# LangChain AI visibility in LLM Observability Evals & Gateways

Canonical: https://devtune.ai/verticals/llm-observability-evals-gateways/langchain

[Website](https://www.langchain.com/)

Latest AI responses: 2026-10-29
Prompts: 25
Runs: 5


## Platforms

- google-ai
- google-ai-mode
- chatgpt-search
- perplexity
- bing-copilot-search

Rank: 4
Total brands: 11
Measured responses: 125
Presence percent: 16
Share of voice percent: 10.153846153846153
Average position: 3.909090909090909
Docs presence percent: 8
Blog presence percent: 0
Brand mention percent: 45.6


## Profile

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.
Product summary: 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 capabilities

- 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



### Target users

- AI/ML engineers and developers building production LLM agents and RAG pipelines
- Enterprise AI platform and infrastructure teams deploying agents at scale
- Data scientists and ML practitioners iterating on agent quality and evaluation
- DevOps/MLOps teams operating and monitoring LLM workloads in production
- Technical product teams at AI-native startups building agentic applications
- Non-technical enterprise users building automated workflows via Fleet (no-code)



### Key use cases

- 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)

Integrations ecosystem: LangSmith is framework-agnostic and supports tracing for any LLM stack via Python, TypeScript, Go, and Java SDKs, as well as OpenTelemetry integration for existing OTel pipelines. The open-source LangChain framework provides 100+ integrations with model providers (OpenAI, Anthropic, Cohere, AWS Bedrock, Google, Ollama), vector databases (Pinecone, Weaviate, Qdrant, Chroma, FAISS, pgvector), and external APIs/data connectors. Cloud deployment options span AWS, GCP, and Azure (cloud-hosted, BYOC, and self-hosted/Kubernetes). LangSmith natively supports MCP (Model Context Protocol) and A2A (Agent-to-Agent) protocols. The GitHub organization (langchain-ai) hosts 230+ repositories. LangChain Academy offers free courses, and an active community forum and partner network extend the ecosystem.
Pricing summary: 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.
Review summary: 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.
Competitive positioning: 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.
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.


### Source urls

- https://www.langchain.com/
- https://www.langchain.com/about
- https://www.langchain.com/pricing
- https://blog.langchain.com/series-b/
- https://techcrunch.com/2025/10/21/open-source-agentic-startup-langchain-hits-1-25b-valuation/
- https://fortune.com/2025/10/20/exclusive-early-ai-darling-langchain-is-now-a-unicorn-with-a-fresh-125-million-in-funding/
- https://sacra.com/c/langchain/
- https://blog.langchain.com/customers-klarna/
- https://blog.langchain.com/customers-chrobinson/
- https://blog.langchain.com/customers-podium/
- https://blog.langchain.com/customers-monday/
- https://github.com/langchain-ai
- https://www.gartner.com/reviews/product/langsmith
- https://docs.langchain.com/langsmith/pricing-faq
- https://tracxn.com/d/companies/langchain/__O9N2dOHcgRE9Nbcn5BFfkUHn-rVk6GTbq8oY-UJ0Ba4/funding-and-investors

Research approved: 2026-05-06T17:53:55.916+00:00


### Customer outcomes

| Customer | Summary | Metric |
| --- | --- | --- |
| Klarna | 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. | 80% reduction in customer query resolution time; ~70% of repetitive support tasks automated |
| C.H. Robinson | C.H. Robinson used LangGraph and LangSmith to automate email-based order processing, automatically parsing shipping requests and creating orders without manual data entry. | 600+ hours saved per day across 5,500 automated orders daily |
| Podium | 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. | 90% reduction in engineering escalations; F1 response quality score improved from 91.7% to 98.6% |
| monday.com (monday Service) | 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. | 8.7x faster evaluation feedback loops (from 162 seconds to 18 seconds) |



### Reviews breakdown





### Review themes



#### 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




### Company facts

Founded year: 2022
Hq: San Francisco, CA, USA


#### Founders

- Harrison Chase
- Ankush Gola

Employees range: Not available
Total funding: ~$160M
Valuation: $1.25B
Arr: Not available
Customer count: 6,000+ active LangSmith customers
Status: Private


