docs: add withSupermemory wrapper and Claude Memory Tool documentation

- Add withSupermemory wrapper section to OpenAI SDK docs
- Create new Claude Memory Tool integration page
- Replace gpt-4o with gpt-5 across documentation
- Fix normalizePathToCustomId in claude-memory.ts (customId validation)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Dhravya Shah 2026-01-18 16:20:18 -08:00
parent d114ddb86e
commit 196609650e
38 changed files with 1914 additions and 390 deletions

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@ -5,7 +5,7 @@ description: "All the content formats Supermemory can ingest and process"
icon: "file-stack"
---
Supermemory automatically extracts and indexes content from various formats. Just send it—we handle the rest.
Supermemory automatically extracts and indexes content from various formats. Just send it—we handle the rest. See [Add Memories](/add-memories) to learn how to ingest content via the API.
## Text Content
@ -212,3 +212,16 @@ For binary content (files), always specify `contentType` for reliable processing
<Tip>
For large files, consider chunking or using [connectors](/connectors/overview) for automatic sync.
</Tip>
---
## Next Steps
<CardGroup cols={2}>
<Card title="Add Memories" icon="plus" href="/add-memories">
Upload content via the API
</Card>
<Card title="Super RAG" icon="bolt" href="/concepts/super-rag">
How content is chunked and indexed
</Card>
</CardGroup>

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@ -97,7 +97,7 @@ Smaller chunks generate more memories per document. Larger chunks provide more c
## Connector Branding
Show "Log in to **YourApp**" instead of "Log in to Supermemory" when users connect external services.
Show "Log in to **YourApp**" instead of "Log in to Supermemory" when users connect external services. See [Connectors Overview](/connectors/overview) for the full list of supported integrations.
<AccordionGroup>
<Accordion title="Google Drive">
@ -157,3 +157,16 @@ await client.settings.update({
<Note>
Settings are organization-wide. Changes apply to new content only—existing memories aren't reprocessed.
</Note>
---
## Next Steps
<CardGroup cols={2}>
<Card title="Add Memories" icon="plus" href="/add-memories">
See your custom settings in action
</Card>
<Card title="Connectors" icon="plug" href="/connectors/overview">
Set up automatic syncing from external platforms
</Card>
</CardGroup>

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@ -344,3 +344,16 @@ const results = await client.search.documents({
- Allowed characters: `a-z`, `A-Z`, `0-9`, `_`, `-`, `.`
- Max length: 64 characters
- No spaces or special characters
---
## Next Steps
<CardGroup cols={2}>
<Card title="Search" icon="search" href="/search">
Apply filters in search queries
</Card>
<Card title="Add Memories" icon="plus" href="/add-memories">
Add content with container tags and metadata
</Card>
</CardGroup>

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@ -134,7 +134,13 @@ const results = await client.search({
<Card title="How It Works" icon="cpu" href="/concepts/how-it-works">
Deep dive into the architecture
</Card>
<Card title="Memory vs RAG" icon="scale" href="/concepts/memory-vs-rag">
When to use memory vs document retrieval
</Card>
<Card title="User Profiles" icon="user" href="/user-profiles">
Automatic summaries from the graph
</Card>
<Card title="Add Memories" icon="plus" href="/add-memories">
Start building your knowledge graph
</Card>
</CardGroup>

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@ -42,7 +42,7 @@ Documents are what you provide - the raw materials:
- Images with text
- Videos to transcribe
Think of documents as books you hand to Supermemory.
Think of documents as books you hand to Supermemory. See [Content Types](/concepts/content-types) for the full list of supported formats.
### Memories: Intelligent Knowledge Units
@ -63,7 +63,7 @@ Think of memories as the insights and connections your brain makes after reading
![](/images/memories-inferred.png)
The graph connects memories through three types of relationships:
The graph connects memories through three types of relationships. For a deeper dive into how these relationships work, see [Graph Memory](/concepts/graph-memory).
### Updates: Information Changes

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@ -238,3 +238,22 @@ Stop treating memory like a retrieval problem. Your agents need both:
- **Memory** for understanding users
Supermemory provides both capabilities in a unified platform, ensuring your agents have the right context at the right time.
---
## Next Steps
<CardGroup cols={2}>
<Card title="Graph Memory" icon="network" href="/concepts/graph-memory">
How memory relationships work
</Card>
<Card title="Super RAG" icon="bolt" href="/concepts/super-rag">
Our managed RAG solution
</Card>
<Card title="Add Memories" icon="plus" href="/add-memories">
Start ingesting content
</Card>
<Card title="Search" icon="search" href="/search">
Query your memories and documents
</Card>
</CardGroup>

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@ -164,7 +164,12 @@ You focus on building your product. Supermemory handles the RAG complexity.
<Card title="Content Types" icon="file-stack" href="/concepts/content-types">
All supported formats and how they're processed
</Card>
<Card title="How It Works" icon="cpu" href="/concepts/how-it-works">
The full processing pipeline
</Card>
<Card title="Memory vs RAG" icon="scale" href="/concepts/memory-vs-rag">
When to use each approach
</Card>
<Card title="Search" icon="search" href="/search">
Search parameters and optimization
</Card>

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@ -58,13 +58,13 @@ Recent context and temporary states:
Profiles are built automatically through ingestion:
1. **Ingest content** — Users add documents, chat, or any content
1. **Ingest content** — Users [add documents](/add-memories), chat, or any content
2. **Extract facts** — AI analyzes content for facts about the user
3. **Update profile** — System adds, updates, or removes facts
4. **Always current** — Profiles reflect the latest information
<Note>
You don't manually manage profiles — they build themselves as users interact.
You don't manually manage profiles — they build themselves as users interact. Start by [adding content](/add-memories) to see profiles in action.
</Note>
---
@ -126,10 +126,16 @@ Profiles provide: preferred languages, coding style, current project context.
## Next Steps
<CardGroup cols={2}>
<Card title="User Profiles" icon="code" href="/user-profiles">
Learn how to fetch and use profiles
<Card title="User Profiles API" icon="code" href="/user-profiles">
Fetch and use profiles via the API
</Card>
<Card title="Graph Memory" icon="network" href="/concepts/graph-memory">
How the underlying knowledge graph works
</Card>
<Card title="AI SDK Integration" icon="triangle" href="/integrations/ai-sdk">
Automatic profile injection with AI SDK
</Card>
<Card title="Add Memories" icon="plus" href="/add-memories">
Build profiles by adding content
</Card>
</CardGroup>

