Skip to content
Stork.AIstork.ai
AI Tool

oqoqo Review

oqoqo is an AI experimentation platform designed for evaluating and benchmarking AI agents in production-like environments.

shipped Aug 10, 2026agentsfreemium
Monthly visits22/mo
agents
oqoqo — product screenshot

Why it matters

1oqoqo was founded in 2025 and launched on Product Hunt in 2026.
2The platform offers a free plan that includes up to 100 runs.
3It provides API documentation at https://docs.oqoqo.ai/ for developers.
4oqoqo supports evaluation experiments at scale in realistic environments using managed cloud infrastructure.

About oqoqo

Business Model
Subscription SaaS
Free Credits
1 run per month on the free plan
Platforms
Web, CLI, MCP
Target Audience
Developers and teams needing to benchmark AI agents and models

Pricing Plans

Free Plan
Free
  • • Adds runs to your organization each month
  • • Unlimited team members and projects
  • • Access to full web app, CLI, and MCP
Paid Top-Up Runs
Variable / per-request
  • • Purchase additional runs that never expire
  • • Larger top-ups cost less per run

Cost Examples

  • • Each run covers Oqoqo platform fees

Leadership

Karthik RaoCo-Founder
API Docs

overview

What is oqoqo?

oqoqo is an AI experimentation platform developed by Oqoqo that enables product builders, developers, and teams working with AI agents to run evaluation experiments at scale in realistic environments. It helps define custom task sets for private benchmarks, measuring how well agents use various products and interfaces. The platform provides a sandbox environment to run experiments around AI agents, offering production-like settings with real repositories, data, and files.

features

Key Features of oqoqo

oqoqo provides a comprehensive suite of features designed for robust AI agent evaluation and experimentation. These capabilities allow users to gain deep insights into agent performance and optimize their interactions with products and interfaces.

  • Run evaluation experiments at scale in realistic environments.
  • Define custom task sets for private benchmarks.
  • Analyze agent performance with pass and fail results.
  • Capture full experiment trajectories, including tool calls and commands.
  • Measure effects across multiple trials, agents, models, and versions.
  • Control variables across agents, models, and versions within experiments.
  • Provide full traces, metrics, and diffs of changes on every run.
  • Integrate with CI/CD workflows to gate releases.
  • Detect model and dependency drift by rerunning workflows on a schedule.

use cases

Who Should Use oqoqo?

oqoqo is primarily designed for product builders, developers, and teams involved in the development and deployment of AI agents. Its capabilities are tailored to ensure agents perform optimally in real-world scenarios and integrate effectively with existing products and systems.

  • Product builders: To create custom benchmarks for products and measure how effectively agents use them.
  • Developers: To test agent-facing interfaces (MCP servers, skills, CLIs, SDKs, APIs) and optimize model spend.
  • Teams working with AI agents: To run scalable agent evaluations, gate releases in CI/CD, and catch model and dependency drift.
  • Organizations needing to optimize model spend: By analyzing token consumption and cost for agentic workflows.
  • Teams requiring continuous evaluation: To power the agent loop by defining experiments, running them in isolated sandboxes, and feeding insights back into development.

how to use

How to Use oqoqo

To begin using oqoqo, users can sign up for the platform and access its managed cloud infrastructure. The process involves defining evaluation experiments and integrating them into development workflows.

  • 1Sign up for an oqoqo account at https://oqoqo.ai/.
  • 2Define custom task sets and benchmarks for specific agent behaviors or product interactions.
  • 3Configure evaluation experiments within the oqoqo platform, specifying agents, models, and environments.
  • 4Run experiments at scale in isolated, production-like sandboxes.
  • 5Analyze the captured full trajectories, metrics, and pass/fail results to identify performance issues.
  • 6Integrate oqoqo into CI/CD pipelines to automate agent workflow testing and prevent regressions.

pricing

oqoqo Pricing & Plans

oqoqo operates on a freemium model, offering a free tier for initial usage and paid options for extended functionality. The platform provides a free trial to allow users to evaluate its capabilities before committing to paid services.

  • Free Plan: Free, includes up to 100 runs.
  • Paid Top-Up Runs: Variable pricing, details available upon booking a demo.

Pros

  • +Provides a managed cloud infrastructure for scalable agent evaluation experiments.
  • +Offers a sandbox environment for production-like testing with real data and files.
  • +Enables definition of custom task sets for private, product-specific benchmarks.
  • +Captures full experiment trajectories, including tool calls, commands, and points of failure.
  • +Supports integration into CI/CD pipelines to prevent agent workflow regressions.
  • +Includes a free plan with 100 runs for initial evaluation.

Cons

  • −Specific pricing details for paid tiers beyond the free plan are not publicly available without a demo.
  • −Requires users to bring their own model keys and potentially manage associated costs.
  • −As a relatively new tool (launched 2026), extensive independent user reviews are not widely available.
  • −Focus on agent evaluation means it may not offer broader LLMOps features like prompt management found in some alternatives.

Similar Tools

oqoqo vs Competitors

oqoqo positions itself as an experimentation platform specifically for the evaluation and benchmarking of AI agents in realistic, production-like environments. This focus differentiates it from broader AI development tools or those with different core functionalities.

1
DeepEval↗

It is a Pythonic, Pytest-native framework for unit testing LLM applications and agents with over 50 research-backed metrics.

DeepEval is a programmatic library, meaning you integrate it directly into your code for evaluation, whereas oqoqo.ai provides a managed cloud infrastructure for running experiments. You gain deep control over your evaluation logic but lose the managed environment and UI for experiment orchestration.

2
Arize PhoenixOn Stork

It is an open-source platform for AI observability and evaluation, allowing self-hosted monitoring and evaluation of LLMs and agents.

Phoenix offers a self-hostable solution for both observability and evaluation, giving you ownership of your data and infrastructure, unlike oqoqo.ai's managed cloud service. While it provides a UI for insights, setting up and maintaining the infrastructure is your responsibility.

3
LangSmithOn Stork

It is specifically designed for debugging, testing, evaluating, and monitoring LLM applications and agents built with the LangChain framework.

LangSmith is tightly integrated with the LangChain ecosystem, making it ideal for users already building agents with LangChain, which oqoqo.ai does not specifically require. It provides a managed platform for evaluation and observability, similar to oqoqo.ai, but its utility is maximized within the LangChain context.

4
BraintrustOn Stork

It is an evaluation-first AI agent observability platform that integrates evaluation directly into CI/CD workflows and provides comprehensive trace capture and automated scoring.

Braintrust focuses heavily on integrating evaluation into CI/CD and production feedback loops, offering a managed platform for agent evaluation and observability. While oqoqo.ai focuses on running experiments at scale, Braintrust emphasizes continuous evaluation throughout the development and deployment lifecycle.

5
AgentaOn Stork

An open-source, self-hostable LLMOps platform that provides a prompt playground, prompt management, and evaluation capabilities for AI agents.

Agenta offers a broader LLMOps suite including prompt management and a playground alongside evaluation, whereas oqoqo.ai is more narrowly focused on agent evaluation experiments. Like Phoenix, it's self-hostable, requiring more setup than oqoqo.ai's managed service but offering full control.

See every oqoqo alternative, compared→

More on Stork

Related AI Tools

Other tools in this category, matched by shared tags

pomelliCleanmails Pruve.devDouble-Oh: The AI Agents Social NetworkBid to UpNanoImage
Visit oqoqo↗
Connect
𝕏
X / Twitterx.com/oqoqo_ai
in
LinkedInwww.linkedin.com/company/oqoqoai/