Best agentic research environments for scientists in 2026

Preclinical teams can now choose from several AI environments whose agents read the literature, run analyses, and help plan the next experiment. Some readers are comparing these tools for the first time, and others want something more capable than what they use now. Either way, finding the best agentic research environment for scientists starts with knowing what separates one platform from the next.

This guide evaluates EMET by BenchSci and six other platforms, listed in the table below. Three criteria cover what each platform brings: proprietary data, workflow depth and integrations, and its service model. Two cover how it is built and governed: model flexibility and neutrality on drug development.

Table reflects each vendor's public materials as of October 2026.

What is an agentic research environment?

An agentic research environment is software where AI agents carry out multi-step research work for a scientist. The scientist states a question in plain language, and the agents plan the steps and gather evidence from literature and databases. They then run the code and return an answer with citations the scientist can check.

In practice, a target question might run like this. One agent gathers genetic association data and expression profiles, a second searches the literature for mechanism and safety signals, and a third runs a differential expression analysis on a public dataset. The environment joins those results into one cited report, and the scientist decides what to test next.

The category sits between tools scientists already know. A chat assistant answers one prompt at a time and leaves the analysis to the user, while a single-purpose tool such as a structure predictor does one job and stops. An electronic lab notebook records what a lab did without reasoning over it, so an agentic research environment is the layer that links these steps from question to evidence to analysis.

What to look for in an agentic research environment

When you compare agentic research environments, look past the demo at five things that decide how useful a platform will be on real programs.

• Proprietary, biology-specific evidence to reason over. The evidence base is the set of papers, databases, and curated experimental data a platform's agents can read when they answer a question. Agents that can read closed-access papers and curated reagent or model-system data find more on a target or safety question than agents limited to public sources. Ask each vendor which sources its agents reach beyond open literature and public databases.
• Depth of biology-grounded workflow and integration. Workflow depth is how much of the preclinical work a platform carries out itself, from a literature search to an omics analysis to an experimental plan. Integration is whether it connects to the systems your lab already runs, such as an electronic lab notebook, a regulatory document system, or lab automation. Check whether the platform runs your common analyses end to end and reads those systems directly, without exports and uploads.
• A service model that drives adoption. The service model is who helps your scientists fit the platform to their real projects after the contract is signed. Ask whether the vendor assigns scientists to your account to build workflows and connectors with your teams, and whether they stay after launch or hand the setup back to your own staff.
• The freedom to use the best model for each task. Model flexibility is whether a platform can send each step of a question to the AI model that handles it best, and switch as better models appear. No single model leads on every scientific task, and a platform tied to one provider inherits that provider's pricing and roadmap. Check how many model providers the platform can use.
• A clear position on whether the vendor develops its own drugs. Your targets, assay results, and failed experiments will pass through the platform, so the vendor's own business matters. Check whether the vendor runs or plans a drug discovery program, and what it commits to about keeping your data out of its own research and model training.

These five capabilities depend on each other: strong evidence lets agents reason well, workflows turn that reasoning into analyses and experiments, and a dedicated team fits those workflows to each program. Model flexibility keeps the results improving as new models ship, and a neutral vendor makes it safe to run sensitive programs on the platform. The platforms in this article differ in how much of that work they cover, and in how much they leave your team to assemble.

1. EMET by BenchSci

EMET by BenchSci is an agentic research environment built only for biopharma R&D, with its deepest coverage in preclinical work from target identification through IND-enabling studies. BenchSci has spent a decade building the data and scientific reasoning underneath it, and the platform is used at 16 of the top 20 pharmaceutical companies. It leads this list because it covers all five criteria in one environment.

What EMET does well

EMET is built around five capabilities, one for each criterion.

The first is the evidence its agents can reach: a biological knowledge graph of 858M nodes and 2.2B relationship edges, built from 38M+ publications. That includes 16M closed-access papers licensed through eight years of publisher partnerships with Elsevier, Springer Nature, Wiley, Oxford University Press, and others.

Beneath the literature sits a reagent and model-system database curated by 60 PhD scientists. It holds 16M antibodies, 22M RNAi entries, 18M CRISPR records, 500K cell lines, and 700K animal models.

The second capability is workflow depth: 200+ proprietary scientific skills and 250+ agents and workflows, organized into evidence workflows for eight teams from discovery to regulatory affairs, with every claim cited to its source. Its agents can also run bioinformatics pipelines inside the conversation and connect to automated lab infrastructure.

That depth reaches into each customer's own systems. EMET can reason over internal data while keeping it isolated to that organization. Sanofi signed a three-year license in October 2025 to deploy BenchSci's ASCEND platform across its global preclinical research organization.