Readiness: Not available


## Ranking

| Display name | Pair count | Total pairs | Presence percent | Avg position |
| --- | --- | --- | --- | --- |
| Arize AI | 39 | 125 | 31.2 | 6.011904761904762 |
| Langfuse | 35 | 125 | 28.000000000000004 | 4.298701298701299 |
| Braintrust | 30 | 125 | 24 | 4.615384615384615 |
| LangChain | 20 | 125 | 16 | 3.909090909090909 |
| LiteLLM | 19 | 125 | 15.2 | 4.75 |
| Portkey | 6 | 125 | 4.8 | 3.727272727272727 |
| Confident AI | 5 | 125 | 4 | 4.166666666666667 |
| Galileo | 5 | 125 | 4 | 6.5 |
| Helicone | 3 | 125 | 2.4 | 8.2 |
| Traceloop | 2 | 125 | 1.6 | 1 |
| Patronus AI | 1 | 125 | 0.8 | 5 |



## Platform breakdown

| Platform | Prompt count | Presence rate |
| --- | --- | --- |
| google-ai | 1 | 4 |
| google-ai-mode | 3 | 12 |
| chatgpt-search | 5 | 20 |
| perplexity | 11 | 44 |
| bing-copilot-search | 0 | 0 |



## Strengths

| Prompt text | Platform count | Avg position |
| --- | --- | --- |
| Which LLM observability tools handle PII redaction and data masking in traces for teams with HIPAA or GDPR compliance requirements? | 1 | 1 |
| What LLM tracing platforms handle high-throughput production workloads — millions of traces per day — without degrading query performance? | 1 | 1 |
| What LLM observability tools do ML engineering teams typically use to annotate and review production traces for quality feedback? | 1 | 1 |
| Which LLM evaluation platforms support custom rubric-based scoring for domain-specific correctness beyond generic faithfulness metrics? | 1 | 1 |
| Which LLM tracing platforms export trace data to a data warehouse so analysts can run custom eval queries alongside product metrics? | 2 | 1 |



## Gaps

| Prompt text | Competitor presence count |
| --- | --- |
| What platforms support end-to-end tracing of multi-agent pipelines including tool calls, retrieval steps, and sub-agent spawning? | 5 |
| What LLM gateway tools integrate best with secret managers and internal auth systems for enterprise teams rolling out to multiple product teams? | 4 |
| I'm evaluating LLM eval platforms — which ones integrate with version control to tie prompt regressions back to specific code or config changes? | 4 |
| Which LLM gateways handle multi-provider fallback and automatic retries while preserving full trace context across the switch? | 4 |
| 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. | 4 |



## Topic scores

| Topic name | Prompt count | Cited prompt count |
| --- | --- | --- |
| Capability | 5 | 2 |
| Developer Experience | 5 | 4 |
| Integrations & Ecosystem | 5 | 3 |
| Performance & Reliability | 5 | 3 |
| Setup & First Run | 5 | 2 |



## Prompt results

- Prompt text: Which LLM observability tools handle PII redaction and data masking in traces for teams with HIPAA or GDPR compliance requirements?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: 1
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Langfuse | 6 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Braintrust | 1 |
| Langfuse | 2 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 4 |
| Braintrust | 9 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Langfuse | 2 |
| Arize AI | 7 |



##### Bing-copilot-search




- Prompt text: Which LLM observability tools work with OpenTelemetry-compatible backends so we can consolidate LLM traces alongside existing service traces?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 5 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Traceloop | 1 |
| Arize AI | 3 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| LiteLLM | 3 |
| Arize AI | 6 |
| Braintrust | 11 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Langfuse | 2 |
| Arize AI | 4 |



##### Bing-copilot-search




- Prompt text: What LLM tracing platforms handle high-throughput production workloads — millions of traces per day — without degrading query performance?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: 1
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Galileo | 1 |
| Braintrust | 2 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 2 |



##### Chatgpt-search





##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 1 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Braintrust | 4 |


