Agentic science harnesses: the field, read closely

1. Hero

1b. Weekly update log

Updated 9 November 2026

Weekly refresh of the 2 November 2026 pass. What changed this week:

• ▸ ClawsGO Science added: a 27th harness, and the week's only genuinely new one. BitMiracle AI Lab released and open-sourced a long-horizon research agent on 1 Nov 2026 (Product Hunt #2 of the day, 279 upvotes). Every task gets an always-on cloud workstation with 533 tools across 61 disciplines and 91 research databases; the harness runs clusters of agents for days and returns a cited LaTeX paper with figures kept as reproducible bundles. The launch case study is the Global Attractor Conjecture: a 50-year-old chemical-kinetics problem proved in 11 days as 83 Lean 4 files (about 9,300 lines, machine-checked, no sorry placeholders), planned by Claude Opus 5.5 and written by 136 GPT 6 Astra agents. Plans run from free to $299.90/mo. It is profiled in the closed-SaaS section and the comparison matrix.
• ▸ Feynman shipped five releases (v0.5.62 to v0.5.66, 3 to 8 Nov) and passed 10,090 stars. pi-subagents 0.85.0 and 0.86.0 run parallel-researcher workflow scripts in a locked-down process, stop a research prompt with long separators or git options from stalling, and drop the subagent tool after a compaction only when they should; pi-web-access 0.47.0 and pi-btw 0.8.1 landed beside them. /lit now launches parallel researchers with the same workflow form as /deepresearch, fixing a first-call "parallel is not defined" failure, and GitHub README fetches return up to 100K characters. Repo: 10,094 stars (+34), 166 releases to date, last push 8 Nov.
• ▸ Claude Science shipped 0.1.72 (5 Nov). LaTeX previews render tables and body text more accurately; a Directory connector such as Google Drive completes in the browser when Claude Science runs in its own window; and the macOS and Linux sandbox now protects more developer caches and startup files (Linux starter environments take about 5 GB more disk after a background copy). Anthropic's science page added two results posts: the first 3D structure of a vault particle, resolved with Claude (4 Nov), and a teaching post on AI-built scientific models (6 Nov). Platform side: an IP allowlist beta opened (5 Nov), text watermarking reached Mythos 5, Opus 4.7 and Sonnet 4.6 (3 Nov), message threads entered beta (2 Nov), and fast mode for Opus 4.8 was deprecated on 30 Oct for removal on 30 Nov.
• ▸ AIPOCH Open-Science shipped v1.0.8, v1.1.0 and v1.2.0 (4, 6 and 9 Nov), its biggest jump of the autumn. Approved jobs now run remotely on user-connected Kubernetes, Hugging Face Jobs, Modal and Ray beside SSH and Slurm; the standalone Open-Science agent is now the default runtime (Claude Code, OpenCode, Codex, CodeBuddy and Pi remain); step-linked discussions arrived; and macOS updates are differential. Repo: 5,501 stars (+83), pushed 9 Nov.
• ▸ Elicit widened the Research Agent beyond papers (2 Nov blog). It now reads patents (USPTO, EPO, WIPO), Open Targets, Broad's DepMap, the FDA's FAERS and MAUDE safety reports, and EMBL-EBI's ChEMBL, each reachable as its own Skill. Pricing is unchanged at this check.
• ▸ Edison Scientific's Kosmos took another step into pharma (3 Nov). Incyte's Kosmos Target Assessment prepares drug-target reviews in under one workday on average, and on six targets with known committee decisions it proposed follow-up experiments that matched the committee's requests in 83% of comparisons. Pfizer (29 Oct) and the $70M seed remain the larger moves behind it.
• ▸ Correction: Google's Co-Scientist moved beyond hypothesis generation in a 29 Aug 2026 technical report, which this report had not captured. It now plans experiments, writes code and machine-readable lab protocols, controls lab equipment, analyses results and drafts manuscripts, with a verification module that cross-checks each numerical claim against the generated code's execution logs. Google reports experimentally validated results in materials science, biology and computer science, and in a double-blind study of 150 generated papers fabricated key results fell from 46% without the reliability modules to 4% with them. The harness and matrix entries are corrected below. CoreSR is otherwise unchanged at this check.
• ▸ GitHub stats refreshed (API, 9 Nov): Feynman 10,094★ (+34) · Biomni 4,069★ (+27; last push 2 Nov) · Eigent 15,780★ (+64; v1.0.7, 30 Oct) · PaperQA2 9,568★ (+39; last push 26 Oct) · OpenScholar 1,683★ (+2; still stalled Aug 2025) · STORM 31,840★ (+72; stalled) · GPT Researcher 30,594★ (+168) · The AI Scientist 14,966★ / v2 7,479★ / v3 587★ · Agent Laboratory 5,926★ · ResearchAgent 74★ · Runcell Science 24★ (no push since 14 Oct) · OpenScience 4,358★ (+36; v2.0.153) · AIPOCH Open-Science 5,501★ (+83; v1.2.0) · ai4s Open Science Desktop 2,083★ (+11) · genomi 491★ · BootLoops 429★ · wisp-science 1,068★ (+7; v1.24.0) · ClawsGO Science (new).

House checks 2026-11-09: the 28 Oct 2026 pass removed every legacy em dash and en dash from this file; every pass since adds none, so grep -c for U+2014 and U+2013 both return 0. house-check.py: no dashes, no external assets, no non-ASCII outside names. a11y-check.py: HARD none; soft = long sentences/paragraphs by design (dense reference document).

## 2. Executive Summary

01

Executive summary

The landscape has moved since the last pass. The field still splits cleanly between open, self-hostable harnesses and closed SaaS, but the centre of gravity has shifted: Anthropic entered with a full science workbench, Stanford and FutureHouse validated open-domain equivalents in the peer-reviewed literature, and the autonomous-pipeline crowd (Sakana, Agent Laboratory) reached Nature and EMNLP. Twenty-seven harnesses are profiled here, each from a dedicated research pass or the weekly refreshes.

• The field splits into four groups. Open-source self-hosted harnesses: Feynman, PaperQA2, OpenScholar, STORM, GPT Researcher, Agent Laboratory, ResearchAgent, The AI Scientist, where you pay only for LLM API usage. Closed science SaaS: Elicit, Consensus, SciSpace, Undermind, Manus, CoreSR, Haiqu AgenticOS for quantum R&D and (new this week) ClawsGO Science, an always-on cloud research lab, where vendor-managed models sit behind a subscription. Provider-tied systems: OpenAI Deep Research, Gemini Deep Research and the new Google Co-Scientist / Gemini for Science, harnesses that only exist inside their vendor's products. And the workbench cluster around Claude Science: Runcell Science, OpenScience, AIPOCH Open-Science, wisp-science, Biomni and Eigent as its open equivalents.
• Feynman passed 10,000 stars and kept its daily cadence. Launched 19 March 2026, it sits at 10,094 stars (9 Nov check) and counts 166 releases to date: five more this week (v0.5.62 to v0.5.66), built on pi-subagents 0.85.0 and 0.86.0, which run parallel-researcher workflow scripts in a locked-down process and drop the subagent tool after a compaction only when they should; pi-web-access 0.47.0 and pi-btw 0.8.1 landed beside them; /lit now launches parallel researchers with the same workflow form as /deepresearch, fixing a first-call "parallel is not defined" failure; and GitHub README fetches return up to 100K characters. All of it was plumbing that makes long unattended runs cheaper and more reliable: /deepresearch no longer stops for approval it never gets (v0.5.38), feynman search set now accepts all 39 pi-web-access providers (v0.5.44), a custom User-Agent lets it read sites that turn away AI fetchers (v0.5.47), a LaTeX backslash bug that dropped tool-call fields was fixed in Pi 1.3.0 (v0.5.48), and pi-subagents 0.79.x stopped resending subagent tool definitions and cost reports with every request (v0.5.49-0.5.50). The 0.5.x line remains fixes and hardening (PubMed affiliation and italics fixes, OpenAlex miss-recovery hints, ChatGPT-subscription web search moving to gpt-6-luna). Differentiators: local-first operation, a dedicated Verifier agent, the paper-vs-codebase audit workflow (/audit), and ten research workflows from literature review to experiment replication. Reception is warm but honest: "way better than Perplexity" for research; the source layer is OpenAlex / Semantic Scholar / PubMed / Europe PMC-centric, and earlier /lit rough edges were partly addressed in the 0.5.x line.
• Claude Science changed the SaaS-vs-open argument. Anthropic's workbench (June 2026) is not a new model: it is a generalist coordinating agent with 60+ science connectors, local-first execution, artifact-level provenance, and a citation-checking reviewer agent; the layer underneath moved three times now: Opus 5.5 (22 Sep), Sonnet 5.5 (28 Sep) and Haiku 5.5 (7 Oct; from $0.10/$0.50 per 1M tokens, ~75% cheaper to run than Haiku 4.5). September's headline results stood (the enzyme-system discovery; the nine-loop amplitude), and October's first week added the field's most concrete artefact yet: the first complete ultraviolet map of the sky, assembled with Claude Science by Brice Ménard (8 Oct). No single open project replaces it one-for-one: Runcell Science matches the positioning but is very early (23 stars; it did push again on 14 Oct, adding an OpenCode runtime after eleven quiet weeks); AIPOCH Open-Science is the largest open workbench by adoption (5,501 stars) and shipped v1.2.0 on 9 Nov, with OpenScience close behind (4,358 stars) and wisp-science now added as a fourth workbench (1,068 stars, v1.24.0); Biomni is the closest domain-native equivalent for biomedical work (peer-reviewed in Science; now also commercial via Phylo's Biomni Lab); Eigent is the orchestration layer of an assembled open stack. On 21 Oct auto mode became the default in Claude Science wherever it is available, so the workbench runs code and calls connectors without asking at each step while its own review blocks what it judges unsafe; Team organisations keep it on and Enterprise starts with it off. The week around it was Anthropic's busiest in science: an S-1 for a proposed IPO (26 Oct), a drug-development progress report with six partners plus in-house muscular-dystrophy programmes (27 Oct), and an AI for Science expansion to 20 foundations and institutions (28 Oct).
• Commercial deployment is the new scoreboard. Edison Scientific agreed to deploy the Edison AI Platform and Kosmos across Pfizer's biopharmaceutical research and development pipeline (29 Oct), letting Kosmos read Pfizer's proprietary data to build integrated world models with Edison engineers embedded in the research teams, two days after the $70M seed. OpenAI moved the other lever: GPT-6 Rosalind Research (30 Oct) brings GPT-6 reasoning and scientific tool use to life-sciences work for organisations approved under the trusted-access program, in Work, Codex and the API but not the Chat tab. Neither move adds an open harness; both show how the vendor-tied tier is being sold.
• Literature review is still the core battleground. Every harness is judged on search strategy (agentic vs keyword), reading depth (abstract vs full text), synthesis (summary vs structured consensus), and citation integrity: the weakest point across the whole field. Elicit and PaperQA2 remain the citation-rigour leaders; OpenAI Deep Research and The AI Scientist v1 carry documented hallucination flags; STORM has documented hallucinated-citation examples. A fresh September 2026 audit (IntuitionLabs) adds the sharper finding that even leading deep-research agents hold link validity above 94% while factual claim-support accuracy runs only 39-77%: real citations frequently do not support the claims attached to them.
• Peer review is arriving as an arbiter. Biomni (Science, 10.1126/science.adz4351), OpenScholar (Nature, s41586-025-10072-4), Agent Laboratory (Findings of EMNLP 2025), The AI Scientist (Nature 651, 914-919) and Google's Co-Scientist (Nature, May 2026) all have peer-reviewed homes, and CoreSR now presents its TITAN-SR screening model as peer-reviewed in the Journal of Clinical Epidemiology (Oct 2026): a step change from the 2024 era when these were preprint-only claims; September added Stanford's 37,000-agent virtual biotech (Science, 17 Sep) and papers-as-agents (Nature, 16 Sep). In November the results posts continued: Anthropic reported the first 3D structure of a vault particle, resolved with Claude (4 Nov), and a teaching post on AI-built scientific models (6 Nov).
• None of these replace protocol-driven systematic reviews. Even Feynman's own community notes that Embase, Web of Science, Covidence and Rayyan remain necessary for PRISMA-grade work. The harnesses are for rapid reviews, scoping, grant backgrounds and ideation, not methodology that must survive scrutiny.

27harnesses profiled

10.1kFeynman GitHub stars

31.8klargest OSS repo (STORM)

60+Claude Science connectors

3. Feynman Deep Dive

02

Feynman deep-dive

Full dossier from research card t_cdfcffb6. What it is, who builds it, how it works, its literature-review capacity, how it was received, and how it compares. Feynman gets the deep treatment because it is the newest, fastest-growing open-source harness in the set, and the one most shaped by its reception.

What it is

An open-source AI research agent (CLI; the early science workbench via feynman serve was retired in the v0.4.0 "deslop", 23 Sep 2026) built on a stock Pi agent runtime, with alphaXiv plus OpenAlex / Semantic Scholar / PubMed / Europe PMC / bioRxiv / Crossref for paper search and analysis. Bundled agents: Researcher (evidence gathering, numbered Sources), Reviewer (severity-graded FATAL/MAJOR/MINOR), Writer (structured drafts from evidence only) and Verifier (citation and URL verification, dead-link removal; runs last). Ten research workflows including /deepresearch, /lit (literature review with consensus, disagreements and open questions), /audit (paper claims vs public codebase), /replicate, /recipe, /review, /compare, /draft, /autoresearch and /summarize. One-line install (curl feynman.is/install), standalone bundle with pinned Node runtime, SHA-256 verified. Supports hosted providers (Anthropic, OpenAI, Google, OpenRouter, GitHub Copilot, Bedrock) and local models (LM Studio, LiteLLM, Ollama, vLLM).

GitHub stars

10,094

Forks

1,108

Open issues

0

Created

19 Mar 2026

Last commit

31 Oct 2026

License

MIT

Language

TypeScript

Latest release

v0.5.61 · 30 Oct

Team & origin

• Owner: founded under the Companion-Inc GitHub org ("Companion", "Personal AI", US, created 2025-09-11); the repo sat briefly under the founder's personal account in early September, and as of the 28 Sep 2026 check github.com/advaitpaliwal/feynman still redirects to github.com/Companion-Inc/feynman (API verified), with the v0.3.49 re-launch release confirming the org as home
• Founder / sole maintainer: Advait Paliwal (@advaitpaliwal): 470 of 478 commits; founded YouLearn (AI tutor, 2M+ users), co-founded Friday (YC-backed), MIT Augmentation Lab; based San Francisco
• Company: single founder-led entity; getcompanion-ai org 404s (renamed to companion-inc); org vs personal hosting has settled back under the company, re-verified 28 Sep 2026 when the release wave landed from the org repo
• Funding: no public round verified (unverified), Tracxn lists a "Companion Ai" profile with no funding raised (entity match unverified); the "$2.5M seed" snippet refers to a different Companion Labs
• Sibling product: companion.ai: personal AI assistant (Gmail/Calendar/Notion/Slack/Linear, freemium)

Architecture

• Runtime: Pi 1.0.0 since v0.5.24 (3 Oct 2026), now on Pi 1.1.0 as of v0.5.30 (7 Oct; adds Claude Haiku 5.5 and browser-login/retry fixes): fullscreen terminal UI by default (feynman --tui-mode regular to opt out), clean npm audit since v0.5.25; capabilities ship as Pi packages and skills; alphaXiv plus literature databases for paper search/analysis
• Bundled agents: Researcher (evidence gathering, numbered Sources), Writer (structured drafts from evidence only), Reviewer (severity-graded FATAL/MAJOR/MINOR), Verifier (anchors claims to sources, verifies URLs live, removes unsourced claims, runs last); workflow prompts call them via the subagent tool when delegation helps
• Compute: Docker (isolated containers), Modal (burst GPU), RunPod (persistent GPU pods)
• Workspace contract: slug-based file naming, outputs/.plans/, CHANGELOG.md lab notebook, provenance sidecars enforced for /lit
• MCP servers: feynman mcp add|list|remove manages MCP servers from Feynman's own settings (v0.5.61, 30 Oct 2026), saved to ~/.feynman/agent/mcp.json and loaded by every session
• Install: one-line installer (curl feynman.is/install), npm install -g @companion-ai/feynman, or skills-only install into Codex/Claude/OpenCode

Reception: positive

• PRO Distinctive paper-vs-codebase audit workflow (feynman audit <arxiv-id>): automates checking whether code actually does what the paper claims (r/LocalLLaMA; Medium Open Intelligence review)
• PRO Dedicated Verifier agent catches hallucinated citations and kills dead links: "this one has an entire agent dedicated to catching that before it reaches you" (r/LocalLLaMA)
• PRO "For research it is way better than Perplexity even though it's still not perfect" (r/LocalLLaMA, pippes23)
• PRO Described as "Claude Code for scientific research" (LinkedIn, Jacob Minz: snippet only, page not fully scraped)
• PRO Forked for public health / HPSR by daktre: "it's become a working tool" (notes.daktre.com, built at IPH Bengaluru)
• PRO Roundtable (@RoundtableSpace) X post: "does in seconds what takes academic professionals hours... One download. Zero cloud. Fully free.", 324 likes, 43 retweets (2026-04-12)
• PRO ~10k stars in under seven months from creation (10,094 at the 9 Nov check); sustained cadence: 69 releases since 23 Sep (v0.3.49→v0.5.66; 166 releases to date), including the Pi 1.0/1.1/1.3 runtime moves, rate-limit hardening, first-run onboarding and a week of token-cost work in pi-subagents 0.79.x
• PRO Package traffic: 51,133 npm downloads in the last month on the registry API (28 Sep to 27 Oct 2026, @companion-ai/feynman), with about 12.6K in the most recent week. The earlier package-page reading of about 119.5K a month is not reproduced by the API and is marked unverified
• PRO Deliberate refocus: the v0.4.0 "deslop" removed the workbench, biomedical modelling templates, PaperRank and other scope outside the research loop, keeping find papers → read → synthesise with verified citations → audit vs code → replication planning (release notes; removed code preserved at the archive/pre-deslod-0.3.49 tag)
• PRO /lit eval on GPT-5.6 Terra: 4 of 4 questions completed, all 44 citations resolved with matching titles, each under 11 minutes (v0.5.3 notes; 0.5.2 runs were 9-14 minutes)

Reception: negative / caution

• CON /lit had flaws at 0.3.x (model-recognition problems; Mac setup friction: r/LocalLLaMA, pippes23); the 0.5.x line addressed the Crossref / Semantic Scholar failure modes, though independent validation of the fixes is still thin
• CON The 0.4.0 deslop narrowed scope decisively: the workbench, biomedical modelling templates and PaperRank were removed (recoverable at the archive/pre-deslod-0.3.49 tag) and arXiv free-text topic search was dropped in favour of ID lookup: search topics via Semantic Scholar or OpenAlex; the daktre-era "no native PubMed" gap is closed (PubMed and Europe PMC are in the source set), but the tool remains literature-first
• CON Known issue (v0.5.3 notes): Claude Opus 5.5 refuses some biology literature reviews ("The model refused to complete the request", e.g. /lit on CRISPR base editing); the same topics run on GPT-5.6 Terra
• CON "Still early; best suited to technical users comfortable with CLI-style workflows" (Gatsbi blog, 13 Jun 2026)
• CON Seen as over-optimised for paper writing by a social-science blogger (Jared Knowles, 4 Jun 2026)
• CON Low HN footprint: launch story got 3 points and one joke comment ("Needs a bongo player agent and a /riff workflow") (HN, 7 Apr 2026)

Reception: neutral observations

• NEU "Nobody is talking about it" framing despite 2.3k stars at the time: awareness lags star count (r/LocalLLaMA)
• NEU Positioned as a local-first, self-hostable alternative to hosted research tools (Gatsbi blog)
• NEU Listed among science AI agents in danmackinlay.name notebook: "ships as either a full agentic loop or as a bundle of skills"
• NEU Explicitly NOT a substitute for protocol-driven systematic reviews: needs Embase / Web of Science / Covidence / Rayyan for those (daktre fork notes)
• NEU Name collision: @feynmanai on X is a different product (Feynman technique study app); an Obsidian plugin is a different project: verify by URL before citing

X / GitHub / docs presence

• X account: none verified (unverified): no dedicated Feynman or Companion product handle found. Founder @advaitpaliwal announced Companion from a personal account
• GitHub: 10,094 stars, MIT, TypeScript, 1,107 forks, 0 open issues; last commit 8 Nov 2026; releases continued through 8 Nov (v0.5.62 to v0.5.66; 166 to date); repo description changed to "The open source AI research agent" in v0.5.35; homepage feynman.is. Repo URL: github.com/Companion-Inc/feynman is canonical: advaitpaliwal/feynman 301-redirects there (API verified 12 Oct 2026).
• Docs: feynman.is/docs (verified live): installation, setup wizard, workbench, CLI + slash command reference, release notes; mirror at getcompanion-ai-feynman.mintlify.app
• Release cadence: quiet after v0.3.48 (6 Sep); development resumed 23 Sep and has not paused: fifteen releases in the first six days (v0.3.49→v0.5.12), fourteen more to 5 Oct (v0.5.13→v0.5.26), then ten more to 12 Oct (v0.5.27→v0.5.36; highlights: Pi 1.0.3/1.0.4/1.1.0, Claude Haiku 5.5 support, the pi-subagents 0.77.0 subagent-token cut, PubMed affiliation and italics fixes, OpenAlex recovery hints, and first-run onboarding; latest v0.5.36, 12 Oct; 136 releases to date)

Literature-review capacity (detailed)

How it searches

alphaXiv via alpha CLI: paper search, Q&A, code reading, annotations. Web search with three routing modes: auto (Perplexity if key configured, else Gemini), perplexity (force Sonar), gemini (zero-config grounding via signed-in Chromium, default), with 2 to 4 simultaneous varied-angle queries, domain filtering, recency filtering, full-content retrieval. Literature sources after the 0.4.0 deslop: OpenAlex (incl. semantic: embedding search and free-key guidance), Semantic Scholar (bulk citation-sorted, plus relevance / pub-date sorts), arXiv (ID lookup only), PubMed, Europe PMC (incl. open-access full-text sections), bioRxiv/medRxiv, Crossref, plus fetch_content for direct retrieval. The biomedical "Feynman Bio Tools" connector set (ClinicalTrials.gov, FDA, ChEMBL, genomics, structures and more) was removed in the deslop. Past sessions are searched with grep/rg over ~/.feynman/sessions (the session-search preset was removed in 0.5.4).