Third, every deployment includes dedicated PhD scientists embedded with the customer. The team runs training, manages change, and builds the custom workflows and connectors that fit each organization's therapeutic areas. Merck renewed its ASCEND agreement with BenchSci in December 2025 for another two years of preclinical disease biology research.

The fourth capability is model flexibility. EMET builds no foundation model of its own, so it routes each task across 40+ models to the one that performs best on it, from frontier language models to specialist models such as ESM-2 and AbLang2. New frontier models are added as they ship, and no customer is locked to one provider.

Finally, BenchSci has committed never to develop its own drugs. Each organization's data stays isolated to that organization.

Where EMET falls short

EMET's full platform is sold through a custom enterprise plan, with no low-cost monthly tier. Academic and nonprofit scientists can use a limited version for free, but a small biotech that wants the closed-access data and custom integrations takes on a bigger commitment than a self-serve subscription.

Getting the most out of EMET takes practice. With 250+ agents and workflows, scientists have to learn which workflows fit their questions and how to direct the agents. BenchSci's account team runs training, but it still takes a while before the platform is part of a researcher's daily work.

Full integration also needs your IT team. Connecting EMET to internal data, an ELN, or lab automation involves the people who manage your security and those systems, so the complete setup takes longer than signing up for a self-serve tool.

Best for

EMET is the clear choice for biopharma R&D organizations that want agents grounded in evidence other platforms cannot reach and connected to their own systems. It suits target, safety, and translational teams that need cited answers they can defend at a go or no-go decision, with a scientific team that fits the platform to their programs.

2. Claude Science

Claude Science is Anthropic's AI workbench for scientists, released in beta on June 30, 2026 for paid Claude plans. A coordinating agent works with more than 60 curated skills and connectors for fields such as genomics, proteomics, and cheminformatics. It serves academic labs and industry research teams that want an environment for computational analysis.

What Claude Science does well

Claude Science makes its work easy to audit. Every figure carries the exact code and environment that produced it, and a reviewer agent flags incorrect citations and untraceable numbers as a pipeline runs. According to Anthropic's launch post, an Allen Institute neuroscientist used it to build a review-writing pipeline in which reviewer agents checked the citations.

It also handles compute for the scientist. Claude Science runs on a lab's own laptop, Linux machine, or HPC login node, so large or sensitive datasets stay on the systems they already sit on. It can submit jobs to a lab's cluster over SSH or to a Modal account and scale an analysis from one GPU to hundreds.

Access is simple for teams already using Claude. The app is included in paid Claude plans with nothing separate to buy, and it connects to open biology models such as Evo 2, Boltz-2, and OpenFold3 through NVIDIA's BioNeMo toolkit.

Where Claude Science falls short

The company behind Claude Science is entering drug discovery itself. At the launch event, Anthropic said it would use the product to pursue its own research into drugs for rare, neglected diseases. In September 2026, Reuters reported that Anthropic had built a wet lab as it ramps that drug program, though a spokesperson said the lab is not specifically for drug discovery.

Its agents reason with a single provider's models. The app pairs Claude with an analysis environment, and open biology models such as Boltz-2 are called as tools within that setup. Its public materials do not describe routing the reasoning to other providers' frontier models as of October 2026.

The evidence its agents reach is public data plus whatever the lab connects. Claude Science queries 60+ scientific databases such as UniProt, Ensembl, and ChEMBL, alongside literature and the lab's own files and pipelines. Its public materials do not describe a licensed proprietary biology dataset of Anthropic's own as of October 2026.

Best for

Claude Science is a strong choice for computational biologists and academic labs that want a reproducible analysis environment on their own hardware or cluster. It suits teams already paying for Claude that want genomics, structural, and single-cell analyses with traceable code behind every figure.

3. Causaly

Causaly is a London-based company founded in 2018 that builds an agentic AI platform for life sciences R&D. Its agents work over a knowledge graph of 500M facts and 70M directional relationships, and it unveiled its Agentic Research platform in 2025. Pharma teams use it from target identification through biomarkers, clinical development, and medical affairs.

What Causaly does well

Causaly turns expert research methods into repeatable processes. Its Scientific Workflows, launched in May 2026, convert a team's standard procedures into multi-agent workflows with human checkpoints and audit trails. Outputs include target assessments, indication reports, and safety dossiers.

It backs the platform with scientific services. Causaly's PhD scientists and its science and change management teams support deployment, adoption, and workflow design, from strategic alignment through ongoing research enablement. In its Eisai case study, Eisai researchers referred colleagues until the team hit its user limit.

Causaly sits on the tooling side of the industry. It sells software to drug developers and describes no drug pipeline of its own as of October 2026, and its enterprise data page states that Causaly never trains on clients' data. It is also available inside Microsoft 365 Copilot and on Microsoft Discovery.

