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## Add customizable prompt templates for memory injection
**Changes:**
- Add `promptTemplate` option to `withSupermemory()` for full control over injected memory format (XML, custom branding, etc.)
- New `MemoryPromptData` interface with `userMemories` and `generalSearchMemories` fields
- Exclude `system` messages from persistence to avoid storing injected prompts
- Add JSDoc comments to all public interfaces for better DevEx
**Usage:**
```typescript
const customPrompt = (data: MemoryPromptData) => `
<user_memories>
${data.userMemories}
${data.generalSearchMemories}
</user_memories>
`.trim()
const model = withSupermemory(openai("gpt-4"), "user-123", {
promptTemplate: customPrompt,
})
```
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done in a similar way to the ai sdk
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Co-authored-by: Mahesh Sanikommmu <[email protected]>
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- small docs miss match on addMemory default option
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relevant files to review:
\- memory-graph.tsx
\- spaces-dropdown.tsx
\- spaces-dropdown.css.ts
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+ new playground (#588)
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includes:
- a package that contains a MemoryGraph component which handles fetching data and rendering the graph
- a playground to test the package
problems:
- the bundle size is huge
- the styles are kinda broken? we are using [https://www.npmjs.com/package/vite-plugin-libgi-inject-css](https://www.npmjs.com/package/vite-plugin-lib-inject-css) to inject the styles
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add openai middleware functionality
fix critical type errors and linting issues
update readme with middleware documentation
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Fixes all following sentry issues
- CONSUMER-APP-FF
- CONSUMER-APP-1T
- CONSUMER-APP-86
- CONSUMER-APP-7H
- CONSUMER-APP-4F
- CONSUMER-APP-7X
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### Added streaming support to the Supermemory middleware and improved memory handling in the AI SDK integration.
### What changed?
- Refactored the middleware architecture to support both streaming and non-streaming responses
- Extracted memory prompt functionality into a separate module (`memory-prompt.ts`)
- Added memory saving capability for streaming responses
- Improved the formatting of memory content with a "User Supermemories:" prefix
- Added utility function to filter out supermemories from content
- Created a new streaming example in the test app with a dedicated route and page
- Updated version from 1.3.0 to 1.3.1 in package.json
- Simplified installation instructions in [README.m](http://README.md)d
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### TL;DR
Added OpenAI SDK middleware support for SuperMemory integration, allowing direct memory injection without AI SDK dependency.
### What changed?
- Added `withSupermemory` middleware for OpenAI SDK that automatically injects relevant memories into chat completions
- Implemented memory search and injection functionality for OpenAI clients
- Restructured the OpenAI module to separate tools and middleware functionality
- Updated README with comprehensive documentation and examples for the new OpenAI middleware
- Added test implementation with a Next.js API route example
- Reorganized package exports to support the new structure
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### TL;DR
Added support for conversation grouping in Supermemory middleware through a new `conversationId` parameter.
### What changed?
- Added a new `conversationId` option to the `withSupermemory` function to group messages into a single document for contextual memory generation
- Updated the middleware to use this conversation ID when adding memories, using a `customId` format of `conversation:{conversationId}`
- Created a new `getConversationContent` function that extracts the full conversation content from the prompt parameters
- Enhanced memory storage to save entire conversations rather than just the last user message
- Updated documentation and examples to demonstrate the new parameter usage
### How to test?
1. Import the `withSupermemory` function from the package
2. Create a model with memory using the new `conversationId` parameter:
```typescript
const modelWithMemory = withSupermemory(openai("gpt-4"), "user-123", {
conversationId: "conversation-456",
mode: "full",
addMemory: "always"
})
```
3. Use the model in a conversation and verify that messages are grouped by the conversation ID
4. Check that memories are being stored with the custom ID format `conversation:{conversationId}`
### Why make this change?
This enhancement improves the contextual understanding of the AI by allowing related messages to be grouped together as a single conversation document. By using a conversation ID, the system can maintain coherent memory across multiple interactions within the same conversation thread, providing better context retrieval and more relevant responses.
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