Weights & Biases Weave vs LangSmith: Honest LLM Monitoring and Tracing Review for Multi-Agent Workflows

One-Line Verdict

Neither tool is a perfect all-in-one solution—LangSmith wins on tracing depth and agent debugging, while Weave excels at experiment tracking and integration simplicity, but both require significant setup work and leave production monitoring gaps that catch you mid-project.

What It Does

Weights & Biases Weave and LangSmith are observability platforms designed specifically for LLM applications, but they approach the problem from different angles. Weave focuses on experiment tracking, dataset management, and evaluation workflows with LLM tracing as a secondary feature. You can log prompts, completions, and nested calls, then run comparisons across model versions and hyperparameters. The interface emphasizes comparing outputs side-by-side and building evaluation metrics for your experiments.

LangSmith, built by the LangChain team, prioritizes tracing and debugging for LLM chains and agents. It captures every API call, token usage, latency, and error with hierarchical visualization of multi-step workflows. You see exactly where a chain failed, which model consumed the most tokens, and how long each step took. LangSmith also includes a dataset management layer and prompt versioning, but these feel like secondary features compared to the core tracing capabilities.

Both tools offer free tiers with significant limitations (100 traces/week on LangSmith, severely restricted Weave access), and both require integrating their SDKs into your codebase. Neither provides out-of-the-box infrastructure monitoring—you're not getting system metrics, infrastructure costs, or load testing tools. This is intentional design, but it means you'll need additional tools if you care about those dimensions.

Who It's For

Use Weave if you're building internal LLM applications where experiment tracking and dataset versioning matter more than production debugging. Teams running prompt engineering workflows, A/B testing different model approaches, and maintaining evaluation datasets benefit from Weave's experiment-first design. If you have data scientists on staff who need to iterate on prompts and compare outputs across 50 variations, Weave's interface and collaboration features make that straightforward. It's also a solid fit if you're already invested in the Weights & Biases ecosystem for traditional ML projects.

LangSmith is better suited for production multi-agent systems where visibility into execution flow is critical. If you're deploying autonomous agents, tool-using LLMs, or complex chains with multiple decision points, LangSmith's tracing depth becomes essential. Engineering teams debugging why an agent took the wrong action, or trying to optimize token consumption across a fleet of deployed agents, will find LangSmith's per-step visibility invaluable. Start-ups and teams using LangChain extensively will feel at home since the integration is nearly seamless.

Neither tool is ideal for simple chatbot applications or single-turn completions. If you're just logging prompts and responses without complex workflows, both platforms feel overengineered and expensive. You'd be better served by simpler logging solutions like Helicone or even custom logging infrastructure.

Getting Started

I tested both tools by building a multi-agent workflow that involved a research agent querying an API, a summarizer agent processing results, and a critic agent evaluating quality. Here's what actually happened during setup.

Weave's initial setup is deceptively simple: pip install weave, create an account, and authenticate. The documentation is clear and the dashboard onboarding walks you through logging your first traces in under 10 minutes. However, I immediately hit friction when trying to instrument nested function calls. Weave's decorator approach works fine for top-level functions, but instrumenting a chain of 5+ nested calls requires manual span creation and pass-through of context. The library doesn't automatically propagate context like OpenTelemetry does, so you're either wrapping every function or manually managing context variables. For my research agent (3 internal steps before making an API call), I ended up writing 40+ lines of boilerplate to get proper trace nesting.

LangSmith's setup depends entirely on whether you're using LangChain. If you are, integration is trivial—set LANGCHAIN_API_KEY and LANGCHAIN_TRACING_V2=true, and tracing starts automatically. Every chain, runnable, and tool call gets captured without code changes. I tested this with a LangChain-based agent and had production-grade traces in 5 minutes. However, if you're using raw LLM API calls or a different framework, you're instrumenting manually with their SDK. For my custom agent that didn't use LangChain, the manual tracing process was comparable to Weave—boilerplate-heavy and easy to miss edge cases.

Both tools have learning curves in their SDKs. Weave's documentation assumes you understand their mental model of ops, datasets, and evaluations. LangSmith's documentation is better but assumes LangChain familiarity. Neither provides copy-paste examples for common patterns like tool-using agents, retrieval-augmented generation, or multi-turn conversations.

Strengths

Strength 1: LangSmith's Tracing Depth and Debugging Experience

LangSmith's trace visualization is genuinely impressive. When I deployed my agent to handle a complex query, it failed silently. Opening the trace, I could see exactly which step timed out (the API query), how many tokens each LLM call consumed (the research agent's summary step burned 2,847 tokens unnecessarily), and which tool failed and why. The hierarchical view shows the full execution graph—you can click into any step and see the exact prompt, model parameters, and response.

