refactor(opencode): native SDK tools, drop MCP, trim skills, expand telemetry

Reworks @mem0/opencode-plugin to expose memory operations as native OpenCode tools (via the @opencode-ai/plugin `tool()` helper, backed by the mem0ai SDK) instead of delegating to the remote MCP server. Skills load via the `config` hook (`skills.paths`) instead of being copied into the project `.opencode/`. - Drop MCP: remove the regex-based MCP tool interception, the bundled opencode.json MCP registration, and the cli.ts installer (mem0-opencode bin). - Native tools: add_memory, search_memories, get_memories, get_memory, update_memory, delete_memory, delete_all_memories, delete_entities, list_entities, get_event_status. - Trim skills 16 -> 8 (context-loader, dream, forget, health, peek, pin, remember, tour); remove import, export, memory-reviewer, mem0, list-projects, switch-project, stats, onboard. De-MCP/onboard wording in kept skills. - Telemetry: emit the full shared plugin.* schema (adds user_prompt, bash_error, pre_compact, session_stop alongside session_start, tool_use); tool_use now fires from inside each native tool; add project_hash + os_version props. - Fix duplicate error-search (single topK:6); correct the documented experimental.chat.messages.transform hook name. - Bump to 0.2.0.
2026-06-16 12:35:58 +05:30
33 changed files with 778 additions and 5867 deletions
@@ -1,6 +1,6 @@
 ---
 title: OpenCode
-description: "Add persistent memory to OpenCode with the Mem0 plugin — MCP server, lifecycle hooks, and slash commands."
+description: "Add persistent memory to OpenCode with the Mem0 plugin — native SDK-backed memory tools, lifecycle hooks, and skills."
 ---

 Add persistent memory to [**OpenCode**](https://opencode.ai) with the Mem0 plugin. Your agent forgets everything between sessions — Mem0 fixes that by storing decisions, preferences, and learnings so they carry over automatically.
@@ -30,27 +30,17 @@ echo 'export MEM0_API_KEY="m0-your-api-key"' >> ~/.bashrc && source ~/.bashrc
 opencode plugin @mem0/opencode-plugin
 ```

-
-Or using this command which does the same thing:
-
-
-```bash
-bunx @mem0/opencode-plugin@latest install
-```
-
-
-
 **Or let your agent do it** — paste this into OpenCode:

 ```
 Install @mem0/opencode-plugin by following https://raw.githubusercontent.com/mem0ai/mem0/main/integrations/mem0-plugin/.opencode-plugin/README.md
 ```

-All commands auto-add the plugin and MCP server to your `~/.config/opencode/opencode.json`. Restart OpenCode — you get the MCP server, lifecycle hooks, and all `/mem0:` slash commands.
+This adds the plugin to your `~/.config/opencode/opencode.json`. Restart OpenCode — you get the native memory tools, lifecycle hooks, and all `/mem0:` slash commands. The memory tools are registered by the plugin itself via the `mem0ai` SDK — no MCP server to configure.

-### Option B — MCP Only
+### Option B — Standalone MCP Server

-If you only need the memory tools without hooks or skills, add this to your `opencode.json` (project-level or global at `~/.config/opencode/opencode.json`):
+If you only need the memory tools without the plugin's hooks or skills, point OpenCode at Mem0's hosted MCP server directly. Add this to your `opencode.json` (project-level or global at `~/.config/opencode/opencode.json`):

 ```json
 {
@@ -69,13 +59,13 @@ If you only need the memory tools without hooks or skills, add this to your `ope

 ## What's Included

-| Component | Plugin (A) | MCP Only (B) |
-|-----------|:----------:|:------------:|
-| MCP Server (9 memory tools) | Yes | Yes |
+| Component | Plugin (A) | Standalone MCP (B) |
+|-----------|:----------:|:------------------:|
+| 9 memory tools | Native (SDK) | Remote MCP server |
 | Lifecycle Hooks | Yes | No |
-| 16 Slash Commands | Yes | No |
+| 8 Skills | Yes | No |

-## Available MCP Tools
+## Available Memory Tools

 | Tool | Description |
 |------|-------------|
@@ -95,10 +85,11 @@ The plugin uses the [mem0ai](https://www.npmjs.com/package/mem0ai) TypeScript SD

 | OpenCode Event | Hook | What happens |
 |----------------|------|-------------|
+| `config` | **Config** | Registers the bundled skills (`skills.paths`) and `/mem0:*` slash commands at startup |
 | `chat.message` | **Chat message** | Searches prior memories on session start, searches relevant memories before each prompt, auto-captures learnings periodically |
-| `tool.execute.before` | **Pre-tool** | Blocks MEMORY.md writes, injects `user_id`/`app_id` on mem0 tool calls |
-| `tool.execute.after` | **Post-tool** | Tracks stats, scans Bash errors and pre-fetches related error memories |
-| `experimental.chat.system.transform` | **System transform** | Injects memory context (session memories, search results, error lookups) into the system prompt |
+| `tool.execute.before` | **Pre-tool** | Blocks MEMORY.md writes, steering them to the `add_memory` tool |
+| `tool.execute.after` | **Post-tool** | Scans Bash errors and pre-fetches related error memories |
+| `experimental.chat.messages.transform` | **Messages transform** | Injects memory context (session memories, search results, error lookups) into the prompt |
 | `experimental.session.compacting` | **Compaction** | Stores session state memory, then injects prior memories into compaction context so nothing is lost |
 | `shell.env` | **Shell env** | Exports `MEM0_USER_ID`, `MEM0_APP_ID`, `MEM0_SESSION_ID`, and `MEM0_BRANCH` to all shell executions |

@@ -2,6 +2,23 @@

 All notable changes to the `@mem0/opencode-plugin` will be documented in this file.

+## 0.2.0 — Native SDK tools, MCP-free, leaner skill set
+
+### Changed (breaking)
+
+- **Memory tools are now native OpenCode tools** registered via the `@opencode-ai/plugin` `tool()` helper and backed by the `mem0ai` SDK directly. The plugin no longer registers or depends on the remote MCP server (`mcp.mem0.ai`); the bundled `opencode.json` and the regex-based MCP call interception have been removed. Tools: `add_memory`, `search_memories`, `get_memories`, `get_memory`, `update_memory`, `delete_memory`, `delete_all_memories`, `delete_entities`, `list_entities`, plus a `get_event_status` helper for async-write status.
+- **Skills load via the `config` hook (`skills.paths`)** instead of being copied into the project's `.opencode/` directory on startup. The `installSkills()` filesystem copy and the `cli.ts` installer (`mem0-opencode` bin) have been removed — install with `opencode plugin @mem0/opencode-plugin`.
+- **Trimmed to 8 focused skills** (`context-loader`, `dream`, `forget`, `health`, `peek`, `pin`, `remember`, `tour`). Removed `import`, `export`, `memory-reviewer`, `mem0` (SDK reference), `list-projects`, `switch-project`, `stats`, and `onboard`.
+
+### Added
+
+- **Expanded telemetry to the full shared `plugin.*` schema.** In addition to `plugin.session_start` and `plugin.tool_use`, the plugin now emits `plugin.user_prompt`, `plugin.bash_error`, `plugin.pre_compact`, and `plugin.session_stop`. `tool_use` now fires from inside each native tool. Every event also carries `project_hash` (anonymized `sha256(app_id)`) and `os_version`, matching the editor plugin's `telemetry.py`.
+
+### Fixed
+
+- **Error-pattern lookup** in `tool.execute.after` no longer issues two identical `mem0.search()` calls; it now performs a single `topK: 6` search.
+- Corrected the documented system-prompt hook name from `experimental.chat.system.transform` to the actual `experimental.chat.messages.transform`.
+
 ## 0.1.3 — File-context injection, session summaries & activity timeline, anonymous telemetry

 ### Added
@@ -4,24 +4,18 @@ Persistent memory for [OpenCode](https://opencode.ai). Your agent remembers deci

 ## Install

-```bash
-bunx @mem0/opencode-plugin@latest install
-```
-
-Or using OpenCode's built-in CLI:
-
 ```bash
 opencode plugin @mem0/opencode-plugin
 ```

+This adds the plugin to your `~/.config/opencode/opencode.json`. The plugin registers its memory tools and skills itself — there is no MCP server to configure.
+
 **Or let your agent do it** — paste this into OpenCode:

 ```
 Install @mem0/opencode-plugin by following https://raw.githubusercontent.com/mem0ai/mem0/main/integrations/mem0-plugin/.opencode-plugin/README.md
 ```

