusercontent.com/u/63950637?v=4" width="100;" alt="Dhravya"/> <br /> <sub><b>Dhravya Shah</b></sub> </a> </td> <td align="center"> <a href="https://github.com/dflor003"> <img src="https://avatars.githubusercontent.com/u/103017?v=4" width="100;" alt="dflor003"/> <br /> <sub><b>Danil Flores</b></sub> </a> </td> <td align="center"> <a href="https://github.com/DAESA24"> <img src="https://avatars.githubusercontent.com/u/173488786?v=4" width="100;" alt="DAESA24"/> <br /> <sub><b>DAESA24</b></sub> </a> </td> <td align="center"> <a href="https://github.com/ammesonb"> <img src="https://avatars.githubusercontent.com/u/2522710?v=4" width="100;" alt="ammesonb"/> <br /> <sub><b>Brett Ammeson</b></sub> </a> </td> <td align="center"> <a href="https://github.com/auto-pr-bot"> <img src="https://avatars.githubusercontent.com/u/245575329?v=4" width="100;" alt="auto-pr-bot"/> <br /> <sub><b>auto-pr-bot</b></sub> </a> </td> <td align="center"> <a href="https://github.com/anush008"> <img src="https://avatars.githubusercontent.com/u/46051506?v=4" width="100;" alt="anush008"/> <br /> <sub><b>Anush</b></sub> </a> </td> </tr> <tr> <td align="center"> <a href="https://github.com/annelchavez-11594"> <img src="https://avatars.githubusercontent.com/u/271067077?v=4" width="100;" alt="annelchavez-11594"/> <br /> <sub><b>Annel Chavez</b></sub> </a> </td> <td align="center"> <a href="https://github.com/atao2004"> <img src="https://avatars.githubusercontent.com/u/148929819?v=4" width="100;" alt="atao2004"/> <br /> <sub><b>Anna Tao</b></sub> </a> </td> </tr> <tbody> </table> <!-- readme: contributors -end -->

docs/associative-recall.md

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Associative recall

Associative recall is not strict truth. It is the mode for pattern matching, emotional continuity, personalization, and reflection. It may surface superseded or contradicted memories, but only ever with an explicit status label and only when the contract allows it. Because it never mutates state, associative recall can never affect strict truth.

Signals

associativeRecall(query, deps) combines several signals per candidate:

  1. Vector similarity — cosine between the query embedding and the memory.

  2. Lexical / BM25 — a BM25 score over the admitted corpus, normalised in-set.

  3. Entity overlap — resolved entities present in the memory.

  4. ACT-R activation — recency + salience + relevance − contradiction, passed through a sigmoid.

  5. Spreading activation — bounded propagation over associative edges (below).

  6. Emotional residue — an emotional-facet boost for personalization.

A small status penalty downranks (but never excludes) superseded and contradicted memories, so strict facts still tend to rank above historical ones.

Policy

associative_recall(query, deps, policy) takes ranking and evidence dilution as one associative_policy struct — not separate modules. Every stage (matrix retrieval, RM3 feedback, evidence/calendar/derivation reranking, Hopfield, session coverage) is a field on that struct, zeroed or flagged off to disable it:

policy.flags fieldEffect when true
matrix_retrievalSeeds spreading activation from a whitened multi-column matrix instead of a flat relevance threshold.
rm3Expands query terms from top feedback documents before BM25 scoring.
evidence_rerankApplies attribution/assertion/exclusion evidence adjustments on top of the base score.
calendar_rerankBoosts memories that fall inside a date window parsed from the query.
derivation_rerankLets evidence support treat paraphrased/derived claims as supporting evidence.
hopfieldRuns a modern-Hopfield pattern snap and attaches it to the result as hopfield (observational only).
session_coverageWidens aggregate/list queries to cover distinct conversations instead of top-K alone.

policy.weights, policy.columns, policy.seeds, policy.evidence, and policy.budgets hold the numeric knobs (scoring weights, matrix columns, seed density, evidence gains, rerank depth) for those same stages. default_associative_policy() returns the production defaults; associative_policy_from_env(env, overrides) maps environment variables onto a copy of those defaults:

