@mastra/core
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type { AssistantContent, CoreMessage, ToolContent, UserContent } from '@internal/ai-sdk-v4';\n\nimport type { AgentExecutionOptions } from '../agent/agent.types';\nimport type { AgentConfig } from '../agent/types';\nexport type { MastraDBMessage } from '../agent';\nimport type { EmbeddingModelId } from '../llm/model/index.js';\nimport type { ModelRouterModelId } from '../llm/model/provider-registry.js';\nimport type { MastraLanguageModel, MastraModelConfig } from '../llm/model/shared.types';\nimport type { RequestContext } from '../request-context';\nimport type { PublicSchema } from '../schema';\nimport type { MastraCompositeStore } from '../storage';\nimport type { DynamicArgument } from '../types';\nimport type { MastraEmbeddingModel, MastraEmbeddingOptions, MastraVector } from '../vector';\nimport type { VectorFilter } from '../vector/filter/base';\nimport type { MemoryProcessor } from '.';\n\nexport type { Message as AiMessageType } from '@internal/ai-sdk-v4';\nexport type { MastraLanguageModel };\n\n// Types for the memory system\nexport type MastraMessageV1 = {\n id: string;\n content: string | UserContent | AssistantContent | ToolContent;\n role: 'system' | 'user' | 'assistant' | 'tool' | 'signal';\n createdAt: Date;\n threadId?: string;\n resourceId?: string;\n toolCallIds?: string[];\n toolCallArgs?: Record<string, unknown>[];\n toolNames?: string[];\n type: 'text' | 'tool-call' | 'tool-result';\n};\n\n/**\n * @deprecated use MastraMessageV1 or MastraDBMessage\n */\nexport type MessageType = MastraMessageV1;\n\nexport type StorageThreadType = {\n id: string;\n title?: string;\n resourceId: string;\n createdAt: Date;\n updatedAt: Date;\n metadata?: Record<string, unknown>;\n};\n\n/**\n * Thread-specific Observational Memory metadata.\n * Stored on thread.metadata.mastra.om to keep thread-specific data\n * separate from the shared resource-level OM record.\n */\nexport type ThreadOMMetadata = {\n /** The current task being worked on in this thread */\n 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!Array.isArray(value);\n}\n\n/**\n * Helper to get OM metadata from a thread's metadata object.\n * Returns undefined if not present or if the structure is invalid.\n */\nexport function getThreadOMMetadata(threadMetadata?: Record<string, unknown>): ThreadOMMetadata | undefined {\n if (!threadMetadata) return undefined;\n const mastra = threadMetadata.mastra;\n if (!isPlainObject(mastra)) return undefined;\n const om = mastra.om;\n if (!isPlainObject(om)) return undefined;\n return om as ThreadOMMetadata;\n}\n\n/**\n * Helper to set OM metadata on a thread's metadata object.\n * Creates the nested structure if it doesn't exist.\n * Returns a new metadata object (does not mutate the original).\n * Safely handles cases where existing mastra/om values are not objects.\n */\nexport function setThreadOMMetadata(\n threadMetadata: Record<string, unknown> | undefined,\n omMetadata: ThreadOMMetadata,\n): Record<string, unknown> {\n const existing = threadMetadata ?? {};\n const existingMastra = isPlainObject(existing.mastra) ? existing.mastra : {};\n const existingOM = isPlainObject(existingMastra.om) ? existingMastra.om : {};\n\n return {\n ...existing,\n mastra: {\n ...existingMastra,\n om: {\n ...existingOM,\n ...omMetadata,\n },\n },\n };\n}\n\n/**\n * Memory-specific context passed via RequestContext under the 'MastraMemory' key\n * This provides processors with access to memory-related execution context\n */\nexport type MemoryRequestContext = {\n thread?: Partial<StorageThreadType> & { id: string };\n resourceId?: string;\n memoryConfig?: MemoryConfigInternal;\n};\n\n/**\n * Parse and validate memory runtime context from RequestContext\n * @param requestContext - The RequestContext to extract memory context from\n * @returns The validated MemoryRequestContext or null if not available\n * @throws Error if the context exists but is malformed\n */\nexport function parseMemoryRequestContext(requestContext?: RequestContext): MemoryRequestContext | null {\n if (!requestContext) {\n return null;\n }\n\n const memoryContext = requestContext.get('MastraMemory');\n if (!memoryContext) {\n return null;\n }\n\n // Validate the structure\n if (typeof memoryContext !== 'object' || memoryContext === null) {\n throw new Error(`Invalid MemoryRequestContext: expected object, got ${typeof memoryContext}`);\n }\n\n const ctx = memoryContext as Record<string, unknown>;\n\n // Validate thread if present\n if (ctx.thread !== undefined) {\n if (typeof ctx.thread !