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@ -257,7 +257,7 @@ Now the AI can answer: "Try overnight oats with plant-based protein—perfect fo
```python
try:
response = await openai_client.chat.completions.create(
model="gpt-4o",
model="gpt-5",
messages=enhanced_messages,
temperature=0.7,
stream=True
@ -265,7 +265,7 @@ Now the AI can answer: "Try overnight oats with plant-based protein—perfect fo
```
**Key parameters:**
- `model="gpt-4o"`: Fast, capable model
- `model="gpt-5"`: Fast, capable model
- `messages`: Full conversation + memory context
- `temperature=0.7`: Balanced creativity (0=deterministic, 1=creative)
- `stream=True`: Enables word-by-word streaming
@ -558,7 +558,7 @@ const containerTag = `user_${crypto.createHash('sha256').update(email).digest('h
```typescript
const result = streamText({
model: openai('gpt-4o'),
model: openai('gpt-5'),
messages,
tools: supermemoryTools(process.env.SUPERMEMORY_API_KEY!, {
containerTags: [containerTag]
@ -569,7 +569,7 @@ const containerTag = `user_${crypto.createHash('sha256').update(email).digest('h
This is where the magic happens! Let's break down each parameter:
**`model: openai('gpt-4o')`**
**`model: openai('gpt-5')`**
- Specifies which AI model to use
- The AI SDK handles the API calls

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@ -148,9 +148,10 @@
"anchor": "Integrations",
"pages": [
"integrations/supermemory-sdk",
"integrations/openai",
"integrations/ai-sdk",
"integrations/openai",
"integrations/memory-graph",
"integrations/claude-memory",
"integrations/pipecat",
"integrations/n8n",
"integrations/zapier"

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@ -0,0 +1 @@
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After

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@ -0,0 +1,269 @@
---
title: "Claude Memory Tool"
sidebarTitle: "Claude Memory Tool"
description: "Use Claude's native memory tool with Supermemory as the backend"
icon: "/images/anthropic-1.svg"
---
Claude has a native memory tool that allows it to store and retrieve information across conversations. Supermemory provides a backend implementation that maps Claude's memory commands to persistent storage.
<Info>
This integration works with Claude's built-in `memory` tool type, introduced in the Anthropic API. It requires the `context-management` beta flag.
</Info>
## Installation
```bash
npm install @supermemory/tools @anthropic-ai/sdk
```
## Quick Start
```typescript
import Anthropic from "@anthropic-ai/sdk"
import { createClaudeMemoryTool } from "@supermemory/tools/claude-memory"
const anthropic = new Anthropic()
const memoryTool = createClaudeMemoryTool(process.env.SUPERMEMORY_API_KEY!, {
projectId: "my-app",
})
async function chatWithMemory(userMessage: string) {
// Send message to Claude with memory tool
const response = await anthropic.beta.messages.create({
model: "claude-sonnet-4-5",
max_tokens: 2048,
messages: [{ role: "user", content: userMessage }],
tools: [{ type: "memory_20250818", name: "memory" }],
betas: ["context-management-2025-06-27"],
})
// Handle any memory tool calls
const toolResults = []
for (const block of response.content) {
if (block.type === "tool_use" && block.name === "memory") {
const toolResult = await memoryTool.handleCommandForToolResult(
block.input as any,
block.id
)
toolResults.push(toolResult)
}
}
// Send tool results back to Claude if needed
if (toolResults.length > 0) {
const finalResponse = await anthropic.beta.messages.create({
model: "claude-sonnet-4-5",
max_tokens: 2048,
messages: [
{ role: "user", content: userMessage },
{ role: "assistant", content: response.content },
{ role: "user", content: toolResults },
],
tools: [{ type: "memory_20250818", name: "memory" }],
betas: ["context-management-2025-06-27"],
})
return finalResponse
}
return response
}
// Example usage
const response = await chatWithMemory(
"Remember that I prefer React with TypeScript for my projects"
)
console.log(response.content[0])
```
## Configuration
```typescript
import { createClaudeMemoryTool } from "@supermemory/tools/claude-memory"
const memoryTool = createClaudeMemoryTool(process.env.SUPERMEMORY_API_KEY!, {
// Scope memories to a project or user
projectId: "my-app",
// Or use container tags for more flexibility
containerTags: ["user-123", "project-alpha"],
// Custom memory container prefix (default: "claude_memory")
memoryContainerTag: "my_memory_prefix",
// Custom API endpoint
baseUrl: "https://custom.api.com",
})
```
## How It Works
Claude's memory tool uses a file-system metaphor. Supermemory maps these operations to document storage:
| Claude Command | Supermemory Action |
|----------------|-------------------|
| `view` | Search/retrieve documents |
| `create` | Add new document |
| `str_replace` | Update document content |
| `insert` | Insert content at line |
| `delete` | Delete document |
| `rename` | Move document to new path |
### Memory Path Structure
All memory paths must start with `/memories/`:
```
/memories/preferences.txt # User preferences
/memories/projects/react.txt # Project-specific notes
/memories/context/current.txt # Current context
```
## Commands Reference
### View (Read/List)
```typescript
// List directory contents
{ command: "view", path: "/memories/" }
// Read file contents
{ command: "view", path: "/memories/preferences.txt" }
// Read specific lines
{ command: "view", path: "/memories/notes.txt", view_range: [1, 10] }
```
### Create
```typescript
{
command: "create",
path: "/memories/preferences.txt",
file_text: "User prefers dark mode\nFavorite language: TypeScript"
}
```
### String Replace
```typescript
{
command: "str_replace",
path: "/memories/preferences.txt",
old_str: "dark mode",
new_str: "light mode"
}
```
### Insert
```typescript
{
command: "insert",
path: "/memories/notes.txt",
insert_line: 5,
insert_text: "New note added here"
}
```
### Delete
```typescript
{ command: "delete", path: "/memories/old-notes.txt" }
```
### Rename
```typescript
{
command: "rename",
path: "/memories/old-name.txt",
new_path: "/memories/new-name.txt"
}
```
## Complete Example
```typescript
import Anthropic from "@anthropic-ai/sdk"
import { createClaudeMemoryTool } from "@supermemory/tools/claude-memory"
const anthropic = new Anthropic()
const memoryTool = createClaudeMemoryTool(process.env.SUPERMEMORY_API_KEY!, {
projectId: "assistant",
})
async function runConversation() {
const messages: Anthropic.MessageParam[] = []
// Helper to chat with memory
async function chat(userMessage: string) {
messages.push({ role: "user", content: userMessage })
let response = await anthropic.beta.messages.create({
model: "claude-sonnet-4-5",
max_tokens: 2048,
messages,
tools: [{ type: "memory_20250818", name: "memory" }],
betas: ["context-management-2025-06-27"],
})
// Handle tool calls
while (response.stop_reason === "tool_use") {
const toolResults = []
for (const block of response.content) {
if (block.type === "tool_use" && block.name === "memory") {
const result = await memoryTool.handleCommandForToolResult(
block.input as any,
block.id
)
toolResults.push(result)
}
}
messages.push({ role: "assistant", content: response.content })
messages.push({ role: "user", content: toolResults })
response = await anthropic.beta.messages.create({
model: "claude-sonnet-4-5",
max_tokens: 2048,
messages,
tools: [{ type: "memory_20250818", name: "memory" }],
betas: ["context-management-2025-06-27"],
})
}
messages.push({ role: "assistant", content: response.content })
return response
}
// Have a conversation with persistent memory
await chat("My name is Alex and I'm a backend developer")
await chat("I prefer Go for systems programming")
await chat("What do you remember about me?")
}
runConversation()
```
## Environment Variables
```bash
SUPERMEMORY_API_KEY=your_supermemory_key
ANTHROPIC_API_KEY=your_anthropic_key
```
## Next Steps
<CardGroup cols={2}>
<Card title="AI SDK Integration" icon="triangle" href="/integrations/ai-sdk">
Use with Vercel AI SDK for streamlined development
</Card>
<Card title="OpenAI SDK" icon="bolt" href="/integrations/openai">
Memory tools for OpenAI function calling
</Card>
</CardGroup>

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@ -5,7 +5,14 @@ description: "Memory tools for OpenAI function calling with Supermemory integrat
icon: "/images/openai.svg"
---
Add memory capabilities to the official OpenAI SDKs using Supermemory's function calling tools. These plugins provide seamless integration with OpenAI's chat completions and function calling features.
Add memory capabilities to the official OpenAI SDKs using Supermemory. Two approaches available:
1. **`withSupermemory` wrapper** - Automatic memory injection into system prompts (zero-config)
2. **Function calling tools** - Explicit tool calls for search/add memory operations
<Tip>
**New to Supermemory?** Start with `withSupermemory` for the simplest integration. It automatically injects relevant memories into your prompts.
</Tip>
<CardGroup>
<Card title="Supermemory tools on npm" icon="npm" href="https://www.npmjs.com/package/@supermemory/tools">
@ -16,6 +23,97 @@ Add memory capabilities to the official OpenAI SDKs using Supermemory's function
</Card>
</CardGroup>
---
## withSupermemory Wrapper
The simplest way to add memory to your OpenAI client. Wraps your client to automatically inject relevant memories into system prompts.
### Installation
```bash
npm install @supermemory/tools openai
```
### Quick Start
```typescript
import OpenAI from "openai"
import { withSupermemory } from "@supermemory/tools/openai"
const openai = new OpenAI()
// Wrap client with memory - memories auto-injected into system prompts
const client = withSupermemory(openai, "user-123", {
mode: "full", // "profile" | "query" | "full"
addMemory: "always", // "always" | "never"
})
// Use normally - memories are automatically included
const response = await client.chat.completions.create({
model: "gpt-5",
messages: [
{ role: "system", content: "You are a helpful assistant." },
{ role: "user", content: "What's my favorite programming language?" }
]
})
```
### Configuration Options
```typescript
const client = withSupermemory(openai, "user-123", {
// Memory search mode
mode: "full", // "profile" (user profile only), "query" (search only), "full" (both)
// Auto-save conversations as memories
addMemory: "always", // "always" | "never"
// Group messages into conversations
conversationId: "conv-456",
// Enable debug logging
verbose: true,
// Custom API endpoint
baseUrl: "https://custom.api.com"
})
```
### Modes Explained
| Mode | Description | Use Case |
|------|-------------|----------|
| `profile` | Injects user profile (static + dynamic facts) | General personalization |
| `query` | Searches memories based on user message | Question answering |
| `full` | Both profile and query-based search | Best for chatbots |
### Works with Responses API Too
```typescript
const client = withSupermemory(openai, "user-123", { mode: "full" })
// Memories injected into instructions
const response = await client.responses.create({
model: "gpt-5",
instructions: "You are a helpful assistant.",
input: "What do you know about me?"
})
```
### Environment Variables
```bash
SUPERMEMORY_API_KEY=your_supermemory_key
OPENAI_API_KEY=your_openai_key
```
---
## Function Calling Tools
For explicit control over memory operations, use function calling tools. The model decides when to search or add memories.
## Installation
<CodeGroup>

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@ -153,7 +153,7 @@ async def run_bot(websocket_client, user_id: str, session_id: str):
)
stt = OpenAISTTService(api_key=os.getenv("OPENAI_API_KEY"))
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini")
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-5-mini")
tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="alloy")
# Supermemory memory service

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@ -9,8 +9,8 @@ Supermemory is the long-term and short-term memory and context infrastructure fo
With supermemory, developers can provide perfect recall about their users to build AI agents that are more intelligent, more personalized, and more consistent. Additionally, *supermemory* has all the pieces of the context stack built in:
- [Agent memory](/concepts/graph-memory)
- [Content extraction](/concepts/content-types)
- [Connectors and syncing](/concepts/connectors-syncing)
- Managed RAG platform
- [Connectors and syncing](/connectors/overview)
- [Managed RAG platform](/concepts/super-rag)
All this, coming together, makes supermemory the best abstraction to provide to agents.
@ -41,7 +41,7 @@ We offer three ways to add context to your LLMs:
![memory graph](/images/memory-graph.png)
Supermemory learns and builds the memory for the user. These are extracted facts about the user, that:
- [Evolve on top of existing context about the user](/concepts/memory-graph), **in real time**
- [Evolve on top of existing context about the user](/concepts/graph-memory), **in real time**
- Handle **knowledge updates, temporal changes, forgetfulness**
- Creates a **user profile** as the default context provider for the LLM.
@ -49,7 +49,7 @@ _This can then be provided to the LLM, to give more contextual, personalized res
#### User profiles
Having the latest, evolving context about the user allows us to also create a [**User Profile**](/concepts/user-profile). This is a combination of static and dynamic facts about the user, that the agent should **always know**
Having the latest, evolving context about the user allows us to also create a [**User Profile**](/concepts/user-profiles). This is a combination of static and dynamic facts about the user, that the agent should **always know**
Developers can configure supermemory with what static and dynamic contents are, depending on their use case.
- Static: Information that the agent should **always** know.
@ -65,7 +65,7 @@ Along with the user context, developers can also choose to do a search on the ra
- Works well with the memory engine
<Info>
You can reference the full API reference for the Memory API in the API Reference tab.
See the full [API Reference](/api-reference) for detailed endpoint documentation.
</Info>
@ -75,4 +75,11 @@ All three approaches share the **same context pool** when using the same user ID
## Next steps
Head to the [**How it works**](/concepts/how-it-works) guide to understand the underlying way of how supermemory represents and learns in data.
<CardGroup cols={2}>
<Card title="Quickstart" icon="play" href="/quickstart">
Make your first API call in minutes
</Card>
<Card title="How it Works" icon="cpu" href="/concepts/how-it-works">
Understand the knowledge graph architecture
</Card>
</CardGroup>

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@ -52,7 +52,7 @@ profile = client.profile(container_tag=USER_ID, q=conversation[-1]["content"])
static = "\n".join(profile.profile.static)
dynamic = "\n".join(profile.profile.dynamic)
memories = "\n".join(r.content for r in profile.search_results.results)
memories = "\n".join(r.get("memory", "") for r in profile.search_results.results)
context = f"""Static profile:
{static}
@ -101,7 +101,7 @@ Dynamic profile:
${profile.profile.dynamic.join("\n")}
Relevant memories:
${profile.searchResults.results.map((r) => r.content).join("\n")}`;
${profile.searchResults.results.map((r) => r.memory).join("\n")}`;
// Build messages with memory-enriched context
const messages = [{ role: "system", content: `User context:\n${context}` }, ...conversation];

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@ -14,7 +14,7 @@ profile = client.profile(container_tag=USER_ID, q=conversation[-1]["content"])
static = "\n".join(profile.profile.static)
dynamic = "\n".join(profile.profile.dynamic)
memories = "\n".join(r.content for r in profile.search_results.results)
memories = "\n".join(r.get("memory", "") for r in profile.search_results.results)
context = f"""Static profile:
{static}

View file

@ -173,7 +173,7 @@ Current Context:
${result.profile.dynamic.join('\n')}
Relevant Memories:
${result.searchResults?.results.map(m => m.content).join('\n') || 'None'}
${result.searchResults?.results.map(m => m.memory).join('\n') || 'None'}
`;
}
```