Where Causaly falls short

Causaly's knowledge graph is built from published sources such as MEDLINE and PubMed Central literature, preprints, and GWAS data. Patents, ClinicalTrials.gov records, and each customer's internal data are added on top. Full text from more than 400 Sage journals is available through a partnership announced in July 2026, sold as a separate add-on.

The platform centers on evidence retrieval, synthesis, and competitive intelligence. Running computational pipelines and connecting to lab automation are not described as native Causaly capabilities as of October 2026. In its Microsoft collaboration, the advanced computing and lab automation sit on the Microsoft Discovery side.

Its services are organized around rollout. Causaly describes its scientists' work in terms of deployment, adoption, and research enablement, and its public materials do not describe a standing scientific team assigned to each account as of October 2026.

Best for

Causaly is a strong choice for R&D and medical teams whose main need is cited evidence synthesis across the biomedical literature. It suits organizations that want that research inside Microsoft tools, with repeatable workflows for target and indication assessments.

4. OpenAI Rosalind

Rosalind is OpenAI's life sciences brand. OpenAI introduced GPT-Rosalind on April 16, 2026 as a reasoning model for biology and drug discovery, then made it available to eligible organizations through a trusted-access program on September 11, 2026. Scientists use it in Rosalind Workbench and Codex, and launch customers include Amgen, Moderna, and Thermo Fisher Scientific.

What OpenAI Rosalind does well

GPT-Rosalind is a model trained for biology, with published benchmark results. OpenAI reports leading performance on BixBench, a bioinformatics benchmark, among models with published scores. In a test with Dyno Therapeutics on unpublished RNA sequences, its best submissions ranked above the 95th percentile of human experts on the prediction task.

OpenAI sits on the tooling side of drug discovery. It sells its model and workspace to drug developers such as Amgen and Novo Nordisk and describes no in-house drug pipeline as of October 2026. Its help center states that OpenAI does not train on enterprise customer data by default.

It is also easy to start with. A free Life Sciences research plugin for Codex connects models to more than 50 public databases, literature sources, and biology tools. The plugin also works with OpenAI's mainline models.

Where OpenAI Rosalind falls short

Rosalind runs on OpenAI's own models. GPT-Rosalind is OpenAI's purpose-built model, and Workbench's Explore mode uses the latest ChatGPT models available to the user. A team that adopts Rosalind ties its research workflows to one provider's model roadmap.

Its agents draw on public data, and working with a company's own data takes setup. The Codex plugin connects to public multi-omics databases and literature. On using an organization's own data, OpenAI's help center says the capability is "not fully available out of the box today" and that some workflows need custom setup.

The workspace is early. Rosalind Workbench is in research preview and starts with guided sequencing analysis. OpenAI's public materials do not describe connectors to ELNs or regulatory systems, or to lab automation, as of October 2026.

Best for

OpenAI Rosalind is a strong choice for bioinformaticians and computational biologists at organizations that already work in ChatGPT Enterprise or Codex. It suits teams that want a biology-trained model for early discovery and hypothesis generation and can build their own workflows around it.

5. Microsoft Discovery

Microsoft Discovery is Microsoft's enterprise platform for agentic R&D, built on Azure. Microsoft announced it at Build in May 2025 and made it generally available in June 2026. It serves science and engineering teams in fields such as pharmaceuticals, materials, and chemicals.

What Microsoft Discovery does well

Microsoft Discovery leaves model choice to the customer. Teams can bring their preferred models, tools, and data into the platform and draw on the models available in Microsoft Foundry. No single model provider is required.

Microsoft sells Discovery as a platform and describes no drug pipeline of its own as of October 2026. Customers deploy it in their own Azure subscription, and Microsoft's documentation states that customer data, knowledge graphs, and models are tenant-isolated and not shared across customers.

It brings large-scale compute and a partner ecosystem. Agents can run models on Azure high-performance computing, and partners such as Wiley and Causaly offer agents inside the platform. GSK's SVP of R&D Technologies said in the general availability announcement that working with Microsoft Discovery gives GSK the opportunity to rapidly iterate on candidate molecules.

Where Microsoft Discovery falls short

The biology evidence comes from what the customer and partners bring. Its agents ground their work in knowledge bases built from your documents and scientific literature, plus partner agents such as Wiley's life sciences research agent. Its public materials do not describe a Microsoft-curated biology dataset as of October 2026.

It is a general R&D platform that each team configures. Organizations define their own agentic workflows across modeling, simulation, and analysis. Microsoft labels lab-integrated experimentation as a scenario whose availability depends on the customer's environment and lab systems.

Delivery runs through partners. Microsoft lists systems integrators such as Capgemini, HCLTech, and Zifo in its Discovery ecosystem, and its public materials do not describe a Microsoft scientific team assigned to each account as of October 2026.

