AI Frameworks · souvikmajumder26

Multi-Agent-Medical-Assistant

Multi-Agent Medical Assistant is an open-source Python chatbot that combines large language models, computer vision, retrieval-augmented generation (RAG), and web search to support medical diagnosis, research, and patient interactions. It is designed for healthcare professionals, researchers, and patients seeking AI-assisted medical insights.

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Source: GitHub — github.com/souvikmajumder26/Multi-Agent-Medical-Assistant

924

GitHub stars

204

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	souvikmajumder26/Multi-Agent-Medical-Assistant
Owner	souvikmajumder26
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	924
Forks	204
Open issues	3
Latest release	v2.1.2 (2025-05-02)
Last updated	2025-05-03
Source	https://github.com/souvikmajumder26/Multi-Agent-Medical-Assistant

What Multi-Agent-Medical-Assistant is

Built on LangGraph and LangChain, the system orchestrates specialized agents for diagnosis, retrieval, and reasoning. It uses Qdrant for hybrid BM25/vector search, Docling for document parsing (text/tables/images), semantic chunking, cross-encoder reranking, and medical imaging models for brain tumor, chest X-ray, and skin lesion analysis. FastAPI serves the backend; Eleven Labs provides voice I/O.

Quickstart

Get the Multi-Agent-Medical-Assistant source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/souvikmajumder26/Multi-Agent-Medical-Assistant.git2cd Multi-Agent-Medical-Assistant3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Healthcare Professional Decision Support

Assist doctors and specialists in cross-referencing patient symptoms against medical literature, recent research, and imaging analysis. The confidence-based routing and human-in-the-loop validation ensure clinical reliability.

Medical Research and Literature Review

Researchers can leverage RAG with semantic chunking and web search integration to retrieve relevant papers, case studies, and findings quickly, with source attribution and reference links.

Patient Education and Triage

Provide patients with evidence-based information about symptoms and conditions in plain language, supported by linked source documents and expert validation before delivery.

Implementation considerations

Requires Python 3.11+, LangGraph 0.3+, LangChain 0.3+, Qdrant 1.13+, FastAPI 0.115+, and external LLM/vision model access. Plan infrastructure for vector database, compute for embeddings, and API credentials (Eleven Labs, web search provider).
Medical imaging components (brain tumor, chest X-ray, skin lesion) integrate computer vision models. Validate model performance against your use case and ensure training data aligns with patient populations.
Semantic chunking and hybrid retrieval are data-dependent. Indexing performance and retrieval quality depend on document quality, chunking strategy, and embedding model choice. Test with representative medical documents.
Human-in-the-loop validation requires UI integration and workflow design for medical professionals to review and approve recommendations before delivery. This adds operational complexity.
Confidence-based routing between agents relies on log probability thresholds. Tuning these thresholds requires domain expertise and testing against failure modes in medical contexts.

When to avoid it — and what to weigh

Standalone Diagnostic System Without Medical Oversight — This system is not suitable as a replacement for licensed medical professionals. It requires human-in-the-loop validation and should never be deployed in clinical settings without active physician oversight.
Regulatory Compliance-Driven Deployments — If your deployment requires FDA approval, HIPAA certification, SOC 2, or other strict healthcare compliance frameworks, this open-source project lacks formal compliance documentation and audit trails.
Low-Resource or Air-Gapped Environments — The system requires external API calls (Eleven Labs, web search, LLM providers), vector database infrastructure (Qdrant), and significant compute. Offline or highly constrained deployments are impractical.
Production Use Without Security Hardening — The project does not include built-in RBAC, encryption-at-rest, audit logging, or data anonymization. Clinical use requires substantial security review and customization before patient data handling.

License & commercial use

Licensed under Apache License 2.0 (Apache-2.0), a permissive OSI-approved license. Allows commercial use, modification, and distribution with liability/warranty disclaimers and retention of license notices. No copyleft requirements.

Apache-2.0 permits commercial use. However, commercial deployment in healthcare contexts requires: (1) independent security audit and compliance review for HIPAA/regulatory requirements, (2) validation of accuracy/safety for patient-facing features, and (3) liability and indemnification terms beyond the open-source license. Recommend legal review before commercial healthcare use.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	High
DEV.co fit	Good
Assessment confidence	High

Security considerations

No formal security audit is referenced. Considerations for healthcare deployment: (1) API keys and credentials are externalized (Eleven Labs, LLM providers, web search) — secure storage/rotation required; (2) No built-in data encryption, RBAC, or audit logging mentioned; (3) RAG retrieval may expose medical document content in logs/traces; (4) Web search agent introduces third-party risk; (5) Medical imaging uploads require validation and sandboxing; (6) Input guardrails exist but their effectiveness against adversarial prompts is Unknown. Conduct threat modeling before patient data access.

Alternatives to consider

Anthropic Claude API with custom retrieval + medical fine-tuning

Simpler single-agent architecture, strong out-of-box instruction following, but lacks multi-agent orchestration, imaging analysis, and voice features. Better for low-complexity retrieval workflows.

OpenAI GPT-4 + RAG frameworks (LlamaIndex, Verba)

Well-documented, mature ecosystem, strong commercial support. However, also closed-source, higher API costs, and lacks built-in medical imaging and voice integration like this project.

Hugging Face open-source LLM + local Ollama + Milvus vector DB

Fully local, no external dependencies, lower API costs. Trade-off: weaker medical reasoning than large models, no voice features, no web search, requires more infrastructure management.

Software development agency

Build on Multi-Agent-Medical-Assistant with DEV.co software developers

Our team can help audit, harden, and deploy this system safely in clinical settings with compliance and security best practices.

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Related open-source tools

Surfaced by semantic similarity across the DEV.co open-source index.

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Multi-Agent-Medical-Assistant FAQ

Is this suitable for direct clinical diagnosis?

No. The system is designed as a decision-support and research assistant. It includes human-in-the-loop validation specifically because AI-only diagnosis is unsafe. Any clinical deployment must have licensed physicians review and approve all outputs before patient use.

What happens if the vector database or external APIs are unavailable?

Unknown. The documentation does not describe fallback behavior, circuit breakers, or graceful degradation. In production, you would need to implement retry logic, failover mechanisms, and queueing to handle service outages.

Can I run this entirely on-premises without external API calls?

Partially. You can run FastAPI, Qdrant, and self-host open-source LLMs (e.g., via Ollama or HuggingFace). However, voice (Eleven Labs) and real-time web search require external calls. Removing these features is feasible but requires code changes.

How do I ensure medical data privacy and HIPAA compliance?

The project does not include built-in HIPAA controls (encryption, audit logs, de-identification). You must add encryption at rest/in-transit, role-based access control, Data Processing Agreements with API providers, and audit logging as custom implementations before handling PHI.

Work with a software development agency

DEV.co helps companies turn open-source tools like Multi-Agent-Medical-Assistant into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your ai frameworks stack.

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