Top AI Tools for ML Engineers

Erich Hellstrom

Erich Hellstrom

— 9 min read
Top AI Tools for ML Engineers
ML Engineer Tools

Machine learning evolves rapidly, especially as Large Language Models (LLMs) become more advanced. For ML engineers, these developments create significant opportunities and introduce a distinct set of technical challenges. Building, deploying, and maintaining LLM applications requires specialized tools—from prompt management and evaluation to monitoring and experiment tracking.

This guide presents essential AI tools for ML engineers, grouped by their core functions. These platforms improve workflow efficiency, support collaboration, and help teams develop reliable AI systems.

Table of Contents

Prompt Management & Collaboration

PromptLayer

PromptLayer acts as an intermediary between your code and LLM providers (such as OpenAI), letting you track, manage, and version every prompt sent to a model.

Core Features

  • Prompt Registry & Versioning: Tracks and versions prompt templates, enabling teams to view edits over time and revert changes as needed.
  • LLM Observability: Automatically logs metadata for each API request—including prompt text, parameters, response, and latency—searchable through a dashboard.
  • Prompt Evaluation & A/B Testing: Supports batch evaluations and regression testing across prompt versions and model variants, surfacing performance differences.
  • Collaboration: Enables non-technical team members, such as product managers and writers, to contribute to prompt development through a no-code interface.

Use Cases

  • Customer Support Automation: Gorgias uses PromptLayer to scale LLM-powered support, running backtests and reviewing logs daily to catch regressions early.
  • Domain Expert Workflows: Legal, healthcare, and education teams work together on prompt iteration without writing code, using PromptLayer’s UI to monitor historical prompt performance.

Observability & Monitoring

LLM-based systems often behave unpredictably, making it essential for engineers to have visibility into their processes. Observability platforms provide tools to trace, debug, and optimize pipelines in real time.

PromptLayer

Beyond basic prompt tracking, PromptLayer treats every prompt as a searchable, filterable, and replayable log entry. It integrates with leading SDKs (OpenAI, Anthropic) and frameworks like LangChain via PromptLayerCallbackHandler.

Core Capabilities

  • Full-Agent Conversation Histories: Logs entire multi-step agent and chain executions. Engineers can filter by execution or user IDs, or custom tags, to pinpoint specific prompts and their context.
  • Metadata-Driven Log Search: Each API call is enriched with detailed metadata. Teams use these filters to quickly find problematic requests, such as latency spikes or unusual token usage.
  • Interactive Debugging Playground: When a log entry shows an error, PromptLayer enables you to re-run and tweak that prompt in a playground interface, comparing results instantly.
  • LangChain Callback Integration: By embedding the callback handler, every component (prompt, retrieval, LLM call) is traced, surfacing latency and token usage for each step.

Use Cases

  • Agent Workflow Debugging: Teams trace the decision points in LangChain agents and see exactly which prompt led to a specific choice.
  • Rapid Triaging of Failures: When a multi-step chain fails, engineers jump directly to the relevant stage in PromptLayer’s playground for focused debugging.

LangSmith

LangSmith, built by the creators of LangChain, provides unified tracing, prompt evaluation, and performance dashboards for AI applications, even if they do not use LangChain.

Core Capabilities

  • End-to-End Tracing: Captures each LLM call, retrieval step, and intermediary logic with OpenTelemetry integration.
  • Prompt & Model Evaluation: Builds evaluation suites within the platform to compare multiple models or prompt variants, highlighting regressions.
  • Real-Time Metrics: Monitors token usage, latency, error rates, and custom tags for detailed performance analysis.
  • Multi-Stage Debugging: Provides guides and sample integrations for major SDKs and platforms.

Use Cases

  • AI Agent Debugging: Teams identify bottlenecks or hallucinations by tracing each decision point.
  • Prompt Regression Detection: Scheduled regression tests flag performance drops automatically.

Arize AI

Arize AI combines LLM observability and evaluation, supporting both prompt optimization and production monitoring.

