Self-driving AI observability
and evals for agents
Trace and evaluate agent behavior without guesswork. Surface issues automatically. Fix what breaks, faster.
Ship through one gateway, not a mess of moving parts.
Promote prompts, models, and workflows straight from the UI into production, with version control, rollout logic, and access to 500+ models through one gateway.
One API for every model
Route OpenAI-style calls through Respan to 500+ models, or keep each provider’s native SDK on a passthrough endpoint—every request is logged.
Stay up when models fail
If a model errors or rate-limits, try the next model in your fallback list, balance load across keys, and retry with backoff from one place.
Control spend and reuse answers
Set soft warnings or hard caps per API key, get Slack or email alerts when a threshold crosses, and cache repeat prompts to cut cost and latency.
One trace for every call
Each gateway call becomes a trace tree with latency on every span. Add customer_identifier and metadata, then filter Logs and Traces.
Keep uptime when models dip
Set fallback_models on the request or in Settings. If the primary errors or rate-limits, the gateway tries the next model in the list.
Know when production shifts - and act before it spreads.
Dashboards for LLM usage, slice metrics by model or user, and get notified when cost, latency, errors, or tokens cross the line you set.
Track usage on one dashboard
See requests, tokens, errors, latency, and cost in one place—broken down by model, API key, and the traffic your product sends through Respan.
Slice metrics by model or user
Switch the same dashboard by model or user to spot spikes, compare traffic, and see which features or keys are driving volume and spend.
Alert when thresholds breach
Monitor error rate, cost, latency, or tokens over a window—and notify Slack, email, or a webhook when production crosses the limit you set.
Turn judgment into a system.
Build evaluation workflows that combine human review, code checks, and LLM judges in one flow - all measured against the metrics that actually matter.
Compose one evaluation flow
Run fast rule checks, LLM judges, and human review in the same workflow—so code, rubric, and ground-truth grading live in one evaluation system.
Score live traffic automatically
Run the same evaluators on sampled production requests so quality issues show up on real spans, not only in offline tests.
Test before you ship
Build a dataset from production traces or a CSV, run experiments across prompt and model variants, and compare scores before merge.
Know exactly what your agents did.
Every prompt, tool call, and response - captured with rich context from real production traffic.
Online evals on production traffic
Run the same evaluators on sampled production logs so scores like faithfulness and json_schema show up on real spans—not only in offline tests.
Reproduce and inspect real sessions
Group related messages in a thread view and see how each turn ties back to spans in the trace—so you keep session context when agents branch or retry.
Alert when production scores drop
Watch evaluator scores such as faithfulness over a rolling window and trigger alerts when they fall below your threshold—before users report the issue.
The AI observability platform behind 80 trillion+ tokens. Loved by world-class founders, engineers, and product teams.
“Imagine jumping to a log immediately after every LLM call. This is the dream for debugging.”
Daniel Wolf
Product Lead, AlphaSense
“We scaled from 5M to 500M+ monthly API calls quickly. Respan gave us the debugging layer to resolve production issues 10x faster.”
Read how Retell builds next-gen voice agents that scale ->Zexia Zhang
CTO, Retell AI
“Respan legit has some of the best UX/DX I’ve ever seen in my life. I truly don’t think I’ve ever integrated a product that was as easy.”
Rahul Behal
Co-founder, Gumloop
“This one felt pretty nice.”
Fabian Hedin
CTO, Lovable
“Such a no brainer choice over LangSmith or anything else and super easy to set up.”
Andy Wang
CEO, Finta
“Respan has been key in helping us scale to trillions of tokens reliably with real-time observability.”
Read how Mem0 builds reliable self-improving AI memory layer ->Deshraj Yadav
CTO, Mem0
“Great product - really love the metrics dashboard.”
Esha Dinne
CTO, Giga
“We scaled from 5M to 500M+ monthly API calls quickly. Respan gave us the debugging layer to resolve production issues 10x faster.”
Read how Retell builds next-gen voice agents that scale ->Zexia Zhang
CTO, Retell AI
“This one felt pretty nice.”
Fabian Hedin
CTO, Lovable
“Respan has been key in helping us scale to trillions of tokens reliably with real-time observability.”
Read how Mem0 builds reliable self-improving AI memory layer ->Deshraj Yadav
CTO, Mem0
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Respan is fully compliant with ISO 27001, the internationally recognized standard for information security management.
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