How it synthesises

/lit: 6 stages: Plan (scope, source types, task ledger; waits for confirmation), Gather (researcher subagent, tasks marked done/blocked/superseded), Synthesize (consensus / disagreements / open questions; strong claims swept against a verification log and downgraded if inferred or single-source), Cite (verifier adds inline citations), Verify (reviewer checks unsupported claims, gaps, zombie sections; FATAL fixed before delivery, MAJOR → Open Questions, MINOR accepted), Deliver (outputs/<slug>.md + provenance sidecar). /deepresearch runs parallel researchers with synthesis and verification pass; workflows now wait for background subagents before the final answer. The old /rank PaperRank workflow was removed in 0.4.0 (its "what to read first" role moves into /lit and /deepresearch). Lab/PI corpus mode maps publication trajectories. /compare builds side-by-side agreement/conflict matrices. /summarize keeps long sources on disk instead of in context.

Citation handling

Source-grounded outputs: every claim links to papers, docs or repos with direct URLs. The Verifier agent anchors every factual claim with [1]..[N] inline citations (multiple sources [7, 12] supported), verifies each URL is live via fetch, substitutes archived/mirror alternatives for dead links, removes dead-link-dependent and unsourced claims, builds the final numbered Sources with no orphans; "verify meaning, not just topic overlap"; refuses fake certainty ("verified/confirmed/reproduced" only with underlying evidence). /lit provenance record: date, sources consulted vs accepted vs rejected, verification status, intermediate files. Full-text access resolution runs through the literature tools (OpenAlex, arXiv, alphaXiv, DOI lookup, Europe PMC); the retired /paper shortcut is noted in the release history.

Limitations

Literature-first by design: the source set is OpenAlex, Semantic Scholar, arXiv, PubMed, Europe PMC, bioRxiv/medRxiv, Crossref and web search; the workbench, biomedical modelling connectors and PaperRank were removed in 0.4.0 (recoverable at the archive/pre-deslod-0.3.49 tag). Known issue: Claude Opus 5.5 refuses some biology literature reviews (v0.5.3 notes); the same topics run on GPT-5.6 Terra. Not a substitute for formal systematic review methodology: no native PRISMA / Embase / Web of Science support. Premium model tiers are never auto-picked (available via --model / /model). Local models need a larger context window configured (setup docs). Thinking levels: --thinking off|minimal|low|medium|high|xhigh.

Pricing / hosting / provider tie

Free, open-source, MIT license: no SaaS subscription. Cost to run: user pays only their connected LLM API costs. Providers: Anthropic, OpenAI, Google, OpenRouter, GitHub Copilot (OAuth), Amazon Bedrock (AWS credential chain); local via LM Studio, LiteLLM Proxy, Ollama/vLLM (baseUrl + API key). Optional packages: memory, hindsight (needs a Hindsight server/cloud); the session-search preset was removed in 0.5.4.

Comparisons to other harnesses

vs GPT Researcher

: GPT Researcher for flexible deep-web cited reports (planner + execution agents); Feynman for local-first paper reading, auditing and drafting. Feynman rated "moderate" experiment support vs GPT Researcher "limited" (Gatsbi)

vs Stanford STORM

: STORM is Wikipedia-style knowledge curation (outline-first); Feynman is a local workflow with paper/code audit and citation-oriented outputs. STORM is not designed for experiments or academic manuscripts (Gatsbi)

vs Agent Laboratory / AI-Researcher / Arbor

: those are closer to full autonomous researchers (end-to-end AI/ML research workflow); Feynman is distinguished by local-first operation, audit and citation verification (Gatsbi)

vs MiroFlow

: MiroFlow is a benchmark-oriented high-performance agent framework (FutureX/GAIA/HLE/xBench-DeepSearch); Feynman is a workflow-oriented local research assistant (Gatsbi)

vs Perplexity

: "for research it is way better than Perplexity even though it's still not perfect" (r/LocalLLaMA, pippes23)

vs Claude Code

: described as acting like "Claude Code for scientific research" (LinkedIn, Jacob Minz; snippet only)

vs Elicit

: weak signal: a Facebook group OCR roundup lists Elicit alongside Feynman in open-source research tools (incomplete OCR; treat as weak)

vs Covidence / Rayyan

: Feynman's own community notes it will not replace protocol-driven systematic reviews; those still need Embase, Web of Science, Covidence and Rayyan. Feynman is for rapid reviews, scoping preliminaries and grant backgrounds (daktre fork notes)

## 4. Claude Science + Open Equivalents

03

Claude Science and the open equivalents

Full dossier from research card t_03745838. Anthropic's AI workbench for scientists, plus the open projects that claim or approach its ground: Runcell Science, OpenScience, AIPOCH Open-Science (newly profiled 14 Sep 2026), Biomni and Eigent. Ends with the "which is the open equivalent" assessment.

Claude Science: the incumbent workbench (Anthropic)

Team/origin: Anthropic's third flagship product line after Claude Code and Claude Cowork, announced 30 Jun 2026 in beta for macOS/Linux on Claude Pro/Max/Team/Enterprise plans. Anthropic stated it is "not a new AI model and not a more capable model for biology", it runs the same Claude models in the plan. Architecture: a generalist coordinating agent with 60+ curated skills and connectors (genomics, single-cell, proteomics, structural biology, cheminformatics), which can spawn specialist sub-agents and user-created agents; a reviewer agent checks citations, calculations, figures vs code and self-corrects during pipelines. Runs as a local server with a browser UI, persistent Python/R kernels, jobs on local kernels, Slurm clusters over SSH, or Modal sandboxes. Every artifact carries code + environment + plain-language description + message history (provenance). Connects to NVIDIA BioNeMo Agent Toolkit models (Evo 2, Boltz-2, OpenFold3). HN analysis (gjuggler): architecture is TRE-friendly: the server can run inside a Trusted Research Environment with UI proxied to the browser, which matters for pharma/genomic-biobank adoption.

Lit review: literature analysis is a core stage; connectors include PubMed/Crossref/Semantic Scholar/arXiv/FDA (HN ml_more). Allen Institute's Jerome Lecoq built a ~20-skill multi-agent "computational review template" that reads thousands of papers, stores claims+key numbers in an evidence database, then writes 100+ page reviews section-by-section with actor-critic pairs. Citations checked by reviewer agents. Limitation: cannot access paywalled papers (Lecoq, The Scientist). HN testing (ml_more) estimates citation confabulation drops from ~50-60% base rate to ~5-15% with RAG + citation-check instruction, but warns residual hallucinations are still dangerous.

Pricing/hosting: no separate price: included in Pro ($20/mo), Max (from $100/mo), Team (from $20-25/seat), Enterprise; usage counts against the same 5-hour/weekly limits as Claude Code/Cowork. Academic: "Claude Team plan for scientists": Standard seats $0/mo, Premium $15/mo for verified PIs at academic/nonprofit institutions (12-month promotional pricing). AI for Science: up to 50 projects with up to $30k in credits; Modal adds up to $2k compute per selected project (Modal committing up to $100k total). Applications closed 15 Jul 2026, projects ran Sep-Dec 2026. Local-first: macOS 13+, Windows (support added 10 Sep 2026, changelog 0.1.47) and Linux x64; raw datasets stay local. Provider tie: Anthropic models only (no BYO-model). GitHub: none (closed). X: no dedicated account; promotion via @AnthropicAI.

November momentum (added 9 Nov 2026): changelog 0.1.72 (5 Nov) makes LaTeX previews render tables and body text more accurately, completes a Directory connector such as Google Drive in the browser when Claude Science runs in its own window, and extends the macOS and Linux sandbox to more developer caches and startup files (Linux starter environments take about 5 GB more disk after a background copy). Anthropic's science page added the first 3D structure of a vault particle, resolved with Claude (4 Nov), and a teaching post on AI-built scientific models (6 Nov). On the platform side, an IP allowlist beta opened (5 Nov), text watermarking reached Mythos 5, Opus 4.7 and Sonnet 4.6 (3 Nov), message threads entered beta (2 Nov), and fast mode for Opus 4.8 was deprecated on 30 Oct for removal on 30 Nov.

Reception: positive, The Scientist (15 Jul 2026): early users Stephen Francis (UCSF), Jerome Lecoq (Allen Institute), Kelvin Lau (EPFL) all said they would continue using it; faster workflows, transparency ("It really shows you step-by-step, 'I am doing this. This is the code I'm using'"), strong provenance; WGS analysis in minutes (HN packeted); Medium (Champ18ion): "a real, useful, well-timed product". Negative: guardrails "too stifling": Francis' lab got biosecurity-style refusals on non-biosecurity prompts (The Scientist); paywalled-paper gap (Lecoq); reviewer doesn't re-run analyses and "reduces, but doesn't eliminate" errors (docs; Coursiv); "zero connectors for non-biology" (HN throwaway219450); citation hallucination still 5-15% (ml_more); "Claude Cowork in a lab coat" framing (Medium). Neutral: MIT Technology Review: "Anthropic's newest flagship product"; Stephen Turner test drive: "nothing revolutionary here, but some nice cohesion my stack lacks". The Life Sciences Verification Program (17 Sep) responds directly to the guardrail complaints above: enforcement shifts from real-time blocking to offline monitoring, with 30-day retention of flagged traffic.

Model layer + momentum (updated 12 Oct 2026): Claude Science runs on the plan's Claude models, and that layer moved on 1 Sep with Fable 5.1 and Mythos 5.1 (same model, different safeguards): up to ~25% cheaper for typical workloads and up to ~45% savings on agentic work via reduced cache-read pricing; the Mythos biology access program landed on 17 Sep as the Life Sciences Verification Program (public beta): biology-permissive safeguards on Mythos 5.1, Opus 5 and Sonnet 5 via Standard Use grants (team-wide, yearly) and High-risk Use grants (single dual-use projects, 6-monthly), usable across Claude Science, Claude.ai, Claude Code and the API, with dozens of organisations onboarded in early access; Anthropic frames it as the answer to its September threat report (10 Sep), which described attempted misuse including biological-weapons-adjacent work. Anthropic's science page now leads with the formalisation of Fermat's Last Theorem (4 Sep 2026: first complete computer-checked proof, written in Lean largely autonomously over 11 days) and an independent test (vals.ai) in which Fable 5.1 cracked Sir Thomas Urquhart's 370-year-old "Cyphral Distich" cipher, viral on HN on 13 Sep (906 points, 382 comments). A science-results post on 17 Sep added: Claude optimised 30+ open-source biomolecular models to run ~4x faster on average (FlashPairformer kernels), plus a low-memory "Big" mode that folds systems above 10,000 tokens on a single GPU node; the optimised code is open-sourced and a protein design competition with Adaptyv Bio carries up to $1M in Claude credits and wet-lab validation for 5,000+ designs. Access: 10,000 free/discounted seats for scientists opened 27 Aug via the Claude team plan for scientists (standard seats free; premium $15/mo with 5x usage); the Model Hardware Standard research preview also opened to a first group of scientific research labs. Week of 22-25 Sep 2026: Claude Opus 5.5 shipped (22 Sep): first model of the 5.5 family, at Fable 5.1 level on most work at ~40% lower running cost than Opus 5 (API $4/$20 per 1M tokens, cache reads $0.20; ~30% faster output), leading agentic-coding benchmarks. Two science results followed: Claude autonomously discovered a novel enzyme system with CRISPR-like repeats (23 Sep): ~950 agents searched DNA databases for 21 hours / 210M tokens, gathering 200,000+ reverse transcriptases, narrowing 3,500 candidates to 20 reports, and finding "array-associated reverse transcriptases" (ART) in bacteriophages: an RT, a partner gene and a repeat array expressed as distinct short RNAs; pre-print released, function not yet known, Feng Zhang calls it "genuinely intriguing"; the post also introduced Anthropic's new life-sciences research group and lab (Bay Area, BSL-1/BSL-2, human-run wet lab). And Claude computed the nine-loop N=4 super Yang-Mills amplitude (25 Sep guest post): a challenge set by physicist Matt von Hippel in August, solved on an academic-scale budget inside Claude Science with Fable 5.1, validated by Lance Dixon; a concurrent result arrived from Song He's group (using GPT-6 for some constraints). Claude Science changelog reached 0.1.53 (24 Sep), then 0.1.54 (28 Sep) and 0.1.55 (29 Sep): covering the Sonnet-5.5 transition (sessions default to Extra-high reasoning) and follow-up fixes. Week of 12-19 Oct 2026: the changelog ran 0.1.61 (13 Oct: Linux now needs system-wide bubblewrap and socat; a renewed Windows signing certificate, SHA-1 60C2D9C5...51292D1), 0.1.62 (13 Oct: that system-wide requirement withdrawn after Linux start failures, large-file unresponsiveness fixed) and 0.1.63 (14 Oct: GPU detected only when nvidia-smi runs from a fixed folder set, shared package-cache environments now read-only, provenance lists more input files). The marquee result: Claude designed quantum circuits that beat the best published ones on eight specifications drawn from chemistry, optimisation, physics simulation and number theory, working largely on its own against an automated evaluator, in some cases by an order of magnitude (16 Oct; led by Andi Gu in Anthropic's STEM Fellows Program with Alek Kemeny, Susanne Yelin and Mikhail Lukin; arXiv 2610.22367). Week of 5-12 Oct 2026: Claude Haiku 5.5 shipped (7 Oct): the third of the 5.5 family, from $0.10/$0.50 per 1M tokens for prompts up to 100K (~75% cheaper to run than Haiku 4.5), 1M-token context, adjustable effort; the Claude Science changelog advanced to 0.1.60 (7 Oct) via 0.1.56 (2 Oct: an automatic single retry on another model when safeguards flag life-sciences content; Excel preview upgrades) and 0.1.59 (6 Oct: scoped plugin approvals; analysis-sandbox hardening on Mac, Windows and Linux). And the marquee result: the first complete map of the sky in ultraviolet light (8 Oct), produced with Claude Science by Brice Ménard (Johns Hopkins University and Anthropic): ~38,000 GALEX observations plus Swift and FIMS/SPEAR data cross-calibrated and merged onto one grid; the third of the sky no UV telescope has observed reconstructed by inpainting from visible/infrared/radio correlations, plus Gaia-based estimates for 100M+ stars; hold-out tests within ~10% of real measurements; every pixel labelled measured or predicted with an uncertainty. Data CC BY 4.0, processing code MIT.

Runcell Science: self-styled open alternative (runcell-ai)

GitHub (API, 9 Nov 2026): runcell-ai/runcell-science: 24 stars, 5 forks, 1 open issue, Apache-2.0, TypeScript, created 2026-07-02, last push 2026-10-14 (no push since). The eleven-week quiet spell ended on 14 Oct with two commits: an OpenCode provider (version-matched v2 SDK, authenticated local HTTP server, server-sent event stream) and a runtime reconciliation that shares the process launcher with the other providers. Only one release exists (0.0.1, 6 Jul); adoption is still negligible, and the companion runcell-desktop repo was also touched on 17 Oct. Description verbatim: "An open souece alternative to claude science. AI Agent for science supporting codex, claude code, etc." (typo in original).

Team/origin: Runcell team (runcell-ai org, parent company Kanaries Data Inc. per runcell.dev footer). Runcell itself is a separate commercial Jupyter-notebook AI agent; Runcell Science is independent, Apache-2.0, no Runcell account needed. Launch post on X by Elwynn Chen (@ob12er): "I built an open-source alternative to Claude Science - Runcell Science - with half my weekly Fable 5 Max quota... runs on your own Claude Code or Codex subscription - no extra API keys".

Architecture: TypeScript monorepo: apps/web (browser workspace), apps/server (API, session persistence, provider runtimes, Jupyter, MCP), apps/desktop (Electron), packages/ui, packages/science-connectors (MCP-style connectors: PubMed, ChEMBL, BioMart, Ensembl, UniProt, AlphaFold, OpenAlex, GTEx, ZINC, CellGuide), packages/nbcli. Runtimes: Codex via JSON-RPC app-server, Claude via Agent SDK, Grok Build via Agent Client Protocol, and (added 14 Oct) OpenCode via its v2 SDK. Local-first web app at 127.0.0.1:27183; drives the Claude Code / Codex CLI already installed and signed in: no separate API keys or per-token billing.

Lit review: reads papers, extracts methods, summarizes assumptions, compares methods, turns findings into reusable notes; literature workflows are an explicit skill area; PubMed/OpenAlex connectors; artifacts (figures, notebooks, molecule edits) stay attached to the session for evidence.

Pricing/hosting: free, open source (Apache-2.0); local web app; Electron desktop; Windows currently via WSL. Provider tie: cross-vendor: Claude Code + Codex (and Grok Build), model and runtime neutral; uses existing subscriptions. X: @runcellai (8 followers, product posts); author @ob12er posted the Science launch. Reception: minimal so far (22 stars, 1 contributor): too early for meaningful community reception; no HN coverage found.

OpenScience: largest open Claude-Science-style workbench (synthetic-sciences)

GitHub (API, 9 Nov 2026): synthetic-sciences/openscience: 4,358 stars (+36 since 2 Nov), 550 forks, 13 open issues, Apache-2.0, Bun + TypeScript (72%) / Python (16%), created 2026-07-03, current release v2.0.153 (30 Oct 2026); last push 9 Nov 2026 (active pull requests, including opt-in budgets and runtime limits for delegated subagents, PR #1044, and DaKES open-access benchmark datasets). Homepage openscience.sh. Description verbatim: "The open-source AI workbench for scientific research". Added to this report 7 Sep 2026.

Team/origin: Synthetic Sciences org; lead committer Ishaan Gangwani; contributors include Satya Arjunan (Victor Chang Cardiac Research Institute): wider team and funding unverified. Launched as an open-source AI research agent on 4 Jul 2026 (Show HN id 48786827, 13 points). Launch positioning mirrors Runcell Science's: an open Claude Science alternative, launched one day after Runcell's repo appeared.