== 'object' || ctx.thread === null) {\n throw new Error(`Invalid MemoryRequestContext.thread: expected object, got ${typeof ctx.thread}`);\n }\n const thread = ctx.thread as Record<string, unknown>;\n if (typeof thread.id !== 'string') {\n throw new Error(`Invalid MemoryRequestContext.thread.id: expected string, got ${typeof thread.id}`);\n }\n }\n\n // Validate resourceId if present\n if (ctx.resourceId !== undefined && typeof ctx.resourceId !== 'string') {\n throw new Error(`Invalid MemoryRequestContext.resourceId: expected string, got ${typeof ctx.resourceId}`);\n }\n\n return memoryContext as MemoryRequestContext;\n}\n\nexport type MessageResponse<T extends 'raw' | 'core_message'> = {\n raw: MastraMessageV1[];\n core_message: CoreMessage[];\n}[T];\n\ntype BaseWorkingMemory = {\n enabled: boolean;\n /**\n * Scope for working memory storage.\n * - 'resource': Memory persists across all threads for the same resource/user (default)\n * - 'thread': Memory is isolated per conversation thread\n *\n * @default 'resource'\n */\n scope?: 'thread' | 'resource';\n /**\n * Experimental: deliver working memory to the model as a state signal instead of folding\n * it into the system message. Storage is unchanged. When `true`, `Memory` auto-attaches\n * a state-signal processor that emits snapshots or deltas with dedup via `cacheKey`, and\n * registers the working-memory tool as `setWorkingMemory` instead of `updateWorkingMemory`.\n *\n * Not supported with template working memory `version: 'vnext'`.\n *\n * @default false\n * @see docs/src/content/en/docs/agents/signals.mdx\n */\n useStateSignals?: boolean;\n /**\n * Whether the main agent manages working memory directly through tool/instruction injection.\n * Set to false when another path, such as Observational Memory extractors,\n * owns working memory updates.\n *\n * @default true\n */\n agentManaged?: boolean;\n /** @deprecated The `use` option has been removed. Working memory always uses tool-call mode. */\n use?: never;\n};\n\ntype TemplateWorkingMemory =\n | (BaseWorkingMemory & {\n template: string;\n schema?: never;\n version?: 'stable';\n })\n | (Omit<BaseWorkingMemory, 'useStateSignals'> & {\n template: string;\n schema?: never;\n version: 'vnext';\n useStateSignals?: false;\n });\n\ntype SchemaWorkingMemory = BaseWorkingMemory & {\n schema: PublicSchema;\n template?: never;\n};\n\ntype WorkingMemoryNone = BaseWorkingMemory & {\n template?: never;\n schema?: never;\n};\n\nexport type WorkingMemory = TemplateWorkingMemory | SchemaWorkingMemory | WorkingMemoryNone;\n\n/**\n * Vector index configuration for optimizing semantic recall performance.\n *\n * These settings are primarily supported by PostgreSQL with pgvector extension.\n * Other vector stores (Pinecone, Qdrant, Chroma, etc.) will use their default\n * configurations and ignore these settings.\n *\n * @see https://mastra.ai/docs/memory/semantic-recall#postgresql-index-optimization\n */\nexport type VectorIndexConfig = {\n /**\n * Type of vector index to create (PostgreSQL/pgvector only).\n * - 'ivfflat': Inverted file index, good balance of speed and recall\n * - 'hnsw': Hierarchical Navigable Small World, best performance for most cases\n * - 'flat': Exact nearest neighbor search, slow but 100% recall\n *\n * @default 'ivfflat'\n * @example\n * ```typescript\n * type: 'hnsw' // Recommended for production\n * ```\n */\n type?: 'ivfflat' | 'hnsw' | 'flat';\n\n /**\n * Distance metric for similarity calculations.\n * - 'cosine': Normalized dot product, good for text similarity\n * - 'euclidean': L2 distance, geometric distance in vector space\n * - 'dotproduct': Inner product, best for OpenAI embeddings\n *\n * Note: While defined here, most vector stores have their own metric configuration.\n *\n * @default 'cosine'\n * @example\n * ```typescript\n * metric: 'dotproduct' // Optimal for OpenAI embeddings\n * ```\n */\n metric?: 'cosine' | 'euclidean' | 'dotproduct';\n\n /**\n * Configuration for IVFFlat index (PostgreSQL only).\n * Controls the number of inverted lists for clustering vectors.\n */\n ivf?: {\n /**\n * Number of inverted lists (clusters) to create.\n * Higher values mean better recall but slower build time.\n * Recommended: rows/1000 for tables with > 1M rows.\n *\n * @default 100\n */\n lists?: number;\n };\n\n /**\n * Configuration for HNSW index (PostgreSQL only).\n * Hierarchical graph-based index with superior query performance.\n */\n hnsw?: {\n /**\n * Maximum number of bi-directional links per node.\n * Higher values increase recall and index size.\n *\n * @default 16\n * @example\n * ```typescript\n * m: 32 // Higher recall, larger index\n * ```\n */\n m?: number;\n\n /**\n * Size of dynamic candidate list during index construction.