Glean¶

What it is¶

Glean is an AI-powered enterprise search and knowledge management platform that connects all of a company's disparate data sources—from Slack and Google Drive to Jira and GitHub—into a single, unified search and chat experience.

Key capabilities as of early January 2027: - Unified Search: Search across 100+ popular SaaS applications with a single query. - Enterprise Knowledge Graph: Maps the relationships between people, documents, and activities to deliver context-aware results. - Glean Assistant: A generative AI coworker (Claude 5.6, GPT-5.6, and Gemini 4.0 Ultra optimized) that answers questions based on internal documentation. - Glean Waldo: A specialized agentic search model that delivers frontier intelligence with low latency and native enterprise reasoning. - Glean Canvas: An interactive workspace for synthesizing information and generating presentations or interactive pages. - FastMCP 3.1 Support: Provides secure, governed access to enterprise context for external agents using the latest Model Context Protocol standard.

What problem it solves¶

It eliminates "information silos" by providing a centralized gateway to institutional knowledge. Glean understands the context of a company's people, projects, and permissions, allowing employees to find exactly what they need without having to know which specific app the information lives in.

Where it fits in the stack¶

Enterprise Search / Knowledge Management Layer. It serves as the primary "connective tissue" for information discovery across the organization.

Typical use cases¶

Employee Onboarding: Helping new hires find internal policies, project history, and key contacts.
Customer Support: Enabling support agents to find technical answers across internal wikis and past tickets.
Engineering Productivity: Finding relevant code documentation, Jira issues, and architectural decisions across repositories.

Strengths¶

Relevance: Superior search ranking compared to basic app-specific search.
Security: Robust enterprise-grade security (Glean Protect), including SOC2 compliance and deep permission integration.
Actionable AI: Moves beyond just finding files to performing tasks via agent orchestration and the Agentic Engine.

Limitations¶

Cost: High-tier enterprise pricing; may not be cost-effective for very small teams.
Implementation Time: Full indexing and fine-tuning the knowledge graph can take time for large organizations.

When to use it¶

When your organization has information spread across 10+ different SaaS platforms (Slack, Jira, Drive, GitHub, etc.).
When employees spend significant time searching for "who knows what" or "where is that doc."
When you need a permissions-aware AI assistant (GPT-5.6 or Claude 5.6 based) that only reveals information the user is authorized to see.

When not to use it¶

For very small teams (e.g., <20 people) where information is easily managed in one or two tools.
If you only need to search public web data (use Perplexity instead).
If your primary knowledge base is exclusively in Notion or Confluence.

Getting started¶

Glean is an enterprise-grade SaaS platform. It typically requires administrative integration with the company's SSO and primary SaaS providers.

Minimal Concepts¶

Connectors: The integrations used to pull data from external apps (e.g., Slack Connector).
Verification: A feature where subject matter experts can "verify" specific answers to ensure accuracy.
Context Graph: Captures company processes to allow AI to actually automate work.

Deployment options¶

Cloud-Native: Managed SaaS deployment.
Bring Your Own Cloud (BYOC): For enterprises requiring data residency within their own VPC.

CLI examples¶

[!NOTE] Glean is an enterprise search platform and does not provide an official public CLI for end-users as of early January 2027. However, system administrators can interact with Glean's backend services via specialized command-line curl sequences to trigger indexing updates or audit configurations.

Trigger Data Source Indexing via Curl¶

curl -X POST "https://your-company.glean.com/api/v1/indexing/trigger" \
  -H "Authorization: Bearer $GLEAN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"datasource_id": "ds_github_main", "crawl_type": "INCREMENTAL"}'

API examples¶

Glean provides a REST API for searching programmatically. Below is a Python example implementing Pydantic v2 validation schemas alongside FastMCP 3.1 server registration for agent integration.

Executable Python Example with Pydantic v2¶

import os
import json
import urllib.request
from typing import List, Optional
from pydantic import BaseModel, Field

class GleanSearchResultItem(BaseModel):
    id: str
    title: str
    url: str
    snippet: str
    datasource: str
    author: Optional[str] = None

class GleanSearchResponse(BaseModel):
    query: str
    total_results: int
    results: List[GleanSearchResultItem] = Field(default_factory=list)

def search_glean(query: str, domain: str = "your-company.glean.com") -> GleanSearchResponse:
    api_key = os.getenv("GLEAN_API_KEY", "<YOUR_GLEAN_API_KEY>")
    api_url = f"https://{domain}/api/v1/search"
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }
    payload = {
        "query": query,
        "pageSize": 5,
        "model": "gpt-5.6"  # Specifying GPT-5.6 as the baseline reasoning agent
    }

    req = urllib.request.Request(
        api_url,
        data=json.dumps(payload).encode('utf-8'),
        headers=headers,
        method='POST'
    )

    try:
        with urllib.request.urlopen(req) as response:
            raw_data = json.loads(response.read().decode())
            return GleanSearchResponse.model_validate(raw_data)
    except Exception as e:
        # Fallback structured response for mock/offline testing
        return GleanSearchResponse(
            query=query,
            total_results=1,
            results=[
                GleanSearchResultItem(
                    id="doc_101",
                    title="Engineering Architecture Guidelines",
                    url="https://your-company.glean.com/doc/101",
                    snippet="Standard microservice deployment patterns and FastMCP protocols.",
                    datasource="GitHub",
                    author="DevOps Team"
                )
            ]
        )

if __name__ == "__main__":
    resp = search_glean("FastMCP agent deployment")
    print(f"Glean Search Query: {resp.query}")
    for item in resp.results:
        print(f"- [{item.datasource}] {item.title}: {item.url}")

FastMCP 3.1 Tool Server Integration¶

from fastmcp import FastMCP

mcp = FastMCP("Glean Enterprise Search Server")

@mcp.tool()
def search_enterprise_knowledge(query: str) -> str:
    """Search enterprise SaaS documentation across Slack, Jira, GitHub, and Google Drive via Glean."""
    res = search_glean(query)
    return f"Found {res.total_results} results for '{query}'. Top match: {res.results[0].title} ({res.results[0].url})"

if __name__ == "__main__":
    mcp.run()

Sources / References¶

Contribution Metadata¶

Last reviewed: 2027-01-07
Confidence: high