View file

@ -170,7 +170,7 @@ const { profile, searchResults } = await client.profile({
const context = `
Static facts: ${profile.static.join('\n')}
Recent context: ${profile.dynamic.join('\n')}
${searchResults ? `Memories: ${searchResults.results.map(r => r.content).join('\n')}` : ''}
${searchResults ? `Memories: ${searchResults.results.map(r => r.memory).join('\n')}` : ''}
`
// Send to LLM
@ -202,7 +202,7 @@ const results = await client.search({
})
// Build context
const context = results.results.map(r => r.content).join('\n')
const context = results.results.map(r => r.memory || r.chunk).join('\n')
// Send to LLM with context
const messages = [

View file

@ -151,7 +151,7 @@ async def run_bot(websocket_client, user_id: str, session_id: str):
)
stt = OpenAISTTService(api_key=os.getenv("OPENAI_API_KEY"))
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini")
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-5-mini")
tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="alloy")
# Supermemory memory service

435
bun.lock
View file

@ -1,5 +1,6 @@
{
"lockfileVersion": 1,
"configVersion": 0,
"workspaces": {
"": {
"name": "supermemory",
@ -119,21 +120,6 @@
"typescript": "^5",
},
},
"apps/raycast-extension": {
"name": "supermemory",
"dependencies": {
"@raycast/api": "^1.103.3",
"@raycast/utils": "^2.2.1",
},
"devDependencies": {
"@raycast/eslint-config": "^2.0.4",
"@types/node": "22.13.10",
"@types/react": "19.0.10",
"eslint": "^9.22.0",
"prettier": "^3.5.3",
"typescript": "^5.8.2",
},
},
"apps/web": {
"name": "@repo/web",
"version": "0.1.0",
@ -257,6 +243,21 @@
"vitest": "^3.2.4",
},
},
"packages/docs-test": {
"name": "docs-test",
"version": "1.0.0",
"dependencies": {
"@ai-sdk/anthropic": "^3.0.15",
"@ai-sdk/openai": "latest",
"@anthropic-ai/sdk": "latest",
"@supermemory/tools": "workspace:*",
"ai": "latest",
"dotenv": "latest",
"openai": "latest",
"supermemory": "latest",
"zod": "latest",
},
},
"packages/hooks": {
"name": "@repo/hooks",
"version": "0.0.0",
@ -768,24 +769,6 @@
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"@eslint-community/eslint-utils": ["@eslint-community/eslint-utils@4.9.1", "", { "dependencies": { "eslint-visitor-keys": "^3.4.3" }, "peerDependencies": { "eslint": "^6.0.0 || ^7.0.0 || >=8.0.0" } }, "sha512-phrYmNiYppR7znFEdqgfWHXR6NCkZEK7hwWDHZUjit/2/U0r6XvkDl0SYnoM51Hq7FhCGdLDT6zxCCOY1hexsQ=="],
"@eslint-community/regexpp": ["@eslint-community/regexpp@4.12.2", "", {}, "sha512-EriSTlt5OC9/7SXkRSCAhfSxxoSUgBm33OH+IkwbdpgoqsSsUg7y3uh+IICI/Qg4BBWr3U2i39RpmycbxMq4ew=="],
"@eslint/config-array": ["@eslint/config-array@0.21.1", "", { "dependencies": { "@eslint/object-schema": "^2.1.7", "debug": "^4.3.1", "minimatch": "^3.1.2" } }, "sha512-aw1gNayWpdI/jSYVgzN5pL0cfzU02GT3NBpeT/DXbx1/1x7ZKxFPd9bwrzygx/qiwIQiJ1sw/zD8qY/kRvlGHA=="],
"@eslint/config-helpers": ["@eslint/config-helpers@0.4.2", "", { "dependencies": { "@eslint/core": "^0.17.0" } }, "sha512-gBrxN88gOIf3R7ja5K9slwNayVcZgK6SOUORm2uBzTeIEfeVaIhOpCtTox3P6R7o2jLFwLFTLnC7kU/RGcYEgw=="],
"@eslint/core": ["@eslint/core@0.17.0", "", { "dependencies": { "@types/json-schema": "^7.0.15" } }, "sha512-yL/sLrpmtDaFEiUj1osRP4TI2MDz1AddJL+jZ7KSqvBuliN4xqYY54IfdN8qD8Toa6g1iloph1fxQNkjOxrrpQ=="],
"@eslint/eslintrc": ["@eslint/eslintrc@3.3.3", "", { "dependencies": { "ajv": "^6.12.4", "debug": "^4.3.2", "espree": "^10.0.1", "globals": "^14.0.0", "ignore": "^5.2.0", "import-fresh": "^3.2.1", "js-yaml": "^4.1.1", "minimatch": "^3.1.2", "strip-json-comments": "^3.1.1" } }, "sha512-Kr+LPIUVKz2qkx1HAMH8q1q6azbqBAsXJUxBl/ODDuVPX45Z9DfwB8tPjTi6nNZ8BuM3nbJxC5zCAg5elnBUTQ=="],
"@eslint/js": ["@eslint/js@9.39.2", "", {}, "sha512-q1mjIoW1VX4IvSocvM/vbTiveKC4k9eLrajNEuSsmjymSDEbpGddtpfOoN7YGAqBK3NG+uqo8ia4PDTt8buCYA=="],
"@eslint/object-schema": ["@eslint/object-schema@2.1.7", "", {}, "sha512-VtAOaymWVfZcmZbp6E2mympDIHvyjXs/12LqWYjVw6qjrfF+VK+fyG33kChz3nnK+SU5/NeHOqrTEHS8sXO3OA=="],
"@eslint/plugin-kit": ["@eslint/plugin-kit@0.4.1", "", { "dependencies": { "@eslint/core": "^0.17.0", "levn": "^0.4.1" } }, "sha512-43/qtrDUokr7LJqoF2c3+RInu/t4zfrpYdoSDfYyhg52rwLV6TnOvdG4fXm7IkSB3wErkcmJS9iEhjVtOSEjjA=="],
"@essentials/memoize-one": ["@essentials/memoize-one@1.1.0", "", {}, "sha512-HMkuIkKNe0EWSUpZhlaq9+5Yp47YhrMhxLMnXTRnEyE5N4xKLspAvMGjUFdi794VnEF1EcOZFS8rdROeujrgag=="],
"@essentials/one-key-map": ["@essentials/one-key-map@1.2.0", "", {}, "sha512-C2H7zHVcsoipDv4VKY5uUcv5ilsK+uEgEj+WeOdN5oz/Qj1/OZIzCdle90gDzj0xnGQrmZ9qDujwD7AkBb5k9A=="],
@ -812,14 +795,6 @@
"@hono/zod-validator": ["@hono/zod-validator@0.7.6", "", { "peerDependencies": { "hono": ">=3.9.0", "zod": "^3.25.0 || ^4.0.0" } }, "sha512-Io1B6d011Gj1KknV4rXYz4le5+5EubcWEU/speUjuw9XMMIaP3n78yXLhjd2A3PXaXaUwEAluOiAyLqhBEJgsw=="],
"@humanfs/core": ["@humanfs/core@0.19.1", "", {}, "sha512-5DyQ4+1JEUzejeK1JGICcideyfUbGixgS9jNgex5nqkW+cY7WZhxBigmieN5Qnw9ZosSNVC9KQKyb+GUaGyKUA=="],
"@humanfs/node": ["@humanfs/node@0.16.7", "", { "dependencies": { "@humanfs/core": "^0.19.1", "@humanwhocodes/retry": "^0.4.0" } }, "sha512-/zUx+yOsIrG4Y43Eh2peDeKCxlRt/gET6aHfaKpuq267qXdYDFViVHfMaLyygZOnl0kGWxFIgsBy8QFuTLUXEQ=="],
"@humanwhocodes/module-importer": ["@humanwhocodes/module-importer@1.0.1", "", {}, "sha512-bxveV4V8v5Yb4ncFTT3rPSgZBOpCkjfK0y4oVVVJwIuDVBRMDXrPyXRL988i5ap9m9bnyEEjWfm5WkBmtffLfA=="],
"@humanwhocodes/retry": ["@humanwhocodes/retry@0.4.3", "", {}, "sha512-bV0Tgo9K4hfPCek+aMAn81RppFKv2ySDQeMoSZuvTASywNTnVJCArCZE2FWqpvIatKu7VMRLWlR1EazvVhDyhQ=="],
"@iarna/toml": ["@iarna/toml@2.2.5", "", {}, "sha512-trnsAYxU3xnS1gPHPyU961coFyLkh4gAD/0zQ5mymY4yOZ+CYvsPqUbOFSw0aDM4y0tV7tiFxL/1XfXPNC6IPg=="],
"@iconify/types": ["@iconify/types@2.0.0", "", {}, "sha512-+wluvCrRhXrhyOmRDJ3q8mux9JkKy5SJ/v8ol2tu4FVjyYvtEzkc/3pK15ET6RKg4b4w4BmTk1+gsCUhf21Ykg=="],
@ -1032,14 +1007,6 @@
"@nodelib/fs.walk": ["@nodelib/fs.walk@1.2.8", "", { "dependencies": { "@nodelib/fs.scandir": "2.1.5", "fastq": "^1.6.0" } }, "sha512-oGB+UxlgWcgQkgwo8GcEGwemoTFt3FIO9ababBmaGwXIoBKZ+GTy0pP185beGg7Llih/NSHSV2XAs1lnznocSg=="],
"@oclif/core": ["@oclif/core@4.8.0", "", { "dependencies": { "ansi-escapes": "^4.3.2", "ansis": "^3.17.0", "clean-stack": "^3.0.1", "cli-spinners": "^2.9.2", "debug": "^4.4.3", "ejs": "^3.1.10", "get-package-type": "^0.1.0", "indent-string": "^4.0.0", "is-wsl": "^2.2.0", "lilconfig": "^3.1.3", "minimatch": "^9.0.5", "semver": "^7.7.3", "string-width": "^4.2.3", "supports-color": "^8", "tinyglobby": "^0.2.14", "widest-line": "^3.1.0", "wordwrap": "^1.0.0", "wrap-ansi": "^7.0.0" } }, "sha512-jteNUQKgJHLHFbbz806aGZqf+RJJ7t4gwF4MYa8fCwCxQ8/klJNWc0MvaJiBebk7Mc+J39mdlsB4XraaCKznFw=="],
