Tools for Effective AI Behavioral Research.
OpenPsy is an open research platform for designing, running and analyzing behavioral experiments, including experiments in which language models take part as participants. The goal of OpenPsy is to enable researchers to understand what is happening beneath their results and to have an audit history in order to understand their research programs. OpenPsy is presently in an internal closed alpha.
Why OpenPsy?
Agentic research can be difficult to parse, monitor, and understand. Often agents will violate rules and assumptions about research design undermining results. OpenPsy was born from this difficulty and aims to assist researchers in agent to agent and agent to human research. The software is designed to improve a researcher's ability to plan, execute, and analyze agentic experiments with or without human participants.
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Introspection
OpenPsy enables the effective design of experiments by providing simulation prior to execution, and saved session introspection for auditability.
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Replicability
OpenPsy saves every version of a design and changes, so a researcher can audit and export the design for replication.
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Context Control
OpenPsy enables researchers to understand exactly what content they are serving agents at each step of an experiment. This experimental introspection allows researchers to improve their confidence in their operations and results.
OpenPsy Builds Experiments as a Flow
In the flow editor, researchers use "blocks" to build ideal experimental process through a node graph interface. All OpenPsy aspects read from the same common database ensuring there is no drift between a design and execution. Below we illustrate an example study in the OpenPsy framework.
Example Study
1Design
Research question
Does a gain or loss framing in a question affect how an LLM responds? Does this response vary across models?
Two factors, four conditions
The sample experiment is a 2x2, two factors with two levels. The first factor is a gain versus loss frame. The second factor is model type, Claude Opus 5 or Claude Sonnet 5.
Participants are randomized across the four conditions.
| Claude Opus 5 | Claude Sonnet 5 | |
|---|---|---|
| Gain frame | Gain + Opus | Gain + Sonnet |
| Loss frame | Loss + Opus | Loss + Sonnet |
Factor 1 · Framing: Gain / Loss
The two scenarios differ only in the highlighted clause.
A new infection is expected to affect 900 people in a region. Health officials are deciding whether to adopt Plan A. If Plan A is adopted, 300 of those people will be protected.
A new infection is expected to affect 900 people in a region. Health officials are deciding whether to adopt Plan A. If Plan A is adopted, 600 of those people will be left unprotected.
OpenPsy ensures the conditions are consistent aside from the manipulation using evaluations prior to and after execution.
Factor 2 · Model type: Opus / Sonnet
The design uses Claude Opus 5 and Claude Sonnet 5 in both framing conditions, with the same procedure and settings for both models.
Procedure
2Execution
The same four cells now track session completion. This simulated monitor shows counts only; responses stay hidden while collection is open.
3Results
In the hypothetical results below, the same four cells show outcomes, with row and column totals and a bar chart.
OpenPsy Roadmap
OpenPsy is in internal closed alpha. It has been tested with invented data only, and no real participant has taken part.
In Progress (Expected Live Q4 2026)
- A researcher can create an experiment, build it as a graph from Library blocks, edit each block's wording, and save every change as a new version.
- Any two saved versions can be compared, and the differences are written out as sentences.
- Library wordings are stored as exact versions, and publishing a revision leaves existing experiments on the version they used.
- A scripted test participant can take an authored study from consent to completion on a phone-sized screen.
- Automated evals pre and post execution
Near Future
- Export and replication of experiments by third parties
- Automated Measure and Execution Pilot Workflows
- Automated Power Analysis
- Co-Pilot Assistant in application.
- Hybrid Human/Agent Experiments
Source Code
The source code, the specification and the product documentation are being prepared for publication on GitHub.
GitHub repository opening soon