iGPT Email Search

Search a user's email by meaning, not just keywords. Hybrid semantic + keyword retrieval across their entire inbox history.

What This Skill Does

This skill queries iGPT's recall/search endpoint to find relevant emails and threads from a user's connected inbox. The search engine:

• Combines semantic vector search (understands meaning) with keyword matching (catches exact terms)
• Searches across the user's full indexed email history (not limited to 90 days like some providers)
• Supports date range filtering for time-bounded queries
• Returns ranked results with relevance scoring
• Includes attachment references when present

When to Use This Skill

• Find emails about a specific topic, project, or person
• Locate threads within a date range
• Retrieve raw email content for further processing
• Feed email context into another tool or agent step
• Check what was discussed about a topic before taking action
• Pull recent correspondence with a specific contact or company

When to Use igpt-email-ask Instead

If you need summarized or synthesized answers, structured data extraction (tasks, decisions, contacts), sentiment analysis, reasoning across multiple threads, or questions that require understanding rather than finding — use igpt-email-ask, not search.

Rule of thumb: if the prompt is a question, use ask. If the prompt is a lookup, use search.

Prerequisites

• An iGPT API key (get one at https://igpt.ai/hub/apikeys/)
• A connected email datasource — the user must have completed OAuth authorization via connectors/authorize before search will return results
• Python >= 3.8 with the igptai package installed

Setup

pip install igptai

Set your API key as an environment variable:

export IGPT_API_KEY="your-api-key-here"

Usage

Basic: Search by topic

from igptai import IGPT
import os

igpt = IGPT(api_key=os.environ["IGPT_API_KEY"], user="user_123")

results = igpt.recall.search(query="board meeting notes")
print(results)

Returns a ranked list of relevant emails and threads matching the query, ordered by relevance.

Search with date range

Narrow results to a specific time window:

results = igpt.recall.search(
    query="budget allocation",
    date_from="2026-01-01",
    date_to="2026-01-31"
)
print(results)

Limit number of results

results = igpt.recall.search(
    query="partnership proposals",
    max_results=10
)
print(results)

Search for a specific person's emails

The semantic engine understands participant context:

results = igpt.recall.search(
    query="emails from Sarah about the product launch",
    date_from="2026-01-01"
)
print(results)

Combine with ask for a two-step workflow

A common pattern: search first to see what's there, then ask for analysis:

# Step 1: Find relevant threads
results = igpt.recall.search(
    query="Acme Corp contract negotiation",
    max_results=20
)
print(f"Found {len(results)} relevant threads")

# Step 2: Ask for structured analysis
analysis = igpt.recall.ask(
    input="Summarize the current status of the Acme Corp contract negotiation. What are the open issues and who owns them?",
    output_format="json"
)
print(analysis)

Parameters

Error Handling

The SDK uses a no-throw pattern. Errors are returned as values, not exceptions:

results = igpt.recall.search(query="Q4 planning")

if isinstance(results, dict) and results.get("error"):
    error = results["error"]
    if error == "auth":
        print("Check your API key")
    elif error == "params":
        print("Check your request parameters")
    elif error == "network_error":
        print("Network issue, retry")
else:
    for result in results:
        print(result)

External Endpoints

This skill communicates exclusively with:

• https://api.igpt.ai/v1/recall/search — the search endpoint
• https://api.igpt.ai/v1/connectors/authorize — only during initial datasource connection setup

Security & Privacy

• API-key scoped: All requests authenticate via IGPT_API_KEY sent as a Bearer token over HTTPS. No shell access, no filesystem access, no system commands.
• Per-user isolation: Every query is scoped to a specific user identifier. User A cannot access User B's email data. Isolation is enforced at the index and execution level, not as a filter layer.
• OAuth read-only: The email datasource connection uses OAuth with read-only scopes. The skill does not send, modify, or delete emails.
• No data retention: Prompts are discarded after execution. Memory is reconstructed on-demand, not stored.
• Transport encryption: All communication occurs over HTTPS. No plaintext endpoints.
• No local persistence: This skill does not write to disk, modify environment files, or create persistent configuration outside of the standard IGPT_API_KEY environment variable.

How It Differs from Basic Email Search

Example Queries

These all work as natural language:

• "board meeting notes" — finds emails about board meetings even if they don't contain that exact phrase
• "emails about the product launch timeline" — semantic understanding of the topic
• "anything from legal about compliance" — understands department and topic context
• "invoices from Q4 2025" — combines topic with implicit date context
• "conversations where deadlines were mentioned" — conceptual search

Companion Skills

Resources

• Get API Key: https://igpt.ai/hub/apikeys/
• Documentation: https://docs.igpt.ai
• API Reference: https://docs.igpt.ai/docs/api-reference/search
• Playground: https://igpt.ai/hub/playground/
• Python SDK: https://pypi.org/project/igptai/
• Node.js SDK: https://www.npmjs.com/package/igptai
• GitHub: https://github.com/igptai/igptai-python