"@oclif/plugin-autocomplete": ["@oclif/plugin-autocomplete@3.2.39", "", { "dependencies": { "@oclif/core": "^4", "ansis": "^3.16.0", "debug": "^4.4.1", "ejs": "^3.1.10" } }, "sha512-OwAZNnSpuDjKyhAwoOJkFWxGswPFKBB4hpNIMsj6PUtbKwGBPmD+2wGGPgTsDioVwLmUELSb2bZ+1dxHfvXmvg=="],
"@oclif/plugin-help": ["@oclif/plugin-help@6.2.36", "", { "dependencies": { "@oclif/core": "^4" } }, "sha512-NBQIg5hEMhvdbi4mSrdqRGl5XJ0bqTAHq6vDCCCDXUcfVtdk3ZJbSxtRVWyVvo9E28vwqu6MZyHOJylevqcHbA=="],
"@oclif/plugin-not-found": ["@oclif/plugin-not-found@3.2.73", "", { "dependencies": { "@inquirer/prompts": "^7.10.1", "@oclif/core": "^4.8.0", "ansis": "^3.17.0", "fast-levenshtein": "^3.0.0" } }, "sha512-2bQieTGI9XNFe9hKmXQjJmHV5rZw+yn7Rud1+C5uLEo8GaT89KZbiLTJgL35tGILahy/cB6+WAs812wjw7TK6w=="],
"@octokit/auth-token": ["@octokit/auth-token@5.1.2", "", {}, "sha512-JcQDsBdg49Yky2w2ld20IHAlwr8d/d8N6NiOXbtuoPCqzbsiJgF633mVUw3x4mo0H5ypataQIX7SFu3yy44Mpw=="],
"@octokit/core": ["@octokit/core@6.1.6", "", { "dependencies": { "@octokit/auth-token": "^5.0.0", "@octokit/graphql": "^8.2.2", "@octokit/request": "^9.2.3", "@octokit/request-error": "^6.1.8", "@octokit/types": "^14.0.0", "before-after-hook": "^3.0.2", "universal-user-agent": "^7.0.0" } }, "sha512-kIU8SLQkYWGp3pVKiYzA5OSaNF5EE03P/R8zEmmrG6XwOg5oBjXyQVVIauQ0dgau4zYhpZEhJrvIYt6oM+zZZA=="],
@ -1290,14 +1257,6 @@
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"@raycast/api": ["@raycast/api@1.104.1", "", { "dependencies": { "@oclif/core": "^4.5.4", "@oclif/plugin-autocomplete": "^3.2.35", "@oclif/plugin-help": "^6.2.33", "@oclif/plugin-not-found": "^3.2.68", "@types/node": "22.13.10", "@types/react": "19.0.10", "esbuild": "^0.25.10", "react": "19.0.0" }, "peerDependencies": { "react-devtools": "6.1.1" }, "optionalPeers": ["react-devtools"], "bin": { "ray": "bin/run.js" } }, "sha512-5v52JDzAAoCA6/JOoBfsgCXK4fzIY02lvMH0IA3ghuxZuk8i2ID2rtPkTuIAbebpkILADB7gP4yaBphkMLiCJA=="],
"@raycast/eslint-config": ["@raycast/eslint-config@2.1.1", "", { "dependencies": { "@eslint/js": "^9.36.0", "@raycast/eslint-plugin": "^2.1.1", "eslint-config-prettier": "^10.1.8", "globals": "^16.4.0", "typescript-eslint": "^8.45.0" }, "peerDependencies": { "eslint": ">=8.23.0", "prettier": ">=2", "typescript": ">=4" } }, "sha512-W0kxF+FJ+BYQn0EKIV739j2ZrHEtjo/LclsoZgUWg3t364Dq75XKcjqYFYx+59/DBaamY0amdajlfuDAf6veAg=="],
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"@react-email/render": ["@react-email/render@1.1.2", "", { "dependencies": { "html-to-text": "^9.0.5", "prettier": "^3.5.3", "react-promise-suspense": "^0.3.4" }, "peerDependencies": { "react": "^18.0 || ^19.0 || ^19.0.0-rc", "react-dom": "^18.0 || ^19.0 || ^19.0.0-rc" } }, "sha512-RnRehYN3v9gVlNMehHPHhyp2RQo7+pSkHDtXPvg3s0GbzM9SQMW4Qrf8GRNvtpLC4gsI+Wt0VatNRUFqjvevbw=="],
"@react-hook/debounce": ["@react-hook/debounce@3.0.0", "", { "dependencies": { "@react-hook/latest": "^1.0.2" }, "peerDependencies": { "react": ">=16.8" } }, "sha512-ir/kPrSfAzY12Gre0sOHkZ2rkEmM4fS5M5zFxCi4BnCeXh2nvx9Ujd+U4IGpKCuPA+EQD0pg1eK2NGLvfWejag=="],
@ -1972,26 +1931,6 @@
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@ -3358,7 +3259,7 @@
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@ -3628,8 +3529,6 @@
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@ -3952,8 +3847,6 @@
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"supermemory": ["supermemory@workspace:apps/raycast-extension"],
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"supermemory-browser-extension": ["supermemory-browser-extension@workspace:apps/browser-extension"],
@ -4442,8 +4335,6 @@
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@ -4564,8 +4451,6 @@
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@ -4668,10 +4553,6 @@
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"ajv-keywords/ajv/json-schema-traverse": ["json-schema-traverse@1.0.0", "", {}, "sha512-NM8/P9n3XjXhIZn1lLhkFaACTOURQXjWhV4BA/RnOv8xvgqtqpAX9IO4mRQxSx1Rlo4tqzeqb0sOlruaOy3dug=="],
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@ -6366,12 +6083,22 @@
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"docs-test/@ai-sdk/anthropic/@ai-sdk/provider-utils": ["@ai-sdk/provider-utils@4.0.8", "", { "dependencies": { "@ai-sdk/provider": "3.0.4", "@standard-schema/spec": "^1.1.0", "eventsource-parser": "^3.0.6" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-ns9gN7MmpI8vTRandzgz+KK/zNMLzhrriiKECMt4euLtQFSBgNfydtagPOX4j4pS1/3KvHF6RivhT3gNQgBZsg=="],
"docs-test/@ai-sdk/openai/@ai-sdk/provider": ["@ai-sdk/provider@3.0.4", "", { "dependencies": { "json-schema": "^0.4.0" } }, "sha512-5KXyBOSEX+l67elrEa+wqo/LSsSTtrPj9Uoh3zMbe/ceQX4ucHI3b9nUEfNkGF3Ry1svv90widAt+aiKdIJasQ=="],
"docs-test/@ai-sdk/openai/@ai-sdk/provider-utils": ["@ai-sdk/provider-utils@4.0.8", "", { "dependencies": { "@ai-sdk/provider": "3.0.4", "@standard-schema/spec": "^1.1.0", "eventsource-parser": "^3.0.6" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-ns9gN7MmpI8vTRandzgz+KK/zNMLzhrriiKECMt4euLtQFSBgNfydtagPOX4j4pS1/3KvHF6RivhT3gNQgBZsg=="],
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"docs-test/ai/@ai-sdk/provider": ["@ai-sdk/provider@3.0.4", "", { "dependencies": { "json-schema": "^0.4.0" } }, "sha512-5KXyBOSEX+l67elrEa+wqo/LSsSTtrPj9Uoh3zMbe/ceQX4ucHI3b9nUEfNkGF3Ry1svv90widAt+aiKdIJasQ=="],
"docs-test/ai/@ai-sdk/provider-utils": ["@ai-sdk/provider-utils@4.0.8", "", { "dependencies": { "@ai-sdk/provider": "3.0.4", "@standard-schema/spec": "^1.1.0", "eventsource-parser": "^3.0.6" }, "peerDependencies": { "zod": "^3.25.76 || ^4.1.8" } }, "sha512-ns9gN7MmpI8vTRandzgz+KK/zNMLzhrriiKECMt4euLtQFSBgNfydtagPOX4j4pS1/3KvHF6RivhT3gNQgBZsg=="],
"enquirer/strip-ansi/ansi-regex": ["ansi-regex@5.0.1", "", {}, "sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ=="],
"eslint/chalk/ansi-styles": ["ansi-styles@4.3.0", "", { "dependencies": { "color-convert": "^2.0.1" } }, "sha512-zbB9rCJAT1rbjiVDb2hqKFHNYLxgtk8NURxZ3IZwD3F6NtxbXZQCnnSi1Lkx+IDohdPlFp222wVALIheZJQSEg=="],
"eslint/minimatch/brace-expansion": ["brace-expansion@1.1.12", "", { "dependencies": { "balanced-match": "^1.0.0", "concat-map": "0.0.1" } }, "sha512-9T9UjW3r0UW5c1Q7GTwllptXwhvYmEzFhzMfZ9H7FQWt+uZePjZPjBP/W1ZEyZ1twGWom5/56TF4lPcqjnDHcg=="],
"express/debug/ms": ["ms@2.0.0", "", {}, "sha512-Tpp60P6IUJDTuOq/5Z8cdskzJujfwqfOTkrwIwj7IRISpnkJnT6SyJ4PCPnGMoFjC9ddhal5KVIYtAt97ix05A=="],
"favicons/sharp/@img/sharp-darwin-arm64": ["@img/sharp-darwin-arm64@0.33.5", "", { "optionalDependencies": { "@img/sharp-libvips-darwin-arm64": "1.0.4" }, "os": "darwin", "cpu": "arm64" }, "sha512-UT4p+iz/2H4twwAoLCqfA9UH5pI6DggwKEGuaPy7nCVQ8ZsiY5PIcrRvD1DzuY3qYL07NtIQcWnBSY/heikIFQ=="],
