OpenVLHarness

OpenVLHarness is an open-source research framework that gives multimodal large language models persistent memory and visual tool use.

OpenVLHarness is a training-free framework designed for real-world visual agentic tool use and multimodal memory. It provides a capability layer and persistent multimodal memory to lift six multimodal large language model backbones across 22 benchmarks. The system features recorded trajectories that can be replayed, paused, resumed, stepped through, or expanded. The agent loop and perception tools run on a server, while models with tool calling orchestrate tasks, write Python code for tools, and summarize web results. It includes components for component-wise ablation, tool usage by domain, and automated framework optimization.

OpenVLHarness at a glance

Best for: Researcher, Software engineer
Runs on: open source, self-hosted
Pricing: Free
Released: October 2026
Category: AI research tool
Website: openvlharness.github.io

Key features

Provides a training-free capability layer and persistent multimodal memory
Supports recorded trajectories with step-by-step playback
Integrates with OpenAI and OpenAI-compatible endpoints with tool calling
Runs perception tools and agent loops on a server
Executes automated framework search to edit system prompts and tool descriptions
Generates new tools by composing existing capabilities

Use it for

Evaluating multimodal language models across 22 benchmarks
Analyzing tool usage share by domain for agent trajectories
Replaying recorded agent trajectories step by step
Optimizing agent workflows through automated root cause analysis

Alternatives to OpenVLHarness

VLMEvalKit — Evaluate large vision-language models across numerous benchmarks with a single command.
OpenRLHF — High-performance distributed architecture and unified agent-based execution
OpenViking — Unifies agent memory, knowledge RAG, and skills into a single directory structure.
OpenCompass — It offers an objective, comprehensive, and credible open-source AI evaluation system.
OpenHarness — Manages Claude Code, Codex, and Hermes from a single command center.
Vlmhub — Run multimodal prompts across multiple local and cloud backends without rewriting inference code.
Lighteval — Evaluates LLMs across multiple backends with thousands of built-in tasks.
LMMs-Eval — Provides open research and tooling for multimodal model evaluation and benchmarks.
ART — Enhances LLM agent performance and reliability using reinforcement learning techniques.
OpenResearch — Run parallel research workflows locally or on remote compute.
HippoRAG — Improves multi-hop retrieval and complex sense-making without sacrificing simple task performance.
SimpleMem — Provides semantic lossless compression to reduce token consumption and maintain long-term context.

All OpenVLHarness alternatives