EnvPolicy fieldDefault
OM_MATRIX_RETRIEVAL !== '0'flags.matrix_retrievaltrue
OM_RM3 === '1'flags.rm3false
LONGMEMORY_EVIDENCE_RERANK !== '0'flags.evidence_reranktrue
LONGMEMORY_CALENDAR_RERANK !== '0'flags.calendar_reranktrue
LONGMEMORY_DERIVATION_RERANK !== '0'flags.derivation_reranktrue
LONGMEMORY_SESSION_COVERAGE === '1'flags.session_coveragefalse

create_memory(config) reads the environment once at construction time and closes over the resulting policy; it is not re-read per call, so live environment edits do not affect an already-open instance. Pass config.associative_policy to override specific fields for one instance without touching the environment — it is not part of the public npm API (src/index.ts), only an instance-level setting.

The ranking helpers that implement these stages (fusion.ts, matrix_fusion.ts, hopfield_recall.ts, rerank.ts, activation_spread.ts, evidence_selection.ts) are private to associative_recall.ts — they are not exported from src/core/recall/index.ts and should not be imported directly. Drive every test or integration through associative_recall(query, deps, policy) instead.

Controlled spreading activation

Activation flows over the graph one hop at a time:

$$ \text{activation}{\text{next}} = (1 - \alpha),\text{query_seed} + \alpha,(T \cdot \text{activation}{\text{current}}) $$

where $T$ is the edge transition matrix (edge weight × confidence, row-normalised on the source). Spreading is bounded: it only visits nodes within maxHops (default 2) of a seed and prunes tiny activations. This guarantees that an irrelevant or disconnected graph neighbourhood can never flood the recall context.

Hopfield-style recall

For associative mode only, a modern-Hopfield update pulls a partial cue toward the nearest stored pattern:

$$ \text{retrieved} = V \cdot \text{softmax}(\beta,K^{\top} q) $$

$\beta$ (inverse temperature) controls how sharply retrieval snaps to a single pattern. This is a pattern operation, not a truth operation — it returns the memory whose pattern is most similar, which may well be a superseded or subjective memory.

Status labeling

Every returned memory carries a status label:

  • superseded — the memory was superseded (always labeled — rule 2).

  • contradicted — the memory is contradicted (always labeled — rule 3).

  • emotional_residue — emotional memory usable for personalization (rule 4).

  • weak_pattern — low-confidence associative match.

  • active — a current, confident memory.

Rules

  1. Associative recall cannot update strict truth (it is read-only).

  2. Superseded memories must be labeled superseded.

  3. Contradicted memories must be labeled contradicted.

  4. Emotional residue may be used for personalization / emotional context.

  5. Graph expansion must be bounded (maxHops, activation pruning).

Example

An old emotional memory — "I felt joy at the beach with my dog" — surfaces associatively for the cue "beach dog" with the label emotional_residue, even though it is years old and would never pass strict recall. A superseded preference still appears, but tagged superseded, and running associative recall never changes what strict recall returns.

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Advantages

  • Supports temporal truth by separating recorded time from valid time, allowing for historical accuracy. evidence: README.md
  • Provides immutable memory where content and provenance are preserved, preventing accidental overwrites. evidence: README.md
  • Enforces governance policies for projects, teams, and roles to ensure secure and scoped memory access. evidence: README.md

Limitations

  • Requires manual configuration for network deployment, including API key management and TLS termination. evidence: README.md
  • The Python SDK requires a running LongMemory server instance for persistent storage. evidence: README.md

Security

Risk level: Low risk

This brain runs

Install scripts (1)

  • Makefile (make install): corepack enable; pnpm install --frozen-lockfile

MCP servers (2)

  • integrations/claude-code-longmemory/.mcp.json (longmemory): longmemory mcp --project current
  • integrations/gemini-cli-longmemory/gemini-extension.json (longmemory): longmemory mcp --project current
  • Low risk: scripts-present (benchmarks/src/official.ts)
  • Low risk: scripts-present (src/cli/index.ts)
  • Low risk: scripts-present (tools/check-integrations.mjs)

Attribution and licence

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@nullure
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Cavira (Community)
Source
CaviraOSS/LongMemory · commit aec98ce
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Apache License 2.0 (files served here)
  • ATTRIBUTION.md (written by AutoGineer)
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