AI visibility report
LangChain ranks #2 in LLM Observability Evals & Gateways AI search.
Outside the top three on 13 of the 25 prompts buyers actually ask.
Braintrust is cited on 10 of those losses.
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Start free trial#2 among 11 vendors · still absent from 77.6% of tracked prompt responses
Top-3 citations across 125 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
Google AI Mode
Gemini Search
Perplexity
ChatGPT
Bing CopilotVisible, but narrative can improve. LangChain ranks #2 on presence but #7 on sentiment. The brand appears relatively often, but competitors may be getting more favorable language when they appear.
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 winning5
I'm looking for an LLM observability platform with a great team collaboration workflow — where engineers and PMs can both review trace quality without SQL knowledge.
Avg # 1.0 · 2 platforms
Which LLM evaluation platforms support custom rubric-based scoring for domain-specific correctness beyond generic faithfulness metrics?
Avg # 1.0 · 1 platform
Which LLM observability tools work with OpenTelemetry-compatible backends so we can consolidate LLM traces alongside existing service traces?
Avg # 1.0 · 1 platform
What LLM tracing platforms handle high-throughput production workloads — millions of traces per day — without degrading query performance?
Avg # 1.5 · 2 platforms
Which LLM observability platforms can a small team get running against a production RAG pipeline in under a day?
Avg # 1.7 · 3 platforms
Where LangChain is losing5
Which LLM tracing platforms export trace data to a data warehouse so analysts can run custom eval queries alongside product metrics?
Competitors on 5 platforms
Track this promptWhich LLM tracing platforms make it easiest to replay a failed multi-step agent run and pinpoint exactly where reasoning went wrong?
Competitors on 4 platforms
Track this promptWhich LLM eval platforms have the best prompt playground experience for iterating on system prompts against a saved test dataset?
Competitors on 4 platforms
Track this promptWhat's the fastest LLM tracing platform for instrumenting a Python-based agent framework without rewriting existing code?
Competitors on 4 platforms
Track this promptWhat LLM gateway tools integrate best with secret managers and internal auth systems for enterprise teams rolling out to multiple product teams?
Competitors on 3 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
Langtrace (Best for Minimalist Setup): An open-source option that frequently requires just two lines of code to initialize ( `pip install langtrace` + init), making it extremely fast to set up for frameworks like LangChain, LlamaIndex, or CrewAI.
What are the best OpenTelemetry-compatible tracing backends for LLM apps that work out of the box without custom span parsing?
LangSmith (LangChain) : While a standalone platform, LangSmith offers robust data retention and exporting capabilities (14-day base, 400-day extended) that can be utilized to move data into downstream analysis tools for complex evaluation workflows.
Which LLM observability tools handle PII redaction and data masking in traces for teams with HIPAA or GDPR compliance requirements?
Langfuse : A highly popular open-source (or managed) platform that excels at integrating tracing and evaluations into existing RAG applications (especially with LangChain or LlamaIndex) in minutes, offering immediate analytics on cost, latency, and quality.
Which LLM observability tools work with OpenTelemetry-compatible backends so we can consolidate LLM traces alongside existing service traces?