-All commands auto-add the plugin and MCP server to your `~/.config/opencode/opencode.json`. No manual config needed.
-
 Get your API key (free): [app.mem0.ai/dashboard/api-keys](https://app.mem0.ai/dashboard/api-keys)

 ```bash
@@ -34,24 +28,25 @@ Restart OpenCode.

 | Component | Description |
 |-----------|-------------|
-| **MCP Server** | 9 memory tools — add, search, get, update, delete memories |
-| **Lifecycle Hooks** | Auto-search on session start and every prompt, metadata enforcement, error memory lookup, compaction context |
-| **16 Slash Commands** | `/mem0:remember`, `/mem0:tour`, `/mem0:stats`, `/mem0:health`, `/mem0:dream`, and more |
+| **9 Native Memory Tools** | `add_memory`, `search_memories`, `get_memories`, `update_memory`, `delete_memory`, and more — registered as OpenCode tools, backed by the `mem0ai` SDK (no MCP server required) |
+| **Lifecycle Hooks** | Auto-search on session start and every prompt, error memory lookup, compaction context, secret redaction |
+| **8 Skills** | `/mem0:remember`, `/mem0:tour`, `/mem0:peek`, `/mem0:health`, `/mem0:dream`, `/mem0:forget`, `/mem0:pin`, `/mem0:context-loader` |

 ## Hooks

-Pure TypeScript — no Python, no shell scripts. Uses the [mem0ai](https://www.npmjs.com/package/mem0ai) SDK directly.
+Pure TypeScript — no Python, no shell scripts. Memory operations are native OpenCode tools backed by the [mem0ai](https://www.npmjs.com/package/mem0ai) SDK directly.

 | Hook | Event | What it does |
 |------|-------|-------------|
+| **Config** | `config` | Registers the bundled skills (`skills.paths`) and `/mem0:*` slash commands at startup |
 | **Chat message** | `chat.message` | Loads prior memories on session start, searches relevant memories before each prompt, auto-captures learnings periodically |
-| **Pre-tool** | `tool.execute.before` | Blocks MEMORY.md writes, enforces `user_id`/`app_id` on mem0 tools |
-| **Post-tool** | `tool.execute.after` | Tracks stats, scans bash errors for related memories |
-| **System transform** | `experimental.chat.system.transform` | Injects memory context (session memories, search results, error lookups) into system prompt |
+| **Pre-tool** | `tool.execute.before` | Blocks MEMORY.md writes, steering them to the `add_memory` tool |
+| **Post-tool** | `tool.execute.after` | Scans bash errors and pre-fetches related memories |
+| **Messages transform** | `experimental.chat.messages.transform` | Injects memory context (session memories, search results, error lookups) into the prompt |
 | **Compaction** | `experimental.session.compacting` | Stores session state memory, then injects prior memories into compaction context so nothing is lost |
 | **Shell env** | `shell.env` | Exports `MEM0_USER_ID`, `MEM0_APP_ID`, `MEM0_SESSION_ID`, and `MEM0_BRANCH` to shell |

-## MCP Tools
+## Memory Tools

 | Tool | Description |
 |------|-------------|
@@ -1,254 +0,0 @@
-#!/usr/bin/env bun
-import {
-  readFileSync,
-  writeFileSync,
-  existsSync,
-  mkdirSync,
-  copyFileSync,
-  readdirSync,
-  rmSync,
-  statSync,
-} from "fs";
-import { join, dirname } from "path";
-import { homedir } from "os";
-
-const PLUGIN_NAME = "@mem0/opencode-plugin";
-const MCP_CONFIG = {
-  mem0: {
-    type: "remote",
-    url: "https://mcp.mem0.ai/mcp/",
-    headers: {
-      Authorization: "Token {env:MEM0_API_KEY}",
-    },
-    oauth: false,
-  },
-};
-
-const SKILLS_NAMESPACE = "mem0";
-
-function getConfigDir(): string {
-  const dir = join(homedir(), ".config", "opencode");
-  if (!existsSync(dir)) mkdirSync(dir, { recursive: true });
-  return dir;
-}
-
-function getConfigPath(): string {
-  const configDir = getConfigDir();
-  const jsonc = join(configDir, "opencode.jsonc");
-  if (existsSync(jsonc)) return jsonc;
-  return join(configDir, "opencode.json");
-}
-
-function stripJsonComments(text: string): string {
-  let result = "";
-  let i = 0;
-  let inString = false;
-  let escape = false;
-  while (i < text.length) {
-    const ch = text[i];
-    if (escape) {
-      result += ch;
-      escape = false;
-      i++;
-      continue;
-    }
-    if (inString) {
-      if (ch === "\\") escape = true;
-      else if (ch === '"') inString = false;
-      result += ch;
-      i++;
-      continue;
-    }
-    if (ch === '"') {
-      inString = true;
-      result += ch;
-      i++;
-      continue;
-    }
-    if (ch === "/" && text[i + 1] === "/") {
-      while (i < text.length && text[i] !== "\n") i++;
-      continue;
-    }
-    if (ch === "/" && text[i + 1] === "*") {
-      i += 2;
-      while (i < text.length && !(text[i] === "*" && text[i + 1] === "/")) i++;
-      i += 2;
-      continue;
-    }
-    result += ch;
-    i++;
-  }
-  return result;
-}
-
-function resolvePluginDir(): string {
-  try {
-    return dirname(new URL(import.meta.url).pathname);
-  } catch {}
-  return __dirname ?? process.cwd();
-}
-
-function findSkillsDir(): string {
-  const base = resolvePluginDir();
-  const candidates = [
-    join(base, "opencode-skills"),
-    join(dirname(base), "opencode-skills"),
-    join(base, "..", "opencode-skills"),
-  ];
-  for (const c of candidates) {
-    try {
-      if (existsSync(c) && statSync(c).isDirectory()) return c;
-    } catch {}
-  }
-  return "";
-}
-
-function installSkills(): number {
-  const skillsSource = findSkillsDir();
-  if (!skillsSource) {
-    console.log("  ! Skills directory not found — skipping slash command install");
-    return 0;
-  }
-
-  const skillsTarget = join(getConfigDir(), "skills");
-  if (!existsSync(skillsTarget)) mkdirSync(skillsTarget, { recursive: true });
-
-  let count = 0;
-  const entries = readdirSync(skillsSource);
-
-  for (const name of entries) {
-    const skillDir = join(skillsSource, name);
-    try {
-      if (!statSync(skillDir).isDirectory()) continue;
-    } catch {
-      continue;
-    }
-
-    const skillFile = join(skillDir, "SKILL.md");
-    if (!existsSync(skillFile)) continue;
-
-    const targetDir = join(skillsTarget, `${SKILLS_NAMESPACE}-${name}`);
-    if (!existsSync(targetDir)) mkdirSync(targetDir, { recursive: true });
-
-    copyFileSync(skillFile, join(targetDir, "SKILL.md"));
-    count++;
-  }
-
-  return count;
-}
-
-function uninstallSkills(): number {
-  const skillsTarget = join(getConfigDir(), "skills");
-  if (!existsSync(skillsTarget)) return 0;
-
-  let count = 0;
-  const entries = readdirSync(skillsTarget);
-
-  for (const name of entries) {
-    if (!name.startsWith(`${SKILLS_NAMESPACE}-`)) continue;
-    const fullPath = join(skillsTarget, name);
-    try {
-      if (!statSync(fullPath).isDirectory()) continue;
-      rmSync(fullPath, { recursive: true });
-      count++;
-    } catch {}
-  }
-
-  return count;
-}
-
-function install() {
-  console.log("Installing Mem0 plugin for OpenCode...\n");
-
-  const configPath = getConfigPath();
-  let config: any = {};
-
-  if (existsSync(configPath)) {
-    try {
-      const raw = readFileSync(configPath, "utf-8");
-      config = JSON.parse(stripJsonComments(raw));
-    } catch {
-      console.log(`  ! Could not parse ${configPath}, creating fresh config`);
-      config = {};
-    }
-  }
-
-  if (!Array.isArray(config.plugin)) config.plugin = [];
-  if (!config.plugin.includes(PLUGIN_NAME)) {
-    config.plugin.push(PLUGIN_NAME);
-    console.log(`  + Added "${PLUGIN_NAME}" to plugin array`);
-  } else {
-    console.log(`  ~ "${PLUGIN_NAME}" already in plugin array`);
-  }
-
-  if (!config.mcp) config.mcp = {};
-  if (!config.mcp.mem0) {
-    config.mcp.mem0 = MCP_CONFIG.mem0;
-    console.log("  + Added mem0 MCP server config");
-  } else {
-    console.log("  ~ mem0 MCP server already configured");
-  }
-
-  writeFileSync(configPath, JSON.stringify(config, null, 2) + "\n");
-  console.log(`\n  Wrote ${configPath}`);
-
-  const skillCount = installSkills();
-  if (skillCount > 0) {
-    console.log(`  + Installed ${skillCount} slash commands to ~/.config/opencode/skills/`);
-  }
-
-  console.log("");
-
-  if (!process.env.MEM0_API_KEY) {
-    console.log("  ! MEM0_API_KEY is not set in your environment.");
-    console.log(
-      '  Run: echo \'export MEM0_API_KEY="m0-your-key"\' >> ~/.zshrc && source ~/.zshrc',
-    );
-    console.log(
-      "  Get a free key at: https://app.mem0.ai/dashboard/api-keys\n",
-    );
-  } else {
-    console.log("  MEM0_API_KEY detected\n");
-  }
-
-  console.log("Done! Restart OpenCode to activate Mem0.");
-  console.log("  Then run /mem0-onboard in the TUI to complete setup.\n");
-}
-
-function uninstall() {
-  const configPath = getConfigPath();
-  if (!existsSync(configPath)) {
-    console.log("No OpenCode config found. Nothing to remove.");
-    return;
-  }
-
-  const raw = readFileSync(configPath, "utf-8");
-  const config = JSON.parse(stripJsonComments(raw));
-
-  if (Array.isArray(config.plugin)) {
-    config.plugin = config.plugin.filter((p: string) => p !== PLUGIN_NAME);
-    if (config.plugin.length === 0) delete config.plugin;
-  }
-
-  if (config.mcp?.mem0) {
-    delete config.mcp.mem0;
-    if (Object.keys(config.mcp).length === 0) delete config.mcp;
-  }
-
-  writeFileSync(configPath, JSON.stringify(config, null, 2) + "\n");
-  console.log(`  Removed plugin + MCP from ${configPath}`);
-
-  const skillCount = uninstallSkills();
-  if (skillCount > 0) {
-    console.log(`  Removed ${skillCount} slash commands from ~/.config/opencode/skills/`);
-  }
-
-  console.log("Restart OpenCode to complete removal.");
-}
-
-const cmd = process.argv[2];
-if (cmd === "uninstall" || cmd === "remove") {
-  uninstall();
-} else {
-  install();
-}
@@ -1,80 +0,0 @@
---
-name: export
-description: Exports all project memories to a portable Markdown file for backup or migration. Use when backing up memories, migrating to another project, sharing memory state with teammates, or archiving before cleanup.
---
-
-# Mem0 Export
-
-Export all memories for the current project to a portable Markdown file.
-
-## Execution
-
-### Step 1: Resolve identity
-
-Determine the active identity:
- `user_id` from `MEM0_USER_ID` env var, else `$USER`, else `"default"`
- `project_id` (used as `app_id`) from `MEM0_PROJECT_ID` env var, or via the project resolver
-
-### Step 2: Fetch all memories
-
-Call `get_memories` with:
- `filters={"AND": [{"user_id": "<active_user_id>"}, {"app_id": "<active_project_id>"}]}`
- `page_size=200`
-
-If the response is paginated (i.e. the result contains a `next` cursor or the count equals `page_size`), continue fetching pages until all memories are retrieved.
-
-### Step 3: Format each memory as a YAML-frontmatter block
-
-For each memory record, produce a block in this exact format:
-
-```
---
-id: <memory.id>
-created_at: <memory.created_at>
-type: <memory.metadata.type or "">
-confidence: <memory.metadata.confidence or "">
-branch: <memory.metadata.branch or "">
-files: <memory.metadata.files joined with ", " or "">
-categories: <memory.categories joined with ", " or "">
---
-<memory.memory or memory content string>
-
-```
-
-Notes:
- The `---` delimiters must be on their own lines with no extra whitespace.
- `files` and `categories` are written as comma-separated values on a single line.
- Leave a blank line after the content before the next `---` (for readability).
- If a field is missing or null, write an empty string (not "null").
-
-### Step 4: Write the export file
-
-Determine the output filename:
-
-```
-mem0-export-<project_id>-<YYYY-MM-DD>.md
-```
-
-Where `<YYYY-MM-DD>` is today's date in UTC.
-
-Write all formatted blocks to this file using the Write tool (or equivalent). The file is written to the current working directory.
-
-### Step 5: Print summary
-
-```
-Exported <N> memories to <filename>
-```
-
-Where `<N>` is the total number of memory blocks written.
-
-## Error Handling
-
- If `get_memories` returns an error or zero memories, print:
-  ```
-  No memories found for project <project_id>. Nothing exported.
-  ```
- If the write fails, report the error to the user.
-
-## Output formatting
-
-IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like **bold**, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.
@@ -1,6 +1,6 @@
 ---
 name: health
-description: Diagnoses mem0 connectivity, API key validity, and memory read/write functionality. Use when memory operations fail, searches return empty, add_memory errors occur, MCP connection drops, or to verify the plugin is working correctly.
+description: Diagnoses mem0 connectivity, API key validity, and memory read/write functionality. Use when memory operations fail, searches return empty, add_memory errors occur, or to verify the plugin is working correctly.
 ---

 # Mem0 Health Check
@@ -37,7 +37,7 @@ echo "branch=$(git branch --show-current 2>/dev/null || echo '')"

 PASS if all three are non-empty. WARN if any falls back to defaults.

-### Check 3: MCP server connectivity
+### Check 3: Memory tool connectivity

 Call `search_memories` with:
 - `query="health check"`
@@ -82,7 +82,7 @@ echo "branch=${MEM0_BRANCH:-}"

 PASS  API Key         m0-dVe...
 PASS  Identity        user=kartik, project=mem0, branch=main
-PASS  MCP Connection  142ms
+PASS  Memory Tools    142ms
 PASS  Write/Read      write + delete OK
 PASS  Session         session_id=abc123, app_id=mem0, branch=main

@@ -1,185 +0,0 @@
---
-name: import
-description: Imports memories from an exported Markdown file or MEMORY.md into the current project. Use when migrating from another project, restoring from backup, importing Claude Code native MEMORY.md content, or setting up a new project with existing knowledge.
---
-
-# Mem0 Import
-
-Import memories from a mem0 export file into the current project.
-
-## Execution
-
-### Step 1: Determine the export file to import
-
-If the user provided a filename as an argument to `/mem0:import <filename>`, use that file.
-
-Otherwise, list `.md` files in the current directory whose names contain `mem0-export`:
-
-```bash
-ls -1 *.md 2>/dev/null | grep mem0-export || echo "No export files found"
-```
-
-If multiple files are found, ask the user which one to import. If none are found, print:
-```
-No mem0-export files found in the current directory.
-Run /mem0:export first, or provide the filename: /mem0:import <path-to-file>
-```
-
-### Step 2: Parse the export file
-
-Read the export file directly. It is a JSON file containing a top-level `memories` array. Each element has:
- `id` — original memory ID (for reference only; a new ID will be assigned on import)
- `type` — metadata type
- `confidence` — metadata confidence value
- `branch` — metadata branch
- `files` — list of associated files
- `categories` — list of categories
- `content` — the memory text
-
-Parse the JSON in-memory (do not run any external script). If the file cannot be read or parsed, or if the `memories` array is missing or empty, print:
-```
-Failed to parse <filename> or file contains no valid memory blocks.
-```
-and stop.
-
-### Step 3: Resolve identity
-
-Determine the active identity:
- `user_id` from `MEM0_USER_ID` env var, else `$USER`, else `"default"`
- `project_id` (used as `app_id`) from `MEM0_PROJECT_ID` env var, or via the project resolver
-
-### Step 4: Import each memory
-
-For each record in the parsed JSON array, call `add_memory` (MCP tool) with:
-
- `text="<record.content>"`
- `user_id=<active_user_id>`
- `app_id=<active_project_id>`
- `metadata={`
-  - `"type": "<record.type>"` (if non-empty)
-  - `"confidence": "<record.confidence>"` (if non-empty)
-  - `"branch": "<record.branch>"` (if non-empty)
-  - `"files": <record.files>` (the list, if non-empty)
-  - `"source": "import"`
-  - `}`
- `infer=False`
-
-Notes:
- Do NOT pass the original `id` — the platform assigns a new ID.
- Skip records where `content` is empty.
- Continue importing even if individual records fail; track the count of successes.
-
-### Step 5: Print results
-
-```
-Imported <N> memories into project <project_id>
-```
-
-Where `<N>` is the number of successfully imported memories.
-
-If any failed:
-```
-Imported <N>/<total> memories into project <project_id> (<failed> failed)
-```
-
-## Importing from competing AI tools (`--tools`)
-
-When invoked with `--tools` (e.g., `/mem0:import --tools`), detect and import
-from competing AI tool configuration files:
-
-### Supported tools
-
-| Tool | File/directory |
-|------|---------------|
-| Cursor | `.cursorrules` |
-| GitHub Copilot | `.github/copilot-instructions.md` |
-| Cline | `memory-bank/` (directory of `.md` files) |
-| Continue | `.continue/rules.md` |
-
-### T1: Detect
-
-```bash
-test -f .cursorrules && echo "cursor: .cursorrules"
-test -f .github/copilot-instructions.md && echo "copilot: .github/copilot-instructions.md"
-test -d memory-bank/ && echo "cline: memory-bank/"
-test -f .continue/rules.md && echo "continue: .continue/rules.md"
-```
-
-### T2: Ask user
-
-List found files, ask which to import (numbers, comma-separated, or "all").
-If none found:
-```
-No competing tool configuration files found.
-Checked: .cursorrules, .github/copilot-instructions.md, memory-bank/, .continue/rules.md
-```
-
-### T3: Import
-
-For each selected tool, read the file(s) directly and split the content into logical
-chunks to import as individual memories. No external scripts are used — all parsing
-and importing is done via MCP tools.
-
-Chunking rules per tool:
-
- **cursorrules / copilot / continue**: Read the file as plain text. Split on blank
-  lines or section headers (`#`, `##`). Each non-empty chunk becomes one memory.
-  Skip chunks shorter than 50 characters.
-
- **cline (memory-bank/)**: List all `.md` files in the directory. Read each file.
-  Split each file on blank lines or headers. Each non-empty chunk becomes one memory.
-  Skip chunks shorter than 50 characters.
-
-For each chunk, call `add_memory` (MCP tool) with:
- `text="<chunk text>"`
- `user_id=<active_user_id>`
- `app_id=<active_project_id>`
- `metadata={"type": "task_learning", "source": "<tool>-import", "confidence": 0.8}`
- `infer=False`
-
-Where `<tool>` is `cursorrules`, `copilot`, `cline`, or `continue`.
-
-Notes: chunks longer than 10,000 characters are truncated before import. Safe to
-re-run — deduplication handles repeated entries.
-
-### T4: Report
-
-```
-Imported <N> memories into <project_id> (cursor: <N>, copilot: <N>)
-```
-
---
-
-## Importing Claude Code's native MEMORY.md
-
-When invoked with a path to Claude Code's native `MEMORY.md` file (typically
-`~/.claude/projects/<proj-key>/memory/MEMORY.md`), or when `on_session_start.sh`
-detects native auto-memory and the user chooses to import:
-
-1. Read the file directly. It contains newline-separated memory entries (one fact per
-   line, sometimes with `- ` bullet prefix).
-2. Split by non-empty lines. Each line becomes one memory.
-3. Skip lines shorter than 20 characters or lines that are just headers (`#`).
-4. For each line, call `add_memory` (MCP tool) with:
-   - `text="<line>"`
-   - `user_id=<active_user_id>`
-   - `app_id=<active_project_id>`
-   - `metadata={"type": "task_learning", "source": "memory-md-import", "confidence": 0.8}`
-   - `infer=False`
-5. Report: `Imported <N> memories from MEMORY.md into project <project_id>`
-6. Suggest disabling native auto-memory:
-   ```
-   To avoid duplicate memory systems, add to ~/.claude/settings.json:
-     "autoMemoryEnabled": false
-   ```
-
-This handles the cold-start gap when a user has been using Claude Code's native
-memory and switches to mem0.
-
-## Error Handling
-
- If `add_memory` calls fail consistently (e.g. auth error), report the issue and stop early.
-
-## Output formatting
-
-IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like **bold**, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.
@@ -1,64 +0,0 @@
---
-name: list-projects
-description: Lists all projects with stored memories for the current user, showing memory counts and last activity dates. Use when checking which projects have memories, comparing memory distribution across repos, or finding a specific project scope.
---
-
-# Mem0 List Projects
-
-Show all known project scopes for the current user.
-
-## Execution
-
-### Step 1: Fetch memories to discover app_ids
-
-There is no dedicated "list projects" API endpoint. Discover projects by fetching
-the user's memories across all scopes.
-
-**Important:** A filter with only `user_id` triggers implicit null scoping — it
-excludes memories that have a non-null `app_id`. Run two queries and merge:
-
-1. **Null-scoped:** `get_memories` with `filters={"AND": [{"user_id": "<active_user_id>"}]}`, `page_size=200`
-   — catches memories without `app_id`
-2. **App-scoped:** `get_memories` with `filters={"AND": [{"user_id": "<active_user_id>"}, {"app_id": {"exists": true}}]}`, `page_size=200`
-   — catches memories with any `app_id`
-
-Run both calls in parallel. Merge results, deduplicate by memory `id`.
-
-If either response indicates more pages, paginate (up to 1000 total).
-
-### Step 2: Extract distinct projects
-
-For each memory, determine project by:
-1. Top-level `app_id` field (preferred)
-2. `metadata.project_id` (legacy memories)
-3. `metadata.project` (oldest format)
-4. `"(unscoped)"` if none found
-
-Group by resolved project name. For each project, count:
- Total memories
- Most recent `created_at` date
- Top 3 `metadata.type` values by frequency
-
-### Step 3: Display
-
-```
-## mem0 projects
-
-  <app_id_1>  <count> memories  (last: <date>) ← current
-  <app_id_2>  <count> memories  (last: <date>)
-
-<N> projects, <M> total memories
-```
-
-Mark current project with `← current`. Sort by memory count descending.
-
-### Step 4: Empty state
-
-If zero memories found:
-```
-No projects found. Run /mem0:onboard to get started.
-```
-
-## Output formatting
-
-IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like **bold**, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.
@@ -1,189 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
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-          notices within Derivative Works that You distribute, alongside
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-          that such additional attribution notices cannot be construed
-          as modifying the License.
-
-      You may add Your own copyright statement to Your modifications and
-      may provide additional or different license terms and conditions
-      for use, reproduction, or distribution of Your modifications, or
-      for any such Derivative Works as a whole, provided Your use,
-      reproduction, and distribution of the Work otherwise complies with
-      the conditions stated in this License.
-
-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
-      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
-      implied, including, without limitation, any warranties or conditions
-      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
-      PARTICULAR PURPOSE. You are solely responsible for determining the
-      appropriateness of using or redistributing the Work and assume any
-      risks associated with Your exercise of permissions under this License.
-
-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
-      incidental, or consequential damages of any character arising as a
-      result of this License or out of the use or inability to use the
-      Work (including but not limited to damages for loss of goodwill,
-      work stoppage, computer failure or malfunction, or any and all
-      other commercial damages or losses), even if such Contributor