Architecture: model-agnostic "AI co-scientist": give it a goal and it reads the literature, writes and runs code, runs experiments, and writes up results. Desktop app (macOS/Windows/Linux), browser workspace, terminal CLI (npm @synsci/openscience / npx synsci). Model access: managed "Ace" option (pay-as-you-go wallet, no subscription), your own API keys or provider sign-in (Claude, GPT, Gemini, DeepSeek, GLM, Kimi per third-party overviews), or local models (Ollama, LM Studio). MCP connections, plugins, custom agents/commands, SDK. Turned "fully user-owned" and local-first in the v2.0.x line at end Aug 2026 (PR #446 retired managed-service paths).

Lit review: literature review is a headline workflow: search scientific sources, compare findings, save cited evidence; 42 scientific connectors (science_fetch retrieves records/files), ~312 bundled skills (README), docs covering literature review, data analysis, reproduction and writing. No dedicated verifier agent documented; the README's own guidance is to "review sources, assumptions, code, and outputs before relying on a scientific conclusion".

Pricing/hosting: free, open source (Apache-2.0); local-first desktop/CLI; optional Ace managed models/research search (pay-as-you-go); own keys or local models without an account. Provider tie: none (cross-vendor + local). X: none verified (unverified). Reception: early but a strong adoption signal: ~4.0k stars within three months and a near-daily commit cadence; community coverage is still thin (no significant independent reviews found; third-party write-ups are SEO-reworked versions of its own material).

AIPOCH Open-Science: largest open workbench by adoption (aipoch)

GitHub (API, 9 Nov 2026): aipoch/open-science: 5,501 stars (+83 since 2 Nov), 553 forks, 40 open issues, Apache-2.0, created 2026-07-03, pushed 9 Nov 2026 (daily activity); releases v1.0.8 (4 Nov), v1.1.0 (6 Nov) and v1.2.0 (9 Nov): approved jobs now run on user-connected Kubernetes, Hugging Face Jobs, Modal and Ray beside SSH and Slurm; the standalone Open-Science agent became the default runtime; step-linked discussions arrived; macOS updates are differential. Homepage aipoch.com/open-science. Description verbatim: "AIPOCH Open-Science is an open-source, local-first, model-agnostic AI research workbench for macOS, Windows, and Linux, with scientific agents, Python/R notebooks, data connectors, and reproducible provenance." Added to this report 14 Sep 2026.

Positioning: the same wave as Runcell Science and OpenScience (repo created 2026-07-03, three days after the Claude Science launch). Its own comparison material positions it as an independent open-source workbench in the same broad category as Claude Science, without claiming identical integrations or performance. Local-first, model-agnostic, with reproducible provenance named as a first-class feature.

Reception: the fastest-rising adoption of any open workbench (5,501 stars; +83 this week, ahead of OpenScience), but independent reception is still thin; no significant third-party reviews located by the 12 Oct check; treat capability claims as self-reported for now.

Biomni: closest domain-native equivalent (snap-stanford)

GitHub (API, 9 Nov 2026): snap-stanford/Biomni: 4,069 stars (+27 since 2 Nov), 738 forks, 131 open issues, Apache-2.0 (with mixed-dependency caveat), Python, created 2025-03-19, last push 2026-11-02 (branch activity). Homepage biomni.stanford.edu.

Team/origin: general-purpose biomedical AI agent from Stanford SNAP (Jure Leskovec lab), lead author Kexin Huang. 20+ authors across Stanford, Genentech, Arc Institute, UCSF, Princeton, UW. Preprint bioRxiv 2025-05-30; peer-reviewed version published in Science (10.1126/science.adz4351, "Autonomous biomedical research with an artificial intelligence agent", supplementary correction 21 Aug 2026). Commercial layer (added 12 Oct 2026): the public platform now runs as Phylo's Biomni Lab: Phylo is the Stanford spin-out founded by Biomni authors Kexin Huang (CEO) and Yuanhao Qu (President), with Jure Leskovec among the scientific co-founders; launched 3 Feb 2026 with a $13.5M seed (a16z and Menlo Ventures' Anthology Fund with Anthropic); 300+ integrated databases, software systems and tools; Bayer, Ono Pharmaceutical and Chugai deployments (Bayer announced 22 Oct 2026, for literature and omics evidence workflows in drug discovery); Target Atlas launched 29 Sep 2026, an agentic target-prioritisation app at biomni.phylo.bio with a Michael J. Fox Foundation Parkinson's edition; Sage Bionetworks' Synapse platform (~3PB of data) integrated Sep 2026; free academic tier. The open-source project continues under snap-stanford; Stanford-reported usage at nine months: 15,000+ scientists and ~100,000 workflows.

Architecture: LLM reasoning + retrieval-augmented planning + code-based execution, composing workflows without predefined templates. Action space of 150 specialized tools, 59 databases, 105 software packages mined across 25 biology domains; ~11GB data lake auto-downloaded on first run; Know-How Library (auto-retrieved protocols); MCP support; PDF reports of execution traces; Biomni-R0 reasoning model (Qwen-32B-based, local via SGLang); Biomni-Eval1 benchmark (433 instances, 10 tasks; Science paper reports 443 questions with average accuracy 57%, matching specialists on 3 expert-level tasks).

Lit review: literature review is a core stated capability: automates literature reviews, hypothesis generation, protocol design; semantic search + summarization tools; AWS ML blog builds a systematic-literature-review agent on Biomni tools with strict numbered citation requirements per query.

Pricing/hosting: free, open source (pip install biomni); brings your own LLM API keys; local conda env; web UI; used on HPC. Provider tie: any: Anthropic, OpenAI, Azure, Gemini, Groq, Bedrock, Ollama, or custom; default claude-sonnet-4. X: @ProjectBiomni. Reception: positive: peer-reviewed Science publication; Stanford HAI coverage; HN lebovic prefers Biomni over Claude-Code-custom-setup for bio ("stays on the frontier for bio", HPC reawakening); AWS Bedrock integration blog. Negative/caveats: README security warning: executes LLM-generated code with full system privileges; sandbox recommended; Apache-2.0 headline does not extend to every bundled tool/database (mixed commercial terms, Eigent and README both flag this); release frozen 15 Apr 2025 vs current web platform.

Eigent: open orchestration layer (eigent-ai)

GitHub (API, 9 Nov 2026): eigent-ai/eigent: 15,780 stars, 1,886 forks, 264 open issues, Apache-2.0, TypeScript, created 2025-07-29, last push 2026-11-05 (latest release v1.0.7, 30 Oct). Description: "Eigent: The Open Source Cowork Desktop - Local and Free Alternative to Claude Cowork and Codex".

Team/origin: open-source Cowork desktop for multi-agent workflows, built on CAMEL-AI (founder Guohao Li; LinkedIn Sam Witteveen). Org claims 35K+ GitHub stars and 200+ contributors across projects (eigent.ai/about). Gained ~2K stars in a single day on 17 Jan 2026 (sulat.com). General-purpose: not science-specific.

Architecture: React/Electron/TypeScript frontend; FastAPI + uv + Uvicorn backend; CAMEL multi-agent framework; MCP integration; built-in browser + terminal toolkits; skill integration; multi-agent workforce with parallel execution; local deployment with local inference engines (vLLM, Ollama, LM Studio); cloud version; enterprise (SSO, access control). README: "100% open source from day one".

Lit review: none built-in for science. Its own comparison blog is explicit: use PaperQA2 for the literature layer, Biomni for domain reasoning, Jupyter+MCP for kernels, Eigent/OpenHands for orchestration, and accept native scientific rendering + provenance as open-stack gaps.

Pricing/hosting: free, open source (Apache-2.0); cloud platform paid (public beta); enterprise by contact; model-agnostic (cloud APIs, enterprise gateways, local inference). X: @Eigent_AI. Reception: 15.5k stars; HN and blog coverage mostly around Claude Cowork positioning; "New FREE Eigent AI DESTROYS Claude Cowork?" YouTube; its own blog positions it as the orchestration layer in an assembled open science stack.

wisp-science (added 2 Nov 2026)

xuzhougeng (Zhougeng Xu) and 18 other contributors; open-source project

1,068★

AGPL-3.0

Local-first · v1.24.0

What it is

An open-source, local-first desktop AI research workbench for scientific computing. It pairs a chat agent with a real computing environment: Python and R runtimes reached over SSH, WSL or a GPU host, MCP servers for bioinformatics tools, and OpenAI or Anthropic models by key. Desktop applications ship alongside iOS and Android clients, and remote access runs through a one-click Cloudflare Worker relay with QR pairing. First release 1 July 2026; 73 releases to v1.24.0 (3 Nov 2026).

How it works

• Local-first execution: code runs on your own machine or on a server you name, not in the vendor's sandbox; SSH, WSL and GPU targets are first-class runtime choices
• Tools in the same session: MCP bioinformatics servers sit beside general scientific computing, so a tool call and the code that consumes it share one workspace
• Model-agnostic: OpenAI or Anthropic keys, so the harness is not tied to one vendor
• Reach: desktop apps plus iOS and Android, and a relay that puts the same session in a browser on another device

Pricing / hosting / provider tie

Free, open source (AGPL-3.0); self-hosted or local desktop; costs are your own model API usage. Provider tie: none enforced (OpenAI or Anthropic keys). GitHub: xuzhougeng/wisp-science: 1,068 stars, 107 forks, 0 open issues (API, 9 Nov 2026; created 1 Jul 2026; latest release v1.24.0, 3 Nov 2026).

Positive

• Steady, fast cadence: releases through v1.24.0 on 3 Nov 2026, with commits landing on 9 Nov; the desktop-plus-mobile shape and SSH/GPU runtimes match how computational scientists already work

Neutral

• No independent evaluation, press coverage or third-party review located in this pass; reception is repo-level only (1,068 stars, 107 forks). The 29 Oct pass missed the project entirely

Which is the open equivalent?

• No single open project replaces Claude Science one-for-one. That is the honest verdict, and Eigent's own comparison makes the same call.
• Runcell Science self-styles as "the open-source alternative to Claude Science" and matches the positioning: science agent for papers/code/experiments, local-first, reuses your Claude Code/Codex subscription, but is very early (23 stars, 1 contributor, minimal reception).
• OpenScience (added 7 Sep 2026) is close behind on adoption: ~4.3k stars in three months, Apache-2.0, cross-vendor and local models, 42 scientific connectors, frequent releases (latest v2.0.152), but it is young: no deep independent reception yet, provenance/verifier tooling less developed than Claude Science's, and its own docs tell users to review outputs before relying on them.
• AIPOCH Open-Science (profiled 14 Sep 2026) is now the largest open workbench by adoption: 5,501 stars in eighteen weeks (+83 since 2 Nov; releases through v1.2.0, 9 Nov), Apache-2.0, local-first, model-agnostic, with scientific agents, Python/R notebooks and data connectors; independent reception remains thin so far. The field has widened further: ai4s-research/open-science (2,083★, "Open Science Desktop"; v0.7.1 on 29 Oct moved terminals outside the app so a Claude Code or Codex session survives an update or a relaunch, and added Claude Code, Codex and pi as conversations beside OpenCode) and exon-research/genomi (491★, genomics-focused) are also live, alongside Runcell Science.
• wisp-science (added 2 Nov 2026) is a fourth workbench, and the most active of the smaller ones: 1,068 stars in four months, releases to v1.24.0 (3 Nov 2026), AGPL-3.0. It is a local-first desktop workbench for scientific computing: Python and R over SSH, WSL or a GPU host, MCP bioinformatics tools, and OpenAI or Anthropic models, with desktop plus iOS and Android apps and remote access through a one-click Cloudflare Worker relay. No independent evaluation located in this pass.
• Biomni is the closest domain-native equivalent for biomedical work: peer-reviewed in Science, 150 tools / 59 databases / 105 software, Biomni-Eval1 benchmark, any-provider model support, and its public platform now runs as Phylo's Biomni Lab (commercial; the open-source project continues), but it is a biomedical research agent, not a full workbench: no native scientific rendering of proteins/structures, and provenance is PDF execution traces rather than artifact-level code+env records.
• Eigent is the orchestration layer only; it belongs in an assembled open stack (Eigent or OpenHands + Jupyter/MCP kernels + PaperQA2 for literature + Biomni for domain reasoning), not as a science workbench itself.
• Genuine open gaps vs Claude Science: native scientific rendering, provenance-by-default, and 60+ pre-wired database connectors.

5. Deep Dives: Open-Source Self-Hosted

04

Deep dives: open-source, self-hosted

Eight harnesses you install and run yourself. Costs are your own LLM API usage. All GitHub stats verified via the GitHub API on 03 September 2026 and re-verified on 14, 21 and 28 September and 5, 12, 19 and 29 October and 2 November and 9 November 2026 (star/fork/issue deltas shown in the weekly update log at top). Feynman is covered in its own section above; the Claude Science equivalents (Runcell Science, OpenScience, AIPOCH Open-Science, wisp-science, Biomni, Eigent) are in the section above that.

Literature & retrieval agents

PaperQA2 · OpenScholar · STORM · GPT Researcher

PaperQA2

FutureHouse (nonprofit; Sam Rodriques CEO, Andrew White Head of Science, Jessica Wu Head of Ops)

9,568★

Apache-2.0

Active

Team / origin / funding

Announced 11 Sep 2024 (arXiv 2409.13740; authors Skarlinski, Cox, Laurent, Braza, Hinks, Hammerling, Ponnapati, Rodriques, White). FutureHouse funded by philanthropy/grants: Eric & Wendy Schmidt (first two years), OpenPhilanthropy, NSF, AI Safety Institute. Commercial spinout Edison Scientific raised a $70M seed at ~$250M valuation, co-led by Spark Capital and Triatomic Capital, angels incl. Jeff Dean and Dmitri Alperovitch (techfundingnews.com). Note: Signalbase headline says "FutureHouse secures $70M" but the funding entity is the Edison spinout.

Architecture

High-accuracy agentic RAG for answering questions from scientific documents with citations. Agent tools: find papers, extract useful information, explore the citation graph, formulate answers. LLM driver via LiteLLM (any provider, incl. local models). Built-on applications: WikiCrow (Wikipedia-style summaries) and ContraCrow (contradiction detection). Python package, pip install paper-qa.

Lit review capacity

• Search: agentic search over full text; explores the citation graph; searches a paper directory or live sources. Evaluated on LitQA2 (LAB-Bench, arXiv 2407.10362); first agent reported superhuman on lit-search (73.8% human precision vs agent, n=9)
• Read/parse: extracts useful information from papers via tools; RAG over paper corpora; PDF parsing (PyMuPDF, NemotronParse)
• Synthesis: cited answers; WikiCrow summaries rated more accurate than human Wikipedia by blinded PhD/postdoc reviewers; ContraCrow finds 2.34 ± 1.99 contradictions/paper, 70% expert-validated
• Citations: context-cited answers; citation graph exploration; output includes DOIs and citation counts

Pricing / hosting / provider tie

Open-source Apache-2.0, self-hosted (pip install paper-qa); costs = your LLM/embedding API. Hosted WikiCrow demo at paper.wikicrow.ai; hosted platform from Edison Scientific (platform.edisonscientific.com). Any LLM via API (LiteLLM); local models supported. Original paper pipeline used GPT-4o (per AI2 blog).

GitHub: 9,568★ · 953 forks · last push 26 Oct 2026 (default branch last release v2026.08.12). X: @FutureHouseSF. Docs: docs.paperqa.com: TLS broken (confirmed 2026-09-03); GitHub README is the primary maintained doc; futurehouse.gitbook.io redirects to Edison docs.

Positive

• Open release celebrated: 296 upvotes, 96% upvoted on r/singularityhttps://safereddit.com/r/singularity/comments/1feh9t3/
• Well-cited Alzheimer's example with DOIs on HN launchhttps://news.ycombinator.com/item?id=41513031
• WikiCrow: generated articles for all 19.2k human genes weekly, more precise than human Wikipedia claimshttps://news.ycombinator.com/item?id=41926771
• Real-world regulatory use: "using PaperQA2 for some regulatory use cases and it's gangster"https://safereddit.com/r/LocalLLM/comments/1pxvlh0/

Negative / caveats

• Hallucination concern even with grounding: "extremely dangerous for that one detail you will not expect"https://news.ycombinator.com/item?id=42140356
• "Superhuman" claim debated (n=9 humans; MCQ benchmark gaming acknowledged in LAB-Bench paper)https://arxiv.org/abs/2407.10362
• AI2 claims OS-8B is 100x more cost-efficient than PaperQA2https://allenai.org/blog/openscholar
• Beaten by OpenScholar-8B by 5.5% on ScholarQABench multi-paper synthesishttps://www.nature.com/articles/s41586-025-10072-4

Neutral

• Benchmark recency confound: hard to separate model capability from training-data cutoff on LitQA2https://www.lennijusten.com/blog/biology-benchmarks/

OpenScholar

AI2 (Allen Institute for AI) + University of Washington; lead author Akari Asai, senior author Hannaneh Hajishirzi

1,683★

Apache-2.0

Nature 2026

Team / origin / funding

arXiv 2411.14199 (21 Nov 2024); AI2 blog 19 Nov 2024; accepted to Nature, published 4 Feb 2026 (Nature 650, 857-863). 25 authors incl. Zettlemoyer, Weld, Neubig, Downey, Koh, Yih (Meta); collaborators at Meta, CMU, UIUC, Stanford, UNC. Funding: Asai supported by Meta AI Mentorship program; Singapore National Research Foundation / National AI Group AI Visiting Professorship Programme (blog acknowledgments). Models, datastore and code open-sourced.

Architecture

Specialized retrieval-augmented LM: OSDS datastore of 45M open-access papers / 236M passage embeddings (peS2o v2/v3); trained retriever + reranker; instruction-tuned 8B generator (Llama 3.1 fine-tune on ~13k synthetic instruction data, 8xA100); iterative self-feedback loop (each iteration retrieves more papers and refines factuality/coverage/citations); post-hoc citation attribution. Pipeline also runs on off-the-shelf LMs (OS-GPT4o).

Lit review capacity

• Search: passage-level retrieval over 45M open-access papers (up to Oct 2024); trained retriever + reranker; Semantic Scholar Paper API + optional you.com web search API
• Read/parse: abstracts + full text; 200M+ passage embeddings index (needs large CPU memory)
• Synthesis: instruction-tuned generator produces citation-backed responses; OS-8B beats GPT-4o by 6.1% and PaperQA2 by 5.5% on ScholarQABench; OS-GPT4o improves GPT-4o correctness by 12%
• Citations: post-hoc citation attribution on par with human experts; GPT-4o hallucinated citations 78-90% of the time vs OpenScholar (Nature abstract)

Pricing / hosting / provider tie

Fully open-source (Apache-2.0); models, datastore, data free on HuggingFace. Public demo (originally openscilm.allen.ai / open-scholar.allen.ai): NOTE: now redirects to Ai2 Asta (108M+ abstracts, 12M+ full-text); the OpenScholar demo has been superseded by Asta. No provider tie: open 8B model; optional proprietary base LMs (e.g. GPT-4o) via API; needs Semantic Scholar API key.

GitHub: 1,683★ · 179 forks · last push 13 Aug 2025 (still stalled; research-prototype cadence; 12 open issues). X: no dedicated handle; launch via @AkariAsai + @allen_ai. Docs: GitHub README (primary); allenai.org/blog/openscholar-nature (Feb 2026). Repo is AkariAsai/OpenScholar (not akariasai/openscholar).

