@companionai/bohr

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Installation

Bohr ships prebuilt standalone bundles for supported macOS, Linux, and Windows machines. You do not need to deploy a running app before changing these commands; you only need the installer scripts and release assets published at reachable URLs.

macOS / Linux:

curl -fsSL https://raw.githubusercontent.com/Dhruvil7694/BohrAI/main/website/public/install | bash

Windows (PowerShell):

irm https://raw.githubusercontent.com/Dhruvil7694/BohrAI/main/website/public/install.ps1 | iex

The one-line installer fetches the latest tagged release. To pin a version, pass it explicitly, for example curl -fsSL https://raw.githubusercontent.com/Dhruvil7694/BohrAI/main/website/public/install | bash -s -- 0.2.18.

The installer downloads a standalone native bundle with its own Node.js runtime.

To upgrade the standalone app later, rerun the installer. bohr update only refreshes installed Pi packages inside Bohr's environment; it does not replace the standalone runtime bundle itself.

For internal client deployments, do not hardcode the GitHub Raw URLs above. Instead set the deployment variables in .env.example such as PUBLIC_BOHR_SITE_URL, PUBLIC_BOHR_INSTALL_BASE_URL, BOHR_RELEASES_LATEST_URL, and BOHR_INSTALL_BASE_URL, then publish the same scripts and release bundles on the client's own host. The website homepage already reads those env-based install URLs at build time; the docs page should be published from the same deployment so users copy the correct host-specific command.

Local models are supported through the custom-provider flow. For Ollama, run bohr setup, choose Custom provider (baseUrl + API key), use openai-completions, and point it at http://localhost:11434/v1.

Skills Only

If you want just the research skills without the full terminal app:

macOS / Linux:

curl -fsSL https://raw.githubusercontent.com/Dhruvil7694/BohrAI/main/website/public/install-skills | bash

Windows (PowerShell):

irm https://raw.githubusercontent.com/Dhruvil7694/BohrAI/main/website/public/install-skills.ps1 | iex

That installs the skill library into ~/.codex/skills/bohr.

For a repo-local install instead:

macOS / Linux:

curl -fsSL https://raw.githubusercontent.com/Dhruvil7694/BohrAI/main/website/public/install-skills | bash -s -- --repo

Windows (PowerShell):

& ([scriptblock]::Create((irm https://raw.githubusercontent.com/Dhruvil7694/BohrAI/main/website/public/install-skills.ps1))) -Scope Repo

That installs into .agents/skills/bohr under the current repository.

These installers download the bundled skills/ and prompts/ trees plus the repo guidance files referenced by those skills. They do not install the Bohr terminal, bundled Node runtime, auth storage, or Pi packages.

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Token usage & caveman mode

Long multi-agent runs burn context fast. Bohr mitigates this in several ways:

• Bundled caveman skill (skills/caveman/caveman.md) — instructs terse, substance-preserving replies. The skill description targets roughly 75% fewer tokens on assistant output versus verbose defaults when the model follows the style; actual savings vary by model and task.
• Paper-generator / Pi subagent path — prepends short [CAVEMAN MODE: lite|full|ultra] instructions, passes minimal JSON context (slug, phase, filenames), and uses file handoffs instead of dumping large intermediates into the prompt.
• Configuration — optional CAVEMAN_MODE_DEFAULT and ENABLE_TOKEN_OPTIMIZATION in .env.example tune subagent compression when using the CLI (dotenv-loaded). See the repo docs: Token optimization & caveman mode.
• Cost planning — illustrative token and dollar scenarios by provider family (OpenAI, Anthropic, Gemini, Grok, DeepSeek, Chinese APIs), tool fees, and self-host FLOPs intuition: Cost & token estimation.

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What you type → what happens

$ bohr "what do we know about scaling laws"
→ Searches papers and web, produces a cited research brief

$ bohr deepresearch "mechanistic interpretability"
→ Multi-agent investigation with parallel researchers, synthesis, verification

$ bohr lit "RLHF alternatives"
→ Literature review with consensus, disagreements, open questions

$ bohr audit 2401.12345
→ Compares paper claims against the public codebase

$ bohr replicate "chain-of-thought improves math"
→ Replicates experiments on local or cloud GPUs

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Workflows

Ask naturally or use slash commands as shortcuts.

