The Best Kitaru Alternatives in 2026

Latitude provides observability for AI agents and helps teams trace failures and verify fixes.

OpenLIT provides observability and evaluation for AI agents and coding agents, tracing models, tools, prompts, costs, and workflows.

Future AGI evaluates and observes AI and agent applications with tracing, simulations, datasets, and guardrails.

traceAI provides open source tracing for AI applications and frameworks using OpenTelemetry.

ORMB packages machine learning models as OCI artifacts, in a workflow described as Docker for ML models.

ONNX-MLIR compiles ONNX models using MLIR compiler infrastructure for model deployment workflows.

E2B provides an open-source secure environment with tools for enterprise agents.

agenttrace audits coding-agent sessions, tracking costs, tokens, latency, failures, and health.

Manifest connects AI agents and tools to multiple model providers through an LLM gateway.

Entroly reduces AI context size with reversible compression and receipts for recovering the original content.

Cog packages machine learning models into containers to support deployment across environments.

DJL is a Java framework for running deep learning models across different engines.

Machine learning operations and large language model operations are the focus of Starwhale’s platform.

The Hydrosphere repository describes an MLOps platform for machine learning operations.

OpenAI compatible API for LLMs and embeddings (LLaMA, Vicuna, ChatGLM and many others).

File Parser optimised for LLM Ingestion with no loss. Parse PDFs, Docx, PPTx in a format that is ideal for LLMs.

ModelDB tracks machine learning model versions, metadata, and experiments.

dstack coordinates AI compute and supports training and inference across GPU clouds, Kubernetes, VMs, and bare metal.

FastChat offers an open platform for training, serving, and evaluating large language models, and hosts release repositories for Vicuna and Chatbot Arena.

MiroShark describes a swarm intelligence engine for simulating scenarios with stated time and cost targets.

Flexible, high-performance serving system for machine learning models.

Model deployment for AI applications is supported by nndeploy, a framework described as easy to use and high performance.

MMDeploy provides a model deployment framework from the OpenMMLab project.

Marquez collects, aggregates, and visualizes metadata about a data ecosystem.