agentspeed.

langchain.com

scanned 7/25/2026, 9:40:53 PM · cached · refresh in 21h · rubric 2026.07.4
Built with Webflow
robots.txt present: passrobots.txt allows AI agents: passContent-Signal directives: failllms.txt present: warnSitemap present: passLink response headers: warnCanonical URL: passMCP server card: failOAuth authorization metadata: failOAuth resource metadata: failAPI catalog / OpenAPI: failWeb Bot Auth: failMarkdown negotiation: failText-to-markup ratio: failContent without JavaScript: skipHeading hierarchy: warnCookie wall blocks content: passPage title: passMeta description: passJSON-LD present: failStructured data validates: skipSchema type coverage: passPaywall / login wall: passAgent Skills manifest: failWebMCP actions: failPrimary action reachable: passReachability: passTime to first byte: passFull render time: skipPage weight: pass70/ 100 · C
Needs work

This is how AI agents (not browsers) experience langchain.com. The score weights five categories of machine-readability; the ticks on the arc are the 30 individual checks behind it.

✉ Email me this report + alert me when it changes ↓

Top fixes

Checks tagged “emerging” are 2026 agent-protocol standards most of the web hasn’t adopted yet — adopting early is an edge, not a defect.

failJSON-LD presentStructured data

No JSON-LD blocks found. Add a <script type="application/ld+json"> with a schema.org type for this page.

Fix · Webflow: Add JSON-LD in Page Settings → Custom Code (inside <head>) for the page’s type.

warnllms.txt presentDiscoverability

Found /llms.txt but missing H1 header and markdown links.

Fix · Webflow: Publish /llms.txt at your domain root. Build a spec-compliant one at /tools/llms-txt-generator.

failText-to-markup ratioReadability

Visible text is 3.7% of HTML weight (5781/155238 bytes). Low. Markup may be crowding out agent-usable text.

Fix: Heavy ad or navigation markup crowds out agent-usable text. Aim for a text-to-DOM ratio above 15%.

warnLink response headersDiscoverability

A Link: header is present but carries none of canonical/alternate/describedby — the relations agents consume.

Fix: Emit `Link:` headers for canonical, alternate-language, and describedby relations. Agents that fetch HEAD-only or stream-render rely on these.

warnHeading hierarchyReadability

Heading issues: 2 skipped level(s).

Fix: Use a single H1 and avoid skipping heading levels (H1 → H3).

failContent-Signal directivesemergingDiscoverability

No Content-Signal directives. Add e.g. `Content-Signal: ai-train=no, ai-summarize=yes` to declare granular AI usage policy beyond binary allow/disallow.

Fix: Add `Content-Signal:` directives to your robots.txt. This is the emerging standard (Cloudflare-driven) for declaring fine-grained AI usage policy beyond a binary allow/disallow.

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Fix it

Generated, ready-to-ship files for the gaps above — copy them or download and drop them into your repo.

Add an llms.txt
Publish this at https://langchain.com/llms.txt
# LangChain: Observe, Evaluate, and Deploy Reliable AI Agents

> LangChain provides the engineering platform and open source frameworks developers use to build, test, and deploy reliable AI agents.

## Key pages
- [Home](https://langchain.com/)
- [Powering the Agent Development Lifecycle](https://langchain.com/)
- [LangSmith powers top AI teams, from startups to global enterprises](https://langchain.com/)
- [Learn from teams running agents in production](https://langchain.com/)
- [Trusted by the largest builder community in AI](https://langchain.com/)
- [Get started with LangSmith](https://langchain.com/)

## About
Describe what langchain.com does, who it's for, and the primary action you want an
agent to be able to complete. Keep it factual and current.
Qualifies for certificationBronze

This score clears the Bronze threshold (70+). Certification turns it into a dated, publicly verifiable attestation — a verification URL, an embeddable badge, and weekly monitoring for a full year. Scores under 70 can’t buy this, which is what makes displaying it mean something.