\n * Higher values mean better recall but slower index creation.\n *\n * @default 64\n * @example\n * ```typescript\n * efConstruction: 128 // Better quality, slower build\n * ```\n */\n efConstruction?: number;\n };\n};\n\n/**\n * Configuration for semantic recall using RAG-based retrieval.\n *\n * Enables agents to retrieve relevant messages from past conversations using vector similarity search.\n * Retrieved messages provide context from beyond the recent conversation history, helping agents\n * maintain continuity across longer interactions.\n *\n * @see https://mastra.ai/docs/memory/semantic-recall\n */\nexport type SemanticRecall = {\n /**\n * Number of semantically similar messages to retrieve from the vector database.\n * Higher values provide more context but increase token usage.\n *\n * @example\n * ```typescript\n * topK: 3 // Retrieve 3 most similar messages\n * ```\n */\n topK: number;\n\n /**\n * Amount of surrounding context to include with each retrieved message.\n * Can be a single number (same before/after) or an object with separate values.\n * Helps provide conversational flow around the matched message.\n *\n * @example\n * ```typescript\n * messageRange: 2 // Include 2 messages before and after\n * messageRange: { before: 1, after: 3 } // 1 before, 3 after\n * ```\n */\n messageRange: number | { before: number; after: number };\n\n /**\n * Scope for semantic search queries.\n * - 'resource': Search across all threads owned by the same resource/user (default)\n * - 'thread': Search only within the current conversation thread\n *\n * @default 'resource'\n * @example\n * ```typescript\n * scope: 'thread' // Limit recall to current thread only\n * ```\n */\n scope?: 'thread' | 'resource';\n\n /**\n * Vector index configuration (PostgreSQL/pgvector specific).\n * Other vector stores will use their default index configurations.\n * HNSW indexes typically provide better performance than IVFFlat.\n *\n * @example\n * ```typescript\n * indexConfig: {\n * type: 'hnsw',\n * metric: 'dotproduct', // Best for OpenAI embeddings\n * hnsw: { m: 16, efConstruction: 64 }\n * }\n * ```\n */\n indexConfig?: VectorIndexConfig;\n\n /**\n * Metadata filter for semantic search queries.\n * Allows filtering results by metadata fields using MongoDB-style query syntax.\n * Works in combination with scope-based filtering (resource_id/thread_id).\n *\n * @example\n * ```typescript\n * filter: {\n * projectId: { $eq: 'project-a' },\n * category: { $in: ['work', 'personal'] }\n * }\n * ```\n */\n filter?: VectorFilter;\n\n /**\n * Minimum similarity score threshold (0-1).\n * Messages below this threshold will be filtered out from semantic search results.\n *\n * @example\n * ```typescript\n * threshold: 0.7 // Only include messages with 70%+ similarity\n * ```\n */\n threshold?: number;\n\n /**\n * Index name for the vector store.\n * If not provided, will be auto-generated based on embedder model.\n *\n * @example\n * ```typescript\n * indexName: 'my-custom-index'\n * ```\n */\n indexName?: string;\n};\n\n/**\n * Model settings for Observer/Reflector agents in Observational Memory.\n * Uses the same settings as Agent.generate() modelSettings (temperature, maxOutputTokens, topP, etc.).\n */\nexport type ObservationalMemoryModelSettings = AgentExecutionOptions['modelSettings'];\n\nexport type ObservationalMemoryActivationTTL = number | string | 'auto' | false;\n\n/**\n * Configuration for the observation step in Observational Memory.\n */\nexport interface ObservationalMemoryObservationConfig {\n /**\n * Model for the Observer agent.\n * Can be a model ID string (e.g., 'openai/gpt-4o'), a LanguageModel instance,\n * a function that returns either (for dynamic model selection),\n * or an array of ModelWithRetries for fallback support.\n *\n * Cannot be set if a top-level `model` is also provided.\n *\n * @default 'google/gemini-2.5-flash'\n */\n model?: AgentConfig['model'];\n\n /**\n * Manage working memory through Observational Memory extraction.\n * When enabled alongside `workingMemory.enabled`, Memory supplies defaults that\n * disable main-agent working memory management and add the WorkingMemoryExtractor.\n * Set `workingMemory.agentManaged: true` to keep main-agent tools/instructions enabled.\n *\n * @default false\n */\n manageWorkingMemory?: boolean;\n\n /**\n * Token count of unobserved messages that triggers observation.\n * When unobserved message tokens exceed this, the Observer is called.\n *\n * @default 30000\n */\n messageTokens?: number;\n\n /**\n * Model settings for the Observer agent.