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

Description and use

Hivemind shares AI agents’ memory in a graph, enabling them to see code changes and avoid conflicts.

Problem

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.

Outcome

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.

Workflow

• 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.

Evidence

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?"

Source

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

Source

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

Source