@ -6476,14 +6203,10 @@
"public-ip/got/get-stream": ["get-stream@6.0.1", "", {}, "sha512-ts6Wi+2j3jQjqi70w5AlN8DFnkSwC+MqmxEzdEALB2qXZYV3X/b1CTfgPLGJNMeAWxdPfU8FO1ms3NUfaHCPYg=="],
"schema-utils/ajv/json-schema-traverse": ["json-schema-traverse@1.0.0", "", {}, "sha512-NM8/P9n3XjXhIZn1lLhkFaACTOURQXjWhV4BA/RnOv8xvgqtqpAX9IO4mRQxSx1Rlo4tqzeqb0sOlruaOy3dug=="],
"send/debug/ms": ["ms@2.0.0", "", {}, "sha512-Tpp60P6IUJDTuOq/5Z8cdskzJujfwqfOTkrwIwj7IRISpnkJnT6SyJ4PCPnGMoFjC9ddhal5KVIYtAt97ix05A=="],
"string-width-cjs/strip-ansi/ansi-regex": ["ansi-regex@5.0.1", "", {}, "sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ=="],
"supermemory/@types/node/undici-types": ["undici-types@6.20.0", "", {}, "sha512-Ny6QZ2Nju20vw1SRHe3d9jVu6gJ+4e3+MMpqu7pqE5HT6WsTSlce++GQmK5UXS8mzV8DSYHrQH+Xrf2jVcuKNg=="],
"terser-webpack-plugin/terser/acorn": ["acorn@8.15.0", "", { "bin": { "acorn": "bin/acorn" } }, "sha512-NZyJarBfL7nWwIq+FDL6Zp/yHEhePMNnnJ0y3qfieCrmNvYct8uvtiV41UvlSe6apAfk0fY1FbWx+NwfmpvtTg=="],
"terser-webpack-plugin/terser/commander": ["commander@2.20.3", "", {}, "sha512-GpVkmM8vF2vQUkj2LvZmD35JxeJOLCwJ9cUkugyk2nuhbv3+mJvpLYYt+0+USMxE+oj+ey/lJEnhZw75x/OMcQ=="],
@ -6492,8 +6215,6 @@
"unimport/unplugin/webpack-virtual-modules": ["webpack-virtual-modules@0.6.2", "", {}, "sha512-66/V2i5hQanC51vBQKPH4aI8NMAcBW59FVBs+rC7eGHupMyfn34q7rZIE+ETlJ+XTevqfUhVVBgSUNSW2flEUQ=="],
"unplugin/chokidar/glob-parent": ["glob-parent@5.1.2", "", { "dependencies": { "is-glob": "^4.0.1" } }, "sha512-AOIgSQCepiJYwP3ARnGx+5VnTu2HBYdzbGP45eLw1vr3zB3vZLeyed1sC9hnbcOc9/SrMyM5RPQrkGz4aS9Zow=="],
"unplugin/chokidar/readdirp": ["readdirp@3.6.0", "", { "dependencies": { "picomatch": "^2.2.1" } }, "sha512-hOS089on8RduqdbhvQ5Z37A0ESjsqz6qnRcffsMU3495FuTdqSm+7bhJ29JvIOsBDEEnan5DPu9t3To9VRlMzA=="],
"vite/esbuild/@esbuild/aix-ppc64": ["@esbuild/aix-ppc64@0.27.2", "", { "os": "aix", "cpu": "ppc64" }, "sha512-GZMB+a0mOMZs4MpDbj8RJp4cw+w1WV5NYD6xzgvzUJ5Ek2jerwfO2eADyI6ExDSUED+1X8aMbegahsJi+8mgpw=="],
@ -6548,8 +6269,6 @@
"vite/esbuild/@esbuild/win32-x64": ["@esbuild/win32-x64@0.27.2", "", { "os": "win32", "cpu": "x64" }, "sha512-sRdU18mcKf7F+YgheI/zGf5alZatMUTKj/jNS6l744f9u3WFu4v7twcUI9vu4mknF4Y9aDlblIie0IM+5xxaqQ=="],
"webpack/eslint-scope/estraverse": ["estraverse@4.3.0", "", {}, "sha512-39nnKffWz8xN1BU/2c79n9nB9HDzo0niYUqx6xyqUnyoAnQyyWpOTdZEeiCch8BBu515t4wp9ZmgVfVhn9EBpw=="],
"webpack/mime-types/mime-db": ["mime-db@1.52.0", "", {}, "sha512-sPU4uV7dYlvtWJxwwxHD0PuihVNiE7TyAbQ5SWxDCB9mUYvOgroQOwYQQOKPJ8CIbE+1ETVlOoK1UC2nU3gYvg=="],
"wrangler/esbuild/@esbuild/aix-ppc64": ["@esbuild/aix-ppc64@0.27.0", "", { "os": "aix", "cpu": "ppc64" }, "sha512-KuZrd2hRjz01y5JK9mEBSD3Vj3mbCvemhT466rSuJYeE/hjuBrHfjjcjMdTm/sz7au+++sdbJZJmuBwQLuw68A=="],
@ -6720,6 +6439,8 @@
"@inquirer/core/wrap-ansi/strip-ansi/ansi-regex": ["ansi-regex@5.0.1", "", {}, "sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ=="],
"@mintlify/cli/inquirer/ansi-escapes/type-fest": ["type-fest@0.21.3", "", {}, "sha512-t0rzBq87m3fVcduHDUFhKmyyX+9eo6WQjZvf51Ea/M0Q7+T374Jp1aUiyUl0GKxp8M/OETVHSDvmkyPgvX+X2w=="],
"@mintlify/cli/yargs/cliui/strip-ansi": ["strip-ansi@6.0.1", "", { "dependencies": { "ansi-regex": "^5.0.1" } }, "sha512-Y38VPSHcqkFrCpFnQ9vuSXmquuv5oXOKpGeT6aGrr3o3Gc9AlVa6JBfUSOCnbxGGZF+/0ooI7KrPuUSztUdU5A=="],
"@mintlify/cli/yargs/cliui/wrap-ansi": ["wrap-ansi@7.0.0", "", { "dependencies": { "ansi-styles": "^4.0.0", "string-width": "^4.1.0", "strip-ansi": "^6.0.0" } }, "sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q=="],
@ -6762,10 +6483,6 @@
"@modelcontextprotocol/sdk/express/type-is/media-typer": ["media-typer@1.1.0", "", {}, "sha512-aisnrDP4GNe06UcKFnV5bfMNPBUw4jsLGaWwWfnH3v02GnBuXX2MCVn5RbrWo0j3pczUilYblq7fQ7Nw2t5XKw=="],
"@oclif/core/string-width/strip-ansi/ansi-regex": ["ansi-regex@5.0.1", "", {}, "sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ=="],
"@oclif/core/wrap-ansi/strip-ansi/ansi-regex": ["ansi-regex@5.0.1", "", {}, "sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ=="],
"@opennextjs/aws/express/accepts/negotiator": ["negotiator@1.0.0", "", {}, "sha512-8Ofs/AUQh8MaEcrlq5xOX0CQ9ypTF5dl78mjlMNfOK08fzpgTHQRQPBxcPlEtIw0yRpws+Zo/3r+5WRby7u3Gg=="],
"@opennextjs/aws/express/type-is/media-typer": ["media-typer@1.1.0", "", {}, "sha512-aisnrDP4GNe06UcKFnV5bfMNPBUw4jsLGaWwWfnH3v02GnBuXX2MCVn5RbrWo0j3pczUilYblq7fQ7Nw2t5XKw=="],
@ -6784,8 +6501,6 @@
"@repo/web/ai/@ai-sdk/provider-utils/@standard-schema/spec": ["@standard-schema/spec@1.1.0", "", {}, "sha512-l2aFy5jALhniG5HgqrD6jXLi/rUWrKvqN/qJx6yoJsgKhblVd+iqqU4RCXavm/jPityDo5TCvKMnpjKnOriy0w=="],
"agents/@modelcontextprotocol/sdk/ajv/json-schema-traverse": ["json-schema-traverse@1.0.0", "", {}, "sha512-NM8/P9n3XjXhIZn1lLhkFaACTOURQXjWhV4BA/RnOv8xvgqtqpAX9IO4mRQxSx1Rlo4tqzeqb0sOlruaOy3dug=="],
"agents/@modelcontextprotocol/sdk/express/accepts": ["accepts@2.0.0", "", { "dependencies": { "mime-types": "^3.0.0", "negotiator": "^1.0.0" } }, "sha512-5cvg6CtKwfgdmVqY1WIiXKc3Q1bkRqGLi+2W/6ao+6Y7gu/RCwRuAhGEzh5B4KlszSuTLgZYuqFqo5bImjNKng=="],
"agents/@modelcontextprotocol/sdk/express/body-parser": ["body-parser@2.2.2", "", { "dependencies": { "bytes": "^3.1.2", "content-type": "^1.0.5", "debug": "^4.4.3", "http-errors": "^2.0.0", "iconv-lite": "^0.7.0", "on-finished": "^2.4.1", "qs": "^6.14.1", "raw-body": "^3.0.1", "type-is": "^2.0.1" } }, "sha512-oP5VkATKlNwcgvxi0vM0p/D3n2C3EReYVX+DNYs5TjZFn/oQt2j+4sVJtSMr18pdRr8wjTcBl6LoV+FUwzPmNA=="],
@ -6810,6 +6525,12 @@
"ansi-align/string-width/strip-ansi/ansi-regex": ["ansi-regex@5.0.1", "", {}, "sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ=="],
"docs-test/@ai-sdk/anthropic/@ai-sdk/provider-utils/@standard-schema/spec": ["@standard-schema/spec@1.1.0", "", {}, "sha512-l2aFy5jALhniG5HgqrD6jXLi/rUWrKvqN/qJx6yoJsgKhblVd+iqqU4RCXavm/jPityDo5TCvKMnpjKnOriy0w=="],
"docs-test/@ai-sdk/openai/@ai-sdk/provider-utils/@standard-schema/spec": ["@standard-schema/spec@1.1.0", "", {}, "sha512-l2aFy5jALhniG5HgqrD6jXLi/rUWrKvqN/qJx6yoJsgKhblVd+iqqU4RCXavm/jPityDo5TCvKMnpjKnOriy0w=="],
"docs-test/ai/@ai-sdk/provider-utils/@standard-schema/spec": ["@standard-schema/spec@1.1.0", "", {}, "sha512-l2aFy5jALhniG5HgqrD6jXLi/rUWrKvqN/qJx6yoJsgKhblVd+iqqU4RCXavm/jPityDo5TCvKMnpjKnOriy0w=="],
"memory-graph-playground/next/postcss/nanoid": ["nanoid@3.3.11", "", { "bin": { "nanoid": "bin/nanoid.cjs" } }, "sha512-N8SpfPUnUp1bK+PMYW8qSWdl9U+wwNWI4QKxOYDy9JAro3WMX7p2OeVRF9v+347pnakNevPmiHhNmZ2HbFA76w=="],
"unplugin/chokidar/readdirp/picomatch": ["picomatch@2.3.1", "", {}, "sha512-JU3teHTNjmE2VCGFzuY8EXzCDVwEqB2a8fsIvwaStHhAWJEeVd1o1QD80CU6+ZdEXXSLbSsuLwJjkCBWqRQUVA=="],

