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Google's agentic development platform and IDE for building software with autonomous, Gemini-powered coding agents.
AI observability and evaluation platform to trace, evaluate and improve LLM agents in production, with an open-source Phoenix core.
Open-source AI-agent observability platform for tracing sessions, clustering failures and running evals on live traffic.
AI gateway and observability suite for governing and optimizing LLM apps; strong dev-tool traffic.
Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.
No public pricing
No public pricing
No public pricing
No public pricing
- ✦Agent-first IDE experience
- ✦Autonomous planning and code execution
- ✦Integrated editor, terminal and browser control
- ✦Powered by Google's Gemini models
- ✦High-level developer supervision
- ✦Agent and LLM tracing
- ✦Large-scale evaluations
- ✦Open-source Phoenix observability
- ✦Alyx AI engineering agent
- ✦OpenTelemetry-based instrumentation
- ✦Experiments and prompt playgrounds
- ✦Agent trace capture and conversation intelligence
- ✦Semantic and exact-text search across all traces
- ✦Automatic issue discovery with Slack/email/webhook alerts
- ✦OpenTelemetry-compatible SDK with no lock-in
- ✦Automated evals and golden dataset generation
- ✦Failure-mode clustering and MCP server integration
- ✦AI Gateway for reliable LLM routing
- ✦Prompt Engineering for collaborative prompt management
- ✦Guardrails for enforcing reliable LLM behavior
- ✦Observability Suite for monitoring costs, quality, and latency
- ✦MCP Client for building AI agents with real-world tool access
- ✦Experiment tracking and visualization for ML training runs
- ✦Model and artifact versioning and management
- ✦Hyperparameter optimization tooling
- ✦Collaborative dashboards and reports for ML teams
- ✦LLM application tracing and evaluation tooling
- →Building apps with AI agents
- →Automating multi-step coding tasks
- →Prototyping and iterating on software
- →Assisting developers on complex work
- →Debugging AI agents in production
- →Measuring LLM output quality
- →Catching regressions before deploy
- →Monitoring AI agents in production
- →Debugging and triaging agent failures
- →Building regression evals from real traffic
- →Getting alerted on new or escalating issues
- →Monitor costs, quality, and latency of AI applications.
- →Route to 250+ LLMs reliably with a single endpoint.
- →Streamline and scale prompt engineering.
- →Enforce reliable LLM behavior with guardrails.
- →Build agents with access to real-world tools.
- →ML engineers tracking and comparing training experiments
- →Research teams versioning datasets and model checkpoints
- →Teams building and evaluating LLM-powered applications
- →Organizations collaborating on machine learning projects