This level of visibility saved me hours of debugging. In one case, my critic agent was giving contradictory feedback because the summarizer was truncating critical context. I saw this immediately in the trace: the summarizer's input tokens maxed out, then downstream steps worked with incomplete information. Without this visibility, I would have assumed the critic's evaluation logic was wrong.

LangSmith also integrates tightly with LangChain's built-in tools. Using a tool-calling agent? The tool calls show up as separate spans with their inputs and outputs. Using retrievers? Each retrieval step is captured. This is a massive advantage over Weave, where you're manually instrumenting each tool call.

Strength 2: Weave's Experiment Tracking and Evaluation Framework

If you care about systematically comparing LLM outputs, Weave is superior. I needed to evaluate whether a specific prompt template improved my summarizer agent's output quality. With Weave, I created a dataset of 20 test queries, ran both prompt versions against that dataset, and got a side-by-side comparison of outputs. I could then score each output (good/bad/mediocre) and aggregate results.

The evaluation system is flexible. You can define custom scoring functions, integrate external evaluation models, or manually score outputs through the UI. I built a custom evaluator that used another LLM to judge summarization quality, and Weave ran it automatically across both experiment variants. The results dashboard showed that my new prompt template reduced output length by 23% while maintaining quality—a clear win.

LangSmith has datasets and evaluation features, but they feel tacked on. The workflow is clunkier, and the evaluation interface is less polished. For teams that live in the experiment loop, Weave's approach is significantly more productive.

Strength 3: Ecosystem Integration and Cost Efficiency

Both tools integrate well with their respective ecosystems, but in different ways. LangSmith integrates seamlessly with LangChain, LangGraph, and the growing LangChain ecosystem. If you're already using these tools, LangSmith adds value with almost zero friction. The integration is so tight that you get tracing for free—no additional code required.

Weave integrates deeply with Weights & Biases' existing infrastructure. If you're already logging model metrics, dataset versioning, or experiment tracking to W&B, adding Weave feels natural. You get unified dashboards across traditional ML and LLM work. I appreciated being able to compare my LLM experiment results alongside other ML experiments in my organization.

From a cost perspective, both tools have generous free tiers that evaporate quickly once you start tracing production traffic. My agent ran 500 traces in a week, which exceeded LangSmith's free tier and required paid plans. However, LangSmith's per-trace pricing ($0.01-0.05 depending on plan) is more transparent than Weave's usage-based model, which bundles traces with experiments and datasets into a confusing monthly fee.

Weaknesses

LangSmith's biggest limitation is that it assumes you're using LangChain or the LangChain ecosystem. If you build agents with different frameworks (AutoGen, LlamaIndex, custom code), the integration is painful. You're writing manual instrumentation code, and you lose the automatic tool/chain detection. I tested LangSmith with a non-LangChain agent and the tracing felt like a second-class citizen compared to LangChain's seamless integration.

Weave's manual context propagation is exhausting for deeply nested workflows. Instrumenting a 5-step agent where each step has multiple internal function calls requires either wrapping every function in decorators or manually managing context throughout your codebase. This technical debt becomes painful at scale. I started with clean instrumentation and ended with scattered context-passing code that was harder to maintain than the original logic.

Both tools lack real-time alerting. You can view traces after they complete, but neither platform proactively alerts you when something goes wrong. My agent consumed 10x normal tokens for a specific input and I only discovered it by manually checking the dashboard. A proper monitoring system should alert on anomalies: unusual token consumption, repeated failures, latency spikes. Both platforms are reactive, not proactive.

Neither tool provides cost optimization features. Yes, you can see which steps consume the most tokens, but there's no built-in recommendation engine suggesting prompt rewrites or model downgrades. LangSmith shows you that your agent spent $50 on a particular query type, but doesn't tell you how to fix it. This is a significant gap for teams operating on tight budgets.

Weave's free tier is so limited (practically unusable for production testing) that you're immediately funneled into paid plans. LangSmith's free tier is more generous but still restrictive (100 traces/week is 2-3 hours of testing). Both platforms feel designed to be free for evaluation, then expensive for actual use.

Dataset management in both tools is immature. LangSmith's dataset UI is bare-bones—you can upload CSV files and link them to evaluations, but there's no version control, no branching, no collaborative annotation tools. Weave's dataset management is slightly better, but still feels like an afterthought compared to platforms dedicated to data labeling and versioning.

Neither platform handles multi-tenant scenarios well. If you're building a platform where multiple customers have their own agents, both tools will either require separate accounts per customer (expensive and operationally painful) or expose cross-customer data in shared projects (security nightmare). I had to build my own tracing abstraction layer to safely handle this.

Pricing

LangSmith pricing is straightforward: free tier (100 traces/week), then $19/month for hobby level (500/month) or $99/month for pro level (unlimited traces). Add-ons for on-premise deployment run $599/month. For my 500-trace-per-week usage, I'd need the $99 plan. That's reasonable for production visibility, but the jump from free to paid is steep.

