Built an open-source tool for causal blame attribution in multi-agent AI pipelines — looking for feedback

[prithvirajualluri]

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Product Feedback

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When a multi-agent pipeline fails, standard tracing tells you what happened but not which agent caused it. The failure point and root cause are often different nodes in the chain.

I built blamr to solve this. It introduces a CausalEdge primitive at every agent handoff that records:
confidence_in / confidence_out
intent_delta (cosine similarity — did the goal drift at this hop?)
influence_score (how much did this agent shape the final output?)
edge_hash (Merkle-chained for tamper-evident audit trail)

When something breaks, Shapley values rank blame across the chain. You get a report like "Agent 2 was responsible for 71% of the outcome degradation" instead of grep-ing logs.

Stack: NestJS · ClickHouse · Redpanda · Valkey · Ollama (no cloud LLM)

Adapters: LangGraph · CrewAI · AutoGen · MCP

License: MIT — self-hosted, free forever

→ https://blamr.ai/

Would love feedback on the CausalEdge data model — specifically whether intent_delta as cosine similarity is the right signal, or if there's a better way to track goal preservation across hops.

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