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Comprehensive Guide: Top Open-Source LLM Observability Tools in 2025

Objective overview with each tool listed.

TL;DR

Why LLM Observability Matters

Observability for large language models enables you to:

  • Trace individual token or prompt calls across microservices
  • Monitor cost and latency by endpoint or model version
  • Detect errors, timeouts, and anomalous behavior (e.g., hallucinations)
  • Correlate embeddings, retrieval calls, and final outputs in RAG pipelines

1. Traceloop (OpenLLMetry)

An OpenTelemetry-compliant SDK for tracing and metrics in LLM applications.

  • Installation: 
  pip install traceloop-sdk
  • Configuration: 
  from traceloop.sdk import Traceloop

  # Initialize with your app name; can disable batching to see traces immediately
  Traceloop.init(app_name="your_app_name", disable_batch=True)
  • Features:
    • Span-based telemetry compatible with Jaeger, Zipkin, and any OTLP receiver
    • Configurable batch sending and sampling through init parameters
    • Built-in semantic tags for errors, retries, and truncated outputs

  • Integration: Works with LangChain, LlamaIndex, Haystack, and native OpenAI SDKs via automatic instrumentation

2. Langfuse

A modular observability and logging framework tailored to LLM chains.

  • Installation: 
  pip install langfuse
  • Configuration: 
  from langfuse import Langfuse

  # Initialize with your API key and optional project name
  Langfuse.init(api_key="YOUR_API_KEY", project="my_project")
  • Features:
    • Structured event logging for prompts, completions, and chain steps
    • Built-in integrations for vector stores: Pinecone, Weaviate, FAISS
    • Web UI dashboards for chain execution flow and performance metrics

  • Integration: Use decorators (@Langfuse.trace) around functions or context managers (with Langfuse.trace())

3. Helicone

A proxy-based solution that captures model calls without SDK changes.

  • Deployment: 
  docker run -d -p 8080:8080 
    -e HELICONE_API_KEY="YOUR_API_KEY" 
    helicone/proxy:latest
  • Configuration: Point your LLM client to the proxy endpoint: 
  export OPENAI_API_BASE_URL="http://localhost:8080/v1"
  • Features:
    • Transparent capture of all API calls via proxy
    • Automated cost and latency reporting
    • Scheduled email summaries of usage metrics

  • Integration: Place in front of any HTTP-based LLM endpoint; no code changes required

4. Lunary

An observability tool focused on retrieval-augmented generation (RAG).

  • Installation: 
  pip install lunary
  • Configuration: 
  from lunary import Client

  client = Client(api_key="YOUR_API_KEY")
  • Features:
    • Traces embedding queries and similarity scores
    • Correlates retrieval latency with generation latency
    • Interactive dashboards for query versus context alignment

  • Integration: Use client.trace_rag() context manager around RAG pipeline execution

5. Phoenix (Arize AI)

A monitoring and anomaly-detection service for LLM metrics.

  • Setup: 
  npm install @arize-ai/phoenix
  • Configuration: 
  import { Phoenix } from "@arize-ai/phoenix";

  const phoenix = new Phoenix({
    apiKey: "YOUR_API_KEY",
    organization: "YOUR_ORG_ID",
    environment: "production"
  });
  • Features:
    • Automatic drift detection across model versions
    • Alerting on latency and error rate thresholds
    • A/B testing support for comparative analysis

  • Integration: Inject phoenix.logInference() calls around model invocation to log inference events

6. TruLens

A semantic-evaluation toolkit from Hugging Face.

  • Installation: 
  pip install trulens-eval
  • Configuration: 
  from trulens_eval import Tru

  tru = Tru(model_name="your-model-name")
  results = tru.run(["prompt1", "prompt2"], metric="coherence")
  • Features:
    • Built-in evaluators for coherence, redundancy, toxicity
    • Batch evaluation of historical outputs
    • Support for custom metric extensions

  • Integration: Use tru.run() in evaluation pipelines or CI workflows to monitor output quality

7. Portkey

A CLI-driven profiler for prompt engineering workflows.