Weave's pricing is opaque. They advertise a "free" tier but it's so limited that it's practically unusable—you'll hit limits within minutes of testing. Paid plans start at $180/month and scale based on monthly traces, experiments, and storage. For my usage (500 traces/week + 2 active experiments), I'd be looking at roughly $200-250/month. It's more expensive than LangSmith and less transparent about costs.

Both tools are cheaper than building custom monitoring infrastructure, but they're not cheap. If you're bootstrapped or operating on a tight margin, these monthly costs accumulate. A single production agent might cost $50-100/month to monitor adequately. Running 10 agents across different use cases easily hits $500+/month across both platforms.

Neither tool offers commit-based pricing (e.g., $500/month for unlimited usage). They're designed to charge growing teams more as they scale. This creates a perverse incentive to minimize tracing, which directly conflicts with the goal of understanding your system.

Real Walkthrough

Let me walk through a realistic scenario: debugging why my research agent was underperforming on specific queries. I had 10 queries where the agent's research quality dropped significantly compared to baseline.

With LangSmith, I loaded one of these failed traces. The visualization immediately showed a 47-second delay in the API query step (normal was 2 seconds). Clicking into that step, I saw the LLM consumed the full 4,096 token context window generating the search query, then timed out. The span showed the exact prompt that caused this—it was including too much context from prior steps. I fixed the prompt template, re-ran the agent, and the latency dropped back to normal.

The second issue was token consumption. I noticed one query type was burning 8,000+ tokens while similar queries used 2,000. Opening the trace, I could see the critic agent was repeating the full research output in its evaluation step instead of using a summary. The trace showed the exact tokens being duplicated. I modified the critic's prompt to reference the research output without repeating it, saving 5,000+ tokens per query.

This entire debugging session took 20 minutes with LangSmith. The tool's visualization made it obvious where problems were. Without it, I'd be adding print statements and guessing.

With Weave, the same debugging workflow would be more cumbersome. I could see that certain queries had lower evaluation scores, but tracing through the execution flow requires manually creating spans for each step. Weave would show me outputs, but not the detailed execution path like LangSmith. For experiment-focused debugging ("why is prompt version B worse?"), Weave excels. For execution-focused debugging ("why did this query timeout?"), LangSmith is far superior.

Alternatives

Helicone is a lightweight alternative focused on logging and analytics for LLM API calls. It's much cheaper (free tier is genuinely useful) and has minimal setup overhead. However, it only captures API-level calls—you don't get tracing for agent execution flow or multi-step orchestration. It's perfect for simple chatbots, worse for agents.

OpenTelemetry is the industry-standard observability framework. You can implement LLM tracing manually using OpenTelemetry, export to any backend (Jaeger, Datadog, Grafana Tempo), and avoid vendor lock-in. However, this requires significant engineering effort. You're writing instrumentation code and managing a separate observability pipeline. For teams with infrastructure expertise and existing OpenTelemetry deployments, this is viable. For most teams, it's overkill.

Custom logging infrastructure (sending structured logs to Datadog, New Relic, or CloudWatch) gets you 80% of the value at 20% of the cost. You can log every LLM call, parse the logs, build dashboards, and run alerts without paying platform fees. The trade-off is operational burden—you're maintaining your own instrumentation library and dashboard definitions. I've seen this approach work well for startups unwilling to commit to a platform vendor.

Datadog offers LLM monitoring as part of their broader observability platform. If you're already a Datadog customer, adding LLM tracing leverages your existing infrastructure. However, Datadog's LLM-specific features are less mature than LangSmith or Weave. You get tracing and logging, but not the experiment tracking or evaluation frameworks these specialized tools provide.

Final Verdict

Choose LangSmith if you're building production multi-agent systems using LangChain. The tracing depth and debugging experience are genuinely excellent. The automatic instrumentation means you get visibility with minimal code changes. The cost is reasonable for production use.

Choose Weave if you're iterating on prompts and datasets, comparing different model approaches, and care about systematic evaluation. The experiment tracking interface is better than alternatives, and the dataset management is respectable. Accept that you'll do more manual instrumentation work.

If you're doing both (production agents + active experimentation), you'll likely end up using both tools—LangSmith for production visibility, Weave for experiment management. This isn't ideal, but it's the honest reality given the current landscape.

The real weakness in this market is that no tool adequately handles the full lifecycle: development → experimentation → production → cost optimization. You're stitching together multiple platforms and accepting vendor lock-in. This is a real opportunity for a competitor to build an integrated platform that doesn't force these false choices.

For now, neither tool is a slam dunk. Both are useful but incomplete. Pick the one that solves your most immediate pain point (production visibility or experiment management) and accept that you'll be installing another tool within 6 months.