LangSmith vs PHBench: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of LangSmith and PHBench — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
LangSmith
LangChain Inc.
Platform to debug, evaluate, monitor, and optimize LLM applications with SDKs, integrations, prompt management, and observability.
Key features
- SDKs for Python and JavaScript: Official client libraries to instrument, send, and query run traces, evaluations, and prompt metadata from LLM applications and agent chains, enabling language-agnostic integration and programmatic access to platform data.
- End-to-end Tracing and Run Storage: Capture detailed step-level traces of LLM calls and agent actions (including inputs, outputs, tools used, timings, and errors) for reproducible debugging and root-cause analysis of complex flows.
- Evaluation & Experimentation: Create datasets, run evaluations, and track experiments with automated scoring (including LLM-based judges) to compare prompts, models, or agent strategies over time and measure improvements.
- Prompt Management and Versioning: Centralized prompt repository and APIs to list, fetch, and manage prompt templates, visibility (public/private), and versions to support prompt reuse, auditing, and A/B testing.
- Conversation & Thread History: Retrieve chronological message histories and thread metadata for conversations, enabling replay, analytics, and context-aware debugging of chat-based applications.
- MCP Server & Integration Components: Optional MCP server and integration layer that bridges language models, agents, and the LangSmith platform, providing endpoints for prompt retrieval, analytics integration, and workspace-scoped API keys.
- Self-hosting & Custom Endpoints: Support for custom LANGSMITH_ENDPOINT configuration and self-hosted deployments to meet data residency, regulatory, or on-premises requirements.
- CLI and Tooling: Command-line utilities (pip-installable) to create datasets, run evaluations, configure API keys, and interact with the LangSmith platform directly from developer workflows.
- Client SDKs for Python and JavaScript for interacting with the LangSmith platform
- Native integration with LangChain (Python and JS) for automatic trace collection
- Trace and conversation history capture with chronological message retrieval
- Evaluation pipelines and tools to run model/agent evaluations and record results
- Prompt management: list, fetch, and retrieve prompts and templates
- Support for self-hosting and custom API endpoints (LANGSMITH_ENDPOINT)
- API key based authentication (LANGSMITH_API_KEY) and optional workspace scoping (LANGSMITH_WORKSPACE_ID)
- PII removal and anonymization utilities (environment flags and custom anonymizers)
- MCP server to bridge models and LangSmith for conversation tracking and analytics integration
- Documentation site and cookbook with tutorials, recipes, and examples
Best for
- Agent Step Debugging: Inspect step-level traces for multi-step agents to identify which tool call or prompt produced incorrect results and rapidly iterate fixes.
- Model Evaluation Experiments: Run controlled experiments comparing model versions or prompt variants against curated datasets using automated scoring and track results over time.
- Production Monitoring: Monitor live LLM applications for errors, latency spikes, or behavioral drift using run telemetry and alerting integrations to reduce downtime.
- Prompt Library Management: Store, version, and fetch canonical prompts across teams to ensure consistency, enable A/B testing, and audit prompt changes in production.
- Conversation Analysis and Support: Retrieve full thread histories to reproduce user issues, analyze user interactions, and improve response quality or routing logic.
- Self-hosted Deployments: Deploy LangSmith endpoints in-region or on-premises for organizations requiring data residency or isolated environments while keeping LangChain integrations.
- Continuous Improvement Workflows: Use the cookbook recipes and SDKs to automate feedback collection, run regular evaluations, and feed insights back into prompt/model tuning pipelines.
- Debugging and tracing multi-step agent executions to find failure points
- Monitoring LLM performance and behavior in production with observability dashboards
- Evaluating prompts and model responses via automated evaluation pipelines
- Managing and retrieving prompt templates and shared prompt libraries
PHBench
Vela Partners
A benchmark dataset and evaluation suite mapping Product Hunt launches to Series A outcomes for predictive modeling of startup funding.
Key features
- Large-Scale Mapping: Links 67,292 featured Product Hunt posts to 528 verified Series A outcomes within an 18-month horizon, enabling longitudinal outcome prediction.
- Engineered Signal Set: Provides 61 engineered features per post including engagement signals (votes, comments, reviews), rank signals (daily/weekly/monthly), maker features (maker count, followers), temporal features, topic flags, and interaction terms to support rich modeling.
- Structured Splits and Imbalanced Labels: Published train/validation/test splits (Train: 47,071; Val: 6,753; Test: 13,468) with measured positive rates (~0.76–0.79%), plus withheld test labels for blind benchmark evaluation.
- Evaluation & Submission Workflow: Test labels are withheld and researchers submit predictions (email to benchmark@vela.partners) for centralized scoring to enable fair comparison between models.
- Open License & Citation: Distributed under CC BY 4.0 (per Hugging Face dataset page) with a required citation (Ihlamur et al., PHBench arXiv 2026) for academic and research use.
- Supporting Code & Graph Tools: Associated code and GNN/graph-analysis workflows are available (Weave project on GitHub) to build graph representations and run node-classification experiments; dataset access may require contacting Vela Partners due to access conditions.
- Mapped dataset of 67,292 Product Hunt featured posts linked to 528 verified Series A outcomes (18-month horizon, 2019–2025).