LangWatch vs Maxim AI (2026)

Langy, LangWatch's agent, changes code through pull requests that a GitHub App opens. LangWatch workflows, which can be published as agents or API endpoints, call outside systems through HTTP nodes and code blocks that use encrypted project secrets. The AI Gateway governs which tools and servers a virtual key may reach.

SourceLangWatch, langwatch.ai/docs/llms.txt (Langy pull requests, workflows, secrets, routing policies)read 2026-09-21

No code agents call third party services through API nodes at any point in a chain and deploy without code changes. One worked example is an agent that categorizes support emails, creates help desk tickets and sends replies. Prompts can use tool calls and connected MCP servers in the playground, and alerts go to Slack and PagerDuty.

SourceMaxim, getmaxim.ai/docs llms.txt (API nodes, agent deployment, customer support email agent guide, prompt tool calls, MCP)read 2026-09-21

A LangWatch workflow is a graph of nodes that runs from an Entry point to an End node, mixing LLM prompt nodes with Python code, HTTP and evaluator nodes. Versions are saved and can be restored, and a workflow can be published as an evaluator, as an agent under test, or as an API the customer's code calls. Workflows do not include loops, branching, retries or fallback paths.

SourceLangWatch, langwatch.ai/docs/workflows/overview and llms.txt (building a workflow, workflow as agent); langwatch.ai/docs/workflows/overview.mdread 2026-09-21

In the no code agent builder, blocks connect in a visual editor, including API nodes that call third party services and loops that repeat steps. Errors show at every node, and agents deploy without code changes for querying through the SDK. API steps that run the same way every time can sit next to prompt steps. Maxim does not describe branching, retries or fallback paths inside a workflow.

SourceMaxim, getmaxim.ai/docs llms.txt (no-code builder nodes, loops, API nodes, deployment, error debugging)read 2026-09-21

Online evaluation monitors run an evaluator on every matching trace or thread as it arrives, alerts and automations catch regressions and notify teams, automations add every new trace matching a filter to a dataset, and Langy can set up alerts and automations from what it finds across traces. Scoring and actions start on incoming traffic without anyone asking.

SourceLangWatch, langwatch.ai/docs/llms.txt (online evaluation, alerts and automations, datasets from traces, Langy insights)read 2026-09-21

Online evaluations score production logs automatically, test runs go out on a schedule and from CI/CD pipelines, and alerts on performance and quality thresholds go to Slack and PagerDuty. Work starts on a schedule, an incoming log or a threshold without a person asking.

SourceMaxim, getmaxim.ai/docs llms.txt (online evals, scheduled runs, alerts and notifications, Slack and PagerDuty integrations)read 2026-09-21

Built in RAG evaluators score retrieval, and cookbooks on embedding tuning and vector versus hybrid search help with the customer's own pipeline. LangWatch has no document ingestion, index, retrieval layer or knowledge API that grounds an agent in company data.

SourceLangWatch, langwatch.ai/docs/llms.txt (built-in evaluators, cookbooks)read 2026-09-21

Retrieval quality is tested, not supplied. The customer connects its own RAG pipeline to prompt tests, and test runs evaluate retrieval. There is no document ingestion, index, retrieval layer or knowledge API that grounds an agent in company data.

SourceMaxim, getmaxim.ai/docs llms.txt (prompt retrieval testing, evaluate retrieval quality)read 2026-09-21

Traces, threads and datasets are stored as records of the customer's agent, and each Langy conversation runs in its own sandboxed worker. Agents have no session, workflow or long term memory that they read and write across runs.

SourceLangWatch, langwatch.ai/docs/llms.txt (concepts, datasets, how Langy works)read 2026-09-21

Logs, sessions, datasets and versioned prompts persist as records of the customer's application. There is no session, workflow or long term memory that an agent reads and writes.

SourceMaxim, getmaxim.ai/docs llms.txtread 2026-09-21

Guardrails run evaluators inline to block or modify harmful responses in real time, the AI Gateway enforces budgets, rate limits and routing policies, including which tools and servers a key may reach, before a request goes through, and Langy's code changes arrive as pull requests that a person reviews before merge.

SourceLangWatch, langwatch.ai/docs/llms.txt (guardrails, AI Gateway budgets and routing policies, Langy pull requests)read 2026-09-21

Human raters review test runs and production logs through human annotation, alongside automated evaluators, and datasets are curated from those annotations. This reviews the agent's outputs. There is no approval step, runtime guardrail or pause and resume control over an agent's actions.

SourceMaxim, getmaxim.ai/docs llms.txt (human annotation, human annotation on logs, curate datasets)read 2026-09-21

Controls include single sign on with directory provisioning and sign in policies, SCIM 2.0 provisioning from Okta, Entra ID and others with group to role mapping, and role based access with custom roles and role bindings at organization, team and project level.

An audit log records every change made through settings, the API and the AI Gateway, with who, where and what changed, and exports to a SIEM in OCSF format. Retention is set per organization, team or project, and secrets are encrypted and write only. LangWatch states ISO 27001 certification and GDPR compliance, with a trust center monitored by Vanta.

SourceLangWatch, langwatch.ai/docs/llms.txt (SSO, SCIM, RBAC, audit log, data retention, secrets, OCSF export), langwatch.ai homepage and pricingread 2026-09-21

Access control runs through organization and workspace roles for team members, workspace level RBAC, and SAML 2.0 single sign on with Okta and Google Workspace, each with its own setup guide, plus two factor authentication and a vault for secrets. Maxim Enterprise adds audit logs, and the platform can be self hosted. There is a trust center at trust.getmaxim.ai, but Maxim's public pages name no SOC 2, ISO 27001 or other attestation.

