Best Open-Source Claude Science Alternatives for Research Teams

There is no open-source project that replaces Claude Science one-for-one, and anyone telling you otherwise is selling something. Anthropic's beta research app bundles four separate things — agent orchestration, domain-wired data access, scientific rendering, and provenance tracking — into one closed desktop application. Open alternatives cover those layers individually, and covering them well is a real option. It is just an assembly job.

This matters because a lot of research groups cannot install the closed version regardless of how good it is. Patient data with residency obligations, unpublished results, and industry IP all create the same constraint: the analysis has to stay on infrastructure you control.

What you are actually replacing

Break Claude Science into its parts before shopping for substitutes:

1. The agent and orchestration layer — plans a task, writes code, runs it, iterates.
2. The execution environment — persistent Python and R kernels, plus compute management from laptop up to HPC over SSH.
3. Domain data access — 60+ pre-wired scientific databases and literature integration.
4. Scientific rendering — proteins, alignments, genomic tracks, chemical structures, PDFs displayed natively.
5. Provenance — every artifact carrying the code, environment, and conversation that produced it.

Layers 1 and 2 are well served by open projects. Layer 3 is partly served. Layers 4 and 5 are where the open ecosystem is genuinely behind, and you should plan for that rather than discover it in month three.

The criteria

Every tool below is judged on: permissive licence, runs fully on your own infrastructure, works with a model you choose (including local inference), executes real code rather than only generating it, and has enough community behind it to still exist next year.

1. Eigent — the general orchestration layer

Licence: Apache 2.0 · Runs: desktop, local backend, cloud, or enterprise deployment

Eigent is an open-source desktop application for running multi-agent workflows, with 15.2k stars at the time of writing. It is not science-specific, which is both the limitation and the point: it supplies the orchestration and tool-use layer that a domain stack plugs into.

The relevant features for a lab are model-agnostic execution, MCP integration, built-in browser and terminal toolkits, and local deployment with local inference engines including vLLM, Ollama, and LM Studio. Multi-agent coordination handles the case where one agent pulls literature while another runs the analysis.

Use it when you want a general agent workforce you control end to end, and you are willing to attach domain tools yourself through MCP. Skip it when you need scientific renderers out of the box, because it does not have them.

2. Biomni — the closest thing to a domain-native equivalent

Licence: Apache 2.0, with the caveat that some bundled tools and databases carry more restrictive commercial terms

Biomni from Stanford is a general-purpose biomedical AI agent, and it is the single closest open analogue to what Claude Science does in life sciences. It combines LLM reasoning with retrieval-augmented planning and code execution, ships an 11GB data lake, supports MCP for external tools, and generates PDF reports of its execution traces.

Documented applications include CRISPR screening, scRNA-seq annotation, ADMET property prediction, GWAS analysis, and rare-disease diagnosis. It installs with pip install biomni and takes an API key for Anthropic, OpenAI, or another provider. It also ships its own benchmark, Biomni-Eval1, with 433 instances across ten biological reasoning tasks — which is more than most tools in this space offer as evidence.

Read the licences before commercial use. The Apache 2.0 headline does not extend to every bundled database, and that is the kind of detail that surfaces during legal review rather than during evaluation.

3. PaperQA2 — the literature layer

Licence: Apache 2.0 · Install: pip install paper-qa>=5, Python 3.11+

PaperQA2 from FutureHouse does one job properly: high-accuracy retrieval-augmented generation over scientific documents, with in-text citations. It handles PDFs, text, Office documents, and source code, retrieves metadata including citation counts with retraction checking, and supports multiple LLM providers through LiteLLM.

Retraction checking deserves a mention on its own. An agent that confidently cites a retracted paper is worse than one that cites nothing, and very few tools check.

This is the substitute for Claude Science's literature integration, and it is a good one. It is not a substitute for the other four layers.

4. OpenHands — the self-hosted control plane

Licence: MIT · 85.8k stars

OpenHands is a self-hosted control centre for coding agents and automations. It runs locally by default via npm, Docker, or source, works with Claude Code, Codex, Gemini, or any agent speaking the Agent-Client Protocol, and supports bring-your-own-model profiles.

Be clear-eyed about the fit: its documentation is squarely about software development, not science. There is no scientific rendering, no database wiring, no kernel model built for analysis. What it gives you is a mature, heavily adopted, MIT-licensed harness for running agents on your own machines, plus scheduling and webhooks.

Use it when your scientific work is really software engineering — maintaining pipelines and packages — and you want it self-hosted.

5. Jupyter plus MCP — the layer you should not rebuild

The persistent-kernel model that makes Claude Science feel right for analysis is Jupyter's model. If you are assembling a stack, the kernel layer already exists, is battle-tested, and runs anywhere. Attaching an agent to it through MCP is a smaller job than reimplementing state management, and it means your researchers keep the interface they know.

This is the least glamorous recommendation here and probably the highest-leverage one.

Comparison

What the open stack still does not give you

Three honest gaps.

Native scientific rendering. Claude Science displays proteins, alignments, genomic tracks, and chemical structures as first-class objects. In an open stack you get this through libraries inside a notebook, which works but is not the same as the agent being able to look at the structure and reason about it.

Provenance by default. Attaching code, environment, and conversation to every artifact automatically is an architectural decision made early. Bolting it on afterwards is possible and rarely happens.

Sixty-plus pre-wired databases. Each connector you build yourself is a week you are not doing science. If your data sits in the common sources, the closed product's integration work is real value you are choosing to redo.

Weigh those against the reasons you are here: data residency, model choice, cost predictability, and not building a research programme on beta software with unpublished pricing.

How to assemble it

A workable stack for a group that cannot send data out:

1. Kernels: JupyterLab on your own hardware, wrapped with MCP.
2. Orchestration: Eigent or OpenHands, pointed at a local model through vLLM or Ollama for sensitive work, and at a frontier API for everything else.
3. Literature: PaperQA2 over a local corpus, with retraction checking on.
4. Domain reasoning: Biomni if you are in biomedicine, licences reviewed first.
5. Provenance: decide the artifact format in week one. Every figure gets its script, its environment lockfile, and its inputs, enforced by convention and CI rather than good intentions.

Start with one workflow, not the whole platform. Pick the analysis you repeat most, and see our guide to building a fully local AI workforce for how to stand the local execution piece up.

If you are still deciding whether you need the science app or the coding one before shopping for substitutes, Claude Science vs Claude Code makes that call first.

Frequently asked questions

Is there a true open-source equivalent? Not as a single project. The closed app bundles five layers; open tools cover them individually, and you assemble the combination you need.

Which one is closest? Biomni, for biomedical work. It is the only open project that pairs domain-native reasoning with code execution and a bundled data lake.

Can I run any of this fully offline? The orchestration and kernel layers, yes, with a local model through vLLM or Ollama. Literature and database access needs a network or a local mirror by definition.

Are they free for commercial use? Apache 2.0 and MIT permit it, but check bundled dependencies separately. Biomni in particular carries tools and databases with more restrictive terms than its headline licence.

What will I miss most? Native scientific rendering and automatic provenance. Both are architectural, so plan for them at the start rather than retrofitting later.

Do I need a GPU? Only for local model inference. Running agents against a hosted API needs none.

Build the layer you control

Eigent gives research teams the orchestration piece as open source under Apache 2.0: multi-agent coordination, MCP tool integration, and local deployment with the model of your choice, so analysis data never has to leave your infrastructure. Turn your most-repeated pipeline into something the agents run by saving the workflow as a skill, or start from the data engineering hub if your bottleneck is upstream of the analysis. Download Eigent to run it on your own hardware.