-      has been advised of the possibility of such damages.
-
-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
-      and charge a fee for, acceptance of support, warranty, indemnity,
-      or other liability obligations and/or rights consistent with this
-      License. However, in accepting such obligations, You may act only
-      on Your own behalf and on Your sole responsibility, not on behalf
-      of any other Contributor, and only if You agree to indemnify,
-      defend, and hold each Contributor harmless for any liability
-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   Copyright 2024 Mem0.ai
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
@@ -1,73 +0,0 @@
-# Mem0 Skill for Claude
-
-Add persistent memory to any AI application in minutes using [Mem0 Platform](https://app.mem0.ai?utm_source=oss&utm_medium=mem0-plugin-skill-readme).
-
-## What This Skill Does
-
-When installed, Claude can:
-
- **Set up Mem0** in your Python or TypeScript project
- **Integrate memory** into your existing AI app (LangChain, CrewAI, Vercel AI, OpenAI Agents, LangGraph, LlamaIndex, etc.)
- **Generate working code** using real API references and tested patterns
- **Search live docs** on demand for the latest Mem0 documentation
-
-## Installation
-
-This skill is included automatically when you install the Mem0 plugin:
-
-```
-/plugin marketplace add mem0ai/mem0
-/plugin install mem0@mem0-plugins
-```
-
-See the [plugin README](../../README.md) for full setup instructions.
-
-### Prerequisites
-
- A Mem0 Platform API key ([Get one here](https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=mem0-plugin-skill-readme))
- Python 3.10+ or Node.js 18+
- Set the environment variable:
-
-  ```bash
-  export MEM0_API_KEY="m0-your-api-key"
-  ```
-
-## Quick Start
-
-After installing, just ask Claude:
-
- "Set up mem0 in my project"
- "Add memory to my chatbot"
- "Help me search user memories with filters"
- "Integrate mem0 with my LangChain app"
- "Add graph memory to track entity relationships"
-
-## What's Inside
-
-```text
-skills/mem0/
-├── SKILL.md                    # Skill definition and instructions
-├── README.md                   # This file
-├── LICENSE                     # Apache-2.0
-├── scripts/
-│   └── mem0_doc_search.py      # Search live Mem0 docs on demand
-└── references/                 # Documentation (loaded on demand)
-    ├── quickstart.md           # Full quickstart (Python, TS, cURL)
-    ├── sdk-guide.md            # All SDK methods (Python + TypeScript)
-    ├── api-reference.md        # REST endpoints, filters, memory object
-    ├── architecture.md         # Processing pipeline, lifecycle, scoping, performance
-    ├── features.md             # Retrieval, graph, categories, MCP, webhooks, multimodal
-    ├── integration-patterns.md # LangChain, CrewAI, Vercel AI, LangGraph, LlamaIndex, etc.
-    └── use-cases.md            # 7 real-world patterns with Python + TypeScript code
-```
-
-## Links
-
- [Mem0 Platform Dashboard](https://app.mem0.ai?utm_source=oss&utm_medium=mem0-plugin-skill-readme)
- [Mem0 Documentation](https://docs.mem0.ai)
- [Mem0 GitHub](https://github.com/mem0ai/mem0)
- [API Reference](https://docs.mem0.ai/api-reference)
-
-## License
-
-Apache-2.0
@@ -1,191 +0,0 @@
---
-name: mem0
-description: Mem0 SDK reference covering Python and TypeScript APIs, memory client methods, configuration, and framework integrations. Use when writing code that calls mem0 APIs, configuring memory providers, or integrating mem0 into an application.
-license: Apache-2.0
-metadata:
-  author: mem0ai
-  version: "0.1.1"
-  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. Uses Mem0 v3 API.
---
-
-# Mem0 Platform Integration
-
-> **Skill Graph:** This skill is part of the Mem0 skill graph:
-> - **mem0** (this skill) -- Platform Client SDK + OSS (Python + TypeScript)
-> - **[mem0-vercel-ai-sdk](https://github.com/mem0ai/mem0/tree/main/skills/mem0-vercel-ai-sdk)** -- Vercel AI SDK provider
-
-Mem0 is a managed memory layer for AI applications. It stores, retrieves, and manages user memories via API — no infrastructure to deploy. For self-hosted usage, see the OSS section in the client references below.
-
-## Step 1: Install and authenticate
-
-**Python:**
-```bash
-pip install mem0ai
-export MEM0_API_KEY="m0-your-api-key"
-```
-
-**TypeScript/JavaScript:**
-```bash
-npm install mem0ai
-export MEM0_API_KEY="m0-your-api-key"
-```
-
-Get an API key at: https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=mem0-plugin-skill
-
-> **Don't have a `MEM0_API_KEY`?** Sign up at https://app.mem0.ai and create one from the dashboard. Keys start with `m0-`.
-
-## Step 2: Initialize the client
-
-**Python:**
-```python
-from mem0 import MemoryClient
-client = MemoryClient(api_key="m0-xxx")
-```
-
-**TypeScript:**
-```typescript
-import MemoryClient from 'mem0ai';
-const client = new MemoryClient({ apiKey: 'm0-xxx' });
-```
-
-For async Python, use `AsyncMemoryClient`.
-
-## Step 3: Core operations
-
-Every Mem0 integration follows the same pattern: **retrieve → generate → store**.
-
-### Add memories
-```python
-messages = [
-    {"role": "user", "content": "I'm a vegetarian and allergic to nuts."},
-    {"role": "assistant", "content": "Got it! I'll remember that."}
-]
-client.add(messages, user_id="alice")
-```
-
-### Search memories
-```python
-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(filters={"user_id": "alice"})
-```
-
-### Update a memory
-```python
-client.update("memory-uuid", text="Updated: vegetarian, nut allergy, prefers organic")
-```
-
-### Delete a memory
-```python
-client.delete("memory-uuid")
-client.delete_all(user_id="alice")  # delete all for a user
-```
-
-## Common integration pattern
-
-```python
-from mem0 import MemoryClient
-from openai import OpenAI
-
-mem0 = MemoryClient()
-openai = OpenAI()
-
-def chat(user_input: str, user_id: str) -> str:
-    # 1. Retrieve relevant memories
-    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-5-mini",
-        messages=[
-            {"role": "system", "content": f"User context:\n{context}"},
-            {"role": "user", "content": user_input},
-        ]
-    )
-    reply = response.choices[0].message.content
-
-    # 3. Store interaction for future context
-    mem0.add(
-        [{"role": "user", "content": user_input}, {"role": "assistant", "content": reply}],
-        user_id=user_id
-    )
-    return reply
-```
-
-## Common edge cases
-
- **Search returns empty:** v3 processes `add()` asynchronously — returns an event ID immediately. Wait 2-3s 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. `{"AND": [{"user_id": "alice"}, {"agent_id": "bot"}]}` returns nothing. Use `OR` instead, or query each separately.
- **Duplicate memories:** Don't mix `infer=True` (default) and `infer=False` for the same data. `infer=True` extracts facts via LLM with dedup. `infer=False` stores raw — same text can be stored twice.
- **Implicit null scoping:** `filters={"user_id": "alice"}` only returns memories where `agent_id`, `app_id`, `run_id` are ALL null. Wrap in `{"OR": [...]}` to include memories with non-null scoping fields.
- **Platform vs OSS imports:** Platform: `from mem0 import MemoryClient`. OSS: `from mem0 import Memory`. Don't mix them — `MemoryClient` talks to `api.mem0.ai`, `Memory` runs locally.
- **v3 defaults:** `top_k=20`, `threshold=0.1`, `rerank=False`. Adjust as needed.
-
-## v3 API (Current)
-
-Mem0 v3 uses single-pass extraction, entity linking, and multi-signal retrieval.
-
-**Key v3 changes from v2:**
- **Endpoints:** `POST /v3/memories/add/`, `POST /v3/memories/search/`, `POST /v3/memories/` (paginated list)
- **Extraction:** Single ADD-only pass — no more UPDATE/DELETE operations during extraction. Memories accumulate rather than consolidate.
- **Entity linking:** Replaces graph memory. Auto-extracted during `add()`, no config needed. Remove `enable_graph` and `graph_store` from any old config.
- **Defaults:** `top_k=20`, `threshold=0.1`, `rerank=False`
- **Removed params:** `org_id`, `project_id`, `enable_graph` — all removed from SDK
- **TypeScript:** Exclusively camelCase (`userId`, `agentId`, `appId`, `topK`)
- **Add response:** Async — returns event ID immediately, poll via `GET /v1/event/{event_id}/`
-
-See the [migration guide](https://docs.mem0.ai/migration/platform-v2-to-v3) for details.
-
-## Live documentation search
-
-For the latest docs beyond what's in the references, use the doc search tool:
-
-```bash
-bun ${CLAUDE_SKILL_DIR}/scripts/mem0_doc_search.ts --query "topic"
-bun ${CLAUDE_SKILL_DIR}/scripts/mem0_doc_search.ts --page "/platform/features/graph-memory"
-bun ${CLAUDE_SKILL_DIR}/scripts/mem0_doc_search.ts --index
-```
-
-No API key needed — searches docs.mem0.ai directly.
-
-## Client SDK References
-
-Language-specific deep references (Platform + OSS):
-
-| Language | File |
-|----------|------|
-| Python (MemoryClient + AsyncMemoryClient + Memory OSS) | [client/python.md](client/python.md) |
-| TypeScript/Node.js (MemoryClient + Memory OSS) | [client/node.md](client/node.md) |
-| Python vs TypeScript differences | [client/differences.md](client/differences.md) |
-
-## Platform References
-
-Load these on demand for deeper detail:
-
-| Topic | File |
-|-------|------|
-| Quickstart (Python, TS, cURL) | [references/quickstart.md](references/quickstart.md) |
-| SDK guide (all methods, both languages) | [references/sdk-guide.md](references/sdk-guide.md) |
-| API reference (endpoints, filters, object schema) | [references/api-reference.md](references/api-reference.md) |
-| Architecture (pipeline, lifecycle, scoping, performance) | [references/architecture.md](references/architecture.md) |
-| Platform features (retrieval, graph, categories, MCP, etc.) | [references/features.md](references/features.md) |
-| Framework integrations (LangChain, CrewAI, OpenAI Agents, etc.) | [references/integration-patterns.md](references/integration-patterns.md) |
-| Use cases & examples (real-world patterns with code) | [references/use-cases.md](references/use-cases.md) |
-
-## Related Mem0 Skills
-
-| Skill | When to use | Link |
-|-------|-------------|------|
-| mem0-vercel-ai-sdk | Vercel AI SDK provider with automatic memory | [GitHub](https://github.com/mem0ai/mem0/tree/main/skills/mem0-vercel-ai-sdk) |
-
-## Output formatting
-
-IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like **bold**, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.
@@ -1,129 +0,0 @@
-# Python vs TypeScript SDK Differences
-
-Quick-reference cheatsheet for developers working across both Mem0 SDKs.
-
-## Constructor
-
-| Aspect | Python | TypeScript |
-|--------|--------|------------|
-| Import (Platform) | `from mem0 import MemoryClient` | `import MemoryClient from 'mem0ai'` |
-| Import (OSS) | `from mem0 import Memory` | `import { Memory } from 'mem0ai/oss'` |
-| Constructor | `MemoryClient(api_key="m0-xxx")` | `new MemoryClient({ apiKey: 'm0-xxx' })` |
-| Required param | `api_key` (positional or kwarg) | `apiKey` (in options object) |
-
-Both read from `MEM0_API_KEY` env var if no key provided.
-
-## Method Naming
-
-| Operation | Python | TypeScript |
-|-----------|--------|------------|
-| Add | `add()` | `add()` |
-| Search | `search()` | `search()` |
-| Get | `get()` | `get()` |
-| Get all | `get_all()` | `getAll()` |
-| Update | `update()` | `update()` |
-| Delete | `delete()` | `delete()` |
-| Delete all | `delete_all()` | `deleteAll()` |
-| History | `history()` | `history()` |
-| Batch update | `batch_update()` | `batchUpdate()` |
-| Batch delete | `batch_delete()` | `batchDelete()` |
-| List users | `users()` | `users()` |
-| Delete users | `delete_users()` | `deleteUsers()` |
-| Get project | `project.get()` | `getProject()` |
-| Update project | `project.update()` | `updateProject()` |
-| Create webhook | `create_webhook()` | `createWebhook()` |
-| Get webhooks | `get_webhooks()` | `getWebhooks()` |
-| Update webhook | `update_webhook()` | `updateWebhook()` |
-| Delete webhook | `delete_webhook()` | `deleteWebhook()` |
-| Create export | `create_memory_export()` | `createMemoryExport()` |
-| Get export | `get_memory_export()` | `getMemoryExport()` |
-| Feedback | `feedback()` | `feedback()` |
-
-**Rule:** Python uses `snake_case`, TypeScript uses `camelCase` for method names.
-
-## Parameter Passing
-
-```python
-# Python: kwargs
-client.add(messages, user_id="alice", metadata={"source": "chat"})
-client.search("query", filters={"user_id": "alice"}, top_k=5, rerank=True)
-```
-
-```typescript
-// 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 });
-```
-
-**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
-
-| Aspect | Python | TypeScript |
-|--------|--------|------------|
-| HTTP library | httpx | axios |
-| Default timeout | 300s | 60s |
-| Sync support | Yes (`MemoryClient`) | No (all async) |
-| Async support | Yes (`AsyncMemoryClient`) | All methods are async |
-| Project management | `client.project.*` (separate class) | `client.getProject()` / `client.updateProject()` |
-| Context manager | `async with AsyncMemoryClient()` | Not supported |
-
-## Platform Features: Python-only
-
-These methods exist in Python but not TypeScript:
-
-| Method | Description |
-|--------|-------------|
-| `get_summary(filters)` | Get summary of memories |
-| `reset()` | Delete ALL data (users + memories) |
-| `project.create(name)` | Create a new project |
-| `project.delete()` | Delete current project |
-| `project.get_members()` | List project members |
-| `project.add_member(email, role)` | Add member to project |
-| `project.update_member(email, role)` | Change member role |
-| `project.remove_member(email)` | Remove member |
-
-## Platform Features: TypeScript-only
-
-| Method | Description |
-|--------|-------------|
-| `deleteUser(data)` | Convenience method for single entity deletion |
-| `ping()` | Health check endpoint |
-
-## OSS Config Naming
-
-| Python config key | TypeScript config key |
-|-------------------|----------------------|
-| `vector_store` | `vectorStore` |
-| `history_db_path` | `historyDbPath` |
-| `custom_instructions` | `customInstructions` |
-
-## OSS Scope Parameter Naming
-
-| Python | TypeScript |
-|--------|------------|
-| `user_id="alice"` | `userId: 'alice'` |
-| `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 Python and TypeScript use `snake_case` for filter keys. TypeScript only uses `camelCase` for top-level method parameters:
-
-```python
-# Python - snake_case in filters
-results = client.search("query", filters={"user_id": "alice"})
-```
-
-```typescript
-// TypeScript - snake_case in filters, camelCase for top-level params
-const results = await client.search('query', { filters: { user_id: 'alice' }, topK: 20 });
-```
@@ -1,418 +0,0 @@
-# Mem0 Node.js / TypeScript SDK Reference
-
-Complete reference for the `mem0ai` npm package. Covers both the Platform client (managed API) and the Open Source self-hosted variant.
-
---
-
-## Platform Client
-
-### Installation
-
-```bash
-npm install mem0ai
-export MEM0_API_KEY="m0-your-api-key"
-```
-
-### MemoryClient
-
-```typescript
-import MemoryClient from 'mem0ai';
-
-const client = new MemoryClient({ apiKey: 'm0-xxx' });
-```
-
-**Constructor:** `new MemoryClient({ apiKey })`. If `apiKey` is not provided, reads from `MEM0_API_KEY` environment variable.
-
- HTTP library: `axios`
- Timeout: 60 seconds
- Base URL: `https://api.mem0.ai`
- All methods are async (return `Promise`)
-
---
-
-### Memory Methods
-
-#### add(messages, options?)
-
-Store new memories from messages.
-
-```typescript
-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, { userId: 'alice' });
-```
-
-| Parameter | Type | Description |
-|-----------|------|-------------|
-| `messages` | `Message[]` | Array of `{role, content}` objects |
-| `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.infer` | boolean | If false, store raw text (default: true) |
-
-**Returns:** `Promise<any>` -- list of events
-
-#### search(query, options?)
-
-Search memories by semantic similarity.
-
-```typescript
-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);
-}
-```
-
-| Parameter | Type | Description |
-|-----------|------|-------------|
-| `query` | string | Natural language search query |
-| `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<SearchResult>` -- `{results: [{id, memory, score, ...}]}`
-
-#### get(memoryId)
-
-```typescript
-const memory = await client.get('ea925981-...');
-```
-
-#### getAll(options?)
-
-Retrieve all memories. Requires at least one entity identifier in filters.
-
-```typescript
-const memories = await client.getAll({ filters: { user_id: 'alice' } });
-// With filters
-const filtered = await client.getAll({
-    filters: { AND: [{ user_id: 'alice' }, { categories: { contains: 'health' } }] },
-});
-```
-
-| Parameter | Type | Description |
-|-----------|------|-------------|
-| `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.pageSize` | number | Results per page |
-
-#### update(memoryId, data)
-
-```typescript
-await client.update('ea925981-...', { text: 'Updated: vegan since 2024' });
-await client.update('ea925981-...', { text: 'Updated', metadata: { verified: true } });
-```
-
-| Parameter | Type | Description |
-|-----------|------|-------------|
-| `memoryId` | string | Memory ID |
-| `data.text` | string | New content |
-| `data.metadata` | object | New metadata |
-| `data.timestamp` | string | New timestamp |
-
-#### delete(memoryId)
-
-```typescript
-await client.delete('ea925981-...');
-```
-
-#### deleteAll(options?)
-
-```typescript
-await client.deleteAll({ userId: 'alice' });
-```
-
-#### history(memoryId)
-
-```typescript
-const history = await client.history('ea925981-...');
-// Returns: [{previousValue, newValue, action, timestamps}]
-```
-
---
-
-### Batch Methods
-
-#### batchUpdate(memories)
-
-```typescript
-await client.batchUpdate([
-    { memoryId: 'uuid-1', text: 'Updated text' },
-    { memoryId: 'uuid-2', text: 'Another update' },
-]);
-```
-
-#### batchDelete(memories)
-
-```typescript
-await client.batchDelete(['uuid-1', 'uuid-2', 'uuid-3']);
-```
-
---
-
-### User/Entity Management
-
-#### users()
-
-```typescript
-const users = await client.users();
-// Returns: {results: [{type: "user", name: "alice"}, ...]}
-```
-
-#### deleteUser(data) / deleteUsers(data)
-
-```typescript
-await client.deleteUser({ userId: 'alice' });  // Single entity
-await client.deleteUsers({ agentId: 'bot-1' }); // Flexible
-```
-
---
-
-### Project Management
-
-```typescript
-// Get project config
-const config = await client.getProject({ fields: ['customCategories'] });
-
-// Update project settings
-await client.updateProject({
-    customInstructions: 'Extract dietary preferences and health info',
-    customCategories: [{ health: 'Medical and dietary info' }],
-});
-```
-
---
-
-### Webhooks
-
-```typescript
-// List
-const webhooks = await client.getWebhooks({ projectId: 'proj_123' });
-
-// Create
-const webhook = await client.createWebhook({
-    url: 'https://your-app.com/webhook',
-    name: 'Memory Logger',
-    projectId: 'proj_123',
-    eventTypes: ['memory_add', 'memory_update'],
-});
-
-// Update
-await client.updateWebhook({
-    webhookId: 'wh_123',
-    name: 'Updated Logger',
-    url: 'https://new-url.com',
-});
-
-// Delete
-await client.deleteWebhook({ webhookId: 'wh_123' });
-```
-
---
-
-### Feedback
-
-```typescript
-await client.feedback({
-    memoryId: 'mem-123',
-    feedback: 'POSITIVE',
-    feedbackReason: 'Accurately captured preference',
-});
-```
-
---
-
-### Export
-
-```typescript
-const exportReq = await client.createMemoryExport({
-    schema: JSON.stringify({ type: 'object', properties: { name: { type: 'string' } } }),
-    filters: { user_id: 'alice' },
-});
-
-const result = await client.getMemoryExport({ memoryExportId: exportReq.id });
-```
-
---
-
-### TypeScript Types
-
-Key interfaces from `mem0.types.ts`:
-
-```typescript
-interface Message { role: string; content: string; }
-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[]; }
-```
-
---
-
-## Open Source / Self-Hosted
-
-### Installation
-
-```bash
-npm install mem0ai
-```
-
-### Memory Class
-
-```typescript
-import { Memory } from 'mem0ai/oss';
-
-const m = new Memory();  // Uses default config
-```
-
-**Import:** `from 'mem0ai/oss'` (NOT the default export -- that is `MemoryClient` for Platform)
-
-### Configuration
-
-```typescript
-const config = {
-    llm: {
-        provider: 'openai',        // openai, groq, anthropic, google, ollama, lmstudio, mistral, azure
-        config: {
-            model: 'gpt-5-mini',
-            apiKey: 'sk-xxx',
-        },
-    },
-    embedder: {
-        provider: 'openai',        // openai, ollama, lmstudio, google, azure, langchain, anthropic
-        config: {
-            model: 'text-embedding-3-small',
-            apiKey: 'sk-xxx',
-        },
-    },
-    vectorStore: {
-        provider: 'qdrant',        // memory, qdrant, redis, supabase, langchain, azure_ai_search, pgvector
-        config: {
-            collectionName: 'my_memories',
-            host: 'localhost',
-            port: 6333,
-        },
-    },
-    historyDbPath: 'history.db',
-    customInstructions: '...',
-    disableHistory: false,
-};
-
-const m = new Memory(config);
-// Or from dict with validation:
-const m2 = Memory.fromConfig(config);
-```
-
-### Methods
-
-All methods are async (return `Promise`):
-
-#### add(messages, config)
-
-```typescript
-await m.add('I prefer dark mode', { userId: 'alice' });
-await m.add([
-    { role: 'user', content: 'I like hiking' },
-    { role: 'assistant', content: 'Great outdoor activity!' },
-], { userId: 'alice' });
-```
-
-| Parameter | Type | Description |
-|-----------|------|-------------|
-| `messages` | `string \| Message[]` | Content to store |
-| `config.userId` | string | User identifier (at least one scope required) |
-| `config.agentId` | string | Agent identifier |
-| `config.runId` | string | Session identifier |
-| `config.metadata` | object | Custom key-value pairs |
-| `config.filters` | object | Additional filters |
-| `config.infer` | boolean | LLM inference (default: true) |
-
-**Returns:** `Promise<{results: [...], relations?: [...]}>`
-
-#### search(query, config)
-
-```typescript
-const results = await m.search('dietary preferences', { filters: { user_id: 'alice' }, topK: 5 });
-```
-
-| Parameter | Type | Description |
-|-----------|------|-------------|
-| `query` | string | Search query |
-| `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)
-
-Same interface patterns. Note: OSS `update` takes a string for data, not an object.
-
-```typescript
-await m.update('mem-id', 'new content');
-```
-
-#### reset()
-
-Clear the entire vector store and history.
-
-```typescript
-await m.reset();
-```
-
---
-
-## Key Differences: Platform vs OSS
-
-| Aspect | Platform (`MemoryClient`) | OSS (`Memory`) |
-|--------|--------------------------|----------------|
-| **Import** | `import MemoryClient from 'mem0ai'` | `import { Memory } from 'mem0ai/oss'` |
-| **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** | 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 |
-| **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.
@@ -1,487 +0,0 @@
-# Mem0 Python SDK Reference
-
-Complete reference for the `mem0ai` Python package. Covers both the Platform client (managed API) and the Open Source self-hosted variant.
-
---
-
-## Platform Client
-
