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.

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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.

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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

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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

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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

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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.

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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.

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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

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