> ## 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 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: - description: List models available through the AI Router. name: Models - name: Guardrail Rules - name: Policies - name: Routing Rules - name: API keys description: >- API keys authenticate programmatic access to the workspace. The unified key model exposes opaque tokens, per-domain access grants, and budget / rate-limit constraints (see ADR 0001 and ADR 0002). - name: Budgets description: >- Budgets govern spend, token usage, and request rate across six scopes: workspace, project, identity, api-key, provider, and model. A budget is hierarchical and defense-in-depth — every applicable budget is a hard gate, and the most restrictive one wins per dimension (see ADR 0007). - name: Documentation description: >- Search the orq.ai documentation. Proxies the workspace's query to the hosted docs search index. - name: Files description: File upload and retrieval operations. - name: Identities description: >- Identities represent end users from your system for usage and engagement tracking. - name: Projects description: Projects organize resources within a workspace - name: Skills description: >- Skills are modular instructions you can use to codify processes and conventions - name: Responses - description: >- Run agents on a cadence — cron, interval, or one-off. Minimum firing interval is 1 hour. name: Agent Schedules - name: Embeddings - 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. 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 - 'null' description: The user ID of the creator updated_by_id: type: - string - 'null' 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 ````