\n * @default { temperature: 0.3, maxOutputTokens: 100_000 }\n */\n modelSettings?: ObservationalMemoryModelSettings;\n\n /**\n * Provider-specific options passed to the Observer model.\n * Use this for provider features like thinking budgets, safety settings, etc.\n *\n * @example\n * ```ts\n * providerOptions: {\n * google: { thinkingConfig: { thinkingBudget: 215 } }\n * }\n * ```\n *\n * @default { google: { thinkingConfig: { thinkingBudget: 215 } } }\n */\n providerOptions?: Record<string, Record<string, unknown> | undefined>;\n\n /**\n * Maximum tokens per batch when observing multiple threads.\n * Threads are chunked into batches of this size and processed in parallel.\n * Lower values = more parallelism but more API calls.\n * Higher values = fewer API calls but less parallelism.\n *\n * @default 10000\n */\n maxTokensPerBatch?: number;\n\n /**\n * Token interval for async background observation buffering.\n * Observations run asynchronously in the background at this interval,\n * storing results in a buffer. When the main `messageTokens` threshold is reached,\n * buffered observations are activated instantly (no blocking LLM call).\n *\n * Can be an absolute token count (e.g. `5_000`) or a fraction of `messageTokens`\n * (e.g. `0.25` means buffer every 25% of the threshold).\n *\n * Set to `false` to explicitly disable async buffering.\n *\n * Must resolve to less than `messageTokens`.\n *\n * @default 0.2 (buffer every 20% of messageTokens)\n * @example\n * ```ts\n * // Buffer every 5k tokens, activate at 20k\n * observation: {\n * messageTokens: 20_000,\n * bufferTokens: 5_000,\n * }\n * // Or equivalently, using a fraction:\n * observation: {\n * messageTokens: 20_000,\n * bufferTokens: 0.25,\n * }\n * // Disable async buffering (use synchronous observation)\n * observation: {\n * bufferTokens: false,\n * }\n * ```\n */\n bufferTokens?: number | false;\n\n /**\n * Ratio (0-1) of buffered observations to activate when threshold is reached.\n * Setting this below 1 keeps some observations in reserve, which helps maintain\n * conversation continuity and provides a buffer for the next activation cycle.\n *\n * Requires `bufferTokens` to also be set.\n *\n * @default 0.8 (activate 80% of buffered observations, keeping 20% in reserve)\n * @example\n * ```ts\n * // Activate 70% of buffered observations, keep 30% in reserve\n * observation: {\n * messageTokens: 20_000,\n * bufferTokens: 0.25,\n * bufferActivation: 0.7,\n * }\n * ```\n */\n bufferActivation?: number;\n\n /**\n * Time before buffered observations are force-activated after inactivity.\n * Accepts milliseconds as a number, a duration string like `\"5m\"` or `\"1hr\"`,\n * `\"auto\"` to choose a provider-aware TTL from the actor model's prompt-cache behavior,\n * or `false` to disable top-level `activateAfterIdle` for observations.\n * If unset, top-level `activateAfterIdle` is used for observations.\n */\n activateAfterIdle?: ObservationalMemoryActivationTTL;\n\n /**\n * Force-activate buffered observations when the actor provider/model changes.\n * If unset, top-level `activateOnProviderChange` is used for observations.\n */\n activateOnProviderChange?: boolean;\n\n /**\n * Token threshold above which synchronous (blocking) observation is forced.\n * When set, the system will never block for observation between `messageTokens`\n * and `blockAfter` — only async buffering and activation are used in that range.\n * Once unobserved tokens exceed `blockAfter`, a synchronous observation runs as a\n * last resort to prevent context window overflow.\n *\n * Accepts either:\n * - A **multiplier** (1 < value < 2): multiplied by `messageTokens`.\n * e.g. `blockAfter: 1.5` with `messageTokens: 20_000` → blocks at 30,000 tokens.\n * - An **absolute token count** (≥ 2): must be greater than `messageTokens`.\n * e.g. `blockAfter: 80_000` → blocks at 80,000 tokens.\n *\n * Only relevant when `bufferTokens` is set. When `bufferTokens` is not set,\n * synchronous observation is used directly at `messageTokens` and this setting has no effect.\n *\n * @default 1.2 (120% of `messageTokens`) when `bufferTokens` is set.\n *\n * @example\n * ```ts\n * // Multiplier: 1.5x messageTokens\n * observation: {\n * messageTokens: 20_000,\n * bufferTokens: 0.25,\n * blockAfter: 1.5, // resolves to 30,000\n * }\n * // Absolute: explicit token count\n * observation: {\n * messageTokens: 20_000,\n * bufferTokens: 5_000,\n * blockAfter: 80_000,\n * }\n * ```\n */\n blockAfter?: number;\n\n /**\n * Optional token budget for observer context.