LangWatch

Every call your AI agents make becomes a searchable trace in LangWatch, and the same platform tests and scores those agents before a new prompt or model reaches users. The Trace Explorer filters by origin, user, model, cost, and evaluator, reads each run as a waterfall, flame graph, topology, or sequence diagram, and takes traces from the Python, TypeScript, and Go SDKs or any OpenTelemetry source. Agent Testing sends simulated users, in text or beta voice calls, through your agent while a judge passes or fails every criterion with its reasoning, adds multi-turn red teaming, and compares two agents side by side. The Experiments workbench scores prompts and agents on datasets with LLM judges, exact match, and RAG checks, then compares runs on cost, latency, and pass rate, while online evaluations monitor live traffic and guardrails block unsafe replies. Prompts keep version history and staging or production tags, Workflows wire prompts, Python code, and HTTP calls on a visual canvas, and annotation queues route traces to human reviewers. Alerts fire when a graph crosses a threshold, automations post to Slack, email, or webhooks, and scheduled reports go out by email or Slack. Claude Code, Codex, Copilot, Gemini CLI, and opencode report session cost, pull request cost, and terminal replays, and an MCP server exposes trace search and prompt management to coding assistants. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 licensed.

LangWatch
LangWatch
LangWatch
LangWatch
LangWatch
LangWatch
LangWatch
LangWatch

Benefits

  • Trace every call, token, and cost
  • Python, TypeScript, and Go SDKs or plain OpenTelemetry capture each LLM call, tool call, token count, and model cost, and the Trace Explorer filters them by user, conversation, and evaluator verdict.
  • Test agents before users meet them
  • A simulated user improvises the customer's words turn by turn, and a judge scores each criterion against the conversation, checking internal actions in the agent's own traces, so regressions surface before a release.
  • Score quality offline and in production
  • Run prompts and agents against datasets in the Experiments workbench, then apply the same evaluators to live traces as online monitors, or call them in code as guardrails that block unsafe responses.
  • Unlimited people and traces on your server
  • Self-hosting without a license caps no members, projects, traces, evaluations, or experiments, and keeps events 49 days by default; SSO, custom roles, SCIM, audit logs, and configurable retention need an Enterprise license.

Features

  • Trace Explorer and Analytics
  • Filter traces by origin, user, model, cost, and evaluator, read runs as waterfall, flame, topology, or sequence views, and chart custom metrics while topic clustering groups conversations by subject.
  • Scenario Agent Testing
  • Simulated users test conversational agents from the platform or CI, plus voice agents in beta through ElevenLabs, OpenAI Realtime, Twilio, Pipecat, and Gemini Live adapters, with multi-turn red teaming.
  • Evaluators and Guardrails
  • LangEvals supplies LLM-as-a-judge, exact match, faithfulness, RAG context precision, and jailbreak detection, usable in dataset experiments, online monitors on live traces, and request guardrails.
  • Prompts and Visual Workflows
  • Version prompts with staging and production tags, then wire LLM calls, Python code, HTTP calls, If/Else branches, and evaluators together on a drag-and-drop workflow canvas.
  • Datasets, Alerts, and Reviews
  • Build datasets from CSV files or matching traces, route traces to annotation queues, and trigger Slack, email, or webhook automations, threshold alerts, and scheduled reports.
  • Coding Agent Monitoring and MCP
  • Wrappers for Claude Code, Codex, GitHub Copilot, Gemini CLI, and opencode report session cost, pull request cost, and terminal replays, while an MCP server exposes traces and prompts.

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