LangWatch

Key features

• End-to-end tracing for agents and assistants End-to-end tracing for agents and assistants: trace every agent run and LLM call with hierarchical spans across chains, tools, retrieval steps, and users, fed by Python and TypeScript SDKs plus more than twenty integrations including OpenTelemetry.[1]

• Scenario simulation tests Scenario simulation tests: create simulation tests for agents and voice agents that replay realistic multi-turn scenarios at temperature zero for deterministic results, instead of hand-checking chat transcripts.[1]

• No-code evaluations with assertions No-code evaluations with assertions: build evals without code using assertion checks, RAG faithfulness and relevance metrics, and PII detection, runnable offline over datasets or online over production traffic.[1]

• Datasets, annotation, and experiments Datasets, annotation, and experiments: collect production traces into versioned datasets, annotate them, and track experiments so prompt and model changes are compared rather than eyeballed.[1]

• Versioned prompt management Versioned prompt management: manage versioned prompts inside the LLM operations workflow and deliver them through prompt-as-an-API, keeping prompt changes reviewable.[1]

• Coding-assistant usage tracking Coding-assistant usage tracking: track company coding assistants launched from common editors and CLIs with per-session, per-PR, and per-team cost and duration analytics plus privacy controls over collected data.[1]

• AI gateway with virtual keys and budgets AI gateway with virtual keys and budgets: route OpenAI and Anthropic traffic through one compatible endpoint with virtual keys, model routing and fallbacks, spending budgets, and credential rotation.[1][2]

• Guardrails and AI governance Guardrails and AI governance: redact PII before it reaches models, set anomaly and cost rules with alerts, and keep an inventory of every AI tool in the company, including findings such as shadow Copilot Studio spend.[1][2][3]

Who will love it

• Teams debugging agents in production: hierarchical traces with session, cost, and latency views isolate looping, failing-tool, and slow-step behavior across agent runs.[1]

• Teams building evaluation loops: no-code assertions, scenario simulations, datasets with annotation, and experiment tracking turn one-off prompt tweaks into repeatable checks.[1]

• Organizations governing company AI use: gateway budgets, PII guardrails, coding-assistant analytics, and a tool inventory give platform teams one place to control spend and risk.[1][2]

Who should avoid it

• Non-technical teams wanting turnkey AI: SDK instrumentation, gateway routing configuration, and cloud or self-hosted setup assume engineers who own code and infrastructure.[1][2]

• Budget-capped teams ignoring usage metering: cloud plans add usage-based credits above seat pricing with per-tier retention, so confirm metering before committing.[1]

• Teams needing audited compliance depth: SOC 2 and GDPR posture is stated with DPA and questionnaire support, but regulated buyers should verify the current audit documents before treating this as a compliance decision.[1][2]

Alternatives and compatibility