Open-Source AI Research Agents: Setup, Tasks and Verification

This guide is for readers who can install, configure and maintain an open-source research tool. Start with the intended task, then inspect the project’s license, setup requirements, model and API dependencies, and the records it retains. A repository’s demonstration or benchmark does not establish suitability for every discipline or private dataset.

The seven project snapshots below were inspected on 18 October 2026. Each commit links to the exact source revision checked; it is not a claim that the project was installed or benchmarked. The list retains AI-Researcher as a public research prototype with an unresolved license-text check, and flags conflicting license metadata in GPT Researcher. Do not treat a public repository alone as confirmation of its reuse terms.

Choose a Project by the Work You Need to Operate

Start by deciding whether the output is a source-backed report, a knowledge-curation article, a computational experiment or a reusable execution framework. Then check the deployment burden. A framework that gives your team more control also leaves your team responsible for configuration, maintenance and evaluation.

These are task categories and adoption checks, not a performance ranking. The model, tools, source access, configuration and evaluation task can change the result even when the repository stays the same.

GPT Researcher

A candidate for configurable web or document research that produces a cited report.

Source: README at the checked commit and package configuration.

STORM and Co-STORM

A candidate for researching a topic, developing an outline and producing a source-grounded article; Co-STORM adds collaborative knowledge curation.

Source: README at the checked commit.

Agent Laboratory

A candidate for a configured research workflow spanning literature review, computational experiments and report writing.

Source: README at the checked commit.

Feynman

A research CLI with commands for literature work, auditing, replication and drafting.

Source: README at the checked commit and package configuration.

MiroFlow

An orchestration framework for configured agents and tools, suited to teams building their own research-task execution environment.

Source: README at the checked commit and package configuration.

AI-Researcher

A public research prototype connecting reference-based ideas with implementation, experiments and manuscript generation.

Source: README at the checked commit and package configuration.

Arbor

A candidate for iterative hypothesis exploration and computational experiments organized around an idea tree.

Source: README at the checked commit and package configuration.

Run a Small Deployment Trial Before Adopting a Project

Pin the repository revision and record your installed dependency versions, configured model identifiers, tool services and operating environment. Repository examples may name models or endpoints that have since changed; confirm the availability and terms of the services you select. Keep credentials out of the research record.

Choose a narrow task with an output you can check independently. For a source report, inspect the passages supporting its central claims. For an experimental workflow, inspect the generated code, input data, actual execution record and evaluation design. Decide in advance what should happen when evidence is missing or a tool call fails.

Run in a workspace with only the data and permissions the task requires. Set spending, runtime and iteration limits. Preserve failed attempts and manual corrections as well as the final output, so that an attractive report does not hide the work needed to produce it.

A useful deployment record contains the pinned code revision, resolved dependencies, model/tool configuration, permitted input, original outputs, checks, corrections and total effort. Compare projects on that record for the same task. No benchmark score or service-cost estimate is supplied here because this review did not run a common evaluation.

Where a Commercial Research Product Fits

Gatsbi publishes this article and is a commercial research product. Its Writer and Reviewer pages describe supported drafting and review workflows. Those may suit readers who want a packaged workflow rather than maintaining a repository and its services.

That is a deployment choice, not evidence that Gatsbi outperforms these projects. Check the relevant product inputs, exports, terms and current plan separately. Likewise, an open-source license does not make hosted model calls, search services, compute or maintenance free.

Making the Adoption Decision

Choose the smallest setup that supports the task and leaves enough evidence for you to verify the result. If a required license, dependency, source record or execution log remains unclear, resolve that specific gap before expanding the deployment. Recheck the pinned configuration when you upgrade; a newer commit is a new configuration to evaluate.

If your project combines tools for evidence synthesis, define the handoffs and update records before connecting them. That guide focuses on the records exchanged between tools; this article focuses on selecting and operating the projects themselves.