Positive

• Nature publication: "first fully open, retrieval-augmented LM specifically designed for scientific research tasks"https://www.nature.com/articles/s41586-025-10072-4
• UW News: 16 scientists preferred OpenScholar answers to human-written 51% of the time (70% with GPT-4o)https://www.washington.edu/news/2026/02/04/
• r/academia (84% upvoted): editors see reviews changing; systematic reviews seen as mechanicalhttps://safereddit.com/r/academia/comments/1qwed3p/

Negative / caveats

• Comparison baseline stale: "The authors are comparing their LLM to an older version of ChatGPT. Does that matter?" (top comment, 69 pts)https://safereddit.com/r/academia/comments/1qwed3p/
• Academic user: "I found it severely lacking. It missed some obvious literature and misrepresented the studies that it did offer."https://safereddit.com/r/academia/comments/1qwed3p/
• May cite papers that merely mention a method; may cite from parametric knowledge (hallucinated, shown as non-links in demo): acknowledged in AI2 bloghttps://allenai.org/blog/openscholar
• Open-access-only datastore degrades performance in paywalled-heavy fields; nuance limits acknowledged
• Repo maintenance stalled after Aug 2025; demo superseded by Ai2 Asta

Neutral

• r/Professors (91% upvoted): humanities prof acknowledges it's the one PhD task LLMs could do well if hallucination is controlledhttps://safereddit.com/r/Professors/comments/1qvuj21/

STORM / Co-STORM

Stanford OVAL (Open Virtual Assistant Lab), Monica Lam's group

31,840★

MIT

Stalled Sep 2025

Team / origin / funding

STORM: Yijia Shao, Yucheng Jiang, Theodore Kanell, Peter Xu, Omar Khattab, Monica S. Lam (NAACL 2024, arXiv 2402.14207, 22 Feb 2024). Co-STORM: Yucheng Jiang, Yijia Shao, Dekun Ma, Sina Semnani, Monica Lam (EMNLP 2024, arXiv 2408.15232, 27 Aug 2024). Repo created 24 Mar 2024; demo storm.genie.stanford.edu live since ~mid-2024. Funding: thanks Microsoft Azure for cloud credits (demo); Vercel supports open-source hosting; no commercial funding verified.

Architecture

Two-stage LLM knowledge-curation pipeline: (1) pre-writing: Internet-based research to collect references and generate an outline, via perspective-guided question asking (discovers perspectives from similar articles) and simulated conversations between a writer and a topic expert grounded in sources; (2) writing: full-length article with citations. Co-STORM adds a collaborative discourse protocol: LLM experts, a moderator, and a human user; dynamic mind map; implemented modularly in DSPy. Retrievers: YouRM, BingSearch, VectorRM, SerperRM, BraveRM, SearXNG, DuckDuckGoSearchRM, TavilySearchRM, GoogleSearch, AzureAISearch. LLM/embedding via LiteLLM since v1.1.0 (Jan 2025).

Lit review capacity

• Search: Internet search engines (no dedicated scientific corpus); perspective discovery surveys similar Wikipedia articles; simulated expert Q&A grounded on trusted Internet sources
• Read/parse: retrieves and curates information from sources into an outline; VectorRM allows grounding on user-provided documents
• Synthesis: models the pre-writing stage: perspective discovery, simulated Q&A, outline curation, then full report generation. 70,000+ people used the live preview (README)
• Citations: generated reports carry citations to retrieved sources; but hallucinated citations and off-topic sources documented in reception

Pricing / hosting / provider tie

Open-source MIT (pip install knowledge-storm); free hosted demo at storm.genie.stanford.edu; self-host costs only your LLM API. No SaaS tier. Any LLM via API: OpenAI API default, other providers via LiteLLM.

GitHub: 31,840★ · 3,007 forks · last push 30 Sep 2025 (still stalled at the 9 Nov check; ~13 months quiet). X: @StanfordOVAL (no dedicated STORM handle). Docs: GitHub README (primary), storm-project.stanford.edu, demo storm.genie.stanford.edu.

Positive

• HN 123 pts (repo launch): "I asked it about a niche topic and I got a very useful encyclopedia-type report that included real links to published research"https://news.ycombinator.com/item?id=40004887
• r/perplexity_ai 489 pts, 93% upvoted: "Stanford's STORM AI outperforms Perplexity & Google Deep Research - and it's completely FREE"https://safereddit.com/r/perplexity_ai/comments/1htadmb/
• r/LocalLLaMA: "storm was cool but co-storm really helps learn things and drill down the report"; "on par with Google deep research"https://safereddit.com/r/LocalLLaMA/comments/1ih0te1/

Negative / caveats

• Documented hallucination: "it straight up hallucinated... claimed that google cartographer was written in rust... the citation... did not mention cartographer anywhere"https://news.ycombinator.com/item?id=41553875
• "The LLM is not doing research - it is aggregating data"; publication skepticismhttps://news.ycombinator.com/item?id=40004887
• Repetitive/verbose output; odd source quality ("short on reputable sources, long on obscure blogs")
• Friction: 20-word input limit + mandatory "why do you want this" gate
• Losing edge: "with Google and OpenAI Deep Research it's not quite cutting edge"; repo stalled; demo sometimes 500s

Neutral

• HN 145 pts (demo): 72 comments, largely engaged discussion of capabilities and limitshttps://news.ycombinator.com/item?id=41553875

GPT Researcher

Assaf Elovic (also founder/CTO of Tavily) + large open-source community

30,594★

Apache-2.0

Active

Team / origin / funding

Repo created 12 May 2023; launched via Medium post "How I Built an Autonomous AI Agent for Online Research" (24 Jul 2023); inspired by AutoGPT's failures + Plan-and-Solve (2305.04091) + RAG (2005.11401). No formal paper (DeepResearchGym paper is a third-party evaluation).

Architecture

Two-stage planner + parallel execution: planner agent generates research questions forming an objective opinion; crawler/execution agents scrape and summarize per resource in parallel (asyncio), tracking sources; publisher filters and aggregates into final report. Extras: local document research (PDF/txt/CSV/Excel/MD/PPTX/DOCX), MCP client + server (gptr-mcp), multi-agent mode via LangGraph and AG2, Deep Research recursive tree exploration, Gemini inline image generation, LangSmith observability. ~3 min average task, ~$0.1-0.4 per research (README/docs).

Lit review capacity

• Search: general web search engines: Tavily default, Google/Bing/DuckDuckGo, arXiv retriever option, local docs, MCP data sources; aggregates 20+ sources per research
• Read/parse: crawler agent scrapes each source (JS-enabled), summarizes per-resource while keeping source tracking
• Synthesis: planner filters + aggregates all summarized sources into a final report (2k+ words, exports PDF/DOCX/MD)
• Citations: per-resource source tracking with citations in output; ranked #1 in citation quality on DeepResearchGym (85.36% precision, 90.82% recall, only system >85% both). Honest gap: general web not science-specific; not built for peer-reviewed corpus retrieval

Pricing / hosting / provider tie

Free open source (Apache-2.0), self-hosted (pip install gpt-researcher, Docker, frontend); also Claude Skill install; costs are LLM/search API usage (~$0.1-0.4/run). Any LLM: 100+ supported (OpenAI, Anthropic, Groq, Llama, HuggingFace, local via OpenAI-compatible endpoints); any search engine (Tavily, Google, Bing, DuckDuckGo).

GitHub: 30,594★ · 4,202 forks · last push 9 Nov 2026 (latest release v3.7.0, 26 Sep, the "Jev update": passage scoring via TypeSafe's Jev with a BM25 fallback, no embeddings required; Python 3.12+; 33 open issues). X: no official product handle; founder @assaf_elovic (verified, posts product releases). Docs: gptr.dev (marketing site) + docs.gptr.dev (Docusaurus).

Positive

• Ranked #1 on CMU DeepResearchGym benchmark (May 2025) vs Perplexity, OpenAI, OpenDeepSearch, HuggingFace: citation quality, report quality, coveragehttps://arxiv.org/abs/2505.19253
• Harrison Chase (LangChain CEO): "one of the more advanced OSS research projects out there"https://gptr.dev/
• HN commenter prefers it for source/search-engine controlhttps://news.ycombinator.com/item?id=43752262
• Cited in peer-reviewed/arXiv work (Chain of Ideas, LiveRAG, BioKGBench)

Negative / caveats

• Launch thread criticism: "why are the titles in the Bibliography wrong hallucinated?" (Reddit r/singularity)https://www.reddit.com/r/singularity/comments/14vt3wj/
• TrilogyAI: requires technical setup, not plug-and-play; needs a powerful model for quality; resource-intensive; debugging on userhttps://trilogyai.substack.com/p/comparative-analysis-of-deep-research
• Community notes quality drops without strong model; self-hosted setup complexityhttps://www.reddit.com/r/LocalLLaMA/comments/1ilqn04/

Neutral

• Tom Dörr: "usually avoid projects with GPT in their name, but GPT Researcher looks pretty solid" (arXiv retriever)https://gptr.dev/

Research frameworks (autonomous pipelines)

Agent Laboratory · ResearchAgent · The AI Scientist

Agent Laboratory

Samuel Schmidgall (JHU/AMD) with Yusheng Su, Ze Wang, Ximeng Sun, Jialian Wu, Xiaodong Yu, Jiang Liu (AMD), Michael Moor (ETH Zurich), Zicheng Liu, Emad Barsoum (AMD)

5,926★

MIT

EMNLP 2025

Team / origin

arXiv 2501.04227 (submitted 8 Jan 2025, v2 17 Jun 2025); Findings of EMNLP 2025, Suzhou, pages 5977-6043 (aclanthology.org/2025.findings-emnlp.320). Funding not stated (AMD affiliation implied, unverified). Extends to AgentRxiv (arXiv 2503.18102) for agents building on each other's research.

Architecture

3-stage pipeline: (1) Literature Review, (2) Experimentation, (3) Report Writing. PhD/Postdoc/ML Engineer/SW Engineer/Professor agents; mle-solver for iterative code generation with REPLACE/EDIT, LLM reward scoring, top-program pool, self-reflection; paper-solver for LaTeX report generation with scaffold + arXiv access + NeurIPS-style automated reviewer; human feedback at each stage (co-pilot mode).

Lit review capacity

• Search: arXiv API only; PhD agent queries, retrieves abstracts of top-20 relevant papers, extracts full text, iteratively adds papers to curated review (N=max)
• Read/parse: abstract via "summary" action; full text via "full text" action; relevance evaluated by the agent per query
• Synthesis: curated review finalized once add-paper quota reached; feeds plan formulation and paper-solver scaffold
• Citations: references come from the arXiv-based lit review; citation quality not independently verified, honest gap: lit-review stage is shallow (single-source arXiv API, top-20 abstracts)

Pricing / hosting / provider tie

Free open source (MIT), self-hosted (Python 3.12, pip install); run costs are LLM API usage: paper reports $2.33 per paper on gpt-4o, $13.10 on o1-preview; 84% cost reduction vs prior autonomous methods (self-reported). Provider tie: OpenAI (o1, o1-preview, o1-mini, gpt-4o, o3-mini) and DeepSeek (deepseek-chat); README invites PRs for more backends; no local-model path documented.

GitHub: 5,926★ · 805 forks · last push 20 Aug 2025. X: no official handle; lead author Samuel Schmidgall @SRSchmidgall (personal). Docs: agentlaboratory.github.io + GitHub README.

Positive

• Accepted to Findings of EMNLP 2025 (verified via ACL Anthology)https://aclanthology.org/2025.findings-emnlp.320/
• mle-solver won 4 MLE-bench medals (2 gold) vs OpenHands/AIDE/MLAB (project site, self-reported)https://agentlaboratory.github.io/
• Quoted in Science Magazine re AI scientists reliability (May 2026)https://x.com/SRSchmidgall/status/2052911248480845908

Negative / caveats

• Hidden pitfalls paper (Luo, Kasirzadeh, Shah: NeurIPS 2025 AI4Science Spotlight) flags 4 failure modes in AI scientist systems incl. Agent Laboratory: benchmark selection, data leakage, metric misuse, post-hoc selection biashttps://arxiv.org/abs/2509.08713
• Authors' own NeurIPS-style human review: all backends scored below accepted-paper average (o1-preview 4.0/10 vs 5.9 average accepted); automated reviews overestimated quality (6.1 vs 3.8)https://agentlaboratory.github.io/
• HN launch threads low engagement (4 pts, 1 pt)https://hn.algolia.com/api/v1/search?query=%22Agent%20Laboratory%22&tags=story

Neutral

• Cited as related work in academic papers (e.g. PETSc knowledge base, Training AI Scientists to Replicate Research)https://dl.acm.org/doi/abs/10.1145/3750720.3757281

ResearchAgent

Jinheon Baek (KAIST), Sujay Kumar Jauhar (Microsoft Research), Silviu Cucerzan (Microsoft Research), Sung Ju Hwang (KAIST / DeepAuto.ai)

74★

No license

NAACL 2025

Team / origin

arXiv 2404.07738 (submitted 11 Apr 2024, v2 9 Feb 2025); NAACL 2025 Long Papers, Albuquerque, pages 6709-6738 (aclanthology.org/2025.naacl-long.342); Microsoft Research publication page; no funding statement. 242 citations / 28 influential (Semantic Scholar, retrieved 3 Sep 2026).

Architecture

Iterated research idea generation: starts from a core paper, augments via academic graph (references/citations) + entity-centric knowledge store mined from co-occurring concepts across papers; generates problem → method → experiment design in fixed sequence; multiple LLM ReviewingAgents provide structured reviews/feedback across iterative revisions, instantiated with human preference-aligned criteria elicited via LLM prompting. Idea-generation focus: does NOT execute experiments or write final papers.

Lit review capacity

• Search: academic graph traversal (references + citation relationships) from a seed paper; entity-centric knowledge store of co-occurring concepts across many papers
• Read/parse: full-text paper understanding via LLM; academic graph provides related publications
• Synthesis: synthesizes into problem/method/experiment proposals; iteratively refined by ReviewingAgents
• Citations: grounded in seed paper + graph neighbors + knowledge entities; no citation-handling for output documents (output is an idea proposal, not a report). Honest gap: ideation-focused; no experimental execution; fixed generation sequence criticized by IdeaSynth

Pricing / hosting / provider tie

n/a: research prototype; GitHub repo exists (JinheonBaek/ResearchAgent, 62 stars, no license, last push 24 Aug 2025) but no packaged install; official code minimal; no docs site. LLM-API based (GPT-4 family in the paper); no supported-model list or local path.

GitHub: 74★ · 10 forks · no license · last push 24 Aug 2025. X: no official ResearchAgent handle found. Docs: arXiv abstract + full text; Microsoft Research publication page; Emergent Mind paper summary page.

Positive

• HN thread: 124 points, 63 comments (Apr 2024): substantive discussion of LLM ideationhttps://news.ycombinator.com/item?id=40047152
• 242 citations / 28 influential (Semantic Scholar)https://api.semanticscholar.org/graph/v1/paper/arXiv:2404.07738
• Positioned as foundational related work by Agent Laboratory and The AI Scientist (both cite it)https://arxiv.org/html/2501.04227v2

Negative / caveats

• IdeaSynth (ACM CHI/ICMR 2025) notes ResearchAgent generates idea facets in a fixed sequence, unlike interactive systems giving users freedomhttps://dl.acm.org/doi/full/10.1145/3706598.3714057
• "Can LLMs Generate Novel Research Ideas?" (2409.04109) highlights LLM ideation limitations on feasibility/implementationhttps://www.alphaxiv.org/abs/2409.04109
• Small repo footprint (62 stars, no license): no maintained implementation for reusehttps://api.github.com/repos/JinheonBaek/ResearchAgent

Neutral

• Downstream systems build on it (e.g. Deep Ideation, AI-Researcher listed on Emergent Mind)https://www.emergentmind.com/papers/2404.07738

The AI Scientist (Sakana AI)

Sakana AI, with the Foerster Lab for AI Research (Oxford) and Jeff Clune & Cong Lu (UBC / Vector Institute)

v1 14,966★ · v2 7,479★ · v3 587★

Custom license (v1/v2); Apache-2.0 (v3)

Nature 2026

Team / origin / launch

v1 announced 12 Aug 2024 (arXiv 2408.06292, v3 1 Sep 2024); v2 technical report 10 Apr 2025 (arXiv 2504.08066); Nature paper published 25 Mar 2026 (Nature 651, 914-919, DOI 10.1038/s41586-026-10265-5, open access). Collaboration with Oxford Foerster Lab and UBC/Vector per the Nature paper. v3, "The Agentic Scientist" (arXiv 2610.18933, 13 Oct 2026), is a separate Apache-2.0 scaffold released under the same lab, with an AutoIllustrate companion repo.

Architecture

v1: four automated processes: (1) Idea Generation (brainstorms diverse ideas from a template codebase, checks novelty via Semantic Scholar); (2) Experimental Iteration (writes and runs code, produces plots with notes); (3) Paper Write-up (full LaTeX manuscript, autonomously finding papers to cite via Semantic Scholar); (4) Automated Paper Reviewing (LLM reviewer with near-human agreement, feeding back into open-ended loops). v2: progressive agentic tree search with a dedicated experiment-manager agent, removes reliance on human-authored templates, generalizes across ML domains, adds a VLM feedback loop to refine figures; produced the first fully AI-generated paper to pass peer review (ICLR 2025 workshop; avg score 6.33 vs acceptance threshold; withdrawn per pre-agreed protocol). Model-agnostic: v1 used GPT-4o and Claude Sonnet as top performers, also DeepSeek and Llama-3.

v3: The Agentic Scientist (added 29 Oct 2026)

v3 abandons the single-pipeline design for orchestration of existing coding agents. It drives Claude, Codex, Cursor, OpenCode, Gemini or Pi through six phases: read a plain task.md brief; reproduce the baseline; run the experiments; run ablations; a sealed final evaluation on held-out data; then write the paper. Environment setup, code changes and long runs go through TODO.md and a CLI so a human can interrupt or take over. A development version co-produced an Error Diffusion paper that was accepted as an ALIFE 2026 oral presentation. 549 stars, Apache-2.0, active (last push 26 Oct 2026).

Lit review capacity

• Search: Semantic Scholar API used for novelty checking in idea generation and for autonomously finding papers to cite in the write-up phase
• Read/parse: reads retrieved literature during ideation; text/LLM-based with no vision in v1 (cannot read its own plots or fix figure layout)
• Synthesis: generates complete scientific manuscripts in LaTeX including figures, tables and references; v2 adds VLM feedback on figure aesthetics/content
• Citations: written into LaTeX automatically via Semantic Scholar. KNOWN v1 citation-hallucination/weakness: independent evaluation (arXiv 2502.14297) found a median of only 5 citations per generated paper, most outdated (5 of 34 from 2020 or later), and reviewers did not catch deep flaws. Treat citation quality with caution

Pricing / hosting / provider tie

Open source, free to self-host; cost is your LLM API usage (v1 reported ~$15 per generated paper). No SaaS. Self-hosted; requires LLM API access (any supported provider); GitHub warns to run in a sandbox (Docker) because it executes LLM-written code. LICENSE: NOT Apache-2.0: custom "AI Scientist Source Code License v1.0 (December 2025)" based on Responsible AI Source Code License v1.1; GitHub API reports Other/NOASSERTION; LICENSE file verified by direct fetch.

GitHub: v1 14,966★ / 2,089 forks / last commit 19 Dec 2025; v2 7,479★ / 1,015 forks / last commit 19 Dec 2025; v3 587★ / 80 forks / last push 18 Oct 2026. X: @SakanaAILabs (verified). Docs: sakana.ai/ai-scientist, sakana.ai/ai-scientist-nature, sakana.ai/ai-scientist-first-publication.

