diff --git a/docs/_snippets/paper-release.mdx b/docs/_snippets/paper-release.mdx deleted file mode 100644 index f6ca2a54a..000000000 --- a/docs/_snippets/paper-release.mdx +++ /dev/null @@ -1,3 +0,0 @@ - - πŸŽ‰ Mem0 1.0.0 is here! Enhanced filtering, reranking, and smarter memory management. - \ No newline at end of file diff --git a/docs/open-source/overview.mdx b/docs/open-source/overview.mdx index e8986fbe3..7b2edb6e5 100644 --- a/docs/open-source/overview.mdx +++ b/docs/open-source/overview.mdx @@ -8,10 +8,6 @@ icon: "house" Mem0 Open Source delivers the same adaptive memory engine as the platform, but packaged for teams that need to run everything on their own infrastructure. You own the stack, the data, and the customizations. - - Mem0 v1.0.0 brought rerankers, async-by-default clients, and Azure OpenAI support. See the release notes for the full rundown before upgrading. - - ## What Mem0 OSS provides - **Full control**: Tune every component, from LLMs to vector stores, inside your environment. diff --git a/docs/platform/features/group-chat.mdx b/docs/platform/features/group-chat.mdx index f0f964336..caf490812 100644 --- a/docs/platform/features/group-chat.mdx +++ b/docs/platform/features/group-chat.mdx @@ -3,8 +3,6 @@ title: Group Chat description: 'Enable multi-participant conversations with automatic memory attribution to individual speakers' --- - - ## Overview The Group Chat feature enables Mem0 to process conversations involving multiple participants and automatically attribute memories to individual speakers. This allows for precise tracking of each participant's preferences, characteristics, and contributions in collaborative discussions, team meetings, or multi-agent conversations. diff --git a/docs/platform/features/v2-memory-filters.mdx b/docs/platform/features/v2-memory-filters.mdx index 95eb7fee9..383feeb31 100644 --- a/docs/platform/features/v2-memory-filters.mdx +++ b/docs/platform/features/v2-memory-filters.mdx @@ -15,10 +15,6 @@ When working with large-scale memory stores, you need precise control over which * **Time-based queries**: Retrieve memories within specific date ranges * **Performance optimization**: Reduce query complexity by pre-filtering - -Filters were introduced in v1.0.0 to provide precise control over memory retrieval. - - ## Filter structure Filters use a nested JSON structure with logical operators at the root: diff --git a/docs/platform/overview.mdx b/docs/platform/overview.mdx index 27e509feb..dfd4d25c0 100644 --- a/docs/platform/overview.mdx +++ b/docs/platform/overview.mdx @@ -8,10 +8,6 @@ icon: "cloud" Mem0 is the memory engine that keeps conversations contextual so users never repeat themselves and your agents respond with continuity. Mem0 Platform delivers that experience as a fully managed serviceβ€”scaling, securing, and enriching memories without any infrastructure work on your side. - - Mem0 v1.0.0 shipped rerankers, async-by-default behavior, and Azure OpenAI support. Catch the full list of changes in the release notes. - - ## Why it matters - **Personalized replies**: Memories persist across users and agents, cutting prompt bloat and repeat questions. diff --git a/skills/mem0-cli/SKILL.md b/skills/mem0-cli/SKILL.md index 565f7feaa..1ca072a79 100644 --- a/skills/mem0-cli/SKILL.md +++ b/skills/mem0-cli/SKILL.md @@ -12,7 +12,7 @@ description: > license: Apache-2.0 metadata: author: mem0ai - version: "1.0.0" + version: "1.1.0" category: ai-memory tags: "cli, terminal, memory, ai, command-line" compatibility: Node.js 18+ (npm install -g @mem0/cli) or Python 3.10+ (pip install mem0-cli), MEM0_API_KEY env var @@ -134,7 +134,6 @@ Choose whichever runtime you already have installed. The behavior is the same. - **`--all` vs `--entity` delete modes:** `mem0 delete --all -u alice` deletes all memories for user alice. `mem0 delete --entity -u alice` deletes the entity itself AND all its memories (cascade). These are mutually exclusive modes. - **Entity ID resolution:** If you pass any explicit scope flag (e.g. `--user-id`), the CLI uses ONLY the explicit IDs and ignores config defaults. If no scope flags are given, all configured defaults apply. - **Stdin detection:** When no text argument is provided and input is piped (not a TTY), the CLI reads from stdin. Works with `add`, `search`, and `update`. -- **Graph tri-state:** `--no-graph` takes precedence over `--graph`, which takes precedence over the config default (`defaults.enable_graph`). ## References diff --git a/skills/mem0-cli/references/command-reference.md b/skills/mem0-cli/references/command-reference.md index d308b12e9..814f6f62c 100644 --- a/skills/mem0-cli/references/command-reference.md +++ b/skills/mem0-cli/references/command-reference.md @@ -77,12 +77,8 @@ Add a memory from text, messages, file, or stdin. | `--messages ` | string | - | Conversation messages as JSON array (e.g. `'[{"role":"user","content":"..."}]'`). | | `-f, --file ` | path | - | Read messages from a JSON file. | | `-m, --metadata ` | string | - | Custom metadata as JSON object (e.g. `'{"source":"cli"}'`). | -| `--immutable` | boolean | false | Prevent future updates to this memory. | | `--no-infer` | boolean | false | Skip inference; store the text verbatim. | -| `--expires ` | string | - | Expiration date in `YYYY-MM-DD` format. | | `--categories ` | string | - | Categories as JSON array or comma-separated string. | -| `--graph` | boolean | false | Enable graph memory extraction for this call. | -| `--no-graph` | boolean | false | Disable graph memory extraction for this call. | | `-o, --output ` | string | `text` | Output format: `text`, `json`, `quiet`. | **Input priority:** `--file` > `--messages` > text argument > stdin (if piped and no text). @@ -134,13 +130,10 @@ Search memories by semantic query. | `--app-id ` | string | - | Filter by app. | | `--run-id ` | string | - | Filter by run. | | `-k, --top-k, --limit ` | integer | 10 | Maximum number of results to return. | -| `--threshold ` | float | 0.3 | Minimum similarity score (0.0 to 1.0). | +| `--threshold ` | float | 0.1 | Minimum similarity score (0.0 to 1.0). | | `--rerank` | boolean | false | Enable reranking for improved relevance (Platform only). | -| `--keyword` | boolean | false | Use keyword search instead of semantic. | | `--filter ` | string | - | Advanced filter expression as JSON (AND/OR operators). | | `--fields ` | string | - | Comma-separated list of fields to return. | -| `--graph` | boolean | false | Enable graph in search. | -| `--no-graph` | boolean | false | Disable graph in search. | | `-o, --output ` | string | `text` | Output format: `text`, `json`, `table`. | **Examples:** @@ -200,8 +193,6 @@ List memories with optional filters and pagination. | `--category ` | string | - | Filter by category. | | `--after ` | string | - | Created after (YYYY-MM-DD). | | `--before ` | string | - | Created before (YYYY-MM-DD). | -| `--graph` | boolean | false | Enable graph in listing. | -| `--no-graph` | boolean | false | Disable graph in listing. | | `-o, --output ` | string | `table` | Output format: `text`, `json`, `table`. | **Examples:** @@ -373,7 +364,7 @@ Get a single configuration value. |------|------|----------|-------------| | `key` | string | Yes | Dotted config key (e.g. `platform.api_key`, `defaults.user_id`). | -**Valid keys:** `platform.api_key`, `platform.base_url`, `defaults.user_id`, `defaults.agent_id`, `defaults.app_id`, `defaults.run_id`, `defaults.enable_graph`. +**Valid keys:** `platform.api_key`, `platform.base_url`, `defaults.user_id`, `defaults.agent_id`, `defaults.app_id`, `defaults.run_id`. API key values are always redacted in output. @@ -404,7 +395,6 @@ Set a configuration value. ```bash mem0 config set defaults.user_id alice mem0 config set platform.base_url https://api.mem0.ai -mem0 config set defaults.enable_graph true ``` --- @@ -638,20 +628,6 @@ This applies to commands with `resolveIds: true`: `add`, `search`, `list`, `dele --- -## Graph Tri-State - -The `enable_graph` parameter follows a three-level precedence: - -``` ---no-graph (explicit disable) > --graph (explicit enable) > config default -``` - -If `--no-graph` is passed, graph is disabled regardless of other settings. If `--graph` is passed (without `--no-graph`), graph is enabled. If neither is passed, the config value `defaults.enable_graph` is used. - -This applies to commands with `resolveGraph: true`: `add`, `search`, `list`. - ---- - ## Filter Building For `search` and `list`, entity IDs and additional filters are composed into the API filter structure: diff --git a/skills/mem0-cli/references/configuration.md b/skills/mem0-cli/references/configuration.md index f585fb691..0c5484cfc 100644 --- a/skills/mem0-cli/references/configuration.md +++ b/skills/mem0-cli/references/configuration.md @@ -24,8 +24,7 @@ The restricted permissions ensure API keys are not world-readable. "user_id": "", "agent_id": "", "app_id": "", - "run_id": "", - "enable_graph": false + "run_id": "" }, "platform": { "api_key": "", @@ -43,7 +42,6 @@ The restricted permissions ensure API keys are not world-readable. | `defaults.agent_id` | string | `""` | Default agent ID for scoping commands. | | `defaults.app_id` | string | `""` | Default app ID for scoping commands. | | `defaults.run_id` | string | `""` | Default run ID for scoping commands. | -| `defaults.enable_graph` | boolean | `false` | Default graph memory extraction toggle. | | `platform.api_key` | string | `""` | API key for the Mem0 Platform. | | `platform.base_url` | string | `"https://api.mem0.ai"` | Base URL for API requests. | @@ -127,7 +125,6 @@ Reads a single configuration value. The key uses dotted notation. ```bash mem0 config get platform.api_key # prints: m0-x...xxxx (redacted) mem0 config get defaults.user_id # prints: alice -mem0 config get defaults.enable_graph # prints: false ``` **Valid keys:** @@ -137,7 +134,6 @@ mem0 config get defaults.enable_graph # prints: false - `defaults.agent_id` - `defaults.app_id` - `defaults.run_id` -- `defaults.enable_graph` Unknown keys print an error message. @@ -148,7 +144,6 @@ Sets a configuration value and saves the config file. ```bash mem0 config set defaults.user_id alice mem0 config set platform.base_url https://api.mem0.ai -mem0 config set defaults.enable_graph true ``` **Type coercion:** @@ -178,20 +173,6 @@ Environment variables override config file values but are overridden by CLI flag | `MEM0_AGENT_ID` | `defaults.agent_id` | string | `""` | | `MEM0_APP_ID` | `defaults.app_id` | string | `""` | | `MEM0_RUN_ID` | `defaults.run_id` | string | `""` | -| `MEM0_ENABLE_GRAPH` | `defaults.enable_graph` | boolean | `false` | - -### Boolean Parsing for `MEM0_ENABLE_GRAPH` - -Accepted truthy values (case-insensitive): `"true"`, `"1"`, `"yes"`. Everything else is treated as `false`. - -```bash -export MEM0_ENABLE_GRAPH=true # enabled -export MEM0_ENABLE_GRAPH=1 # enabled -export MEM0_ENABLE_GRAPH=yes # enabled -export MEM0_ENABLE_GRAPH=false # disabled -export MEM0_ENABLE_GRAPH=0 # disabled -export MEM0_ENABLE_GRAPH="" # disabled -``` --- @@ -200,8 +181,8 @@ export MEM0_ENABLE_GRAPH="" # disabled Configuration values are resolved in this order (highest priority first): ``` -1. CLI flags --api-key, --user-id, --base-url, --graph, --no-graph, etc. -2. Environment vars MEM0_API_KEY, MEM0_USER_ID, MEM0_ENABLE_GRAPH, etc. +1. CLI flags --api-key, --user-id, --base-url, etc. +2. Environment vars MEM0_API_KEY, MEM0_USER_ID, etc. 3. Config file ~/.mem0/config.json 4. Defaults Hardcoded defaults (empty strings, false, https://api.mem0.ai) ``` @@ -241,4 +222,3 @@ The `config get` and `config set` commands use dotted key paths. Here is the ful | `defaults.agent_id` | defaults | agent_id | | `defaults.app_id` | defaults | app_id | | `defaults.run_id` | defaults | run_id | -| `defaults.enable_graph` | defaults | enable_graph | diff --git a/skills/mem0-vercel-ai-sdk/SKILL.md b/skills/mem0-vercel-ai-sdk/SKILL.md index 65353a7f5..d0d49a3d0 100644 --- a/skills/mem0-vercel-ai-sdk/SKILL.md +++ b/skills/mem0-vercel-ai-sdk/SKILL.md @@ -12,7 +12,7 @@ description: > license: Apache-2.0 metadata: author: mem0ai - version: "1.0.0" + version: "1.1.0" category: ai-memory tags: "vercel, ai-sdk, memory, nextjs, typescript, provider" compatibility: Node.js 18+, npm install @mem0/vercel-ai-provider, Vercel AI SDK v5 (ai package), MEM0_API_KEY + LLM provider API key @@ -47,16 +47,16 @@ import { createMem0 } from "@mem0/vercel-ai-provider"; const mem0 = createMem0(); const { text } = await generateText({ - model: mem0("gpt-4-turbo", { user_id: "alice" }), + model: mem0("gpt-5-mini", { user_id: "alice" }), prompt: "Recommend a restaurant", }); ``` What happens under the hood: -1. The prompt is sent to Mem0 search (`POST /v2/memories/search/`) to retrieve relevant memories +1. The prompt is sent to Mem0 search (`POST /v3/memories/search/`) to retrieve relevant memories 2. Retrieved memories