\n * When set, the \"Previous Observations\" section is truncated from the end\n * to keep the most recent observations within this budget, and pending\n * buffered reflections replace the raw observations they summarized.\n * Set to `0` for full truncation (omit previous observations entirely), or `false` to disable.\n *\n * @default undefined (disabled)\n */\n previousObserverTokens?: number | false;\n\n /**\n * Custom instructions appended to the Observer agent's system prompt.\n * Use this to customize what the Observer focuses on or how it formats observations.\n *\n * @example\n * ```ts\n * observation: {\n * instruction: 'Focus on user dietary preferences and allergies.',\n * }\n * ```\n */\n instruction?: string;\n\n /**\n * When enabled, the Observer suggests a short thread title based on the conversation.\n * The title is updated on the thread whenever the Observer runs.\n *\n * @default false\n */\n threadTitle?: boolean;\n\n /**\n * Whether image/file attachment parts are forwarded to the Observer LLM.\n * - `true` forwards attachments\n * - `false` drops attachments and leaves placeholder text\n * - `'auto'` checks model capabilities to decide\n *\n * @default true\n */\n observeAttachments?: 'auto' | boolean;\n}\n\n/**\n * Configuration for the reflection step in Observational Memory.\n */\nexport interface ObservationalMemoryReflectionConfig {\n /**\n * Model for the Reflector agent.\n * Can be a model ID string (e.g., 'openai/gpt-4o'), a LanguageModel instance,\n * a function that returns either (for dynamic model selection),\n * or an array of ModelWithRetries for fallback support.\n *\n * Cannot be set if a top-level `model` is also provided.\n *\n * @default 'google/gemini-2.5-flash'\n */\n model?: AgentConfig['model'];\n\n /**\n * Token count of observations that triggers reflection.\n * When observation tokens exceed this, the Reflector is called to condense them.\n *\n * @default 40000\n */\n observationTokens?: number;\n\n /**\n * Model settings for the Reflector agent.\n * @default { temperature: 0, maxOutputTokens: 100_000 }\n */\n modelSettings?: ObservationalMemoryModelSettings;\n\n /**\n * Provider-specific options passed to the Reflector model.\n * Use this for provider features like thinking budgets, safety settings, etc.\n *\n * @example\n * ```ts\n * providerOptions: {\n * google: { thinkingConfig: { thinkingBudget: 1024 } }\n * }\n * ```\n *\n * @default { google: { thinkingConfig: { thinkingBudget: 1024 } } }\n */\n providerOptions?: Record<string, Record<string, unknown> | undefined>;\n\n /**\n * Token threshold above which synchronous (blocking) reflection is forced.\n * When set with async reflection enabled, the system will not block for\n * reflection between `observationTokens` and `blockAfter` — only async\n * buffering and activation are used in that range. Once observation tokens\n * exceed `blockAfter`, a synchronous reflection runs as a last resort.\n *\n * Accepts either:\n * - A **multiplier** (1 < value < 2): multiplied by `observationTokens`.\n * e.g. `blockAfter: 1.5` with `observationTokens: 30_000` → blocks at 45,000 tokens.\n * - An **absolute token count** (≥ 2): must be greater than `observationTokens`.\n * e.g. `blockAfter: 50_000` → blocks at 50,000 tokens.\n *\n * Only relevant when `bufferActivation` is set. When `bufferActivation` is not set,\n * synchronous reflection is used directly at `observationTokens` and this setting has no effect.\n *\n * @default 1.2 (120% of `observationTokens`) when `bufferActivation` is set.\n */\n blockAfter?: number;\n\n /**\n * Time before buffered reflections are force-activated after inactivity.\n * Accepts milliseconds as a number, a duration string like `\"5m\"` or `\"1hr\"`,\n * `\"auto\"` to choose a provider-aware TTL from the actor model's prompt-cache behavior,\n * or `false` to disable idle activation for reflections.\n * Reflections do not inherit top-level `activateAfterIdle`; set this explicitly to enable.\n */\n activateAfterIdle?: ObservationalMemoryActivationTTL;\n\n /**\n * Force-activate buffered reflections when the actor provider/model changes.\n * Reflections do not inherit top-level `activateOnProviderChange`; set this explicitly to enable.\n */\n activateOnProviderChange?: boolean;\n\n /**\n * Ratio (0-1) controlling when async reflection buffering starts.\n * When observation tokens reach `observationTokens * bufferActivation`,\n * reflection runs asynchronously in the background. When the full\n * `observationTokens` threshold is reached, the buffered reflection\n * is spliced into the observation content instantly (no blocking LLM call).