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Open-source alternatives to LangSmith Observability

Compare community-driven replacements for LangSmith Observability in llm evaluation & observability workflows. We curate active, self-hostable options with transparent licensing so you can evaluate the right fit quickly.

LangSmith Observability logo

LangSmith Observability

LangSmith Observability gives visibility into agent behavior with end-to-end tracing, real-time monitoring and alerting, dashboards, and automations—plus tight integration with LangSmith evaluations.Read more
LLM Evaluation & Observability
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Key stats

  • 12Alternatives
  • 3Support self-hosting

    Run on infrastructure you control

  • 12Active development

    Recent commits in the last 6 months

  • 8Permissive licenses

    MIT, Apache, and similar licenses

Counts reflect projects currently indexed as alternatives to LangSmith Observability.

All open-source alternatives

Agenta logo

Agenta

Accelerate production LLM apps with integrated prompt, evaluation, observability

Active developmentFast to deployIntegration-friendlyTypeScript

Why teams choose it

  • Interactive prompt playground with versioned branching for SME collaboration
  • Built‑in evaluation framework offering 20+ LLM‑as‑judge evaluators and custom test sets
  • Real‑time observability of cost, latency, and traces via OpenTelemetry standards

Watch for

Self‑hosting requires Docker and environment configuration

Migration highlight

Customer support chatbot refinement

SMEs iteratively improve prompts, run evaluations against real tickets, and monitor latency to ensure SLA compliance.

Phoenix logo

Phoenix

AI observability platform for tracing, evaluation, and prompt management

Active developmentFast to deployIntegration-friendlyJupyter Notebook

Why teams choose it

  • Unified tracing via OpenTelemetry
  • LLM‑specific evaluation suite
  • Versioned dataset and experiment tracking

Watch for

Requires instrumentation of your code

Migration highlight

Prompt Optimization

Iteratively test prompt variations, compare model responses, and select the best performing version.

Opik logo

Opik

Open-source platform for tracing, evaluating, and optimizing LLM applications

Active developmentPermissive licensePrivacy-firstPython

Why teams choose it

  • Deep tracing of LLM calls and agent activity
  • LLM‑as‑a‑judge evaluation with custom metrics
  • Scalable production monitoring (40M+ traces/day)

Watch for

Self‑hosting adds operational overhead and requires container expertise

Migration highlight

RAG chatbot performance tuning

Iteratively refine prompts and retrieval strategies, reducing hallucinations as measured by LLM‑as‑a‑judge metrics.

Coze Loop logo

Coze Loop

Full‑life‑cycle platform for building, testing, and monitoring AI agents

Active developmentPermissive licenseFast to deployGo

Why teams choose it

  • Playground for interactive prompt debugging and version management
  • Automated, multi‑dimensional evaluation of prompts and agents
  • SDK‑based trace reporting with end‑to‑end execution observability

Watch for

Advanced features from the commercial edition are not included

Migration highlight

Prompt Iteration

Developers quickly test, compare, and version prompts across multiple LLMs, reducing debugging time.

Evidently logo

Evidently

Evaluate, test, and monitor ML & LLM systems effortlessly

Active developmentPermissive licenseIntegration-friendlyJupyter Notebook

Why teams choose it

  • 100+ built‑in metrics for tabular and generative tasks
  • Modular Reports that can be converted into pass/fail Test Suites
  • Self‑hosted monitoring UI with optional managed Cloud service

Watch for

Requires a Python environment; not native to other languages

Migration highlight

Detect data drift between training and production

Early alerts when feature distributions shift, preventing model degradation

Helicone logo

Helicone

Open-source LLM observability and developer platform for AI applications

Self-host friendlyActive developmentPermissive licenseTypeScript

Why teams choose it

  • One-line integration with 20+ LLM providers including OpenAI, Anthropic, and Gemini
  • Agent and session tracing with cost, latency, and quality metrics
  • Prompt versioning and playground for rapid iteration with production data

Watch for

Self-hosting requires managing five separate services (Web, Worker, Jawn, Supabase, ClickHouse, MinIO)

Migration highlight

Multi-Agent System Debugging

Trace complex agent interactions across sessions to identify bottlenecks, track costs per agent, and optimize prompt chains using production data in the playground.