- Prompt text: Which LLM eval platforms support async evaluation at scale without blocking the inference path or adding latency for end users?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: 2
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 1 |



##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Braintrust | 4 |
| Arize AI | 8 |
| Galileo | 10 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 2 |
| Langfuse | 3 |
| Arize AI | 4 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Arize AI | 4 |
| Galileo | 6 |


- Prompt text: Which LLM observability platforms stay reliable under traffic spikes from batch eval jobs running thousands of LLM calls simultaneously?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: 3
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| LangChain | 3 |
| Arize AI | 15 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 2 |



##### Bing-copilot-search




- Prompt text: I'm evaluating LLM gateway solutions for a startup — which ones have the simplest self-hosted setup with a working UI on day one?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LiteLLM | 3 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |
| Portkey | 5 |



##### Bing-copilot-search




- Prompt text: Which evaluation platforms for LLM outputs are easiest to plug into an existing CI pipeline for a five-engineer team?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: 6
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Braintrust | 2 |



##### Chatgpt-search





##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 6 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Confident AI | 1 |
| Arize AI | 2 |


- Prompt text: What are the most production-hardened LLM gateway options for an enterprise team needing 99.9% uptime with circuit-breaker support?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 2 |



##### Google-ai-mode





##### Chatgpt-search





##### Perplexity

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |



##### Bing-copilot-search




- Prompt text: Which LLM tracing platforms make it easiest to replay a failed multi-step agent run and pinpoint exactly where reasoning went wrong?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: 2
Chatgpt-search: 1
Perplexity: 1
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 2 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| LangChain | 2 |
| Braintrust | 3 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Arize AI | 3 |
| Langfuse | 5 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Arize AI | 2 |
| Langfuse | 4 |



##### Bing-copilot-search




- Prompt text: What LLM observability tools do ML engineering teams typically use to annotate and review production traces for quality feedback?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: 1
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Confident AI | 1 |



##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LangChain | 1 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 3 |
| Braintrust | 5 |



##### Bing-copilot-search




- Prompt text: Which LLM gateways add the least latency overhead when routing between LLM providers — safe to use in production for sub-500ms SLAs?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LiteLLM | 4 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |



##### Bing-copilot-search




- Prompt text: What LLM gateway tools integrate best with secret managers and internal auth systems for enterprise teams rolling out to multiple product teams?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LiteLLM | 2 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |
| Portkey | 4 |



##### Bing-copilot-search




- Prompt text: Which LLM eval platforms have the best prompt playground experience for iterating on system prompts against a saved test dataset?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: 3
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 2 |
| Arize AI | 4 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Arize AI | 1 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Braintrust | 1 |
| Langfuse | 3 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Braintrust | 1 |
| LangChain | 3 |
| Langfuse | 6 |



##### Bing-copilot-search




- Prompt text: Which LLM evaluation platforms support custom rubric-based scoring for domain-specific correctness beyond generic faithfulness metrics?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: 1
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Galileo | 4 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Galileo | 3 |



##### Chatgpt-search





##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Braintrust | 4 |



##### Bing-copilot-search




- Prompt text: Looking for an eval platform that supports automated safety and toxicity scoring on LLM outputs at scale — what are my options?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Braintrust | 3 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Arize AI | 7 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Arize AI | 3 |
| Patronus AI | 4 |



##### Bing-copilot-search




- Prompt text: Which LLM observability platforms integrate natively with the most popular agent frameworks so traces appear automatically with no manual instrumentation?


#### Brand position by platform

Google-ai: 1
Google-ai-mode: 2
Chatgpt-search: Not available
Perplexity: 3
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| LangChain | 1 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Arize AI | 1 |
| LangChain | 2 |
| Langfuse | 3 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 3 |
| Arize AI | 4 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Arize AI | 1 |
| LangChain | 3 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Arize AI | 2 |


- Prompt text: I'm evaluating LLM eval platforms — which ones integrate with version control to tie prompt regressions back to specific code or config changes?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: 8
Perplexity: 4
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 1 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Braintrust | 4 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Braintrust | 1 |
| Langfuse | 2 |
| LangChain | 8 |
| Arize AI | 10 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Braintrust | 1 |
| Langfuse | 3 |
| LangChain | 4 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Confident AI | 1 |
| Arize AI | 5 |


