agentlens logo
agentlens
★ 24
vs
lakevision logo
lakevision
★ 52

agentlens vs lakevision

agentlens: AgentLens is an open-source observability tool designed for AI agents, capturing every LLM call, tool invocation, and decision. It stands out with a tamper-evident audit trail, cryptographically verifying events to ensure compliance and data integrity.; lakevision: Lakevision is a comprehensive tool designed to provide deep insights into Data Lakehouses built on the Apache Iceberg table format. It allows users to explore namespaces, tables, schemas, properties, and change history, facilitating efficient data governance and understanding.

01

TL;DR

agentlens logo
agentlensObservability · ★ 24

Monitoring and debugging AI agent runs in real-time.

lakevision logo
lakevisionObservability · ★ 52

Quickly understand the data layout, file locations, and change history within an Apache Iceberg-based Data Lakehouse.

02

Side-by-Side Comparison

Field
agentlens logoagentlens
lakevision logolakevision
Category
Observability
Observability
Stars
★ 24
★ 52
License
MIT
Apache-2.0
Install Command
docker run -p 3400:3400 -e AUTH_DISABLED=true -e JWT_SECRET=dev-secret ghcr.io/agentkitai/agentlens
git clone https://github.com/lakevision-project/lakevision.git && cd lakevision && docker build -t lakevision:1.0 . && docker run --env-file .env -p 8081:8081 lakevision:1.0 /app/start.sh
Updated
5d ago
1mo ago
Open Source
Yes
Yes
Tags
Observability, AI Agents, Audit Trail
Data Lakehouse, Apache Iceberg, Data Catalog
03

Features

agentlens logoagentlens
01Python Auto-Instrumentation for 9 LLM providers
02Tamper-Evident Audit Trail with SHA-256 hash chains
03Real-Time Web Dashboard for monitoring and analytics
04LLM Call Tracking, cost aggregation, and latency measurement
05Cost Optimization with model recommendation engine
06Session Replay to step-through past agent runs
lakevision logolakevision
01Search and view all namespaces and tables, including their schema, properties, partition specs, and snapshots.
02Graphical summary of record additions over time and detailed partition information like record count, file count, and size.
03Lakehouse Health Feature for running, scheduling, and monitoring data quality and health checks.
04OIDC/OAuth-based authentication support and pluggable authorization for secure access control.
05Optional "Chat with Lakehouse" capability using an LLM.
04

Use Cases

agentlens logoagentlens
↳Monitoring and debugging AI agent runs in real-time.
↳Ensuring compliance with regulations like EU AI Act Article 12 through verifiable audit logs.
↳Tracking and optimizing LLM costs and performance across agents and models.
↳A/B testing and benchmarking different agent variants for improved performance.
↳Analyzing past agent sessions with full context reconstruction for understanding behavior.
lakevision logolakevision
↳Quickly understand the data layout, file locations, and change history within an Apache Iceberg-based Data Lakehouse.
↳Implement and monitor data quality standards and health checks across your lakehouse environment.
↳Securely manage and govern data access by integrating OIDC/OAuth authentication and custom authorization plugins.
↳Explore and analyze table-level metadata, such as schemas, partition details, and snapshots, for data management tasks.
↳Deploy a robust data lakehouse observability solution on Kubernetes or OpenShift for production environments.
FAQ

FAQ

What is the difference between agentlens and lakevision?
Both agentlens and lakevision are in the Observability category. agentlens has 24 stars, while lakevision has 52 stars.
Which is better, agentlens or lakevision?
The best choice depends on your use case. Choose agentlens if Monitoring and debugging AI agent runs in real-time., and lakevision if Quickly understand the data layout, file locations, and change history within an Apache Iceberg-based Data Lakehouse..
Is agentlens free or open source?
Yes, agentlens is open source on GitHub (MIT).
Is lakevision free or open source?
Yes, lakevision is open source on GitHub (Apache-2.0).
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