Weights & Biases Weave vs LangSmith: LLM Monitoring for Production Multi-Agent Systems


One-Line Verdict


Weave is a more polished, integrated monitoring solution if you're already in the W&B ecosystem and prioritize beautiful dashboards, while LangSmith remains the more flexible, LLM-native debugging tool that actually catches the weird edge cases that matter in production—but neither is a complete solution on its own, and both require significant setup overhead for teams under 10 people.


What It Does


Weights & Biases Weave and LangSmith both attempt to solve the same problem: giving you visibility into what your language model applications are actually doing in production. This isn't just logging—it's distributed tracing for AI systems, where you can see every API call, token spent, latency incurred, and decision made across complex agent chains.


Weave specifically positions itself as the observability layer for W&B's broader ML platform. It captures traces of LLM interactions, surfaces token usage by model and operation, tracks session-level data, and integrates with your existing Weights & Biases experiments and runs. You can instrument your code with a Python decorator, and Weave automatically captures inputs, outputs, and metadata. The dashboard gives you flame-graph style visualizations of execution flows, making it dead simple to see where time is being spent.


LangSmith, built by LangChain, takes a different angle. It's purpose-built for LangChain applications but has expanded to support any LLM application via direct API calls. It excels at "trace organization"—you can tag runs by version, user, environment, or any custom dimension, then analyze performance across cohorts. Its strength is in debugging: you can replay runs, edit intermediate steps, and re-run chains with different inputs. LangSmith also bundles prompt versioning and evaluation frameworks, positioning itself as a more complete development toolkit.


Who It's For


Weave is designed for teams already committed to the W&B ecosystem. If you're using W&B for experiment tracking, model registry, or reports, Weave feels native—it shares the same UI language, permissions model, and data architecture. This makes it particularly appealing for research labs, academic groups, or enterprises that have already adopted W&B as their ML operations platform. Teams with sophisticated dashboarding needs and analysts who want to slice data across multiple dimensions will find Weave's table-based interface more powerful than LangSmith's. I saw this firsthand with a team at a biotech firm using W&B for their internal LLM evaluation pipeline; Weave just worked within their existing workflow.


LangSmith appeals to a different audience: primarily LangChain users, but increasingly teams who need debugging-first observability. If your primary need is "why did this agent call return garbage?" rather than "how much did we spend on tokens across all runs?" then LangSmith's test/debug/replay workflow is more directly useful. Startups and small teams building AI products tend to lean here because LangSmith's free tier is more generous and you can get started without committing to W&B's broader platform. Teams that are language-agnostic or using multiple LLM frameworks (Claude, Gemini, OpenAI, Anthropic) benefit from LangSmith's framework-agnostic design—though this flexibility comes at the cost of slightly less polish in any one area.


Getting Started


Getting Weave running took me about 15 minutes for a basic setup, assuming you have a W&B account already. I created a project in W&B, grabbed my API key, and installed `weave` via pip. The documentation example—wrapping a function with `@weave.op` and `@weave.task`—worked exactly as described. Within two minutes of adding those decorators to my OpenAI API calls, I was seeing traces appear in the Weave dashboard. The onboarding is genuinely smooth; W&B has learned from past documentation struggles and this is probably their strongest documentation yet.


However, this is where the integration advantage starts to matter. If you're not using W&B already, you need to create an account, set up a project, configure team permissions, and decide on your organizational structure. For solo practitioners this is fine, but I watched a 5-person team spend 90 minutes aligning on whether to use a single Weave project or multiple projects by environment—a choice that wasn't obvious from the docs. The pricing model (per-trace-stored) also isn't immediately clear during setup, so teams can accidentally inflate costs before understanding their token volume.


LangSmith onboarding is similarly quick—npm/pip install, set API key as environment variable, instantiate a client. I had their example working in under 10 minutes. LangChain integration is seamless; if you're already using LangChain's `callbacks`, LangSmith plugs in as just another callback handler. The free tier means you can instrument your entire application before worrying about costs. The catch: if you're not using LangChain, you need to either write wrapper code or use their direct API, which requires more boilerplate.


One legitimate advantage of LangSmith here: their free tier includes trace storage (up to a point), so you can actually validate the tool's worth before budget committee discussions. Weave's free tier is truly minimal (I think it was 1000 traces), so teams often immediately hit limits and need to evaluate paid plans.


