AI visibility report
AI visibility report for LangChain in LLM Observability Evals & Gateways.
Outside the top three on 15 of the 25 prompts buyers actually ask.
Braintrust is cited on 7 of those losses.
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Start free trialStill absent from 86.7% of tracked prompt responses
Top-3 citations across 75 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
Gemini Search
ChatGPT
PerplexityHow to read this. LangChain appears in 13.3% of tracked prompt responses. Presence is absolute coverage; share of voice is relative citation share; sentiment measures tone only when the brand appears.
Where LangChain is losing
Prompts where competitors are visible and LangChain is not.
These prompt-level losses are the first prompts to track and repair.
Where LangChain is winning3
What's the fastest way to start tracing my LLM application calls without rewriting my code?
Avg # 2.0 · 2 platforms
What tools provide model-graded evaluation with calibrated reference-free scoring for chatbots?
Avg # 4.0 · 1 platform
Which LLM platforms have the best workflows for human annotation and labeling of model outputs?
Avg # 6.0 · 1 platform
Where LangChain is losing5
Which LLM eval platforms support running automated evaluations on production traces with custom metrics?
Competitors on 3 platforms
Track this promptWhat are the best tools for detecting hallucinations and faithfulness issues in RAG pipelines?
Competitors on 3 platforms
Track this promptWhat AI eval platforms support on-premise or VPC deployment for regulated industries?
Competitors on 2 platforms
Track this promptWhich observability tools include real-time alerting on quality drops, not just latency?
Competitors on 2 platforms
Track this promptWhich evaluation platforms let me convert development-time evals into production guardrails automatically?
Competitors on 2 platforms
Track this prompt
Track LangChain daily before the next report refresh.
Track these gapsResearch dossierCapabilities, use cases, sources, reviews, pricing, and FAQ
Overview
LangChain is a San Francisco-based AI infrastructure company offering the LangSmith agent engineering platform and a suite of open-source frameworks (LangChain, LangGraph, Deep Agents) for building, observing, evaluating, and deploying LLM-powered agents. Founded in late 2022 by Harrison Chase and Ankush Gola, LangChain began as a widely adopted open-source project before expanding into a commercial platform. LangSmith provides production-grade observability, evaluation tooling, agent deployment infrastructure, and a no-code agent builder (Fleet). With over 100 million monthly open-source downloads, 131,000+ GitHub stars, 6,000+ active LangSmith customers, and 5 of the Fortune 10 as customers, LangChain serves both AI-native startups and global enterprises seeking to ship reliable agents faster across the full development lifecycle.
LangChain offers an integrated agent engineering stack: LangSmith (commercial SaaS) for observability, evaluation, deployment, and no-code Fleet agents; LangChain (open source) for rapid LLM application development with 100+ provider integrations; LangGraph (open source) for graph-based, stateful multi-agent orchestration; and Deep Agents for long-horizon autonomous task execution. LangSmith is framework-agnostic and supports any LLM stack via Python, TypeScript, Go, and Java SDKs plus OpenTelemetry, targeting the full agent development lifecycle from prototype to production.
Key Facts
- Founded
- 2022
- HQ
- San Francisco, CA, USA
- Founders
- Harrison Chase, Ankush Gola
- Funding
- ~$160M
- Customers
- 6,000+ active LangSmith customers
- Valuation
- $1.25B
- Status
- Private
Target users
Key Capabilities10
- Full-stack LLM and agent observability with step-by-step trace timelines (LangSmith)
- Offline and online LLM-as-judge and multi-turn evaluation pipelines
- Production agent deployment with durable checkpointing, memory, and human-in-the-loop
- Graph-based agent orchestration with stateful, low-level control (LangGraph)
- Prompt management, playground, and meta-prompting for iterative optimization
- Online monitoring with AI-driven pattern detection and failure mode clustering
- No-code enterprise agent builder (LangSmith Fleet) with MCP integration
- Self-hosted, BYOC, and managed cloud deployment options for data residency
- OpenTelemetry-compatible tracing for existing observability pipelines
- Multi-SDK support (Python, TypeScript, Go, Java) and 100+ LLM/vector DB integrations
Key Use Cases8
- Production observability and debugging for LLM agents and RAG pipelines
- Iterative agent evaluation using curated datasets, LLM-as-judge, and human feedback
- Multi-agent system development with stateful graph orchestration (LangGraph)
- Customer support and service automation at enterprise scale
- Automated order processing and logistics document workflows
- Prompt engineering, versioning, and regression testing across model updates
- Enterprise AI deployment with compliance, security, and human-in-the-loop controls
- No-code autonomous agent deployment for non-technical enterprise users (Fleet)
LangChain customer outcomes
80% reduction in customer query resolution time; ~70% of repetitive support tasks automated
Klarna's AI Assistant, built on LangGraph and LangSmith, handles multi-departmental escalations for 85 million active users, automating customer support at scale. The assistant performs work equivalent to 700 full-time staff across 2.5 million conversations.