are injected as a system message at the start of the prompt -3. The underlying LLM (e.g., OpenAI gpt-4-turbo) generates a response using the enriched prompt -4. The conversation is stored back to Mem0 (`POST /v1/memories/`) as a fire-and-forget async call (no await) +3. The underlying LLM (e.g., OpenAI gpt-5-mini) generates a response using the enriched prompt +4. The conversation is stored back to Mem0 (`POST /v3/memories/add/`) as a fire-and-forget async call (no await) ## Pattern 2: Standalone Utilities @@ -77,7 +77,7 @@ const memories = await retrieveMemories(prompt, { // Generate using any provider with injected memories const { text } = await generateText({ - model: openai("gpt-4-turbo"), + model: openai("gpt-5-mini"), prompt, system: memories, }); @@ -102,7 +102,7 @@ import { createMem0 } from "@mem0/vercel-ai-provider"; const mem0 = createMem0(); const result = streamText({ - model: mem0("gpt-4-turbo", { user_id: "alice" }), + model: mem0("gpt-5-mini", { user_id: "alice" }), prompt: "What should I cook for dinner?", }); @@ -128,7 +128,7 @@ Select a provider when creating the Mem0 instance: ```typescript const mem0 = createMem0({ provider: "anthropic" }); const { text } = await generateText({ - model: mem0("claude-sonnet-4-20250514", { user_id: "alice" }), + model: mem0("gpt-5-mini", { user_id: "alice" }), prompt: "Hello!", }); ``` @@ -139,7 +139,7 @@ const { text } = await generateText({ ``` User prompt - --> searchInternalMemories (POST /v2/memories/search/) + --> searchInternalMemories (POST /v3/memories/search/) --> memories injected as system message at start of prompt --> underlying LLM generates response (doGenerate or doStream) --> processMemories fires addMemories as fire-and-forget (no await) @@ -161,7 +161,7 @@ User controls each step: | Function | Returns | Use when | |----------|---------|----------| | `retrieveMemories` | Formatted system prompt **string** | Injecting directly into `system` parameter | -| `getMemories` | Raw memory **array** (or full response if `enable_graph`) | Processing memories programmatically | +| `getMemories` | Raw memory **array** | Processing memories programmatically | | `searchMemories` | Full search **response** (results + relations) | Need relations, scores, metadata | | `addMemories` | API response | Storing new messages to Mem0 | @@ -171,7 +171,6 @@ All four accept `LanguageModelV2Prompt | string` as the first argument and optio - **Always provide `user_id`** (or `agent_id`/`app_id`/`run_id`) for consistent memory retrieval. Without an entity identifier, memories cannot be scoped. - **Standalone utilities require explicit API key**: pass `mem0ApiKey` in the config object, or set the `MEM0_API_KEY` environment variable. -- **Graph memories**: set `enable_graph: true` in the config to retrieve graph relations alongside text memories. When enabled, `getMemories` returns the full response (with `results` and `relations`), not just the array. - **This uses Vercel AI SDK v5** (LanguageModelV2 / ProviderV2 interfaces). It is not compatible with AI SDK v3 or v4. - **`processMemories` fires `addMemories` as fire-and-forget** (`.then()` without `await`). Memory storage happens asynchronously and does not block the LLM response. - **The `"gemini"` alias** exists in the provider switch but is NOT in the `supportedProviders` list. Use `"google"` instead. diff --git a/skills/mem0-vercel-ai-sdk/references/memory-utilities.md b/skills/mem0-vercel-ai-sdk/references/memory-utilities.md index be91f84ae..56a287805 100644 --- a/skills/mem0-vercel-ai-sdk/references/memory-utilities.md +++ b/skills/mem0-vercel-ai-sdk/references/memory-utilities.md @@ -79,8 +79,7 @@ async function retrieveMemories( 1. Flattens the prompt to a plain string (extracts text from `LanguageModelV2Prompt` parts) 2. Calls `searchInternalMemories` (`POST /v2/memories/search/`) 3. Formats each memory as `"Memory: {memory.memory}\n\n"` -4. If `enable_graph: true`, also appends graph relations as `"Relation: {source} -> {relationship} -> {target}\n\n"` -5. Wraps everything in a system prompt preamble +4. Wraps everything in a system prompt preamble **Returns:** A **string** containing the formatted system prompt with embedded memories. Returns `""` (empty string) if no memories found. @@ -91,17 +90,13 @@ System Message: These are the memories I have stored. Give more weightage to the Memory: User loves Italian food Memory: User is vegetarian - -HERE ARE THE GRAPHS RELATIONS FOR THE PREFERENCES OF THE USER: - -Relation: Alice -> likes -> Italian cuisine ``` --- ## `getMemories(prompt, config?)` -Retrieves memories and returns the **raw memory array** (or full response if graph is enabled). +Retrieves memories and returns the **raw memory array**. ```typescript import { getMemories } from "@mem0/vercel-ai-provider"; @@ -111,14 +106,6 @@ const memories = await getMemories("What are my preferences?", { mem0ApiKey: "m0-xxx", }); // Returns: [{ memory: "User loves Italian food", id: "...", ... }, ...] - -// With graph enabled: -const graphMemories = await getMemories("What are my preferences?", { - user_id: "alice", - mem0ApiKey: "m0-xxx", - enable_graph: true, -}); -// Returns: { results: [...], relations: [...] } ``` **Signature:** @@ -140,10 +127,9 @@ async function getMemories( **Behavior:** 1. Flattens the prompt to a plain string 2. Calls `searchInternalMemories` (`POST /v2/memories/search/`) -3. If `enable_graph` is **not** set: returns `memories.results` (the array of memory objects) -4. If `enable_graph` is set: returns the full response object (with both `results` and `relations`) +3. Returns `memories.results` (the array of memory objects) -**Returns:** Memory object array, or full response object when graph is enabled. +**Returns:** Memory object array. --- @@ -184,7 +170,7 @@ async function searchMemories( **Returns:** The complete API response object. On error, returns `[]`. -**Note:** Unlike `getMemories`, this always returns the full response regardless of `enable_graph` setting. +**Note:** Unlike `getMemories`, this always returns the full response. --- @@ -193,8 +179,8 @@ async function searchMemories( | Function | Returns | Use when | |----------|---------|----------| | `retrieveMemories` | Formatted system prompt **string** | Injecting directly into a `system` parameter for `generateText`/`streamText` | -| `getMemories` | Memory **array** (or full response if `enable_graph`) | Processing memories programmatically (filtering, transforming, counting) | -| `searchMemories` | Full API **response** (results + relations) | Need relations, similarity scores, or complete metadata regardless of graph setting | +| `getMemories` | Memory **array** | Processing memories programmatically (filtering, transforming, counting) | +| `searchMemories` | Full API **response** (results + relations) | Need relations, similarity scores, or complete metadata | | `addMemories` | API response | Storing new conversation messages as memories | ## Internal: `searchInternalMemories(query, config?, top_k?)` @@ -210,15 +196,13 @@ async function searchInternalMemories( ``` **Behavior:** -1. Builds an `OR` filter from entity identifiers (`user_id`, `app_id`, `agent_id`, `run_id`) -2. Resolves org/project identifiers (`org_id` takes precedence over `org_name`) +1. Builds a `filters` object from entity identifiers (`user_id`, `app_id`, `agent_id`, `run_id`) +2. Resolves entity identifiers 3. Loads the API key from `config.mem0ApiKey` or `MEM0_API_KEY` env var 4. Calls `POST {host}/v2/memories/search/` with: - `query`: the search string - - `filters`: the OR filter object + - `filters`: the filter object with entity identifiers - `top_k`: from config or default 5 - - `version`: `"v2"` - - `output_format`: `"v1.1"` - All other config fields spread into the request body **Default host:** `https://api.mem0.ai` @@ -264,10 +248,6 @@ All fields are optional. Used across all utility functions. | `app_id` | `string` | -- | Scope memories to an application | | `agent_id` | `string` | -- | Scope memories to an agent | | `run_id` | `string` | -- | Scope memories to a session/run | -| `org_name` | `string` | -- | Organization name (fallback if `org_id` not set) | -| `project_name` | `string` | -- | Project name (fallback if `org_id` not set) | -| `org_id` | `string` | -- | Organization ID (takes precedence) | -| `project_id` | `string` | -- | Project ID | | `metadata` | `Record` | -- | Custom metadata | | `filters` | `Record` | -- | Custom search filters | | `infer` | `boolean` | -- | Enable inference | @@ -277,8 +257,4 @@ All fields are optional. Used across all utility functions. | `top_k` | `number` | `5` | Number of memories to retrieve | | `threshold` | `number` | -- | Minimum similarity score | | `rerank` | `boolean` | -- | Enable re-ranking | -| `enable_graph` | `boolean` | -- | Enable graph memory (relations) | | `host` | `string` | `https://api.mem0.ai` | Custom API host | -| `output_format` | `string` | -- | Output format version | -| `filter_memories` | `boolean` | -- | Enable memory filtering | -| `async_mode` | `boolean` | -- | Enable async processing | diff --git a/skills/mem0-vercel-ai-sdk/references/provider-api.md b/skills/mem0-vercel-ai-sdk/references/provider-api.md index 4a876d274..3a59f9883 100644 --- a/skills/mem0-vercel-ai-sdk/references/provider-api.md +++ b/skills/mem0-vercel-ai-sdk/references/provider-api.md @@ -9,8 +9,8 @@ Factory function that creates a `Mem0Provider` instance. This is the primary ent ```typescript import { createMem0 } from "@mem0/vercel-ai-provider"; -const mem0 = createMem0(); // defaults: provider "openai" -const mem0 = createMem0({ provider: "anthropic" }); // use Anthropic as LLM backend +const mem0 = createMem0(); // defaults: provider "openai" +const mem0 = createMem0({ provider: "anthropic" }); // use Anthropic as LLM backend ``` **Signature:** @@ -39,7 +39,7 @@ interface Mem0Provider extends ProviderV2 { } ``` -- **Direct call** (`mem0("gpt-4-turbo", {...})`): creates a generic language model (neither chat nor completion mode forced). +- **Direct call** (`mem0("gpt-5-mini", {...})`): creates a generic language model (neither chat nor completion mode forced). - **`chat()`**: creates a model with `modelType: "chat"` (note: in the current source, the chat constructor sets `modelType: "completion"` -- this appears to be a bug; functionally equivalent to `completion()` at present). - **`completion()`**: creates a model with `modelType: "completion"`. - **`languageModel()`**: alias for the generic model (same as direct call). @@ -73,7 +73,7 @@ interface Mem0ProviderSettings { | `provider` | Which LLM backend to use | `"openai"`, `"anthropic"`, `"google"`, `"groq"`, `"cohere"` | | `mem0ApiKey` | Mem0 Platform API key | `"m0-xxx"` | | `apiKey` | LLM provider API key | `"sk-xxx"` (OpenAI), `"sk-ant-xxx"` (Anthropic) | -| `mem0Config` | Default Mem0 settings for all calls | `{ user_id: "alice", enable_graph: true }` | +| `mem0Config` | Default Mem0 settings for all calls | `{ user_id: "alice" }` | | `config` | Provider-specific SDK settings | `{ organization: "org-xxx" }` for OpenAI | | `baseURL` | Override LLM provider base URL | `"https://my-proxy.example.com"` | @@ -85,7 +85,7 @@ A pre-configured instance using default settings (OpenAI provider, no API keys s import { mem0 } from "@mem0/vercel-ai-provider"; const { text } = await generateText({ - model: mem0("gpt-4-turbo", { user_id: "alice" }), + model: mem0("gpt-5-mini", { user_id: "alice" }), prompt: "Hello", }); ``` @@ -102,10 +102,6 @@ interface Mem0ConfigSettings { app_id?: string; // Scope memories to an application agent_id?: string; // Scope memories to an agent run_id?: string; // Scope memories to a specific run/session - org_name?: string; // Organization name (used if org_id not set) - project_name?: string; // Project name (used if org_id not set) - org_id?: string; // Organization ID (takes precedence over org_name) - project_id?: string; // Project ID (takes precedence over project_name) metadata?: Record; // Custom metadata attached to memories filters?: Record; // Custom filters for memory search infer?: boolean; // Enable inference during memory operations @@ -113,13 +109,9 @@ interface Mem0ConfigSettings { page_size?: number; // Pagination: results per page mem0ApiKey?: string; // Mem0 API key (overrides provider-level key) top_k?: number; // Number of memories to retrieve (default: 5) - threshold?: number; // Minimum similarity score for retrieval - rerank?: boolean; // Enable re-ranking of search results - enable_graph?: boolean; // Enable graph memory (returns relations) + threshold?: number; // Minimum similarity score for retrieval (default: 0.1) + rerank?: boolean; // Enable re-ranking of search results (default: false) host?: string; // Custom Mem0 API host (default: "https://api.mem0.ai") - output_format?: string; // Output format version - filter_memories?: boolean; // Enable memory filtering - async_mode?: boolean; // Enable async processing of memory operations } ``` @@ -138,9 +130,9 @@ This means a `Mem0ChatConfig` has all fields from both `Mem0ConfigSettings` and Alias for `Mem0ConfigSettings`. Passed as the second argument when creating a model: ```typescript -mem0("gpt-4-turbo", { user_id: "alice", enable_graph: true }) -// ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -// This object is Mem0ChatSettings +mem0("gpt-5-mini", { user_id: "alice" }) +// ^^^^^^^^^^^^^^^^^^ +// This object is Mem0ChatSettings ``` ## `LLMProviderSettings` Type @@ -188,8 +180,8 @@ An alternative exported class that creates models directly without the callable- import { Mem0 } from "@mem0/vercel-ai-provider"; const mem0 = new Mem0({ provider: "openai" }); -const chatModel = mem0.chat("gpt-4-turbo", { user_id: "alice" }); -const completionModel = mem0.completion("gpt-3.5-turbo-instruct"); +const chatModel = mem0.chat("gpt-5-mini", { user_id: "alice" }); +const completionModel = mem0.completion("gpt-5-mini"); ``` The facade defaults its base URL to `"http://127.0.0.1:11434/api"` (Ollama-style) rather than `"http://api.openai.com"`. It always uses `"openai"` as the provider for created models. @@ -210,7 +202,7 @@ class Mem0GenericLanguageModel implements LanguageModelV2 { readonly supportedUrls: Record = { '*': [/.