Egregore
Egregore provides an AI-native collaboration environment where development teams work alongside AI agents within a shared, persistent knowledge graph. The platform enables multiplayer sessions, structured handoffs, and automated git workflows, ensuring that context and reasoning accumulate across the organization. It transforms scattered team knowledge into a living substrate that AI systems draw from to produce more relevant and tailored outputs.
- Artificial Intelligence
- AI Agents
- Developer Tools
- Software Only
Funding
Founders
Product
Problem
Development teams using AI coding tools often work in fragmented environments where context is lost between sessions, and handoffs between team members or AI agents lack the underlying reasoning and decision history. This leads to repetitive work, misaligned priorities, and AI outputs that are generic rather than tailored to the organization's specific needs.
Solution
Egregore provides a persistent, AI-native collaboration workspace where teams and AI agents share a growing knowledge graph of accumulated context, decisions, and workflows. The platform enables multiplayer sessions where teammates can be invited with one command, gaining immediate access to shared memory and organizational context. Structured handoffs capture not just the state of work but the reasoning behind it, while automated git workflows streamline versioning and pull request creation. A deep-reflection feature analyzes past artifacts to surface hidden tensions and consensus points, helping teams resolve disagreements by revealing underlying variables. The environment functions as a "context garden," where organizational intelligence emerges organically from team interactions rather than being manually engineered.
Target Audience
Primary users are software development teams and engineering organizations that rely on AI coding assistants and need a shared, persistent context layer to coordinate work across multiple human and AI agents.
Features
- One-command teammate invitation that grants access to shared memory, knowledge graph, and coordination workflows from the first session
- Structured handoff system that captures decisions, trade-offs, open threads, and reasoning context for seamless AI-to-AI and human-to-AI transitions
- Automated git workflow with single-command staging, committing, pushing, and PR creation with automatic reviewer assignment
- Deep-reflection agent that analyzes historical artifacts across quests to identify consensus, tensions, and hidden variables in team disagreements
- Persistent knowledge graph that grows from actual collaboration patterns rather than administrator-defined schemas
- Context gardening approach where AI systems operate on living, emergent context rather than static retrieval sets