Case study / CipherClaw
CipherClaw, debugging the agents that other tools cannot see.
An open-source debugger and observability layer for multi-agent AI systems. Built when production teams kept asking the same question: why did the chain stop?
Apache 2.0. Used by teams running Claude, OpenAI, and mixed-model agent stacks.
02 / Problem
Multi-agent failures are invisible by default.
When five agents pass tasks between each other, a single bad tool call cascades into silence. Logs scatter across processes. Traces stop at the model boundary. Teams ship blind.
03 / Approach
Trace every agent, every tool, every retry.
Span-level traces
Every agent call, tool invocation, and model response captured with parent-child links.
Replay
Re-run a failed chain locally with the exact inputs that broke production.
Eval harness
Pin golden tasks. Regress on every model upgrade.
Open source
Apache 2.0. No vendor lock-in, no surprise bills.
04 / Outcome
Production multi-agent debugging without the rebuild.
Adopted by teams shipping Claude agents in finance, legal, and customer ops. Listed in the Anthropic ecosystem. Powers internal observability across my own production AI work.
FAQ
Common questions.
- What is CipherClaw?
- An open-source toolkit for debugging multi-agent AI systems. Traces, evals, replay. Apache 2.0 licensed.
- Who is it for?
- Teams running more than one agent in production who need to see why a chain failed instead of guessing.
- Is it free?
- Yes. Open source under Apache 2.0. Source on GitHub, live site at cipherclaw.com.
Have a multi-agent system stuck in pilot?
The $1,000 AI Production Readiness Audit names the blockers in 48 hours. CipherClaw is one of the tools we use to find them.