Glen
Glen provides a memory and context layer for AI agents that automatically captures organizational knowledge from agent sessions, Slack, GitHub, tickets, calls, and docs into a unified, queryable store. The platform injects this accumulated context into every agent prompt, reducing token usage and improving response consistency. Teams can query the entire company knowledge base through Slack, MCP-compatible agents, or the web app, and a PR reviewer explains code changes by querying clones of the original agents.
- Artificial Intelligence
- AI Agents
- Developer Tools
- Enterprise Software
- Software Only
Funding
Raised to date
$500KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Founder details are not available yet.
Product
Problem
AI agents working within organizations often lack the broader context of company knowledge, leading to inconsistent, incomplete, or duplicated responses. Critical information is scattered across Slack threads, tickets, calls, code repositories, and agent sessions, making it difficult for both agents and people to access the full picture when making decisions.
Solution
Glen provides a memory and context layer for AI agents and applications that automatically captures and synthesizes organizational knowledge from multiple sources into a unified, queryable store. The platform continuously ingests agent sessions from tools like Claude Code, Codex, and Cursor, plus Slack, GitHub, tickets, calls, and docs, with no manual effort required. Glen injects this accumulated context into every agent prompt, enabling faster, more consistent responses on fewer tokens. Teams can also ask the entire company knowledge base questions through Slack, any MCP-compatible agent, or the web app. The platform includes a PR reviewer that queries clones of the original agents to explain the reasoning behind code changes, and supports shared sessions, skills, and artifacts for what the company calls "multiplayer AI."
Target Audience
Primary customers are engineering and knowledge-work teams that rely on AI agents and want to unify organizational context for more consistent, higher-quality automated outputs.
Features
- Continuous automatic capture from agent sessions (Claude Code, Codex, Cursor), Slack, GitHub, tickets, calls, and docs, synthesized into a single organizational memory store
- Automatic context injection into every agent prompt, reducing token usage and improving response consistency
- Company brain query interface accessible via Slack, MCP server, command-line tool, or web app
- PR reviewer that queries clones of the original agents to explain the reasoning behind code changes, with diff highlighting for "why does this line exist" questions
- Shared learning layer with skills that distill from team work and propagate across the org, enabling non-experts to perform work the way top performers do
- MCP server and command-line tool integrations for custom-built agents, plus TypeScript SDK and plugin support for rapid integration