Observability & Tracing

with abv, developers can easily implement observability and tracing into their llm applications the platform captures everything happening during an llm interaction inputs, outputs, tool usage, retries, latencies and costs observability is the broader concept of understanding what is happening under the hood of you llm application traces are entities that can be considered individual 'units' observability why should you setup tracing? accelerate development & debugging quickly pinpoint issues and understand agent/llm behavior track costs in real time gain visibility into expenses for every model call optimize performance identify bottlenecks and reduce application latency enable evaluations build a strong foundation of data that enables you to use abv’s suite of evaluation tools for systematic testing and benchmarking streamline customer support save hours by diagnosing user issues with complete trace visibility why trace with abv? all in one platform abv platform provides the most complete development experience for ai app developers multi modal abv supports text, images, audio, and more multi model abv supports all major llm providers language agnostic abv natively supports python and javascript via our sdks, along with an open api for other languages where to start use the quickstart guides to add tracing to your llm app quickstart (python sdk) docid\ bmfrsuoiye9cuoj8exsl quickstart (js/ts sdk) docid\ ibpkub 8mrevbnhi0nwny core features sessions https //docs abv dev/sessions group related traces by user journey or job to see end to end behavior users https //docs abv dev/user tracking link traces to user accounts for faster reproduction and targeted support environments https //docs abv dev/environments separate dev/staging/prod to compare performance and isolate issues safely metadata https //docs abv dev/metadata attach structured context (e g , model, region, tenant) to make queries precise tags https //docs abv dev/tags add flexible labels to categorize, filter, and slice traces quickly trace ids https //docs abv dev/trace ids and distributed tracing correlate events across services for reliable root cause analysis multi modality and attachments https //docs abv dev/multi modality and attachments store text, images, audio, and files for richer debugging context advanced features model usage & cost tracking https //docs abv dev/model usage and cost tracking monitor tokens, runtime, and spend to optimize model selection masking of sensitive llm data https //docs abv dev/masking of sensitive llm data redact pii, secrets, and prompts to stay compliant while logging log levels https //docs abv dev/log levels prioritize noise vs signal by severity to focus on what matters releases & versioning https //docs abv dev/releases and versioning annotate traces with code/model versions to spot post deploy regressions comments on objects https //docs abv dev/comments on objects collaborate inline on traces/sessions for reviews, handoffs, and decisions trace urls https //docs abv dev/trace urls share deep links to any trace/span for reproducible bug reports and reviews sampling https //docs abv dev/sampling control volume and cost with rule or rate based capture policies