Auditi

Auditi is an open-source AI agent observability and evaluation tool that combines tracing, logging, and built-in evaluation in a single platform. In this Auditi review, we examine how it positions itself against LangSmith and Langfuse as a simpler alternative for monitoring LLM-powered applications and AI agents.

Overview

Auditi is an open-source tool hosted on GitHub that provides tracing, evaluation, and logging for AI agents and LLM applications. The project was created to address a gap in the AI observability space: most tools either focus on tracing (LangSmith, Langfuse) or evaluation (Ragas, DeepEval) but rarely combine both in a lightweight, open-source package.

The tool offers 2-line auto-instrumentation — meaning developers can add observability to existing LLM applications with minimal code changes. Auditi captures traces of agent execution (which tools were called, what prompts were sent, what responses were received) and runs evaluation metrics on the results automatically.

Key Features and Architecture

2-Line Auto-Instrumentation

Auditi integrates into existing Python applications with just two lines of code. The auto-instrumentation captures LLM calls, tool invocations, and agent decision chains without requiring manual span creation or decorator patterns that other tools demand.

Combined Tracing and Evaluation

Unlike tools that separate tracing (observing what happened) from evaluation (measuring quality), Auditi runs both in a single pipeline. Traces capture the execution flow, and built-in evaluators score outputs for relevance, faithfulness, toxicity, and custom metrics.

Open-Source and Self-Hosted

Hosted on GitHub under an open-source license, Auditi can be deployed on your own infrastructure. This addresses data privacy concerns that prevent some organizations from sending LLM traces to third-party SaaS platforms like LangSmith.

Agent Observability

Purpose-built for AI agents (not just simple LLM calls), Auditi traces multi-step agent workflows including tool selection, memory retrieval, planning steps, and final output generation. This is critical for debugging complex agentic systems where failures can occur at any step.

Ideal Use Cases

Teams Building AI Agents Who Need Debugging Visibility

Developers building multi-step AI agents need to understand why an agent chose a particular tool, what context it retrieved, and where failures occur. Auditi's tracing provides this visibility without the complexity of setting up a full observability stack.

Organizations with Data Privacy Requirements

Companies that cannot send LLM inputs/outputs to third-party services (healthcare, finance, government) can self-host Auditi to maintain full control over sensitive data while still getting observability and evaluation capabilities.

Early-Stage AI Projects Needing Lightweight Evaluation

Teams that want to evaluate LLM output quality (relevance, faithfulness, hallucination detection) without setting up separate evaluation frameworks like Ragas or DeepEval can use Auditi's built-in evaluators.

Pricing and Licensing

Auditi is free and open-source:

For context, comparable tools: LangSmith offers a free tier (5,000 traces/month) with paid plans from $39/seat/month, Langfuse is open-source with a cloud offering starting at $59/month, and Arize Phoenix is open-source for local tracing. Auditi's advantage is combining tracing and evaluation in a single lightweight tool at zero cost.

For budget planning, organizations should factor in not just licensing costs but also infrastructure requirements, team training, integration effort, and ongoing maintenance when calculating total cost of ownership.

Pros and Cons

Pros

• 2-line integration — minimal setup effort compared to LangSmith's SDK or Langfuse's decorator patterns
• Combined tracing + evaluation — eliminates the need for separate observability and evaluation tools
• Open-source and self-hosted — full data control for privacy-sensitive organizations
• Agent-native — designed for multi-step agent workflows, not just simple LLM calls

Cons

• Very early-stage — limited community, documentation, and production track record
• No managed cloud offering — self-hosting requires infrastructure management; no SaaS option for teams that want zero ops
• Smaller evaluation library — fewer built-in evaluators compared to dedicated frameworks like Ragas or DeepEval
• Single-developer risk — open-source projects without commercial backing can stall if the maintainer moves on
• No UI dashboard mentioned — unclear if Auditi provides a visual interface for exploring traces or only CLI/API access

Getting Started

Getting started with Auditi is straightforward. Visit the official website to create a free account or download the application. The onboarding process typically takes under 5 minutes, and most users can be productive within their first session. For teams evaluating Auditi against alternatives, we recommend a 2-week trial period to assess whether the feature set and user experience align with your specific workflow requirements. Documentation and community resources are available to help with initial setup and configuration.

Alternatives and How It Compares

LangSmith

LangSmith (by LangChain) is the most widely used LLM observability platform with tracing, evaluation, datasets, and a polished UI. It's closed-source SaaS with a free tier (5,000 traces/month) and paid plans from $39/seat/month. LangSmith is more mature and feature-rich than Auditi but requires sending data to LangChain's servers.

Langfuse

Langfuse is an open-source LLM observability platform with tracing, scoring, and prompt management. It offers both self-hosted and cloud options (from $59/month). Langfuse is more established than Auditi with a larger community, but Auditi claims to be simpler with built-in evaluation that Langfuse lacks natively.

Arize Phoenix

Phoenix is an open-source tool for LLM tracing and evaluation with a local-first approach. It provides a visual UI for exploring traces and runs evaluations using LLM-as-judge patterns. Phoenix is more mature than Auditi and offers a richer visual experience.