Core Capabilities

  • Prompt Optimization & Serving: An interface for prompt iteration, comparison, and deployment.
  • RAG Evaluation: Benchmarks retrieval-augmented generation (RAG) pipelines to detect poor retrievals or hallucination patterns.
  • CI/CD Experiments: Automated evaluation testing in CI pipelines.
  • Tracing & Spans: Visualizes every LLM call and integrates with Phoenix for vendor-agnostic instrumentation.
  • Real-Time Monitoring & Drift Detection: Highlights shifts in token distributions or performance metrics.
  • Human-in-the-Loop Annotation: Queues outputs for quick human review and labeling.

Use Cases

  • Enterprise LLM Debugging: Detects and surfaces problems in minutes rather than days.
  • Phoenix Integration: Teams use Phoenix locally and send metrics to Arize for centralized dashboards.

Phoenix (by Arize AI)

Phoenix is an open-source, vendor-neutral observability platform for experimentation and troubleshooting LLM pipelines. It integrates with frameworks such as LangChain, LlamaIndex, and major LLM providers.

Core Features

  • Tracing: Automatically records latency, token counts, and retrieval performance.
  • Evaluation Modules: Benchmarks model responses using metrics like accuracy, ROUGE, BLEU, and supports retrieval evaluation for RAG pipelines.
  • Dataset Versioning: Maintains versioned datasets for reproducible experimentation.
  • Playground: Interactive UI for replaying and comparing model outputs.

Use Cases

  • Local Debugging: Engineers use Phoenix locally to identify misaligned prompts or latency outliers.
  • Cross-Vendor Comparisons: Teams compare results from multiple LLM providers using identical prompt sets.

Evaluation Frameworks

Reliable evaluation is essential to prevent regressions when prompts, models, or retrieval strategies change. The following tools provide systematic evaluation pipelines.

PromptLayer

PromptLayer offers a complete evaluation framework accessible via CLI or API, integrating directly with CI/CD pipelines.

Core Capabilities

  • A/B & Regression Testing: Define evaluation suites (JSONL/YAML) with prompts and expected outputs. The backend runs automated regression tests when prompts or models change, highlighting drops in performance.
  • Custom Evaluation Metrics: Supports Python or LLM-as-judge metrics (e.g., using GPT-4 to grade summaries). Results appear in the dashboard with pass/fail rates.
  • Batch Model Comparison: Evaluate multiple model endpoints on the same prompt sets, comparing side-by-side results, token usage, and response times.
  • CI/CD Integration: Run evaluations on every pull request in CI. If accuracy drops past a threshold, the PR is flagged.

Use Cases

  • Automated Regression Guardrails: Teams catch silent regressions before they reach production by integrating evaluation runs into pull requests.
  • Model Selection: Data teams compare open-source models against GPT benchmarks for domain-specific tasks.

OpenAI Evals

OpenAI Evals is an open-source framework for systematic evaluation of LLMs and their outputs.

Core Features

  • Eval Registry: Includes community benchmarks for summarization, code generation, and Q&A.
  • Custom Eval Creation: Define new evaluations with YAML, specifying datasets and metrics.
  • Model-Graded Evals: Supports automated, scalable model-as-judge evaluations.
  • CLI & Dashboard Integration: Run locally or in CI and export results to dashboards.

Use Cases

  • Continuous Integration: Engineering teams automatically reject merges that degrade core benchmark scores.
  • Production Monitoring: Periodic re-evaluation of outputs to detect concept drift.

HuggingFace Eval & EvalHarness

HuggingFace Eval tools, part of the Transformers library, enable benchmarking LLMs on standard NLP tasks and custom datasets.

Core Features

  • Wide Benchmark Suite: Provides loaders and metrics for GLUE, SuperGLUE, MMLU, and more.
  • Extensibility: Any model from the Hub can be evaluated on local or remote datasets.
  • Community Contributions: YAML configurations for domain-specific tasks encourage rapid adoption.

Use Cases

  • Benchmarking New Models: ML engineers compare open-source models to GPT-4 on responsibility and factuality.
  • Internal Leaderboards: Teams create private leaderboards for specialized evaluation tasks.

