How I Built an AI Agent That Uses a Graph Database as Its Memory

说明与用途

ContriGraph 使用 FalkorDB 推荐开源问题，并将每次交互记录为类型化边，创建持久记忆，无需会话状态。

问题

Finding good open source issues to contribute to is harder than it looks. The real problem is memory and context, not discovery.

结果

ContriGraph delivers issue recommendations in under 1 ms per query, filters out previously skipped or applied issues at the database level, and uses graph edges as persistent memory, eliminating the need for session state or separate action tables.

工作流

• User types a query in the browser
• FastAPI streaming endpoint receives the query
• Claude AI agent decides which tool to call
• Tool executes a Cypher query against FalkorDB
• Result streams back via SSE to the browser
• Agent writes VIEWED, BOOKMARKED, SKIPPED, or APPLIED_TO edges to FalkorDB
• Next query uses NOT clauses to filter out previously interacted issues

证据

Finding good open source issues to contribute to is harder than it looks.

来源

ContriGraph is an AI agent that traverses this graph to find the right issue for you — and remembers every interaction by writing it back to the graph as a typed edge.

来源

When you click Bookmark, Skip, or Apply on a card: CREATE (d)-[:BOOKMARKED {timestamp:$ts, session_id:$session}]->(i)

来源