600+ hours saved per day across 5,500 automated orders daily
C.H. Robinson used LangGraph and LangSmith to automate email-based order processing, automatically parsing shipping requests and creating orders without manual data entry.
90% reduction in engineering escalations; F1 response quality score improved from 91.7% to 98.6%
Podium used LangSmith for dataset curation, model fine-tuning, and trace-based debugging of their AI Employee agent, enabling non-engineering support staff to resolve most issues independently.
8.7x faster evaluation feedback loops (from 162 seconds to 18 seconds)
monday Service embedded LangSmith into a code-first, eval-driven development framework for their LangGraph-based AI service workforce, parallelizing offline evaluations with Vitest integration.
Recent Trend
How AI describes LangChain3
Deployment: Can be self-hosted (LangChain stack), or run in private environments * Strengths: * Prompt/version tracking * Dataset-driven evals * Agent tracing * Tradeoff:...
What AI eval platforms support on-premise or VPC deployment for regulated industries?
* ### LangSmith Very widely used if you’re in the LangChain ecosystem. * Built-in trace inspection (you see full agent runs) * Human feedback on traces (thumbs up/down, rubric scoring, annotations) * Tight loop from prompt → run → r...
Which LLM platforms have the best workflows for human annotation and labeling of model outputs?
...ommonly used options are: | Tool | Best at | What you can inspect in a single session | | --- | --- | --- | | LangSmith | LangChain/LangGraph ecosystems | Full execution graph, prompts, tool calls, state, token usage, replay, run comparison | | Langfuse...
What tools let me drill into a single user session to debug exactly what my agent did at each step?
Most cited sources6
78 LLM Observability Tools to Monitor & Eval AI Agents
langchain.com·Listicle
- D3
Alerts in LangSmith - Docs by LangChain
docs.langchain.com·Documentation
- D2
Trace an LLM application tutorial - Docs by LangChain
docs.langchain.com·Documentation
- 2
LLM Evals: Production Monitoring to Regression Tests - LangChain
langchain.com·Home
- D1
Agent Evals - Docs by LangChain
docs.langchain.com·Documentation
- G1
langchain-ai/openevals: Readymade evaluators for your ...
github.com·Documentation
Alternatives in LLM Observability Evals & Gateways6
LangChain positions itself as the full-lifecycle 'agent engineering platform,' uniquely combining a commercial observability/eval/deployment product (LangSmith) with the most widely adopted open-source LLM frameworks (LangChain, LangGraph, Deep Agents).
- Unlike pure-play observability vendors (Langfuse, Arize AI, Traceloop) or standalone evaluation tools (Braintrust, Galileo, Patronus AI, Confident AI), LangChain offers an integrated build-observe-evaluate-deploy stack.
- Unlike LLM gateway competitors (LiteLLM, Portkey, Helicone), LangChain's value proposition centers on agent reliability and lifecycle management rather than routing or cost optimization alone.
- Its dominant open-source community (131K+ GitHub stars, 100M+ monthly downloads) creates a powerful developer acquisition flywheel into the paid LangSmith platform, targeting both AI-native startups and Fortune 500 enterprises.
- The company explicitly benchmarks against Datadog and CrowdStrike as infrastructure category analogies.
Reviews
Praised
- Deep step-by-step observability into agent execution via LangSmith
- Rich integration ecosystem with 100+ LLM providers and vector databases
- Modular, flexible architecture enabling rapid prototyping
- Strong open-source community and active documentation improvements
- Framework-agnostic LangSmith tracing works with any LLM stack
- LLM-as-judge and dataset-driven evaluation workflows
- Smooth path from prototype to production-grade deployment
Criticized
- Steep learning curve for developers new to LLM frameworks
- Heavy abstractions increase codebase complexity and debuggability
- Breaking changes in updates require frequent code adjustments
- Documentation gaps for advanced or non-standard use cases
- Ecosystem can feel biased toward LangSmith over third-party observability tools
- LangSmith UI becomes cluttered with large volumes of experiments or traces
- Multi-modal evaluation (images, audio) requires custom implementation
User sentiment across review platforms and developer forums is generally positive, particularly for LangSmith's deep observability into agent execution, ease of integration with existing LangChain projects, and comprehensive tracing UI. The evaluation framework (LLM-as-judge, dataset curation, pairwise evals) is frequently cited as a key differentiator. Common criticisms include a steep initial learning curve, the complexity introduced by LangChain's layered abstractions, breaking API changes across versions, and documentation gaps for advanced use cases. Non-LangChain users note that LangSmith works well as a standalone observability tool but that the ecosystem can feel biased toward proprietary tooling.
Pricing
LangSmith is offered on three self-serve tiers plus a startup program. The Developer plan is free (1 seat, 5,000 base traces/month, 14-day retention). The Plus plan costs $39/user/month (up to 10 seats, 10,000 base traces/month included). Both plans charge $2.50 per 1,000 additional base traces and $5.00 per 1,000 extended traces (400-day retention). LangSmith Deployment is available on Plus with one free dev deployment included. Enterprise pricing is custom (annual invoicing, self-hosted/BYOC/Kubernetes options, unlimited seats, higher rate limits). A Startup plan with discounted rates is available for early-stage funded companies. LangChain and LangGraph open-source frameworks are free under the MIT license.