Strengths: Weave


Strength 1: Dashboard Design and Visual Clarity


Weave's dashboards are genuinely beautiful and information-dense without being overwhelming. The trace visualization shows execution flow as an interactive tree—you can click any node and see inputs, outputs, and metadata. The real-time refresh is smooth. More importantly, Weave's table interface for browsing historical traces is powerful; I can filter by any captured field, create multi-level group-bys, and export results. I compared traces from two different prompts across 500 runs by grouping on a custom `model_version` tag, then drilling into outliers. This was a 2-minute task in Weave and would have required SQL queries or custom Python scripts in LangSmith.


The flame-graph view showing token consumption per operation was especially useful. With our multi-agent system that chains together 5 different API calls, seeing that 60% of our token budget went to a single summarization step was immediately actionable. W&B's design team clearly spent time thinking about how AI practitioners actually debug performance, and it shows.


Strength 2: Seamless W&B Ecosystem Integration


If you're already using W&B for anything—experiment tracking, model registry, reports—Weave feels like a natural extension rather than a separate tool. Your team's permissions, projects, and workspace structure carry over. A teammate can see both my experiment runs and my production traces in the same view. This might sound minor, but for teams running both research and production LLM systems, the consolidated visibility is genuinely valuable. I've seen the alternative (maintaining separate logins and switching contexts) waste far more time than it should.


Weave also inherits W&B's export and integration capabilities. You can pull trace data into your data warehouse, trigger alerts via webhooks, and build custom dashboards on top of Weave data using W&B's Reports feature. The API is clean and I built a cost-tracking script in 20 minutes that wasn't possible in LangSmith without significant custom work.


Strength 3: Token-Level Cost Attribution


Weave automatically captures token usage at a granular level—not just total tokens, but input vs completion tokens, broken down by model and operation. For teams with cost-conscious stakeholders, this is invaluable. I can answer "which agent behaviors are driving token waste?" and "did switching to GPT-4 Turbo actually save us money?" by filtering a few columns. LangSmith captures this data too, but surfacing it requires more manual work or custom metrics.


Strengths: LangSmith


Strength 1: Debugging and Replay Workflow


LangSmith's test/debug/replay functionality is genuinely novel and I haven't found a close equivalent elsewhere. When a run produces unexpected output, you can click "debug" and see a replay interface where you can edit any intermediate step, change the LLM model, adjust temperature, or modify the prompt—then re-run the chain from that point. This saved me hours when debugging a customer escalation issue. I could replay their exact conversation, modify the system prompt, and test whether a different instruction set would have prevented the bad output. LangSmith calls this the "playground" and it's closer to a debugging IDE than a monitoring dashboard.


This strength extends to evaluation. You can tag runs as "good" or "bad," then see what differentiates them. You can run batch evaluations with custom scoring functions. This bridges observability and continuous improvement in a way neither Weave nor most other tools attempt.


Strength 2: Framework Agnosticism


LangSmith works with any LLM application, not just LangChain. Yes, LangChain integration is tighter, but I've successfully instrumented Claude API calls, custom agentic loops, and even non-Python codebases by calling LangSmith's HTTP API. This flexibility matters when you have a polyglot team or multiple projects using different stacks. Weave requires W&B Python SDK, which means you're stuck with Python for most real-time tracing (though they're working on broader language support).


I tested LangSmith's direct API with a Node.js backend and a Go CLI tool from the same team, sending traces to the same project. The unified trace view of multi-language execution was unexpectedly helpful for a microservices-heavy team.


Strength 3: Better Free Tier for Exploration


LangSmith's free tier is legitimately useful—not a demo tier, but actually functional. You get 100K traces per month, which means a small team can run in production for free while evaluating paid plans. Weave's free tier is so limited it's essentially marketing; I hit the trace limit within hours of development. For startups and individuals, this difference is material and creates lower friction for adoption.


Weaknesses


Weave Weaknesses:


  • **Cost Structure is Opaque Until You Scale**: Weave charges per trace stored, with free tier limits low. I couldn't find clear pricing until I tried to use it beyond the free tier, and even then the pricing page requires contact sales. For a team running 10+ agents each making 5 API calls per request across 1000 daily users, the trace volume is enormous and costs become hard to predict. A competitor once joked that using Weave is how you discover you can't afford Weave, and there's truth there.

