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Alternatives

Products that do what LangSmith General Availability does

LLM application development, monitoring, and testing

  1. 1

    Evaluate & optimize your LLM performance with DSPy

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  2. 2

    Improve your LLM apps with open-source observability tool

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    Build LLM apps and plug AI into your team's operations

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  4. 4

    A collection of production-ready reference architectures

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  5. 5

    Validate, monitor, and safeguard LLM-based apps

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  6. 6LO

    Hey HN, Ola and Karthik here. We are working on Langtrace(https://github.com/Scale3-Labs/langtrace), an open source, open telemetry based SDK and monitoring/evaluations client for LLM based applications. The SDK generates OTEL standard spans and traces for popular LLMs like OpenAI, Anthropic and Cohere, popular frameworks like Langchain and LlamaIndex and vectorDBs like ChromaDB and Pinecone. The LLM monitoring/evaluations space has seen a number of products off late, both open source and closed source ones. But, a couple of things we have observed are: lack of…

    2024

  7. 7

    Aggregate uptime monitoring across OpenAI, Claude, and more

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  8. 8
    Kuzco216

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  9. 9
    Astra AI142

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  10. 10

    Open-source LLM tracing for agent visibility

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  11. 11
    liteLLM120

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  12. 12

    Find your best LLM for a local inference

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  13. 13

    Trace LLM requests + costs with OpenTelemetry monitoring

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  14. 14
    Langflow139

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  15. 15
    TraceLLM100

    OpenTelemetry for production AI applications

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  16. 16

    Let Llama take over your desktop

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  17. 17LS

    Ever had json.loads() explode halfway through an LLM stream? langdiff fixes that with a schema + callback approach. Define your schema → attach callbacks → push streaming tokens → get structured events immediately.

    2025 · github.com

  18. 18HL

    At testup.io we have been working for a while to bring artificial intelligence to the field of test automation. Just a few years ago, the primary challenge laid in accurately identifying UI elements following minor structural changes, such as updates to IDs or paths. The emergence of Large Language Models (LLMs) raised the bar for what it meant to be smart. Now, we anticipate the robot to do lots of things autonomously, such as retry in cases of unresponsiveness or handle minor error reports. A more challenging, but soon expected feature, would involve the test robot navigating your web shop…

    2024 · github.com

  19. 19LI

    Hey HN! We built Lunon to make LLM development way less of a headache. Ever wanted to see how different models handle the same prompt without all the setup hassle? That's what we fixed. Our API lets you compare Claude, GPT, Mistral and others in real-time with just a few lines of code. No more complex infrastructure or managing multiple API connections - we handle all that boring stuff behind the scenes. Plus, you can cut costs by intelligently routing requests to the right model for each task. Use the powerful (expensive) models only when you really need them. If you're building with LLMs…

    2025 · lunon.com

  20. 20LA

    You build LLM applications with YAML files, that define an execution graph. Nodes can be either LLM API calls, regular function executions or other graphs themselves. Because you can nest graphs easily, building complex applications is not an issue, but at the same time you don't lose control. The YAML basically states what are the tasks that need to be done and how they connect. Other than that, you only write individual python functions to be called during the execution. No new classes and abstractions to learn.

    2024 · github.com

  21. 21GB

    Hey HN, We’re excited to share PySpur, an open-source tool that provides a graph-based interface for building, debugging, and evaluating LLM workflows. Why we built this: Before this, we built several LLM-powered applications that collectively served thousands of users. The biggest challenge we faced was ensuring reliability: making sure the workflows were robust enough to handle edge cases and deliver consistent results. In practice, achieving this reliability meant repeatedly: 1. Breaking down complex goals into simpler steps: Composing prompts, tool calls, parsing steps, and branching…

    2024 · github.com

  22. 22LO
  23. 23EL

    Hey HN! I built Experiment to solve a common frustration in LLM development: the lack of proper tools for prompt engineering experimentation. Here's what makes it different: Key Features: - Load and edit chat completion logs from CSV files - Fork and modify specific conversation entries - Run inference via Anthropic, Mistral, and OpenAI - Define custom tools using JSONSchema format - Visual tool usage analysis with collapsible, sorted key-value pairs - Full mobile support and available as installable PWA Technical Highlights: - Built with React using custom isomorphic architecture -…

    2025 · github.com

  24. 24LC

    2023 · github.com

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