> ## Documentation Index
> Fetch the complete documentation index at: https://docs.orq.ai/llms.txt
> Use this file to discover all available pages before exploring further.

> Create a new memory store to persist context across AI agent sessions. Configure storage settings for long-term memory and conversation recall.

# Create memory store



## OpenAPI

````yaml post /v2/memory-stores
openapi: 3.1.0
info:
  title: orq.ai API
  version: '2.0'
  description: orq.ai API documentation
servers:
  - url: https://api.orq.ai
security:
  - ApiKey: []
tags:
  - name: Guardrail Rules
  - name: Policies
  - name: Routing Rules
  - name: Files
    description: File upload and retrieval operations.
  - name: FilesService
  - name: Projects
    description: Projects organize resources within a workspace
  - name: ProjectsService
  - name: Skills
    description: >-
      Skills are modular instructions you can use to codify processes and
      conventions
  - name: SkillsService
  - name: Responses
  - description: >-
      Run agents on a cadence — cron, interval, or one-off. Minimum firing
      interval is 1 hour.
    name: Agent Schedules
  - name: Reporting
    description: >-
      GenAI reporting API over canonical analytics rollups. Accepts a metric
      name, time range, grain, group-by, and filters; returns a typed time
      series and optional totals.
  - name: ReportingService
    description: |-
      ReportingService exposes a single QueryReport RPC that maps allowlisted
       analytics payloads onto safe rollup queries. Callers never send SQL;
       the backend picks the rollup family and grain from the metric
       catalogue, the requested range, and the requested grouping.
externalDocs:
  url: https://docs.orq.ai
  description: orq.ai Documentation
paths:
  /v2/memory-stores:
    post:
      tags:
        - Memory Stores
      summary: Create memory store
      operationId: CreateMemoryStore
      requestBody:
        content:
          application/json:
            schema:
              type: object
              properties:
                key:
                  type: string
                  minLength: 1
                  maxLength: 255
                  pattern: ^[A-Za-z]([A-Za-z0-9]*([._][A-Za-z0-9]+)*)?$
                  description: >-
                    The unique key of the memory store. The key is unique and
                    inmmutable and cannot be repeated within the same workspace.
                embedding_config:
                  type: object
                  properties:
                    model:
                      type: string
                      description: >-
                        The embeddings model to use for the knowledge base in
                        the format "provider/model" for public models or
                        "workspaceKey@provider/model" for private workspace
                        models. This model will be used to embed the chunks when
                        they are added to the knowledge base. Refer to the
                        (Supported models)[/docs/proxy/supported-models] to
                        browse available models.
                  required:
                    - model
                description:
                  type: string
                  description: >-
                    The description of the memory store. Be as precise as
                    possible to help the AI to understand the purpose of the
                    memory store.
                ttl:
                  type: number
                  description: >-
                    The default time to live of every memory document created
                    within the memory store. Useful to control if the documents
                    in the memory should be store for short or long term.
                path:
                  type: string
                  description: >-
                    Entity storage path.


                    With workspace-level API keys, use the format
                    `project/folder/subfolder/...`. The first element identifies
                    the project, followed by nested folders (auto-created as
                    needed). Example: `Default/agents`.


                    With project-level API keys, the project is predetermined by
                    the API key, so the path is relative to that project.
                    Example: `agents`. For backward compatibility, a leading
                    project name is ignored when it matches the scoped project.
                  example: Default
              required:
                - key
                - embedding_config
                - description
                - path
      responses:
        '201':
          description: Memory store successfully created with the specified configuration.
          content:
            application/json:
              schema:
                type: object
                properties:
                  _id:
                    type: string
                    description: The unique identifier of the memory store
                  key:
                    type: string
                    minLength: 1
                    maxLength: 255
                    pattern: ^[A-Za-z]([A-Za-z0-9]*([._][A-Za-z0-9]+)*)?$
                    description: >-
                      The unique key of the memory store. The key is unique and
                      inmmutable and cannot be repeated within the same
                      workspace.
                  description:
                    type: string
                    description: >-
                      The description of the memory store. Be as precise as
                      possible to help the AI to understand the purpose of the
                      memory store.
                  created_by_id:
                    type: string
                    description: The user ID of the creator
                  updated_by_id:
                    type: string
                    description: The user ID of the last updater
                  created:
                    type: string
                    description: The creation date of the memory store
                  updated:
                    type: string
                    description: The last update date of the memory store
                  ttl:
                    type: number
                    description: >-
                      The default time to live of every memory document created
                      within the memory store. Useful to control if the
                      documents in the memory should be store for short or long
                      term.
                  embedding_config:
                    type: object
                    properties:
                      model:
                        type: string
                        description: >-
                          The embeddings model to use for the knowledge base in
                          the format "provider/model" for public models or
                          "workspaceKey@provider/model" for private workspace
                          models. This model will be used to embed the chunks
                          when they are added to the knowledge base. Refer to
                          the (Supported models)[/docs/proxy/supported-models]
                          to browse available models.
                    required:
                      - model
                required:
                  - _id
                  - key
                  - description
                  - created
                  - updated
                  - embedding_config
components:
  securitySchemes:
    ApiKey:
      type: http
      scheme: bearer
      bearerFormat: JWT

````