> ## 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. # Add Language Model Endpoint > Add a new language model configuration at runtime. Creates a new language model with the specified configuration and attempts to initialize it immediately. This allows adding models without restarting the server. The configuration is persisted to the configuration file. **Security Warning**: API keys and credentials are stored in plain text in the configuration file. Only use this API in protected environments. To avoid storing secrets in plain text, use environment variable references (e.g., `"api_key": "$OPENAI_API_KEY"`) which will be resolved at runtime. Supported providers: - `openai-responses`: OpenAI models using the Responses API (requires api_key, model) - `openai-chat-completions`: OpenAI models using Chat Completions API (requires api_key, model) - `amazon-bedrock`: AWS Bedrock models (requires region, model_id) The response indicates whether the model was successfully initialized: - `success: true, status: ready`: Model is available for use - `success: false, status: failed`: Initialization failed (check error field) If initialization fails, the model configuration is still stored and can be retried later using the retry endpoint when the underlying service becomes available. Returns 422 if the provider type or configuration is invalid. ## OpenAPI ````yaml /openapi.json post /api/v2/config/resources/language_models 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/config/resources/language_models: post: tags: - Resources summary: Add Language Model Endpoint description: |- Add a new language model configuration at runtime. Creates a new language model with the specified configuration and attempts to initialize it immediately. This allows adding models without restarting the server. The configuration is persisted to the configuration file. **Security Warning**: API keys and credentials are stored in plain text in the configuration file. Only use this API in protected environments. To avoid storing secrets in plain text, use environment variable references (e.g., `"api_key": "$OPENAI_API_KEY"`) which will be resolved at runtime. Supported providers: - `openai-responses`: OpenAI models using the Responses API (requires api_key, model) - `openai-chat-completions`: OpenAI models using Chat Completions API (requires api_key, model) - `amazon-bedrock`: AWS Bedrock models (requires region, model_id) The response indicates whether the model was successfully initialized: - `success: true, status: ready`: Model is available for use - `success: false, status: failed`: Initialization failed (check error field) If initialization fails, the model configuration is still stored and can be retried later using the retry endpoint when the underlying service becomes available. Returns 422 if the provider type or configuration is invalid. operationId: add_language_model requestBody: content: application/json: schema: $ref: '#/components/schemas/AddLanguageModelSpec' required: true responses: '201': description: Successful Response content: application/json: schema: $ref: '#/components/schemas/UpdateResourceResponse' '422': description: Validation Error content: application/json: schema: $ref: '#/components/schemas/HTTPValidationError' components: schemas: AddLanguageModelSpec: properties: name: type: string title: Name description: |2- Unique name/identifier for the language model. provider: type: string enum: - openai-responses - openai-chat-completions - amazon-bedrock title: Provider description: |2- The language model provider type (e.g., 'openai-responses', 'openai-chat-completions', 'amazon-bedrock'). config: additionalProperties: true type: object title: Config description: |2- Provider-specific configuration settings. type: object required: - name - provider - config title: AddLanguageModelSpec description: Specification for adding a new language model. UpdateResourceResponse: properties: success: type: boolean title: Success description: |2- Whether the operation succeeded. status: $ref: '#/components/schemas/ResourceStatus' description: |2- Current status of the resource after the operation. error: anyOf: - type: string - type: 'null' title: Error description: |2- Error message if the operation failed. type: object required: - success - status title: UpdateResourceResponse description: Response model for resource update operations. HTTPValidationError: properties: detail: items: $ref: '#/components/schemas/ValidationError' type: array title: Detail type: object title: HTTPValidationError ResourceStatus: type: string enum: - ready - failed - pending title: ResourceStatus description: Status of a resource. 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 ````