Limitations
- Heavy abstractions in the LangChain framework can make codebases complex and harder to debug, with some users reporting a sense of vendor lock-in toward LangSmith.
- Frequent breaking changes across versions require ongoing code maintenance.
- Documentation, while improving, has gaps for advanced use cases and can be difficult to navigate across the LangChain/LangGraph/LangSmith product split.
- The LangSmith UI can become cluttered and harder to navigate when managing large numbers of experiments or concurrent traces.
- Multi-modal evaluation (images, audio) requires custom implementation.
- The Plus plan has a 10-seat cap, pushing larger teams to enterprise pricing.
- LangGraph's open-source version lacks built-in scheduling/cron and requires manual LangSmith integration for full observability.
Frequently asked questions
Topic coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
| Prompt | Gemini Search | ChatGPT | Perplexity |
|---|---|---|---|
Evaluation3/5 cited (60%) | |||
Which LLM platforms have the best workflows for human annotation and labeling of model outputs? | |||
What tools provide model-graded evaluation with calibrated reference-free scoring for chatbots? | |||
Which LLM eval platforms support running automated evaluations on production traces with custom metrics? | |||
What are the best tools for detecting hallucinations and faithfulness issues in RAG pipelines? | |||
Which evaluation platforms let me convert development-time evals into production guardrails automatically? | |||
Gateways & Routing0/5 cited (0%) | |||
What gateways have the lowest latency overhead when routing high-volume LLM traffic? | |||
Which LLM gateways are open-source and self-hostable for teams that don't want a SaaS dependency? | |||
Which AI gateways let me route between OpenAI, Anthropic, and open-source models with a single API call? | |||
What LLM gateway platforms support automatic fallbacks, retries, and load balancing across providers? | |||
Which AI proxies handle rate limiting, key rotation, and cost tracking across teams centrally? | |||
Production Readiness2/5 cited (40%) | |||
What AI eval platforms support on-premise or VPC deployment for regulated industries? | |||
What LLM monitoring platforms integrate with PagerDuty, Slack, or Datadog for alerting workflows? | |||
Which observability tools include real-time alerting on quality drops, not just latency? | |||
Which AI guardrail platforms provide pre-execution intervention to block unsafe agent actions before they run? | |||
Which LLM observability platforms scale to billions of traces per month at enterprise volumes? | |||
Setup & First Run4/5 cited (80%) | |||
Which AI observability platforms can be self-hosted with one command using Docker Compose? | |||
Which LLM observability tools work with OpenTelemetry so I don't have to add yet another vendor SDK? | |||
I want to add eval tracking to my agent — which platforms have the simplest Python decorator-style integration? | |||
What's the easiest way to log every LLM call my app makes for debugging without changing my application architecture? | |||
What's the fastest way to start tracing my LLM application calls without rewriting my code? | |||
Tracing & Debugging0/5 cited (0%) | |||
Which LLM observability tools show token usage, latency, and cost per step in an agent pipeline? | |||
What platforms support replaying production traces in development for reproducible debugging? | |||
Which observability platforms offer the best agent execution tracing for multi-step LLM workflows? | |||
What tools let me drill into a single user session to debug exactly what my agent did at each step? | |||
Which AI observability tools surface unknown failure patterns I wouldn't have written tests for? | |||
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Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Braintrust | 26.7% | 26.4% | 2.7% | 0.0% | 26.7% | #8.5 | +0.39 |
| 2 | Confident AI | 13.3% | 8.0% | 0.0% | 4.0% | 13.3% | #5.0 | +0.37 |
| 3 | LangChain | 13.3% | 6.9% | 5.3% | 0.0% | 13.3% | #9.3 | +0.44 |
| 4 | Langfuse | 13.3% | 18.4% | 6.7% | 2.7% | 13.3% | #12.1 | +0.51 |
| 5 | Galileo | 12.0% | 10.9% | 0.0% | 12.0% | 12.0% | #5.5 | +0.52 |
| 6 | Arize AI | 12.0% | 13.8% | 0.0% | 0.0% | 12.0% | #12.9 | +0.45 |
| 7 | BerriAI (LiteLLM) | 5.3% | 2.3% | 4.0% | 0.0% | 2.7% | #9.0 | +0.40 |
| 8 | Helicone | 5.3% | 10.3% | 1.3% | 5.3% | 5.3% | #18.2 | +0.32 |
| 9 | Traceloop | 4.0% | 1.7% | 0.0% | 4.0% | 4.0% | #3.7 | +0.20 |
| 10 | Portkey | 2.7% | 1.1% | 0.0% | 0.0% | 2.7% | #11.0 | +0.42 |
| 11 | Patronus AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
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