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BRAHM

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BRAHM

Bi-directional Research & Analysis Hub for Multimodal Science

A personal project — a self-hosted, multi-agent AI platform for automating materials science research workflows. Built to reduce the time between a scientific idea and a validated result.

What It Does

Modern research is slowed by information overload, fragmented knowledge, repetitive analysis, and documentation overhead. BRAHM automates the mechanical parts of the research process while keeping the researcher in control of every scientific decision.

The pipeline moves from literature to knowledge to insight to communication through a unified, evidence-driven workflow.

Related MCP server: tooluniverse

Architecture

BRAHM is built as an ecosystem of specialised agents, each responsible for one layer of the research process.

Literature Discovery → Knowledge Extraction → Gap Analysis → Simulation → Documentation
      SHANI                  SHANI              SHANI       Vishwakarma      GANESH
                                          Chitragupta

Agents

Agent

Role

SHANI

Literature pipeline — discovery, download, content extraction, knowledge extraction

Chitragupta

Knowledge custodian — context management, research memory, database access

GANESH

Document synthesis — literature reviews, research reports, manuscript drafts

VIDUR

Characterisation analysis — XRD, Raman, UV-Vis, SEM/EDS interpretation

Vishwakarma

Computational engine — structure generation, DFT via Quantum ESPRESSO

Pipeline Stages

SHANI — Literature Pipeline

Stage

Description

S1

Workflow initialisation and query generation

S2

Paper discovery via Semantic Scholar and arXiv

S3

PDF download and resolution

S4

Content extraction with section-aware parsing

S4.5

PaperContent normalisation — canonical section names, noise removal

S5

LLM-driven knowledge extraction into structured records

S5.5

Finding reconstruction — connecting extracted signals into grounded claims

GANESH — Document Pipeline

Stage

Description

G1

Context loading from Chitragupta

G2

Document planning

G3

Section graph construction

G4

Section-by-section generation

G5

Document integration and export

Tech Stack

  • Runtime: Python, FastAPI, SQLite, FAISS

  • LLMs: Groq, Gemini, Cerebras (cloud) + Ollama (local, for sensitive data)

  • Computation: Quantum ESPRESSO 7.5 for DFT

  • Embeddings: all-MiniLM-L6-v2 for vector search

  • Infrastructure: WSL2 on Windows 11, self-hosted

Design Principles

Human judgment remains central. BRAHM assists research. Researchers direct research. Scientific decisions always belong to the researcher.

Scientific integrity above speed. Evidence is more important than confidence. Uncertainty is surfaced rather than hidden.

Privacy by design. Instrument data and DFT structures never leave the local machine. Only published paper text reaches cloud APIs.

LLM-agnostic. Each agent uses the best available model for its task. The orchestration layer is model-independent.

Status

Active development. Core pipeline (S1→S5) is operational. Document generation (GANESH G1→G5) is functional. S5.5 finding reconstruction and full grounded document generation are in progress.

Project Structure

brahm/
├── agents/
│   ├── shani/          # Literature pipeline
│   ├── chitragupta/    # Knowledge custodian
│   ├── ganesh/         # Document generation
│   ├── vidur/          # Characterisation analysis
│   └── vishwakarma/    # DFT computation
├── brahm/              # Shared registry and utilities
├── brahm_dashboard.py  # Service health and control UI
└── mcp_server.py       # MCP entry point

Built by Shardul Khanduri — MSc Physics, materials science and AI systems.

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Maintenance

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