  • Installation: 
  npm install -g portkey
  • Configuration: 
  portkey init --api-key YOUR_API_KEY
  • Features:
    • Auto-instruments OpenAI, Anthropic, and Hugging Face SDK calls
    • Captures system metrics (CPU, memory) alongside token costs
    • Local replay mode for comparative benchmarks

  • Usage: Run portkey audit ./path-to-your-code to generate a trace report

8. PostHog

A product-analytics platform with an LLM observability plugin.

  • Installation: 
  npm install posthog-node @posthog/plugin-llm
  • Configuration: 
  import PostHog from 'posthog-node';

  const posthog = new PostHog('YOUR_PROJECT_API_KEY', { host: 'https://app.posthog.com' });
  • Features:
    • Treats each LLM call as an analytics event
    • Funnel and cohort analysis on prompt usage
    • Alerting on custom error or latency conditions

  • Integration: Use posthog.capture() around your model calls to log events; plugin enriches events with LLM metadata

9. Keywords AI

An intent-tagging and alerting tool based on keyword rules.

  • Installation: 
  pip install keywords-ai
  • Configuration: 
  from keywords_ai import Client

  client = Client(api_key="YOUR_API_KEY")
  intents = client.analyze("Which model should I use for medical diagnosis?")
  • Features:
    • Intent classification via configurable keyword lists
    • Emits metrics when specified intents (e.g., “legal,” “medical”) occur
    • Custom alerting hooks for regulatory workflows

  • Integration: Middleware pattern for any LLM request pipeline, call client.analyze() before or after completion

10. Langsmith

The official LangChain observability extension.

  • Installation: 
  pip install langsmith
  • Configuration: 
  from langsmith import Client, trace

  client = Client(api_key="YOUR_API_KEY")
  @trace(client)
  def my_chain(...):
      # chain logic here
      pass
  • Features:
    • Decorators for instrumenting sync/async functions
    • Visual chain graphs in Jupyter and CLI reports
    • Metadata tagging for run context and environment

  • Integration: Use @trace(client) decorator or with trace(client): context manager around LangChain executions

11. Opik & OpenLIT (Emerging)

Lightweight community projects for minimal-overhead instrumentation.

  • Opik (JavaScript SDK, ~10 KB):

    • Installation: 
    npm install @opik/sdk
    • Configuration: 
    import { Opik } from "@opik/sdk";

const opik = new Opik({ apiKey: "YOUR_API_KEY" });
opik.track("prompt text", { model: "gpt-4", tokens: 120 });

  • OpenLIT (Python, <2 ms overhead):

    • Installation: 
    pip install openlit
    • Configuration: 
    from openlit import tracer

tracer.configure(service_name="my_service")
tracer.trace_llm("text-davinci-003", prompt="Hello world")

Conclusion & Next Steps

  1. Identify your primary observability needs (tracing, cost reporting, RAG metrics, semantic evaluation).
  2. Select one or more tools from this list based on compatibility and feature focus.
  3. Integrate and monitor within staging before rolling out to production.
  4. Compare metrics and adjust sampling rates or alert thresholds to balance overhead and insight.

FAQ

Q1: Which tool emits OpenTelemetry spans?
A1: Traceloop (OpenLLMetry) and OpenLIT both emit OTLP-compatible spans.

Q2: How can I capture cost reports without code changes?
A2: Helicone operates as a proxy in front of your LLM endpoint and generates cost reports automatically.

Q3: What’s the easiest way to trace RAG pipelines?
A3: Lunary captures embedding and retrieval metrics alongside generation latency in a single dashboard.

Q4: Can I analyze LLM calls as product-analytics events?
A4: Yes—PostHog’s LLM plugin treats each API call as an event for funnel and cohort analysis.

Q5: Are there lightweight front-end options for prompt observability?
A5: Opik’s JavaScript SDK (≈10 KB) can be embedded in web applications for real-time prompt tracking.

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