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A multi-agent due-diligence system on LangGraph that researches crypto projects across on-chain data, GitHub activity, team signals and social channels — compressing 8 hours of analyst work into roughly 10 minutes.
Crypto due diligence requires pulling together on-chain metrics, GitHub commit patterns, team backgrounds, tokenomics and community signals — work that typically takes an analyst the better part of a day.
We wanted to explore how far multi-agent orchestration could compress this, and to build a reference architecture for future client projects in the multi-agent space.
A LangGraph-orchestrated system of specialised agents — on-chain analyst, GitHub analyst, team analyst, social analyst, synthesiser — each with its own tools, retrieval scope and output schema.
A coordinator agent routes tasks, enforces budget limits, and assembles the final report with full source attribution.
A coordinator graph in LangGraph dispatches sub-tasks to specialist agents, each with a narrow tool surface (on-chain APIs, GitHub API, search) and a typed output schema.
Intermediate results are stored in a shared scratchpad so agents can build on each other without re-fetching.
A synthesiser agent assembles the final report, with every claim linked back to the agent and the source it came from. Cost and tool-call budgets are enforced at the coordinator level.