\n *\n * Only one buffered reflection is maintained at a time. On activation,\n * the buffered reflection replaces the line range it was generated from,\n * and any new observations appended after that range are preserved.\n *\n * Requires `observation.bufferTokens` to also be set (async observation).\n *\n * @example\n * ```ts\n * reflection: {\n * observationTokens: 30_000,\n * bufferActivation: 0.5, // Start buffering at 15k tokens\n * }\n * ```\n */\n bufferActivation?: number;\n\n /**\n * Custom instructions appended to the Reflector agent's system prompt.\n * Use this to customize how the Reflector consolidates observations.\n *\n * @example\n * ```ts\n * reflection: {\n * instruction: 'Consolidate observations and remove duplicates.',\n * }\n * ```\n */\n instruction?: string;\n}\n\n/**\n * Configuration for Observational Memory.\n *\n * Observational Memory is a three-tier memory system that uses an Observer agent\n * to extract observations from conversations and a Reflector agent to compress them.\n * This enables efficient long-term memory with minimal context usage.\n *\n * Can be set to `true` to enable with defaults, or an object to customize.\n *\n * @example\n * ```typescript\n * // Enable with defaults\n * observationalMemory: true\n *\n * // Custom configuration\n * observationalMemory: {\n * scope: 'resource',\n * model: 'google/gemini-2.5-flash',\n * observation: {\n * messageTokens: 20_000,\n * },\n * reflection: {\n * observationTokens: 90_000,\n * },\n * }\n * ```\n */\nexport interface ObservationalMemoryOptions {\n /**\n * Enable or disable Observational Memory.\n * When omitted, defaults to `true` (enabled).\n * Only `enabled: false` explicitly disables it.\n *\n * @default true\n */\n enabled?: boolean;\n\n /**\n * Model for both Observer and Reflector agents.\n * Sets the model for both agents at once. Cannot be used together with\n * `observation.model` or `reflection.model` — an error will be thrown.\n *\n * @default 'google/gemini-2.5-flash'\n */\n model?: AgentConfig['model'];\n\n /**\n * Observation step configuration for extracting observations from conversations.\n */\n observation?: ObservationalMemoryObservationConfig;\n\n /**\n * Reflection step configuration for compressing observations.\n */\n reflection?: ObservationalMemoryReflectionConfig;\n\n /**\n * Memory scope for observations.\n * - 'resource': Observations span all threads for a resource (cross-thread memory)\n * - 'thread': Observations are per-thread (default)\n *\n * @default 'thread'\n */\n scope?: 'resource' | 'thread';\n\n /**\n * Time before buffered observations are force-activated after inactivity.\n * Accepts milliseconds as a number, a duration string like `\"5m\"` or `\"1hr\"`,\n * or `\"auto\"` to choose a provider-aware TTL from the actor model's prompt-cache behavior.\n * When the gap between the current time and the last assistant message part's `createdAt`\n * exceeds this value, buffered observations activate regardless of whether the\n * token threshold has been reached. Useful to align with prompt cache TTLs.\n *\n * Reflections do not inherit this setting. Use `reflection.activateAfterIdle` to\n * opt reflections into idle activation.\n *\n * @example 300_000\n * @example \"5m\"\n * @example \"1hr\"\n * @example \"auto\"\n */\n activateAfterIdle?: ObservationalMemoryActivationTTL;\n\n /**\n * Force-activate buffered observations when the actor provider/model changes.\n * Useful when switching between models that do not share prompt caches.\n *\n * Reflections do not inherit this setting. Use `reflection.activateOnProviderChange`\n * to opt reflections into provider-change activation.\n */\n activateOnProviderChange?: boolean;\n\n /**\n * Share the token budget between messages and observations.\n * When true, the total budget = observation.messageTokens + reflection.observationTokens.\n * - Messages can use more space when observations are small\n * - Observations can use more space when messages are small\n *\n * This helps maximize context usage by allowing flexible allocation.\n *\n * @default false\n */\n shareTokenBudget?: boolean;\n\n /**\n * When true, inserts temporal-gap reminder markers before new user messages after\n * significant inactivity. These markers are persisted in memory and also emitted\n * as inline reminder events for clients that want to render them specially.\n *\n * @default false\n */\n temporalMarkers?: boolean;\n\n /**\n * Enable retrieval-mode observation groups as durable pointers\n * to raw message history. When enabled, observation groups keep `_range`\n * metadata visible in context and a `recall` tool is registered so the actor\n * can inspect raw messages behind a stored observation summary.