Weights & Biases (W&B)

Weights & Biases extends its experiment tracking to LLM evaluation and monitoring.

Core Features

  • W&B Traces: Visualizes each step in an LLM workflow, identifying latency spikes or error patterns.
  • W&B Evaluations: Define and monitor evaluation pipelines, surfacing regressions over time.
  • LLM Monitoring: Out-of-the-box analytics for embedding drift, token-usage trends, and more.
  • LangChain Integration: Directly logs chain-of-thought steps and agent actions.

Use Cases

  • LLM Regression Alerts: Teams set up alerts for drops in validation accuracy after model updates.
  • Feedback Loop Integration: Production feedback links real-world issues to experiment logs.

Experiment Tracking & Model Registry

Tracking experiments and artifacts ensures reproducibility and bridges development and production.

PromptLayer

PromptLayer focuses on prompt-centric experiment logging. Instead of tracking entire model checkpoints, it maintains prompt versions and their evaluation metrics.

Core Capabilities

  • Prompt Version Lineage: Every prompt edit creates a new version with a clear history and annotated changes, tied to evaluation results.
  • Metric Dashboards & Analytics: Aggregates usage metrics and evaluation scores, allowing engineers to track trends over time.
  • Integration with W&B: Bi-directional sync enables prompt versions and results to appear alongside training logs in W&B.
  • Lightweight Model Registry: Teams tag prompt versions as “baseline,” “v2,” or “production-ready,” making it easy to select the best prompt for a task.

Use Cases

  • Prompt-Centric Experiment Tracking: Provides a prompt-specific audit trail that is simpler to manage than traditional MLOps tools.
  • Cross-Tool Audit Trail: Teams can keep prompt data in sync with model training logs in W&B or MLflow.

Weights & Biases (W&B)

W&B is essential for experiment tracking—logging hyperparameters, metrics, models, and datasets. Its Model Registry helps version LLM checkpoints and share them across teams.

Core Features

  • Experiment Tracking: Logs metrics live during training or fine-tuning, with interactive dashboards.
  • Model Registry: Versions and manages checkpoints with clear lineage.
  • Artifacts & Datasets: Stores tokenized datasets, prompt corpora, and embeddings for quick rollbacks.
  • Reports & Collaboration: Combines charts, code snippets, and evaluation summaries for sharing.

Use Cases

  • LLM Fine-Tuning: Teams monitor and compare learning curves and hyperparameter sweeps.
  • Artifact Promotion: “Best-of” checkpoints are promoted for deployment, triggering CI pipelines.

Orchestration & Prompt Chaining

Developing complex applications often involves chaining LLM calls and retrievals. These frameworks simplify development and debugging.

PromptLayer

PromptLayer traces each step in multi-step pipelines—prompts, retrieval calls, LLM invocations, rerankers—within a unified dashboard.

Core Capabilities

  • LangChain Callback for Full-Pipeline Tracing: Tracks every component and visualizes a timeline of calls, including prompt text, latency, and token usage.
  • Tracing Agent Decision Points: Logs each step in agent workflows, showing which prompt and context led to specific decisions.
  • Mid-Chain Playground Debugging: Engineers can jump directly to any stage of a failing chain and debug with previous context loaded.
  • Orchestration Dashboards: Visualizes common sub-chains, helping teams identify opportunities to optimize for latency or cost.

Use Cases

  • Orchestration Debugging: Engineers quickly diagnose why an agent chose a particular action.
  • Sub-Chain Optimization: Teams identify and optimize repeated sub-chains for efficiency.

LangChain

LangChain is the leading library for building conversational agents, text-based pipelines, and retrieval-augmented generation (RAG) applications. It offers modular components for LLMs, prompts, chains, agents, and memory, enabling rapid prototyping and robust deployment.

Core Components

  • LLM Wrappers: Abstracts multiple providers under one interface.
  • Chains: Organizes sequences of LLM calls with built-in error handling.
  • Agents: Allows runtime decisions, such as calling an LLM or running custom code.
  • Callback & Logging: Integrates with tracing and monitoring tools.