*/] }; provider: string; // e.g., "openai" - modelId: string; // e.g., "gpt-4-turbo" + modelId: string; // e.g., "gpt-5-mini" settings: Mem0ChatSettings; config: Mem0ChatConfig; @@ -230,10 +222,12 @@ Both `doGenerate` and `doStream` follow the same internal flow: 4. Delegate to the underlying model's `doGenerate` or `doStream` 5. Return the result +**Note:** Entity identifier fields use snake_case (`user_id`, `app_id`, `agent_id`, `run_id`) to match the Mem0 API. + ## Type: `Mem0ChatModelId` ```typescript type Mem0ChatModelId = string & NonNullable; ``` -Any non-null string. The model ID is passed through to the underlying provider (e.g., `"gpt-4-turbo"`, `"claude-sonnet-4-20250514"`, `"gemini-pro"`). +Any non-null string. The model ID is passed through to the underlying provider (e.g., `"gpt-5-mini"`, `"gemini-pro"`). diff --git a/skills/mem0-vercel-ai-sdk/references/usage-patterns.md b/skills/mem0-vercel-ai-sdk/references/usage-patterns.md index b33be250d..bd1e66518 100644 --- a/skills/mem0-vercel-ai-sdk/references/usage-patterns.md +++ b/skills/mem0-vercel-ai-sdk/references/usage-patterns.md @@ -13,7 +13,7 @@ import { createMem0 } from "@mem0/vercel-ai-provider"; const mem0 = createMem0(); const { text } = await generateText({ - model: mem0("gpt-4-turbo", { user_id: "alice" }), + model: mem0("gpt-5-mini", { user_id: "alice" }), prompt: "Recommend a restaurant based on my preferences", }); @@ -33,7 +33,7 @@ import { createMem0 } from "@mem0/vercel-ai-provider"; const mem0 = createMem0(); const result = streamText({ - model: mem0("gpt-4-turbo", { user_id: "alice" }), + model: mem0("gpt-5-mini", { user_id: "alice" }), prompt: "What should I cook for dinner tonight?", }); @@ -53,17 +53,17 @@ import { openai } from "@ai-sdk/openai"; import { generateText } from "ai"; import { retrieveMemories, addMemories } from "@mem0/vercel-ai-provider"; -const userId = "alice"; +const user_id = "alice"; const prompt = "Suggest a weekend trip"; // Step 1: Retrieve memories as a formatted system prompt const memories = await retrieveMemories(prompt, { - user_id: userId, + user_id: user_id, }); // Step 2: Generate with the memories injected as system context const { text } = await generateText({ - model: openai("gpt-4-turbo"), + model: openai("gpt-5-mini"), prompt, system: memories, }); @@ -76,7 +76,7 @@ await addMemories( { role: "user", content: [{ type: "text", text: prompt }] }, { role: "assistant", content: [{ type: "text", text }] }, ], - { user_id: userId } + { user_id: user_id } ); ``` @@ -121,7 +121,7 @@ import { z } from "zod"; const mem0 = createMem0(); const { object } = await generateObject({ - model: mem0("gpt-4-turbo", { user_id: "alice" }), + model: mem0("gpt-5-mini", { user_id: "alice" }), prompt: "Suggest a meal plan for today", schema: z.object({ breakfast: z.string(), @@ -138,53 +138,7 @@ console.log(object); The `defaultObjectGenerationMode` is `"json"`, so structured output works out of the box. -## 6. Graph Memory Enabled - -Retrieve both text memories and entity relationship graphs. - -```typescript -import { generateText } from "ai"; -import { createMem0 } from "@mem0/vercel-ai-provider"; - -const mem0 = createMem0(); - -const { text } = await generateText({ - model: mem0("gpt-4-turbo", { - user_id: "alice", - enable_graph: true, - }), - prompt: "What connections do you know about between my friends?", -}); - -console.log(text); -``` - -With `enable_graph: true`, the system prompt includes both: -- **Text memories**: `"Memory: Alice is friends with Bob"` -- **Graph relations**: `"Relation: Alice -> friends_with -> Bob"` - -### Using graph with standalone utilities - -```typescript -import { getMemories, searchMemories } from "@mem0/vercel-ai-provider"; - -// getMemories with graph returns the full response -const graphResult = await getMemories("my social connections", { - user_id: "alice", - enable_graph: true, -}); -console.log(graphResult.results); // memory objects -console.log(graphResult.relations); // graph relations - -// searchMemories always returns the full response -const fullResponse = await searchMemories("my social connections", { - user_id: "alice", -}); -console.log(fullResponse.results); -console.log(fullResponse.relations); -``` - -## 7. Multi-Provider Setup +## 6. Multi-Provider Setup Configure different LLM providers with the wrapped model. @@ -194,7 +148,7 @@ Configure different LLM providers with the wrapped model. import { createMem0 } from "@mem0/vercel-ai-provider"; const mem0 = createMem0(); // defaults to "openai" -const model = mem0("gpt-4-turbo", { user_id: "alice" }); +const model = mem0("gpt-5-mini", { user_id: "alice" }); ``` ### Anthropic @@ -235,7 +189,7 @@ const mem0 = createMem0({ }); ``` -## 8. Next.js API Route Integration +## 7. Next.js API Route Integration A POST handler that uses the wrapped model in a Next.js App Router API route. @@ -247,12 +201,12 @@ import { createMem0 } from "@mem0/vercel-ai-provider"; const mem0 = createMem0(); export async function POST(req: Request) { - const { messages, userId } = await req.json(); + const { messages, user_id } = await req.json(); const lastMessage = messages[messages.length - 1]; const result = streamText({ - model: mem0("gpt-4-turbo", { user_id: userId }), + model: mem0("gpt-5-mini", { user_id }), prompt: lastMessage.content, }); @@ -269,17 +223,17 @@ import { streamText } from "ai"; import { retrieveMemories, addMemories } from "@mem0/vercel-ai-provider"; export async function POST(req: Request) { - const { messages, userId } = await req.json(); + const { messages, user_id } = await req.json(); const lastMessage = messages[messages.length - 1]; // Retrieve relevant memories const memories = await retrieveMemories(lastMessage.content, { - user_id: userId, + user_id, }); // Stream the response const result = streamText({ - model: openai("gpt-4-turbo"), + model: openai("gpt-5-mini"), prompt: lastMessage.content, system: memories, }); @@ -291,7 +245,7 @@ export async function POST(req: Request) { { role: "user", content: [{ type: "text", text: lastMessage.content }] }, { role: "assistant", content: [{ type: "text", text }] }, ], - { user_id: userId } + { user_id } ); }); @@ -299,7 +253,7 @@ export async function POST(req: Request) { } ``` -## 9. How Memory Processing Works Internally +## 8. How Memory Processing Works Internally ### Wrapped model flow (doGenerate / doStream) @@ -308,10 +262,10 @@ export async function POST(req: Request) { 2. processMemories(messagesPrompts, mem0Config): a. addMemories(messagesPrompts, mem0Config) --> fire-and-forget: .then().catch(), NO await - --> POST /v1/memories/ with converted messages + --> POST /v3/memories/add/ with converted messages b. await getMemories(messagesPrompts, mem0Config) - --> POST /v2/memories/search/ with flattened prompt - --> returns memory array (or {results, relations} if enable_graph) + --> POST /v3/memories/search/ with flattened prompt + --> returns memory array c. Format memories into system message string d. Prepend system message to messagesPrompts array e. Return { memories, messagesPrompts } @@ -337,7 +291,7 @@ The memories are injected as a system message at position 0 of the prompt array: } ``` -## 10. Custom Configuration +## 9. Custom Configuration ### Custom Mem0 API host @@ -358,32 +312,15 @@ const memories = await retrieveMemories(prompt, { }); ``` -### Organization and project scoping - -```typescript -const mem0 = createMem0(); -const { text } = await generateText({ - model: mem0("gpt-4-turbo", { - user_id: "alice", - org_id: "org-123", - project_id: "proj-456", - }), - prompt: "Hello", -}); -``` - -Note: `org_id` takes precedence over `org_name`. If `org_id` is set, `org_name` and `project_name` are not sent in the request. - ### Memory filtering and ranking ```typescript const mem0 = createMem0(); -const model = mem0("gpt-4-turbo", { +const model = mem0("gpt-5-mini", { user_id: "alice", top_k: 10, // retrieve up to 10 memories (default: 5) threshold: 0.8, // only memories with score >= 0.8 - rerank: true, // enable re-ranking of results - filter_memories: true, + rerank: true, // enable re-ranking of results }); ``` @@ -409,14 +346,13 @@ Set defaults at the provider level that apply to every model created: const mem0 = createMem0({ mem0Config: { user_id: "alice", - enable_graph: true, top_k: 10, }, }); -// These calls inherit user_id, enable_graph, and top_k from mem0Config +// These calls inherit user_id and top_k from mem0Config const { text } = await generateText({ - model: mem0("gpt-4-turbo"), + model: mem0("gpt-5-mini"), prompt: "Hello", }); ``` @@ -425,7 +361,7 @@ Per-call settings (passed as the second argument to `mem0()`) are merged on top ```typescript // Override user_id for this specific call -const model = mem0("gpt-4-turbo", { user_id: "bob" }); +const model = mem0("gpt-5-mini", { user_id: "bob" }); ``` The merge order is: `config.mem0Config` (provider defaults) < `settings` (per-call overrides). diff --git a/skills/mem0/SKILL.md b/skills/mem0/SKILL.md index c5e8d6a27..0c8ab9e60 100644 --- a/skills/mem0/SKILL.md +++ b/skills/mem0/SKILL.md @@ -15,10 +15,10 @@ description: > license: Apache-2.0 metadata: author: mem0ai - version: "2.0.0" + version: "3.0.0" category: ai-memory tags: "memory, personalization, ai, python, typescript, vector-search" -compatibility: Requires Python 3.10+ or Node.js 18+, pip install mem0ai or npm install mem0ai, MEM0_API_KEY env var (Platform), and internet access to api.mem0.ai +compatibility: Requires Python 3.10+ or Node.js 18+, pip install mem0ai or npm install mem0ai, MEM0_API_KEY env var (Platform), and internet access to api.mem0.ai. SDK v3 with v2 compatibility mode available. --- # Mem0 Platform Integration @@ -77,14 +77,14 @@ client.add(messages, user_id="alice") ### Search memories ```python -results = client.search("dietary preferences", user_id="alice") +results = client.search("dietary preferences", filters={"user_id": "alice"}) for mem in results.get("results", []): print(mem["memory"]) ``` ### Get all memories ```python -all_memories = client.get_all(user_id="alice") +all_memories = client.get_all(filters={"user_id": "alice"}) ``` ### Update a memory @@ -109,12 +109,12 @@ openai = OpenAI() def chat(user_input: str, user_id: str) -> str: # 1. Retrieve relevant memories - memories = mem0.search(user_input, user_id=user_id) + memories = mem0.search(user_input, filters={"user_id": user_id}) context = "\n".join([m["memory"] for m in memories.get("results", [])]) # 2. Generate response with memory context response = openai.chat.completions.create( - model="gpt-4.1-nano-2025-04-14", + model="gpt-5-mini", messages=[ {"role": "system", "content": f"User context:\n{context}"}, {"role": "user", "content": user_input}, @@ -132,11 +132,20 @@ def chat(user_input: str, user_id: str) -> str: ## Common edge cases -- **Search returns empty:** Memories process asynchronously. Wait 2-3s after `add()` before searching. Also verify `user_id` matches exactly (case-sensitive). +- **Search returns empty:** Memories process asynchronously. Wait 2-3s after `add()` before searching. Also verify `user_id` matches exactly (case-sensitive) and use `filters={"user_id": "..."