Ramanujan – Multi-Model Agent for Research in Computational Mathematics

Developer AniketWathore released Ramanujan, a free terminal-based multi-model agentic workbench for research in computational mathematics, available on GitHub. Ramanujan spawns N parallel subagents across any OpenAI-compatible providers, runs each in an isolated worktree with local journal, budgets, and stall detection, and verifies results deterministically using SymPy and Z3 with independent double-verification. The tool requires macOS or Linux, Node.js 22+, npm, Python 3.12+ (SymPy, Z3, Pydantic), and at least one provider API key, and is still under active development.

Multi Model Agentic Workbench for Research in Computational Mathematics Ramanujan is a terminal based multi model agentic workbench for research in computational mathematics. It is a free tool to help with research in maths. You can chat normally and when you want to dig deeper it structures your problem, surveys the literature, runs parallel subagents and brings back a consolidated report. Everything runs locally and every verdict is computed deterministically. Disclaimer: This project is still under active development. If you find any bugs, issues, or have suggestions, please open an issue on the GitHub Issues page https://github.com/AniketWathore/Ramanujan/issues . - Multi-model parallel subagents — spawn N subagents on different models at once, with live progress and contradiction flagging. - Isolated worktrees — each run gets its own worktree with local journal, budgets, and stall detection. - Multiple providers & models — mix any OpenAI-compatible providers, multi-pick models, save them as presets. - Verified literature survey — every source carries a real URL or an explicit model-memory, unverified tag. - Deterministic checking — SymPy + Z3 kill-check with independent double-verification; cross-provider panel only advises where no check applies. - Checkpoints & consolidated report — confirm / revise at every stage, then get a plain-language report with technical appendix. Open ramanujan with an empty config → provider picker → API key → main-model picker → computational pool existing or new providers, multi-pick models → save preset → confirm → home screen. Zero commands. Type a problem in chat "Goldbach's Conjecture: Every even integer 2 is sum of two primes — prove it" . The agent structures it, shows the spec, and waits for your confirm / revise / typed feedback before the next stage. Sources, data, and related work land in a text store; you see the important entries, verify them, confirm-and-continue or type what to change. Pick the preset or providers/models manually and the headcount. Subagents work in parallel with live progress, shared orchestration files, cross-help, and contradiction flagging — then one consolidated findings report. | Requirement | Details | |---|---| | OS | macOS tested , Linux | | Runtime | Node.js 22+ | | Package Manager | npm | | Python | 3.12+ math engine: SymPy, Z3, Pydantic | | LLM access | At least one provider API key any OpenAI-compatible endpoint | TypeScript TUI agent on the pi substrate vendored + frozen Python math engine spawned as a subprocess. The agent renders; only the engine computes. Clone git clone https://github.com/AniketWathore/Ramanujan.git cd Ramanujan Install builds the agent, installs the ramanujan bin globally, puts ramanujan-engine on PATH — never touches any pi install ./scripts/install.sh Fresh machine, no checkout handy? The script is self-contained — prerequisites are just Node 22+, npm, and Python 3.12+. Run ramanujan with an empty config — the setup wizard starts automatically: 1. Pick a provider from the list. 2. Paste your API key. 3. Pick your main model from the live list. 4. Build your computational pool reuse providers or add new ones, multi-pick models . 5. Save it as the default preset and confirm — you land on the home screen. ramanujan That's it. First launch opens the setup wizard; afterwards you land on the ASCII home + chatbox. Talk like a normal chatbot, or ask it to research something and follow the checkpoints. To re-run setup any time, remove the config and relaunch: mv ~/.config/ramanujan/config.toml ~/ramanujan-config.bak && ramanujan | Symptom | Fix | |---|---| | Chat footer shows a different model than the setup pick | Re-run setup command above — the pick is now written once and never overwritten by pool providers; /model still switches anytime | | Setup text invisible / wrong colours | Fixed — the wizard queries the real terminal background before the first screen; update with git pull + ./scripts/install.sh | | ramanujan opens pi, or pi opens Ramanujan | Fixed — separate bins ramanujan vs pi and separate dirs ~/.ramanujan vs ~/.pi ; reinstall both cleanly and the collision is gone | | Setup keeps re-appearing | Config isn't persisting — check ~/.config/ramanujan/config.toml exists 0600 ; skip once with RAMANUJAN NO SETUP=1 ramanujan | | No models listed for a provider | The wizard falls back to the built-in catalog, then manual slug entry — any of the three works | uv run pytest -q 153 passed — engine: journal, killcheck, stages, calibration uv run ruff check . clean npm run test --prefix agent/packages/bridge 20 passed npm run test --prefix agent/packages/config 18 passed npm run test --prefix agent/packages/math-tools 33 passed ./scripts/install-obscura.sh minimal no-render obscura 45M+40M, tools/obscura/bin/obscura --version tools/obscura/bin/obscura fetch https://example.com --dump text Example Domain uv run python -m ramanujan.obscura client is available true Manual: 1. Fresh config → ramanujan → wizard → home + chat, zero commands 2. Research prompt → spec card → confirm → literature arXiv+Scholar+websites via Obscura, per-category text → confirm → worktrees → report 3. pi --version still genuine; ~/.pi untouched - pi agent https://github.com/badlogic/pi-mono MIT — TUI substrate, vendored verbatim except the documented fork diffs. - Obscura https://github.com/h4ckf0r0day/obscura Apache-2.0 — minimal headless browser fetch --dump markdown , no-render bundled at tools/obscura/bin/obscura for literature web collection papers, books, websites, blogs, articles, discussions as text . - SymPy https://www.sympy.org/ and Z3 https://github.com/Z3Prover/z3 — the deterministic math backend. - Srinivasa Ramanujan — the name, and the standard. Distributed under the MIT License /AniketWathore/Ramanujan/blob/main/LICENSE . See LICENSE /AniketWathore/Ramanujan/blob/main/LICENSE for more information.