My AI agent didn't know what yours was doing, so I gave them one shared brain

说明与用途

Hivemind 将 AI 代理的记忆共享到图中，使其能够看到代码更改并避免冲突。

问题

Multiple AI coding agents work independently on the same codebase, each with its own memory, leading to conflicting changes and failures that Git and code review catch only after the fact.

结果

Hivemind enabled agents to share intent‑aware memory, reducing conflicting edits and speeding up resolution of failures. The shared graph allowed agents to query who is changing a function and what has been tried, preventing repeated mistakes.

工作流

• 1. Parse repo with Python ast to build a function call graph in FalkorDB.
• 2. Attach agent memories (claims, failures, decisions) to the relevant Function nodes.
• 3. Before editing, an agent queries the graph for CALLS edges from the target function to identify active changes and recent failures.
• 4. Agent proceeds with edits, writes new memories to the graph.
• 5. Hivemind re‑indexes only modified files, comparing AST hashes to detect real changes.
• 6. Other agents receive updated graph data and adjust their plans accordingly.

证据

If memories hang off functions, and functions are connected by calls, then "what should my agent know before it edits checkout?" turns into "what is connected to checkout?"

来源

Hivemind puts every agent on one graph. It holds the code's call graph, who is changing what, what has been tried…

来源

The design came together once I noticed that every memory worth sharing is about a specific function.

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