}` syntax. - **AND filter with user_id + agent_id returns empty:** Entities are stored separately. Use `OR` instead, or query separately. - **Duplicate memories:** Don't mix `infer=True` (default) and `infer=False` for the same data. Stick to one mode. - **Wrong import:** Always use `from mem0 import MemoryClient` (or `AsyncMemoryClient` for async). Do not use `from mem0 import Memory`. -- **Immutable memories:** Cannot be updated or deleted once created. Use `client.history(memory_id)` to track changes over time. +- **v3 defaults:** `top_k=20`, `threshold=0.1`, `rerank=False`. Adjust as needed for your use case. + +## v2 Compatibility + +If you're using SDK v2.x, note these differences: +- **Entity IDs:** Pass `user_id` as top-level kwarg to `search()` instead of inside `filters` +- **Defaults:** `top_k=100`, no threshold, `rerank=True` +- **Graph memory:** Available via `enable_graph=True` + +See the [migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) for details. ## Live documentation search diff --git a/skills/mem0/client/differences.md b/skills/mem0/client/differences.md index 37344b277..e5e80e250 100644 --- a/skills/mem0/client/differences.md +++ b/skills/mem0/client/differences.md @@ -46,18 +46,16 @@ Both read from `MEM0_API_KEY` env var if no key provided. ```python # Python: kwargs client.add(messages, user_id="alice", metadata={"source": "chat"}) -client.search("query", user_id="alice", top_k=5, rerank=True) +client.search("query", filters={"user_id": "alice"}, top_k=5, rerank=True) ``` ```typescript -// TypeScript: options object -await client.add(messages, { user_id: 'alice', metadata: { source: 'chat' } }); -await client.search('query', { user_id: 'alice', top_k: 5, rerank: true }); +// TypeScript: options object with camelCase for top-level params, snake_case for filter keys +await client.add(messages, { userId: 'alice', metadata: { source: 'chat' } }); +await client.search('query', { filters: { user_id: 'alice' }, topK: 5, rerank: true }); ``` -**Important:** Both use `snake_case` for API parameter names (`user_id`, `agent_id`, `top_k`, etc.). Only method names differ. - -Exception: OSS TypeScript uses `camelCase` for config params (`userId`, `agentId`, `runId`). +**v3:** Python uses `snake_case` everywhere. TypeScript uses `camelCase` for top-level params (`userId`, `topK`) but `snake_case` for filter keys (`user_id`, `agent_id`). ## Architectural Differences @@ -97,10 +95,8 @@ These methods exist in Python but not TypeScript: | Python config key | TypeScript config key | |-------------------|----------------------| | `vector_store` | `vectorStore` | -| `graph_store` | `graphStore` | | `history_db_path` | `historyDbPath` | | `custom_instructions` | `customInstructions` | -| `enable_graph` | `enableGraph` | ## OSS Scope Parameter Naming @@ -110,16 +106,24 @@ These methods exist in Python but not TypeScript: | `agent_id="bot"` | `agentId: 'bot'` | | `run_id="session"` | `runId: 'session'` | +## Entity ID Passing (v3) + +| Method | Python | TypeScript | +|--------|--------|------------| +| add() | Top-level: `user_id="alice"` | Top-level: `{ userId: 'alice' }` | +| search() | In filters: `filters={"user_id": "alice"}` | In filters: `{ filters: { user_id: 'alice' } }` | +| get_all() | In filters: `filters={"user_id": "alice"}` | In filters: `{ filters: { user_id: 'alice' } }` | + ## Common Gotcha -When searching/filtering, **both SDKs use `snake_case`** for filter keys: +When searching/filtering, both Python and TypeScript use `snake_case` for filter keys. TypeScript only uses `camelCase` for top-level method parameters: ```python -# Python -filters = {"AND": [{"user_id": "alice"}, {"categories": {"contains": "health"}}]} +# Python - snake_case in filters +results = client.search("query", filters={"user_id": "alice"}) ``` ```typescript -// TypeScript -- same snake_case in filter objects! -const filters = { AND: [{ user_id: 'alice' }, { categories: { contains: 'health' } }] }; +// TypeScript - snake_case in filters, camelCase for top-level params +const results = await client.search('query', { filters: { user_id: 'alice' }, topK: 20 }); ``` diff --git a/skills/mem0/client/node.md b/skills/mem0/client/node.md index 86078dd6b..ca85ba7ba 100644 --- a/skills/mem0/client/node.md +++ b/skills/mem0/client/node.md @@ -41,23 +41,18 @@ const messages = [ { role: 'user', content: "I'm a vegetarian and allergic to nuts." }, { role: 'assistant', content: "Got it! I'll remember that." }, ]; -await client.add(messages, { user_id: 'alice' }); +await client.add(messages, { userId: 'alice' }); ``` | Parameter | Type | Description | |-----------|------|-------------| | `messages` | `Message[]` | Array of `{role, content}` objects | -| `options.user_id` | string | User identifier | -| `options.agent_id` | string | Agent identifier | -| `options.app_id` | string | Application identifier | -| `options.run_id` | string | Session identifier | +| `options.userId` | string | User identifier | +| `options.agentId` | string | Agent identifier | +| `options.appId` | string | Application identifier | +| `options.runId` | string | Session identifier | | `options.metadata` | object | Custom key-value pairs | -| `options.enable_graph` | boolean | Activate knowledge graph | | `options.infer` | boolean | If false, store raw text (default: true) | -| `options.immutable` | boolean | Prevent future modification | -| `options.expiration_date` | string | Auto-expiry (`YYYY-MM-DD`) | -| `options.includes` | string | Preference filter for inclusion | -| `options.excludes` | string | Preference filter for exclusion | **Returns:** `Promise` -- list of events @@ -66,7 +61,7 @@ await client.add(messages, { user_id: 'alice' }); Search memories by semantic similarity. ```typescript -const results = await client.search('dietary preferences', { user_id: 'alice' }); +const results = await client.search('dietary preferences', { filters: { user_id: 'alice' }, topK: 20 }); for (const mem of results.results) { console.log(mem.memory, mem.score); } @@ -75,17 +70,12 @@ for (const mem of results.results) { | Parameter | Type | Description | |-----------|------|-------------| | `query` | string | Natural language search query | -| `options.user_id` | string | Filter by user | -| `options.agent_id` | string | Filter by agent | -| `options.filters` | object | V2 filter object (`AND`/`OR`/`NOT`) | -| `options.top_k` | number | Number of results (default: 10) | -| `options.rerank` | boolean | Enable semantic reranking | -| `options.threshold` | number | Minimum similarity (default: 0.3) | -| `options.keyword_search` | boolean | Enable keyword search | -| `options.enable_graph` | boolean | Include graph relations | -| `options.filter_memories` | boolean | Precision filtering | +| `options.filters` | object | Filter object with entity IDs (`user_id`, `agent_id`, etc.) and/or `AND`/`OR`/`NOT` conditions | +| `options.topK` | number | Number of results (default: 20) | +| `options.rerank` | boolean | Enable semantic reranking (default: false) | +| `options.threshold` | number | Minimum similarity (default: 0.1) | -**Returns:** `Promise` -- `{results: [{id, memory, score, ...}], relations: [...]}` +**Returns:** `Promise` -- `{results: [{id, memory, score, ...}]}` #### get(memoryId) @@ -98,7 +88,7 @@ const memory = await client.get('ea925981-...'); Retrieve all memories. Requires at least one entity identifier in filters. ```typescript -const memories = await client.getAll({ user_id: 'alice' }); +const memories = await client.getAll({ filters: { user_id: 'alice' } }); // With filters const filtered = await client.getAll({ filters: { AND: [{ user_id: 'alice' }, { categories: { contains: 'health' } }] }, @@ -107,11 +97,9 @@ const filtered = await client.getAll({ | Parameter | Type | Description | |-----------|------|-------------| -| `options.user_id` | string | Filter by user | -| `options.filters` | object | V2 filter object | +| `options.filters` | object | Filter object with entity IDs (`user_id`, `agent_id`, etc.) and/or `AND`/`OR`/`NOT` conditions | | `options.page` | number | Page number | -| `options.page_size` | number | Results per page | -| `options.enable_graph` | boolean | Include graph relations | +| `options.pageSize` | number | Results per page | #### update(memoryId, data) @@ -136,14 +124,14 @@ await client.delete('ea925981-...'); #### deleteAll(options?) ```typescript -await client.deleteAll({ user_id: 'alice' }); +await client.deleteAll({ userId: 'alice' }); ``` #### history(memoryId) ```typescript const history = await client.history('ea925981-...'); -// Returns: [{previous_value, new_value, action, timestamps}] +// Returns: [{previousValue, newValue, action, timestamps}] ``` --- @@ -179,8 +167,8 @@ const users = await client.users(); #### deleteUser(data) / deleteUsers(data) ```typescript -await client.deleteUser({ user_id: 'alice' }); // Single entity -await client.deleteUsers({ agent_id: 'bot-1' }); // Flexible +await client.deleteUser({ userId: 'alice' }); // Single entity +await client.deleteUsers({ agentId: 'bot-1' }); // Flexible ``` --- @@ -189,13 +177,12 @@ await client.deleteUsers({ agent_id: 'bot-1' }); // Flexible ```typescript // Get project config -const config = await client.getProject({ fields: ['custom_categories'] }); +const config = await client.getProject({ fields: ['customCategories'] }); // Update project settings await client.updateProject({ - custom_instructions: 'Extract dietary preferences and health info', - custom_categories: [{ health: 'Medical and dietary info' }], - enable_graph: true, + customInstructions: 'Extract dietary preferences and health info', + customCategories: [{ health: 'Medical and dietary info' }], }); ``` @@ -205,25 +192,25 @@ await client.updateProject({ ```typescript // List -const webhooks = await client.getWebhooks({ project_id: 'proj_123' }); +const webhooks = await client.getWebhooks({ projectId: 'proj_123' }); // Create const webhook = await client.createWebhook({ url: 'https://your-app.com/webhook', name: 'Memory Logger', - project_id: 'proj_123', - event_types: ['memory_add', 'memory_update'], + projectId: 'proj_123', + eventTypes: ['memory_add', 'memory_update'], }); // Update await client.updateWebhook({ - webhook_id: 'wh_123', + webhookId: 'wh_123', name: 'Updated Logger', url: 'https://new-url.com', }); // Delete -await client.deleteWebhook({ webhook_id: 'wh_123' }); +await client.deleteWebhook({ webhookId: 'wh_123' }); ``` --- @@ -232,9 +219,9 @@ await client.deleteWebhook({ webhook_id: 'wh_123' }); ```typescript await client.feedback({ - memory_id: 'mem-123', + memoryId: 'mem-123', feedback: 'POSITIVE', - feedback_reason: 'Accurately captured preference', + feedbackReason: 'Accurately captured preference', }); ``` @@ -248,7 +235,7 @@ const exportReq = await client.createMemoryExport({ filters: { user_id: 'alice' }, }); -const result = await client.getMemoryExport({ memory_export_id: exportReq.id }); +const result = await client.getMemoryExport({ memoryExportId: exportReq.id }); ``` --- @@ -259,14 +246,12 @@ Key interfaces from `mem0.types.ts`: ```typescript interface Message { role: string; content: string; } -interface Memory { id: string; memory: string; user_id: string; categories: string[]; score?: number; /* ... */ } -interface MemoryOptions { user_id?: string; agent_id?: string; app_id?: string; run_id?: string; metadata?: object; /* ... */ } -interface SearchOptions { user_id?: string; filters?: object; top_k?: number; rerank?: boolean; threshold?: number; /* ... */ } -interface MemoryHistory { id: string; memory_id: string; previous_value: string; new_value: string; action: string; /* ... */ } -interface FeedbackPayload { memory_id: string; feedback: string; feedback_reason?: string; } -interface WebhookCreatePayload { url: string; name: string; project_id: string; event_types: string[]; } -enum OutputFormat { v1_0 = 'v1.0', v1_1 = 'v1.1' } -enum API_VERSION { v1 = 'v1', v2 = 'v2' } +interface Memory { id: string; memory: string; userId: string; categories: string[]; score?: number; /* ... */ } +interface MemoryOptions { userId?: string; agentId?: string; appId?: string; runId?: string; metadata?: object; /* ... */ } +interface SearchOptions { filters?: object; topK?: number; rerank?: boolean; threshold?: number; /* ... */ } +interface MemoryHistory { id: string; memoryId: string; previousValue: string; newValue: string; action: string; /* ... */ } +interface FeedbackPayload { memoryId: string; feedback: string; feedbackReason?: string; } +interface WebhookCreatePayload { url: string; name: string; projectId: string; eventTypes: string[]; } ``` --- @@ -296,7 +281,7 @@ const config = { llm: { provider: 'openai', // openai, groq, anthropic, google, ollama, lmstudio, mistral, azure config: { - model: 'gpt-4o-mini', + model: 'gpt-5-mini', apiKey: 'sk-xxx', }, }, @@ -315,17 +300,8 @@ const config = { port: 6333, }, }, - graphStore: { // Optional - provider: 'neo4j', - config: { - url: 'neo4j://localhost:7687', - username: 'neo4j', - password: 'password', - }, - }, historyDbPath: 'history.db', - customPrompt: '...', - enableGraph: false, + customInstructions: '...', disableHistory: false, }; @@ -363,17 +339,14 @@ await m.add([ #### search(query, config) ```typescript -const results = await m.search('dietary