- Prompt text: What are the best OpenTelemetry-compatible tracing backends for LLM apps that work out of the box without custom span parsing?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Braintrust | 2 |
| Langfuse | 3 |
| Arize AI | 5 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LiteLLM | 2 |
| Arize AI | 4 |
| Langfuse | 6 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Arize AI | 1 |
| Langfuse | 3 |



##### Bing-copilot-search




- Prompt text: What platforms support end-to-end tracing of multi-agent pipelines including tool calls, retrieval steps, and sub-agent spawning?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Arize AI | 2 |
| Langfuse | 4 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 3 |
| Braintrust | 4 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 3 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Arize AI | 1 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Braintrust | 2 |
| Arize AI | 3 |


- Prompt text: Which LLM gateways handle multi-provider fallback and automatic retries while preserving full trace context across the switch?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |
| Portkey | 2 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |
| Portkey | 4 |
| Helicone | 11 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Portkey | 1 |
| LiteLLM | 5 |



##### Bing-copilot-search




- Prompt text: What's the fastest LLM tracing platform for instrumenting a Python-based agent framework without rewriting existing code?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Traceloop | 1 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 2 |
| Arize AI | 5 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Arize AI | 1 |



##### Bing-copilot-search




- Prompt text: Which LLM observability platforms can a small team get running against a production RAG pipeline in under a day?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: 2
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 1 |



##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Arize AI | 3 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| LangChain | 2 |
| Arize AI | 3 |



##### Bing-copilot-search




- Prompt text: Which LLM tracing platforms export trace data to a data warehouse so analysts can run custom eval queries alongside product metrics?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: 1
Chatgpt-search: Not available
Perplexity: 1
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Braintrust | 2 |
| Langfuse | 4 |
| Helicone | 5 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 9 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Langfuse | 2 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Confident AI | 4 |


- Prompt text: Which LLM gateway tools give developers the best real-time cost and token usage visibility across multiple LLM providers during development?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LiteLLM | 2 |
| Portkey | 4 |



##### Chatgpt-search





##### Perplexity

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |
| Helicone | 6 |



##### Bing-copilot-search




- Prompt text: 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.


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: 5
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Confident AI | 1 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Braintrust | 3 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Braintrust | 3 |
| LangChain | 5 |
| Arize AI | 6 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Braintrust | 4 |



##### Bing-copilot-search







## Top sources

| Url | Title | Domain | Logo url | Source vertical | Content type | Citation count | Last30d count |
| --- | --- | --- | --- | --- | --- | --- | --- |
| https://www.langchain.com/langsmith/evaluation | LangSmith: AI Agent & LLM Model Evaluation Platform | langchain.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/b1dc0a348272cb295a5c0bee793df9c37a8202c2.png | commercial | product_page | 23 | 23 |
| https://www.langchain.com/resources/llm-observability-tools | 9 Best LLM Observability Tools for Production AI Agents | langchain.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/b1dc0a348272cb295a5c0bee793df9c37a8202c2.png | commercial | product_page | 19 | 19 |
| https://www.langchain.com/langsmith/observability | LangSmith: Agent & LLM Observability Platform | langchain.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/b1dc0a348272cb295a5c0bee793df9c37a8202c2.png | commercial | article | 15 | 15 |
| https://www.langchain.com/resources/agent-observability | AI Agent Observability: Tracing, Testing, and Improving Agents | langchain.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/b1dc0a348272cb295a5c0bee793df9c37a8202c2.png | commercial | product_page | 9 | 9 |
| https://docs.langchain.com/langsmith/data-export | Bulk export trace data - Docs by LangChain | docs.langchain.com | Not available | commercial | documentation | 7 | 7 |
| https://www.langchain.com/resources/langsmith-vs-arize | Faq | langchain.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/b1dc0a348272cb295a5c0bee793df9c37a8202c2.png | commercial | comparison | 5 | 5 |
| https://docs.langchain.com/langsmith/run-evaluation-from-playground | Run an evaluation from the Playground - Docs by LangChain | docs.langchain.com | Not available | commercial | documentation | 5 | 5 |
| https://docs.langchain.com/langsmith/evaluation-types | Evaluation types - Docs by LangChain | docs.langchain.com | Not available | commercial | documentation | 5 | 5 |