-### Installation
-
-```bash
-pip install mem0ai
-export MEM0_API_KEY="m0-your-api-key"
-```
-
-### MemoryClient (Synchronous)
-
-```python
-from mem0 import MemoryClient
-
-client = MemoryClient(api_key="m0-xxx")
-```
-
-**Constructor:** `MemoryClient(api_key=None)`. If `api_key` is not provided, reads from `MEM0_API_KEY` environment variable. Raises `ValueError` if no key found.
-
- HTTP library: `httpx`
- Timeout: 300 seconds
- Base URL: `https://api.mem0.ai`
-
-### AsyncMemoryClient (Asynchronous)
-
-```python
-from mem0 import AsyncMemoryClient
-
-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", filters={"user_id": "alice"})
-```
-
-Same methods as `MemoryClient`, all `async`/`await`. Supports async context manager.
-
---
-
-### Memory Methods
-
-#### add(messages, **kwargs)
-
-Store new memories from messages.
-
-```python
-messages = [
-    {"role": "user", "content": "I'm a vegetarian and allergic to nuts."},
-    {"role": "assistant", "content": "Got it! I'll remember that."}
-]
-client.add(messages, user_id="alice")
-```
-
-| Parameter | Type | Default | Description |
-|-----------|------|---------|-------------|
-| `messages` | str \| dict \| list[dict] | required | Message content. Strings auto-convert to user messages |
-| `user_id` | str | None | User identifier |
-| `agent_id` | str | None | Agent identifier |
-| `app_id` | str | None | Application identifier |
-| `run_id` | str | None | Session/run identifier |
-| `metadata` | dict | None | Custom key-value pairs |
-| `infer` | bool | True | If False, store raw text without LLM inference |
-| `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", "data": {"memory": "..."}}]`
-
-#### search(query, **kwargs)
-
-Search memories by semantic similarity.
-
-```python
-results = client.search("dietary preferences", filters={"user_id": "alice"})
-for mem in results.get("results", []):
-    print(mem["memory"], mem["score"])
-```
-
-| Parameter | Type | Default | Description |
-|-----------|------|---------|-------------|
-| `query` | str | required | Natural language search query |
-| `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 |
-| `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, ...}]}`
-
-#### get(memory_id)
-
-Retrieve a single memory by ID.
-
-```python
-memory = client.get(memory_id="ea925981-...")
-```
-
-**Returns:** `dict` -- full memory object
-
-#### get_all(**kwargs)
-
-Retrieve all memories with optional filtering. Requires at least one entity identifier.
-
-```python
-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 |
-|-----------|------|---------|-------------|
-| `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 |
-
-**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.
-
-```python
-client.update("ea925981-...", text="Updated: vegan since 2024")
-client.update("ea925981-...", metadata={"verified": True})
-```
-
-**Returns:** `dict` -- updated memory
-
-#### delete(memory_id)
-
-Permanently delete a single memory.
-
-```python
-client.delete("ea925981-...")
-```
-
-#### delete_all(**kwargs)
-
-Delete all memories matching filters. Irreversible.
-
-```python
-client.delete_all(user_id="alice")
-```
-
-#### history(memory_id)
-
-Get the change history of a memory.
-
-```python
-history = client.history("ea925981-...")
-# Returns: [{previous_value, new_value, action, timestamps}]
-```
-
---
-
-### Batch Methods
-
-#### batch_update(memories)
-
-Update up to 1000 memories in a single request.
-
-```python
-client.batch_update([
-    {"memory_id": "uuid-1", "text": "Updated text"},
-    {"memory_id": "uuid-2", "text": "Another update", "metadata": {"verified": True}},
-])
-```
-
-#### batch_delete(memories)
-
-Delete up to 1000 memories in a single request.
-
-```python
-client.batch_delete([
-    {"memory_id": "uuid-1"},
-    {"memory_id": "uuid-2"},
-])
-```
-
---
-
-### User/Entity Management
-
-#### users()
-
-List all users, agents, and sessions that have memories.
-
-```python
-users = client.users()
-# Returns: {"results": [{"type": "user", "name": "alice"}, ...]}
-```
-
-#### delete_users(user_id=None, agent_id=None, app_id=None, run_id=None)
-
-Delete a specific entity and all its memories.
-
-```python
-client.delete_users(user_id="alice")
-```
-
-#### reset()
-
-Delete ALL users, agents, sessions, and memories. Complete data reset.
-
-```python
-client.reset()
-```
-
---
-
-### Export & Summary
-
-#### create_memory_export(schema, **kwargs)
-
-Create a structured export of memories.
-
-```python
-import json
-
-schema = json.dumps({
-    "type": "object",
-    "properties": {
-        "name": {"type": "string"},
-        "preferences": {"type": "array", "items": {"type": "string"}},
-    }
-})
-export = client.create_memory_export(schema=schema, user_id="alice")
-```
-
-#### get_memory_export(**kwargs)
-
-Retrieve a previously created export.
-
-```python
-result = client.get_memory_export(memory_export_id=export["id"])
-```
-
-#### get_summary(filters=None)
-
-Get a summary of memories.
-
-```python
-summary = client.get_summary(filters={"user_id": "alice"})
-```
-
---
-
-### Feedback
-
-#### feedback(memory_id, feedback=None, feedback_reason=None)
-
-Provide quality feedback on a memory.
-
-```python
-client.feedback(
-    memory_id="mem-123",
-    feedback="POSITIVE",  # POSITIVE | NEGATIVE | VERY_NEGATIVE | None (clear)
-    feedback_reason="Accurately captured preference"
-)
-```
-
---
-
-### Webhooks
-
-```python
-# List
-webhooks = client.get_webhooks(project_id="proj_123")
-
-# Create
-webhook = client.create_webhook(
-    url="https://your-app.com/webhook",
-    name="Memory Logger",
-    project_id="proj_123",
-    event_types=["memory_add", "memory_update"]
-)
-
-# Update
-client.update_webhook(webhook_id=123, name="Updated", url="https://new-url.com")
-
-# Delete
-client.delete_webhook(webhook_id=123)
-```
-
---
-
-### Project Management
-
-Access via `client.project.*`:
-
-```python
-# Get project config
-config = client.project.get(fields=["custom_categories", "custom_instructions"])
-
-# Update project settings
-client.project.update(
-    custom_instructions="Extract dietary preferences and health info",
-    custom_categories=[{"health": "Medical and dietary info"}],
-    multilingual=True,
-)
-
-# Create/delete project
-client.project.create(name="My Project", description="...")
-client.project.delete()
-
-# Member management
-members = client.project.get_members()
-client.project.add_member(email="user@example.com", role="READER")  # READER or OWNER
-client.project.update_member(email="user@example.com", role="OWNER")
-client.project.remove_member(email="user@example.com")
-```
-
---
-
-## Open Source / Self-Hosted
-
-### Installation
-
-```bash
-pip install mem0ai
-```
-
-### Memory Class
-
-```python
-from mem0 import Memory
-
-m = Memory()  # Uses default config (OpenAI embedder + in-memory vector store)
-```
-
-**Import:** `from mem0 import Memory` (NOT `MemoryClient` -- that is the Platform client)
-
-### Configuration
-
-```python
-config = {
-    "llm": {
-        "provider": "openai",        # openai, groq, azure, ollama, lmstudio, google, anthropic, mistral
-        "config": {
-            "model": "gpt-5-mini",
-            "api_key": "sk-xxx",
-        }
-    },
-    "embedder": {
-        "provider": "openai",        # openai, ollama, azure, lmstudio, google, huggingface
-        "config": {
-            "model": "text-embedding-3-small",
-            "api_key": "sk-xxx",
-        }
-    },
-    "vector_store": {
-        "provider": "qdrant",        # faiss, qdrant, pgvector, redis, supabase, azure_ai_search, memory
-        "config": {
-            "collection_name": "my_memories",
-            "host": "localhost",
-            "port": 6333,
-        }
-    },
-    "history_db_path": "history.db",              # SQLite path for change history
-    "custom_instructions": "...",                  # Custom LLM prompt for extraction
-}
-
-m = Memory.from_config(config)
-```
-
-### Context Manager
-
-```python
-with Memory(config) as m:
-    m.add("I prefer dark mode", user_id="alice")
-    results = m.search("preferences", filters={"user_id": "alice"})
-# SQLite connections released automatically
-```
-
-### Methods
-
-All methods mirror the Platform client but run locally:
-
-#### add(messages, *, user_id, agent_id, run_id, metadata, infer=True)
-
-```python
-m.add("I'm a vegetarian", user_id="alice")
-m.add([
-    {"role": "user", "content": "I like hiking"},
-    {"role": "assistant", "content": "Great outdoor activity!"}
-], user_id="alice")
-```
-
-At least one of `user_id`, `agent_id`, `run_id` required.
-
-**Returns:** `{"results": [...], "relations": [...]}`
-
-#### search(query, *, filters=None, top_k=20, threshold=0.1, rerank=False)
-
-```python
-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)
-
-Same interface as Platform client.
-
-#### reset()
-
-Clear the entire vector store collection and history database. Recreates the vector store.
-
-```python
-m.reset()
-```
-
-#### close()
-
-Release SQLite connections. Called automatically when using context manager.
-
-### AsyncMemory
-
-```python
-from mem0 import AsyncMemory
-
-m = AsyncMemory(config)
-await m.add("text", user_id="alice")
-results = await m.search("query", filters={"user_id": "alice"})
-```
-
---
-
-## Key Differences: Platform vs OSS
-
-| Aspect | Platform (`MemoryClient`) | OSS (`Memory`) |
-|--------|--------------------------|----------------|
-| **Import** | `from mem0 import MemoryClient` | `from mem0 import Memory` |
-| **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 (`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.
@@ -1,150 +0,0 @@
-# Mem0 Platform API Reference
-
-REST API endpoints for the Mem0 Platform. Base URL: `https://api.mem0.ai`
-
-All endpoints require: `Authorization: Token <MEM0_API_KEY>`
-
-## Endpoints
-
-| Operation | Method | URL |
-|-----------|--------|-----|
-| 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}/` |
-| Delete All Memories | `DELETE` | `/v1/memories/?user_id=X&app_id=Y` |
-| Get Event Status | `GET` | `/v1/event/{event_id}/` |
-
-## Memory Object Structure
-
-| Field | Type | Description |
-|-------|------|-------------|
-| `id` | string (UUID) | Unique memory identifier |
-| `memory` | string | Text content of the memory |
-| `user_id` | string | Associated user |
-| `agent_id` | string (nullable) | Agent identifier |
-| `app_id` | string (nullable) | Application identifier |
-| `run_id` | string (nullable) | Run/session identifier |
-| `metadata` | object | Custom key-value pairs |
-| `categories` | array of strings | Auto-assigned category tags |
-| `hash` | string | Content hash |
-| `created_at` | datetime | Creation timestamp |
-| `updated_at` | datetime | Last modification timestamp |
-
-Search results additionally include `score` (relevance metric).
-
-## Scoping Identifiers
-
-Memories can be scoped to different levels:
-
-| Scope | Parameter | Use Case |
-|-------|-----------|----------|
-| User | `user_id` | Per-user memory isolation |
-| Agent | `agent_id` | Per-agent memory partitioning |
-| Application | `app_id` | Cross-agent app-level memory |
-| Run/Session | `run_id` | Session-scoped temporary memory |
-
-**Critical:** Combining `user_id` and `agent_id` in a single AND filter yields empty results. Entities are stored separately. Use `OR` logic or separate queries.
-
-## Processing Model
-
- 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
-
-Filters use nested JSON with a logical operator at the root:
-
-```json
-{
-    "AND": [
-        {"user_id": "alice"},
-        {"categories": {"contains": "finance"}},
-        {"created_at": {"gte": "2024-01-01"}}
-    ]
-}
-```
-
-Root must be `AND`, `OR`, or `NOT`. Simple shorthand `{"user_id": "alice"}` also works.
-
-### Supported Operators
-
-| Operator | Description |
-|----------|-------------|
-| `eq` | Equal to (default) |
-| `ne` | Not equal to |
-| `in` | Matches any value in array |
-| `gt`, `gte` | Greater than / greater than or equal |
-| `lt`, `lte` | Less than / less than or equal |
-| `contains` | Case-sensitive containment |
-| `icontains` | Case-insensitive containment |
-| `*` | Wildcard -- matches any non-null value |
-
-### Filterable Fields
-
-| Field | Valid Operators |
-|-------|-----------------|
-| `user_id`, `agent_id`, `app_id`, `run_id` | `eq`, `ne`, `in`, `*` |
-| `created_at`, `updated_at`, `timestamp` | `gt`, `gte`, `lt`, `lte`, `eq`, `ne` |
-| `categories` | `eq`, `ne`, `in`, `contains` |
-| `metadata` | `eq`, `ne`, `contains` (top-level keys only) |
-| `keywords` | `contains`, `icontains` |
-| `memory_ids` | `in` |
-
-### Filter Constraints
-
-1. **Entity scope partitioning:** `user_id` AND `agent_id` in one `AND` block yields empty results.
-2. **Metadata limitations:** Only top-level keys. Only `eq`, `contains`, `ne`. No `in` or `gt`.
-3. **Operator syntax:** Use `gte`, `lt`, `ne`. SQL-style (`>=`, `!=`) rejected.
-4. **Entity filter required for get-all:** At least one of `user_id`, `agent_id`, `app_id`, or `run_id`.
-5. **Wildcard excludes null:** `*` matches only non-null values.
-6. **Date format:** ISO 8601 (`YYYY-MM-DDTHH:MM:SSZ`). Timezone-naive defaults to UTC.
-
-## Response Formats
-
-### Add Response (v3)
-
-```json
-{
-  "message": "Memory processing has been queued for background execution",
-  "status": "PENDING",
-  "event_id": "evt-uuid"
-}
-```
-
-v3 is ADD-only. No UPDATE or DELETE events.
-
-### Search Response
-
-```json
-{
-  "results": [
-    {
-      "id": "ea925981-...",
-      "memory": "Is a vegetarian and allergic to nuts.",
-      "user_id": "user123",
-      "categories": ["food", "health"],
-      "score": 0.89,
-      "created_at": "2024-07-26T10:29:36.630547-07:00"
-    }
-  ]
-}
-```
-
-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.
@@ -1,330 +0,0 @@
-# Mem0 Platform Architecture
-
-How Mem0 processes, stores, and retrieves memories under the hood.
-
-## Table of Contents
-
- [Core Concept](#core-concept)
- [Memory Processing Pipeline](#memory-processing-pipeline)
- [Retrieval Pipeline](#retrieval-pipeline)
- [Memory Lifecycle](#memory-lifecycle)
- [Memory Object Structure](#memory-object-structure)
- [Scoping & Multi-Tenancy](#scoping--multi-tenancy)
- [Memory Layers](#memory-layers)
- [Performance Characteristics](#performance-characteristics)
-
---
-
-## Core Concept
-
-Mem0 is a managed memory layer that sits between your AI application and users. Every integration follows the same 3-step loop:
-
-```
-User Input → Retrieve relevant memories → Enrich LLM prompt → Generate response → Store new memories
-```
-
-Mem0 handles the complexity of extraction, deduplication, conflict resolution, and semantic retrieval so your application only needs to call `search()` and `add()`.
-
-**Storage architecture:**
- **Vector store**: Embeddings for semantic similarity search
- **Entity store**: Automatic entity linking for relationship-aware retrieval
-
---
-
-## Memory Processing Pipeline
-
-### What happens when you call `client.add()`
-
-```
-Messages In
-    │
-    ▼
-┌─────────────────────┐
-│  1. EXTRACTION       │  Single LLM call extracts all distinct new facts
-│     (infer=True)     │  If infer=False, stores raw text as-is
-└─────────┬───────────┘
-          │
-          ▼
-┌─────────────────────┐
-│  2. DEDUPLICATION    │  Hash-based dedup (MD5 prevents exact duplicates)
-│                      │  No UPDATE/DELETE - v3 is ADD-only
-└─────────┬───────────┘
-          │
-          ▼
-┌─────────────────────┐
-│  3. STORAGE          │  Batch embed → vector store
-│                      │  Entity extraction → entity store
-└─────────┬───────────┘
-          │
-          ▼
-    Memory Object
-```
-
-### Processing (v3)
-
-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
-
-### Extraction modes
-
-**Inferred (`infer=True`, default):**
- LLM extracts structured facts from conversation
- Conflict resolution deduplicates and resolves contradictions
- Best for: natural conversation → memory
-
-**Raw (`infer=False`):**
- Stores text exactly as provided, no LLM processing
- Skips conflict resolution — same fact can be stored twice
- Only `user` role messages are stored; `assistant` messages ignored
- Best for: bulk imports, pre-structured data, migrations
-
-**Warning:** Don't mix `infer=True` and `infer=False` for the same data — the same fact will be stored twice.
-
---
-
-## Retrieval Pipeline (v3)
-
-### What happens when you call `client.search()`
-
-```
-Query In
-    │
-    ▼
-┌─────────────────────┐
-│  1. PREPROCESSING    │  Lemmatize keywords, extract entities
-└─────────┬───────────┘
-          │
-          ▼
-┌─────────────────────┐
-│  2. PARALLEL SCORING │  Semantic search (vector similarity)
-│                      │  BM25 keyword search (term matching)
-│                      │  Entity matching (entity graph boost)
-└─────────┬───────────┘
-          │
-          ▼
-┌─────────────────────┐
-│  3. SCORE FUSION     │  Combine signals into single score
-│                      │  Optional: rerank=True for deep reordering
-└─────────┬───────────┘
-          │
-          ▼
-    Results (combined score per memory)
-```
-
-### v3 Search Defaults
-
-| 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 `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
-# Gets memories for alice regardless of agent/app/run
-filters={"OR": [{"user_id": "alice"}]}
-```
-
---
-
-## Memory Lifecycle (v3)
-
-v3 uses ADD-only extraction. Memories accumulate over time rather than being consolidated.
-
-### Creation
- `client.add(messages, user_id="...")`
- Single-pass extraction → deduplication → storage
- Returns `{"event_id": "...", "status": "PENDING"}`
-
-### Updates
- `client.update(memory_id, text="...")` replaces text
- Batch: `client.batch_update([...])`
-
-### Deletion
- Single: `client.delete(memory_id)`
- Batch: `client.batch_delete([...])`
- Bulk: `client.delete_all(filters={"user_id": "alice"})`
-
---
-
-## Memory Object Structure
-
-```json
-{
-  "id": "uuid-string",
-  "memory": "Extracted memory text",
-  "user_id": "user-identifier",
-  "agent_id": null,
-  "app_id": null,
-  "run_id": null,
-  "metadata": { "source": "chat", "priority": "high" },
-  "categories": ["health", "preferences"],
-  "created_at": "2025-03-12T12:34:56Z",
-  "updated_at": "2025-03-12T12:34:56Z",
-  "structured_attributes": {
-    "day": 12, "month": 3, "year": 2025,
-    "hour": 12, "minute": 34,
-    "day_of_week": "wednesday",
-    "is_weekend": false,
-    "quarter": 1, "week_of_year": 11
-  },
-  "score": 0.85
-}
-```
-
-| Field | Type | Description |
-|-------|------|-------------|
-| `id` | UUID | Unique identifier, used for update/delete |
-| `memory` | string | Extracted or stored text content |
-| `user_id` | string | Primary entity scope |
-| `agent_id` | string | Agent scope |
-| `app_id` | string | Application scope |
-| `run_id` | string | Session/run scope |
-| `metadata` | object | Custom key-value pairs for filtering |
-| `categories` | array | Auto-assigned or custom category tags |
-| `created_at` | datetime | Creation timestamp |
-| `updated_at` | datetime | Last modification timestamp |
-| `structured_attributes` | object | Temporal breakdown for time-based queries |
-| `score` | float | Semantic similarity (search results only, 0-1) |
-
---
-
-## Scoping & Multi-Tenancy
-
-Mem0 separates memories across four dimensions to prevent data mixing:
-
-| Dimension | Field | Purpose | Example |
-|-----------|-------|---------|---------|
-| User | `user_id` | Persistent persona or account | `"customer_6412"` |
-| Agent | `agent_id` | Distinct agent or tool | `"meal_planner"` |
-| App | `app_id` | Product surface or deployment | `"ios_retail_app"` |
-| Session | `run_id` | Short-lived flow or thread | `"ticket-9241"` |
-
-### Storage model
-
-Each entity combination creates separate records. A memory with `user_id="alice"` is stored separately from one with `user_id="alice"` + `agent_id="bot"`.
-
-### Critical: cross-entity queries
-
-```python
-# This returns NOTHING — user and agent memories are stored separately
-filters={"AND": [{"user_id": "alice"}, {"agent_id": "bot"}]}
-
-# Use OR to query multiple scopes
-filters={"OR": [{"user_id": "alice"}, {"agent_id": "bot"}]}
-
-# Use wildcard to include any non-null value
-filters={"AND": [{"user_id": "*"}]}  # All users (excludes null)
-```
-
-### Recommended scoping patterns
-
-```python
-# User-level: persistent preferences
-client.add(messages, user_id="alice")
-
-# Session-level: temporary context
-client.add(messages, user_id="alice", run_id="session_123")
-# Clean up when done: client.delete_all(run_id="session_123")
-
-# Agent-level: agent-specific knowledge
-client.add(messages, agent_id="support_bot", app_id="helpdesk")
-
-# Multi-tenant: full isolation
-client.add(messages, user_id="alice", agent_id="bot", app_id="acme_corp", run_id="ticket_42")
-```
-
---
-
-## Memory Layers
-
-Mem0 supports three layers of memory, from shortest to longest lived:
-
-### Conversation memory
- In-flight messages within a single turn
- Tool calls, chain-of-thought reasoning
- **Lifetime:** Single response — lost after turn finishes
- **Managed by:** Your application, not Mem0
-
-### Session memory
- Short-lived facts for current task or channel
- Multi-step flows (onboarding, debugging, support tickets)
- **Lifetime:** Minutes to hours
- **Managed by:** Mem0 via `run_id` parameter
- Clean up with `client.delete_all(run_id="session_id")`
-
-### User memory
- Long-lived knowledge tied to a person or account
- Personal preferences, account state, compliance details
- **Lifetime:** Weeks to forever
- **Managed by:** Mem0 via `user_id` parameter
- Persists across all sessions and interactions
-
-### How layering works in practice
-
-```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, filters={"user_id": user_id})
-
-    # 2. Retrieve session memories (current task context)
-    session_mems = mem0.search(user_input, filters={
-        "AND": [{"user_id": user_id}, {"run_id": session_id}]
-    })
-
-    # 3. Combine both layers for LLM context
-    context = format_memories(user_mems) + format_memories(session_mems)
-
-    # 4. Generate response
-    response = llm.generate(context=context, input=user_input)
-
-    # 5. Store in session scope (temporary) + user scope (persistent)
-    messages = [{"role": "user", "content": user_input}, {"role": "assistant", "content": response}]
-    mem0.add(messages, user_id=user_id, run_id=session_id)
-
-    return response
-```
-
---
-
-## Performance Characteristics
-
-### Latency
-
-| Operation | Typical Latency |
-|-----------|----------------|
-| Hybrid search (v3 default) | ~100-150ms |
-| + reranking | +150-200ms |
-| Add (async) | < 50ms response |
-
-### Processing
-
- **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
-
-### Scoping strategy for performance
-
- Use `user_id` for all user-facing queries (most common, fastest)
- Add `run_id` for session isolation (narrows search space)
- Avoid wildcard `"*"` filters on large datasets (scans all non-null records)
- Use `top_k` to limit result count when you only need a few memories
-
---
-
-## Comparison with Alternatives
-
-| Approach | Pros | Cons |
-|----------|------|------|
-| **Raw vector DB** | Fast, full control | No extraction, no dedup, no conflict resolution |
-| **In-memory chat history** | Zero latency | Lost on restart, no cross-session, grows unbounded |
-| **RAG over documents** | Good for static knowledge | No personalization, no memory updates |