Most cited sources8
578 LLM Observability Tools to Monitor & Eval AI Agents
langchain.com·Product Page
- 4
LangSmith - Observability
langchain.com·Article
- 3
LangSmith
langchain.com·Product Page
- D3
Prevent logging of sensitive data in traces - Docs by LangChain
docs.langchain.com·Documentation
- 2
LangChain: Observe, Evaluate, and Deploy Reliable AI Agents
langchain.com·Product Page
- 2
LLM Evaluation Metrics: Measuring What Matters for Your Users
langchain.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 | Bing Copilot | Perplexity | Google AI Mode | ChatGPT | Gemini Search |
|---|---|---|---|---|---|
Capability4/5 cited (80%) | |||||
Which LLM observability tools handle PII redaction and data masking in traces for teams with HIPAA or GDPR compliance requirements? | |||||
Looking for an eval platform that supports automated safety and toxicity scoring on LLM outputs at scale — what are my options? | |||||
What platforms support end-to-end tracing of multi-agent pipelines including tool calls, retrieval steps, and sub-agent spawning? | |||||
Which LLM evaluation platforms support custom rubric-based scoring for domain-specific correctness beyond generic faithfulness metrics? | |||||
Which LLM gateways handle multi-provider fallback and automatic retries while preserving full trace context across the switch? | |||||
Developer Experience3/5 cited (60%) | |||||
Which LLM tracing platforms make it easiest to replay a failed multi-step agent run and pinpoint exactly where reasoning went wrong? | |||||
I'm looking for an LLM observability platform with a great team collaboration workflow — where engineers and PMs can both review trace quality without SQL knowledge. | |||||
Which LLM eval platforms have the best prompt playground experience for iterating on system prompts against a saved test dataset? | |||||
What LLM observability tools do ML engineering teams typically use to annotate and review production traces for quality feedback? | |||||
Which LLM gateway tools give developers the best real-time cost and token usage visibility across multiple LLM providers during development? | |||||
Integrations & Ecosystem4/5 cited (80%) | |||||
I'm evaluating LLM eval platforms — which ones integrate with version control to tie prompt regressions back to specific code or config changes? | |||||
What LLM gateway tools integrate best with secret managers and internal auth systems for enterprise teams rolling out to multiple product teams? | |||||
Which LLM tracing platforms export trace data to a data warehouse so analysts can run custom eval queries alongside product metrics? | |||||
Which LLM observability platforms integrate natively with the most popular agent frameworks so traces appear automatically with no manual instrumentation? | |||||
Which LLM observability tools work with OpenTelemetry-compatible backends so we can consolidate LLM traces alongside existing service traces? | |||||
Performance & Reliability2/5 cited (40%) | |||||
Which LLM gateways add the least latency overhead when routing between LLM providers — safe to use in production for sub-500ms SLAs? | |||||
Which LLM observability platforms stay reliable under traffic spikes from batch eval jobs running thousands of LLM calls simultaneously? | |||||
What are the most production-hardened LLM gateway options for an enterprise team needing 99.9% uptime with circuit-breaker support? | |||||
What LLM tracing platforms handle high-throughput production workloads — millions of traces per day — without degrading query performance? | |||||
Which LLM eval platforms support async evaluation at scale without blocking the inference path or adding latency for end users? | |||||
Setup & First Run2/5 cited (40%) | |||||
Which LLM observability platforms can a small team get running against a production RAG pipeline in under a day? | |||||
What are the best OpenTelemetry-compatible tracing backends for LLM apps that work out of the box without custom span parsing? | |||||
I'm evaluating LLM gateway solutions for a startup — which ones have the simplest self-hosted setup with a working UI on day one? | |||||
What's the fastest LLM tracing platform for instrumenting a Python-based agent framework without rewriting existing code? | |||||
Which evaluation platforms for LLM outputs are easiest to plug into an existing CI pipeline for a five-engineer team? | |||||
Turn this matrix into daily prompt monitoring.
Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Braintrust | 42.4% | 33.9% | 0.8% | 0.8% | 45.6% | #6.3 | +0.45 |
| 2 | LangChain | 22.4% | 12.4% | 1.6% | 0.0% | 51.2% | #11.7 | +0.53 |
| 3 | Galileo | 20.0% | 10.6% | 0.0% | 19.2% | 16.0% | #6.4 | +0.46 |
| 4 | Confident AI | 15.2% | 10.3% | 0.0% | 0.0% | 12.0% | #6.0 | +0.45 |
| 5 | Arize AI | 15.2% | 9.8% | 0.0% | 6.4% | 10.4% | #11.5 | +0.54 |
| 6 | Traceloop | 9.6% | 4.3% | 0.0% | 7.2% | 11.2% | #4.4 | +0.55 |
| 7 | Langfuse | 9.6% | 6.6% | 1.6% | 0.0% | 52.8% | #10.7 | +0.67 |
| 8 | LiteLLM | 5.6% | 3.4% | 4.0% | 0.0% | 0.0% | #7.3 | +0.63 |
| 9 | Helicone | 4.8% | 4.3% | 1.6% | 2.4% | 25.6% | #24.5 | +0.52 |
| 10 | Portkey | 4.0% | 4.0% | 0.0% | 0.8% | 16.0% | #8.6 | +0.56 |
| 11 | Patronus AI | 0.8% | 0.3% | 0.8% | 0.0% | 2.4% | #1.0 | +0.70 |
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