Best for

Microsoft Discovery is a strong choice for organizations standardized on Azure that want to build their own agentic R&D workflows across science and engineering. It suits teams with platform engineers and systems-integrator support, especially where chemistry, materials, or simulation work sits alongside biology.

6. FutureHouse (Edison Scientific)

FutureHouse is a nonprofit AI-for-science lab in San Francisco, founded in 2023 by Sam Rodriques and Andrew White. Edison Scientific, a for-profit company that spun out of FutureHouse in 2025, now commercializes its tools, including the agents first built under the FutureHouse name. In June 2026, Edison, whose flagship product is the Kosmos AI scientist, announced a partnership to co-build biotechs with the incubator Population Health Partners.

What FutureHouse does well

Edison stays open on models. Its site says Kosmos uses the best frontier models and remains model agnostic as new state-of-the-art models emerge.

Kosmos runs long, autonomous research. In its launch announcement, Edison reported that a single Kosmos run reads about 1,500 papers and runs about 42,000 lines of analysis code. Incyte has embedded Kosmos across its discovery and development work, starting with target discovery and validation.

Edison also staffs its enterprise accounts. Its site describes forward-deployed scientists embedded in the customer's team, along with an on-premises platform deployed in the customer's environment.

Where FutureHouse falls short

Its licensed content is at an early stage. Edison's literature agent draws on more than 175M papers, trials, and patents. In September 2026, Edison signed an agreement bringing licensed Springer Nature content into Kosmos, which it describes as the first of several content partnerships.

Kosmos works over literature and the datasets and files a customer connects. As of October 2026, Edison's public materials do not describe live connectors to ELNs or regulatory systems, or a connection to lab automation.

Best for

FutureHouse's tools, through Edison Scientific, are a strong choice for research teams that want long autonomous research runs across literature and data. They suit organizations that value a research-lab pedigree and want forward-deployed scientists to help train Kosmos on their own data.

7. Phylo

Phylo is an applied AI research lab that grew out of Stanford's open-source Biomni project. It launched Biomni Lab on February 3, 2026 as an integrated environment for AI-native biology, with 100+ pre-built agentic workflows. Phylo reports that 74K+ scientists at 7K+ life science organizations use Biomni, and Ono Pharmaceutical and Chugai have signed enterprise collaborations.

What Phylo does well

Phylo does not tie Biomni Lab to one model provider. It describes the platform as model-agnostic, matching research tasks to the best-performing models while keeping costs in check.

It sits on the tooling side of drug discovery. Phylo sells Biomni Lab to drug developers and describes no drug pipeline of its own as of October 2026. It states that customer data is never used to train models and can be deployed into the customer's own VPC.

It is easy to start with. Phylo's pricing runs from a free tier to $100 per month for Pro, and enterprise plans add white-glove onboarding and support. A UCSD project scientist quoted on Phylo's site said Biomni Lab cut a CRISPR screen workflow from nearly 20 hours to under 30 minutes.

Where Phylo falls short

Biomni Lab's agents reason over public and partner resources plus each customer's data. Phylo's launch announcement describes 300+ integrated databases and tools, with partner sources such as Consensus, COSMIC, and Addgene. Enterprise deployments connect to internal data, as in its Chugai collaboration.

Its workflows center on computational analysis run inside Biomni Lab's own workspaces. As of October 2026, Phylo's public materials do not describe live connectors to R&D record systems such as ELNs, and they do not describe a connection to lab automation.

Hands-on support is tied to the Enterprise plan. Phylo's pricing lists white-glove onboarding and support at that tier, and its public materials do not describe a standing scientific team assigned to each account as of October 2026.

Best for

Phylo is a strong choice for scientists who want to run a wide range of computational biology on public tools and data, self-serve and at a low starting price. It suits academic labs and biotech teams that want broad analysis coverage, from single-cell work to virtual screening.

Which agentic research environment is right for you?

If you need reproducible computational analysis on your own laptop or cluster, Claude Science may be enough. If your main need is cited literature synthesis inside Microsoft tools, Causaly is worth a look.

If your team already works in ChatGPT Enterprise or Codex and wants a biology-trained model for early discovery, OpenAI Rosalind is worth a look. If you are building custom agentic workflows across science and engineering on Azure, Microsoft Discovery may be enough.

If you want long autonomous research runs across literature and data, FutureHouse's tools through Edison Scientific are worth a look. If individual scientists want broad, self-serve computational biology, Phylo's Biomni Lab may be enough.

If your preclinical programs depend on evidence beyond public sources, connected to your own systems and fitted to your teams, choose EMET by BenchSci, which is built for that work. Book a demo to see it on your own targets.