\n *\n * - `true` — recall tool with cross-thread browsing by default\n * - `{ vector: true }` — also enables semantic search using Memory-level vector/embedder\n * - `{ scope: 'thread' }` — restricts the recall tool to the current thread only\n * - `{ vector: true, scope: 'thread' }` — current-thread browsing + semantic search\n *\n * `scope` defaults to `'resource'` (cross-thread browsing, thread listing, and search).\n * Set to `'thread'` to restrict to the current thread only.\n *\n * @default false\n */\n retrieval?: boolean | { vector?: boolean; scope?: 'thread' | 'resource' };\n}\n\n/**\n * Check if observational memory is enabled from a `boolean | ObservationalMemoryOptions` value.\n *\n * - `true` → enabled\n * - `false` → disabled\n * - `{ enabled: false }` → disabled\n * - `{ ... }` (without `enabled: false`) → enabled\n * - `undefined` → disabled\n */\nexport function isObservationalMemoryEnabled(\n config: boolean | ObservationalMemoryOptions | undefined,\n): config is true | ObservationalMemoryOptions {\n if (config === true) return true;\n if (config === false || config === undefined) return false;\n return config.enabled !== false;\n}\n\n/**\n * Configuration for memory behaviors and retrieval strategies.\n *\n * Controls three types of memory: conversation history (recent messages), semantic recall\n * (RAG-based retrieval of relevant past messages), and working memory (persistent user data).\n * All memory types are combined into a single context window for the LLM.\n *\n * @see https://mastra.ai/docs/memory/overview\n */\ntype BaseMemoryConfig = {\n /**\n * When true, prevents memory from saving new messages.\n * Useful for internal agents (like routing agents) that should read memory but not modify it.\n *\n * @default false\n * @example\n * ```typescript\n * readOnly: true // Agent can read memory but won't save new messages\n * ```\n */\n readOnly?: boolean;\n\n /**\n * Number of recent messages from the current thread to include in context.\n * Provides short-term conversational continuity.\n * Set to false to disable conversation history entirely.\n *\n * @default 10\n * @example\n * ```typescript\n * lastMessages: 5 // Include last 5 messages\n * lastMessages: false // Disable conversation history\n * ```\n */\n lastMessages?: number | false;\n\n /**\n * Semantic recall configuration for RAG-based retrieval of relevant past messages.\n * Uses vector embeddings for similarity search across conversation history.\n * Can be a boolean to enable/disable with defaults, or an object for detailed configuration.\n *\n * @default false (disabled by default)\n * @example\n * ```typescript\n * semanticRecall: false // Disable semantic recall\n * semanticRecall: {\n * topK: 5,\n * messageRange: 2,\n * scope: 'resource' // Search across all resource (user) threads\n * }\n * ```\n */\n semanticRecall?: boolean | SemanticRecall;\n\n /**\n * Working memory configuration for persistent user data and preferences.\n * Maintains a structured record (Markdown or schema-based) that agents update over time.\n * Can be thread-scoped (per conversation) or resource-scoped (across all user threads).\n *\n * @example\n * ```typescript\n * workingMemory: {\n * enabled: true,\n * scope: 'resource', // Persist across all resource (user) conversations\n * template: '# User Profile\\n- **Name**:\\n- **Preferences**:',\n * schema: z.object({\n * name: z.string(),\n * preferences: z.object({\n * communicationStyle: z.string(),\n * projectGoal: z.string(),\n * deadlines: z.array(z.string()),\n * }),\n * }),\n * }\n * ```\n */\n workingMemory?: WorkingMemory;\n\n /**\n * Observational Memory configuration for long-term memory with automatic observation and reflection.\n *\n * Uses an Observer agent to extract observations from conversations and a Reflector agent\n * to compress them when they grow too large. This enables efficient long-term memory\n * that maintains context across many conversations.\n *\n * Set to `true` to enable with defaults, `false` to disable, or an object to customize.\n *\n * @example\n * ```typescript\n * // Enable with defaults\n * observationalMemory: true\n *\n * // Custom configuration\n * observationalMemory: {\n * scope: 'resource',\n * model: 'google/gemini-2.5-flash',\n * observation: {\n * messageTokens: 20_000,\n * },\n * reflection: {\n * observationTokens: 90_000,\n * },\n * }\n * ```\n */\n observationalMemory?: boolean | ObservationalMemoryOptions;\n\n /**\n * Automatically generate descriptive thread titles based on the first user message.