TwinMind SDK Patterns

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

Examples

Full Meeting Workflow

client = TwinMindClient()
# After meeting ends
ctx = client.create_meeting_context(
    meeting_id="mtg-123",
    transcript=transcript_text,
    participants=["[email protected]", "[email protected]"]
)
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