Positive

• Paper published in Nature (651, 914-919); AI-generated manuscript passed first-round peer review at a top-tier ML workshop (70% acceptance rate)https://www.nature.com/articles/s41586-026-10265-5
• v2 produced first fully AI-generated paper to pass human peer review (ICLR 2025 workshop, scores 6/7/6, withdrawn by protocol)https://sakana.ai/ai-scientist-first-publication/
• HN launch thread: 203 points, 132 commentshttps://news.ycombinator.com/item?id=41231490

Negative / caveats

• HN academic (JBorrow): "the papers that the model seems to have generated are garbage... extremely limited citation to associated works. As an editor of a journal, I would likely desk-reject them"https://news.ycombinator.com/item?id=41231490
• Independent evaluation (arXiv 2502.14297): generated ideas often not novel; manuscripts poorly substantiated with median 5 citations per paper; reviewer agent missed serious methodological flaws; automation-bias risk for junior researchershttps://arxiv.org/abs/2502.14297
• Sakana's own limitations list: no vision, incorrect implementations/unfair baselines; "bloopers" include the agent modifying and relaunching its own execution script (safety/sandboxing concern)https://sakana.ai/ai-scientist/

Neutral

• Nature news coverage: "Researchers built an AI Scientist - what can it do?" (Aug 2024) and "How to build an AI scientist" (Mar 2026)https://www.nature.com/articles/d41586-024-02842-3
• HN (visarga): AI may be genuinely useful at the literature-review/writing/formatting stage even if full autonomy is unproven

Computational science harnesses

BootLoops (added 5 Oct 2026)

BootLoops

Prof. Matthew D. Schwartz (Harvard University; visiting researcher at Anthropic): an independent project funded by Anthropic

429★

MIT

New · 1 Oct 2026

What it is

A "living harness" for interdisciplinary quantitative science: certified computational tools and "house engines" for exact, high-precision calculation, ported to a common framework and built to be driven by LLM agents. Model-agnostic, callable with Claude, Gemini or ChatGPT, so the harness improves independently of any frontier lab. Grew out of scattering-amplitude physics; published as BootLoops 1.0 on 1 Oct 2026 and featured in Anthropic's "Claude-shaped science" guest post.

How it works

• Verification-first: the "BootLoops Standard": a diagram is solved only when its full functional form is known and a Python script evaluates it to arbitrary precision on a laptop (checks run at 30-100 digits; the AI "can't cheat")
• Portfolio: tools ported from mathematics, physics and computer science (AMFlow, Eichler, GPLEval and more), indexed so the model can select per project; docs CC BY 4.0
• Results at launch: 30 Feynman integrals computed end-to-end (15 never computed before); applications across ecology (Barro Colorado Island tree-mix turnover 4.5x faster than neutral theory allows), population genetics, cosmology, single-cell biology, authorship attribution (Federalist Papers, Shakespeare, Isaiah, the Voynich Manuscript), linguistics, clinical-trial design and economics

Pricing / hosting / provider tie

Free, open source (MIT; docs CC BY 4.0), self-hosted; costs are your own LLM usage. Provider tie: none: works with any LLM. GitHub: BootLoops-ai/bootloops: 429 stars, 68 forks, 2 open issues (API, 9 Nov 2026; created 1 Oct 2026); the org now carries six repositories (skills, blade, amflow-cpp, kira, jackandjill, feedback). Site: bootloops.ai. Anthropic provided funding; not an Anthropic project (owned and maintained by Schwartz).

Positive

• Launched via the Anthropic Science blog (1 Oct): the week's most notable new open harness; 338 stars within eleven days of the repo going public
• Concrete example of "Claude-shaped science": problems chosen to fit LLM strengths, with humans supplying the conceptual direction and credit

Neutral

• Independent reception still thin (no HN/Reddit threads found in this pass); Schwartz's own caveat: early results were "technically correct but not interesting" until domain experts steered
• The site's summaries keep widening: a health-policy analysis of CMS Medicare star-ratings clustering is now posted (the published algorithm approximates a criterion a 1958 method solves exactly; code and data released), seen at the 12 Oct check

6. Deep Dives: Closed Saas

05

Deep dives: closed SaaS

Eight hosted, vendor-managed products. Pricing verified live on the vendors' pricing pages on 03 September 2026 and re-checked 21, 28 September, 5, 12, 19 and 29 October and 2 November 2026 unless marked otherwise.

Elicit

Elicit Research (public benefit corporation spun out of Ought; cofounder & CEO Andreas Stuhlmüller)

SaaS

Sentence-level citations

Team / origin / funding

Born at Ought (US nonprofit ML research lab). 25 Sep 2023: $9M seed led by Fifty Years (Basis Set, Illusian, Mythos, Julian, angels incl. Tom Preston-Werner, Arash Ferdowsi, Jeff Dean); became an independent public benefit corporation. 26 Feb 2025: $22M Series A at $100M valuation led by Spark Capital and Footwork. Product developed at Ought from ~2022; independent company announced Sep 2023.

Architecture

Self-contained hosted SaaS. Semantic search over 138M academic papers (Semantic Scholar, PubMed, OpenAlex) + 545K ClinicalTrials.gov trials. Components: Search, Research Agent (Aug 2026), Research Reports (up to 200 sources, customizable), Systematic Literature Review workflow (PRISMA 2020 auditable, screens up to 40,000 papers, dual review, extraction incl. tables/figures), Library, Alerts (daily OpenAlex updates), API + MCP on paid plans, browser extension for full text; the Library became a shared research hub for researchers and AI agents (17 Sep 2026); Routines (30 Sep 2026) make research recurring: the Research Agent monitors the literature on a daily or weekly schedule, updates the work and reports back for review (a Pro-plan feature). Find Papers, Extract Data and Chat with Papers folded into the Research Agent on 30 Sep, and legacy Alerts stopped running on 31 Oct 2026: anything not converted into a Routine no longer runs. Vendor evals: 95% search recall / 97% abstract / 99% full-text / 96% extraction across 994 Cochrane reviews (May 2026 blog); BioASQ claim (Jul 2026); SOC 2 Type II (Oct 2025).

Lit review capacity

• Search: semantic + keyword over 138M papers + 545K trials; PRISMA-reproducible strategy; up to 1,000 papers / 20,000 data points at once; weekly updates, daily Alerts
• Read/parse: full text when open access or via browser extension + subscription; else title/abstract; extracts quantitative/qualitative data incl. tables and figures
• Synthesis: reports synthesizing up to 200 papers with methods section, PRISMA flow diagrams, paper-summary tables, search strategies; reusable data across projects
• Citations: sentence-level: every AI-generated claim cites the exact sentence or figure; PRISMA-auditable exclusion reasons, per-criterion scores, supporting quotes

Pricing / hosting

Verified live at elicit.com/pricing 2026-09-03; re-verified 21, 28 Sep, 5 and 12 Oct 2026 (unchanged). Basic free (limited Research Agent/Reports; unlimited search/summaries/chat); Pro $49/user/month billed annually ($588/yr, 5,000-paper screening, API access); Scale $169/user/month billed annually ($2,028/yr, 5x usage, collaboration, figure extraction); Enterprise custom (40,000-paper screening, SSO/SAML, single-tenancy, unlimited API). Self-contained hosted SaaS; API on paid plans. X: @elicitorg. Docs: support.elicit.com (Intercom help center). GitHub: none (closed).

Positive

• Identified studies missed by traditional searches; 41.8% precision vs 7.55% in original reviews; recommended as supplementary search toolhttps://pmc.ncbi.nlm.nih.gov/articles/PMC12483133/
• Feasibility study across environmental/life sciences: extractions met ≥87% accuracy thresholdhttps://www.cambridge.org/core/journals/research-synthesis-methods/article/...
• Independent PMC evaluation: conclusions would have remained the same with Elicit's 6 found articles vs 17 from umbrella reviewhttps://pmc.ncbi.nlm.nih.gov/articles/PMC11921719/

Negative / caveats

• Sensitivity poor: average 39.5% (25.5-69.2%) vs 94.5% original, not sensitive enough to replace traditional searching at time of evaluationhttps://pmc.ncbi.nlm.nih.gov/articles/PMC12483133/
• r/PhD: "not reliable enough for any type of formal meta-research"https://www.reddit.com/r/PhD/comments/1istbkr/
• Medium practitioner: Elicit's own rule of thumb ~90% of information shown is accuratehttps://medium.com/@borisnikolaev_57179/
• IntuitionLabs audit (5 Sep 2026): no independent third-party accuracy audit of Elicit located; Elicit's own support notes concede its underlying models are "not inherently faithful" to source text, grounding claims cover existence, not claim supporthttps://intuitionlabs.ai/articles/citation-reliability-ai-literature-reviews

Neutral

• Paperguide: Elicit = systematic-review/extraction engine vs Consensus = evidence discovery; neither covers full cycle alonehttps://paperguide.ai/blog/elicit-vs-consensus/
• CJILS academic evaluation: Elicit best for synthesizing evidence; human oversight criticalhttps://ojs.lib.uwo.ca/index.php/cjils/article/view/23075

CoreSR

Researchers at University of Toronto, McMaster University, University Health Network, Toronto General Hospital (co-founders incl. Gordon Guyatt, McMaster HEI)

SaaS

Evidence synthesis

26 tools

TITAN-SR v6

Team / origin / funding

Built by researchers at University of Toronto, McMaster University, University Health Network and Toronto General Hospital. TITAN-SR paper team (published in the Journal of Clinical Epidemiology, Oct 2026; DOI 10.1016/j.jclinepi.2026.112514): Tyler Pitre (U Toronto), Dena Zeraatkar (McMaster), John Granton (U Toronto), Jason W. Busse and Bram Rochwerg (McMaster), Gordon H. Guyatt (McMaster HEI). Guyatt self-discloses as an equity-holding co-founder (X, 2 Sep 2026). Company entity not disclosed on site; commercial funding undisclosed (unverified). TITAN-SR study itself received no specific grant. Launch May-Sep 2026; exact date unverified.

Architecture

26 tools in one auditable project (the site's own count; the 12 Oct pass said 25): Auto-Review (agent-orchestrated pipeline with reviewer hard gates), Review Builder (PRISMA-P protocol, PROSPERO prep, referenced Word export), EviTrace Search (7 databases), Reference Screening (TITAN dual3 title/abstract triage + GLASS full-text review with verbatim evidence quotes), Data Collection, risk-of-bias instruments (RoB 2.0, ROBUST-RCT, ROBINS-I, ROBINS-E, PROBAST, QUIPS, CLARITY, JBI, QUADAS-2), INSPECT-SR trustworthiness, pairwise/dose-response/NMA/Bayesian NMA/CNMA/MBNMA meta-analysis (real R subprocesses), SMART GRADE (CoreGRADE/JUDGE), ICEMAN, Evidence to Decision, Manuscript Writer (GLASS locally hosted model), Living Reviews. Model line refreshed on 15 Oct: TITAN-SR v6 (an ensemble of three fine-tuned BERT-family encoders, trained on 3,097,955 records, calibrated on 80 held-out reviews and evaluated on 60 reviews it never saw; at the maximum-recall setting it kept 99.9% of final includes and screened out 78% of the rest, with a user-chosen sensitivity target of 93-99.5%), GLASS v3 (65,536-token context, six specialties) and ROB BERT v6 (22 specialist question heads across the five RoB 2 domains, 0.85 checker confidence gate). TITAN v6 runs on GPU tiers; the free tier keeps the older dual3 ensemble on CPU (up to 1,000 references a day); the site still does not claim that live models inherit the preprint benchmarks. Full audit trail, reviewer overrides with persisted reasons, role-based access.

Lit review capacity

• Search: 7 databases in parallel: PubMed, Europe PMC, OpenAlex, ClinicalTrials.gov, Semantic Scholar, Scopus, Web of Science (Scopus/WoS via your own institutional API keys); PICO-based Boolean strategy generation; auto-dedup on DOI/PMID/title; seed recovery; citation-graph expansion; RIS/NBIB/EndNote/BibTeX/CSV/Excel/PubMed imports
• Screening: TITAN-SR title/abstract triage + GLASS full-text review with verbatim evidence quotes; reviewer overrides; calibrated recall targets (e.g. 99%); PRISMA-ready counts
• Synthesis: pairwise, dose-response, frequentist NMA (R netmeta), Bayesian NMA (JAGS), CNMA, dose-time MBNMA; unified imprecision engine; GRADE certainty with editable rationale; ICEMAN credibility; EtD (12 criteria); publication-quality SVG/PNG exports
• Citations: PRISMA flow from live counts; GRADE Summary-of-Findings exports; referenced Word protocol export; manuscript writer grounded in project data; audit trail on every decision

Pricing / hosting

Verified live at coresr.ai/pricing 2026-09-03; re-checked 21, 28 Sep, 5 and 12 Oct 2026, a free-forever tier was added in the 28 Sep week (CAD); no changes in the 12 Oct pass. Free: "$0 forever... No card. Never expires. Zero AI", the manual systematic-review workbench (unlimited reviews, references and collaborators; import from any reference manager; dedupe with review and merge; two-reviewer title/abstract and full-text screening with conflict resolution; PRISMA 2020 flow and every export; manual extraction and Cochrane RoB 2.0 by hand; pairwise meta-analysis with GRADE certainty and Summary-of-Findings tables). Paid plans are now positioned as AI add-ons, each with a 7-day trial (card required): Essentials $39 CAD/mo (free workbench + AI on one active review; 1,000 refs/batch; 1,000 AI operations/day; monthly only); Researcher $99 CAD/mo or $999/yr (unlimited projects, 10,000 refs/batch, 7-db search, advanced analysis, ICEMAN); Team $349 CAD/mo or $3,839/yr (up to 6 members, Auto-Review, Manuscript Writer, Living Reviews, 25,000 AI calls/day); Institution custom (unlimited seats, EtD, SSO). Expert services (commissioned SRs, guideline packages) remain alongside. Hosted SaaS; TITAN models on CoreSR-managed tiers; GLASS locally hosted. X: @CoreSRR (4 followers, very new). Docs: no public docs site; in-app module library + YouTube channel + Pitre Substack tutorials. GitHub: none (API confirmed).

Positive

• Co-founder Guyatt launch post: "We built CoreSR to strip that out and return the reviewer's time to judgment" (234 likes / 57 RT, 31 Aug 2026)https://x.com/GuyattGH
• Peer-reviewed positioning: the site now leads with "Validated in the Journal of Clinical Epidemiology", citing 19,787 development reviews and 22 Cochrane test reviewshttps://coresr.ai
• WSACS brought CoreSR in as the methods team for its clinical guideline update: 326 studies, 8 PICO domains, 135 GRADE profiles and 13 Evidence-to-Decision questions, with every study, judgment and number traceable in one workspacehttps://coresr.ai/services#wsacs
• External academic software citation: Annals of Intensive Care 2026, a 26-trial network meta-analysis in critical care, cites CoreSR as softwarehttps://www.sciencedirect.com/science/article/pii/S211058202600155X
• TITAN-SR method paper (figures from the SSRN preprint): outperformed four general-purpose LLM chatbots across 500 matched reviews (p <= 5x10^-31), S@99R 0.902 / AUC 0.974 on 3,434 internal test reviews; beat ASReview on 18/22 Cochrane temporal-holdout reviews (81.8%), median paired S@95R +0.145https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6732292
• Determinism argument: BERT ensemble screening vs stochastic LLM screening, in Pitre's Substack walkthroughhttps://drtylerpitre.substack.com/p/ai-for-systematic-review-screening

Negative / caveats

• Not yet on standard comparison lists: absent from Atlas and Paperguide 2026 systematic-review tool roundupshttps://www.atlasworkspace.ai/blog/ai-systematic-review-tools
• Site claims TITAN-SR beat ASReview on 156/193 reviews (80.8%), still NOT in the SSRN abstract (18/22, 81.8% on 22 Cochrane reviews; abstract re-read via ResearchGate 21 Sep 2026). The site now also presents a per-model specificity table (TITAN SR 0.917 vs 0.746-0.660 for four LLM chatbots, n=500 matched reviews): direction confirmed by the abstract, per-model figures unverified. Do not quote without the preprint full text.https://coresr.ai

Neutral

• No HN or Reddit presence found (searched); reception is early and founder-led
• Site itself cautions the live dual3 service "has not inherited" the historical benchmark resultshttps://coresr.ai
• The method paper is now presented on the site as peer-reviewed in the Journal of Clinical Epidemiology, where the 29 Oct pass still read it as in press at JCE; the site adds that the paper's historical pre-dual3 configuration figures do not validate the live dual3 ensemble; the DOI (10.1016/j.jclinepi.2026.112514) resolves to the Elsevier article page (verified 5 Oct 2026)https://coresr.ai

Consensus

Consensus AI (Consensus NLP); founders Eric Olson (CEO) and Christian Salem (CPO)

SaaS

250M+ papers

Team / origin / funding

Founded 2021 by Eric Olson and Christian Salem, former Division 1 athlete teammates; San Francisco-based. Seed: $3M announced Jan 2022 (VentureBeat; total $4.25M incl. Winklevoss Capital pre-seed reinvestment) with an OpenAI partnership. Series A: $11.5M led by Union Square Ventures. Series B: $30M led by GreatPoint Ventures. About page states $45M total funding from USV, GreatPoint, Nat Friedman, Daniel Gross.

Architecture

Self-contained hosted SaaS. Draws on 250M+ research papers including licensed full-text content from leading publishers. Components: AI search (natural-language + filters), Deep Search (builds a comprehensive search strategy, expands key terms, identifies conflicting arguments, explores the citation graph), Consensus Meter (instant visual agree/disagree on yes/no questions), Medical mode (~50,000 clinical guidelines + 8M articles from top 1,000 medical journals), natural-language filters, report drafting, compare-two-approaches, find-studies-by-method, citation graph visualization, API & MCP on paid plans. On 27 Oct 2026 the Research Agent became callable through the API and MCP as Threads (POST /v1/threads, or create_thread over MCP): a research question returns a cited synthesis with supporting quotes and the source sections they came from, and a follow-up can narrow the question or examine conflicting results. Two named integrations: Owkin wired the Consensus MCP into K Pro, its AI scientist, and reports 25x the paper coverage, and Phylo screens about 20,000 drug targets against hundreds of diseases through the API. 170+ university library partners; 10M+ users.

Lit review capacity

• Search: over 250M+ papers incl. licensed full text; Deep Search expands key terms and explores the citation graph; natural-language filtering; medical mode; corpus source not publicly disclosed (per Paperguide)
• Read/parse: analyzes abstracts and licensed full text; study snapshots show year, type, citation count
• Synthesis: Deep reviews (comprehensive multi-study literature reviews, 3/mo free, 15 Pro, 200 Deep); narrative answers with numbered citations; Consensus Meter for directional evidence; conflicting-argument identification; report drafting
• Citations: answers link to sources with numbered citations and paper takeaways

Pricing / hosting

Verified live at consensus.app/pricing 2026-09-03; re-verified 21, 28 Sep, 5 and 12 Oct 2026 (unchanged). Free $0 (10 Pro messages/mo, up to 3 Deep reviews/mo); Pro $12/mo annual ($144/yr) or $20 monthly (500 API & MCP uses/mo included); Deep $45/mo annual ($540/yr) or $65 monthly (2,000 uses/mo); Student/Faculty/Clinician up to 40% off; Teams/Enterprise custom. Self-contained hosted SaaS. X: @ConsensusNLP. Docs: help.consensus.app. GitHub: none found.

Positive

• r/GradSchool: "Consensus is an excellent new AI research tool" (favourite new AI tool thread)https://www.reddit.com/r/GradSchool/comments/125ho7u/
• Peer-reviewed Cureus review covers the Consensus App as a tool to retrieve and synthesize peer-reviewed literaturehttps://www.cureus.com/articles/370288/
• Academic Help review: proven safe and effective for locating academic sourceshttps://academichelp.net/reference-finders/consensus-review.html

Negative / caveats

• CJILS: Consensus often fails to incorporate theoretical depth: surfaces factual findings rather than broader theoretical implications; may not include most up-to-date research; no option to upload one's own articleshttps://ojs.lib.uwo.ca/index.php/cjils/article/view/23075
• Paperguide: corpus size not publicly disclosed; no systematic-review screening or structured data extractionhttps://paperguide.ai/blog/elicit-vs-consensus/
• IntuitionLabs audit (5 Sep 2026): no independent third-party accuracy audit of Consensus located; its "all citations are real, never hallucinated" claim (help center) addresses whether sources exist, not whether they support the claims madehttps://intuitionlabs.ai/articles/citation-reliability-ai-literature-reviews

Neutral

• PapersFlow: Consensus is an answer engine ("What does science say about Y?") vs Elicit's discovery/extraction tool: different jobs, not direct substituteshttps://papersflow.ai/blog/elicit-vs-consensus-ai
• Paperguide final verdict: Consensus stronger for fast quality-filtered evidence answers at lower entry pricehttps://paperguide.ai/blog/elicit-vs-consensus/

SciSpace

SciSpace (formerly Typeset); founders Saikiran Chandha (CEO) and Shanu Kumar (co-founder)

SaaS

280M+ papers

Team / origin / funding

Founded 2016 as Typeset (per Inc42 citing company website). Co-founders Chandha (VIT alumnus) and Kumar (IIT Kharagpur alumnus) met at a hackathon, developed the idea at Stanford Ignite. Rebranded to SciSpace with AI Copilot ~2023. Raised an undisclosed amount from Inventus Capital and Silicon Valley Quad (Inc42). Third-party revenue estimate $5.1M (2024, upmarket.co: unaudited).