Get certified — $199/year →

What the agent receives

The agent’s-eye view
LangChain: Observe, Evaluate, and Deploy Reliable AI Agents Interrupt is coming to London &amp; NYC in the fall! Get tickets Products LangSmith Platform Agent Improvement Engine Improve agents autonomously Observability See exactly what your agents are doing Evaluation Score and improve agent performance Agent Infrastructure Deployment Ship and scale agents in production Sandboxes Run agent-generated code safely No-Code Agents Fleet Agents for the whole company Open Source Frameworks deepagents Build long-running agents for complex tasks langgraph Build reliable agents with low-level control langchain Quick start agents with any model provider Learn Resources Blog Customer Stories Guides Max Agency How-To LangChain Academy YouTube Documentation Community LangSmith for Startups Meetups Community Docs Company About Careers Partners Events Pricing Try LangSmith Get a demo Try LangSmith Get a demo Powering the Agent Development Lifecycle Make experimentation repeatable, iterate faster, and gain momentum with LangSmith. Start building Get a demo Build Test Deploy Monitor LangSmith powers top AI teams, from startups to global enterprises LangSmith Agent Engineering Platform Observe, evaluate, and deploy agents with LangSmith. LangSmith is framework-agnostic: trace your preferred framework or integrate LangSmith with any agent stack using our Python, TypeScript, Go, or Java SDKs. LangSmith Engine Observability Evaluation Deployment Fleet NEW RELEASE Improve agents faster with LangSmith Engine Surface and diagnose undetected issues autonomously to improve agents faster. LangSmith Engine clusters production failures into prioritized issues, finds the root cause in your traces and code, and proposes the fix for your review. Read the announcement Observability Understand exactly what your agent is doing Agents can be hard to debug and understand. Long context, branching logic, and many tools make it difficult to pinpoint where things went wrong. Tracing breaks each run into a structured timeline of steps so you can see exactly what happened, in what order, and why. Native tracing for popular agent frameworks and OpenTelemetry SDKs for Python, TypeScript, Go, and Java Message threading for multi-turn chat interactions Analytics and AI-driven insights to uncover patterns across traces LangSmith Observability Evaluation Use real-world usage for iterative improvement Capture production traces, turn them into test cases, and score agents with a mix of human review and automated evals. Each iteration makes your agent measurably better. Reusable LLM-as-judge and multi-turn evals Eval calibration with human feedback Human feedback annotations Online and offline scoring LangSmith Evaluation Deployment Ship and scale agents in production Unlike traditional web apps, agents work for long durations and need to handle async collaboration with humans and other agents. The agent server provides memory, conversational threads, and durable checkpointing out of the box - on infrastructure that’s fault-tolerant and scales to handle any workload. Supports human-in-the-loop interactions, input concurrency, and background agents Type-safe streaming of messages, UI components, and custom events Scalable, distributed runtime to handle agent swarms Native protocol support for A2A &amp; MCP LangSmith Deployment Fleet Agents for the whole company Routine tasks like research, follow-ups, and status checks eat up your day. Describe what you need in plain language, and Fleet takes action on it across your daily tools. Turn any question or task into a recurring agent that improves with feedback and acts autonomously. Designed with enterprise security and admin in mind. Bring your own models Use first-party integrations or extend with any MCP server Export agent files for pro-code development Integrated LangSmith tracing Agents improve with user feedback LangSmith Fleet Build with our open source frameworks Build agents fast with any model provider. Choose the right framework for the job from batteries included to low-level control. deepagents Build intelligent agents for open-ended work For highly autonomous, long-running agents Explore deepagents langchain Quick start agents with any model provider For building agents fast with templates Explore langchain langgraph Build reliable agents with low-level control For production agents that require some determinism Explore langgraph Learn from teams running agents in production More customer stories Klarna’s AI assistant reduced case resolution time by 80% with LangSmith Read Use Case Monday Service achieved 8.7x faster feedback loops for evals with LangSmith Read Use Case Podium reduced engineering escalations by 90% with LangSmith Read Use Case C.H. Robinson automated 5,500 orders per day, saving 600+ hours daily with LangSmith Read Use Case ServiceNow orchestrates agents across 8 customer stages using LangSmith Read Use Case More use cases Trusted by the largest builder community in AI 100M+ Monthly open source downloads 6K+ Active LangSmith customers 5 Of the Fortune 10 are LangSmith customers Get started with LangSmith Start building Get a demo Use LangSmith, the agent engineering platform, to improve every step of the agent development lifecycle. Products LangSmith Platform LangSmith Observability LangSmith Evaluation LangSmith Deployment LangSmith Fleet LangSmith Sandboxes Deep Agents LangChain LangGraph Resources Blog Customer Stories Guides Community Changelog Docs Support LangChain Academy Company About Careers Partners Trust Center Marketing Assets Events Sign up for our newsletter to stay up to date Thank you! Your submission has been received! Oops! Something went wrong while submitting the form. All systems operational Privacy policy Terms of service