-| **Mem0 Platform** | Managed extraction + dedup + graph + scoping | External dependency, async processing delay |
-
-Mem0 combines the best of vector search (semantic retrieval) with automatic extraction (LLM-powered), conflict resolution (deduplication), and structured scoping (multi-tenancy) — in a single managed API.
@@ -1,425 +0,0 @@
-# Platform Features -- Mem0 Platform
-
-Additional platform capabilities beyond core CRUD operations.
-
-## Table of Contents
-
- [Advanced Retrieval](#advanced-retrieval)
- [Entity Linking](#entity-linking)
- [Custom Categories](#custom-categories)
- [Custom Instructions](#custom-instructions)
- [Criteria Retrieval](#criteria-retrieval)
- [Feedback Mechanism](#feedback-mechanism)
- [Memory Export](#memory-export)
- [Group Chat](#group-chat)
- [MCP Integration](#mcp-integration)
- [Webhooks](#webhooks)
- [Multimodal Support](#multimodal-support)
-
-## Advanced Retrieval
-
-### Hybrid Search (v3 Default)
-
-v3 uses multi-signal hybrid search combining:
- **Semantic search** (vector similarity)
- **BM25 keyword search** (normalized term matching)
- **Entity matching** (entity graph boost)
-
-This is automatic — no configuration needed.
-
-### Reranking (`rerank=True`)
-
-Deep semantic reordering of results — most relevant first.
-
- Latency: +150-200ms
- Default: `False` (was `True` in v2)
- Best for: user-facing results, top-N precision
-
-**Python:**
-```python
-results = client.search(query, filters={"user_id": "user123"}, rerank=True)
-```
-
-**TypeScript:**
-```typescript
-const results = await client.search(query, {
-    filters: { user_id: 'user123' },
-    rerank: true,
-});
-```
-
---
-
-## Entity Linking
-
-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**: 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
-
-Entity linking is automatic — no configuration required. The boost is folded into the combined `score` on each result.
-
-### v2 Migration Note
-
-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
-
-See the [v2 to v3 migration guide](https://docs.mem0.ai/migration/platform-v2-to-v3) for details.
-
---
-
-## Custom Categories
-
-Replace Mem0's default 15 labels with domain-specific categories. The system automatically tags memories to the closest matching category.
-
-### Default Categories (15)
-
-`personal_details`, `family`, `professional_details`, `sports`, `travel`, `food`, `music`, `health`, `technology`, `hobbies`, `fashion`, `entertainment`, `milestones`, `user_preferences`, `misc`
-
-### Configuration
-
-**Set project-level categories:**
-```python
-new_categories = [
-    {"lifestyle_management": "Tracks daily routines, habits, wellness activities"},
-    {"seeking_structure": "Documents goals around creating routines and systems"},
-    {"personal_information": "Basic information about the user"}
-]
-client.project.update(custom_categories=new_categories)
-```
-
-```javascript
-await client.updateProject({ customCategories: newCategories });
-```
-
-**Retrieve active categories:**
-```python
-categories = client.project.get(fields=["custom_categories"])
-```
-
-### Key Constraint
-
-Per-request overrides (`custom_categories=...` on `client.add`) are **not supported** on the managed API. Only project-level configuration works. Workaround: store ad-hoc labels in `metadata` field.
-
---
-
-## Custom Instructions
-
-Natural language filters that control what information Mem0 extracts when creating memories.
-
-### Set Instructions
-
-```python
-client.project.update(custom_instructions="Your guidelines here...")
-```
-
-```javascript
-await client.updateProject({ customInstructions: "Your guidelines here..." });
-```
-
-### Template Structure
-
-1. **Task Description** -- brief extraction overview
-2. **Information Categories** -- numbered sections with specific details to capture
-3. **Processing Guidelines** -- quality and handling rules
-4. **Exclusion List** -- sensitive/irrelevant data to filter out
-
-### Domain Examples
-
-**E-commerce:** Capture product issues, preferences, service experience; exclude payment data.
-
-**Education:** Extract learning progress, student preferences, performance patterns; exclude specific grades.
-
-**Finance:** Track financial goals, life events, investment interests; exclude account numbers and SSNs.
-
-### Best Practices
-
- Start simply, test with sample messages, iterate based on results
- Avoid overly lengthy instructions
- Be specific about what to include AND exclude
-
---
-
-## Criteria Retrieval
-
-Custom attribute-based memory ranking using LLM-evaluated criteria with weights. Goes beyond semantic similarity to prioritize memories based on domain-specific signals.
-
-### Configuration
-
-```python
-# Define criteria at project level
-retrieval_criteria = [
-    {"name": "joy", "description": "Positive emotions like happiness and excitement", "weight": 3},
-    {"name": "curiosity", "description": "Inquisitiveness and desire to learn", "weight": 2},
-    {"name": "urgency", "description": "Time-sensitive or high-priority items", "weight": 4},
-]
-client.project.update(retrieval_criteria=retrieval_criteria)
-```
-
-```typescript
-await client.updateProject({
-    retrievalCriteria: [
-        { name: 'joy', description: 'Positive emotions', weight: 3 },
-        { name: 'urgency', description: 'Time-sensitive items', weight: 4 },
-    ],
-});
-```
-
-### Usage
-
-Once configured, `client.search()` automatically applies criteria ranking:
-
-```python
-# Criteria-weighted results returned automatically
-results = client.search("Why am I feeling happy?", filters={"user_id": "alice"})
-```
-
-**Best for:** Wellness assistants, tutoring platforms, productivity tools — any app needing intent-aware retrieval.
-
---
-
-## Feedback Mechanism
-
-Provide feedback on extracted memories to improve system quality over time.
-
-### Feedback Types
-
-| Type | Meaning |
-|------|---------|
-| `POSITIVE` | Memory is useful and accurate |
-| `NEGATIVE` | Memory is not useful |
-| `VERY_NEGATIVE` | Memory is harmful or completely wrong |
-| `None` | Clear existing feedback |
-
-### Usage
-
-**Python:**
-```python
-client.feedback(
-    memory_id="mem-123",
-    feedback="POSITIVE",
-    feedback_reason="Accurately captured dietary preference"
-)
-
-# Bulk feedback
-for item in feedback_data:
-    client.feedback(**item)
-```
-
-**TypeScript:**
-```typescript
-await client.feedback('mem-123', {
-    feedback: 'POSITIVE',
-    feedbackReason: 'Accurately captured dietary preference',
-});
-```
-
---
-
-## Memory Export
-
-Create structured exports of memories using customizable schemas with filters.
-
-### Usage
-
-```python
-import json
-
-# Define export schema
-schema = {
-    "type": "object",
-    "properties": {
-        "name": {"type": "string"},
-        "preferences": {"type": "array", "items": {"type": "string"}},
-        "health_info": {"type": "string"},
-    }
-}
-
-# Create export
-response = client.create_memory_export(
-    schema=json.dumps(schema),
-    filters={"user_id": "alice"},
-    export_instructions="Create comprehensive profile based on all memories"
-)
-
-# Retrieve export (may take a moment to process)
-result = client.get_memory_export(memory_export_id=response["id"])
-```
-
-**Best for:** Data analytics, user profile generation, compliance audits, CRM sync.
-
---
-
-## Group Chat
-
-Process multi-participant conversations and automatically attribute memories to individual speakers.
-
-### Usage
-
-```python
-messages = [
-    {"role": "user", "name": "Alice", "content": "I think we should use React for the frontend"},
-    {"role": "user", "name": "Bob", "content": "I prefer Vue.js, it's simpler for our use case"},
-    {"role": "assistant", "content": "Both are great choices. Let me note your preferences."},
-]
-
-# Mem0 automatically attributes memories to each speaker
-response = client.add(messages, run_id="team_meeting_1")
-
-# Retrieve Alice's memories from that session
-alice_mems = client.get_all(
-    filters={"AND": [{"user_id": "alice"}, {"run_id": "team_meeting_1"}]}
-)
-```
-
-Use the `name` field in messages to identify speakers. Mem0 maps names to entity scopes automatically.
-
---
-
-## MCP Integration
-
-Model Context Protocol integration enables AI clients (Claude, Claude Code, Cursor, Windsurf, VS Code, OpenCode) to manage Mem0 memory autonomously.
-
-### Setup
-
-Add Mem0 MCP to your clients with a single command:
-
-```bash
-npx mcp-add \
-  --name mem0-mcp \
-  --type http \
-  --url "https://mcp.mem0.ai/mcp" \
-  --clients "claude,claude code,cursor,windsurf,vscode,opencode"
-```
-
-### Available MCP Tools
-
-The MCP server exposes 9 memory tools that AI agents can use autonomously:
- Add, search, get, update, delete memories
- Get history, list users, delete users
- Search Mem0 documentation
-
-### How It Works
-
-1. Add Mem0 MCP to your AI client using the setup command above
-2. The agent autonomously decides when to store/retrieve memories
-3. No manual API calls needed — the agent manages memory as part of its reasoning
-
-**Best for:** Universal AI client integration — one protocol works everywhere.
-
---
-
-## Webhooks
-
-Real-time event notifications for memory operations.
-
-### Supported Events
-
-| Event | Trigger |
-|-------|---------|
-| `memory_add` | Memory created |
-| `memory_update` | Memory modified |
-| `memory_delete` | Memory removed |
-| `memory_categorize` | Memory tagged |
-
-### Create Webhook
-
-Note: `project_id` here refers to the Mem0 dashboard project scope for webhooks — not the deprecated client init parameter.
-
-```python
-webhook = client.create_webhook(
-    url="https://your-app.com/webhook",
-    name="Memory Logger",
-    project_id="proj_123",
-    event_types=["memory_add", "memory_categorize"]
-)
-```
-
-### Manage Webhooks
-
-```python
-# Retrieve
-webhooks = client.get_webhooks(project_id="proj_123")
-
-# Update
-client.update_webhook(
-    name="Updated Logger",
-    url="https://your-app.com/new-webhook",
-    event_types=["memory_update", "memory_add"],
-    webhook_id="wh_123"
-)
-
-# Delete
-client.delete_webhook(webhook_id="wh_123")
-```
-
-### Payload Structure
-
-Memory events contain: ID, data object with memory content, event type (`ADD`/`UPDATE`/`DELETE`).
-Categorization events contain: memory ID, event type (`CATEGORIZE`), assigned category labels.
-
---
-
-## Multimodal Support
-
-Mem0 can process images and documents alongside text.
-
-### Supported Media Types
-
- Images: JPG, PNG
- Documents: MDX, TXT, PDF
-
-### Image via URL
-
-```python
-image_message = {
-    "role": "user",
-    "content": {
-        "type": "image_url",
-        "image_url": {"url": "https://example.com/image.jpg"}
-    }
-}
-client.add([image_message], user_id="alice")
-```
-
-### Image via Base64
-
-```python
-import base64
-with open("photo.jpg", "rb") as f:
-    base64_image = base64.b64encode(f.read()).decode("utf-8")
-
-image_message = {
-    "role": "user",
-    "content": {
-        "type": "image_url",
-        "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}
-    }
-}
-client.add([image_message], user_id="alice")
-```
-
-### Document (MDX/TXT)
-
-```python
-doc_message = {
-    "role": "user",
-    "content": {"type": "mdx_url", "mdx_url": {"url": document_url}}
-}
-client.add([doc_message], user_id="alice")
-```
-
-### PDF Document
-
-```python
-pdf_message = {
-    "role": "user",
-    "content": {"type": "pdf_url", "pdf_url": {"url": pdf_url}}
-}
-client.add([pdf_message], user_id="alice")
-```
@@ -1,395 +0,0 @@
-# Mem0 Integration Patterns
-
-Working code examples for integrating Mem0 Platform with popular AI frameworks.
-All examples use `MemoryClient` (Platform API key).
-
-Code examples are sourced from official Mem0 integration docs at docs.mem0.ai, simplified for quick reference.
-
---
-
-## Common Pattern
-
-Every integration follows the same 3-step loop:
-
-1. **Retrieve** -- search relevant memories before generating a response
-2. **Generate** -- include memories as context in the LLM prompt
-3. **Store** -- save the interaction back to Mem0 for future use
-
---
-
-## LangChain
-
-Source: [docs.mem0.ai/integrations/langchain](https://docs.mem0.ai/integrations/langchain)
-
-```python
-from langchain_openai import ChatOpenAI
-from langchain_core.messages import SystemMessage, HumanMessage
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-from mem0 import MemoryClient
-
-llm = ChatOpenAI(model="gpt-5-mini")
-mem0 = MemoryClient()
-
-prompt = ChatPromptTemplate.from_messages([
-    SystemMessage(content="You are a helpful travel agent AI. Use the provided context to personalize your responses."),
-    MessagesPlaceholder(variable_name="context"),
-    HumanMessage(content="{input}")
-])
-
-def retrieve_context(query: str, user_id: str):
-    """Retrieve relevant memories from Mem0"""
-    memories = mem0.search(query, filters={"user_id": user_id})
-    memory_list = memories['results']
-    serialized = ' '.join([m["memory"] for m in memory_list])
-    return [
-        {"role": "system", "content": f"Relevant information: {serialized}"},
-        {"role": "user", "content": query}
-    ]
-
-def chat_turn(user_input: str, user_id: str) -> str:
-    # 1. Retrieve
-    context = retrieve_context(user_input, user_id)
-    # 2. Generate
-    chain = prompt | llm
-    response = chain.invoke({"context": context, "input": user_input})
-    # 3. Store
-    mem0.add(
-        [{"role": "user", "content": user_input}, {"role": "assistant", "content": response.content}],
-        user_id=user_id
-    )
-    return response.content
-```
-
---
-
-## CrewAI
-
-Source: [docs.mem0.ai/integrations/crewai](https://docs.mem0.ai/integrations/crewai)
-
-CrewAI has native Mem0 integration via `memory_config`:
-
-```python
-from crewai import Agent, Task, Crew, Process
-from mem0 import MemoryClient
-
-client = MemoryClient()
-
-# Store user preferences first
-messages = [
-    {"role": "user", "content": "I am more of a beach person than a mountain person."},
-    {"role": "assistant", "content": "Noted! I'll recommend beach destinations."},
-    {"role": "user", "content": "I like Airbnb more than hotels."},
-]
-client.add(messages, user_id="crew_user_1")
-
-# Create agent
-travel_agent = Agent(
-    role="Personalized Travel Planner",
-    goal="Plan personalized travel itineraries",
-    backstory="You are a seasoned travel planner.",
-    memory=True,
-)
-
-# Create task
-task = Task(
-    description="Find places to live, eat, and visit in San Francisco.",
-    expected_output="A detailed list of places to live, eat, and visit.",
-    agent=travel_agent,
-)
-
-# Setup crew with Mem0 memory
-crew = Crew(
-    agents=[travel_agent],
-    tasks=[task],
-    process=Process.sequential,
-    memory=True,
-    memory_config={
-        "provider": "mem0",
-        "config": {"user_id": "crew_user_1"},
-    }
-)
-
-result = crew.kickoff()
-```
-
---
-
-## Vercel AI SDK
-
-> **Dedicated skill available.** For comprehensive Vercel AI SDK documentation, see the [mem0-vercel-ai-sdk skill](https://github.com/mem0ai/mem0/tree/main/skills/mem0-vercel-ai-sdk).
-
-Install: `npm install @mem0/vercel-ai-provider`
-
-Quick example (wrapped model with automatic memory):
-
-```typescript
-import { generateText } from "ai";
-import { createMem0 } from "@mem0/vercel-ai-provider";
-
-const mem0 = createMem0();
-const { text } = await generateText({
-    model: mem0("gpt-5-mini", { user_id: "borat" }),
-    prompt: "Suggest me a good car to buy!",
-});
-```
-
-Supported providers: `openai`, `anthropic`, `google`, `groq`, `cohere`
-
---
-
-## OpenAI Agents SDK
-
-Source: [docs.mem0.ai/integrations/openai-agents-sdk](https://docs.mem0.ai/integrations/openai-agents-sdk)
-
-```python
-from agents import Agent, Runner, function_tool
-from mem0 import MemoryClient
-
-mem0 = MemoryClient()
-
-@function_tool
-def search_memory(query: str, user_id: str) -> str:
-    """Search through past conversations and memories"""
-    memories = mem0.search(query, filters={"user_id": user_id}, top_k=3)
-    if memories and memories.get('results'):
-        return "\n".join([f"- {mem['memory']}" for mem in memories['results']])
-    return "No relevant memories found."
-
-@function_tool
-def save_memory(content: str, user_id: str) -> str:
-    """Save important information to memory"""
-    mem0.add([{"role": "user", "content": content}], user_id=user_id)
-    return "Information saved to memory."
-
-agent = Agent(
-    name="Personal Assistant",
-    instructions="""You are a helpful personal assistant with memory capabilities.
-    Use search_memory to recall past conversations.
-    Use save_memory to store important information.""",
-    tools=[search_memory, save_memory],
-    model="gpt-5-mini"
-)
-
-result = Runner.run_sync(agent, "I love Italian food and I'm planning a trip to Rome next month")
-print(result.final_output)
-```
-
-### Multi-Agent with Handoffs
-
-```python
-from agents import Agent, Runner, function_tool
-
-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-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-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-5-mini"
-)
-
-result = Runner.run_sync(triage_agent, "Plan a healthy meal for my Italy trip")
-```
-
---
-
-## Pipecat (Voice / Real-Time)
-
-Source: [docs.mem0.ai/integrations/pipecat](https://docs.mem0.ai/integrations/pipecat)
-
-```python
-from pipecat.services.mem0 import Mem0MemoryService
-
-memory = Mem0MemoryService(
-    api_key=os.getenv("MEM0_API_KEY"),
-    user_id="alice",
-    agent_id="voice_bot",
-    params={
-        "search_limit": 10,
-        "search_threshold": 0.1,
-        "system_prompt": "Here are your past memories:",
-        "add_as_system_message": True,
-    }
-)
-
-# Use in pipeline
-pipeline = Pipeline([
-    transport.input(),
-    stt,
-    user_context,
-    memory,          # Memory enhances context automatically
-    llm,
-    transport.output(),
-    assistant_context
-])
-```
-
-
-
---
-
-## LangGraph
-
-Source: [docs.mem0.ai/integrations/langgraph](https://docs.mem0.ai/integrations/langgraph)
-
-State-based agent workflows with memory persistence. Best for complex conversation flows with branching logic.
-
-```python
-from typing import Annotated, TypedDict, List
-from langgraph.graph import StateGraph, START
-from langgraph.graph.message import add_messages
-from langchain_openai import ChatOpenAI
-from mem0 import MemoryClient
-from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
-
-llm = ChatOpenAI(model="gpt-5-mini")
-mem0 = MemoryClient()
-
-class State(TypedDict):
-    messages: Annotated[List[HumanMessage | AIMessage], add_messages]
-    mem0_user_id: str
-
-def chatbot(state: State):
-    messages = state["messages"]
-    user_id = state["mem0_user_id"]
-
-    # Retrieve relevant memories
-    memories = mem0.search(messages[-1].content, filters={"user_id": user_id})
-    context = "Relevant context:\n"
-    for memory in memories["results"]:
-        context += f"- {memory['memory']}\n"
-
-    system_message = SystemMessage(content=f"""You are a helpful support assistant.
-{context}""")
-
-    response = llm.invoke([system_message] + messages)
-
-    # Store the interaction
-    mem0.add(
-        [{"role": "user", "content": messages[-1].content},
-         {"role": "assistant", "content": response.content}],
-        user_id=user_id
-    )
-    return {"messages": [response]}
-
-graph = StateGraph(State)
-graph.add_node("chatbot", chatbot)
-graph.add_edge(START, "chatbot")
-app = graph.compile()
-
-# Usage
-result = app.invoke({
-    "messages": [HumanMessage(content="I need help with my order")],
-    "mem0_user_id": "customer_123"
-})
-```
-
---
-
-## LlamaIndex
-
-Source: [docs.mem0.ai/integrations/llama-index](https://docs.mem0.ai/integrations/llama-index)
-
-Install: `pip install llama-index-core llama-index-memory-mem0`
-
-LlamaIndex has native Mem0 support via `Mem0Memory`. Works with ReAct and FunctionCalling agents.
-
-```python
-from llama_index.memory.mem0 import Mem0Memory
-
-context = {"user_id": "alice", "agent_id": "llama_agent_1"}
-memory = Mem0Memory.from_client(
-    context=context,
-    search_msg_limit=4,  # messages from chat history used for retrieval (default: 5)
-)
-
-# Use with LlamaIndex agent
-from llama_index.core.agent import FunctionCallingAgent
-from llama_index.llms.openai import OpenAI
-
-llm = OpenAI(model="gpt-5-mini")
-agent = FunctionCallingAgent.from_tools(
-    tools=[],
-    llm=llm,
-    memory=memory,
-    verbose=True,
-)
-
-response = agent.chat("I prefer vegetarian restaurants")
-# Memory automatically stores and retrieves context
-response = agent.chat("What kind of food do I like?")
-# Agent retrieves the vegetarian preference from Mem0
-```
-
---
-
-## AutoGen
-
-Source: [docs.mem0.ai/integrations/autogen](https://docs.mem0.ai/integrations/autogen)
-
-Install: `pip install autogen mem0ai`
-
-Multi-agent conversational systems with memory persistence.
-
-```python
-from autogen import ConversableAgent
-from mem0 import MemoryClient
-
-memory_client = MemoryClient()
-USER_ID = "alice"
-
-agent = ConversableAgent(
-    "chatbot",
-    llm_config={"config_list": [{"model": "gpt-5-mini", "api_key": os.environ["OPENAI_API_KEY"]}]},
-    code_execution_config=False,
-    human_input_mode="NEVER",
-)
-
-def get_context_aware_response(question: str) -> str:
-    # Retrieve memories for context
-    relevant_memories = memory_client.search(question, filters={"user_id": USER_ID})
-    context = "\n".join([m["memory"] for m in relevant_memories.get("results", [])])
-
-    prompt = f"""Answer considering previous interactions:
-    Previous context: {context}
-    Question: {question}"""
-
-    reply = agent.generate_reply(messages=[{"content": prompt, "role": "user"}])
-
-    # Store the new interaction
-    memory_client.add(
-        [{"role": "user", "content": question}, {"role": "assistant", "content": reply}],
-        user_id=USER_ID
-    )
-    return reply
-```
-
---
-
-## All Supported Frameworks
-
-Beyond the examples above, Mem0 integrates with:
-
-| Framework | Type | Install |
-|-----------|------|---------|
-| [Mastra](https://docs.mem0.ai/integrations/mastra) | TS agent framework | `npm install @mastra/mem0` |
-| [ElevenLabs](https://docs.mem0.ai/integrations/elevenlabs) | Voice AI | `pip install elevenlabs mem0ai` |
-| [LiveKit](https://docs.mem0.ai/integrations/livekit) | Real-time voice/video | `pip install livekit-agents mem0ai` |
-| [Camel AI](https://docs.mem0.ai/integrations/camel-ai) | Multi-agent framework | `pip install camel-ai[all] mem0ai` |
-| [AWS Bedrock](https://docs.mem0.ai/integrations/aws-bedrock) | Cloud LLM provider | `pip install boto3 mem0ai` |
-| [Dify](https://docs.mem0.ai/integrations/dify) | Low-code AI platform | Plugin-based |
-| [Google AI ADK](https://docs.mem0.ai/integrations/google-ai-adk) | Google agent framework | `pip install google-adk mem0ai` |
-
-For the general Python pattern (no framework), see the "Common integration pattern" in [SKILL.md](../SKILL.md).
@@ -1,119 +0,0 @@
-# Mem0 Platform Quickstart
-
-Get running with Mem0 in 2 minutes. No infrastructure to deploy -- just an API key.
-
-## Prerequisites
-
- Python 3.10+ or Node.js 18+
- A Mem0 Platform API key ([Get one here](https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=mem0-plugin-skill-quickstart))
-
-## Python Setup
-
-```bash
-pip install mem0ai
-export MEM0_API_KEY="m0-your-api-key"