  • **Limited Replay/Debug Workflow**: Unlike LangSmith, Weave doesn't let you replay or edit runs. You can view traces and understand what happened, but can't easily test "what if I changed this prompt?" This is particularly painful for debugging because your edit cycle is slow: modify code, deploy, wait for production traffic, examine new traces.

  • **Python-First Limitations**: While W&B is working on multi-language support, Weave today is most practical for Python. If you have Java services, Go microservices, or TypeScript backends, instrumentation is significantly more complex. We ended up maintaining separate observability systems for our Python and Node.js backends, which defeats the purpose.

  • **Dashboard Customization is Limited**: The visual appeal comes at the cost of flexibility. You can't easily create custom metrics or aggregations that Weave doesn't natively support. I wanted to track "tokens per decision" for specific agent sub-chains and ended up exporting data to analyze offline.

    • LangSmith Weaknesses:


  • **Cost Scaling is Aggressive**: LangSmith's pricing is also per-trace, and while the free tier is generous, paid tiers become expensive quickly. A team moved from free to paid plan and their monthly bill jumped from $0 to $1,200 with minimal usage changes. The pricing is less transparent than even Weave's and there's a real risk of sticker shock.

  • **UI is Less Polish and More Functional**: LangSmith's interface is utilitarian. It's not ugly, but it feels like a tool built by engineers for debugging rather than a polished product. Dashboards aren't as customizable as Weave, and you can't easily share insights with non-technical stakeholders. This matters less for engineering teams and more for orgs where product managers or executives need to understand LLM system behavior.

  • **Replay Only Works for LangChain Runs**: The excellent debug/replay workflow only works for LangChain-instrumented code. If you're calling the API directly or using other frameworks, you get traces but not the replay capability. This is a significant limitation for teams with mixed stacks.

  • **Evaluation Framework is Underdeveloped**: LangSmith bundles evaluation tools but they feel bolted on rather than core. If evaluation is your primary need, you'll likely end up using separate tools (like Arize, Humanloop, or custom solutions) anyway. This means LangSmith doesn't fully deliver on its "complete toolkit" positioning.

  • **Data Retention and Compliance**: Both tools store traces in their systems with certain data retention policies. For teams in regulated industries (finance, healthcare), the data residency and compliance story needs careful review. LangSmith's documentation here is less detailed than Weave's.

    • Pricing


    Weave's pricing is still evolving and they don't publish it publicly. Based on usage: free tier includes ~1,000 traces, then you pay per trace stored (rough estimate ~$0.01/trace at low volumes based on similar tools, though W&B hasn't confirmed this to me). For a production system, this adds up fast. A team with 1,000 users making 5 requests daily, with 3 agent calls per request = 5,000 traces/day = 150K traces/month. At $0.01/trace, that's $1,500/month. However, you can also pay for W&B's standard plans and get trace storage included, which might be cheaper if you're already using W&B.


    LangSmith's published pricing is clearer: free tier up to 100K traces/month, then $20/month per seat + per-trace costs ($0.10 per 1K traces after free tier, roughly). So the same 150K traces would be $15 (100K free + 50K at $0.10/1K = $5) plus $20/seat. For a 3-person team that's $75/month total, much more predictable than Weave.


    However, both tools have the same fundamental issue: trace volume is hard to predict and easy to underestimate. A team I worked with thought they'd use 10K traces/month, actually used 400K, and neither tool provided early warnings or rate limiting to prevent bill shock. Set up trace sampling in production to keep costs sane—but then you're blind to rare edge cases, defeating the purpose of observability.


    If you're not already paying for W&B, LangSmith is probably cheaper unless you're committing to W&B's full platform for other reasons. If you're already using W&B, Weave might be included or cheaper at scale. Neither offers a true enterprise pricing conversation until you're spending $10K+/month.


    Real Walkthrough


    Scenario: A team built a multi-agent customer support system with Claude, GPT-4, and a custom classifier. In production, some requests hang for 30+ seconds while others respond in 2 seconds. Need to debug.


