Parea AI

Intercepts and logs all LLM API calls (OpenAI, Anthropic, LiteLLM, etc.) using language-specific decorators (@trace in Python, trace() in JavaScript) or SDK wrapping patterns (wrap_openai_client). Captures prompts, completions, latency, token counts, and cost without modifying application logic. Works by patching the underlying LLM client libraries at runtime, forwarding call metadata to Parea's logging backend while maintaining transparent pass-through of responses.

Enables users to create multiple prompt variants and run them against the same test dataset in parallel, displaying results side-by-side with metrics (accuracy, latency, cost, custom evaluations). The Prompt Playground provides a UI for editing prompts and selecting LLM parameters; variants are versioned and can be deployed independently. Comparison is powered by running each variant through the same evaluation pipeline, aggregating results into a comparative dashboard showing win rates and metric deltas.

Provides native integrations with popular LLM frameworks (LangChain, Instructor, DSPy, Maven, SGLang) through SDK adapters. These adapters automatically trace LLM calls, chain steps, and structured outputs without requiring explicit instrumentation. For LangChain, Parea provides callbacks that hook into the LangChain callback system. For Instructor, Parea traces validation and retry logic. For DSPy, Parea captures module execution and optimization steps. Integrations are transparent — users add a single line of code to enable tracing.

Generates domain-specific evaluation metrics automatically based on user-provided context (use case description, expected output format, quality criteria). Uses LLM-based analysis to create evaluation prompts that score outputs on relevant dimensions. Generated metrics are stored as reusable evaluation functions and can be customized by users. This capability is listed as an AI Consulting service, suggesting it may be semi-automated or require human review. Mechanism for automation is not fully documented.

Maintains a complete history of all experiments run on a prompt, including results, dataset versions, evaluation functions, and LLM parameters. Users can compare experiments side-by-side across different time periods, visualizing metric trends (accuracy over time, cost reduction, latency improvements). Comparisons are powered by filtering and aggregating experiment metadata. Experiment history enables root cause analysis (e.g., 'why did accuracy drop after this change?') by correlating metric changes with prompt/parameter changes. Supports exporting experiment data for external analysis.

Analyzes production LLM usage patterns and recommends cost optimizations: switching to cheaper models, adjusting temperature/max_tokens, or batching requests. Recommendations are based on historical cost and quality data (from experiments and production logs). For example, if a lower-cost model achieves similar quality on a task, Parea recommends the switch with estimated savings. Recommendations are presented in the observability dashboard with impact estimates (cost reduction, quality impact). Mechanism for generating recommendations is not fully documented.

Allows users to define evaluation functions as Python callables (or LLM-based evaluators) that score LLM outputs against expected results. Metrics can be deterministic (exact match, regex, code execution) or LLM-based (using Claude or GPT to judge quality). Evaluation functions are registered via decorator (@eval_func) or passed directly to experiment/comparison runs. Parea executes these functions in parallel across test datasets, aggregating results into scorecards and comparison dashboards. Supports both synchronous and asynchronous evaluation functions.

Provides a centralized repository for managing test datasets used in prompt evaluation and experimentation. Datasets are uploaded as structured records (JSON, CSV, or via SDK) and versioned automatically. Each dataset version is immutable, enabling reproducible evaluations across time. Datasets can be filtered, sampled, and linked to experiments. The platform tracks which experiments used which dataset versions, enabling traceability and preventing evaluation drift from dataset changes.

Runs a prompt variant against an entire test dataset, executing the prompt for each test case and collecting outputs. The experiment() method (Python/JS SDK) orchestrates this: it iterates over dataset records, calls the LLM with each input, runs evaluation functions on outputs, and aggregates results into a scorecard. Experiments can be compared side-by-side, and results are persisted for historical analysis. Supports both synchronous and asynchronous execution with configurable concurrency.

Captures and aggregates metrics from production LLM calls: token usage, API costs, latency, error rates, and user feedback. Metrics are displayed in dashboards with time-series visualization and filtering by model, endpoint, or user. Cost tracking integrates with LLM provider pricing (OpenAI, Anthropic, etc.) to calculate real-time spend. Latency is measured end-to-end (including network and LLM processing time). Data is retained for 1 month (free), 3 months (team), or longer (enterprise), enabling trend analysis and anomaly detection.

Runs evaluation functions on production LLM outputs in real-time, without blocking user requests. Evaluations are asynchronous and can include LLM-based scorers or lightweight heuristics. User feedback (thumbs up/down, ratings, comments) is captured via UI components or API calls and stored alongside LLM outputs. Feedback is aggregated into quality dashboards and can be used to retrain evaluation models or identify failure cases. Supports A/B testing in production by running different prompts for different users and comparing feedback-based metrics.

Deploys prompt variants from the Prompt Playground to a managed endpoint, making them callable via API. Each deployment is versioned and can be rolled back. Deployed prompts are accessible via REST API with configurable parameters (model, temperature, etc.). Deployment slots are limited by plan tier (10 free, 100 team, unlimited enterprise). Deployments are tracked with metadata (creation date, creator, deployment history), enabling audit trails. Supports canary deployments by routing a percentage of traffic to new prompt versions.

Abstracts over multiple LLM providers (OpenAI, Anthropic, LiteLLM, etc.) through a unified SDK interface. Users can switch between providers or models without changing application code by updating configuration. The SDK handles provider-specific API differences (message formats, parameter names, response structures) transparently. Supports both synchronous and asynchronous calls. Integrates with LiteLLM for additional provider support (Cohere, Replicate, local models, etc.). Cost tracking automatically adapts to each provider's pricing model.

Provides a UI for human reviewers to annotate LLM outputs, assign quality scores, or provide feedback on evaluation results. Reviewers can filter outputs by model, date, or evaluation score, and annotate in bulk. Annotations are stored and can be used to retrain evaluation models or identify systematic failures. Supports role-based access control (reviewer, admin, etc.). Annotations are versioned and tracked with reviewer metadata (who, when, why).