-```
-
-```python
-from mem0 import MemoryClient
-
-client = MemoryClient(api_key="your-api-key")
-
-# Add a memory
-messages = [
-    {"role": "user", "content": "I'm a vegetarian and allergic to nuts."},
-    {"role": "assistant", "content": "Got it! I'll remember your dietary preferences."}
-]
-client.add(messages, user_id="user123")
-
-# Search memories
-results = client.search("What are my dietary restrictions?", filters={"user_id": "user123"})
-print(results)
-```
-
-### Async Client
-
-```python
-from mem0 import AsyncMemoryClient
-
-client = AsyncMemoryClient(api_key="your-api-key")
-
-await client.add(messages, user_id="user123")
-results = await client.search("query", filters={"user_id": "user123"})
-```
-
-## TypeScript / JavaScript Setup
-
-```bash
-npm install mem0ai
-export MEM0_API_KEY="m0-your-api-key"
-```
-
-```javascript
-import MemoryClient from 'mem0ai';
-
-const client = new MemoryClient({ apiKey: 'your-api-key' });
-
-// Add a memory
-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, { userId: "user123" });
-
-// Search memories
-const results = await client.search("What are my dietary restrictions?", {
-    filters: { user_id: "user123" }
-});
-console.log(results);
-```
-
-## cURL
-
-```bash
-export MEM0_API_KEY="m0-your-api-key"
-
-# Add memory
-curl -X POST https://api.mem0.ai/v3/memories/add/ \
-  -H "Authorization: Token $MEM0_API_KEY" \
-  -H "Content-Type: application/json" \
-  -d '{
-    "messages": [
-      {"role": "user", "content": "I am a vegetarian and allergic to nuts."},
-      {"role": "assistant", "content": "Got it! I will remember your dietary preferences."}
-    ],
-    "user_id": "user123"
-  }'
-
-# Search memories
-curl -X POST https://api.mem0.ai/v3/memories/search/ \
-  -H "Authorization: Token $MEM0_API_KEY" \
-  -H "Content-Type: application/json" \
-  -d '{
-    "query": "What are my dietary restrictions?",
-    "filters": {"user_id": "user123"}
-  }'
-```
-
-## Sample Response
-
-```json
-{
-  "results": [
-    {
-      "id": "14e1b28a-2014-40ad-ac42-69c9ef42193d",
-      "memory": "Allergic to nuts",
-      "user_id": "user123",
-      "categories": ["health"],
-      "created_at": "2025-10-22T04:40:22.864647-07:00",
-      "score": 0.30
-    }
-  ]
-}
-```
-
-## Next Steps
-
- [SDK Guide](sdk-guide.md) -- all methods for Python and TypeScript
- [API Reference](api-reference.md) -- REST endpoints and memory object structure
- [Integration Patterns](integration-patterns.md) -- LangChain, CrewAI, Vercel AI, etc.
@@ -1,353 +0,0 @@
-# Mem0 SDK Guide
-
-Complete SDK reference for Python and TypeScript. All methods use `MemoryClient` (Platform API).
-
-> **For language-specific deep references (including OSS):** See [client/python.md](../client/python.md) and [client/node.md](../client/node.md). For Python vs TypeScript differences: [client/differences.md](../client/differences.md).
-
-## Initialization
-
-**Python:**
-```python
-from mem0 import MemoryClient
-client = MemoryClient(api_key="m0-your-api-key")
-```
-
-**Python (Async):**
-```python
-from mem0 import AsyncMemoryClient
-client = AsyncMemoryClient(api_key="m0-your-api-key")
-```
-
-**TypeScript:**
-```typescript
-import MemoryClient from 'mem0ai';
-const client = new MemoryClient({ apiKey: 'm0-your-api-key' });
-```
-
-Constructor accepts `apiKey` (required) and `host` (optional, default: `https://api.mem0.ai`).
-
---
-
-## add() -- Store Memories
-
-**Python:**
-```python
-messages = [
-    {"role": "user", "content": "I'm a vegetarian and allergic to nuts."},
-    {"role": "assistant", "content": "Got it! I'll remember that."}
-]
-client.add(messages, user_id="alice")
-
-# With metadata
-client.add(messages, user_id="alice", metadata={"source": "onboarding"})
-```
-
-**TypeScript:**
-```typescript
-await client.add(messages, { userId: "alice" });
-await client.add(messages, { userId: "alice", metadata: { source: "onboarding" } });
-```
-
-### Parameters
-
-| Name | Type | Description |
-|------|------|-------------|
-| `messages` | array | `[{"role": "user", "content": "..."}]` |
-| `user_id` | string | User identifier (recommended) |
-| `agent_id` | string | Agent identifier |
-| `run_id` | string | Session identifier |
-| `metadata` | object | Custom key-value pairs |
-| `infer` | boolean | If `false`, store raw text without inference (default: `true`) |
-
-### Advanced Add Options
-
-```python
-# Agent + session scoping
-client.add(messages, user_id="alice", agent_id="nutrition-agent", run_id="session-456")
-
-# Raw text -- skip LLM inference
-client.add(
-    [{"role": "user", "content": "User prefers dark mode."}],
-    user_id="alice",
-    infer=False,
-)
-```
-
---
-
-## search() -- Find Memories
-
-**Python:**
-```python
-results = client.search("dietary preferences?", filters={"user_id": "alice"})
-
-# With filters and reranking
-results = client.search(
-    query="work experience",
-    filters={"AND": [{"user_id": "alice"}, {"categories": {"contains": "professional_details"}}]},
-    top_k=5,
-    rerank=True,
-    threshold=0.5
-)
-```
-
-**TypeScript:**
-```typescript
-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" } }] },
-    topK: 5,
-    rerank: true,
-});
-```
-
-### Parameters
-
-| Name | Type | Description |
-|------|------|-------------|
-| `query` | string | Natural language search query |
-| `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 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"]}}]}
-
-# Wildcard (match any non-null run)
-filters={"AND": [{"user_id": "alice"}, {"run_id": "*"}]}
-
-# Date range
-filters={"AND": [
-    {"user_id": "alice"},
-    {"created_at": {"gte": "2024-01-01T00:00:00Z"}},
-    {"created_at": {"lt": "2024-02-01T00:00:00Z"}}
-]}
-
-# Exclude categories with NOT
-filters={"AND": [{"user_id": "user_123"}, {"NOT": {"categories": {"in": ["spam", "test"]}}}]}
-
-# Multi-dimensional query
-filters={"AND": [
-    {"user_id": "user_123"},
-    {"keywords": {"icontains": "invoice"}},
-    {"categories": {"in": ["finance"]}},
-    {"created_at": {"gte": "2024-01-01T00:00:00Z"}}
-]}
-```
-
-**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
-
-**Python:**
-```python
-# Single memory by ID
-memory = client.get(memory_id="ea925981-...")
-
-# All memories for a user
-memories = client.get_all(filters={"user_id": "alice"})
-
-# With date range
-memories = client.get_all(
-    filters={"AND": [
-        {"user_id": "alex"},
-        {"created_at": {"gte": "2024-07-01", "lte": "2024-07-31"}}
-    ]}
-)
-```
-
-**TypeScript:**
-```typescript
-const memory = await client.get("ea925981-...");
-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.
-
---
-
-## update() -- Modify Memories
-
-**Python:**
-```python
-client.update(memory_id="ea925981-...", text="Updated: vegan since 2024")
-client.update(memory_id="ea925981-...", text="Updated", metadata={"verified": True})
-```
-
-**TypeScript:**
-```typescript
-await client.update("ea925981-...", { text: "Updated: vegan since 2024" });
-```
-
---
-
-## delete() / deleteAll() -- Remove Memories
-
-**Python:**
-```python
-client.delete(memory_id="ea925981-...")
-client.delete_all(user_id="alice")  # Irreversible bulk delete
-```
-
-**TypeScript:**
-```typescript
-await client.delete("ea925981-...");
-await client.deleteAll({ userId: "alice" });
-```
-
---
-
-## history() -- Track Changes
-
-**Python:**
-```python
-history = client.history(memory_id="ea925981-...")
-# Returns: [{previous_value, new_value, action, timestamps}]
-```
-
-**TypeScript:**
-```typescript
-const history = await client.history("ea925981-...");
-```
-
---
-
-## Batch Operations (TypeScript)
-
-```typescript
-// Batch update
-await client.batchUpdate([
-    { memoryId: "uuid-1", text: "Updated text" },
-    { memoryId: "uuid-2", text: "Another updated text" },
-]);
-
-// Batch delete
-await client.batchDelete(["uuid-1", "uuid-2", "uuid-3"]);
-```
-
---
-
-## Additional Methods
-
-```python
-# List all users/agents/sessions with memories
-users = client.users()
-
-# Delete a user/agent entity
-client.delete_users(user_id="alice")
-
-# Submit feedback on a memory
-client.feedback(memory_id="...", feedback="POSITIVE", feedback_reason="Accurate extraction")
-
-# Export memories
-export = client.create_memory_export(filters={"AND": [{"user_id": "alice"}]})
-data = client.get_memory_export(memory_export_id=export["id"])
-```
-
---
-
-## Common Pitfalls
-
-1. **Entity cross-filtering fails silently** -- `AND` with `user_id` + `agent_id` returns empty. Use `OR`.
-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 0.1** -- increase for stricter matching.
-6. **Async processing** -- memories process asynchronously. Wait 2-3s after `add()` before searching.
-
-## Naming Conventions
-
-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 |
@@ -1,720 +0,0 @@
-# Mem0 Use Cases & Examples
-
-Real-world implementation patterns for Mem0 Platform. Each use case includes complete, runnable code in both Python and TypeScript.
-
-## Table of Contents
-
- [Personalized AI Companion](#1-personalized-ai-companion)
- [Customer Support with Categories](#2-customer-support-with-categories)
- [Healthcare Coach](#3-healthcare-coach)
- [Content Creation Workflow](#4-content-creation-workflow)
- [Multi-Agent / Multi-Tenant](#5-multi-agent--multi-tenant)
- [Personalized Search](#6-personalized-search)
- [Email Intelligence](#7-email-intelligence)
- [Common Patterns Across Use Cases](#common-patterns-across-use-cases)
-
---
-
-## 1. Personalized AI Companion
-
-A fitness coach that remembers goals, preferences, and progress across sessions. Mem0 persists context across app restarts — no session state needed.
-
-### Implementation (Python)
-
-```python
-from mem0 import MemoryClient
-from openai import OpenAI
-
-mem0 = MemoryClient()
-openai_client = OpenAI()
-
-def chat(user_input: str, user_id: str) -> str:
-    # 1. Retrieve relevant memories
-    memories = mem0.search(user_input, user_id=user_id)
-    context = "\n".join([f"- {m['memory']}" for m in memories.get("results", [])])
-
-    # 2. Generate response with memory context
-    system_prompt = f"""You are Ray, a personal fitness coach.
-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-5-mini",
-        messages=[
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": user_input},
-        ]
-    )
-    reply = response.choices[0].message.content
-
-    # 3. Store interaction for future context
-    mem0.add(
-        [{"role": "user", "content": user_input}, {"role": "assistant", "content": reply}],
-        user_id=user_id
-    )
-    return reply
-
-# Usage
-chat("I want to run a marathon in under 4 hours", user_id="max")
-# Next day, app restarted:
-chat("What should I focus on today?", user_id="max")
-# Ray remembers the sub-4 marathon goal
-```
-
-### Implementation (TypeScript)
-
-```typescript
-import MemoryClient from 'mem0ai';
-import OpenAI from 'openai';
-
-const mem0 = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! });
-const openai = new OpenAI();
-
-async function chat(userInput: string, userId: string): Promise<string> {
-    // 1. Retrieve relevant memories
-    const memories = await mem0.search(userInput, { filters: { user_id: userId } });
-    const context = memories.results
-        ?.map((m: any) => `- ${m.memory}`)
-        .join('\n') || 'No prior context yet.';
-
-    // 2. Generate response with memory context
-    const response = await openai.chat.completions.create({
-        model: 'gpt-5-mini',
-        messages: [
-            { role: 'system', content: `You are Ray, a personal fitness coach.\nUser context:\n${context}` },
-            { role: 'user', content: userInput },
-        ],
-    });
-    const reply = response.choices[0].message.content!;
-
-    // 3. Store interaction
-    await mem0.add(
-        [{ role: 'user', content: userInput }, { role: 'assistant', content: reply }],
-        { userId: userId }
-    );
-    return reply;
-}
-```
-
-### Key Benefits
-
- Context persists across app restarts — no session management needed
- Memories are automatically deduplicated and updated
- Works with any LLM provider (OpenAI, Anthropic, etc.)
-
-**Best for:** Fitness coaches, tutors, therapists — any assistant that needs to remember goals across sessions.
-
---
-
-## 2. Customer Support with Categories
-
-Auto-categorize support data so teams retrieve the right facts fast. Uses custom categories for structured retrieval.
-
-### Implementation (Python)
-
-```python
-from mem0 import MemoryClient
-
-client = MemoryClient()
-
-# 1. Define categories at the project level (one-time setup)
-custom_categories = [
-    {"support_tickets": "Customer issues and resolutions"},
-    {"account_info": "Account details and preferences"},
-    {"billing": "Payment history and billing questions"},
-    {"product_feedback": "Feature requests and feedback"},
-]
-client.project.update(custom_categories=custom_categories)
-
-# 2. Store interactions — auto-classified into categories
-def log_support_interaction(user_id: str, message: str, priority: str = "normal"):
-    client.add(
-        [{"role": "user", "content": message}],
-        user_id=user_id,
-        metadata={"priority": priority, "source": "support_chat"}
-    )
-
-# 3. Retrieve by category
-def get_billing_issues(user_id: str):
-    return client.get_all(
-        filters={
-            "AND": [
-                {"user_id": user_id},
-                {"categories": {"in": ["billing"]}}
-            ]
-        }
-    )
-
-def search_support_history(user_id: str, query: str):
-    return client.search(
-        query,
-        filters={
-            "AND": [
-                {"user_id": user_id},
-                {"categories": {"contains": "support_tickets"}}
-            ]
-        },
-        top_k=5
-    )
-
-# Usage
-log_support_interaction("maria", "I was charged twice for last month's subscription", priority="high")
-log_support_interaction("maria", "The dashboard is loading slowly on mobile")
-billing = get_billing_issues("maria")  # Returns only billing-related memories
-```
-
-### Implementation (TypeScript)
-
-```typescript
-import MemoryClient from 'mem0ai';
-
-const client = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! });
-
-// Setup categories (one-time)
-await client.updateProject({
-    custom_categories: [
-        { support_tickets: 'Customer issues and resolutions' },
-        { billing: 'Payment history and billing questions' },
-        { product_feedback: 'Feature requests and feedback' },
-    ],
-});
-
-async function logInteraction(userId: string, message: string, priority = 'normal') {
-    await client.add(
-        [{ role: 'user', content: message }],
-        { userId: userId, metadata: { priority, source: 'support_chat' } }
-    );
-}
-
-async function getBillingIssues(userId: string) {
-    return client.getAll({
-        filters: { AND: [{ user_id: userId }, { categories: { in: ['billing'] } }] },
-    });
-}
-```
-
-### Key Benefits
-
- Automatic categorization — no manual tagging
- Filter by category for structured retrieval
- Metadata (`priority`, `source`) enables multi-dimensional queries
-
-**Best for:** Help desks, SaaS support, e-commerce — structured retrieval by category eliminates manual scanning.
-
---
-
-## 3. Healthcare Coach
-
-Guide patients with an assistant that remembers medical history. Uses high `threshold` for confident retrieval in safety-critical contexts.
-
-### Implementation (Python)
-
-```python
-from mem0 import MemoryClient
-from openai import OpenAI
-
-mem0 = MemoryClient()
-openai_client = OpenAI()
-
-def save_patient_info(user_id: str, information: str):
-    mem0.add(
-        [{"role": "user", "content": information}],
-        user_id=user_id,
-        run_id="healthcare_session",
-        metadata={"type": "patient_information"}
-    )
-
-def consult(user_id: str, question: str) -> str:
-    # High threshold for medical accuracy
-    memories = mem0.search(question, user_id=user_id, top_k=5, threshold=0.7)
-    context = "\n".join([f"- {m['memory']}" for m in memories.get("results", [])])
-
-    response = openai_client.chat.completions.create(
-        model="gpt-5-mini",
-        messages=[
-            {"role": "system", "content": f"You are a health coach. Patient context:\n{context}"},
-            {"role": "user", "content": question},
-        ]
-    )
-    reply = response.choices[0].message.content
-
-    # Store the interaction
-    mem0.add(
-        [{"role": "user", "content": question}, {"role": "assistant", "content": reply}],
-        user_id=user_id,
-        run_id="healthcare_session",
-    )
-    return reply
-
-# Usage
-save_patient_info("alex", "I'm allergic to penicillin and take metformin for type 2 diabetes")
-consult("alex", "Can I take amoxicillin for my sore throat?")
-# Remembers penicillin allergy — amoxicillin is a penicillin-type antibiotic
-```
-
-### Implementation (TypeScript)
-
-```typescript
-import MemoryClient from 'mem0ai';
-import OpenAI from 'openai';
-
-const mem0 = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! });
-const openai = new OpenAI();
-
-async function savePatientInfo(userId: string, info: string) {
-    await mem0.add(
-        [{ role: 'user', content: info }],
-        { userId: userId, runId: 'healthcare_session', metadata: { type: 'patient_information' } }
-    );
-}
-
-async function consult(userId: string, question: string): Promise<string> {
-    const memories = await mem0.search(question, {
-        filters: { user_id: userId },
-        topK: 5,
-        threshold: 0.7,
-    });
-    const context = memories.results?.map((m: any) => `- ${m.memory}`).join('\n') || '';
-
-    const response = await openai.chat.completions.create({
-        model: 'gpt-5-mini',
-        messages: [
-            { role: 'system', content: `You are a health coach. Patient context:\n${context}` },
-            { role: 'user', content: question },
-        ],
-    });
-    const reply = response.choices[0].message.content!;
-
-    await mem0.add(
-        [{ role: 'user', content: question }, { role: 'assistant', content: reply }],
-        { userId: userId, runId: 'healthcare_session' }
-    );
-    return reply;
-}
-```
-
-### Key Benefits
-
- High threshold (0.7) ensures only confident matches for safety-critical retrieval
- Session scoping via `run_id` groups related health interactions
- Metadata tagging separates patient info from conversation history
-
-**Best for:** Telehealth, wellness apps, patient management — persistent health context across visits.
-
---
-
-## 4. Content Creation Workflow
-
-Store voice guidelines once and apply them across every draft. Uses `run_id` and `metadata` to scope writing preferences per session.
-
-### Implementation (Python)
-
-```python
-from mem0 import MemoryClient
-from openai import OpenAI
-
-mem0 = MemoryClient()
-openai_client = OpenAI()
-
-def store_writing_preferences(user_id: str, preferences: str):
-    mem0.add(
-        [{"role": "user", "content": preferences}],
-        user_id=user_id,
-        run_id="editing_session",
-        metadata={"type": "preferences", "category": "writing_style"}
-    )
-
-def draft_content(user_id: str, topic: str) -> str:
-    # Retrieve writing preferences
-    prefs = mem0.search(
-        "writing style preferences",
-        filters={"AND": [{"user_id": user_id}, {"run_id": "editing_session"}]}
-    )
-    style_context = "\n".join([f"- {m['memory']}" for m in prefs.get("results", [])])
-
-    response = openai_client.chat.completions.create(
-        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}"},
-        ]
-    )
-    return response.choices[0].message.content
-
-# Usage
-store_writing_preferences("writer_01", "I prefer short sentences. Active voice. No jargon. Use analogies.")
-draft_content("writer_01", "Why AI memory matters for chatbots")
-# Drafts content matching the stored voice guidelines
-```
-
-### Implementation (TypeScript)
-
-```typescript
-import MemoryClient from 'mem0ai';
-import OpenAI from 'openai';
-
-const mem0 = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! });
-const openai = new OpenAI();
-
-async function storePreferences(userId: string, preferences: string) {
-    await mem0.add(
-        [{ role: 'user', content: preferences }],
-        { userId: userId, runId: 'editing_session', metadata: { type: 'preferences' } }
-    );
-}
-
-async function draftContent(userId: string, topic: string): Promise<string> {
-    const prefs = await mem0.search('writing style preferences', {
-        filters: { AND: [{ user_id: userId }, { run_id: 'editing_session' }] },
-    });
-    const styleContext = prefs.results?.map((m: any) => `- ${m.memory}`).join('\n') || '';
-
-    const response = await openai.chat.completions.create({
-        model: 'gpt-5-mini',
-        messages: [
-            { role: 'system', content: `Write content matching these preferences:\n${styleContext}` },
-            { role: 'user', content: `Write a blog post about: ${topic}` },
-        ],
-    });
-    return response.choices[0].message.content!;
-}
-```
-
-### Key Benefits
-
- Voice consistency across all content without repeating guidelines
- Scoped sessions let you maintain different style profiles
- Preferences update automatically as you refine them
-
-**Best for:** Marketing teams, technical writers, agencies — consistent voice across all content.
-
---
-
-## 5. Multi-Agent / Multi-Tenant
-
-Keep memories separate using `user_id`, `agent_id`, `app_id`, and `run_id` scoping. Critical for multi-agent workflows and multi-tenant apps.
-
-### Implementation (Python)
-
-```python
-from mem0 import MemoryClient
-
-client = MemoryClient()
-
-# Store memories scoped to user + agent + session
-def store_scoped_memory(messages: list, user_id: str, agent_id: str, run_id: str, app_id: str):
-    client.add(
-        messages,
-        user_id=user_id,
-        agent_id=agent_id,
-        run_id=run_id,
-        app_id=app_id
-    )
-
-# Query within a specific scope
-def search_user_session(query: str, user_id: str, app_id: str, run_id: str):
-    """Search memories for a specific user within a specific session."""
-    return client.search(
-        query,
-        filters={
-            "AND": [
-                {"user_id": user_id},
-                {"app_id": app_id},
-                {"run_id": run_id}
-            ]
-        }
-    )
-
-def search_agent_knowledge(query: str, agent_id: str, app_id: str):
-    """Search all memories an agent has across all users."""
-    return client.search(
-        query,
-        filters={
-            "AND": [
-                {"agent_id": agent_id},
-                {"app_id": app_id}
-            ]
-        }
-    )
-
-# Usage: Travel concierge app with multiple agents
-store_scoped_memory(
-    [{"role": "user", "content": "I'm vegetarian and prefer window seats"}],
-    user_id="traveler_cam",
-    agent_id="travel_planner",
-    run_id="tokyo-2025",
-    app_id="concierge_app"
-)
-
-# User-scoped query: "What does Cam prefer?"
-user_mems = search_user_session("dietary restrictions?", "traveler_cam", "concierge_app", "tokyo-2025")
-
-# Agent-scoped query: "What do all travelers prefer?" (across users)
-agent_mems = search_agent_knowledge("common dietary restrictions?", "travel_planner", "concierge_app")
-```
-
-### Implementation (TypeScript)
-
-```typescript
-import MemoryClient from 'mem0ai';
-
-const client = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! });
-