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AgentSpeed score for langchain.com
<a href="https://agentspeed.com/scan/langchain.com"><img src="https://agentspeed.com/badge/langchain.com" alt="AgentSpeed agent-readiness score" height="56"></a>

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Full breakdown

13 pass3 warn11 fail3 skip
Discoverability
pass
robots.txt present
A robots.txt is reachable at the site root.
100/100
pass
robots.txt allows AI agents
All 8 answer-time access agents allowed.
100/100
fail
Content-Signal directivesemerging
No Content-Signal directives. Add e.g. `Content-Signal: ai-train=no, ai-summarize=yes` to declare granular AI usage policy beyond binary allow/disallow.
0/100
warn
llms.txt present
Found /llms.txt but missing H1 header and markdown links.
60/100
pass
Sitemap present
sitemap.xml reachable and referenced from robots.txt.
100/100
warn
Link response headers
A Link: header is present but carries none of canonical/alternate/describedby — the relations agents consume.
60/100
pass
Canonical URL
Canonical points to self: https://www.langchain.com/.
100/100
fail
MCP server cardemerging
No valid MCP Server Card at /.well-known/mcp/server-card.json. Publish one so agents can discover your tools without HTML scraping.
0/100
fail
OAuth authorization metadataemerging
No RFC 8414 metadata at /.well-known/oauth-authorization-server. Agents that act on behalf of users need this to discover your authorization endpoints.
0/100
fail
OAuth resource metadataemerging
No RFC 9728 metadata at /.well-known/oauth-protected-resource. Publish it so agents can discover required scopes without hand-coded credentials.
0/100
fail
API catalog / OpenAPIemerging
No /.well-known/api-catalog (RFC 9727) and no /openapi.json|yaml. Publish one so agents that integrate with APIs can discover your endpoints.
0/100
fail
Web Bot Authemerging
No Web Bot Auth JWKS at /.well-known/http-message-signatures-directory.json. Publish one to allow trusted agents while keeping a default-deny posture for the rest.
0/100
Readability
fail
Markdown negotiationemerging
Accept: text/markdown returns HTML, not markdown. Serve a markdown variant of primary content when requested; agents summarize and cite it more reliably.
0/100
fail
Text-to-markup ratio
Visible text is 3.7% of HTML weight (5781/155238 bytes). Low. Markup may be crowding out agent-usable text.
28/100
skip
Content without JavaScript
Browser pass unavailable; cannot measure JS dependency.
warn
Heading hierarchy
Heading issues: 2 skipped level(s).
75/100
pass
Cookie wall blocks content
No common consent-modal markers detected.
100/100
pass
Page title
A concise <title> is present (59 chars).
100/100
pass
Meta description
A well-sized meta description is present (132 chars).
100/100
Structured data
fail
JSON-LD present
No JSON-LD blocks found. Add a <script type="application/ld+json"> with a schema.org type for this page.
0/100
skip
Structured data validates
No JSON-LD blocks present; nothing to validate (covered by structured_data.jsonld_present).
pass
Schema type coverage
Page intent unclear; no specific schema.org type expected.
100/100
Actionability
pass
Paywall / login wall
No paywall or login-wall detected on the landing URL.
100/100
fail
Agent Skills manifestemerging
No Agent Skills manifest at /.well-known/agent-skills.json. Enumerate the tasks agents can perform (search, add-to-cart, contact-support) so they pick the right one without scraping.
0/100
fail
WebMCP actionsemerging
No WebMCP detected. On pages with first-class actions (cart, support, account), embed a WebMCP server so on-page agents invoke tools directly.
0/100
pass
Primary action reachable
Primary offering identified ("Agent engineering platform") with price/CTA ("Get a demo").
100/100
pass
Reachability
HTTP 200 after 1 redirect.
100/100
Performance
pass
Time to first byte
TTFB 175ms. Healthy.
100/100
skip
Full render time
Full-render time unavailable (browser pass skipped or failed).
pass
Page weight
Initial document is a lean 152 KB.
100/100
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