by raystack · MCP Server · ★ 67
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
Compass Compass is a context engine that builds a temporal knowledge graph of your organization — entities, relationships, and documents across every system — queryable by humans and AI agents alike. Search it. Traverse it. Reason over it. Key Features AI-Native -- Composable MCP tools let AI agents search, traverse, assess impact, and read documents over your organizational graph. Knowledge Graph -- Typed, directed, temporal relationships between entities including lineage, ownership, and custom edge types. Context Assembly -- Multi-hop bidirectional traversal builds a subgraph around any entity — the full picture in one call. Impact Analysis -- Blast radius analysis traces what depends on what and what breaks when something changes. Hybrid Search -- Keyword precision with semantic similarity using Postgres-native full-text search and pgvector embeddings. Documents -- Attach any document to entities — ru
| Stars | 67 |
| Forks | 8 |
| Language | Go |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 39.8/100 |
| Open Issues | 4 |
| Last Updated | 2026-04-21 |
| Created | 2021-03-22 |
| Platforms | go, mcp |
| Est. Tokens | ~320k |
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compass is Compass is a context engine that builds a knowledge graph of your organization's metadata, capturing entities, relationships, and lineage across systems and time, making it discoverable and queryabl. It is categorized as a MCP Server with 67 GitHub stars.
compass is primarily written in Go. It covers topics such as ai-agents, context-engine, context-engineering.
You can find installation instructions and usage details in the compass GitHub repository at github.com/raystack/compass. The project has 67 stars and 8 forks, indicating an active community.
compass is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to compass on Agent Skills Hub include omega-memory, mengram, mcp-memory-libsql. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.