Langfuse logo

Langfuse

Collaborative platform for building, monitoring, and debugging LLM applications.

Self-host friendlyActive developmentPrivacy-firstTypeScript

Why teams choose it

  • Unified tracing of LLM calls, retrievals, embeddings, and agent actions
  • Prompt management with version control and low‑latency caching
  • Flexible evaluation pipelines supporting LLM‑as‑judge and human feedback

Watch for

Production self‑hosting may require container or Kubernetes expertise

Migration highlight

Debugging a multi‑step agent workflow

Trace each LLM call, retrieval, and tool use to pinpoint failures and iterate via the integrated playground.

Laminar logo

Laminar

Trace, evaluate, and scale AI applications with minimal code.

Self-host friendlyActive developmentPermissive licenseTypeScript

Why teams choose it

  • OpenTelemetry‑based automatic tracing for major LLM frameworks
  • Built‑in observability of latency, cost, and token usage
  • Parallel evaluation SDK with dataset integration

Watch for

Self‑hosting requires managing multiple services (Postgres, ClickHouse, RabbitMQ)

Migration highlight

Real‑time latency monitoring for a chatbot

Detect and alert on response slowdowns, reducing user‑perceived latency.

OpenLIT logo

OpenLIT

Unified observability and management platform for LLM applications

Active developmentPermissive licenseFast to deployPython

Why teams choose it

  • OpenTelemetry‑native SDKs for vendor‑agnostic tracing and metrics
  • Analytics dashboard with cost, performance, and exception monitoring
  • Prompt Hub and Vault for secure prompt versioning and API key management

Watch for

Requires running the OpenLIT stack (ClickHouse, collector) adding infrastructure overhead

Migration highlight

Monitor LLM latency and token usage in production

Identify performance bottlenecks and optimize model selection, reducing response times by up to 30%.

Langtrace logo

Langtrace

Observability platform for LLM applications with real‑time tracing

Active developmentPrivacy-firstFast to deployTypeScript

Why teams choose it

  • OpenTelemetry‑compliant tracing across LLM providers and vector databases
  • Real‑time monitoring of API calls, latency, and cost
  • Performance analytics with visual dashboards

Watch for

Some frameworks lack TypeScript SDK coverage (e.g., Langchain, Langgraph)

Migration highlight

Debugging LLM API latency spikes

Identify slow calls, reduce response times, and lower usage costs

OpenLLMetry logo

OpenLLMetry

Full‑stack observability for LLM applications via OpenTelemetry

Active developmentPermissive licenseIntegration-friendlyPython

Why teams choose it

  • Native OpenTelemetry compatibility
  • Instrumentation for major LLM providers, vector DBs, and AI frameworks
  • Plug‑and‑play SDK with one‑line initialization

Watch for

Instrumentation limited to providers listed in documentation

Migration highlight

Debug LLM prompt failures

Trace each prompt, response, and token usage across providers, pinpointing latency spikes or error patterns.

TruLens logo

TruLens

Systematically evaluate, track, and improve your LLM applications

Active developmentPermissive licenseIntegration-friendlyPython

Why teams choose it

  • Stack‑agnostic instrumentation for prompts, models, retrievers, and knowledge sources
  • Customizable feedback functions covering honesty, harmlessness, helpfulness, and RAG metrics
  • Interactive UI for comparing experiment runs and visualizing evaluation results

Watch for

Requires a Python environment; not language‑agnostic

Migration highlight

RAG pipeline benchmarking

Identify which retriever‑model combination yields highest relevance and factuality scores

Choosing a llm evaluation & observability alternative

Teams replacing LangSmith Observability in llm evaluation & observability workflows typically weigh self-hosting needs, integration coverage, and licensing obligations.

  • 3 projects let you self-host and keep customer data on infrastructure you control.
  • 12 options are actively maintained with recent commits.

Tip: shortlist one hosted and one self-hosted option so stakeholders can compare trade-offs before migrating away from LangSmith Observability.