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Openllmetry Alternatives
Similar projects and alternatives to openllmetry
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SaaSHub
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ollama
Get up and running with Kimi-K2.6, GLM-5.2, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models.
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Strapi
🚀 Strapi is the leading open-source headless CMS. It’s 100% JavaScript/TypeScript, fully customizable, and developer-first.
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PostHog
:hedgehog: PostHog is the leading platform for building self-driving products. Our developer tools – AI observability, analytics, session replay, flags, experiments, error tracking, logs, and more – capture all the context agents need to diagnose problems, uncover opportunities, and ship fixes. Steer it all from Slack, web, desktop, or the MCP.
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FLiPStackWeekly
FLaNK AI Weekly covering Apache NiFi, Apache Flink, Apache Kafka, Apache Spark, Apache Iceberg, Apache Ozone, Apache Pulsar, and more...
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tracetest
🔠Tracetest - Build integration and end-to-end tests in minutes, instead of days, using OpenTelemetry and trace-based testing.
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self-hosted
Sentry, feature-complete and packaged up for low-volume deployments and proofs-of-concept
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langfuse-docs
🪢 Langfuse documentation -- Langfuse is the open source LLM Engineering Platform. Observability, evals, prompt management, playground and metrics to debug and improve LLM apps
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openlit
Open source platform for AI Engineering: OpenTelemetry-native LLM Observability, GPU Monitoring, Guardrails, Evaluations, Prompt Management, Vault, Playground. 🚀💻 Integrates with 50+ LLM Providers, VectorDBs, Agent Frameworks and GPUs.
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better openllmetry alternative or higher similarity.
openllmetry discussion
openllmetry reviews and mentions
Posts with mentions or reviews of openllmetry.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2026-07-01.
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Semantic Observability: Engineering Reliability for Production RAG
Injecting these into standard logs is a recipe for storage-cost disasters and PII leaks. Instead, we use OpenLLMetry, an open-source extension of OTel that introduces semantic conventions for AI. It auto-instruments calls to providers like OpenAI or Anthropic, capturing the input and output as attributes within a span.
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What is OpenTelemetry, and why does it matter for AI agents?
OpenLLMetry: Pre-built instrumentations for LangChain, Anthropic, OpenAI, LlamaIndex, Ollama, Qdrant, and others. Reduces boilerplate if your agent uses popular frameworks.
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AI: Introduction to Ollama for local LLM launch
For monitoring, there are separate full-fledged monitoring solutions like Opik, PostHog, Langfuse or OpenLLMetry, maybe will try some next time.
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Top Open Source Tools for LLM Observability in 2025
Traceloop is also an open-source project that provides end-to-end tracing for LLM applications. It uses OpenTelemetry standards to offer visibility into the request flow through code, especially in agent-based and multi-step workflows. Traceloop focuses exclusively on tracing and requires an existing OpenTelemetry setup to unlock its full potential.
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Testing LLM Apps with Trace-based Tests
To instrument our app you will use the CLI tool opentelemetry-instrument that automatically sets up auto-instrumentation in your code without needing to do boilerplate configuration, and OTel Python SDK and OpenLLMetry to do manual instrumentation and specific instrumentation for LLM SDKs, like the file ./app/telemetry.py in the example:
- Show HN: Laminar – Open-Source Datadog and PostHog for LLM Apps, Built in Rust
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Launch HN: Traceloop (YC W23) – Detecting LLM Hallucinations with OpenTelemetry
2. Our soft-faithfulness metric was able to detect cases in summarization tasks where a model was completely making up stuff that never appeared in the original text.
One of the challenges we faced was figuring out how to collect the data that we need from our customers' LLM apps. That’s where OpenTelemetry came in handy. We built OpenLLMetry (https://github.com/traceloop/openllmetry), and announced it here almost a year ago. It standardized the use of OpenTelemetry to observe LLM apps. We realized that the concepts of traces, spans, metrics, and logs that were standardized with OpenTelemetry can easily extend to gen AI. We partnered with 20+ observability platforms to make sure that OpenLLMetry becomes the standard for GenAI observability and that the data that we collect can be sent to other platforms as well.
We plan to extend the metrics we provide to support agents that use tools, vision models, and other amazing developments in our fast-paced industry.
We invite you to give Traceloop a spin and are eager for your feedback! How do you track and debug hallucinations? How much has that been an issue for you? What types of hallucinations have you encountered?
- FLaNK-AIM Weekly 13 May 2024
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Pydantic Logfire
I’m also aware of other OSS initiatives doing similar initiatives, so I wouldn’t say no one has ever done what your doing.
[1] https://github.com/traceloop/openllmetry
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Show HN: You don't need to adopt new tools for LLM observability
So why should it be different when the app you're building happened to be using LLMs?
So today we're open-sourcing OpenLLMetry-JS. It's an open protocol and SDK, based on OpenTelemetry, that provides traces and metrics for LLM JS/TS applications and can be connected to any of the 15+ tools that already support OpenTelemetry. Here's the repo: https://github.com/traceloop/openllmetry-js
A few months ago we launched the python flavor here (https://news.ycombinator.com/item?id=37843907) and we've now built a compatible one for Node.js.
Would love to hear your thoughts and opinions!
Check it out -
Docs: https://www.traceloop.com/docs/openllmetry/getting-started-t...
Github:
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A note from our sponsor - SaaSHub
www.saashub.com | 14 Aug 2026
Stats
Basic openllmetry repo stats
10
7,361
9.2
10 days ago
traceloop/openllmetry is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of openllmetry is Python.
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