## Response excerpts

| Prompt text | Platform | Excerpt |
| --- | --- | --- |
| What platforms support end-to-end tracing of multi-agent pipelines including tool calls, retrieval steps, and sub-agent spawning? | perplexity | LangSmith — traces runs as nested steps, including model calls, retrieval, and tools, with particularly strong native integrations for LangChain and LangGraph. |
| What LLM tracing platforms handle high-throughput production workloads — millions of traces per day — without degrading query performance? | perplexity | LangChain also reports benchmark figures for its cloud platform: over 100M runs per day, with published latencies for trace loading, search, and filtering. |
| Which LLM eval platforms support async evaluation at scale without blocking the inference path or adding latency for end users? | perplexity | LangSmith — Its SDK sends traces to a distributed collector through an async callback handler; LangChain says this keeps tracing from impacting application performance. |



## Competitor excerpts

| Platform | Competitor name | Excerpt |
| --- | --- | --- |
| google-ai-mode | Langfuse | AI Agent Observability, Tracing & Evaluation with LangfuseAI Observability Tools: 8 Platforms for Tracing AI Agents in 2026 The le... |
| chatgpt-search | Langfuse | Website: LangSmith ### 2. Langfuse Best open-source all-rounder Captures hierarchical traces with distinct observation types for agents, tools, model generations, chains, and retrieval. |
| bing-copilot-search | Braintrust | Leading options include MLflow, Braintrust, LangSmith, Langfuse, Arize AX/Phoenix, Helicone, ONES.com, and New Relic.[Best LLM Tracing Tools for Multi-Agent Systems in 2026 \| MLflow](https://mlflow.org/articles/best-llm-tracing-tools-for-multi-agent-s... |
| google-ai | LiteLLM | ...\| Gateway Tool \| Best Suited For \| Secret Manager Integration \| Internal Auth & IAM Integration \| \| --- \| --- \| --- \| --- \| \| LiteLLM Proxy (Self-Hosted / Enterprise) \| Highly customizable, open-source proxy with robust virtual key mapping per team. |
| google-ai-mode | LiteLLM | LiteLLM Enterprise/Proxy and Kong AI Gateway are the top LLM gateway tools for multi-team enterprise rollouts that require deep integration with corporate secret managers and internal authentication (SSO / OIDC / RBAC) systems. |
| chatgpt-search | LiteLLM | LiteLLM — a strong starting point for a shared, self-hosted gateway with virtual keys, team budgets, model access controls, and integrations with enterprise infrastructure. |
| perplexity | LiteLLM | For an enterprise rollout across multiple product teams, LiteLLM and Portkey are the clearest fits in the results. |
| google-ai | Braintrust | Braintrust * How it works with Version Control: Braintrust offers a bidirectional sync workflow. |
| chatgpt-search | Braintrust | ## 1. Braintrust Best for commit-level traceability Braintrust records evaluation experiments and can associate them with Git branches and commit SHAs. |
| chatgpt-search | Langfuse | Documentation: GitHub Actions eval pipeline ## 2. Langfuse Best for prompt-to-Git synchronization Langfuse versions prompts immutably, supports prompt diffs and environment labels, and can sync prompt versions to GitHub. |
| perplexity | Braintrust | ...Git-based changes, but they do it at different levels: \| Platform \| How it ties regressions to changes \| Best fit \| \|---\|---\|---\| \| Braintrust \| Run evals on every pull request with GitHub Actions, gate merges on results, and post scores in the PR. |
| perplexity | Langfuse | \| \| Langfuse \| Its GitHub Action runs experiments in CI, posts scores as a PR comment, and can fail the pipeline when thresholds are missed. |



## Trend

Visibility delta: -12.799999999999997
Avg position delta: -0.6261203585147248
Citation count delta: -38