langwatch.ai's rivals

Provides a platform for orchestrating, evaluating, and observing AI workflows.

Provides observability, telemetry, and debugging for software and artificial intelligence agents.

Traces, evaluates, and improves AI agents using production data.

Provides an end-to-end platform for testing and monitoring voice AI and chat AI agents. It facilitates pre-production simulations across different personas. The platform also monitors production conversations to assess…

Provides a platform for developing, testing, and monitoring structured prompts for large language models. It allows users to design prompt templates and define test queries with expected results, automating the process…

Detects spiraling artificial intelligence agents through behavioral anomaly detection, agent replay, and cross-provider forensics.

Provides open-source observability for AI agents, enabling tracing, evaluation, and debugging. It automatically identifies agent failures by capturing LLM, tool, and sub-agent calls along with costs and tokens.

Provides simulation, evaluation, and guardrails for AI agents to improve reliability. It is used to develop trusted AI systems for production use. The platform incorporates research-backed methods for creating robust…

Provides a platform for evaluating AI agents in production. It continuously scores agents for quality, drift, and risk. The platform offers features for comparisons, integrations, and support.

Monitors AI agents in production to detect failures and pinpoint issues quickly. It helps trace problems and verify fixes. It offers real-time alerts for silent agent failures.

Provides tools for observability, evaluation, tracing, and experimentation of AI agents. It helps identify issues and suggests fixes for AI agents.

Provides observability, evaluation, governance, and runtime policy enforcement for production AI agents.

Provides observability and runtime control for production AI agents. It allows tracing LLM calls, enforcing runtime limits, and investigating failures. Developers can instrument processes using a lightweight SDK, and…

Generates shareable evaluation reports for AI agents based on goal completion, tool accuracy, efficiency, quality, and safety.

Enables running evaluation experiments at scale in realistic cloud environments. Users can define custom benchmarks to measure agent performance with various products and models.

Evaluates AI agents through adversarial testing, synthetic environments, and real-time production monitoring.

Records, replays, and debugs AI agent executions by tracking LLM calls, tool calls, and errors.

Traces large language model calls and AI agents using OpenTelemetry to inspect token usage, cost, latency, and errors.

Provides tools for AI engineering teams, including agent building, evaluation, and cost tracking. It supports Python and TypeScript agents. It offers features like trace production agents and turn failures into…

Validates AI agents to automate coverage, catch regressions, and verify vendor systems without building test infrastructure.

An open-source framework for evaluating LLM applications. It provides 50+ metrics for testing AI agents, RAG, and chatbots. This framework supports building reliable evaluation pipelines.

Evaluates artificial intelligence agents by inspecting data, models, and code to produce evidence packages of their behavior.

Provides open-source observability for AI agents to monitor their behavior. It enables insights into AI agent self-healing capabilities.

A platform for enterprises to build, test, evaluate, run, and improve AI agents with audit trails and cost tracking.

Provides risk management and evaluation tools for deploying agentic AI systems.

Provides a platform to trace LLMs and AI agents using OpenTelemetry. It enables running LLM evaluations and managing prompts. Developers can compare models and store API keys using Vault. The platform is available for…

Provides a continuous feedback loop from evaluations and monitoring for LLM applications. It aims to improve each release by automating the process of understanding performance.

Provides observability for AI agents built on the Vercel AI SDK, tracking costs, latency, tokens, traces, evaluations, and alerts.

Tests artificial intelligence agents and learned policies to identify operational failures prior to deployment.

Provides a vendor-neutral JSON-RPC protocol for grading AI agent outputs, tool calls, and audit context across different frameworks, models, and CI systems.