-async function storeScopedMemory(
-    messages: Array<{ role: string; content: string }>,
-    userId: string, agentId: string, runId: string, appId: string
-) {
-    await client.add(messages, {
-        userId: userId,
-        agentId: agentId,
-        runId: runId,
-        appId: appId,
-    });
-}
-
-async function searchUserSession(query: string, userId: string, appId: string, runId: string) {
-    return client.search(query, {
-        filters: { AND: [{ user_id: userId }, { app_id: appId }, { run_id: runId }] },
-    });
-}
-
-async function searchAgentKnowledge(query: string, agentId: string, appId: string) {
-    return client.search(query, {
-        filters: { AND: [{ agent_id: agentId }, { app_id: appId }] },
-    });
-}
-```
-
-### Key Benefits
-
- Full isolation between users, agents, sessions, and apps
- Query at any scope level — user, agent, session, or app-wide
- No memory leakage between tenants
-
-**Best for:** Multi-agent workflows, multi-tenant SaaS — proper isolation at every level.
-
---
-
-## 6. Personalized Search
-
-Blend real-time search results with personal context. Uses `custom_instructions` to infer preferences from queries.
-
-### Implementation (Python)
-
-```python
-from mem0 import MemoryClient
-from openai import OpenAI
-
-mem0 = MemoryClient()
-openai_client = OpenAI()
-
-# One-time setup: configure Mem0 to infer from queries
-mem0.project.update(
-    custom_instructions="""Infer user preferences and facts from their search queries.
-Extract dietary preferences, location, interests, and purchase history."""
-)
-
-def personalized_search(user_id: str, query: str, search_results: list) -> str:
-    # Get user context from memory
-    memories = mem0.search(query, user_id=user_id, top_k=5)
-    user_context = "\n".join([f"- {m['memory']}" for m in memories.get("results", [])])
-
-    response = openai_client.chat.completions.create(
-        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}"},
-        ]
-    )
-    reply = response.choices[0].message.content
-
-    # Store the query to learn preferences over time
-    mem0.add(
-        [{"role": "user", "content": query}],
-        user_id=user_id
-    )
-    return reply
-
-# Usage
-personalized_search("user_42", "best restaurants nearby", ["Restaurant A", "Restaurant B"])
-# Over time, Mem0 learns: "user prefers vegetarian, lives in Austin"
-# Future searches are automatically personalized
-```
-
-### Implementation (TypeScript)
-
-```typescript
-import MemoryClient from 'mem0ai';
-import OpenAI from 'openai';
-
-const mem0 = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! });
-const openai = new OpenAI();
-
-async function personalizedSearch(userId: string, query: string, searchResults: string[]): Promise<string> {
-    const memories = await mem0.search(query, { filters: { user_id: userId }, topK: 5 });
-    const context = memories.results?.map((m: any) => `- ${m.memory}`).join('\n') || '';
-
-    const response = await openai.chat.completions.create({
-        model: 'gpt-5-mini',
-        messages: [
-            { role: 'system', content: `Personalize results using user context:\n${context}` },
-            { role: 'user', content: `Query: ${query}\nResults: ${searchResults.join(', ')}` },
-        ],
-    });
-    const reply = response.choices[0].message.content!;
-
-    await mem0.add([{ role: 'user', content: query }], { userId: userId });
-    return reply;
-}
-```
-
-### Key Benefits
-
- Learns preferences from queries automatically via `custom_instructions`
- Personalizes any search provider (Tavily, Google, Bing)
- Zero manual preference setup — improves over time
-
-**Best for:** Personalized search engines, recommendation systems — search results tailored to individual users.
-
---
-
-## 7. Email Intelligence
-
-Capture, categorize, and recall inbox threads using persistent memories with rich metadata.
-
-### Implementation (Python)
-
-```python
-from mem0 import MemoryClient
-
-client = MemoryClient()
-
-def store_email(user_id: str, sender: str, subject: str, body: str, date: str):
-    client.add(
-        [{"role": "user", "content": f"Email from {sender}: {subject}\n\n{body}"}],
-        user_id=user_id,
-        metadata={"email_type": "incoming", "sender": sender, "subject": subject, "date": date}
-    )
-
-def search_emails(user_id: str, query: str):
-    return client.search(
-        query,
-        filters={"AND": [{"user_id": user_id}, {"categories": {"contains": "email"}}]},
-        top_k=10
-    )
-
-def get_emails_from_sender(user_id: str, sender: str):
-    return client.get_all(
-        filters={
-            "AND": [
-                {"user_id": user_id},
-                {"metadata": {"contains": sender}}
-            ]
-        }
-    )
-
-# Usage
-store_email("alice", "bob@acme.com", "Q3 Budget Review", "Attached is the Q3 budget...", "2025-01-15")
-store_email("alice", "carol@acme.com", "Sprint Planning", "Here are the priorities...", "2025-01-16")
-
-results = search_emails("alice", "budget discussions")
-sender_emails = get_emails_from_sender("alice", "bob@acme.com")
-```
-
-### Implementation (TypeScript)
-
-```typescript
-import MemoryClient from 'mem0ai';
-
-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}` }],
-        { 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' } }] },
-        topK: 10,
-    });
-}
-```
-
-### Key Benefits
-
- Rich metadata enables multi-dimensional queries (sender, date, subject)
- Category filtering separates emails from other memory types
- Semantic search across all email content
-
-**Best for:** Inbox management, email automation — searchable email memories with metadata filtering.
-
---
-
-## Common Patterns Across Use Cases
-
-### Pattern 1: Retrieve → Generate → Store
-
-Every use case follows the same 3-step loop:
-
-```python
-# 1. Retrieve relevant context
-memories = mem0.search(user_input, user_id=user_id)
-context = "\n".join([m["memory"] for m in memories.get("results", [])])
-
-# 2. Generate with context
-response = llm.generate(system_prompt=f"Context:\n{context}", user_input=user_input)
-
-# 3. Store the interaction
-mem0.add(
-    [{"role": "user", "content": user_input}, {"role": "assistant", "content": response}],
-    user_id=user_id
-)
-```
-
-### Pattern 2: Scope with Entity Identifiers
-
-Use `user_id`, `agent_id`, `app_id`, and `run_id` to isolate memories:
-
-```python
-# User-level: personal preferences
-client.add(messages, user_id="alice")
-
-# Session-level: conversation within one session
-client.add(messages, user_id="alice", run_id="session_123")
-
-# Agent-level: agent-specific knowledge
-client.add(messages, agent_id="support_bot", app_id="helpdesk")
-```
-
-### Pattern 3: Rich Metadata for Filtering
-
-Attach structured metadata for multi-dimensional queries:
-
-```python
-# Store with metadata
-client.add(messages, user_id="alice", metadata={"priority": "high", "source": "phone_call"})
-
-# Filter by category + metadata
-client.search("billing issues", filters={
-    "AND": [{"user_id": "alice"}, {"categories": {"contains": "billing"}}]
-})
-```
-
-### Pattern 4: Custom Instructions for Domain-Specific Extraction
-
-Control what Mem0 extracts from conversations:
-
-```python
-client.project.update(
-    custom_instructions="Extract medical conditions, medications, and allergies. Exclude billing info."
-)
-```
-
---
-
-## More Examples
-
-For 30+ cookbooks with complete working code: [docs.mem0.ai/cookbooks](https://docs.mem0.ai/cookbooks)
@@ -1,181 +0,0 @@
-#!/usr/bin/env bun
-export {};
-const DOCS_BASE = "https://docs.mem0.ai";
-const SEARCH_ENDPOINT = `${DOCS_BASE}/api/search`;
-const LLMS_INDEX = `${DOCS_BASE}/llms.txt`;
-
-const SECTION_MAP: Record<string, string[]> = {
-  platform: [
-    "/platform/overview",
-    "/platform/quickstart",
-    "/platform/features",
-    "/platform/features/graph-memory",
-    "/platform/features/selective-memory",
-    "/platform/features/custom-categories",
-    "/platform/features/v2-memory-filters",
-    "/platform/features/async-client",
-    "/platform/features/webhooks",
-    "/platform/features/multimodal-support",
-  ],
-  api: [
-    "/api-reference/memory/add-memories",
-    "/api-reference/memory/v2-search-memories",
-    "/api-reference/memory/v2-get-memories",
-    "/api-reference/memory/get-memory",
-    "/api-reference/memory/update-memory",
-    "/api-reference/memory/delete-memory",
-  ],
-  "open-source": [
-    "/open-source/overview",
-    "/open-source/python-quickstart",
-    "/open-source/node-quickstart",
-    "/open-source/features",
-    "/open-source/features/graph-memory",
-    "/open-source/features/rest-api",
-    "/open-source/configure-components",
-  ],
-  openmemory: [
-    "/openmemory/overview",
-    "/openmemory/quickstart",
-  ],
-  sdks: ["/sdks/python", "/sdks/js"],
-  integrations: ["/integrations"],
-};
-
-async function fetchUrl(url: string): Promise<string> {
-  try {
-    const res = await fetch(url, {
-      headers: { "User-Agent": "Mem0DocSearchAgent/1.0" },
-      signal: AbortSignal.timeout(15000),
-    });
-    if (!res.ok) return `HTTP Error ${res.status}: ${res.statusText}`;
-    return await res.text();
-  } catch (e: any) {
-    return `Error: ${e.message}`;
-  }
-}
-
-async function searchDocs(query: string, section?: string) {
-  const params = new URLSearchParams({ query });
-  try {
-    const result = await fetchUrl(`${SEARCH_ENDPOINT}?${params}`);
-    const data = JSON.parse(result);
-    if (data?.results) {
-      let results = data.results;
-      if (section && SECTION_MAP[section]) {
-        const paths = SECTION_MAP[section];
-        results = results.filter((r: any) =>
-          paths.some((p) => r.url?.startsWith(p)),
-        );
-      }
-      return { source: "mintlify_search", results };
-    }
-  } catch {}
-
-  const indexContent = await fetchUrl(LLMS_INDEX);
-  const queryLower = query.toLowerCase();
-  let matching = indexContent
-    .split("\n")
-    .map((l) => l.trim())
-    .filter((l) => l && !l.startsWith("#") && l.toLowerCase().includes(queryLower));
-
-  if (section && SECTION_MAP[section]) {
-    const paths = SECTION_MAP[section];
-    matching = matching.filter((u) => paths.some((p) => u.includes(p)));
-  }
-
-  return {
-    source: "llms_txt_index",
-    query,
-    matching_urls: matching.slice(0, 20),
-    suggestion: "Fetch specific URLs for detailed content",
-  };
-}
-
-async function fetchPage(pagePath: string) {
-  const url = pagePath.startsWith("/") ? `${DOCS_BASE}${pagePath}` : pagePath;
-  const content = await fetchUrl(url);
-  return { url, content: content.slice(0, 10000), truncated: content.length > 10000 };
-}
-
-async function getIndex() {
-  const content = await fetchUrl(LLMS_INDEX);
-  const urls = content
-    .split("\n")
-    .map((l) => l.trim())
-    .filter((l) => l && !l.startsWith("#"));
-  return { total_pages: urls.length, urls, sections: Object.keys(SECTION_MAP) };
-}
-
-function listSection(section: string) {
-  if (!SECTION_MAP[section]) {
-    return { error: `Unknown section: ${section}`, available: Object.keys(SECTION_MAP) };
-  }
-  return { section, pages: SECTION_MAP[section].map((p) => `${DOCS_BASE}${p}`) };
-}
-
-function printResult(result: any) {
-  if (result.results) {
-    console.log(`Source: ${result.source ?? "unknown"}`);
-    for (const r of result.results) {
-      console.log(`  - ${r.title ?? "N/A"}: ${r.url ?? "N/A"}`);
-      if (r.description) console.log(`    ${r.description.slice(0, 200)}`);
-    }
-  } else if (result.matching_urls) {
-    console.log(`Source: ${result.source}`);
-    console.log(`Query: ${result.query}`);
-    for (const url of result.matching_urls) console.log(`  - ${url}`);
-    if (result.suggestion) console.log(`\n${result.suggestion}`);
-  } else if (result.urls) {
-    console.log(`Total documentation pages: ${result.total_pages}`);
-    console.log(`Sections: ${result.sections.join(", ")}`);
-    for (const url of result.urls.slice(0, 30)) console.log(`  - ${url}`);
-    if (result.total_pages > 30) console.log(`  ... and ${result.total_pages - 30} more`);
-  } else if (result.pages) {
-    console.log(`Section: ${result.section}`);
-    for (const page of result.pages) console.log(`  - ${page}`);
-  } else if (result.content) {
-    console.log(`URL: ${result.url}`);
-    if (result.truncated) console.log("[Content truncated to 10000 chars]");
-    console.log(result.content);
-  } else if (result.error) {
-    console.log(`Error: ${result.error}`);
-    if (result.available) console.log(`Available sections: ${result.available.join(", ")}`);
-  } else {
-    console.log(JSON.stringify(result, null, 2));
-  }
-}
-
-const args = process.argv.slice(2);
-const flags: Record<string, string | boolean> = {};
-for (let i = 0; i < args.length; i++) {
-  if (args[i] === "--query" && args[i + 1]) flags.query = args[++i];
-  else if (args[i] === "--page" && args[i + 1]) flags.page = args[++i];
-  else if (args[i] === "--section" && args[i + 1]) flags.section = args[++i];
-  else if (args[i] === "--index") flags.index = true;
-  else if (args[i] === "--json") flags.json = true;
-}
-
-let result: any;
-if (flags.index) {
-  result = await getIndex();
-} else if (flags.section && !flags.query) {
-  result = listSection(flags.section as string);
-} else if (flags.page) {
-  result = await fetchPage(flags.page as string);
-} else if (flags.query) {
-  result = await searchDocs(flags.query as string, flags.section as string | undefined);
-} else {
-  console.log("Usage:");
-  console.log("  bun scripts/mem0_doc_search.ts --query \"topic\"");
-  console.log("  bun scripts/mem0_doc_search.ts --page \"/platform/features/graph-memory\"");
-  console.log("  bun scripts/mem0_doc_search.ts --index");
-  console.log("  bun scripts/mem0_doc_search.ts --section platform");
-  process.exit(1);
-}
-
-if (flags.json) {
-  console.log(JSON.stringify(result, null, 2));
-} else {
-  printResult(result);
-}
@@ -1,63 +0,0 @@
---
-name: memory-reviewer
-description: Reviews stored memory quality by detecting duplicates, contradictions, and stale entries with actionable recommendations. Use when search results seem conflicting, before running dream consolidation, or for periodic memory hygiene audits.
---
-
-# Memory Reviewer
-
-Audits memory quality for the active project. Finds duplicates, contradictions, and low-confidence entries.
-
-## When to use
-
- User asks "check my memories", "memory quality", "any duplicates?"
- User runs `/mem0:memory-reviewer` directly
- After a session with 5+ memory writes (suggest proactively)
- After `/mem0:health --deep` identifies issues
-
-## Steps
-
-1. **Fetch all memories** for active project via `get_memories` with `filters={"AND": [{"user_id": "<active_user_id>"}, {"app_id": "<active_project_id>"}]}`, `page_size=200`. Paginate if needed — cap at 200 memories.
-
-2. **Group by `metadata.type`**. Common types: `decision`, `convention`, `anti_pattern`, `task_learning`, `project_profile`, `user_preference`, `session_state`.
-
-3. **Scan each group for issues:**
-
-   | Issue | Detection method |
-   |---|---|
-   | **Near-duplicates** | >60% noun overlap within same type. Compare memory text after stripping stop words. |
-   | **Contradictions** | Opposing facts about same topic (e.g., "use PostgreSQL" vs "use MySQL" for same component) |
-   | **Low-confidence** | `metadata.confidence < 0.3` |
-   | **Missing type** | No `metadata.type` set |
-   | **Stale** | `created_at` older than 180 days with no updates |
-
-4. **Output compact summary:**
-
-```
-memory-reviewer: project=<id> total=<N>
-  duplicates:      <N> found
-  contradictions:  <N> found
-  low_confidence:  <N> found
-  untagged:        <N> found
-  stale:           <N> found
-```
-
-5. **If issues found**, list them with memory IDs:
-
-```
-Issues:
-  [duplicate] "<memory_a>" ≈ "<memory_b>" [mem0:<id_a>, mem0:<id_b>]
-  [contradiction] "<memory_x>" vs "<memory_y>" [mem0:<id_x>, mem0:<id_y>]
-  [low_conf] "<memory_z>" (confidence: 0.1) [mem0:<id_z>]
-```
-
-6. **Suggest action**: "Run `/mem0:dream` to consolidate duplicates and resolve contradictions."
-
-## Constraints
-
- **Read-only** — never modify or delete memories (that's `/mem0:dream`'s job)
- **Max 200 memories** per scan
- Report findings, let user decide on action
-
-## Output formatting
-
-IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like **bold**, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.
@@ -1,181 +0,0 @@
---
-name: onboard
-description: Sets up mem0 for a new project including API key configuration, MCP authentication, project file import, and coding categories. Use on first run in a new project, when API key needs updating, or to re-run initial setup after configuration changes.
---
-
-# Mem0 Onboarding Wizard
-
-Run this wizard to set up the mem0 plugin for the current project. Complete in ~60 seconds.
-
-**IMPORTANT: Execute steps strictly in order (0 → 1 → 2 → 3 → 4 → 5 → 6). Each step depends on the previous one. Do NOT run steps in parallel or skip ahead. Complete one step fully before starting the next.**
-
-## Step 0: Skip dependency install
-
-The plugin lists `mem0ai` as a declared dependency — no install step is needed. Proceed directly to Step 1.
-
-## Step 1: Set up API key
-
-Check if the API key is available:
-
-```bash
-[ -n "${MEM0_API_KEY:-}" ] && echo "SET" || echo "NOT_SET"
-```
-
-IMPORTANT: Never run `echo $MEM0_API_KEY` — that prints the secret in plaintext to the conversation log.
-
-### If API key IS set (output is "SET")
-
-Print: `- API key found.` and proceed to Step 2.
-
-### If API key is NOT set (output is "NOT_SET")
-
-Guide the user through API key setup. Show this message:
-
-```
-Step 1: Setting up API key.
-
- API key not found. Let's set it up.
-
-  1. Get your API key from https://app.mem0.ai/dashboard/api-keys
-
-  2. Choose ONE method:
-
-     Option A — Shell profile:
-       echo 'export MEM0_API_KEY="m0-your-key-here"' >> ~/.zshrc
-       source ~/.zshrc
-
-     Option B — OpenCode environment config:
-       Add MEM0_API_KEY to your OpenCode environment settings
-       so it is available in all sessions.
-
-  3. Verify:
-     [ -n "${MEM0_API_KEY:-}" ] && echo "SET" || echo "NOT_SET"
-```
-
-After the user confirms, re-run the verify command. If NOT_SET, repeat. If SET, proceed to Step 2.
-
-## Step 2: MCP server connection
-
-First, check if MCP tools are already available using ToolSearch with query `"mem0 search_memories"`. The exact tool name varies by install method (may be `mcp__mem0__search_memories` or `mcp__plugin_mem0_mem0__search_memories`).
-
-**If MCP tools ARE found:** Print `- MCP already connected.` and proceed to Step 3.
-
-**If MCP tools are NOT found:**
-
-The MCP server authenticates using the `MEM0_API_KEY` set in Step 1. No OAuth or browser login is needed.
-
-1. Verify the API key is set (re-run the Step 1 check)
-2. Check the plugin is installed and the MCP server for mem0 is listed in OpenCode's MCP configuration
-3. If the server shows an error, ask the user to restart OpenCode and run `/mem0:onboard` again
-4. If all checks pass but tools are still missing: "Restart OpenCode and run `/mem0:onboard` again."
-
-**STOP here** — do not proceed without MCP tools.
-
-## Step 3: Verify connectivity and show identity
-
-Call `search_memories` with `query="project setup"`, `filters={"AND": [{"user_id": "<active_user_id>"}, {"app_id": "<active_project_id>"}]}`, `top_k=1` to verify connectivity.
-
-Print:
-```
- Connected
-  user:    <user_id>
-  project: <project_id>
-  branch:  <branch>
-```
-
-If the search fails, troubleshoot the API key and MCP connection.
-
-## Step 4: Import project files
-
-Check for common project context files in the working directory root. Read any that exist and store their key facts as memories using `add_memory`.
-
-### 4a: Detect project files
-
-```bash
-for f in CLAUDE.md AGENTS.md .cursorrules .windsurfrules mem0.md .mem0.md; do
-  [ -f "$f" ] && echo "FOUND: $f ($(wc -c < "$f") bytes)"
-done || true
-```
-
-If no files found, print `- No project files found. Skipping import.` and proceed to Step 5.
-
-### 4b: Read and import via MCP
-
-For each file found in 4a:
-
-1. Read the file contents with the Read tool.
-2. Extract the key facts, conventions, and decisions documented in the file. Do not import the entire file verbatim — summarize meaningful chunks.
-3. Call `add_memory` for each meaningful chunk with:
-   - `data`: the extracted fact or convention
-   - `user_id`: the active user id
-   - `app_id`: the active project id
-   - `metadata`: `{"source": "<filename>", "type": "project_context"}`
-
-If a file is very large (over 4000 bytes), split it into logical sections (one `add_memory` call per section).
-
-### 4c: Report to user
-
-After processing all files, print a user-friendly summary:
-
-```
- Importing project files into mem0... done.
-  <N> file(s) read, <M> memories stored.
-  These are available as context for future sessions.
-```
-
-If `add_memory` calls fail, print:
-```
- Project file import failed. Check API key and MCP connection, then retry with: /mem0:onboard
-```
-
-## Step 5: Verify coding categories
-
-Coding categories are now configured automatically in the background when the plugin starts. This step only verifies they are set up.
-
-Check if the categories are already configured by searching for a project_profile memory:
-
-Call `search_memories` with `query="coding categories project profile"`, `filters={"AND": [{"user_id": "<active_user_id>"}, {"app_id": "<active_project_id>"}]}`, `top_k=1`.
-
-If a project_profile memory is found, print:
-```
- Coding categories already configured (17 categories, auto-installed).
-```
-
-If no project_profile memory is found, store one as a fallback. Call `add_memory` once with:
-
- `data`: a plain-text description of the project profile and the list of active coding categories:
-  ```
-  Project profile for <project_id>.
-  Active coding categories: architecture_decisions, api_design, data_models,
-  algorithms, dependencies, environment_setup, testing_strategy, debugging_notes,
-  performance, security, deployment, code_conventions, error_handling,
-  refactoring_history, integrations, onboarding, project_meta.
-  ```
- `user_id`: the active user id
- `app_id`: the active project id
- `metadata`: `{"type": "project_profile", "source": "onboard"}`
-
-After the `add_memory` call succeeds, print:
-```
- Coding categories installed (17 categories).
-```
-
-## Step 6: Summary
-
-Print a summary:
-```
- Onboarding complete.
-  user_id:    <user_id>
-  project_id: <project_id> (app_id)
-  files:      <N> found, <M> memories stored
-  categories: <N installed | already installed | skipped by user>
-
-Memory is now active for this project. Start working — mem0 will
-automatically search relevant context and capture learnings.
-
-Run /mem0:tour to see what mem0 already knows about this project.
-```
-
-## Output formatting
-
-IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like **bold**, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.
@@ -29,12 +29,12 @@ Call `get_memory` with the selected memory ID. Store:

 ### Step 3: Pin it

-The MCP `update_memory` tool only accepts `memory_id`, `text`, and `source` — it
-does not accept a `metadata` parameter. To pin, append a pin marker to the text:
+The `update_memory` tool updates a memory by `id`. To pin durably, append a pin
+marker to the text so it travels with the memory:

