| name | igpt-email-search |
| description | Secure, per-user-isolated semantic email search via the iGPT API. Hybrid semantic + keyword retrieval across a user's full Gmail, Outlook, or IMAP inbox history — no shell access, no filesystem access, API-key scoped only. Returns relevant messages and threads ranked by meaning, not just keyword overlap. Use when the user needs to find specific emails, threads, or conversations by topic, participant, date range, or content. Retrieval only — for reasoning, summaries, or structured extraction, use the companion skill igpt-email-ask.
|
| homepage | https://igpt.ai/hub/playground/ |
| metadata | {"clawdbot":{"emoji":"📧","requires":{"env":"[Truncated]"},"primaryEnv":"IGPT_API_KEY"},"author":"igptai","version":"1.0.0","license":"MIT","tags":["email","search","retrieval","semantic-search","context","productivity"]} |
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
This is retrieval only. It finds and returns email content. It does not reason over it, summarize it, or extract structured data. For that, use igpt-email-ask — the companion skill that runs iGPT's Context Engine for analysis, summarization, and structured extraction.
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:
results = igpt.recall.search(
query="Acme Corp contract negotiation",
max_results=20
)
print(f"Found {len(results)} relevant threads")
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
| Parameter | Type | Required | Description |
|---|
| query | string | Yes | Search query. Supports natural language (semantic) and exact terms (keyword). |
| user | string | Yes (or set in constructor) | Unique user identifier scoping the query to their connected data. |
| date_from | string | No | Start date filter in YYYY-MM-DD format. |
| date_to | string | No | End date filter in YYYY-MM-DD format. |
| max_results | integer | No | Maximum number of results to return. |
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 endpointhttps://api.igpt.ai/v1/connectors/authorize — only during initial datasource connection setup
No other external endpoints are contacted. No data is sent to any third-party service. The igptai PyPI package source is available at https://github.com/igptai/igptai-python.
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.
For the full security model, see https://docs.igpt.ai/docs/security/model.
How It Differs from Basic Email Search
| Basic email/Gmail search | iGPT Email Search |
|---|
| Keyword matching only | Semantic + keyword hybrid |
| Misses related content using different words | Understands meaning, finds conceptually related emails |
| Limited to Gmail's search operators | Natural language queries work |
| Provider-specific (Gmail OR Outlook) | Searches across all connected providers |
| Often limited history (Nylas: 90 days) | Full email history indexed |
| Returns raw MIME data | Returns clean, structured results |
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
| Skill | What it does | When to use it |
|---|
| igpt-email-ask | Reasoning, summaries, structured extraction, sentiment | When you need answers, not just results |
| igpt-email-search (this skill) | Hybrid semantic + keyword retrieval | When you need to find and retrieve emails |
Both skills use the same IGPT_API_KEY and connected datasources. Install both for the full search → analyze workflow.
Resources