SourceMaxim, getmaxim.ai/docs llms.txt (members and roles, workspace RBAC, SAML SSO, two factor authentication, vault, audit logs)read 2026-10-01

LangWatch traces LLM calls, tool executions and retrieval steps through OpenTelemetry native SDKs and integrations, including coding assistants such as Claude Code and Codex. An audit log records every change through settings, the API and the gateway, governance events export to a SIEM as OCSF records, and analytics export into the customer's dashboards. Default retention is published per plan and can be adjusted per organization, team or project.

SourceLangWatch, langwatch.ai/docs/llms.txt (observability, audit log, OCSF export, data retention) and langwatch.ai/docs/pricingread 2026-09-21

Production traffic is logged into repositories with distributed tracing of sessions, traces and spans. The platform also ingests OpenTelemetry traces over OTLP, tracks live issues, runs automated quality checks on production logs, and exports logs with their evaluation data to CSV with filters.

SourceMaxim, getmaxim.ai/docs overview and llms.txt (tracing, OTLP ingestion, exports)read 2026-09-21

Customers can run LangWatch as managed multi tenant SaaS in the EU, US, UK or APAC, self hosted with Docker, Kubernetes and Helm or in their own VPC, or hybrid, with the data plane on their infrastructure and the control plane on LangWatch's. The open source repository includes every feature for local use, and an Enterprise license adds longer retention to self hosted deployments.

SourceLangWatch, langwatch.ai homepage and langwatch.ai/docs/llms.txt (self-hosting overview, editions and licensing, hybrid setup)read 2026-09-21

Deployment is either a managed service or self hosted, with full VPC isolation (Zero Touch) or a hybrid setup that places the data plane in the customer's own cloud.

SourceMaxim, getmaxim.ai/docs llms.txt (self-hosting overview, data plane deployment)read 2026-09-21

LangWatch ships Langy, an automated AI engineer that reads traces and evals, writes Scenario tests, optimizes prompts and opens pull requests, and a Skills Directory of installable skills, including skills for product managers and domain experts, that set a coding assistant up to work with LangWatch. There is one packaged agent and a set of setup skills, but no set of ready made agents or workflow templates to choose from.

SourceLangWatch, langwatch.ai/docs/llms.txt (Langy, skills directory) and langwatch.ai homepageread 2026-09-21

The ready made material is for evaluation. It includes an evaluator store of off the shelf evaluators, test presets, and guides that walk through building agents such as a customer support email agent. There are no ready made agents, templates or packaged workflows a customer adopts for their own work.

SourceMaxim, getmaxim.ai/docs llms.txt (evaluator store, presets, guides)read 2026-09-21

The LangWatch AI Gateway offers one endpoint, compatible with OpenAI and Anthropic, across providers including OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, xAI, Groq, Cerebras and DeepSeek. Provider credentials are held in LangWatch, each application gets a virtual key, and routing policies set which providers a key routes through, the fallback order and model tiers, with budgets and rate limits enforced before the request. Model providers are stored once per organization, team or project for evaluators, workflows and Langy.

SourceLangWatch, langwatch.ai/docs/llms.txt (AI Gateway, routing policies, budgets, providers, model providers)read 2026-09-21

Teams set up their own model providers before running prompts, agents and evaluators, and the playground compares output quality, cost and latency across combinations of prompts, models and parameters, with custom token pricing for accurate cost reporting.

SourceMaxim, getmaxim.ai/docs llms.txt (running your first eval, custom pricing) and overviewread 2026-09-21

LangWatch offers a REST API, including the Workflows and Instant Evals APIs, SDKs in Python, TypeScript, Go and Java, and the langwatch CLI, which drives the platform from the terminal for people and coding assistants. An MCP server gives coding assistants access to traces, tests and evaluations, alongside installable skills, personal and service API keys for development and CI/CD, and CI integration that gates merges on eval results.

SourceLangWatch, langwatch.ai/docs/llms.txt (CLI, MCP server, API keys, API reference) and langwatch.ai/pricingread 2026-09-21

There are Python and TypeScript SDKs and public REST APIs, and the OpenAPI description covers prompts, prompt tools, evaluators, workflows, datasets, alerts and integrations. Evaluations run in CI/CD through GitHub Actions, and MCP servers connect to prompts in the playground.

SourceMaxim, getmaxim.ai/docs llms.txt (SDKs, public APIs, CI/CD, MCP)read 2026-09-21

Experiments run over datasets with built in, saved and workflow built evaluators. LangWatch also tests agents with multi turn scenarios in which a simulated user acts against the agent and a judge scores the conversation. It groups scenarios into test suites, compares agents side by side on pass rate, cost and latency, gates merges on eval results in CI, scores production traffic with online monitors, and optimizes prompts and tool contracts with the scenario suite as the quality gate.

SourceLangWatch, langwatch.ai/docs/llms.txt (agent testing, evaluations, improve your agent) and langwatch.ai/pricingread 2026-09-21

Offline evaluations run prompts, no code agents and HTTP endpoint agents against datasets, using an evaluator store and custom AI, programmatic, statistical and human evaluators. The platform also simulates multi turn conversations, compares versions in run reports, schedules test runs, runs evaluations in CI/CD through GitHub Actions, and evaluates production logs online.

SourceMaxim, getmaxim.ai/docs llms.txt (offline evals, simulation, scheduled runs, CI/CD, online evals) and overviewread 2026-09-21

The product covers tracing, agent testing, evaluation, the AI Gateway, governance, Langy and self hosting, and no agent controls a browser, desktop or computer. Langy works in a sandbox through the CLI and code.

SourceLangWatch, langwatch.ai/docs/llms.txtread 2026-09-21

Maxim covers evaluation, simulation, observability and a no code agent builder. There is no browser, desktop or computer control by an agent.

SourceMaxim, getmaxim.ai/docs llms.txtread 2026-09-21