\n * Can be a boolean to enable with defaults, or an object to customize the model and instructions.\n * Title generation runs asynchronously and doesn't affect response time.\n *\n * @default false\n * @example\n * ```typescript\n * generateTitle: true // Use agent's model for title generation\n * generateTitle: {\n * model: openai(\"gpt-4o-mini\"),\n * instructions: \"Generate a concise title (max 5 words)\"\n * }\n * ```\n */\n generateTitle?:\n | boolean\n | {\n /**\n * Language model to use for title generation.\n * Can be static or a function that receives request context for dynamic selection.\n * Accepts both Mastra models and standard AI SDK LanguageModelV1/V2.\n */\n model: DynamicArgument<MastraModelConfig>;\n /**\n * Custom instructions for title generation.\n * Can be static or a function that receives request context for dynamic customization.\n */\n instructions?: DynamicArgument<string>;\n };\n\n /**\n * Whether to filter out incomplete (suspended) tool calls when sending messages to the LLM.\n * When true, tool calls in `input-available` state are stripped from the prompt,\n * preventing the agent from seeing its own suspended tool calls in thread history.\n *\n * Set to false to allow the agent to see suspended tool calls in context.\n * This is useful for suspend/resume patterns where the agent should be aware of pending interactions.\n *\n * Note: Some providers (e.g. OpenAI) may return errors when incomplete tool calls are included.\n * Anthropic handles incomplete tool calls without issues.\n *\n * @default true\n * @example\n * ```typescript\n * filterIncompleteToolCalls: false // Keep suspended tool calls visible in context\n * ```\n */\n filterIncompleteToolCalls?: boolean;\n\n /**\n * Thread management configuration.\n * @deprecated The `threads` object is deprecated. Use top-level `generateTitle` instead of `threads.generateTitle`.\n */\n threads?: {\n /**\n * @deprecated Moved to top-level `generateTitle`. Using `threads.generateTitle` will throw an error.\n */\n generateTitle?:\n | boolean\n | {\n model: DynamicArgument<MastraModelConfig>;\n instructions?: DynamicArgument<string>;\n };\n };\n};\n\nexport type MemoryConfigInternal = BaseMemoryConfig & {\n /**\n * Working memory configuration for persistent user data and preferences.\n * Maintains a structured record (Markdown or schema-based) that agents update over time.\n * Can be thread-scoped (per conversation) or resource-scoped (across all user threads).\n *\n * @example\n * ```typescript\n * workingMemory: {\n * enabled: true,\n * scope: 'resource', // Persist across all resource (user) conversations\n * template: '# User Profile\\n- **Name**:\\n- **Preferences**:',\n * schema: z.object({\n * name: z.string(),\n * preferences: z.object({\n * communicationStyle: z.string(),\n * projectGoal: z.string(),\n * deadlines: z.array(z.string()),\n * }),\n * }),\n * }\n * ```\n */\n workingMemory?: WorkingMemory;\n};\n\nexport type MemoryConfig = BaseMemoryConfig & {\n /**\n * Working memory configuration for persistent user data and preferences.\n * Maintains a structured record (Markdown or schema-based) that agents update over time.\n * Can be thread-scoped (per conversation) or resource-scoped (across all user threads).\n *\n * @example\n * ```typescript\n * workingMemory: {\n * enabled: true,\n * scope: 'resource', // Persist across all resource (user) conversations\n * template: '# User Profile\\n- **Name**:\\n- **Preferences**:',\n * schema: z.object({\n * name: z.string(),\n * preferences: z.object({\n * communicationStyle: z.string(),\n * projectGoal: z.string(),\n * deadlines: z.array(z.string()),\n * }),\n * }),\n * }\n * ```\n */\n workingMemory?: TemplateWorkingMemory | SchemaWorkingMemory | WorkingMemoryNone;\n};\n\n/**\n * Configuration for Mastra's memory system.\n *\n * Enables agents to persist and recall information across conversations using storage providers,\n * vector databases for semantic search, and processors for context management. Memory can be\n * scoped to individual threads or shared across all conversations for a resource (user).\n *\n * @see https://mastra.ai/docs/memory/overview\n */\nexport type SharedMemoryConfig = {\n /**\n * Storage adapter for persisting conversation threads, messages, and working memory.\n *\n * @example\n * ```typescript\n * storage: new LibSQLStore({ id: 'agent-memory-storage', url: \"file:./agent-memory.db\" })\n * ```\n */\n storage?: MastraCompositeStore;\n\n /**\n * Configuration for memory behaviors including conversation history, semantic recall,\n * working memory, and thread management. Controls how messages are retrieved and\n