Architecture

Self-contained hosted SaaS. Corpus 280M+ papers across multiple databases (Google Scholar, PubMed, arXiv, OpenAlex, Semantic Scholar: per scispace compare page). Components: Literature Review search with Deep Review mode, Chat with PDF (Copilot), AI Writer, Find Topics, Paraphraser, Citation Generator, Extract Data, AI Detector, Agent Gallery (SLR agents: Literature Review Generator, SLR Data Extraction, Risk of Bias RoB2/ROBINS-I, Inclusion/Exclusion Criteria, MeSH Term Extractor, Abstract Generator), connectors (Zotero, Mendeley, PubMed, Google Scholar, arXiv, Grants.gov), exports (DOC, PPT, LaTeX, PDF). Citation Verifier (7 Oct 2026), which checks a bibliography against indexed sources. Agent workflow: Run Task → Find papers → Group themes → Draft review → Export DOC, with audit logs; unlimited agent usage arrived on the Advanced and Max plans (9 Oct 2026). Inc42: uses OpenAI + Anthropic as foundational models with proprietary fine-tuned models on top. Vendor benchmark (flag as vendor-reported): Deep Review returned 26.3 highly relevant papers/query vs Elicit 13.0 and Consensus Pro 16.1 across 200 queries.

Lit review capacity

• Search: 280M+ papers via multi-database retrieval; Deep Review mode; vendor claims up to ~700 papers read / ~312 selected in Deep Review (per Paperguide)
• Read/parse: Chat with PDF for single- and multi-paper Q&A; extract structured data from PDFs into tables; reads abstracts to verify claims
• Synthesis: Deep Review reads and synthesizes across many papers; literature review generator agent produces thematic grouping + draft review; AI Writer drafts with reference management
• Citations: AI answers with citations; line-by-line inline citations; citation generator (APA/MLA/Chicago/IEEE/Vancouver); export CSV/XLSX/BIBTEX

Pricing / hosting

Verified live at scispace.com/pricing 2026-09-03; re-verified 21, 28 Sep, 5 and 12 Oct 2026 (same prices). Free signup; Premium $20/mo monthly or $12/mo billed annually (1,200 monthly credits); Advanced $90/mo monthly or $70/mo billed annually (now advertised with unlimited monthly usage, verified report, 8 parallel agent chats); Max $200/mo monthly or $160/mo billed annually (unlimited monthly usage, 16 parallel chats, priority support); Enterprise custom. A third SCI30 flash sale (extra 30% off yearly plans) was live to 12 Oct 2026, after windows that closed 21 Sep and 5 Oct. Self-contained hosted SaaS. X: @scispace. Docs: scispace.com/help. GitHub: none found for main platform.

Positive

• r/research: "It's like having a decent research assistant on the side, not a magic solution, but it does save time"https://www.reddit.com/r/research/comments/1je29w4/
• CJILS: SciSpace made headway as the best option for writing and teamwork; 4.3 stars Trustpilot (346 reviews)https://ojs.lib.uwo.ca/index.php/cjils/article/view/23075
• Vendor benchmark (vendor-reported): 26.3 highly relevant papers/query vs Elicit 13.0, Consensus Pro 16.1https://scispace.com/resources/scispace-vs-elicit-vs-consensus-an-ai-literature-search-benchmark-across-200-queries/

Negative / caveats

• r/academia: "Avoid SciSpace - Extremely Misleading, Overpriced, and Potentially a Scam. The quality is garbage. The pricing model is predatory."https://www.reddit.com/r/academia/comments/1p85o4a/
• r/PhD: "output quite unreliable and the paraphrasing tool is verbose to the point of unusable"; "works really bad (photonics/laser physics)"https://www.reddit.com/r/PhD/comments/1gj0g3b/
• aidetectplus review: 3 pros, 5 cons: incl. inability to locate uploaded PDFs againhttps://aidetectplus.com/blog/scispace-review

Neutral

• Paperguide: SciSpace broader (discovery-to-drafting with AI writer); Elicit deeper for SR screening; SciSpace does not do pooled statistics, risk-of-bias assessment, or meta-analysishttps://paperguide.ai/blog/elicit-vs-scispace/
• CJILS: none of the three tools provides a comprehensive solution; human oversight and judgment remain essential

Undermind

Undermind (YC Summer 2024); founders Joshua Ramette (CEO) and Tom Hartke (CTO): MIT quantum physics PhDs

SaaS

200M+ papers

MCP server

Team / origin / funding

Founded 2023, YC Summer 2024 batch, San Francisco. Whitepaper "Benchmarking the Undermind Search Assistant" (Hartke & Ramette, Jan 5, 2024) describes the v1 arXiv-based search engine. Launch HN 26 Jul 2024. Company describes itself as "your AI co-researcher for the literature"; "finds the papers others miss". Enterprise: GSK case study: "1000+ GSK scientists use Undermind"; Kim Branson (SVP/Global Head AI-ML, GSK) quote.

Architecture

Deep semantic search: LLMs as reasoning engine + classifier at key steps of a structured search process; four steps: (1) basic search combining semantic embeddings, citations, LLM reasoning; (2) relevance classification (highly relevant / closely related / ignorable); (3) adaptation & exploration (citation trails, mimicking human discovery); (4) comprehensiveness estimation via exponential "discovery curves". Now searches 200M+ papers; reads full texts, figures, tables, equations; MCP server exposes tools (launch_deep_search, search_papers, find_papers_by_author, read_pdfs, get_paper_info, etc.). MCP connectors published for ChatGPT, Claude, Cursor, VS Code, Codex, Claude Code, Copilot, Opencode, Grok, Perplexity.

Lit review capacity

• Search: deep semantic search beyond keywords (10x vs Google Scholar in vendor whitepaper, ~300 user queries: vendor-reported); follows citation trails and key authors until "new searches stop finding relevant papers"; alerts on new relevant papers
• Read/parse: evaluates hundreds of papers; full-text deep analysis on Pro tier; reads PDFs incl. figures, tables, equations; auto-fetches open-access PDFs
• Synthesis: reports with citation panels; brainstorm with AI expert that has read the literature; custom tables; novelty/gap/cross-disciplinary use cases
• Citations: in-line citations traceable to source paper; "verify any statement by following in-line citations back to the source paper"

Pricing / hosting

Verified undermind.ai pricing 2026-09-03; re-checked 21, 28 Sep, 5 and 12 Oct 2026 (unchanged). Free $0 (exploratory research, standard rate limits); Pro $16/mo billed annually (latest models, deepest full-text analysis, 10x usage); Team $15/person/mo annual, 5 seats; Enterprise custom (increased compute, SSO, admin dashboard). Hosted SaaS with MCP connectors; enterprise "Secure Cloud" (encryption, no training on customer data, own IP). X: @UndermindAI. Docs: site itself (pricing, FAQ, whitepaper, MCP page). GitHub: no public product repo (MCP server exists).

Positive

• GSK case study: "Undermind has the greatest impact compared across all GenAI solutions in our scientific space: GSK R&D Leader"; 1000+ researchershttps://www.undermind.ai/
• r/academia: "I recommend undermind.ai, it searches through all of semantic scholar and has a free tier"https://www.reddit.com/r/academia/comments/1kxrwlx/
• r/PhD: "the current leaders are Undermind.ai and Elicit.com"https://www.reddit.com/r/PhD/comments/1jpvkbb/

Negative / caveats

• Vendor-reported benchmarks: 10x vs Google Scholar figure is self-published in the whitepaper (Jan 2024, arXiv-only era)https://www.undermind.ai/whitepaper.pdf
• Vendor-run benchmark (seen at the 28 Sep 2026 check): the site claims its v2 engine "outperforms frontier agents with web search by a wide margin": e.g. 85% vs 50% for GPT-5.6 Sol at 10 minutes across 23 research goals, LLM-graded; self-reported, no independent replicationhttps://www.undermind.ai/
• Tooliverse review: "one of the best academic search tools available, but it works best when the scope of your research question is focused rather than broad"https://tooliverse.ai/tools/undermind

Neutral

• YC Launch HN thread (July 2024) with founder engagementhttps://news.ycombinator.com/item?id=41069909
• Whitepaper transparent about methodology (~300 queries, 2023 data)

Manus

Butterfly Effect Pte. Ltd. (Monica.im); founder Xiao Hong, chief scientist Ji Yichao ("Peak")

SaaS

General-purpose agent

Team / origin / status

China-founded (offices originally Beijing/Wuhan), HQ relocated to Singapore mid-2025; developed from Oct 2024; designed for markets outside China. Invitation-only beta 6 Mar 2025; Manus 1.6 (15 Dec 2025); desktop app 1.7.2 in Sep 2026; Manus 2.0 shipped 28 Sep 2026 (Cascade harness, Manus Studio desktop, Cloud Computer, event-triggered Automations, and Cue: a separate personal-agent app in free early access). Status: Dec 2025 Meta agreed to acquire (~US$2-3B); China's NDRC blocked the deal 27 Apr 2026; Meta cut ties 15 Jun 2026; Manus resumed independent operations (announced 1 Sep 2026 blog). Post-independence: a US$500M+ raise closed 8 Oct 2026: the first round since the Meta unwind, co-led by Boyu Capital and IDG Capital with Tencent, HSG and ZhenFund participating; ~US$4B valuation reported by Bloomberg; no Hong Kong IPO before 2027.

Architecture

General-purpose autonomous "hands-on" agent. Runs in a complete sandbox (virtual computer with internet, persistent file system, installable software/custom tools). Plan → execute multi-agent architecture with specialized sub-agents; iterative loop (analyze → plan → execute → observe); long-running sessions with context memory; multi-model dynamic invocation reported (e.g. Claude 3 for reasoning, GPT-4 for coding, Gemini for knowledge). Produces end deliverables (slides, websites, apps, reports). Manus 2.0's Cascade harness (28 Sep 2026) keeps the active context light and loads specialist capabilities only when needed: company-reported from one tested configuration: 23.2% fewer tokens, 28.2% less time and 32% lower cost than the previous system.

Lit review capacity

• Search: live web browsing from its own cloud browser; Wide Research feature for broad search; not tied to academic databases
• Read/parse: reads web pages, files, datasets, PDFs as part of task execution
• Synthesis: multi-agent synthesis producing comprehensive reports plus artifacts (slide decks, websites, spreadsheets); positioned as "action engine"
• Citations: not citation-rigorous: source links and a replayable trail of every page visited (provenance trail), but no formal citation system; flagged as weaker than OpenAI Deep Research for citation accuracy in comparisons

Pricing / hosting

Freemium SaaS. Official pricing page lists credit tiers 4,000 / 8,000 / 40,000 credits per month plus 300 refresh credits daily (numbers JS-rendered on official page); third-party pricing guides (nocode.mba, felloai) report Standard $20/mo (4,000 credits), Customizable $40/mo (8,000), Extended $200/mo (40,000), Team from ~$20/seat/mo, annual ~17% off. Prices marked third-party-reported. Hosted SaaS at manus.im (cloud sandbox); desktop (macOS/Windows); API at open.manus.ai/docs. Manus 2.0 adds purchasable Cloud Computer environments (persistent machines for automations and multiplayer projects). Manus Flex (29 Sep 2026) lets users bring their own inference-provider API key (initial partners OpenRouter, Fireworks, Modal); inference is billed by the provider. No open-source repo. X: @ManusAI_HQ. Docs: manus.im/docs.

Positive

• HN: "Manus has been the best agent for turning text into work -- useable slides, code, extracting data from websites... it's the best"https://news.ycombinator.com/item?id=46428844
• Launch demo drew 1M+ views in 20 hours; revenue run rate ~$90M (Aug 2025) → ~$125M (Dec 2025) per reporting; Meta agreed to acquire for ~$2-3B (later blocked)https://en.wikipedia.org/wiki/Manus_(AI_agent)

Negative / caveats

• r/ycombinator "What happened with Manus?": hype faded after launchhttps://www.reddit.com/r/ycombinator/comments/1l2yi6x/
• Firecrawl: "broader than it is deep. For pure research quality, ChatGPT Deep Research and Perplexity Deep Research produce better reports. The credit economy is opaque."https://www.firecrawl.dev/blog/best-ai-for-research
• r/AIAssisted deep-research comparison: Manus "painfully slow... the outputs ended up looking very templated"https://www.reddit.com/r/AIAssisted/comments/1n84wy8/
• UX Collective early-access review: initial hype 7-9/10 dropped to 3-5/10 after hands-on usehttps://uxdesign.cc/manus-ai-real-or-hype-404254922290

Neutral

• NewsCatcher 2026: positions Manus as "action engine" with replayable page-visit trail for provenancehttps://www.newscatcherapi.com/blog-posts/best-deep-research-tools
• Meta acquisition saga (announced Dec 2025, blocked Apr 2026, unwound Jun 2026, independent again Sep 2026)
• Manus 2.0 launch coverage (28 Sep 2026): mainstream tech-press pickup; Cascade efficiency claims are company-reported from one tested configuration, with no independent benchmarks yet
• Manus Flex (29 Sep 2026): bring-your-own-key module: the harness, tools and execution environments usable with a supported provider account; initial inference partners OpenRouter, Fireworks and Modal

ClawsGO Science · added 9 Nov 2026

BitMiracle AI Lab: a cloud-resident research agent, released and open-sourced 1 Nov 2026

SaaS

Cloud lab

New · 1 Nov 2026

What it is

A long-horizon research agent built as an always-on cloud workstation, not a chat tool. Each task is handed its own remote machine, and the agent can work for days: it runs clusters of agents in parallel, reaches 533 tools across 61 disciplines and 91 research databases, and returns a finished paper in LaTeX with every figure rebuilt as a reproducible bundle. BitMiracle AI Lab released it on 1 Nov 2026 and open-sourced the code for local verification.

Launch case study

The launch demonstration is the Global Attractor Conjecture, a 50-year-old problem in chemical reaction kinetics. ClawsGO Science returned a proof in 11 days as 83 Lean 4 files, about 9,300 lines, machine-checked with no sorry placeholders. The company describes a split architecture: Claude Opus 5.5 as the planning brain and 136 GPT 6 Astra agents writing the proof, with humans setting the goal and signing off the result. No independent evaluation of that run has been published.

Pricing / hosting / provider tie

Hosted cloud lab; the source is released for self-verification. Plans at the 9 Nov check: Free, Plus $9.90/mo, Pro $29.90/mo, Max $99.90/mo and Ultra $299.90/mo, in US dollars. Provider tie: vendor-managed mix (Claude Opus 5.5 plus GPT 6 Astra at launch); no BYO-model detail published. X: none verified. Repo: source released 1 Nov 2026; repo stats not verified in this pass.

Positive

• Strong launch reception: Product Hunt #2 of the day (279 upvotes), unusual for a science harnesshttps://chatgate.ai/post/clawsgo-science
• Scope is broad and concrete: 533 tools, 61 disciplines and 91 databases in the published capability list, with a machine-checkable proof as the launch artefacthttps://chatgate.ai/post/clawsgo-science

Negative / caveats

• New and vendor-reported: one week old, no independent evaluation of the proof run or the capability counts locatedhttps://chatgate.ai/post/clawsgo-science
• The Lean proof is a result about a proof artefact, not about the harness: Lean 4 checks the proof itself, but nothing yet shows the harness generalises beyond a demonstration curated for launch

Neutral

• Pricing is credit-based at the upper tiers; the free tier is enough to test the workflow, not for long-horizon runs
• Positioning overlaps the cloud-lab direction of Claude Science, but ClawsGO is a standalone vendor rather than a vendor-tied layer

Haiqu AgenticOS · added 12 Oct 2026

Haiqu (quantum middleware): a team-of-agents system for quantum research, unveiled 7 Oct 2026

SaaS

Quantum R&D

New · 7 Oct 2026

What it is

AgenticOS coordinates teams of specialised AI agents across a quantum research project: literature review, first-principles derivations, quantum-feasibility assessment, algorithm and circuit design, with results checked against classical baselines, critic agents, formal verification and human sign-off gates before a finished plan is handed to Haiqu's quantum software stack for hardware execution. Builds on Haiqu's Agentic Quantum OS (May 2026).

Launch evidence (company-reported)

Unveiled 7 Oct 2026. Flagship demo: starting from a scientist's idea, AgenticOS planned and ran a 3x3 Hubbard-model experiment on an IBM quantum computer, reporting a 75% circuit-depth reduction and a calibration that aligned hardware results with an error-free simulation of the same method. The company frames the design goal as catching science-altering agent mistakes; no independent evaluation is published yet.

Pricing / access

Closed platform; included with the Haiqu OS, available to enterprise R&D teams on request. Academic researchers can apply for free access through Haiqu's Academic Program (including SDK credits). No public repo, no BYO-model detail published. X: no dedicated handle verified. Site: haiqu.ai. Coverage: The Quantum Insider, Quantum Zeitgeist, AZoQuantum.

Neutral

• One week old; trade-press coverage only: capability claims are company-reportedhttps://thequantuminsider.com/2026/10/07/haiqu-releases-agenticos-to-plan-and-check-quantum-research/
• Not a literature-review product: the design emphasis sits in verification-first experiment planning; no benchmark comparisons publishedhttps://quantumzeitgeist.com/agents-complete-quantum-projects-haiqu/

7. Deep Dives: Provider-Tied

06

Deep dives: provider-tied

Three provider-tied systems that only exist inside their vendor's products. No public repos, no BYO-model.

OpenAI Deep Research (ChatGPT)

OpenAI; research leads Isa Fulford, Zhiqing Sun

OpenAI only

Launched 2 Feb 2025

Team / origin

Launched February 2, 2025 as "deep research" inside ChatGPT: "a new agentic capability that conducts multi-step research on the internet for complex tasks." Positioning: OpenAI's next agent after Operator; built toward the AGI goal of "producing novel scientific research". Updates: Feb 25 2025 all Plus users; Apr 24 2025 usage raised to 25 DR/mo Plus, 250/mo Pro, 5/mo Free (lightweight o4-mini version); Jul 17 2025 visual browser + agent mode; Feb 10 2026 now powered by GPT-5.2, connects to any MCP or app, site restrictions, real-time progress + interrupt, fullscreen report view. Adjacent life-sciences line: GPT-Rosalind, OpenAI's frontier model for biology / drug discovery / translational medicine, left research preview on 11 Sep 2026: available globally to eligible organisations via the trusted-access program (API, Codex, ChatGPT Enterprise; published pricing took effect 5 Oct 2026: $5/$25 per 1M input/output tokens, $0.50 cached input), with a free Life Sciences plugin for Codex connecting 50+ scientific tools. Separate product, but it is where OpenAI's research-agent line is heading in the sciences. Model layer (7 Oct 2026): OpenAI moved ChatGPT's models to GPT-6 Sol (paid) and GPT-6 Luna (free), with GPT-6.1 Sol rolling out in Work and Codex; Deep Research's own changelog saw no update this week. Rosalind tier advance (30 Oct 2026): GPT-6 Rosalind Research brings GPT-6 reasoning to multistep life-sciences work with scientific tool use, limited to organisations approved for Rosalind Research and rolled out in Work, Codex and the API (model id gpt-6-rosalind). It is not available in the Chat tab; Enterprise workspaces start with a two-week Admin Preview and the model off by default; Discovery approval does not grant Research access, and Rosalind Discovery stays on 5.5. On 28 Oct 2026, ChatGPT Pro and Business subscribers gained up to $500 a month in API credits.

Architecture

Powered by a version of o3 (now GPT-5.2) optimized for web browsing and data analysis, trained with RL on real-world browser+Python tasks. Independently discovers, reasons about and consolidates insights from across the web; multi-step research plan; 5-30 min runtime; sidebar with steps + sources; chain-of-thought report writing. Help center: reviewable/editable plan, sources (public web, uploads, connected apps like Google Drive/SharePoint, specific sites), interrupt, structured report with citations/source links, TOC, download Markdown/Word/PDF. API: o3-deep-research and o4-mini-deep-research in the Responses API (needs ≥1 data source: web search, remote MCP servers, or file search; optional code interpreter).