Command	What it does
/deepresearch <topic>	Source-heavy multi-agent investigation
/lit <topic>	Literature review from paper search and primary sources
/lit-review <topic>	Full literature pipeline with themes, contradictions, gaps, and validator pass
/paper <topic-or-artifact>	Publication-grade paper pipeline with method, figures, and compliance
/paper-pro <topic-or-artifact>	Professional paper pipeline with evidence map, adversarial checks, citations, and provenance
/review <artifact>	Simulated peer review with severity and revision plan
/audit <item>	Paper vs. codebase mismatch audit
/replicate <paper>	Replicate experiments on local or cloud GPUs
/compare <topic>	Source comparison matrix
/draft <topic>	Paper-style draft from research findings
/autoresearch <idea>	Autonomous experiment loop
/watch <topic>	Recurring research watch
/hypothesis <topic-or-artifact>	Ranked, testable hypotheses with explicit falsifiers
/contradict <claim-or-artifact>	Adversarial contradiction analysis for conclusions
/evidence-score <artifact-or-topic>	Claim-level evidence strength scoring
/experiment <hypothesis-or-goal>	Minimal decisive experiment workflow
/citation-check <artifact>	Sentence-level citation integrity validation
/memory-log <project-or-topic>	Durable memory consolidation for long-running work
/plan-research <topic-or-goal>	Planner workflow for agent ordering and stop criteria
/knowledge-graph <artifact-or-topic>	Build reusable knowledge graph for cross-topic reasoning
/reasoning-validate <artifact>	Validate logic chain from evidence to conclusions
/iterate <topic-or-artifact>	Iterative loop controller until confidence/contradiction targets
/outputs	Browse all research artifacts

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Agents

Ten bundled research agents, dispatched automatically.

• Researcher — gather evidence across papers, web, repos, docs
• Reviewer — simulated peer review with severity-graded feedback
• Writer — structured drafts from research notes
• Verifier — inline citations, source URL verification, dead link cleanup
• Hypothesis — generate testable hypotheses and ranked falsifiers
• Contradiction — find counter-evidence, edge cases, and fragile assumptions
• Evidence Scorer — score claim support quality with a transparent rubric
• Experiment — design and run minimal experiments to reduce uncertainty
• Citation Integrity — validate claim-to-citation alignment at sentence level
• Memory — maintain durable project memory across sessions
• Research Planner — orchestration brain for agent selection, ordering, and stop rules
• Knowledge Graph — convert findings into reusable entities and relations
• Reasoning Validator — detect invalid inference chains and logic drift
• Iteration Controller — drive multi-loop convergence with explicit thresholds
• Literature Collector — collect paper candidates and metadata
• Literature Quality — score/filter papers by relevance and recency
• Literature Synthesizer — cluster papers into structured themes
• Literature Contradiction — detect conflicting claims across papers
• Literature Gap — identify missing areas and open problems
• Literature Review Writer — write survey-style thematic review
• Literature Review Validator — validate completeness, flow, and perspective balance
• Method Math — formalize notation, equations, and derivation consistency
• Figures Tables — produce publication-ready visuals and captions
• Paper Compliance — enforce manuscript structure and submission-style checks

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Skills & Tools

• AlphaXiv — paper search, Q&A, code reading, annotations (via alpha CLI)
• Docker — isolated container execution for safe experiments on your machine
• Web search — Gemini or Perplexity, zero-config default
• Session search — indexed recall across prior research sessions
• Preview — browser and PDF export of generated artifacts
• Modal — serverless GPU compute for burst training and inference
• RunPod — persistent GPU pods with SSH access for long-running experiments

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How it works

Built on Pi for the agent runtime, alphaXiv for paper search and analysis, and CLI tools for compute and execution. Capabilities are delivered as Pi skills — Markdown instruction files synced to ~/.bohr/agent/skills/ on startup. Every output is source-grounded — claims link to papers, docs, or repos with direct URLs.

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Contributing

See CONTRIBUTING.md for the full contributor guide.

git clone https://github.com/Dhruvil7694/BohrAI.git
cd BohrAI
nvm use || nvm install
npm install
npm test
npm run typecheck
npm run build

Docs · Proprietary License