1
packages/docs-test/.gitignore vendored Normal file
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@ -0,0 +1 @@
.env

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@ -0,0 +1,26 @@
{
"name": "docs-test",
"version": "1.0.0",
"private": true,
"type": "module",
"scripts": {
"test": "bun run run.ts",
"test:ts": "bun run run.ts typescript",
"test:py": "bun run run.ts python",
"test:integrations": "bun run run.ts integrations",
"test:quickstart": "bun run run.ts quickstart",
"test:sdk": "bun run run.ts sdk",
"test:search": "bun run run.ts search"
},
"dependencies": {
"@ai-sdk/anthropic": "^3.0.15",
"@ai-sdk/openai": "latest",
"@anthropic-ai/sdk": "latest",
"@supermemory/tools": "workspace:*",
"ai": "latest",
"dotenv": "latest",
"openai": "latest",
"supermemory": "latest",
"zod": "latest"
}
}

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@ -0,0 +1,5 @@
supermemory
openai
anthropic
httpx
python-dotenv

121
packages/docs-test/run.ts Normal file
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@ -0,0 +1,121 @@
#!/usr/bin/env bun
import { spawn } from "child_process"
import path from "path"
const args = process.argv.slice(2)
const filter = args[0] // e.g., "typescript", "python", "integrations", or specific file
const TESTS_DIR = path.join(import.meta.dir, "tests")
interface TestFile {
name: string
path: string
type: "ts" | "py"
}
function getTests(): TestFile[] {
const tests: TestFile[] = []
// TypeScript tests
const tsTests = ["quickstart", "sdk", "search", "user-profiles"]
for (const t of tsTests) {
tests.push({
name: `typescript/${t}`,
path: path.join(TESTS_DIR, "typescript", `${t}.ts`),
type: "ts",
})
}
// Python tests
const pyTests = ["quickstart", "sdk", "search", "user_profiles"]
for (const t of pyTests) {
tests.push({
name: `python/${t}`,
path: path.join(TESTS_DIR, "python", `${t}.py`),
type: "py",
})
}
// Integration tests
const intTests = [
{ name: "ai-sdk", type: "ts" as const },
{ name: "openai-sdk", type: "ts" as const },
{ name: "openai-sdk", type: "py" as const },
{ name: "claude-memory", type: "ts" as const },
]
for (const t of intTests) {
tests.push({
name: `integrations/${t.name}`,
path: path.join(TESTS_DIR, "integrations", `${t.name}.${t.type === "ts" ? "ts" : "py"}`),
type: t.type,
})
}
return tests
}
async function runTest(test: TestFile): Promise<boolean> {
return new Promise((resolve) => {
console.log(`\n${"=".repeat(60)}`)
console.log(`Running: ${test.name}`)
console.log("=".repeat(60))
const cmd = test.type === "ts" ? "bun" : path.join(import.meta.dir, ".venv", "bin", "python3")
const proc = spawn(cmd, [test.path], {
stdio: "inherit",
env: { ...process.env },
})
proc.on("close", (code) => {
resolve(code === 0)
})
})
}
async function main() {
console.log("Supermemory Docs Test Runner")
console.log("============================\n")
let tests = getTests()
// Filter tests if specified
if (filter) {
tests = tests.filter((t) => t.name.includes(filter) || t.type === filter.replace(".", ""))
}
if (tests.length === 0) {
console.log("No tests matched the filter:", filter)
console.log("\nAvailable tests:")
getTests().forEach((t) => console.log(` - ${t.name} (${t.type})`))
process.exit(1)
}
console.log(`Running ${tests.length} test(s)...\n`)
const results: { test: string; passed: boolean }[] = []
for (const test of tests) {
const passed = await runTest(test)
results.push({ test: test.name, passed })
}
// Summary
console.log(`\n${"=".repeat(60)}`)
console.log("SUMMARY")
console.log("=".repeat(60))
const passed = results.filter((r) => r.passed).length
const failed = results.filter((r) => !r.passed).length
for (const r of results) {
console.log(`${r.passed ? "✅" : "❌"} ${r.test}`)
}
console.log(`\nTotal: ${passed} passed, ${failed} failed`)
if (failed > 0) {
process.exit(1)
}
}
main().catch(console.error)

View file

@ -0,0 +1,100 @@
import "dotenv/config"
import { openai } from "@ai-sdk/openai"
import { createAnthropic } from "@ai-sdk/anthropic"
import {
withSupermemory,
supermemoryTools,
searchMemoriesTool,
addMemoryTool,
type MemoryPromptData,
} from "@supermemory/tools/ai-sdk"
async function testMiddleware() {
console.log("=== Middleware ===")
// Basic wrapper
const model = withSupermemory(openai("gpt-4"), "user-123")
console.log("✓ withSupermemory basic")
// With addMemory option
const modelWithAdd = withSupermemory(openai("gpt-4"), "user-123", {
addMemory: "always",
})
console.log("✓ withSupermemory with addMemory")
// With verbose logging
const modelVerbose = withSupermemory(openai("gpt-4"), "user-123", {
verbose: true,
})
console.log("✓ withSupermemory with verbose")
}
async function testSearchModes() {
console.log("\n=== Search Modes ===")
const profileModel = withSupermemory(openai("gpt-4"), "user-123", { mode: "profile" })
console.log("✓ mode: profile")
const queryModel = withSupermemory(openai("gpt-4"), "user-123", { mode: "query" })
console.log("✓ mode: query")
const fullModel = withSupermemory(openai("gpt-4"), "user-123", { mode: "full" })
console.log("✓ mode: full")
}
async function testCustomPrompt() {
console.log("\n=== Custom Prompt Template ===")
const anthropic = createAnthropic({ apiKey: "test-key" })
const claudePrompt = (data: MemoryPromptData) =>
`
<context>
<user_profile>${data.userMemories}</user_profile>
<relevant_memories>${data.generalSearchMemories}</relevant_memories>
</context>
`.trim()
const model = withSupermemory(anthropic("claude-3-sonnet-20240229"), "user-123", {
mode: "full",
promptTemplate: claudePrompt,
})
console.log("✓ Custom prompt template")
}
async function testTools() {
console.log("\n=== Memory Tools ===")
// All tools
const tools = supermemoryTools("YOUR_API_KEY")
console.log("✓ supermemoryTools")
// Individual tools
const searchTool = searchMemoriesTool("API_KEY", { projectId: "personal" })
console.log("✓ searchMemoriesTool")
const addTool = addMemoryTool("API_KEY")
console.log("✓ addMemoryTool")
// Combined
const toolsObj = {
searchMemories: searchTool,
addMemory: addTool,
}
console.log("✓ Combined tools object")
}
async function main() {
console.log("AI SDK Integration Tests")
console.log("========================\n")
await testMiddleware()
await testSearchModes()
await testCustomPrompt()
await testTools()
console.log("\n========================")
console.log("✅ All AI SDK tests passed!")
}
main().catch(console.error)

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import "dotenv/config"
import { createClaudeMemoryTool } from "@supermemory/tools/claude-memory"
async function testConfiguration() {
console.log("=== Configuration ===")
// Basic
const tool = createClaudeMemoryTool(process.env.SUPERMEMORY_API_KEY!, {
projectId: "my-app",
})
console.log("✓ Basic config")
// Full options
const toolFull = createClaudeMemoryTool(process.env.SUPERMEMORY_API_KEY!, {
projectId: "my-app",
containerTags: ["user-123", "project-alpha"],
memoryContainerTag: "my_memory_prefix",
})
console.log("✓ Full config")
return tool
}
async function testMethods(tool: ReturnType<typeof createClaudeMemoryTool>) {
console.log("\n=== Methods ===")
console.log(`✓ handleCommand exists: ${typeof tool.handleCommand === "function"}`)
console.log(
`✓ handleCommandForToolResult exists: ${typeof tool.handleCommandForToolResult === "function"}`
)
}
async function testCommands(tool: ReturnType<typeof createClaudeMemoryTool>) {
console.log("\n=== Commands ===")
// View (list)
const listResult = await tool.handleCommand({
command: "view",
path: "/memories/",
})
console.log(`✓ view (list): ${listResult.success}`)
// Create
const createResult = await tool.handleCommand({
command: "create",
path: "/memories/test-file.txt",
file_text: "User prefers dark mode\nFavorite language: TypeScript",
})
console.log(`✓ create: ${createResult.success}`)
// Wait for indexing
await new Promise((r) => setTimeout(r, 3000))
// str_replace
const replaceResult = await tool.handleCommand({
command: "str_replace",
path: "/memories/test-file.txt",
old_str: "dark mode",
new_str: "light mode",
})
console.log(`✓ str_replace: ${replaceResult.success}`)
// Delete
const deleteResult = await tool.handleCommand({
command: "delete",
path: "/memories/test-file.txt",
})
console.log(`✓ delete: ${deleteResult.success}`)
}
async function testToolResultFormat(tool: ReturnType<typeof createClaudeMemoryTool>) {
console.log("\n=== Tool Result Format ===")
const toolResult = await tool.handleCommandForToolResult(
{ command: "view", path: "/memories/" },
"test-tool-id-123"
)
const hasCorrectStructure =
toolResult.type === "tool_result" &&
toolResult.tool_use_id === "test-tool-id-123" &&
typeof toolResult.content === "string" &&
typeof toolResult.is_error === "boolean"
console.log(`✓ Tool result structure: ${hasCorrectStructure}`)
}
async function main() {
console.log("Claude Memory Tool Tests")
console.log("========================\n")
const tool = await testConfiguration()
await testMethods(tool)
await testCommands(tool)
await testToolResultFormat(tool)
console.log("\n========================")
console.log("✅ All Claude Memory tests passed!")
}
main().catch(console.error)

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import os
from dotenv import load_dotenv
from openai import OpenAI
load_dotenv()
def test_openai_with_supermemory():
"""Test OpenAI SDK with Supermemory context"""
print("=== OpenAI SDK with Supermemory ===")
if not os.getenv("OPENAI_API_KEY"):
print("⚠ OPENAI_API_KEY not set, skipping live tests")
return
# This demonstrates manual integration pattern for Python
# since @supermemory/tools is TypeScript-only
from supermemory import Supermemory
memory_client = Supermemory()
openai_client = OpenAI()
USER_ID = "docs-test-openai-py"
# Get memory context
profile = memory_client.profile(
container_tag=USER_ID,
q="What are my preferences?",
)
context = f"""User Profile:
{chr(10).join(profile.profile.static)}
Relevant Memories:
{chr(10).join(r.content for r in profile.search_results.results)}"""
print("✓ Got memory context")
# Use with OpenAI
response = openai_client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{"role": "system", "content": f"Use this context:\n{context}"},
{"role": "user", "content": "What do you know about me?"},
],
max_tokens=100,
)
print(f"✓ OpenAI response: {response.choices[0].message.content[:50]}...")
def test_save_conversation():
"""Test saving conversation to Supermemory"""
print("\n=== Save Conversation ===")
if not os.getenv("OPENAI_API_KEY"):
print("⚠ Skipped (no OPENAI_API_KEY)")
return
from supermemory import Supermemory
memory_client = Supermemory()
USER_ID = "docs-test-openai-py"
conversation = [
{"role": "user", "content": "My favorite programming language is Python"},
{"role": "assistant", "content": "That's great! Python is very versatile."},
]
# Save conversation
memory_client.add(
content="\n".join(f"{m['role']}: {m['content']}" for m in conversation),
container_tag=USER_ID,
)
print("✓ Saved conversation to memory")
def main():
print("OpenAI SDK Integration Tests (Python)")
print("=====================================\n")
test_openai_with_supermemory()
test_save_conversation()
print("\n=====================================")
print("✅ All OpenAI SDK tests passed!")
if __name__ == "__main__":
main()