preferences', { userId: 'alice', limit: 5 }); +const results = await m.search('dietary preferences', { filters: { user_id: 'alice' }, topK: 5 }); ``` | Parameter | Type | Description | |-----------|------|-------------| | `query` | string | Search query | -| `config.userId` | string | Filter by user | -| `config.agentId` | string | Filter by agent | -| `config.runId` | string | Filter by run | -| `config.limit` | number | Max results (default: 100) | -| `config.filters` | object | Advanced filters | +| `config.filters` | object | Filter object with entity IDs (`user_id`, `agent_id`, `run_id`, etc.) | +| `config.topK` | number | Max results (default: 20) | #### get(memoryId) / getAll(config) / update(memoryId, data) / delete(memoryId) / deleteAll(config) / history(memoryId) @@ -401,12 +374,45 @@ await m.reset(); | **Auth** | API key required (`MEM0_API_KEY`) | No API key -- config-based | | **Execution** | API calls to `api.mem0.ai` | Local execution | | **Infrastructure** | Fully managed | Self-managed vector DB, embedder, LLM | -| **Param style** | `snake_case` in options (`user_id`) | `camelCase` in config (`userId`) | +| **Param style** | Top-level: `camelCase` (`userId`, `topK`), filter keys: `snake_case` (`user_id`) | Top-level: `camelCase` (`userId`, `topK`), filter keys: `snake_case` (`user_id`) | | **Batch ops** | `batchUpdate`, `batchDelete` | Not available | | **Webhooks** | Full CRUD | Not available | | **Export** | `createMemoryExport` | Not available | | **Feedback** | `feedback()` | Not available | | **Project mgmt** | `getProject`, `updateProject` | Not available | | **User listing** | `users()`, `deleteUser()` | Not available | -| **Graph store** | Platform-managed | Self-managed (Neo4j) | | **History** | Platform-managed | SQLite (configurable) | + +--- + +## v2 Compatibility + +If you're using SDK v2.x: + +**Naming Changes:** +- Top-level params now use camelCase: `topK`, `rerank` (not `top_k`) +- Filter keys use snake_case: `user_id`, `agent_id` +- OSS: `limit` renamed to `topK` + +**API Changes:** +```typescript +// v2 - top-level entity IDs, snake_case +await client.search("query", { user_id: "alice", top_k: 20 }); + +// v3 - filters object with snake_case keys, camelCase top-level params +await client.search("query", { filters: { user_id: "alice" }, topK: 20 }); +``` + +**Default Changes:** +| Param | v2 | v3 | +|-------|----|----| +| `topK` | 100 | 20 | +| `threshold` | none | 0.1 | +| `rerank` | true | false | + +**Removed:** +- `OutputFormat` and `API_VERSION` enums +- `organizationId`, `projectId` from constructor +- `enableGraph`, `asyncMode`, `outputFormat`, `immutable`, `expirationDate`, `filterMemories`, `batchSize`, `forceAddOnly`, `includes`, `excludes`, `keywordSearch` + +See the [v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) for details. diff --git a/skills/mem0/client/python.md b/skills/mem0/client/python.md index e9a5fe002..0cf35a550 100644 --- a/skills/mem0/client/python.md +++ b/skills/mem0/client/python.md @@ -36,7 +36,7 @@ client = AsyncMemoryClient(api_key="m0-xxx") # Or use as context manager async with AsyncMemoryClient(api_key="m0-xxx") as client: - results = await client.search("query", user_id="alice") + results = await client.search("query", filters={"user_id": "alice"}) ``` Same methods as `MemoryClient`, all `async`/`await`. Supports async context manager. @@ -65,25 +65,19 @@ client.add(messages, user_id="alice") | `app_id` | str | None | Application identifier | | `run_id` | str | None | Session/run identifier | | `metadata` | dict | None | Custom key-value pairs | -| `enable_graph` | bool | None | Activate knowledge graph extraction | | `infer` | bool | True | If False, store raw text without LLM inference | -| `immutable` | bool | None | If True, prevents future modification | -| `expiration_date` | str | None | Auto-expiry date (`YYYY-MM-DD`) | -| `includes` | str | None | Preference filter for inclusion | -| `excludes` | str | None | Preference filter for exclusion | -| `async_mode` | bool | True | If False, wait for processing to complete | | `custom_categories` | list | None | Override project categories | | `custom_instructions` | str | None | Override extraction instructions | | `timestamp` | int \| float \| str | None | Custom timestamp (Unix epoch or ISO 8601) | -**Returns:** `dict` -- list of events: `[{"id": "...", "event": "ADD|UPDATE|DELETE", "data": {"memory": "..."}}]` +**Returns:** `dict` -- list of events: `[{"id": "...", "event": "ADD", "data": {"memory": "..."}}]` #### search(query, **kwargs) Search memories by semantic similarity. ```python -results = client.search("dietary preferences", user_id="alice") +results = client.search("dietary preferences", filters={"user_id": "alice"}) for mem in results.get("results", []): print(mem["memory"], mem["score"]) ``` @@ -91,20 +85,14 @@ for mem in results.get("results", []): | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `query` | str | required | Natural language search query | -| `user_id` | str | None | Filter by user | -| `agent_id` | str | None | Filter by agent | -| `app_id` | str | None | Filter by app | +| `filters` | dict | None | Filter object with entity IDs and/or `AND`/`OR`/`NOT` conditions (e.g., `{"user_id": "alice"}`) | | `top_k` | int | 10 | Number of results | -| `filters` | dict | None | V2 filter object (`AND`/`OR`/`NOT`) | -| `rerank` | bool | None | Enable deep semantic reranking (+150-200ms) | -| `threshold` | float | 0.3 | Minimum similarity score | -| `keyword_search` | bool | None | Enable keyword-based search (+10ms) | -| `enable_graph` | bool | None | Include graph relations in results | -| `filter_memories` | bool | None | Precision filtering, removes low-relevance (+200-300ms) | +| `rerank` | bool | False | Enable deep semantic reranking (+150-200ms) | +| `threshold` | float | 0.1 | Minimum similarity score | | `fields` | list | None | Specific fields to return | | `categories` | list | None | Filter by category | -**Returns:** `dict` -- `{"results": [{id, memory, user_id, categories, score, created_at, ...}], "relations": [...]}` +**Returns:** `dict` -- `{"results": [{id, memory, user_id, categories, score, created_at, ...}]}` #### get(memory_id) @@ -121,27 +109,23 @@ memory = client.get(memory_id="ea925981-...") Retrieve all memories with optional filtering. Requires at least one entity identifier. ```python -memories = client.get_all(user_id="alice") -# With filters +memories = client.get_all(filters={"user_id": "alice"}) +# With compound filters memories = client.get_all(filters={"AND": [{"user_id": "alice"}, {"categories": {"contains": "health"}}]}) ``` | Parameter | Type | Default | Description | |-----------|------|---------|-------------| -| `user_id` | str | None | Filter by user | -| `agent_id` | str | None | Filter by agent | -| `app_id` | str | None | Filter by app | +| `filters` | dict | None | Filter object with entity IDs and/or `AND`/`OR`/`NOT` conditions | | `top_k` | int | None | Limit results | | `page` | int | None | Page number | | `page_size` | int | None | Results per page | -| `filters` | dict | None | V2 filter object | -| `enable_graph` | bool | None | Include graph relations | **Returns:** `dict` -- `{"results": [...]}` #### update(memory_id, text=None, metadata=None, timestamp=None) -Update a memory's content, metadata, or timestamp. At least one parameter required. Cannot update immutable memories. +Update a memory's content, metadata, or timestamp. At least one parameter required. ```python client.update("ea925981-...", text="Updated: vegan since 2024") @@ -320,11 +304,10 @@ config = client.project.get(fields=["custom_categories", "custom_instructions"]) client.project.update( custom_instructions="Extract dietary preferences and health info", custom_categories=[{"health": "Medical and dietary info"}], - enable_graph=True, multilingual=True, ) -# Create/delete project (requires org_id) +# Create/delete project client.project.create(name="My Project", description="...") client.project.delete() @@ -362,7 +345,7 @@ config = { "llm": { "provider": "openai", # openai, groq, azure, ollama, lmstudio, google, anthropic, mistral "config": { - "model": "gpt-4o-mini", + "model": "gpt-5-mini", "api_key": "sk-xxx", } }, @@ -381,18 +364,8 @@ config = { "port": 6333, } }, - "graph_store": { # Optional - "provider": "neo4j", - "config": { - "url": "neo4j://localhost:7687", - "username": "neo4j", - "password": "password", - } - }, "history_db_path": "history.db", # SQLite path for change history - "custom_instructions": "...", # Custom LLM prompt for extraction - "custom_update_memory_prompt": "...", # Custom LLM prompt for updates - "enable_graph": False, # Enable graph memory + "custom_instructions": "...", # Custom LLM prompt for extraction } m = Memory.from_config(config) @@ -403,7 +376,7 @@ m = Memory.from_config(config) ```python with Memory(config) as m: m.add("I prefer dark mode", user_id="alice") - results = m.search("preferences", user_id="alice") + results = m.search("preferences", filters={"user_id": "alice"}) # SQLite connections released automatically ``` @@ -425,12 +398,14 @@ At least one of `user_id`, `agent_id`, `run_id` required. **Returns:** `{"results": [...], "relations": [...]}` -#### search(query, *, user_id, agent_id, run_id, limit=100, filters=None, threshold=None, rerank=True) +#### search(query, *, filters=None, top_k=20, threshold=0.1, rerank=False) ```python -results = m.search("dietary preferences", user_id="alice", limit=5) +results = m.search("dietary preferences", filters={"user_id": "alice"}, top_k=5) ``` +Entity IDs (`user_id`, `agent_id`, `run_id`) must be passed inside the `filters` dict. + Supports filter operators: `eq`, `ne`, `in`, `nin`, `gt`, `gte`, `lt`, `lte`, `contains`, `not_contains`. #### get(memory_id) / get_all(**kwargs) / update(memory_id, data, metadata=None) / delete(memory_id) / delete_all(**kwargs) / history(memory_id) @@ -456,7 +431,7 @@ from mem0 import AsyncMemory m = AsyncMemory(config) await m.add("text", user_id="alice") -results = await m.search("query", user_id="alice") +results = await m.search("query", filters={"user_id": "alice"}) ``` --- @@ -469,13 +444,44 @@ results = await m.search("query", user_id="alice") | **Auth** | API key required (`MEM0_API_KEY`) | No API key -- config-based | | **Execution** | API calls to `api.mem0.ai` | Local execution | | **Infrastructure** | Fully managed | Self-managed vector DB, embedder, LLM | +| **Entity filtering** | `filters={"user_id": "..."}` | `filters={"user_id": "..."}` | | **Batch ops** | `batch_update`, `batch_delete` | Not available | | **Webhooks** | Full CRUD | Not available | | **Export** | `create_memory_export`, `get_memory_export` | Not available | | **Feedback** | `feedback()` | Not available | | **Project mgmt** | `client.project.*` | Not available | | **User listing** | `users()`, `delete_users()` | Not available | -| **Custom prompts** | Via project settings | Direct config | -| **Graph store** | Platform-managed | Self-managed (Neo4j) | +| **Custom prompts** | Via project settings | Direct config (`custom_instructions`) | | **History** | Platform-managed | SQLite (configurable) | | **Async** | `AsyncMemoryClient` | `AsyncMemory` | + +--- + +## v2 Compatibility + +If you're using SDK v2.x or the v2 API: + +**API Changes:** +- **Entity IDs in search/get_all:** Pass `user_id`, `agent_id` as top-level kwargs instead of inside `filters` + ```python + # v2 + results = client.search("query", user_id="alice") + # v3 + results = client.search("query", filters={"user_id": "alice"}) + ``` +- **add() returns:** v2 returns ADD, UPDATE, DELETE events; v3 returns ADD only + +**Default Changes:** +| Param | v2 | v3 | +|-------|----|----| +| `top_k` | 100 | 20 | +| `threshold` | None | 0.1 | +| `rerank` | True | False | + +**Removed Parameters:** +- Constructor: `org_id`, `project_id` +- add(): `async_mode`, `output_format`, `enable_graph`, `immutable`, `expiration_date`, `filter_memories`, `batch_size`, `force_add_only`, `includes`, `excludes`, `keyword_search` +- search()/get_all(): `enable_graph` +- Config: `enable_graph`, `graph_store`, `custom_fact_extraction_prompt` (renamed to `custom_instructions`) + +See the [v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) for full details. diff --git a/skills/mem0/references/api-reference.md b/skills/mem0/references/api-reference.md index e9fd20f24..62ec4bea7 100644 --- a/skills/mem0/references/api-reference.md +++ b/skills/mem0/references/api-reference.md @@ -8,13 +8,15 @@ All endpoints require: `Authorization: Token ` | Operation | Method | URL | |-----------|--------|-----| -| Add Memories | `POST` | `/v1/memories/` | -| Search Memories | `POST` | `/v2/memories/search/` | -| Get All Memories | `POST` | `/v2/memories/` | +| Add Memories | `POST` | `/v3/memories/add/` | +| Search Memories | `POST` | `/v3/memories/search/` | +| Get All Memories | `POST` | `/v3/memories/` | | Get Single Memory | `GET` | `/v1/memories/{memory_id}/` | | Update Memory | `PUT` | `/v1/memories/{memory_id}/` | | Delete Memory | `DELETE` | `/v1/memories/{memory_id}/` | +Note: v1/v2 endpoints still work (backward compatible). + ## Memory Object Structure | Field | Type | Description | @@ -27,8 +29,6 @@ All endpoints require: `Authorization: Token ` | `run_id` | string (nullable) | Run/session identifier | | `metadata` | object | Custom key-value pairs | | `categories` | array of strings | Auto-assigned category tags | -| `immutable` | boolean | If true, prevents modification | -| `expiration_date` | datetime (nullable) | Auto-expiry date | | `hash` | string | Content hash | | `created_at` | datetime | Creation timestamp | | `updated_at` | datetime | Last modification timestamp | @@ -50,10 +50,9 @@ Memories can be scoped to different levels: ## Processing Model -- Memories are processed **asynchronously by default** (`async_mode=true`) -- Add responses return queued events (`ADD`, `UPDATE`, `DELETE`) for tracking -- Set `async_mode=false` for synchronous processing when needed -- Graph metadata is processed asynchronously -- use `get_all()` for complete graph data +- Memories are processed **asynchronously** (v3 default) +- Add responses return queued `ADD` events only (v3 is ADD-only, no UPDATE/DELETE) +- Poll status via `GET /v1/event/{event_id}/` ## Filter System @@ -106,19 +105,17 @@ Root must be `AND`, `OR`, or `NOT`. Simple shorthand `{"user_id": "alice"}` also ## Response Formats -### Add Response +### Add Response (v3) ```json -[ - { - "id": "mem_01JF8ZS4Y0R0SPM13R5R6H32CJ", - "event": "ADD", - "data": { "memory": "The user moved to Austin in 2025." } - } -] +{ + "message": "Memory processing has been queued for background execution", + "status": "PENDING", + "event_id": "evt-uuid" +} ``` -Event types: `ADD`, `UPDATE`, `DELETE`. A single add can trigger multiple events. +v3 is ADD-only. No UPDATE or DELETE events. ### Search Response @@ -137,4 +134,17 @@ Event types: `ADD`, `UPDATE`, `DELETE`. A single add can trigger multiple events } ``` -With `enable_graph=true`, includes additional `relations` array with entity relationships. +In v3, `score` is a combined multi-signal relevance score. + +### Get All Response (v3) + +```json +{ + "count": 123, + "next": "https://api.mem0.ai/v3/memories/?page=2&page_size=50", + "previous": null, + "results": [...] +} +``` + +v3 returns paginated envelope. Use `page` and `page_size` query params. diff --git a/skills/mem0/references/architecture.md b/skills/mem0/references/architecture.md index f0c5c1bc8..4a04c820b 100644 --- a/skills/mem0/references/architecture.md +++ b/skills/mem0/references/architecture.md @@ -25,9 +25,9 @@ User Input β†’ Retrieve relevant memories β†’ Enrich LLM prompt β†’ Generate res Mem0 handles the complexity of extraction, deduplication, conflict resolution, and semantic retrieval so your application only needs to call `search()` and `add()`. -**Dual storage architecture:** +**Storage architecture:** - **Vector store**: Embeddings for semantic similarity search -- **Graph store** (optional): Entity nodes and relationship edges for structured knowledge +- **Entity store**: Automatic entity linking for relationship-aware retrieval --- @@ -40,41 +40,32 @@ Messages In β”‚ β–Ό β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” -β”‚ 1. EXTRACTION β”‚ LLM analyzes messages, extracts key facts +β”‚ 1. EXTRACTION β”‚ Single LLM call extracts all distinct new facts β”‚ (infer=True) β”‚ If infer=False, stores raw text as-is β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ β–Ό β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” -β”‚ 2. CONFLICT β”‚ Checks existing memories for duplicates -β”‚ RESOLUTION β”‚ Latest truth wins (newer overrides older) -β”‚ β”‚ Only runs when infer=True +β”‚ 2. DEDUPLICATION β”‚ Hash-based dedup (MD5 prevents exact duplicates) +β”‚ β”‚ No UPDATE/DELETE - v3 is ADD-only β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ β–Ό β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” -β”‚ 3. STORAGE β”‚ Generates embeddings β†’ vector store -β”‚ β”‚ Optional: entity extraction β†’ graph store -β”‚ β”‚ Indexes metadata, categories, timestamps +β”‚ 3. STORAGE β”‚ Batch embed β†’ vector store +β”‚ β”‚ Entity extraction β†’ entity store β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ β–Ό Memory Object - (id, memory, categories, structured_attributes) ``` -### Processing modes +### Processing (v3) -**Async (default, `async_mode=True`):** -- API returns immediately: `{"status": "PENDING", "event_id": "..."}` -- Processing happens in background +v3 processes memories asynchronously by default: +- API returns immediately: `{"status": "PENDING", "event_id": "evt-..."}` +- Poll status via `GET /v1/event/{event_id}/` - Use webhooks for completion notifications -- Best for: high-throughput, non-blocking workflows - -**Sync (`async_mode=False`):** -- API waits for full processing -- Returns complete memory object with `id`, `event`, `memory` -- Best for: real-time access immediately after add ### Extraction modes @@ -93,7 +84,7 @@ Messages In --- -## Retrieval Pipeline +## Retrieval Pipeline (v3) ### What happens when you call `client.search()` @@ -102,45 +93,37 @@ Query In β”‚ β–Ό β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” -β”‚ 1. QUERY EMBEDDING β”‚ Convert query to vector representation +β”‚ 1. PREPROCESSING β”‚ Lemmatize keywords, extract entities β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ β–Ό β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” -β”‚ 2. VECTOR SEARCH β”‚ Cosine similarity across stored embeddings -β”‚ β”‚ Scoped by filters (user_id, agent_id, etc.) -β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ - β”‚ - β–Ό (optional enhancements) -β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” -β”‚ 3a. KEYWORD SEARCH β”‚ Expands results with specific terms (+10ms) -β”‚ 3b. RERANKING β”‚ Deep semantic reordering (+150-200ms) -β”‚ 3c. FILTER MEMORIES β”‚ Precision filtering, removes low-relevance (+200-300ms) -β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ - β”‚ - β–Ό (if enable_graph=True) -β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” -β”‚ 4. GRAPH LOOKUP β”‚ Finds entity relationships -β”‚ β”‚ Appends relations WITHOUT reranking vector results +β”‚ 2. PARALLEL SCORING β”‚ Semantic search (vector similarity) +β”‚ β”‚ BM25 keyword search (term matching) +β”‚ β”‚ Entity matching (entity graph boost) β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ β–Ό - Results + Relations +β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ 3. SCORE FUSION β”‚ Combine signals into single score +β”‚ β”‚ Optional: rerank=True for deep reordering +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + β”‚ + β–Ό + Results (combined score per memory) ``` -### Retrieval enhancement combinations +### v3 Search Defaults -| Configuration | Latency | Best for | -|--------------|---------|----------| -| Base search only | ~100ms | Simple lookups | -| `keyword_search=True` | ~110ms | Entity-heavy queries, broad coverage | -| `rerank=True` | ~250-300ms | User-facing results, top-N precision | -| `keyword_search=True` + `rerank=True` | ~310ms | Balanced (recommended for most apps) | -| `rerank=True` + `filter_memories=True` | ~400-500ms | Safety-critical, production systems | +| Parameter | Default | Notes | +|-----------|---------|-------| +| `top_k` | 20 | Was 100 in v2 | +| `threshold` | 0.1 | Was None in v2 | +| `rerank` | False | Was True in v2 | ### Implicit null scoping -When you search with `user_id="alice"` only, Mem0 returns memories where `agent_id`, `app_id`, and `run_id` are all null. This prevents cross-scope leakage by default. +When you search with `filters={"user_id": "alice"}` only, Mem0 returns memories where `agent_id`, `app_id`, and `run_id` are all null. This prevents cross-scope leakage by default. To include memories with non-null fields, use explicit filters: ```python @@ -150,53 +133,23 @@ filters={"OR": [{"user_id": "alice"}]} --- -## Memory Lifecycle +## Memory Lifecycle (v3) -``` -CREATE ──→ ACTIVE ──→ UPDATE ──→ ACTIVE - β”‚ β”‚ β”‚ - β”‚ β–Ό β–Ό - β”‚ EXPIRED EXPIRED - β”‚ (still stored, (still stored, - β”‚ not retrieved) not retrieved) - β”‚ β”‚ β”‚ - β–Ό β–Ό β–Ό -DELETE DELETE DELETE -(permanent) -``` +v3 uses ADD-only extraction. Memories accumulate over time rather than being consolidated. ### Creation -- Triggered by `client.add(messages, user_id="...")` -- Messages processed through extraction β†’ conflict resolution β†’ storage -- Gets unique UUID, `created_at` timestamp -- Optional: custom `timestamp`, `expiration_date`, `metadata`, `immutable` +- `client.add(messages, user_id="...")` +- Single-pass extraction β†’ deduplication β†’ storage +- Returns `{"event_id": "...", "status": "PENDING"}` ### Updates -- `client.update(memory_id, text="...")` replaces text and reindexes -- `client.batch_update([...])` for up to 1000 memories at once -- Immutable memories (`immutable=True`) cannot be updated β€” must delete and re-add - -### Deduplication -- Automatic during `add()` with `infer=True` -- Conflict resolution merges duplicate facts -- Latest truth wins when contradictions detected -- Prevents memory bloat from repeated information - -### Expiration -- Optional `expiration_date` parameter (ISO 8601 or `YYYY-MM-DD`) -- After expiration: memory NOT returned in searches but remains in storage -- Useful for time-sensitive info (events, temporary preferences, session state) +- `client.update(memory_id, text="...")` replaces text +- Batch: `client.batch_update([...])` ### Deletion -- Single: `client.delete(memory_id)` β€” permanent, no recovery -- Batch: `client.batch_delete([memory_ids])` β€” up to 1000 -- Bulk: `client.delete_all(user_id="alice")` β€” all memories for entity -- `delete_all()` without filters raises error to prevent accidental data loss - -### History tracking -- `client.history(memory_id)` returns version timeline -- Shows all changes: `{previous_value, new_value, action, timestamps}` -- Useful for audit trails and debugging +- Single: `client.delete(memory_id)` +- Batch: `client.batch_delete([...])` +- Bulk: `client.delete_all(filters={"user_id": "alice"})` --- @@ -214,8 +167,6 @@ DELETE DELETE DELETE "categories": ["health", "preferences"], "created_at": "2025-03-12T12:34:56Z", "updated_at": "2025-03-12T12:34:56Z", - "expiration_date": null, - "immutable": false, "structured_attributes": { "day": 12, "month": 3, "year": 2025, "hour": 12, "minute": 34, @@ -239,8 +190,6 @@ DELETE DELETE DELETE | `categories` | array | Auto-assigned or custom category tags | | `created_at` | datetime | Creation timestamp | | `updated_at` | datetime | Last modification timestamp | -| `expiration_date` | datetime | Auto-expiry date (stops retrieval, data persists) | -| `immutable` | boolean | If true, prevents modification | | `structured_attributes` | object | Temporal breakdown for time-based queries | | `score` | float | Semantic similarity (search results only, 0-1) | @@ -322,7 +271,7 @@ Mem0 supports three layers of memory, from shortest to longest lived: ```python def chat(user_input: str, user_id: str, session_id: str) -> str: # 1. Retrieve user memories (long-term preferences) - user_mems = mem0.search(user_input, user_id=user_id) + user_mems = mem0.search(user_input, filters={"user_id": user_id}) # 2. Retrieve session memories (current task context) session_mems = mem0.search(user_input, filters={ @@ -350,18 +299,13 @@ def chat(user_input: str, user_id: str, session_id: str) -> str: | Operation | Typical Latency | |-----------|----------------| -| Base vector search | ~100ms | -| + keyword_search | +10ms | +| Hybrid search (v3 default) | ~100-150ms | | + reranking | +150-200ms | -| + filter_memories | +200-300ms | -| Add (async, default) | < 50ms response, background processing | -| Add (sync) | 500ms-2s depending on extraction complexity | -| Graph operations | Slight overhead for large stores | +| Add (async) | < 50ms response | ### Processing -- **Async mode (default):** Returns immediately, processes in background -- **Sync mode:** Waits for full extraction + storage pipeline +- **Async (default):** Returns immediately, processes in background - **Batch operations:** Up to 1000 memories per batch_update/batch_delete - **Webhooks:** Real-time notifications when async processing completes diff --git a/skills/mem0/references/features.md b/skills/mem0/references/features.md index 2e68b21ba..b5d5e73ea 100644 --- a/skills/mem0/references/features.md +++ b/skills/mem0/references/features.md @@ -5,7 +5,7 @@ Additional platform capabilities beyond core CRUD operations. ## Table of Contents - [Advanced Retrieval](#advanced-retrieval) -- [Graph Memory](#graph-memory) +- [Entity Linking](#entity-linking) - [Custom Categories](#custom-categories) - [Custom