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Insights on AI pipeline debugging and observability.

Record, Inspect, Replay. A Better Way to Build AI Agents

AI development has three expensive problems. Invisible failures, fragile tests, and surprise bills. Here's how a single recording proxy solves all of them.

Let Your AI Debug Your AI. Agent-Driven Triage with MCP

Your coding assistant can't fix what it can't see. Connect it to Orchid's MCP server and it can investigate failed agent runs with real evidence.

Zero-Cost AI Testing. Record Once, Replay Forever

Stop choosing between flaky live-API tests and hand-written mocks that rot. Record real LLM responses once and replay them deterministically in CI.

Know What Every Agent Run Costs Before the Bill Arrives

Token costs are tiny per call and shocking in aggregate. Orchid attributes real USD costs to every exchange, session, and pipeline step as they happen.

No SDK Required. LLM Observability in Any Language with Two Headers

Orchid ships SDKs for Python and TypeScript, but you don't need them. Any HTTP client in any language can capture and replay LLM traffic with a base URL and a couple of headers.

Benchmarking Your Agent Logic Without the Network Noise

Your agent feels slow, but is it your code or the API? Replay mode removes network latency and provider variance so you can profile what you actually control.

Local-First Observability. Why Your LLM Traffic Should Stay Home

Your prompts contain your business logic, your customer data, and sometimes your secrets. Here's the case for observability that never leaves your infrastructure.

Why Debugging AI Pipelines Is Broken (And How to Fix It)

AI pipelines fail in ways traditional debugging tools can't handle. Here's why grepping logs doesn't work anymore, and what does.

How to Debug a Stuck LangChain Agent in 30 Seconds

Your LangChain agent is looping and the logs won't tell you why. Here's how to find the root cause in a recorded session with three clicks.