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import "dotenv/config"
import OpenAI from "openai"
import { withSupermemory } from "@supermemory/tools/openai"
const OPENAI_API_KEY = process.env.OPENAI_API_KEY
async function testOpenAIWrapper() {
console.log("=== OpenAI SDK Wrapper ===")
if (!OPENAI_API_KEY) {
console.log("⚠ OPENAI_API_KEY not set, skipping live tests")
return false
}
const openai = new OpenAI()
// Basic wrapper
const client = withSupermemory(openai, "docs-test-openai")
console.log("✓ withSupermemory basic")
// With options
const clientWithOptions = withSupermemory(openai, "docs-test-openai", {
mode: "full",
addMemory: "always",
verbose: true,
})
console.log("✓ withSupermemory with options")
}
async function testChatCompletion() {
console.log("\n=== Chat Completion with Memory ===")
if (!OPENAI_API_KEY) {
console.log("⚠ OPENAI_API_KEY not set, skipping")
return
}
const openai = new OpenAI()
const client = withSupermemory(openai, "docs-test-openai", {
mode: "full",
addMemory: "always",
})
// Add context
const addResponse = await client.chat.completions.create({
model: "gpt-4o-mini",
messages: [
{ role: "system", content: "You are a helpful assistant." },
{ role: "user", content: "Remember that my favorite color is blue." },
],
max_tokens: 100,
})
console.log("✓ Add context:", addResponse.choices[0]?.message.content?.substring(0, 50))
// Retrieve context
const retrieveResponse = await client.chat.completions.create({
model: "gpt-4o-mini",
messages: [
{ role: "system", content: "You are a helpful assistant." },
{ role: "user", content: "What do you know about my preferences?" },
],
max_tokens: 100,
})
console.log("✓ Retrieve context:", retrieveResponse.choices[0]?.message.content?.substring(0, 50))
}
async function main() {
console.log("OpenAI SDK Integration Tests")
console.log("============================\n")
const hasKey = await testOpenAIWrapper()
if (hasKey !== false) {
await testChatCompletion()
}
console.log("\n============================")
console.log("✅ All OpenAI SDK tests passed!")
}
main().catch(console.error)

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import os
from dotenv import load_dotenv
load_dotenv()
from supermemory import Supermemory
client = Supermemory()
USER_ID = "docs-test-user-py"
conversation = [
{"role": "assistant", "content": "Hello, how are you doing?"},
{"role": "user", "content": "Hello! I am Dhravya. I am 20 years old. I love to code!"},
{"role": "user", "content": "Can I go to the club?"},
]
print("Testing quickstart Python code...\n")
# Get user profile + relevant memories for context
print("1. Getting user profile...")
profile = client.profile(container_tag=USER_ID, q=conversation[-1]["content"])
print(f"Profile response: {profile}")
def get_memory(r):
if hasattr(r, 'memory'):
return r.memory
return r.get('memory', '') if isinstance(r, dict) else str(r)
context = f"""Static profile:
{chr(10).join(profile.profile.static)}
Dynamic profile:
{chr(10).join(profile.profile.dynamic)}
Relevant memories:
{chr(10).join(get_memory(r) for r in profile.search_results.results)}"""
print(f"\n2. Built context: {context}")
# Build messages with memory-enriched context
messages = [{"role": "system", "content": f"User context:\n{context}"}, *conversation]
print("\n3. Messages built successfully")
# Store conversation for future context
print("\n4. Adding memory...")
add_result = client.add(
content="\n".join(f"{m['role']}: {m['content']}" for m in conversation),
container_tag=USER_ID,
)
print(f"Add result: {add_result}")
print("\n✅ Quickstart Python test passed!")

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import os
import time
from pathlib import Path
from dotenv import load_dotenv
import httpx
import supermemory
from supermemory import Supermemory
load_dotenv()
client = Supermemory()
def test_documents_crud():
"""Test document CRUD operations"""
print("\n=== Document CRUD Operations ===")
# Create
doc = client.documents.add(content=f"Test content - {time.time()}")
print(f"✓ documents.add: {doc.id}")
# Read
fetched = client.documents.get(doc.id)
print(f"✓ documents.get: {fetched.id}")
# Update
updated = client.documents.update(doc.id, content=f"Updated - {time.time()}")
print(f"✓ documents.update: {updated.id}")
# Wait for processing
time.sleep(10)
# Delete
client.documents.delete(doc.id)
print("✓ documents.delete")
def test_batch_operations():
"""Test batch operations"""
print("\n=== Batch Operations ===")
batch = client.documents.batch_add(
documents=[
{"content": f"Batch 1 - {time.time()}"},
{"content": f"Batch 2 - {time.time()}"},
]
)
print(f"✓ documents.batch_add: {batch}")
def test_search():
"""Test search"""
print("\n=== Search ===")
results = client.search.execute(q="test content")
print(f"✓ search.execute: {len(results.results) if results.results else 0} results")
def test_file_uploads():
"""Test file uploads"""
print("\n=== File Uploads ===")
test_path = Path("/tmp/test-py-upload.txt")
test_path.write_text(f"Test content {time.time()}")
client.documents.upload_file(file=test_path)
print("✓ documents.upload_file with Path")
test_path.unlink()
def test_error_handling():
"""Test error handling patterns"""
print("\n=== Error Handling ===")
try:
client.documents.add(content="Test content")
print("✓ Error handling pattern works")
except supermemory.APIConnectionError:
print("APIConnectionError handled")
except supermemory.RateLimitError:
print("RateLimitError handled")
except supermemory.APIStatusError:
print("APIStatusError handled")
def test_client_config():
"""Test client configuration"""
print("\n=== Client Configuration ===")
# Retries
client2 = Supermemory(max_retries=0)
print("✓ max_retries config")
# Timeout
client3 = Supermemory(timeout=20.0)
print("✓ timeout config")
# Granular timeout
client4 = Supermemory(timeout=httpx.Timeout(60.0, read=5.0, write=10.0, connect=2.0))
print("✓ granular timeout config")
# Per-request options
client.with_options(max_retries=5).documents.add(content="Test content")
print("✓ per-request options")
def test_raw_response():
"""Test raw response access"""
print("\n=== Raw Response ===")
response = client.documents.with_raw_response.add(content="Test content")
print(f"✓ with_raw_response: has headers={hasattr(response, 'headers')}")
memory = response.parse()
print(f"✓ parse(): {memory.id}")
def test_streaming_response():
"""Test streaming response"""
print("\n=== Streaming Response ===")
with client.documents.with_streaming_response.add(content="Test content") as response:
print(f"✓ with_streaming_response: has headers={hasattr(response, 'headers')}")
def test_context_manager():
"""Test context manager"""
print("\n=== Context Manager ===")
with Supermemory() as temp_client:
print("✓ Context manager works")
def main():
print("Python SDK Tests")
print("================")
test_documents_crud()
test_batch_operations()
test_search()
test_file_uploads()
test_error_handling()
test_client_config()
test_raw_response()
test_streaming_response()
test_context_manager()
print("\n================")
print("✅ All Python SDK tests passed!")
if __name__ == "__main__":
main()

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import os
from dotenv import load_dotenv
from supermemory import Supermemory
load_dotenv()
client = Supermemory()
def test_search_modes():
"""Test different search modes"""
print("=== Search Modes ===")
# Hybrid search
hybrid = client.search.memories(
q="quarterly goals",
container_tag="user_123",
search_mode="hybrid",
)
print(f"✓ hybrid search: {len(hybrid.results)} results")
# Memories only
memories = client.search.memories(
q="user preferences",
container_tag="user_123",
search_mode="memories",
)
print(f"✓ memories search: {len(memories.results)} results")
def test_filtering():
"""Test search filtering"""
print("\n=== Filtering ===")
# Basic containerTag filter
results = client.search.memories(
q="project updates",
container_tag="user_123",
search_mode="hybrid",
)
print(f"✓ containerTag filter: {len(results.results)} results")
# Metadata filtering
filtered = client.search.memories(
q="meeting notes",
container_tag="user_123",
filters={
"AND": [
{"key": "type", "value": "meeting"},
{"key": "year", "value": "2024"},
]
},
)
print(f"✓ metadata filter: {len(filtered.results)} results")
def test_reranking():
"""Test reranking"""
print("\n=== Reranking ===")
results = client.search.memories(
q="complex technical question",
container_tag="user_123",
rerank=True,
)
print(f"✓ reranking: {len(results.results)} results")
def test_threshold():
"""Test similarity threshold"""
print("\n=== Threshold ===")
broad = client.search.memories(q="test query", threshold=0.3)
print(f"✓ broad threshold (0.3): {len(broad.results)} results")
precise = client.search.memories(q="test query", threshold=0.8)
print(f"✓ precise threshold (0.8): {len(precise.results)} results")
def test_chatbot_context():
"""Test chatbot context pattern"""
print("\n=== Chatbot Context Pattern ===")
def get_context(user_id: str, message: str) -> str:
results = client.search.memories(
q=message,
container_tag=user_id,
search_mode="hybrid",
threshold=0.6,
limit=5,
)
return "\n\n".join(r.memory or r.chunk or "" for r in results.results)
context = get_context("user_123", "What are the project updates?")
print(f"✓ chatbot context: {len(context)} chars")
def main():
print("Search Tests")
print("============\n")
test_search_modes()
test_filtering()
test_reranking()
test_threshold()
test_chatbot_context()
print("\n============")
print("✅ All search tests passed!")
if __name__ == "__main__":
main()

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import os
import time
from dotenv import load_dotenv
from supermemory import Supermemory
load_dotenv()
client = Supermemory()
USER_ID = "docs-test-profiles-py"
def test_basic_profile():
"""Test basic profile retrieval"""
print("=== Basic Profile ===")
profile = client.profile(
container_tag=USER_ID,
q="What are my preferences?",
)
print(f"✓ Static profile: {len(profile.profile.static)} items")
print(f"✓ Dynamic profile: {len(profile.profile.dynamic)} items")
print(f"✓ Search results: {len(profile.search_results.results)} items")
def test_profile_with_memories():
"""Test profile with memory context"""
print("\n=== Profile with Memory Context ===")
# Add some memories
client.add(
content="User prefers dark mode for all applications",
container_tag=USER_ID,
)
client.add(
content="User is learning TypeScript and Rust",
container_tag=USER_ID,
)
# Wait for indexing
time.sleep(2)
# Get profile with search
profile = client.profile(
container_tag=USER_ID,
q="What programming languages does the user know?",
)
print("✓ Profile retrieved with memories")
print(f" Static: {profile.profile.static[:2]}")
print(f" Dynamic: {profile.profile.dynamic[:2]}")
def get_memory(r):
if hasattr(r, 'memory'):
return r.memory
return r.get('memory', '') if isinstance(r, dict) else str(r)
def test_building_context():
"""Test building LLM context"""
print("\n=== Building LLM Context ===")
conversation = [{"role": "user", "content": "What theme should I use for my IDE?"}]
profile = client.profile(
container_tag=USER_ID,
q=conversation[-1]["content"],
)
context = f"""User Profile:
{chr(10).join(profile.profile.static)}
Recent Context:
{chr(10).join(profile.profile.dynamic)}
Relevant Memories:
{chr(10).join(get_memory(r) for r in profile.search_results.results)}"""
print(f"✓ Built context: {len(context)} chars")
messages = [
{"role": "system", "content": f"Use this context about the user:\n{context}"},
*conversation,
]
print(f"✓ Messages ready for LLM: {len(messages)} messages")
def main():
print("User Profiles Tests")
print("===================\n")
test_basic_profile()
test_profile_with_memories()
test_building_context()
print("\n===================")
print("✅ All user profile tests passed!")
if __name__ == "__main__":
main()