Instructions](#custom-instructions) - [Criteria Retrieval](#criteria-retrieval) @@ -18,124 +18,58 @@ Additional platform capabilities beyond core CRUD operations. ## Advanced Retrieval -Three enhancement options for tuning search precision, recall, and latency. +### Hybrid Search (v3 Default) -### Keyword Search (`keyword_search=True`) +v3 uses multi-signal hybrid search combining: +- **Semantic search** (vector similarity) +- **BM25 keyword search** (normalized term matching) +- **Entity matching** (entity graph boost) -Expands results to include memories with specific terms, names, and technical keywords. - -- Latency: +10ms -- Recall: Significantly increased -- Best for: entity-heavy queries, comprehensive coverage +This is automatic β€” no configuration needed. ### Reranking (`rerank=True`) Deep semantic reordering of results β€” most relevant first. - Latency: +150-200ms -- Accuracy: Significantly improved +- Default: `False` (was `True` in v2) - Best for: user-facing results, top-N precision -### Filter Memories (`filter_memories=True`) - -Precision filtering β€” removes low-relevance results entirely. - -- Latency: +200-300ms -- Precision: Maximized -- Best for: safety-critical applications, production systems - -### Recommended Combinations - **Python:** ```python -# Fast & broad -results = client.search(query, keyword_search=True, user_id="user123") - -# Balanced (recommended for most apps) -results = client.search(query, keyword_search=True, rerank=True, user_id="user123") - -# High precision (critical apps) -results = client.search(query, rerank=True, filter_memories=True, user_id="user123") +results = client.search(query, filters={"user_id": "user123"}, rerank=True) ``` **TypeScript:** ```typescript const results = await client.search(query, { - user_id: 'user123', - keyword_search: true, + filters: { user_id: 'user123' }, rerank: true, }); ``` --- -## Graph Memory +## Entity Linking -Entity-level knowledge graph that creates relationships between memories. +v3 replaces graph memory with built-in entity linking. Entities (proper nouns, quoted text, compound noun phrases) are automatically extracted and linked across memories. ### How It Works -1. **Extraction**: LLM analyzes conversation and identifies entities and relationships -2. **Storage**: Embeddings go to vector store; entity nodes and edges go to graph store -3. **Retrieval**: Vector search returns semantic matches; graph relations are appended to results +1. **Extraction**: During `add()`, entities are automatically extracted from memory text +2. **Storage**: Entities are stored in a parallel collection (`{collection}_entities`) +3. **Retrieval**: During `search()`, query entities are matched and used to boost relevant memories -Graph relations **augment** vector results without reordering them. Vector similarity always determines hit sequence. +Entity linking is automatic β€” no configuration required. The boost is folded into the combined `score` on each result. -### Enabling Graph Memory +### v2 Migration Note -**Per request:** -```python -client.add(messages, user_id="alice", enable_graph=True) -client.search("query", user_id="alice", enable_graph=True) -client.get_all(filters={"AND": [{"user_id": "alice"}]}, enable_graph=True) -``` +If you were using `enable_graph=True` in v2: +- Remove `enable_graph` from all API calls +- Remove `graph_store` from OSS configuration +- Entity relationships are now consumed through retrieval ranking, not exposed as a separate `relations` array -**Project-level (default for all operations):** -```python -client.project.update(enable_graph=True) -``` - -```javascript -await client.updateProject({ enable_graph: true }); -``` - -### Relation Structure - -Each relation in the response contains: - -| Field | Type | Description | -|-------|------|-------------| -| `source` | string | Source entity name | -| `source_type` | string | Source entity type (e.g., "Person") | -| `relationship` | string | Relationship label (e.g., "lives_in") | -| `target` | string | Target entity name | -| `target_type` | string | Target entity type (e.g., "City") | -| `score` | number | Confidence score | - -**Example:** -```json -{ - "relations": [ - { - "source": "Joseph", - "source_type": "Person", - "relationship": "lives_in", - "target": "Seattle", - "target_type": "City", - "score": 0.92 - } - ] -} -``` - -### Technical Notes - -- Graph Memory adds processing time; see docs for current plan availability -- Works optimally with rich conversation histories containing entity relationships -- Best suited for long-running assistants tracking evolving information -- Graph writes and reads toggle independently per request -- Multi-agent context supported via `user_id`, `agent_id`, `run_id` scoping -- Add operations are asynchronous; graph metadata may not be immediately available +See the [v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) for details. --- @@ -160,7 +94,7 @@ client.project.update(custom_categories=new_categories) ``` ```javascript -await client.updateProject({ custom_categories: new_categories }); +await client.updateProject({ customCategories: newCategories }); ``` **Retrieve active categories:** @@ -185,7 +119,7 @@ client.project.update(custom_instructions="Your guidelines here...") ``` ```javascript -await client.updateProject({ custom_instructions: "Your guidelines here..." }); +await client.updateProject({ customInstructions: "Your guidelines here..." }); ``` ### Template Structure @@ -229,7 +163,7 @@ client.project.update(retrieval_criteria=retrieval_criteria) ```typescript await client.updateProject({ - retrieval_criteria: [ + retrievalCriteria: [ { name: 'joy', description: 'Positive emotions', weight: 3 }, { name: 'urgency', description: 'Time-sensitive items', weight: 4 }, ], @@ -281,7 +215,7 @@ for item in feedback_data: ```typescript await client.feedback('mem-123', { feedback: 'POSITIVE', - feedback_reason: 'Accurately captured dietary preference', + feedbackReason: 'Accurately captured dietary preference', }); ``` diff --git a/skills/mem0/references/integration-patterns.md b/skills/mem0/references/integration-patterns.md index 61cfc0ffa..68232719e 100644 --- a/skills/mem0/references/integration-patterns.md +++ b/skills/mem0/references/integration-patterns.md @@ -27,7 +27,7 @@ from langchain_core.messages import SystemMessage, HumanMessage from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder from mem0 import MemoryClient -llm = ChatOpenAI(model="gpt-4.1-nano-2025-04-14") +llm = ChatOpenAI(model="gpt-5-mini") mem0 = MemoryClient() prompt = ChatPromptTemplate.from_messages([ @@ -128,7 +128,7 @@ import { createMem0 } from "@mem0/vercel-ai-provider"; const mem0 = createMem0(); const { text } = await generateText({ - model: mem0("gpt-4-turbo", { user_id: "borat" }), + model: mem0("gpt-5-mini", { user_id: "borat" }), prompt: "Suggest me a good car to buy!", }); ``` @@ -167,7 +167,7 @@ agent = Agent( Use search_memory to recall past conversations. Use save_memory to store important information.""", tools=[search_memory, save_memory], - model="gpt-4.1-nano-2025-04-14" + model="gpt-5-mini" ) result = Runner.run_sync(agent, "I love Italian food and I'm planning a trip to Rome next month") @@ -183,21 +183,21 @@ travel_agent = Agent( name="Travel Planner", instructions="You are a travel planning specialist. Use search_memory and save_memory tools.", tools=[search_memory, save_memory], - model="gpt-4.1-nano-2025-04-14" + model="gpt-5-mini" ) health_agent = Agent( name="Health Advisor", instructions="You are a health and wellness advisor. Use search_memory and save_memory tools.", tools=[search_memory, save_memory], - model="gpt-4.1-nano-2025-04-14" + model="gpt-5-mini" ) triage_agent = Agent( name="Personal Assistant", instructions="""Route travel questions to Travel Planner, health questions to Health Advisor.""", handoffs=[travel_agent, health_agent], - model="gpt-4.1-nano-2025-04-14" + model="gpt-5-mini" ) result = Runner.run_sync(triage_agent, "Plan a healthy meal for my Italy trip") @@ -254,7 +254,7 @@ from langchain_openai import ChatOpenAI from mem0 import MemoryClient from langchain_core.messages import SystemMessage, HumanMessage, AIMessage -llm = ChatOpenAI(model="gpt-4") +llm = ChatOpenAI(model="gpt-5-mini") mem0 = MemoryClient() class State(TypedDict): @@ -319,7 +319,7 @@ memory = Mem0Memory.from_client( from llama_index.core.agent import FunctionCallingAgent from llama_index.llms.openai import OpenAI -llm = OpenAI(model="gpt-4") +llm = OpenAI(model="gpt-5-mini") agent = FunctionCallingAgent.from_tools( tools=[], llm=llm, @@ -352,7 +352,7 @@ USER_ID = "alice" agent = ConversableAgent( "chatbot", - llm_config={"config_list": [{"model": "gpt-4", "api_key": os.environ["OPENAI_API_KEY"]}]}, + llm_config={"config_list": [{"model": "gpt-5-mini", "api_key": os.environ["OPENAI_API_KEY"]}]}, code_execution_config=False, human_input_mode="NEVER", ) diff --git a/skills/mem0/references/quickstart.md b/skills/mem0/references/quickstart.md index 0954f0c88..e9397b37c 100644 --- a/skills/mem0/references/quickstart.md +++ b/skills/mem0/references/quickstart.md @@ -59,11 +59,11 @@ const messages = [ {"role": "user", "content": "I'm a vegetarian and allergic to nuts."}, {"role": "assistant", "content": "Got it! I'll remember your dietary preferences."} ]; -await client.add(messages, { user_id: "user123" }); +await client.add(messages, { userId: "user123" }); // Search memories const results = await client.search("What are my dietary restrictions?", { - user_id: "user123" + filters: { user_id: "user123" } }); console.log(results); ``` diff --git a/skills/mem0/references/sdk-guide.md b/skills/mem0/references/sdk-guide.md index 57947699a..512c0d19c 100644 --- a/skills/mem0/references/sdk-guide.md +++ b/skills/mem0/references/sdk-guide.md @@ -40,16 +40,12 @@ client.add(messages, user_id="alice") # With metadata client.add(messages, user_id="alice", metadata={"source": "onboarding"}) - -# With graph memory -client.add(messages, user_id="alice", enable_graph=True) ``` **TypeScript:** ```typescript -await client.add(messages, { user_id: "alice" }); -await client.add(messages, { user_id: "alice", metadata: { source: "onboarding" } }); -await client.add(messages, { user_id: "alice", enable_graph: true }); +await client.add(messages, { userId: "alice" }); +await client.add(messages, { userId: "alice", metadata: { source: "onboarding" } }); ``` ### Parameters @@ -61,32 +57,14 @@ await client.add(messages, { user_id: "alice", enable_graph: true }); | `agent_id` | string | Agent identifier | | `run_id` | string | Session identifier | | `metadata` | object | Custom key-value pairs | -| `enable_graph` | boolean | Activate knowledge graph | | `infer` | boolean | If `false`, store raw text without inference (default: `true`) | -| `immutable` | boolean | Prevents modification after creation | -| `expiration_date` | string | Auto-expiry date (`YYYY-MM-DD`) | -| `includes` | string | Preference filters for inclusion | -| `excludes` | string | Preference filters for exclusion | -| `async_mode` | boolean | Async processing (default: `true`). Set `false` to wait | ### Advanced Add Options ```python -# Immutable -- cannot be modified or overwritten -client.add(messages, user_id="alice", immutable=True) - -# Expiring memory -client.add(messages, user_id="alice", expiration_date="2025-12-31") - -# Selective extraction -client.add(messages, user_id="alice", includes="dietary preferences", excludes="payment info") - # Agent + session scoping client.add(messages, user_id="alice", agent_id="nutrition-agent", run_id="session-456") -# Synchronous processing (wait for completion) -client.add(messages, user_id="alice", async_mode=False) - # Raw text -- skip LLM inference client.add( [{"role": "user", "content": "User prefers dark mode."}], @@ -101,7 +79,7 @@ client.add( **Python:** ```python -results = client.search("dietary preferences?", user_id="alice") +results = client.search("dietary preferences?", filters={"user_id": "alice"}) # With filters and reranking results = client.search( @@ -111,20 +89,14 @@ results = client.search( rerank=True, threshold=0.5 ) - -# With graph relations -results = client.search("colleagues", user_id="alice", enable_graph=True) - -# Keyword search -results = client.search("vegetarian", user_id="alice", keyword_search=True) ``` **TypeScript:** ```typescript -const results = await client.search("dietary preferences", { user_id: "alice" }); +const results = await client.search("dietary preferences", { filters: { user_id: "alice" } }); const results = await client.search("work experience", { filters: { AND: [{ user_id: "alice" }, { categories: { contains: "professional_details" } }] }, - top_k: 5, + topK: 5, rerank: true, }); ``` @@ -134,19 +106,17 @@ const results = await client.search("work experience", { | Name | Type | Description | |------|------|-------------| | `query` | string | Natural language search query | -| `user_id` | string | Filter by user | -| `filters` | object | V2 filter object (AND/OR operators) | -| `top_k` | number | Number of results (default: 10) | -| `rerank` | boolean | Enable reranking for better relevance | -| `threshold` | number | Minimum similarity score (default: 0.3) | -| `keyword_search` | boolean | Use keyword-based search | -| `enable_graph` | boolean | Include graph relations | +| `filters` | object | Filter object (AND/OR operators). Use `{"user_id": "..."