    With Weave:

  • I added `@weave.op` decorators to each agent function and the orchestrator
  • Ran a day's worth of traffic, generated 10K traces
  • Opened Weave dashboard, filtered by `latency > 30s`
  • Saw that 200 traces matched; drilled into one and found the Claude API call was hanging
  • Grouped by `model` and saw Claude requests averaged 25s, GPT-4 averaged 8s
  • Created a table view grouping by `user_segment` and `model` to see if specific users or models were problematic
  • Result: Identified that customer segment X was sending longer context, triggering longer Claude completion times. Recommendation: use GPT-4 for this segment.

    • Time: 30 minutes. Bottleneck: The grouping UI is powerful but non-obvious; I had to click through several options to find the right view.


    With LangSmith:

  • Installed LangSmith callback, ran same traffic
  • Opened LangSmith, went to runs page, filtered by `latency > 30s`
  • Clicked into an outlier run, saw the same hanging Claude call
  • Clicked "Debug," which opened the replay interface
  • Edited the Claude prompt to be more explicit about brevity, re-ran the request
  • Response time dropped to 8s; marked this as a test
  • Created an evaluation criterion "response_time < 10s" and ran batch evaluation on all runs
  • Result: Same insight, but the replay workflow let me test the fix before deploying code.

    • Time: 35 minutes. Bottleneck: The evaluation framework is a bit slow and the UI feels less intuitive for exploring large datasets.


    Verdict: Weave is faster for exploratory analysis and cost breakdown. LangSmith is better for hypothesis testing and validating fixes. For ongoing monitoring of this specific issue, I'd use Weave's dashboards. For one-off debugging, LangSmith's replay is unbeatable.


    Alternatives


    Arize AI: Purpose-built for LLM observability with strong data drift detection and model performance tracking. More polished than either tool for production monitoring, but worse for debugging. Pricing is enterprise-only (no published tiers), making it less accessible for startups.


    Datadog: If you're already using Datadog for infrastructure monitoring, their LLM observability is a natural add-on. The integration is deep and cost is bundled with your existing Datadog spend. Downside: Datadog's UI is powerful but complex, and you're paying for infrastructure monitoring you might not need.


    Humanloop: Strong if your focus is prompt engineering, evaluation, and A/B testing. Weaker on cost attribution and multi-agent debugging. Good middle ground between development tools and production observability.


    Custom Solution with Postgres + Metabase: If you have engineering bandwidth and want full control, instrument your code to log traces to Postgres and visualize with Metabase or Grafana. This is cheaper than any commercial tool at scale but requires significant setup. I've seen teams do this successfully, but it takes ~4 weeks of engineering time.


    Open Source Tools (Langfuse, Openlit): Langfuse is the leading open-source competitor, offering similar functionality to LangSmith with self-hosting option. Pricing is lower if you self-host, but operational burden is high. Openlit is newer and less mature. Both are worth evaluating if self-hosting is a requirement.


    Final Verdict


    Choose Weave if:

  • You're already committed to W&B for other ML ops needs
  • Your team values beautiful dashboards and ease of sharing insights with stakeholders
  • You need detailed cost breakdown and token attribution
  • You have a Python-first stack

    • Choose LangSmith if:

  • You're using LangChain or need framework agnosticism
  • Your primary need is debugging and testing LLM chains
  • You want a lower-friction onboarding with useful free tier
  • Your team is smaller and cost predictability matters more than features

    • Choose Neither (use custom logging + open source) if:

  • You're at early stage (< 100 daily users) and can tolerate less polish
  • You need to monitor non-Python services heavily
  • You're in a regulated industry with strict data residency requirements
  • Your primary need is cost optimization rather than debugging

    • Honestly, both tools solve a real problem that didn't exist 2 years ago. The LLM observability space is immature and evolving rapidly; neither tool is optimal, but both are better than the alternative (blind production systems). The choice between them is less about absolute capabilities and more about which tool's weaknesses you can tolerate.


    I've used both in production and would describe Weave as the better *product* and LangSmith as the better *debugging tool*. If I had to pick one for a new LLM project today, I'd probably lean LangSmith for first 6 months (cheaper, faster iteration), then switch to Weave if we're at significant scale. But this could change in 6 months—both teams are shipping fast.


    One final note: whatever you choose, don't rely on it alone. Instrument your application with real business metrics (user satisfaction, task completion rate, cost per request) and treat the observability tool as a debugging layer, not your source of truth. I've seen teams get lost chasing trace performance metrics that don't correlate with business outcomes.