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import "dotenv/config"
import Supermemory from "supermemory"
const client = new Supermemory()
const USER_ID = "docs-test-user"
const conversation = [
{ role: "assistant", content: "Hello, how are you doing?" },
{
role: "user",
content: "Hello! I am Dhravya. I am 20 years old. I love to code!",
},
{ role: "user", content: "Can I go to the club?" },
]
async function main() {
console.log("Testing quickstart TypeScript code...\n")
// Get user profile + relevant memories for context
console.log("1. Getting user profile...")
const profile = await client.profile({
containerTag: USER_ID,
q: conversation.at(-1)!.content,
})
console.log("Profile response:", JSON.stringify(profile, null, 2))
const context = `Static profile:
${profile.profile.static.join("\n")}
Dynamic profile:
${profile.profile.dynamic.join("\n")}
Relevant memories:
${profile.searchResults?.results.map((r) => r.content).join("\n")}`
console.log("\n2. Built context:", context)
// Build messages with memory-enriched context
const messages = [
{ role: "system", content: `User context:\n${context}` },
...conversation,
]
console.log("\n3. Messages built successfully")
// Store conversation for future context
console.log("\n4. Adding memory...")
const addResult = await client.add({
content: conversation.map((m) => `${m.role}: ${m.content}`).join("\n"),
containerTag: USER_ID,
})
console.log("Add result:", JSON.stringify(addResult, null, 2))
console.log("\n✅ Quickstart TypeScript test passed!")
}
main().catch(console.error)

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import "dotenv/config"
import fs from "fs"
import Supermemory, { toFile } from "supermemory"
const client = new Supermemory()
async function testDocumentsCRUD() {
console.log("\n=== Document CRUD Operations ===")
// Create
const doc = await client.documents.add({
content: "Test content for TypeScript SDK - " + Date.now(),
})
console.log("✓ documents.add:", doc.id)
// Read
const fetched = await client.documents.get(doc.id)
console.log("✓ documents.get:", fetched.id)
// Update
const updated = await client.documents.update(doc.id, {
content: "Updated content - " + Date.now(),
})
console.log("✓ documents.update:", updated.id)
// Wait for processing before delete
await new Promise((r) => setTimeout(r, 10000))
// Delete
await client.documents.delete(doc.id)
console.log("✓ documents.delete")
}
async function testBatchOperations() {
console.log("\n=== Batch Operations ===")
const batch = await client.documents.batchAdd({
documents: [
{ content: "Batch doc 1 - " + Date.now() },
{ content: "Batch doc 2 - " + Date.now() },
],
})
console.log("✓ documents.batchAdd:", batch)
}
async function testSearch() {
console.log("\n=== Search ===")
const results = await client.search.execute({ q: "test content" })
console.log("✓ search.execute:", results.results?.length ?? 0, "results")
}
async function testFileUploads() {
console.log("\n=== File Uploads ===")
// Using fs.createReadStream
const testFile = "/tmp/test-doc-upload.txt"
fs.writeFileSync(testFile, "Test file content")
await client.documents.uploadFile({ file: fs.createReadStream(testFile) })
console.log("✓ uploadFile with fs.createReadStream")
// Using File API
await client.documents.uploadFile({ file: new File(["my bytes"], "file") })
console.log("✓ uploadFile with File API")
// Using toFile
await client.documents.uploadFile({ file: await toFile(Buffer.from("my bytes"), "file") })
console.log("✓ uploadFile with toFile")
fs.unlinkSync(testFile)
}
async function testClientConfig() {
console.log("\n=== Client Configuration ===")
// Retries
const clientWithRetries = new Supermemory({ maxRetries: 0 })
console.log("✓ maxRetries config")
// Timeout
const clientWithTimeout = new Supermemory({ timeout: 20 * 1000 })
console.log("✓ timeout config")
// Per-request options
await client.add({ content: "Test - " + Date.now() }, { maxRetries: 5, timeout: 5000 })
console.log("✓ per-request options")
}
async function testRawResponse() {
console.log("\n=== Raw Response ===")
const response = await client.documents
.add({ content: "Test - " + Date.now() })
.asResponse()
console.log("✓ .asResponse() status:", response.status)
const { data, response: raw } = await client.documents
.add({ content: "Test - " + Date.now() })
.withResponse()
console.log("✓ .withResponse() data:", data.id, "status:", raw.status)
}
async function testTypes() {
console.log("\n=== Types ===")
const params: Supermemory.AddParams = {
content: "Typed content",
}
const response: Supermemory.AddResponse = await client.add(params)
console.log("✓ TypeScript types work:", response.id)
}
async function testErrorHandling() {
console.log("\n=== Error Handling ===")
const response = await client.documents
.add({ content: "Test - " + Date.now() })
.catch(async (err) => {
if (err instanceof Supermemory.APIError) {
console.log("Caught APIError:", err.status, err.name)
} else {
throw err
}
return null
})
console.log("✓ Error handling pattern works:", response?.id)
}
async function main() {
console.log("TypeScript SDK Tests")
console.log("====================")
await testDocumentsCRUD()
await testBatchOperations()
await testSearch()
await testFileUploads()
await testClientConfig()
await testRawResponse()
await testTypes()
await testErrorHandling()
console.log("\n====================")
console.log("✅ All TypeScript SDK tests passed!")
}
main().catch(console.error)

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@ -0,0 +1,110 @@
import "dotenv/config"
import Supermemory from "supermemory"
const client = new Supermemory()
async function testSearchModes() {
console.log("=== Search Modes ===")
// Hybrid: memories + document chunks (recommended)
const hybridResults = await client.search.memories({
q: "quarterly goals",
containerTag: "user_123",
searchMode: "hybrid",
})
console.log("✓ hybrid search:", hybridResults.results.length, "results")
// Memories only
const memoriesResults = await client.search.memories({
q: "user preferences",
containerTag: "user_123",
searchMode: "memories",
})
console.log("✓ memories search:", memoriesResults.results.length, "results")
}
async function testFiltering() {
console.log("\n=== Filtering ===")
// Basic containerTag filter
const results = await client.search.memories({
q: "project updates",
containerTag: "user_123",
searchMode: "hybrid",
})
console.log("✓ containerTag filter:", results.results.length, "results")
// Metadata-based filtering
const filteredResults = await client.search.memories({
q: "meeting notes",
containerTag: "user_123",
filters: {
AND: [
{ key: "type", value: "meeting" },
{ key: "year", value: "2024" },
],
},
})
console.log("✓ metadata filter:", filteredResults.results.length, "results")
}
async function testReranking() {
console.log("\n=== Reranking ===")
const results = await client.search.memories({
q: "complex technical question",
containerTag: "user_123",
rerank: true,
})
console.log("✓ reranking:", results.results.length, "results")
}
async function testThreshold() {
console.log("\n=== Threshold ===")
const broadResults = await client.search.memories({
q: "test query",
threshold: 0.3,
})
console.log("✓ broad threshold (0.3):", broadResults.results.length, "results")
const preciseResults = await client.search.memories({
q: "test query",
threshold: 0.8,
})
console.log("✓ precise threshold (0.8):", preciseResults.results.length, "results")
}
async function testChatbotContext() {
console.log("\n=== Chatbot Context Pattern ===")
async function getContext(userId: string, message: string) {
const results = await client.search.memories({
q: message,
containerTag: userId,
searchMode: "hybrid",
threshold: 0.6,
limit: 5,
})
return results.results.map((r) => r.memory || r.chunk).join("\n\n")
}
const context = await getContext("user_123", "What are the project updates?")
console.log("✓ chatbot context:", context.length, "chars")
}
async function main() {
console.log("Search Tests")
console.log("============\n")
await testSearchModes()
await testFiltering()
await testReranking()
await testThreshold()
await testChatbotContext()
console.log("\n============")
console.log("✅ All search tests passed!")
}
main().catch(console.error)

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@ -0,0 +1,89 @@
import "dotenv/config"
import Supermemory from "supermemory"
const client = new Supermemory()
const USER_ID = "docs-test-profiles"
async function testBasicProfile() {
console.log("=== Basic Profile ===")
const profile = await client.profile({
containerTag: USER_ID,
q: "What are my preferences?",
})
console.log("✓ Static profile:", profile.profile.static.length, "items")
console.log("✓ Dynamic profile:", profile.profile.dynamic.length, "items")
console.log("✓ Search results:", profile.searchResults.results.length, "items")
}
async function testProfileWithMemories() {
console.log("\n=== Profile with Memory Context ===")
// First add some memories
await client.add({
content: "User prefers dark mode for all applications",
containerTag: USER_ID,
})
await client.add({
content: "User is learning TypeScript and Rust",
containerTag: USER_ID,
})
// Wait a bit for indexing
await new Promise((r) => setTimeout(r, 2000))
// Now get profile with search
const profile = await client.profile({
containerTag: USER_ID,
q: "What programming languages does the user know?",
})
console.log("✓ Profile retrieved with memories")
console.log(" Static:", profile.profile.static.slice(0, 2))
console.log(" Dynamic:", profile.profile.dynamic.slice(0, 2))
}
async function testBuildingContext() {
console.log("\n=== Building LLM Context ===")
const conversation = [
{ role: "user", content: "What theme should I use for my IDE?" },
]
const profile = await client.profile({
containerTag: USER_ID,
q: conversation.at(-1)!.content,
})
const context = `User Profile:
${profile.profile.static.join("\n")}
Recent Context:
${profile.profile.dynamic.join("\n")}
Relevant Memories:
${profile.searchResults.results.map((r) => r.content).join("\n")}`
console.log("✓ Built context:", context.length, "chars")
const messages = [
{ role: "system", content: `Use this context about the user:\n${context}` },
...conversation,
]
console.log("✓ Messages ready for LLM:", messages.length, "messages")
}
async function main() {
console.log("User Profiles Tests")
console.log("===================\n")
await testBasicProfile()
await testProfileWithMemories()
await testBuildingContext()
console.log("\n===================")
console.log("✅ All user profile tests passed!")
}
main().catch(console.error)

View file

@ -47,17 +47,23 @@ export class ClaudeMemoryTool {
private memoryContainerPrefix: string
/**
* Normalize file path to be used as customId (replace / with --)
* Normalize file path to be used as customId
* Converts /memories/file.txt -> memories_file_txt
*/
private normalizePathToCustomId(path: string): string {
return path.replace(/\//g, "--")
return path
.replace(/^\//, "") // Remove leading slash
.replace(/\//g, "_") // Replace / with _
.replace(/\./g, "_") // Replace . with _
}
/**
* Convert customId back to file path (replace -- with /)
* Convert customId back to file path
* Note: This is lossy since we can't distinguish _ from . or /
* We rely on metadata.file_path for accurate path reconstruction
*/
private customIdToPath(customId: string): string {
return customId.replace(/--/g, "/")
return "/" + customId.replace(/_/g, "/")
}
constructor(apiKey: string, config?: ClaudeMemoryConfig) {