All four guides now show the mount pattern as the primary approach:
mount SMFS inside the sandbox, then run a Claude agent with bash
tool access so it reads/writes memory using standard commands.
- E2B: tested end-to-end (install, login, mount, read, write, grep)
- Daytona: mount pattern with TLS warning
- Vercel AI SDK: SMFS mounted on host, bash tool for the agent
- Cloudflare: Container with SMFS pre-installed in Dockerfile
- E2B/Daytona: Python tabs now show mount pattern (not @supermemory/bash
which is TypeScript-only)
- E2B/Daytona: section intros updated to explain per-language approach
- Daytona: removed redundant 'Alternative: Mount' section (Python tab
already covers the mount pattern with TLS warning)
- E2B/Daytona: Python install commands no longer list supermemory package
- E2B: fix Python example in section 3 to use bash tool pattern (matching TS tab)
- E2B: add sudo write example and note about FUSE mount ownership
- Daytona: fix Python example in section 3 to use bash tool pattern (matching TS tab)
- Daytona: add Warning to 'Alternative: Mount' section about TLS restrictions
- Add smfs/providers/daytona.mdx with full tutorial (mount + bash tool patterns)
- Add smfs/providers/e2b.mdx with sandbox integration guide
- Add smfs/providers/vercel.mdx with AI SDK tool-calling examples
- Add smfs/providers/cloudflare.mdx with Workers + Containers guide
- Update docs.json navigation with Providers group under SMFS
- Update smfs/overview.mdx with provider cards
**`withSupermemory`** **(AI SDK)**
- **`skipMemoryOnError`** **defaults to** **`true`**. memory errors/timeouts log and the model runs on the **original** prompt unless you set `skipMemoryOnError: false`.
- **Pre-LLM** **`/v4/profile`** **is aborted after 5s** via `AbortSigna`
**Docs**
- `packages/tools/README.md`, **`apps/docs/integrations/ai-sdk.md`**
### TL;DR
Added Python SDK for integrating Supermemory with Cartesia Line voice agents, enabling persistent memory capabilities.
### What changed?
Created a new Python SDK package (`supermemory_cartesia`) that provides:
- `SupermemoryCartesiaAgent` wrapper class that enhances Cartesia Line agents with memory capabilities
- Memory retrieval and storage functionality that integrates with the Supermemory API
- Utility functions for memory formatting, deduplication, and time formatting
- Custom exception classes for error handling
- Comprehensive documentation and type hints
The implementation includes:
- Memory enrichment for user queries
- Automatic storage of conversation history
- Configurable memory retrieval modes (profile, query, full)
- Background processing to avoid blocking the main conversation flow
### How to test?
```python
from supermemory_cartesia import SupermemoryCartesiaAgent
from line.llm_agent import LlmAgent, LlmConfig
import os
# Create base LLM agent
base_agent = LlmAgent(
model="gemini/gemini-2.5-flash-preview-09-2025",
config=LlmConfig(
system_prompt="You are a helpful assistant.",
introduction="Hello!"
)
)
# Wrap with Supermemory
memory_agent = SupermemoryCartesiaAgent(
agent=base_agent,
api_key=os.getenv("SUPERMEMORY_API_KEY"),
user_id="user-123",
)
# Use memory_agent in your Cartesia Line application
```
### Why make this change?
This SDK enables Cartesia Line voice agents to maintain persistent memory across conversations, enhancing user experience by:
1. Providing contextual awareness of past interactions
2. Remembering user preferences and important information
3. Reducing repetition in conversations
4. Creating more personalized and natural voice interactions
The integration is designed to be lightweight and non-blocking, ensuring that memory operations don't impact the responsiveness of voice interactions.
- Switch to infinite query with viewport-triggered pagination (loads more when user zooms out 3x past node bounds)
- Remove maxNodes cap so all data renders
- Remove background color and dot pattern from graph
- Make document-memory edges light grey
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
