> ## Documentation Index > Fetch the complete documentation index at: https://docs.memmachine.ai/llms.txt > Use this file to discover all available pages before exploring further. # Get Project > Retrieve a project. Returns the project identified by `org_id` and `project_id`, following the same rules as project creation. Each project acts as an isolated memory namespace. Queries and operations only access memories (episodes) stored within this project. No data from other projects is visible or included in any background processing, such as memory summarization or knowledge extraction. The response includes the project's description and effective configuration. If the project does not exist, a not-found error is returned. ## OpenAPI ````yaml /openapi.json post /api/v2/projects/get openapi: 3.1.0 info: title: MemMachine API version: 0.3.10 description: >- Architectural Memory Systems for AI Agents. Specialized in episodic and semantic memory management with strict namespace isolation. servers: - url: http://localhost:8080 security: [] tags: - name: Configuration description: System overview and memory subsystem configuration. - name: Episodic Configuration description: Per-project episodic memory subsystem configuration. - name: Memories description: Core operations for episodic and semantic memory ingestion and retrieval. - name: Projects description: Lifecycle management for isolated memory namespaces. - name: Resources description: Embedder, language model, and reranker lifecycle management. - name: 'Semantic Memory: Categories' description: Category, template, and tag management for semantic sets. - name: 'Semantic Memory: Features' description: Add, retrieve, and update individual semantic features. - name: 'Semantic Memory: Sets' description: Set type and set ID lifecycle, listing, and configuration. - name: System description: Infrastructure, health, and observability. paths: /api/v2/projects/get: post: tags: - Projects summary: Get Project description: |- Retrieve a project. Returns the project identified by `org_id` and `project_id`, following the same rules as project creation. Each project acts as an isolated memory namespace. Queries and operations only access memories (episodes) stored within this project. No data from other projects is visible or included in any background processing, such as memory summarization or knowledge extraction. The response includes the project's description and effective configuration. If the project does not exist, a not-found error is returned. operationId: get_project requestBody: content: application/json: schema: $ref: '#/components/schemas/GetProjectSpec' required: true responses: '200': description: Successful Response content: application/json: schema: $ref: '#/components/schemas/ProjectResponse' '422': description: Validation Error content: application/json: schema: $ref: '#/components/schemas/HTTPValidationError' components: schemas: GetProjectSpec: properties: org_id: type: string title: Org Id description: |2- The unique identifier of the organization. - Must not contain slashes (`/`). - Must contain only letters, numbers, underscores, hyphens, colon, and Unicode characters (e.g., Chinese/Japanese/Korean). No slashes or other symbols are allowed. This value determines the namespace the project belongs to. examples: - MemVerge - AI_Labs project_id: type: string title: Project Id description: |2- The identifier of the project. - Must be unique within the organization. - Must not contain slashes (`/`). - Must contain only letters, numbers, underscores, hyphens, colon, and Unicode characters (e.g., Chinese/Japanese/Korean). No slashes or other symbols are allowed. This ID is used in API paths and resource locations. examples: - memmachine - research123 - qa_pipeline type: object required: - org_id - project_id title: GetProjectSpec description: |- Specification model for retrieving a project. This model defines the parameters required to fetch an existing project. Both the organization ID and project ID follow the standard `SafeId` validation rules. The combination of `org_id` and `project_id` uniquely identifies the project to retrieve. ProjectResponse: properties: org_id: type: string title: Org Id description: |2- The unique identifier of the organization this project belongs to. Returned exactly as stored by the system. project_id: type: string title: Project Id description: |2- The identifier of the project. This value uniquely identifies the project within the organization. description: type: string title: Description description: |2- A human-readable description of the project. Used for display purposes in UIs and dashboards. Optional; defaults to an empty string. default: '' config: $ref: '#/components/schemas/ProjectConfig' description: |2- Configuration settings associated with this project. Defines which models (reranker, embedder) to use. If any values within `ProjectConfig` are empty, global defaults are applied. type: object required: - org_id - project_id title: ProjectResponse description: >- Response model returned after project operations (e.g., creation, update, fetch). Contains the resolved identifiers and configuration of the project as stored in the system. Field formats follow the same validation rules as in `CreateProjectSpec`. HTTPValidationError: properties: detail: items: $ref: '#/components/schemas/ValidationError' type: array title: Detail type: object title: HTTPValidationError ProjectConfig: properties: backend: anyOf: - type: string enum: - declarative - event - type: 'null' title: Backend description: Long-term memory backend. New projects should set 'event'. reranker: type: string title: Reranker description: |2- The name of the reranker model to use for this project. - Must refer to a reranker model defined in the system configuration. - If set to an empty string (default), the globally configured reranker will be used. Rerankers typically re-score retrieved documents to improve result quality. default: '' examples: - bge-reranker-large - my-custom-reranker embedder: type: string title: Embedder description: |2- The name of the embedder model to use for this project. - Must refer to an embedder model defined in the system configuration. - If set to an empty string (default), the globally configured embedder will be used. Embedders generate vector embeddings for text to support semantic search and similarity operations. default: '' examples: - bge-base-en - my-embedder vector_graph_store: type: string title: Vector Graph Store description: VectorGraphStore resource id (declarative backend only) default: '' vector_store: type: string title: Vector Store description: VectorStore resource id (event backend only) default: '' segment_store: type: string title: Segment Store description: >- SQL engine resource id backing the segment store (event backend only) default: '' properties_schema: additionalProperties: type: string type: object title: Properties Schema description: >- User-defined filterable properties (event backend only). Maps name to type ("bool", "int", "float", "str", "datetime"). type: object title: ProjectConfig description: >- Project configuration model. Identifies the long-term-memory backend and the resource ids it should bind to. If any string field is left empty (""), the server falls back to its globally-configured defaults. Backend selection: - `backend="event"` (default for new projects): uses VectorStore + SegmentStore. Requires `vector_store` and `segment_store`. - `backend="declarative"`: uses VectorGraphStore. Requires `vector_graph_store`. - Empty/null `backend`: server falls back to its parse-time default (declarative, for backwards compatibility with pre-discriminator clients). ValidationError: properties: loc: items: anyOf: - type: string - type: integer type: array title: Location msg: type: string title: Message type: type: string title: Error Type type: object required: - loc - msg - type title: ValidationError ````