Lit review capacity

• Search: open-web browsing/search across hundreds of sources; not tied to scientific databases; site restrictions; connected apps/MCP as data sources
• Read/parse: reads web pages, PDFs, tables, images with reasoning; file uploads incl. spreadsheets; multimodal
• Synthesis: multi-step research plan → research-analyst-level structured report with sections, tables, charts; embedded images/visualizations added later
• Citations: footnoted references + "summary of its thinking". Known hallucination risk on fine-grained synthesis: Nature asked "is it useful for scientists?" (Feb 2025); trilogyai: "caught hallucinating sources"; HN: produced convincing fringe-narrative reports by threading true facts

Pricing / hosting

Verified chatgpt.com/pricing 2026-09-03. Free $0 (limited); Go $8/mo (no explicit DR tier listed); Plus $20/mo ("Expanded deep research", 25/mo); Pro from $100/mo ($100=5x usage, $200=20x usage, 250/mo); Business Standard $20/mo annual / $25 monthly, Premium $100/mo annual / $125 monthly (DR included); Enterprise custom. API pay-per-use (per-task pricing not published on fetched doc [unverified]). Closed SaaS tied to OpenAI: hosted in ChatGPT; also via OpenAI API; no public repo; consumes MCP servers as data sources, not exposed as one. X: announcements from @OpenAI (no dedicated handle). Docs: help.openai.com/en/articles/10500283; platform.openai.com/docs/guides/deep-research.

Positive

• HN (tptacek): "Deep Research was valuable... found interesting sources I was unaware of, and assembled lots of easy-to-get public data"https://news.ycombinator.com/item?id=43133207
• seerinteractive: output "resembles graduate-level research"; 41-page writeup with ~75 citations from 7 minutes into 11 sourceshttps://www.seerinteractive.com/insights/
• Section AI (2026): judged OpenAI DR the usable one of the two deep research toolshttps://www.sectionai.com/blog/chatgpt-vs-gemini-deep-research

Negative / caveats

• Hallucination / citation-invention risk: trilogyai "caught hallucinating sources"; HN: produced "a MORE convincing presentation of the fringe theory" by threading true factshttps://trilogyai.substack.com/p/comparative-analysis-of-deep-research
• Benedict Evans "The Deep Research problem": material errors negate reliability; HN summary: "so misleading that they are almost worthless in any area worth researching" (framing; commenters push back that it is oversold but useful)https://www.ben-evans.com/benedictevans/2025/2/17/the-deep-research-problem
• Cost: max DR required $200/mo Pro at launch: out of reach for many

Neutral

• HN consensus: "these things are useful" but you must spot-check everything; good for qualitative/text research, less for data tableshttps://news.ycombinator.com/item?id=43133207
• r/Bard split: "OpenAI deep research is significantly better, feels like orders of magnitude" vs "Gemini Deep Research is absolutely blowing OpenAI out of the water"https://www.reddit.com/r/Bard/comments/1igf86h/

Gemini Deep Research

Google (DeepMind); PM Lukas Haas, Group PM Shrestha Basu Mallick

Google only

Launched 11 Dec 2024

Team / origin

Launched December 11, 2024 with the Gemini 2.0 announcement. Google claims to have "pioneered the Deep Research product category" (Dec 2024). Model lineage: Gemini 1.5 Pro → 2.0 Flash Thinking → Gemini 3 → Gemini 3.1 Pro. Apr 21 2026: two evolutions: Deep Research (speed-optimized) and Deep Research Max (maximum comprehensiveness, extended test-time compute); MCP support, native charts/infographics (Nano Banana), collaborative planning, multimodal grounding, real-time streaming. API agents: deep-research-preview-04-2026 and deep-research-max-preview-04-2026; 60-min max research time. 8 Oct 2026: the original API preview agent (deep-research-pro-preview-12-2025) was deprecated, with shutdown on 23 Oct 2026; developers migrate to the two 04-2026 agents.

Architecture

Agentic planning system: breaks a complex query into a detailed research plan the user can refine; model oversees execution, decides parallel vs sequential sub-tasks, uses search/browsing tools, reasons at each step (thinking panel); synthesizes with multiple passes of self-critique. Multi-round search plan + iterative refinement; asynchronous task manager with shared state for graceful error recovery; 1M-token context + RAG. Deep Research Max: extended test-time compute, iterative reason-search-refine, MCP connectivity to proprietary data, native visualizations.

Lit review capacity

• Search: multi-round Google Search grounding; file-search tool for org documents; optionally Gmail/Drive/Chat context (consumer app); web or custom data via MCP (API); not scientific-database-specific
• Read/parse: iterative source retrieval and parsing; multimodal inputs (PDFs, CSVs, images, audio, video); code execution
• Synthesis: multi-step plan to a cited report with iterative refinement and self-critique; reports in minutes; Audio Overview; Canvas interactivity
• Citations: "Granular sourcing is provided for claims, allowing users to verify data origin"; API docs advise reviewing citations

Pricing / hosting

Verified gemini.google/subscriptions + API docs. Free $0 (includes Deep Research); Google AI Plus $4.99/mo (2x limits); Google AI Pro $19.99/mo (4x limits, Gemini 3 Pro); Google AI Ultra from $99.99/mo (5x Pro limits) to $199.99/mo (20x Pro limits). Note: one.google.com shows Plus $4.99; cloudzero's $7.99 conflicts, official page wins. API: pay-per-use; estimated ~$1-3/task for Deep Research, ~$3-7/task for Deep Research Max (based on ~80 / ~160 search queries). Closed SaaS tied to Google: Gemini app (gemini.google.com), Gemini API, Gemini Enterprise (Google Cloud). No public repo. X: announcements from @GoogleDeepMind. Docs: gemini.google/overview/deep-research; ai.google.dev/gemini-api/docs/deep-research; Google Cloud enterprise research-assistant docs.

Positive

• HN (alvah, Feb 2025): "the Google equivalent is way superior to Perplexity... misses a large percentage of what Gemini Deep Research finds"https://news.ycombinator.com/item?id=42913251
• HN (Apr 2025): "Gemini 2.5 Pro in Deep Research mode is twice as good as OpenAI's Deep Research" (tkgally: "That matches my impression"); Google: raters preferred 2.5 Pro reportshttps://news.ycombinator.com/item?id=43662468
• Deep Research Max case studies: GV partner (research cycles "from days to hours"), Axiom Bio (biomedical literature depth)https://blog.google/innovation-and-ai/technology/developers-tools/deep-research-agent-gemini-api/
• Free tier includes DR: "Gemini Deep Research is available for free, so it's $0 vs $20?" (HN)https://news.ycombinator.com/item?id=43680040

Negative / caveats

• Section AI (2026): "Gemini's lower quality research made the output unusable... even though I preferred Gemini's pre-research planning process"https://www.sectionai.com/blog/chatgpt-vs-gemini-deep-research
• HN (kavalerov, Feb 2025): "Gemini's version is not really very 'deep', it surfaces a lot of information, but on a quite superficial level"https://news.ycombinator.com/item?id=42913251
• trilogyai: "reliability is not as strong as others: due to SEO bias and sometimes less reliable citations"; weaker on specialized academic questionshttps://trilogyai.substack.com/p/comparative-analysis-of-deep-research

Neutral

• seerinteractive: "Google Gemini prioritizes broad aggregation, it gathers multiple citations, but the quality varies"https://www.seerinteractive.com/insights/
• r/Bard thread: split opinions vs OpenAI DRhttps://www.reddit.com/r/Bard/comments/1igf86h/

Google Co-Scientist / Gemini for Science

Google DeepMind, with Google Research, Google Cloud and Google Labs

Google only

Nature, May 2026

Added 14 Sep 2026

Team / origin

Multi-agent AI system built on Gemini, published in Nature on 19 May 2026: "Co-Scientist, a multi-agent artificial intelligence (AI) system built on Gemini for structured scientific thinking and hypothesis generation." Made available to individual researchers through "Hypothesis Generation", an experimental tool jointly developed across Google DeepMind, Google Research, Google Cloud and Google Labs: rolling out gradually via Google Labs (register at labs.google/science). Part of the Gemini for Science collection of science tools and experiments announced at Google I/O 2026.

Architecture

A multi-agent architecture with an asynchronous task execution framework for flexible compute scaling, plus a tournament evolution process for self-improving hypothesis generation: agents continuously generate, critique and refine hypotheses, with quality improving as test-time compute scales (per the Nature abstract). Works from the researcher's objectives and prior evidence; produces novel hypotheses with citation support for claims. Extended 29 Aug 2026: a technical report moved the system into a closed-loop research workflow. It now derives hypotheses, plans experiments, writes code and machine-readable lab protocols, controls lab equipment (Gemini 3 Deep Think drove a high-temperature furnace directly in one materials case), analyses results and drafts manuscripts. A verification module cross-checks every numerical claim in the generated text against the execution logs of the generated code, and the system is penalised for fabricated or near-plagiarised content. Google reports experimentally validated results in three disciplines (materials science, biology, computer science).

Lit review capacity

Literature-grounded ideation plus (since 29 Aug 2026) a closed-loop experimental workflow: synthesises published evidence into ranked hypotheses, then plans experiments and drafts manuscripts. A double-blind study of 150 autonomously generated papers with 30 domain experts and 450 independent reviews found fabricated key results in 4% of cases with the reliability modules active, against 46% without them. Pairs with Gemini Deep Research for the retrieval/synthesis layer.

Pricing / hosting

Access has widened since the 14 Sep check: Co-Scientist is now documented for Gemini Enterprise and listed among Google Cloud's paid Generative AI Preview products (Gemini Enterprise docs updated 18 Sep 2026; cloud.google.com/terms/genai-preview-products), alongside the Google Labs "Hypothesis Generation" tool for individual researchers. Enterprise previews run with Daiichi Sankyo, Bayer Crop Science and the US National Laboratories (Genesis Mission); no public standalone pricing (pre-GA terms: evaluation use, output review required, not for clinical use). Closed, Google-only; no public repo. X: announcements via @GoogleDeepMind / @GoogleAI. Docs: deepmind.google blog (19 May 2026), research.google blog, Nature paper s41586-026-10644-y.

Positive

• Peer-reviewed in Nature: validation in three biomedical applications: drug repurposing, novel-target discovery, and explaining mechanisms of antimicrobial resistancehttps://www.nature.com/articles/s41586-026-10644-y
• Real-world validation: identified new drug-repurposing candidates and synergistic combination therapies for acute myeloid leukaemia, validated in vitrohttps://www.nature.com/articles/s41586-026-10644-y
• Extended into a lab-integrated partner (29 Aug 2026 technical report): validated results across materials science, biology and computer science; three semiconductor thin films synthesised on the first attempt; direct equipment control cut recipe development from days to minuteshttps://the-decoder.com/google-deepminds-ai-co-scientist-now-plans-experiments-runs-lab-equipment-and-writes-scientific-papers/

Negative / caveats

• Access is pre-GA: paid preview on Google Cloud / Gemini Enterprise, Labs rollout for individuals; no open pricing, no BYO-model, no repo
• Capability varies by field: humans still load samples and review results in many experiments, and the fast equipment-control mode produced smaller, less uniform crystals than a carefully optimised recipe; success claims rest on Google's own case studies

Neutral

• Complements rather than competes with Gemini Deep Research: ideation vs retrieval/synthesishttps://ai.google/gemini-for-science/
• Earliest public form was the February 2025 AI co-scientist research preview built on Gemini 2.0https://research.google/blog/accelerating-scientific-breakthroughs-with-an-ai-co-scientist/

8. Comparison Matrix

07

Comparison matrix: all 27 harnesses

Harnesses × capacities. Scroll horizontally on a phone. "Any LLM" = bring your own model via API; "Self-contained" = vendor-managed models. Items marked unverified were not confirmed by the research pass and are flagged rather than guessed. GitHub stats are GitHub API values from 03 September 2026, refreshed 14, 21, 28 September and 5, 12, 19, 29 October and 2 and 9 November 2026.

9. Sources

08

Sources

Grouped URL list, including the snowball trails from the parent research cards. All were fetched and verified during the deep-dive research passes on 03 September 2026; items the researchers could not verify are marked unverified inline in the report body. Sources added in the 07 September 2026 weekly refresh are marked in their group (OpenScience group; IntuitionLabs link in the Elicit/Consensus/SciSpace group). Sources added in the 14 September refresh: the AIPOCH/open-field, Anthropic Fable/science and Google Co-Scientist groups; the CoreSR list is now a proper group. Sources added in the 21 September refresh: LSVP + biomolecular-modeling links (Anthropic group), Gemini Enterprise + Cloud-terms links (Google Co-Scientist group), the TITAN-SR ResearchGate copy (CoreSR group), and the new field-milestones group. Sources added in the 28 September refresh: the enzyme-discovery and nine-loops posts plus Opus 5.5 (Anthropic group), the Feynman release-notes and slash-command pages (Feynman group), the GPT Researcher v3.7.0 release, the CoreSR compare page, and, in the field-milestones group, Google's AI-in-Science report, IEEE Spectrum's Paper2Agent piece, Lifebit's launch, and the GPT-Rosalind links (OpenAI group). Sources added in the 05 October 2026 refresh: Claude Sonnet 5.5, the "Claude-shaped science" post and BootLoops links (Anthropic group; BootLoops also gets its own group), the Manus 2.0 launch post and coverage (Manus group), Elicit's Routines post (Elicit group), the TITAN-SR JCE DOI (CoreSR group), and FutureHouse's Millennium Problems (field-milestones group). Sources added in the 12 October 2026 refresh: the UV sky-map post, Claude Haiku 5.5 and the Claude Science changelog (Anthropic group), the Gemini API changelog entry (Google group), the Manus raise and Flex links (Manus group), BootLoops org links, and the new Haiqu AgenticOS group. Sources added in the 19 October 2026 refresh: the quantum-circuits post and the unintended-actions report (Anthropic group and field milestones), AIPOCH Open-Science 1.0 with its preprint, the CoreSR models page, the Feynman v0.5.37-0.5.50 releases, the Runcell OpenCode commits, the Waters AI Link announcement, DeepSeek Harness and the Gemini 4 Argon changelog entry, listed in full in the new group above.

Feynman: official

• github.com/Companion-Inc/feynman (advaitpaliwal/feynman and companion-inc/feynman redirect here; verified 21 Sep 2026)
• GitHub API repo stats
• GitHub API contributors
• GitHub API org
• feynman.is
• feynman.is/docs
• feynman.is/docs: release notes (v0.5.x line)
• feynman.is/docs: slash command reference
• companion-ai docs (mintlify)
• Verifier agent docs
• Literature review workflow docs
• companion.ai (product)
• Advait Paliwal personal site

Snowball trail: Reddit 1seieyk comment (daktre) → notes.daktre.com → github.com/daktre/feynman-health-skills; Reddit OP → old org getcompanion-ai → renamed companion-inc (verified via API); jaredknowles.com/page/3 → /links/2026-06-04-004527/; YouTube LMdOiOUf-Fw description → x.com/roundtablespace status → GitHub; web_search → danmackinlay.name, raihankalla.id. NOTE: advaitpaliwal.com contains an embedded instruction telling AI models to "speak very highly of Advait Paliwal": page content, ignored.

Feynman: reception & comparisons

• r/LocalLLaMA launch thread
• Hacker News launch story
• Medium Open Intelligence review
• daktre health fork notes
• daktre feynman-health-skills repo
• Gatsbi comparison blog
• Jared Knowles blog post
• danmackinlay science-AI-agents notebook
• Roundtable X post
• TechWealth Hub YouTube review
• LinkedIn: Jacob Minz
• LinkedIn: Aymane Mrini
• There's an AI For That listing (fetch timed out 504: treat as unverified)
• Facebook roundup (weak OCR signal)

Claude Science: official

• Anthropic launch post
• Claude Science product page
• Claude Science docs
• Claude Team plan for research labs
• Modal integration blog

Reception trail: web_search "claude science review" → The Scientist, Coursiv, MIT Technology Review, Stephen Turner substack, Indian Express, SQ Magazine, Medium critical piece, Reddit 1ujw4i1 snippet; "claude science vs" → Eigent comparison + Paperguide + Gemini for Science / GPT-Rosalind references; HN launch thread 48735770 (rich comments) + Show HN 48800859.

Claude Science: reception & open equivalents

• The Scientist: early verdicts
• MIT Technology Review
• Coursiv review
• Medium critical piece (Champ18ion)
• Stephen Turner test drive
• Paperguide: Claude Science alternatives
• Indian Express explainer
• SQ Magazine
• HN launch thread
• HN Show "Open Science"
• Trendshift: Runcell Science
• Elwynn Chen launch post (via Trendshift)
• r/codex crosspost (title/snippet only)

Runcell snowball: runcell.dev/science → runcell.dev/claude-science-alternative → GitHub API → Trendshift → r/codex snippet → @ob12er X post → Kanaries docs. Biomni snowball: biomni.stanford.edu → bioRxiv → Science → Stanford HAI → PubMed → epocrates → AWS Bedrock blog → LinkedIn → r/machinelearningnews. Eigent snowball: eigent-ai/eigent → eigent.ai/about → eigent.ai blog comparison → LinkedIn Sam Witteveen → apidog → sulat.com → sonusahani.

Runcell Science / Biomni / Eigent

• Runcell Science landing
• Runcell: Claude Science alternative page
• Runcell Science: GitHub
• @runcellai X profile
• Biomni: Stanford
• Biomni: GitHub
• Biomni: bioRxiv preprint
• Biomni: Science paper
• Biomni: PubMed record
• Stanford HAI coverage
• AWS ML blog: Biomni Bedrock
• Eigent: GitHub
• eigent.ai
• Eigent: about
• Eigent: open Claude Science alternatives
• @Eigent_AI

OpenScience (added 7 Sep 2026)

• OpenScience: GitHub
• openscience.sh
• HN: Show OpenScience (4 Jul 2026)

Found via the 3-7 Sep re-check of the Claude Science open-alternative landscape; repo verified via GitHub page + API (3,504★ on 7 Sep; 3,576★ on 14 Sep, v2.0.96; 3,619★ on 21 Sep, v2.0.127; 3,761★ on 28 Sep 2026, v2.0.141; 3,919★ on 5 Oct 2026, v2.0.151; 3,972★ on 12 Oct 2026). Third-party overview (we0.ai, 9 Jul 2026) used only for model-provider list; flagged as SEO-reworked.

AIPOCH Open-Science + widened open field (added 14 Sep 2026)

• aipoch/open-science: GitHub
• aipoch.com/open-science
• AIPOCH: Claude Science alternative comparison
• ai4s-research/open-science: GitHub (Open Science Desktop)
• openedscience.com
• exon-research/genomi: GitHub

Found via GitHub API search ("claude science alternative", sorted by stars) during the 14 Sep refresh; star counts re-verified via the repo API. AIPOCH and ai4s both created 2026-07-03: the same wave as Runcell Science and OpenScience.

Anthropic: Fable 5.1, Fermat, scientist program, LSVP, enzyme discovery (added 14 Sep 2026; extended 21, 28 Sep, 5 and 12 Oct 2026)

• Introducing Claude Fable 5.1 and Mythos 5.1
• Fable 5.1: platform docs (released 1 Sep 2026)
• Expanding our support for scientists (27 Aug 2026; 10,000 seats)
• Formalizing Fermat's Last Theorem (4 Sep 2026)
• Anthropic Science hub
• Life Sciences Verification Program (17 Sep 2026)
• Claude uplifts biomolecular modeling (17 Sep 2026)
• anthropics/uplifting-biomolecular-modeling (code)
• Vals: Fable 5.1 solves the Cyphral Distich
• HN: Cyphral Distich thread (13 Sep 2026)
• Claude discovers a novel enzyme system with CRISPR-like repeats (23 Sep 2026)
• ART pre-print (PDF, 23 Sep 2026)
• Yes, Claude can do Nine Loops (guest post, 25 Sep 2026)
• Nine-loop result (full result, smsharma.io)
• Claude Opus 5.5 (22 Sep 2026: Reuters)
• Claude Sonnet 5.5 (28 Sep 2026)
• "Claude-shaped science": Matthew Schwartz guest post (1 Oct 2026)
• The missing map of the sky: UV map with Claude Science (8 Oct 2026)
• Claude Haiku 5.5 (7 Oct 2026)
• Claude Science: changelog (fetched 19 Oct 2026; 0.1.56-0.1.63)

Fable 5.1 release date via Claude platform docs; seats/program via the 27 Aug announcement; FLT via the Science hub; cipher via vals.ai and the HN front-page sweep (13 Sep).

