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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/answering.md

Optional Evidence-First Answering

answer_from_evidence is a TypeScript library adapter above memory retrieval. It works with caller-supplied evidence and a caller-owned language model. It does not change createMemory, ingestion, recall, HTTP routes, MCP tools, or model configuration. There is no built-in hosted provider, automatic search, retry, training step, or extra verifier model.

Use it for evidence-grounded questions about documents, projects, personal memories, historical records, or fictional material. It is not an unrestricted chat or creative-writing API: answers require cited evidence. Existing recall remains available for applications that need different answering behavior.

Use Authorized Recall Results

import {
    answer_from_evidence,
    type evidence_reader_model,
    type long_memory,
    type public_recall_query,
} from 'longmemory';

export async function answer_authorized_query(
    memory: long_memory,
    model: evidence_reader_model,
    scoped_query: public_recall_query,
    signal?: AbortSignal,
) {
    const recalled = await memory.recall(scoped_query);
    if (!('context' in recalled)) throw new Error('Recall did not return an evidence context');
    return answer_from_evidence(
        {
            question: scoped_query.text,
            sources: recalled.context.evidence,
            knowledge: 'evidence-only',
            signal,
        },
        model,
    );
}

Construct scoped_query using your application's authenticated identity, permissions, selected world, and temporal scope. Do not let an untrusted caller choose another user's scope. The adapter itself has no database access and does not authorize supplied sources. Only pass evidence the user may access and that your selected model is allowed to process; apply redaction and retention rules before calling it.

You can instead supply your own evidence objects. Each has a unique id and text, with optional speaker, observed_at (Unix milliseconds), and status (current, superseded, or contradicted). Other fields are stripped before generation, including arbitrary metadata. IDs and quotes are preserved exactly. Returned references refer to these supplied evidence objects; when the text is a multi-source bundle, keep your original bundle provenance for further inspection.

Model Contract

Provide an object with generate(request): Promise<{ text: string }>. The request contains system, user, json: true, max_tokens, and an AbortSignal. Map these to your model client's corresponding options and return its response text. Local and hosted models use the same interface; no SDK is required by the adapter. Use a model that can follow structured-output instructions.

The callback must disable tools/browsing, use the supplied prompts without injecting private ambient context, and honor the output limit and abort signal. The adapter makes at most one callback invocation, but cannot prevent retries or tool calls performed internally by your callback. Keep that transport policy explicit. The adapter will stop waiting on timeout/cancellation even if the callback ignores the signal; it cannot force that provider to stop computing or billing. Its post-response size check likewise cannot prevent upstream allocation or token spending, so enforce streaming/response limits in your client too.

Policies and Limits

OptionDefaultBehavior
knowledgeevidence-onlyNo separate general-knowledge inference; deductions may use supplied evidence
knowledge: allow-general-inferenceOpt-inAllows recommendations/inference based on grounded premises, with an inference field separate from source-backed facts
output_languageQuestion languageModel instruction; source excerpts remain in their original language
question_timeUnspecifiedOptional Unix-millisecond anchor for relative-time questions
max_context_tokens2048; maximum 32768Estimated allowance for serialized sources, not the whole prompt or a provider-exact token count
max_output_tokens2048; maximum 8192Requested limit passed to the model callback
max_response_chars32768; maximum 131072Rejects larger returned strings before JSON parsing
timeout_ms60000; maximum 300000Per-call timeout; combined with caller cancellation

Up to 64 sources are accepted, each at most 262144 UTF-16 code units. Sources are considered in supplied order. Oversized sources are omitted whole, not silently truncated; later smaller sources may still fit. used_source_ids means sent to the model, not necessarily cited. omitted_source_ids identifies skipped sources. No model call occurs if there is no usable evidence. Retrieval is the caller's responsibility, and the adapter does not fetch omitted or missing data.

The model returns a small ledger of subject/relation/object facts with exact source excerpts, a final answer, optional general inference, and missing-evidence descriptions. These are declarative facts, not a reasoning transcript. The output allows at most 32 facts with eight citations each; excessively large schemas fail closed. This is intentionally bounded, not an exhaustive whole-corpus report API.

Results and Errors

  • answered: a nonempty answer and at least one fact whose citations pass the reference/excerpt checks. missing_evidence is empty.

  • insufficient_evidence: answer and inference are null; missing evidence or ambiguity is described. Partial cited facts may be returned. Applications can render their own localized abstention; no fixed English user-facing answer is imposed. The no-source diagnostic message is internal English text.

  • invalid_response: malformed, oversized, inconsistent, or unsupported output. answer is null and draft facts/inference are discarded. validation_errors contains a reason code such as invalid_json, invalid_schema, unknown_source, or quote_not_in_source. There is no automatic repair call.

Invalid requests and duplicate source IDs throw. Model errors, timeout, and caller cancellation also reject the promise; they are not reported as successful abstentions. Handle errors at your application boundary and do not expose provider error details or private evidence in public logs.

citations_verified means only that every returned citation refers to a source actually sent and that its quote is an exact contiguous excerpt. It does not verify semantic entailment, source reliability, completeness, actor attribution, arithmetic, language compliance, or the truth of the answer. A model can still misinterpret a valid quote or leave unsupported claims in its answer. Evidence is serialized as data and the prompt rejects source instructions, but this is not a prompt-injection-proof security sandbox. Treat all returned strings as untrusted when rendering them. Use appropriate review for consequential decisions.

The adapter never writes facts, infers supersession, changes memory contracts, or merges event identities. Those remain core/application decisions. Automatic follow-up retrieval is intentionally absent; a future bounded extension needs independent evaluation and must preserve the original authorization and time scope.

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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

Created by
CaviraOSS
First committed by
@nullure
Declared authors
  • CaviraOSS (declared in package.json)
Publisher
Cavira (Community)
Source
CaviraOSS/LongMemory · commit aec98ce
Licence
Apache License 2.0 (files served here)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

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Install from source

459 of its files are not served here, so there is no ZIP. CaviraOSS/LongMemory at aec98ce