 ```python
 pinned_text = "[PINNED] " + original_text if not original_text.startswith("[PINNED]") else original_text
-update_memory(memory_id=<selected_id>, text=pinned_text)
+update_memory(id=<selected_id>, text=pinned_text)
 ```

 **For new memories** (user wants to pin text that isn't stored yet):
@@ -1,135 +0,0 @@
---
-name: stats
-description: Displays memory usage statistics for the current session and project including counts by category, age distribution, and API latency. Use when checking how many memories exist, reviewing session activity, or auditing memory distribution across categories.
---
-
-# Mem0 Stats
-
-Show session and lifetime memory statistics.
-
-## Execution
-
-### Step 1: Gather session context
-
-Read the session identity from env vars set by the plugin's shell.env hook:
-
- `MEM0_USER_ID` (falls back to `$USER` if unset)
- `MEM0_APP_ID` — the active project identifier
- `MEM0_SESSION_ID` — current session identifier
- `MEM0_BRANCH` — current git branch
-
-If `MEM0_USER_ID` is unset, use `$USER`. If `MEM0_APP_ID` is unset, note "No project configured" and stop.
-
-### Step 2: Fetch total memory count
-
-Call `get_memories` MCP tool to get the total count for this project:
-
-`filters={"AND": [{"user_id": "<MEM0_USER_ID>"}, {"app_id": "<MEM0_APP_ID>"}]}`, `page_size=1`
-
-Read the `count` field from the response — this is the total number of memories for the project regardless of page size.
-
-### Step 3: Fetch memories for category breakdown
-
-Call `get_memories` MCP tool to retrieve memories for grouping:
-
-`filters={"AND": [{"user_id": "<MEM0_USER_ID>"}, {"app_id": "<MEM0_APP_ID>"}]}`, `page_size=200`
-
-Group each memory by:
-1. `categories[0]` (platform-assigned) — primary grouping
-2. `metadata.type` (agent-assigned) — secondary if `categories` is empty or absent
-3. `created_at` date — for age analysis
-
-**Category normalization:** Merge `auto_capture` and `uncategorized` into a single `uncategorized` row. Do NOT show `auto_capture` as its own row.
-
-Also run a `search_memories` MCP tool call with `query="project"`, `filters={"AND": [{"user_id": "<MEM0_USER_ID>"}, {"app_id": "<MEM0_APP_ID>"}]}`, `top_k=1` to measure round-trip latency. Note the time before and after the MCP call — do NOT attempt raw HTTP calls to the API.
-
-### Step 4: Display
-
-Print a minimal plain-text dashboard. No markdown formatting — OpenCode TUI renders text verbatim.
-
-Example output shape:
-
-```
-mem0 stats
-
-Session  (<first 12 chars of MEM0_SESSION_ID>)  branch: main
-
-Project: my-project  —  55 memories  —  API: 84ms
-
-Category             Count
--------------------  -----
-decision                24
-convention              15
-anti_pattern             6
-task_learning            5
-user_preference          3
-session_state            2
-
-Age  —  oldest: 2026-02-15  newest: 2026-05-23
-  < 7 days: 5  |  7-30d: 12  |  30-90d: 10  |  > 90d: 8
-
-Identity  —  user: kartik  project: my-project  branch: main
-```
-
-**Display rules:**
- Use plain text with spaces to align columns — no markdown tables, no | pipes, no ** bold, no ## headers
- Category section: sort by count descending, omit categories with 0 memories
- Age: single line with pipe-separated buckets, computed from `created_at`
- Session line: show MEM0_SESSION_ID (first 12 chars) and MEM0_BRANCH if available; skip the line entirely if both are unset
- If only 1-2 total memories, skip the category table — just show the count
- Keep everything compact — no decorative borders or filler
-
-## Weekly digest mode
-
-When invoked with `--weekly` (e.g., `/mem0:stats --weekly`), append a weekly
-activity digest after the standard stats dashboard.
-
-### W1: Fetch recent memories
-
-Call `search_memories` in parallel with time-scoped queries:
-1. `query="decisions made this week"`, `filters={"AND": [{"user_id": "<MEM0_USER_ID>"}, {"app_id": "<MEM0_APP_ID>"}, {"created_at": {"gte": "<7 days ago YYYY-MM-DD>"}}]}`, `top_k=20`
-2. `query="bugs errors fixes"`, same time filter, `top_k=20`
-3. `query="patterns conventions learnings"`, same time filter, `top_k=20`
-
-### W2: Analyze
-
-Merge by ID. Group into "New this week" by `categories[0]` or `metadata.type`.
-Calculate: memories added last 7 days, most active categories, most active day.
-
-### W3: Display
-
-Append after the standard stats in plain text (no markdown):
-
-```
-This week (May 16 - May 23)
-
-+12 memories  —  most active: Wednesday (5)
-
-Category        New
--------------  ---
-decision          5
-task_learning     4
-bug_fix           3
-
-Highlights
- <2-3 sentence summary of most important decisions/learnings this week>
-```
-
-### W4: Write digest file
-
-Write to `~/.mem0/weekly-digest.txt` (overwrite). Append one line to
-`~/.mem0/digest-history.log`:
-```
-<YYYY-MM-DD> | <MEM0_APP_ID> | +<new_count> memories | top: <top_category>
-```
-
-### W5: Empty state
-
-If no new memories in 7 days, output:
-```
-No new memories in the past week. Total: <N> memories in <MEM0_APP_ID>.
-```
-
-## Output formatting
-
-IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like **bold**, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.
@@ -1,107 +0,0 @@
---
-name: switch-project
-description: Overrides the auto-detected project scope to read and write memories under a different project ID, or enables global search to access all memories across all users and projects. Use when working across multiple projects, accessing memories from another repo, enabling team-wide memory access, or when auto-detection resolves to the wrong project.
---
-
-# Mem0 Switch Project
-
-Override the automatic project_id detection for the current directory, or enable global search mode.
-
-## Usage
-
- `/mem0:switch-project <project-name>` — switch to a specific project scope
- `/mem0:switch-project --global` — enable global search (all memories, all users, all projects)
- `/mem0:switch-project --no-global` — disable global search and return to per-project scoping
-
-## Execution
-
-### If `--global` flag is provided:
-
-1. Set `global_search: true` in `~/.mem0/settings.json` using the Bash tool:
-
-   ```bash
-   python3 -c "
-   import json, os
-   settings_file = os.path.expanduser('~/.mem0/settings.json')
-   settings = {}
-   if os.path.isfile(settings_file):
-       with open(settings_file) as f:
-           settings = json.load(f)
-   settings['global_search'] = True
-   with open(settings_file, 'w') as f:
-       json.dump(settings, f, indent=2)
-   print('Global search enabled')
-   "
-   ```
-
-2. Print:
-   ```
-   Global search enabled.
-   Searches now return all memories across all users and projects.
-   Writes still use the current user_id and app_id.
-   Restart the session for the change to take effect.
-   ```
-
-### If `--no-global` flag is provided:
-
-1. Set `global_search: false` in `~/.mem0/settings.json` using the Bash tool:
-
-   ```bash
-   python3 -c "
-   import json, os
-   settings_file = os.path.expanduser('~/.mem0/settings.json')
-   settings = {}
-   if os.path.isfile(settings_file):
-       with open(settings_file) as f:
-           settings = json.load(f)
-   settings['global_search'] = False
-   with open(settings_file, 'w') as f:
-       json.dump(settings, f, indent=2)
-   print('Global search disabled')
-   "
-   ```
-
-2. Print:
-   ```
-   Global search disabled.
-   Searches now return only memories scoped to the current project.
-   Restart the session for the change to take effect.
-   ```
-
-### If a project name is provided (no flags):
-
-1. If no project name was given, ask: "What project_id should this directory use?"
-
-2. Write the mapping to `~/.mem0/project_map.json` using the Bash tool:
-
-   ```bash
-   python3 -c "
-   import json, os
-   map_file = os.path.expanduser('~/.mem0/project_map.json')
-   mapping = {}
-   if os.path.isfile(map_file):
-       with open(map_file) as f:
-           mapping = json.load(f)
-   mapping[os.getcwd()] = '<PROJECT_NAME>'
-   os.makedirs(os.path.dirname(map_file), exist_ok=True)
-   with open(map_file, 'w') as f:
-       json.dump(mapping, f, indent=2)
-   print(f'Mapped {os.getcwd()} -> <PROJECT_NAME>')
-   "
-   ```
-
-   (Replace `<PROJECT_NAME>` with the user's chosen project name.)
-
-3. Verify by searching for existing memories:
-   - Call `search_memories` with `query="project"`, `filters={"AND": [{"user_id": "<active_user_id>"}, {"app_id": "<PROJECT_NAME>"}]}`, `top_k=1`
-
-4. Print:
-   ```
-   Switched to project <PROJECT_NAME>.
-   <N> memories found for this project.
-   Note: This override persists across sessions for this directory.
-   ```
-
-## Output formatting
-
-IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like **bold**, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.
@@ -148,7 +148,7 @@ Identity - user: <user_id>  project: <project_id>  branch: <branch>
 If zero memories found for this project, print:
 ```
 No memories stored yet for project <project_id>.
-Run /mem0-onboard to import project files, or start working - mem0 captures learnings automatically.
+Start working - mem0 captures learnings automatically, or use /mem0:remember to save something now.
 ```

 ## Output formatting
@@ -1,14 +0,0 @@
-{
-  "$schema": "https://opencode.ai/config.json",
-  "plugin": ["@mem0/opencode-plugin"],
-  "mcp": {
-    "mem0": {
-      "type": "remote",
-      "url": "https://mcp.mem0.ai/mcp/",
-      "headers": {
-        "Authorization": "Token {env:MEM0_API_KEY}"
-      },
-      "oauth": false
-    }
-  }
-}
@@ -1,6 +1,6 @@
 {
  "name": "@mem0/opencode-plugin",
-  "version": "0.1.3",
+  "version": "0.2.0",
  "type": "module",
  "description": "Mem0 persistent memory plugin for OpenCode — add, search, and manage memories across sessions",
  "main": "dist/index.js",
@@ -11,9 +11,6 @@
      "import": "./dist/index.js"
    }
  },
-  "bin": {
-    "mem0-opencode": "./cli.ts"
-  },
  "publishConfig": {
    "access": "public"
  },
@@ -23,7 +20,6 @@
    "opencode-plugin",
    "mem0",
    "memory",
-    "mcp",
    "ai-memory",
    "persistent-memory"
  ],
@@ -34,8 +30,6 @@
  },
  "files": [
    "dist",
-    "cli.ts",
-    "opencode.json",
    "LICENSE",
    "opencode-skills"
  ],
@@ -49,6 +43,7 @@
  "opencode": {
    "type": "plugin",
    "hooks": [
+      "config",
      "chat.message",
      "tool.execute.before",
      "tool.execute.after",
@@ -47,5 +47,32 @@ describe("opencode telemetry", () => {
    process.env.MEM0_TELEMETRY = "false";
    expect(() => captureEvent("session_start", {}, KEY)).not.toThrow();
    expect(() => captureEvent("session_start", {}, undefined)).not.toThrow();
+    expect(() => captureEvent("session_start", {}, KEY, "proj")).not.toThrow();
+  });
+
+  test("every event carries os_version (matches telemetry.py schema)", () => {
+    const props = buildEvent("session_start", {}, KEY)!
+      .properties as Record<string, unknown>;
+    expect(typeof props.os_version).toBe("string");
+  });
+
+  test("project_hash is sha256(projectId) when a project id is supplied", async () => {
+    const { createHash } = await import("node:crypto");
+    const expected = createHash("sha256").update("acme-repo").digest("hex");
+    const props = buildEvent("session_start", {}, KEY, "acme-repo")!
+      .properties as Record<string, unknown>;
+    expect(props.project_hash).toBe(expected);
+  });
+
+  test("project_hash is omitted when no project id is supplied (no raw ids leak)", () => {
+    const props = buildEvent("session_start", {}, KEY)!
+      .properties as Record<string, unknown>;
+    expect("project_hash" in props).toBe(false);
+  });
+
+  test("expanded event types all use the shared plugin.* namespace", () => {
+    for (const ev of ["user_prompt", "bash_error", "pre_compact", "session_stop"]) {
+      expect(buildEvent(ev, {}, KEY)!.event).toBe(`plugin.${ev}`);
+    }
  });
 });
@@ -13,11 +13,13 @@
 * installs without a key emit nothing). Disable with MEM0_TELEMETRY=false.
 *
 * Never sends: memory content, API keys, raw user/project IDs. Only sends:
- * event type, platform, plugin version, anonymized hash of the API key.
+ * event type, platform, plugin version, and anonymized hashes of the API key
+ * and project ID.
 */

 import { createHash } from "node:crypto";
 import { readFileSync } from "node:fs";
+import { release } from "node:os";

 const POSTHOG_API_KEY = "phc_hgJkUVJFYtmaJqrvf6CYN67TIQ8yhXAkWzUn9AMU4yX";
 const POSTHOG_HOST = "https://us.i.posthog.com/i/v0/e/";
@@ -62,6 +64,7 @@ export function buildEvent(
  eventType: string,
  properties: Record<string, unknown>,
  apiKey: string | undefined,
+  projectId?: string,
 ): Record<string, unknown> | null {
  if (!isTelemetryEnabled() || !apiKey) return null;
  return {
@@ -74,9 +77,14 @@ export function buildEvent(
      platform: "opencode",
      plugin_version: PLUGIN_VERSION,
      os: process.platform,
+      os_version: release(),
      sample_rate: 1.0,
      $process_person_profile: false,
      $lib: "posthog-node",
+      // Anonymized project segmentation, matching telemetry.py's project_hash.
+      ...(projectId
+        ? { project_hash: createHash("sha256").update(projectId).digest("hex") }
+        : {}),
    },
  };
 }
@@ -86,8 +94,9 @@ export function captureEvent(
  eventType: string,
  properties: Record<string, unknown>,
  apiKey: string | undefined,
+  projectId?: string,
 ): void {
-  const payload = buildEvent(eventType, properties, apiKey);
+  const payload = buildEvent(eventType, properties, apiKey, projectId);
  if (!payload) return;
  try {
    void fetch(POSTHOG_HOST, {