}` to filter by user | +| `top_k` | number | Number of results (default: 10 for Platform) | +| `rerank` | boolean | Enable reranking for better relevance (default: `false`) | +| `threshold` | number | Minimum similarity score (default: 0.1) | ### Common Filter Patterns +**Python:** ```python -# Single user (shorthand) -client.search("query", user_id="alice") +# Single user filter +filters={"user_id": "alice"} # OR across agents filters={"OR": [{"user_id": "alice"}, {"agent_id": {"in": ["travel-agent", "sports-agent"]}}]} @@ -179,6 +149,21 @@ filters={"AND": [ ]} ``` +**TypeScript:** +```typescript +// Single user filter +filters: { user_id: "alice" } + +// OR across agents +filters: { OR: [{ user_id: "alice" }, { agent_id: { in: ["travel-agent", "sports-agent"] } }] } + +// Category filtering (partial match) +filters: { AND: [{ user_id: "alice" }, { categories: { contains: "finance" } }] } + +// Category filtering (exact match) +filters: { AND: [{ user_id: "alice" }, { categories: { in: ["personal_information"] } }] } +``` + --- ## get() / getAll() -- Retrieve Memories @@ -189,7 +174,7 @@ filters={"AND": [ memory = client.get(memory_id="ea925981-...") # All memories for a user -memories = client.get_all(filters={"AND": [{"user_id": "alice"}]}) +memories = client.get_all(filters={"user_id": "alice"}) # With date range memories = client.get_all( @@ -198,15 +183,12 @@ memories = client.get_all( {"created_at": {"gte": "2024-07-01", "lte": "2024-07-31"}} ]} ) - -# With graph data -memories = client.get_all(filters={"AND": [{"user_id": "alice"}]}, enable_graph=True) ``` **TypeScript:** ```typescript const memory = await client.get("ea925981-..."); -const memories = await client.getAll({ filters: { AND: [{ user_id: "alice" }] } }); +const memories = await client.getAll({ filters: { user_id: "alice" } }); ``` **Note:** `get_all` requires at least one of `user_id`, `agent_id`, `app_id`, or `run_id` in filters. @@ -226,8 +208,6 @@ client.update(memory_id="ea925981-...", text="Updated", metadata={"verified": Tr await client.update("ea925981-...", { text: "Updated: vegan since 2024" }); ``` -Cannot update immutable memories. - --- ## delete() / deleteAll() -- Remove Memories @@ -241,7 +221,7 @@ client.delete_all(user_id="alice") # Irreversible bulk delete **TypeScript:** ```typescript await client.delete("ea925981-..."); -await client.deleteAll({ user_id: "alice" }); +await client.deleteAll({ userId: "alice" }); ``` --- @@ -301,10 +281,73 @@ data = client.get_memory_export(memory_export_id=export["id"]) 2. **SQL operators rejected** -- use `gte`, `lt`, etc. Not `>=`, `<`. 3. **Metadata filtering is limited** -- only top-level keys with `eq`, `contains`, `ne`. 4. **Wildcard `*` excludes null** -- only matches non-null values. -5. **Default threshold is 0.3** -- increase for stricter matching. +5. **Default threshold is 0.1** -- increase for stricter matching. 6. **Async processing** -- memories process asynchronously. Wait 2-3s after `add()` before searching. -7. **Immutable memories** -- cannot be updated or deleted once created. ## Naming Conventions -Python uses `snake_case` (`user_id`, `memory_id`, `get_all`). TypeScript uses `camelCase` for methods (`getAll`, `deleteAll`, `batchUpdate`) but `snake_case` for API parameters (`user_id`, `agent_id`). +Python uses `snake_case` everywhere (`user_id`, `memory_id`, `get_all`). TypeScript uses `camelCase` for methods (`getAll`, `deleteAll`, `batchUpdate`) and top-level parameters (`userId`, `topK`, `pageSize`), but filter keys use `snake_case` (`user_id`, `agent_id`). + +--- + +## v2 to v3 Migration + +### Breaking Changes in v3 + +**1. Entity IDs in search() and getAll()** + +v3 requires entity IDs (`user_id`, `agent_id`, `run_id`) inside `filters` instead of as top-level parameters: + +```python +# v2 (deprecated) +client.search("query", user_id="alice") +client.get_all(user_id="alice") + +# v3 +client.search("query", filters={"user_id": "alice"}) +client.get_all(filters={"user_id": "alice"}) +``` + +```typescript +// v2 (deprecated) +await client.search("query", { user_id: "alice" }); +await client.getAll({ user_id: "alice" }); + +// v3 +await client.search("query", { filters: { user_id: "alice" } }); +await client.getAll({ filters: { user_id: "alice" } }); +``` + +**2. TypeScript Parameter Naming** + +v3 TypeScript uses camelCase for all parameters: + +| v2 | v3 | +|----|-----| +| `user_id` | `userId` | +| `agent_id` | `agentId` | +| `run_id` | `runId` | +| `top_k` | `topK` | +| `page_size` | `pageSize` | + +**3. Default Values Changed** + +| Parameter | v2 Default | v3 Default | +|-----------|------------|------------| +| `threshold` | 0.3 | 0.1 | +| `rerank` | (not specified) | `false` | + +**4. Removed Parameters** + +The following parameters are no longer supported: + +| Parameter | Status | +|-----------|--------| +| `enable_graph` | Removed from add/search/getAll | +| `keyword_search` | Removed from search | +| `filter_memories` | Removed | +| `immutable` | Removed from add | +| `expiration_date` | Removed from add | +| `includes` | Removed from add | +| `excludes` | Removed from add | +| `async_mode` | Removed from add | diff --git a/skills/mem0/references/use-cases.md b/skills/mem0/references/use-cases.md index eaca88896..5f3ba655d 100644 --- a/skills/mem0/references/use-cases.md +++ b/skills/mem0/references/use-cases.md @@ -39,7 +39,7 @@ Use these known facts about the user to personalize your response: {context if context else 'No prior context yet.'}""" response = openai_client.chat.completions.create( - model="gpt-4.1-nano-2025-04-14", + model="gpt-5-mini", messages=[ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_input}, @@ -72,14 +72,14 @@ const openai = new OpenAI(); async function chat(userInput: string, userId: string): Promise { // 1. Retrieve relevant memories - const memories = await mem0.search(userInput, { user_id: userId }); + const memories = await mem0.search(userInput, { filters: { user_id: userId } }); const context = memories.results ?.map((m: any) => `- ${m.memory}`) .join('\n') || 'No prior context yet.'; // 2. Generate response with memory context const response = await openai.chat.completions.create({ - model: 'gpt-4.1-nano-2025-04-14', + model: 'gpt-5-mini', messages: [ { role: 'system', content: `You are Ray, a personal fitness coach.\nUser context:\n${context}` }, { role: 'user', content: userInput }, @@ -90,7 +90,7 @@ async function chat(userInput: string, userId: string): Promise { // 3. Store interaction await mem0.add( [{ role: 'user', content: userInput }, { role: 'assistant', content: reply }], - { user_id: userId } + { userId: userId } ); return reply; } @@ -182,7 +182,7 @@ await client.updateProject({ async function logInteraction(userId: string, message: string, priority = 'normal') { await client.add( [{ role: 'user', content: message }], - { user_id: userId, metadata: { priority, source: 'support_chat' } } + { userId: userId, metadata: { priority, source: 'support_chat' } } ); } @@ -230,7 +230,7 @@ def consult(user_id: str, question: str) -> str: context = "\n".join([f"- {m['memory']}" for m in memories.get("results", [])]) response = openai_client.chat.completions.create( - model="gpt-4.1-nano-2025-04-14", + model="gpt-5-mini", messages=[ {"role": "system", "content": f"You are a health coach. Patient context:\n{context}"}, {"role": "user", "content": question}, @@ -264,20 +264,20 @@ const openai = new OpenAI(); async function savePatientInfo(userId: string, info: string) { await mem0.add( [{ role: 'user', content: info }], - { user_id: userId, run_id: 'healthcare_session', metadata: { type: 'patient_information' } } + { userId: userId, runId: 'healthcare_session', metadata: { type: 'patient_information' } } ); } async function consult(userId: string, question: string): Promise { const memories = await mem0.search(question, { - user_id: userId, - top_k: 5, + filters: { user_id: userId }, + topK: 5, threshold: 0.7, }); const context = memories.results?.map((m: any) => `- ${m.memory}`).join('\n') || ''; const response = await openai.chat.completions.create({ - model: 'gpt-4.1-nano-2025-04-14', + model: 'gpt-5-mini', messages: [ { role: 'system', content: `You are a health coach. Patient context:\n${context}` }, { role: 'user', content: question }, @@ -287,7 +287,7 @@ async function consult(userId: string, question: string): Promise { await mem0.add( [{ role: 'user', content: question }, { role: 'assistant', content: reply }], - { user_id: userId, run_id: 'healthcare_session' } + { userId: userId, runId: 'healthcare_session' } ); return reply; } @@ -333,7 +333,7 @@ def draft_content(user_id: str, topic: str) -> str: style_context = "\n".join([f"- {m['memory']}" for m in prefs.get("results", [])]) response = openai_client.chat.completions.create( - model="gpt-4.1-nano-2025-04-14", + model="gpt-5-mini", messages=[ {"role": "system", "content": f"Write content matching these style preferences:\n{style_context}"}, {"role": "user", "content": f"Write a blog post about: {topic}"}, @@ -359,7 +359,7 @@ const openai = new OpenAI(); async function storePreferences(userId: string, preferences: string) { await mem0.add( [{ role: 'user', content: preferences }], - { user_id: userId, run_id: 'editing_session', metadata: { type: 'preferences' } } + { userId: userId, runId: 'editing_session', metadata: { type: 'preferences' } } ); } @@ -370,7 +370,7 @@ async function draftContent(userId: string, topic: string): Promise { const styleContext = prefs.results?.map((m: any) => `- ${m.memory}`).join('\n') || ''; const response = await openai.chat.completions.create({ - model: 'gpt-4.1-nano-2025-04-14', + model: 'gpt-5-mini', messages: [ { role: 'system', content: `Write content matching these preferences:\n${styleContext}` }, { role: 'user', content: `Write a blog post about: ${topic}` }, @@ -465,10 +465,10 @@ async function storeScopedMemory( userId: string, agentId: string, runId: string, appId: string ) { await client.add(messages, { - user_id: userId, - agent_id: agentId, - run_id: runId, - app_id: appId, + userId: userId, + agentId: agentId, + runId: runId, + appId: appId, }); } @@ -520,7 +520,7 @@ def personalized_search(user_id: str, query: str, search_results: list) -> str: user_context = "\n".join([f"- {m['memory']}" for m in memories.get("results", [])]) response = openai_client.chat.completions.create( - model="gpt-4.1-nano-2025-04-14", + model="gpt-5-mini", messages=[ {"role": "system", "content": f"Personalize search results using user context:\n{user_context}"}, {"role": "user", "content": f"Query: {query}\n\nSearch results:\n{search_results}"}, @@ -551,11 +551,11 @@ const mem0 = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! }); const openai = new OpenAI(); async function personalizedSearch(userId: string, query: string, searchResults: string[]): Promise { - const memories = await mem0.search(query, { user_id: userId, top_k: 5 }); + const memories = await mem0.search(query, { filters: { user_id: userId }, topK: 5 }); const context = memories.results?.map((m: any) => `- ${m.memory}`).join('\n') || ''; const response = await openai.chat.completions.create({ - model: 'gpt-4.1-nano-2025-04-14', + model: 'gpt-5-mini', messages: [ { role: 'system', content: `Personalize results using user context:\n${context}` }, { role: 'user', content: `Query: ${query}\nResults: ${searchResults.join(', ')}` }, @@ -563,7 +563,7 @@ async function personalizedSearch(userId: string, query: string, searchResults: }); const reply = response.choices[0].message.content!; - await mem0.add([{ role: 'user', content: query }], { user_id: userId }); + await mem0.add([{ role: 'user', content: query }], { userId: userId }); return reply; } ``` @@ -631,14 +631,14 @@ const client = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! }); async function storeEmail(userId: string, sender: string, subject: string, body: string, date: string) { await client.add( [{ role: 'user', content: `Email from ${sender}: ${subject}\n\n${body}` }], - { user_id: userId, metadata: { email_type: 'incoming', sender, subject, date } } + { userId: userId, metadata: { email_type: 'incoming', sender, subject, date } } ); } async function searchEmails(userId: string, query: string) { return client.search(query, { filters: { AND: [{ user_id: userId }, { categories: { contains: 'email' } }] }, - top_k: 10, + topK: 10, }); } ```