Use Cases

  • RAG Pipelines: Combine vector database queries with LLM summarization.
  • Multi-Step Agents: Build support bots capable of escalating to humans as needed.

LlamaIndex (formerly GPT-Index)

LlamaIndex simplifies data ingestion, indexing, and retrieval, converting documents into embeddings and building indices optimized for LLMs.

Core Features

  • Indexing Wrappers: Supports various index types and handles document splitting.
  • Query Rerankers: Uses LLMs to rerank retrieved passages.
  • Integration with Phoenix/Arize: Traces retrieval spans for easy diagnosis.

Use Cases

  • Knowledge Base Chatbots: Ingest manuals as embeddings and answer user questions using LLMs.
  • Domain-Specific Search: Indexes and prioritizes legal documents for research tasks.

Retrieval & Vector Databases

Fast, relevant retrieval is critical for applications like RAG, where LLMs depend on access to external documents. The following databases work well with LLM frameworks.

PromptLayer

While not a vector database, PromptLayer logs retrieval metadata during RAG pipelines, helping teams pinpoint retrieval issues.

Capabilities

  • Retrieval Metadata Logging: Tracks retrieval queries, hit counts, and similarity scores.
  • Provider Comparison: Teams can benchmark RAG accuracy and cost across different vector databases.

Use Cases

  • RAG Debugging: Engineers trace hallucinations to the quality of retrieved documents.
  • Provider Benchmarking: Quantifies the effectiveness of different vector stores.

Pinecone

Pinecone is a managed vector database optimized for low-latency, high-throughput similarity search, capable of handling millions to billions of embeddings.

Core Features

  • Scalable Indexes: Shards and replicates embeddings for scale and reliability.
  • Metadata Filtering: Enables complex queries combining metadata and vector similarity.
  • Hybrid Search: Merges dense and sparse retrieval for accurate rankings.

Use Cases

  • Personalized Recommendations: Suggests articles or products based on embedding similarity.
  • Contextual RAG: Stores knowledge-base embeddings for fast LLM retrieval.

Chroma

Chroma is an open-source embedding database designed for simplicity and on-premises deployment.

Core Features

  • Embeddings Manager: Maintains embedding dimension consistency.
  • Simple Python Client: Quick CRUD operations for fast prototyping.
  • Batch Operations: Supports bulk inserts and queries for efficiency.

Use Cases

  • Local Prototyping: Enables RAG application development offline.
  • Privacy-Focused Deployments: Keeps sensitive data on-premises for compliance.

Weaviate

Weaviate is a popular open-source vector store with built-in ML modules and semantic search filters. It is often chosen for enterprise self-hosting.

Core Features

  • GraphQL API: Combines vector and attribute filters for advanced search.
  • Modular Embedding Providers: Supports several embedding services natively.

Use Cases

  • Semantic Document Search: Enables complex, filtered queries for knowledge graphs.

Additional Tools & Emerging Research

Beyond production-ready solutions, emerging research tools provide new capabilities for ML engineers.

Guardrails & Safety

  • Guardrails by Shoreline: Defines rules to prevent harmful LLM outputs, such as profanity or privacy violations.
  • AgentOps: Establishes standards for tracing and auditing autonomous LLM agents.

Log Analytics & Anomaly Detection

  • LogAI: A Salesforce open-source library for summarizing and detecting anomalies in LLM pipeline logs.

CI/CD Integration

  • LogSage: An LLM-based framework for automated CI/CD failure detection and remediation.
  • LLMPrism: Diagnoses large-scale LLM training performance using network flow data.

Conclusion

To develop robust, scalable LLM systems, ML engineers should combine specialized tools for prompt management, observability, evaluation, and experiment tracking. Platforms like PromptLayer, LangSmith, W&B, and leading vector stores provide the foundation for effective development and production workflows. By integrating these tools, teams can deliver reliable AI applications that continuously improve.

About PromptLayer

PromptLayer is a prompt management system that helps you iterate on prompts faster — further speeding up the development cycle! Use their prompt CMS to update a prompt, run evaluations, and deploy it to production in minutes. Check them out here. 🍰

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