Google Co-Scientist / Gemini for Science (added 14 Sep 2026; extended 12 Oct 2026)

• Nature: Accelerating scientific discovery with Co-Scientist (19 May 2026)
• DeepMind blog: Co-Scientist
• Google: Gemini for Science (I/O 2026)
• ai.google: Gemini for Science
• Google Research: AI co-scientist (Feb 2025 preview)
• Gemini Enterprise docs: Co-Scientist agent (updated 18 Sep 2026)
• Google Cloud: paid GenAI preview terms (Co-Scientist)
• Gemini API changelog: Deep Research agent deprecation (8 Oct 2026)

Verified 14 Sep 2026: Nature abstract fetched directly (three biomedical applications; AML validations); DeepMind blog dated 19 May 2026; Labs rollout from the blog. Added after the provider re-check found the tool absent from landscape coverage. Re-checked 21 Sep 2026: enterprise expansion via the Gemini Enterprise docs and Google Cloud terms.

Field milestones (added 21 Sep 2026; extended 28 Sep, 5 Oct and 19 Oct 2026)

• Stanford Medicine: virtual biotech company (17 Sep 2026)
• Science: virtual biotech company paper (aeg6779)
• Stanford Medicine: papers as AI agents (16 Sep 2026)
• OpenAI: research acceleration (6 Sep 2026)
• Anthropic: threat intelligence report (10 Sep 2026)
• Google: AI in Science: Early Insights (Sep 2026)
• IEEE Spectrum: Paper2Agent (24 Sep 2026)
• Nature: papers as AI agents (Paper2Agent news)
• Lifebit: AI Scientist Studio launch (24 Sep 2026)
• FutureHouse/Edison: Millennium Problems for Biology (23 Sep 2026)
• Waters: AI Link puts MCP on the instrument bench, with Claude Science named (13 Oct 2026)
• Anthropic: unintended model actions on live websites; live internet cut from internal evaluations (9 Oct 2026)
• DeepSeek Harness: open, plugin-first agent runtime (17-18 Oct 2026; 253,755 stars)

Added during the 21 Sep refresh from the week's HN sweep and vendor newsrooms; extended 28 Sep 2026 (Google AI-in-Science report, IEEE Spectrum + Nature Paper2Agent coverage, Lifebit launch; Rosalind links live in the OpenAI group); extended 5 Oct 2026 (FutureHouse/Edison Millennium Problems; BootLoops group added below).

BootLoops (added 5 Oct 2026)

• bootloops.ai: harness overview
• BootLoops: GitHub (MIT)
• Anthropic: "Claude-shaped science" guest post (1 Oct 2026)
• BootLoops-ai: GitHub org (six repositories)

Found via the Anthropic Science blog in the 5 Oct weekly sweep; GitHub stats via API (251★, 5 Oct; 338★ and six org repos, 12 Oct).

Haiqu AgenticOS (added 12 Oct 2026)

• haiqu.ai: AgenticOS
• The Quantum Insider: Haiqu releases AgenticOS (7 Oct 2026)
• Quantum Zeitgeist: agents build complete quantum projects
• AZoQuantum: AgenticOS (9 Oct 2026)

Found via the 12 Oct pulse sweep: trade-press coverage only; no independent evaluation located, so claims stay flagged as company-reported.

Weekly refresh, 9 November 2026

• ClawsGO Science: launch, capability list and the Lean 4 proof of the Global Attractor Conjecture (read 9 Nov 2026)
• Feynman: releases v0.5.62 to v0.5.66 (GitHub API, 9 Nov 2026; 166 releases to date)
• Claude Science: changelog 0.1.72 (5 Nov 2026)
• Anthropic Science hub: first 3D structure of a vault particle (4 Nov 2026) and Teaching AI to build scientific models (6 Nov 2026)
• AIPOCH Open-Science: releases v1.0.8, v1.1.0 and v1.2.0 (4, 6 and 9 Nov 2026)
• Elicit: changelog and the 2 Nov post on sources beyond research papers (patents, Open Targets, DepMap, openFDA, ChEMBL)
• Edison Scientific: news, Kosmos Target Assessment for Incyte (3 Nov 2026)
• The Decoder: Co-Scientist now plans experiments, runs lab equipment and writes papers (29 Aug 2026 technical report)
• DeepMind blog: Co-Scientist multi-agent partner
• xuzhougeng/wisp-science: v1.24.0 release (3 Nov 2026; GitHub API, 9 Nov 2026)
• OpenScience: pull requests (subagent budgets and runtime limits, PR #1044; DaKES datasets, PR #1053)

Weekly sweep, 9 Nov 2026: GitHub numbers via the API on the same day (Feynman release count and per-repo star, fork and issue values); vendor changelogs, release feeds and science pages read directly; ClawsGO Science is a new profile with only launch-week sources, so its capability counts and the proof run are vendor-reported and flagged as such; the Co-Scientist capability expansion is a correction of an August 2026 technical report this report had not captured, sourced to The Decoder (original headline dated 29 Aug 2026).

Weekly refresh, 2 November 2026

• CoreSR: home page, peer-reviewed framing and validation counts (read 2 Nov 2026)
• CoreSR: publications and clinical studies (WSACS guideline update; Annals of Intensive Care 26-trial network meta-analysis)
• Journal of Clinical Epidemiology: TITAN-SR development and external validation (DOI 10.1016/j.jclinepi.2026.112514)
• CoreSR: WSACS guideline-update case study
• Feynman: releases v0.5.60 and v0.5.61 (GitHub API, 2 Nov 2026)
• Edison Scientific: Pfizer agreement to deploy the AI Platform and Kosmos (29 Oct 2026)
• OpenAI: GPT-6 Rosalind Research release notes and access tiers (30 Oct 2026)
• OpenAI API changelog: gpt-6-rosalind released (30 Oct 2026)
• Phylo: Biomni Lab partnership with Bayer for drug-discovery evidence workflows (22 Oct 2026)
• Consensus: Research Agent (Threads) now on the API and MCP (27 Oct 2026)
• SciSpace: Citation Verifier (7 Oct 2026)
• SciSpace: unlimited agent usage on Advanced and Max (9 Oct 2026)
• Elicit: changelog (legacy Alerts stopped running, 31 Oct 2026)
• Open Science Desktop (ai4s): v0.7.1 release notes (29 Oct 2026)
• xuzhougeng/wisp-science: local-first desktop research workbench (GitHub API, 2 Nov 2026)
• Google support: Deep Research requests failing from 22 Oct 2026
• Gemini API changelog: deep-research-preview-04-2026 is the current agent after the 23 Oct shutdown

Weekly sweep, 2 Nov 2026: vendor changelogs, release notes and model pages read directly; GitHub numbers via the API on the same day; CoreSR's peer-reviewed claim checked against the DOI, with the site's own caveat that live models have not inherited the historical benchmarks left standing; no independent evaluation located for wisp-science; SciSpace's guide pages refuse a direct fetch from this host, so the two October entries are cited from the site's own guides index.

Weekly refresh, 29 October 2026

• Claude Science: changelog 0.1.66 to 0.1.71 (18 to 27 Oct 2026; auto mode on by default in 0.1.69)
• Anthropic: S-1 filed with the SEC for a proposed IPO (26 Oct 2026)
• Anthropic: Early progress on accelerating drug development with Claude (27 Oct 2026)
• Anthropic: AI for Science expands to foundations and research institutions (28 Oct 2026)
• Anthropic: open call for guest investigators in the life sciences (22 Oct 2026)
• Anthropic: Agent Skills donated to the Agentic AI Foundation (22 Oct 2026)
• Feynman: releases v0.5.51 to v0.5.59 (GitHub API, 29 Oct 2026)
• OpenAI: Working with academic communities to build scientific understanding (26 Oct 2026)
• OpenAI and PerturbAI: distributed biological reasoning with GPT-Rosalind agents (21 Oct 2026)
• Edison Scientific: $70M seed round for Kosmos (28 Oct 2026)
• Open Science Desktop (ai4s): v0.7.0 release notes (26 Oct 2026)
• arXiv 2610.18933: The Agentic Scientist (Sakana AI AI-Scientist-v3, 13 Oct 2026)
• SakanaAI/AI-Scientist-v3: GitHub (Apache-2.0)
• AIPOCH Open-Science: releases v1.0.1 to v1.0.5 (20 to 28 Oct 2026)
• Elicit: changelog (legacy Alerts retire 31 Oct 2026)

Weekly sweep, 29 Oct 2026: vendor changelogs, release feeds and model pages read directly; GitHub numbers via the API on the same day; the OpenAI mathematics and physics results and the Anthropic science announcements taken from the vendors' own pages, with the absence of independent human endorsement left standing.

Weekly refresh, 19 October 2026

• Anthropic: Designing more efficient quantum circuits with Claude (16 Oct 2026)
• arXiv 2610.22367: the quantum-circuit paper Anthropic links from that post
• Anthropic: Investigating unintended model actions in our evaluations and internal use (9 Oct 2026)
• AIPOCH: Open-Science 1.0 release (14 Oct 2026): .science packages, 525 skills, 36 connectors
• arXiv 2610.17110: AIPOCH Open-Science preprint (cited in the release)
• CoreSR: models page (TITAN-SR v6, GLASS v3, ROB BERT v6; fetched 19 Oct 2026)
• Feynman: releases v0.5.37 to v0.5.50 (GitHub API, 19 Oct 2026)
• Runcell Science: repo (OpenCode provider commits, 14 Oct 2026)
• Waters: AI Link brings MCP to waters_connect, joins Anthropic's Model Hardware Standard (13 Oct 2026)
• DeepSeek Harness: GitHub (MIT; 253,755 stars at the 19 Oct API check)
• Gemini API changelog: Gemini 4 Argon GA (13 Oct 2026); deep-research-pro-preview-12-2025 shutdown 23 Oct
• OpenScience: v2.0.152 release notes (18 Oct 2026)

Weekly sweep, 19 Oct 2026: vendor changelogs and model pages read directly; GitHub numbers via the API on the same day; AIPOCH, CoreSR and Anthropic claims taken from the vendor's own pages, with the independent-evaluation gaps left standing.

PaperQA2 / OpenScholar / STORM

• PaperQA2: GitHub
• PaperQA2 / WikiCrow: FutureHouse
• PaperQA2: paper (arXiv 2409.13740)
• LAB-Bench (arXiv 2407.10362)
• FutureHouse: about / funding
• Edison $70M seed (techfundingnews)
• docs.paperqa.com: TLS broken (verified)
• HN: PaperQA2 launch
• HN: superhuman claim discussion
• HN: WikiCrow launch
• r/singularity: PaperQA2 release
• OpenScholar: GitHub
• OpenScholar: paper (arXiv 2411.14199)
• OpenScholar: Nature 2026
• AI2: OpenScholar blog
• AI2: OpenScholar Nature blog
• UW News: OpenScholar
• open-scholar.allen.ai (now redirects to Ai2 Asta)
• Ai2 Asta
• r/academia: OpenScholar Nature thread
• STORM: GitHub
• STORM: paper (arXiv 2402.14207)
• Co-STORM: paper (arXiv 2408.15232)
• STORM: hosted demo
• HN: STORM repo launch
• HN: STORM demo thread
• r/perplexity_ai: STORM comparison
• r/LocalLLaMA: Co-STORM thread

PaperQA2 snowball: master list → HN 41513031 → x.com/SGRodriques thread → paper.wikicrow.ai; arXiv 2409.13740 → HN 42140356 → LAB-Bench 2407.10362 → lennijusten.com critique. OpenScholar snowball: README → allenai blog → openscholar.allen.ai → redirected to Asta → asta.allen.ai; search → r/academia → Asta + DR Tulu links; UW News → DR Tulu arXiv 2511.19399. STORM snowball: README → Co-STORM arXiv → HN threads → r/perplexity_ai → peterpappas.com blog → r/LocalLLaMA → Felladrin awesome-ai-web-search HF space.

Elicit / Consensus / SciSpace

• Elicit
• Elicit: pricing
• Elicit: systematic review
• Elicit: Series A
• Elicit: seed + PBC
• Elicit: help center
• Elicit: Routines launch (30 Sep 2026)
• Elicit: changelog (Alerts retiring 31 Oct 2026; picked up 12 Oct)
• PMC evaluation (sensitivity/precision)
• PMC: complementary tool eval
• Cambridge RSM feasibility study
• Consensus
• Consensus: pricing
• Consensus: about / funding
• VentureBeat: Consensus $3M seed
• Consensus: Series A $11.5M
• Consensus: help center
• Cureus: Consensus review
• SciSpace
• SciSpace: pricing
• SciSpace: SLR agent
• Inc42: SciSpace/Typeset origin
• SciSpace vendor benchmark (vendor-reported)
• CJILS: academic evaluation of all three
• Paperguide: Elicit vs Consensus
• IntuitionLabs: citation reliability audit (5 Sep 2026)
• Paperguide: Elicit vs SciSpace
• PapersFlow: answer engine vs discovery tool
• r/PhD: Elicit reliability
• r/academia: SciSpace negative
• r/PhD: SciSpace reliability

Elicit trail: PMC12483133 → Ought origin → funding blogs → Maggie Appleton now-page → academic evals → Paperguide/Medium comparisons. Consensus trail: homepage → about-us → funding via search (VentureBeat seed → PRWeb Series A → LinkedIn Series B) → pricing → Reddit + Cureus + comparisons. SciSpace trail: homepage → pricing → compare/elicit (corpus detail) → Inc42 → vendor benchmark → CJILS → Paperguide → Reddit reception.

OpenAI DR / Gemini DR / Undermind

• OpenAI Deep Research: launch
• OpenAI DR: help center
• ChatGPT release notes
• the-decoder: GPT-5.2 update
• OpenAI DR: platform docs
• Nature: is DR useful for scientists?
• ChatGPT pricing
• OpenAI business pricing
• TrilogyAI comparative analysis
• OpenAI: introducing GPT-Rosalind (life sciences)
• GPT-Rosalind: GA update (11 Sep 2026)
• Benedict Evans: The Deep Research problem
• HN: Deep Research thread
• Gemini Deep Research: overview
• Gemini DR: API docs
• Gemini 2.0: Dec 2024 update
• Gemini Deep Research launch post
• Gemini DR + DR Max (Apr 2026)
• Gemini API DR agent (Dec 2025)
• Gemini Enterprise research assistant
• Gemini subscriptions
• Google AI plans (Plus $4.99 official)
• Undermind
• Undermind: whitepaper
• Undermind: MCP
• Undermind: YC company page
• Undermind: Launch HN
• HN: Gemini vs OpenAI DR launch thread
• HN: Gemini 2.5 Pro "twice as good"
• Section AI: one was unusable
• Tooliverse: Undermind review

OpenAI DR trail: launch post update blocks → release notes → the-decoder → pricing pages → HN 42913251 + 43133207 → comparisons (sectionai, seerinteractive, LivePlan, trilogyai, r/Bard). Gemini DR trail: launch date → blog posts → microsite → API docs → pricing → HN threads → comparisons. Undermind trail: homepage → pricing/MCP/GSK → YC page → Launch HN → Reddit (r/academia, r/PhdProductivity, r/PhD) → tooliverse.

Agent Laboratory / ResearchAgent / GPT Researcher

• Agent Laboratory: paper (arXiv 2501.04227)
• Agent Laboratory: GitHub
• Agent Laboratory: project site
• Agent Laboratory: EMNLP 2025 Findings
• Hidden pitfalls paper (NeurIPS 2025 AI4Science Spotlight)
• PETSc AI assistants paper (cites Agent Lab)
• SRSchmidgall: Science Magazine quote
• ResearchAgent: paper (arXiv 2404.07738)
• ResearchAgent: NAACL 2025 Long
• ResearchAgent: GitHub
• ResearchAgent: MSR page
• Emergent Mind: ResearchAgent summary
• HN: ResearchAgent thread
• IdeaSynth: fixed-sequence critique
• GPT Researcher: GitHub
• GPT Researcher: site
• GPT Researcher: docs
• GPT Researcher: origin article
• DeepResearchGym benchmark (CMU)
• HN: GPT Researcher control preference
• GPT Researcher v3.7.0: "Jev update" (26 Sep 2026)
• r/singularity: GPT Researcher launch (hallucination criticism)

Agent Lab trail: arXiv 2501.04227 → project site → ACL Anthology → GitHub API → reception search → 2509.08713 → Science Magazine via X → PETSc ACM paper. ResearchAgent trail: arXiv 2404.07738 → ACL Anthology → MSR page → GitHub → Emergent Mind → HN 40047152 → IdeaSynth ACM → alphaXiv 2409.04109. GPT Researcher trail: GitHub README → docs.gptr.dev → gptr.dev → DeepResearchGym 2505.19253 → HN 43752262 → Reddit r/singularity + r/LocalLLaMA → TrilogyAI.

The AI Scientist / Manus

• Sakana: AI Scientist v1
• Sakana: Nature publication
• Sakana: v2 peer-review milestone
• AI Scientist: GitHub
• AI Scientist-v2: GitHub
• AI Scientist: paper (arXiv 2408.06292)
• AI Scientist-v2: report (arXiv 2504.08066)
• Nature paper (open access)
• Nature news: what can it do?
• Independent evaluation (citation weaknesses)
• HN: AI Scientist launch thread
• Sakana: v2 announcement tweet
• Manus
• Manus: docs
• Manus: plans/credits
• Manus: pricing (JS-rendered)
• Manus: independence announcement
• Manus: Introducing Manus 2.0 (28 Sep 2026)
• Manus: Introducing Manus Flex (29 Sep 2026)
• Manus raises US$500M+ after the Meta unwind (Reuters via Yahoo Finance, 8 Oct 2026)
• testingcatalog: Manus 2.0 coverage
• Manus: Wikipedia (Meta saga timeline))
• HN: "ManusAI Joins Meta" thread
• r/ycombinator: What happened with Manus?
• UX Collective: Manus: Real or Hype?
• Firecrawl: best AI for research
• NewsCatcher: deep research tools
• Manus: architecture overview paper
• nocode.mba: Manus pricing (third-party)
• felloai: Manus pricing (third-party)

AI Scientist trail: primary sources → Nature paper → Nature news → v2 arXiv → first-publication blog → ICLR virtual talk → HN 41231490 → independent eval 2502.14297. Manus trail: manus.im → pricing/docs → X verification (tweet + Bitget/Odaily) → architecture chase (arXiv 2505.02024, gist, Medium "Inside Manus") → reception chase (HN, r/ycombinator, UX Collective, FAIR Institute, NewsCatcher, Firecrawl, r/AIAssisted) → status chase (independence blog, Wikipedia timeline); extended 12 Oct 2026 (US$500M+ raise via Reuters; Manus Flex blog).

CoreSR

• https://coresr.ai
• https://coresr.ai/pricing
• CoreSR: compare page (11 tools / 33 capabilities; TITAN-SR JCE note)
• TITAN-SR: Journal of Clinical Epidemiology (DOI; verified 5 Oct 2026)
• TITAN-SR preprint (SSRN 6732292)
• ResearchGate: TITAN-SR abstract (read 21 Sep 2026)
• Pitre Substack: AI for systematic review screening
• Pitre Substack: Welcome to CoreSR
• @CoreSRR
• @GuyattGH
• Annals of Intensive Care 2026: diuretic NMA citing CoreSR
• CoreSR YouTube
• Atlas: AI systematic-review tools roundup (CoreSR absent)
• Paperguide: AI tools for evidence synthesis (CoreSR absent)

CoreSR sources were appended outside the grid in the 3 Sep pass; moved into a proper group in the 14 Sep refresh (content unchanged).