---

name: twinmind-sdk-patterns description: 'Apply production-ready TwinMind SDK patterns for TypeScript and Python.

Use when implementing TwinMind integrations, refactoring API usage,

or establishing team coding standards for meeting AI integration.

Trigger with phrases like "twinmind SDK patterns", "twinmind best practices",

"twinmind code patterns", "idiomatic twinmind".

' allowed-tools: Read, Write, Edit version: 1.13.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io tags:

• saas
• twinmind
• api
• python
• typescript compatibility: Designed for Claude Code

---

TwinMind SDK Patterns

Overview

Production patterns for TwinMind's AI memory and meeting intelligence REST API. TwinMind captures, organizes, and retrieves contextual memories from conversations and meetings.

Prerequisites

• TwinMind API key configured
• Understanding of REST API patterns
• Familiarity with memory/context retrieval concepts

Instructions

Step 1: Client Wrapper with Authentication

import requests
import os

class TwinMindClient:
    def __init__(self, api_key: str = None, base_url: str = "https://api.twinmind.com/v1"):
        self.api_key = api_key or os.environ["TWINMIND_API_KEY"]
        self.base_url = base_url
        self.session = requests.Session()
        self.session.headers.update({
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json"
        })

    def _request(self, method: str, path: str, **kwargs):
        response = self.session.request(method, f"{self.base_url}{path}", **kwargs)
        response.raise_for_status()
        return response.json()

Step 2: Memory Storage and Retrieval

class TwinMindClient:
    # ... (continued from Step 1)

    def store_memory(self, content: str, context: dict = None, tags: list = None) -> dict:
        return self._request("POST", "/memories", json={
            "content": content,
            "context": context or {},
            "tags": tags or [],
            "timestamp": datetime.utcnow().isoformat()
        })

    def search_memories(self, query: str, limit: int = 10, tags: list = None) -> list:
        params = {"q": query, "limit": limit}
        if tags:
            params["tags"] = ",".join(tags)
        return self._request("GET", "/memories/search", params=params)

    def get_memory(self, memory_id: str) -> dict:
        return self._request("GET", f"/memories/{memory_id}")

Step 3: Meeting Context Integration

    def create_meeting_context(self, meeting_id: str, transcript: str, participants: list) -> dict:
        return self._request("POST", "/contexts/meeting", json={
            "meeting_id": meeting_id,
            "transcript": transcript,
            "participants": participants,
            "extract_action_items": True,
            "extract_decisions": True
        })

    def get_meeting_insights(self, meeting_id: str) -> dict:
        return self._request("GET", f"/contexts/meeting/{meeting_id}/insights")

Step 4: Batch Operations with Rate Limiting

import time

def batch_store_memories(client: TwinMindClient, memories: list, batch_size: int = 20):
    results = []
    for i in range(0, len(memories), batch_size):
        batch = memories[i:i+batch_size]
        for memory in batch:
            try:
                result = client.store_memory(**memory)
                results.append({"status": "ok", "id": result["id"]})
            except requests.HTTPError as e:
                if e.response.status_code == 429:  # HTTP 429 Too Many Requests
                    time.sleep(int(e.response.headers.get("Retry-After", 5)))
                    result = client.store_memory(**memory)
                    results.append({"status": "ok", "id": result["id"]})
                else:
                    results.append({"status": "error", "error": str(e)})
        time.sleep(1)  # rate limit between batches
    return results

Error Handling

Error	Cause	Solution
401 Unauthorized	Invalid API key	Verify TWINMIND_API_KEY
429 Rate Limited	Too many requests	Respect Retry-After header
404 Not Found	Invalid memory/meeting ID	Validate IDs before lookup
Empty search results	Query too specific	Broaden query terms

Examples

Full Meeting Workflow

client = TwinMindClient()
# After meeting ends
ctx = client.create_meeting_context(
    meeting_id="mtg-123",
    transcript=transcript_text,
    participants=["alice@co.com", "bob@co.com"]
)
insights = client.get_meeting_insights("mtg-123")
for item in insights.get("action_items", []):
    print(f"- [{item['assignee']}] {item['task']}")

Resources

• TwinMind API

Output

• Configuration files or code changes applied to the project
• Validation report confirming correct implementation
• Summary of changes made and their rationale