Revise README for Aegis project overview and details

Updated README to reflect project name change and detailed features.

Author: felixk29

README.md

@@ -1,2 +1,118 @@
-# junction-hack
-Junction Hack Finland
+<img width="1104" height="639" alt="{1AEBB550-3E8D-40BD-B4A7-0D42D4ADA5D4}" src="https://github.com/user-attachments/assets/4e8936ad-7206-48ba-9ce2-c5202ece5e80" />
+
+# Aegis - Trust Evaluation Platform
+**"Security decisions in seconds"** - Aegis is an AI-powered security assessment platform that automates vendor trust evaluations. It generates instant, source-grounded reports with transparent trust scores, and includes an interactive chat agent to provide further insights and answer specific questions, enabling security teams to make fast, informed decisions.
+
+## 🚀 Overview 
+![Structural Diagram](https://github.com/user-attachments/assets/d23e5034-869f-411f-9b26-beac3a536c44)
+Aegis consists of two main components:
+ - **Web-Client** - A next.js web application providing an easy to use interface to evaluate products, discuss the evaluation with an agent, and a vault giving oversight over prior scoring.
+ - **Deep Research Agent** - An agent based on OpenAi & Langgraph, utilizing various specialist API's to thoroughly investigate the product provided.   
+
+## Highlights
+ - 🔒 Firebase Auth + Profiles – Email/password and Google SSO with enriched user metadata captured in Firestore.
+ - 📥 Submission Hub – Text prompt + binary upload workflow for requesting assessments.
+ - 🤖 Multi-LLM Research Agent – Configurable OpenAI/Anthropic stacks for summarize→research→compress→report loops.
+ - 🔎 Search + MCP Integrations – Pluggable Tavily, OpenAI native search, Anthropic native search, and custom MCP toolchains.
+ - 📊 Reports Vault – High-signal trust brief cards with risk tags, source counts, and sharing links.
+ - 🧪 Benchmark Harness – Pre-wired Deep Research Bench evaluation scripts to validate agent quality.
+## 🏗️ Project Structure
+junction-hack/
+├── junction-app/                # Next.js frontend│   
+├── app/                     # App Router routes (landing, auth, dashboard, reports)│   
+├── components/              # Shared UI (AppChrome, landing sections)
+│   ├── contexts/AuthContext.tsx # Client-side auth/session provider│   ├── lib/firebase.ts          # Firebase initialization│   
+├── public/                  # Static assets
+│   └── README.md
+├── deep_security/               # LangGraph / Open Deep Research backend
+│   ├── src/open_deep_research/  # Config + runtime
+│   ├── src/security/            # Auth helpers
+│   ├── tests/                   # Benchmark + evaluation scripts
+│   ├── README.md
+│   └── pyproject.toml
+└── example_data.csv             # Sample assessment data
+## 🎨 Frontend (Next.js)
+Modern App Router experience focusing on security analyst workflows:
+Tech Stack: Next.js 15, TypeScript, Tailwind, shadcn/ui, Lucide icons.
+Auth Flow: AuthContext wraps Firebase Auth; guards dashboard and reports routes.
+Key Screens:
+Landing page with hero/demo/trust-score highlights.
+/auth multi-step login/register with Google SSO fallback.
+/dashboard submission form (text + file upload) and quick links to reports.
+/reports gallery of trust briefs with status, sources, and risk chips.
+## ⚙️ Deep Research Service
+LangGraph-backed agent toolbox housed in deep_security/:
+ - Configuration Surface: src/open_deep_research/configuration.py exposes sliders/toggles for structured-output retries, concurrency, model choices, search providers, and MCP settings.
+ - Model Pipeline: Separate slots for summarization, researcher, compression, and final-report models (defaults to OpenAI gpt-4.1 / gpt-4.1-mini, but swappable to Anthropic, GPT-5, etc.).
+ - Search & MCP: Built-in support for Tavily, OpenAI native, Anthropic native search plus external MCP servers for custom tools/data.
+ - Evaluation: tests/run_evaluate.py and tests/extract_langsmith_data.py automate Deep Research Bench submissions (LangSmith integration).
+## 🧭 Data Flow
+ 1. User Authenticates – Firebase Auth session hydrates AuthContext.
+ 2. Submission – Dashboard posts text/binary payload to a Next.js API route or edge function (placeholder today).
+ 3. Assessment Orchestration – API proxies request to LangGraph runtime (Deep Research service).
+ 4. LLM + Search Loop – Agent fans out to configured LLMs, search APIs, and MCP tools, storing intermediate notes.
+ 5. Report Storage – Final trust brief, scores, and citation metadata saved back to Firestore.
+ 6. Consumption – Reports UI reads Firestore entries for sharing/export.
+## 🚀 Quick Start
+### Prerequisites
+ - Node.js 18+ (or Bun), npm/yarn/pnpm.
+ - Python 3.11, uv or pip.
+ - Firebase project (Auth + Firestore) + service credentials.
+ - OpenAI and/or Anthropic API keys (plus Tavily key if using default search).
+ - LangSmith account if running benchmarks.
+### Frontend Setup
+'''
+cd junction-app
+cp .env.example .env.local   # fill Firebase + API vars
+npm installnpm
+run dev
+'''
+Visit [http://localhost:3000](http://localhost:3000).
+### Backend Setup
+'''
+cd deep_securityuv venv && source .venv/bin/activate    # or python -m venvuv 
+sync                                 # installs LangChain/LangGraph dep
+scp .env.example .env                    # configure LLM/search/MCP keys
+uvx --from "langgraph-cli[inmem]" langgraph dev --allow-blocking
+'''
+LangGraph Studio UI available at the printed URL (default [http://127.0.0.1:2024)](http://127.0.0.1:2024)).
+## 🛠️ Environment Variables
+Component	Variable	Description
+junction-app	NEXT_PUBLIC_FIREBASE_*	Firebase web config (auth domain, project ID…)
+NEXT_PUBLIC_ASSESSMENT_API_URL	(Future) API route for submissions
+deep_security	SUMMARIZATION_MODEL, RESEARCH_MODEL…	Override default LLMs per stage
+SEARCH_API	tavily, openai, anthropic, or none
+MCP_CONFIG_URL, MCP_CONFIG_TOOLS	Optional MCP server info
+Shared	OPENAI_API_KEY, ANTHROPIC_API_KEY	Provider credentials
+Shared	TAVILY_API_KEY	Web search enrichment
+## 📚 Documentation
+junction-app/README.md – Frontend development tips.
+deep_security/README.md – LangGraph configuration, benchmarking, LangSmith usage.
+LangChain docs for MCP + multi-provider LLM setup.
+Firebase docs for Auth/Firestore provisioning.
+## 🚢 Deployment
+Layer	Recommended Target
+Frontend	Vercel / Netlify (set Firebase/public env vars)
+API Routes	Vercel Edge Functions or Next.js serverless runtime
+LangGraph	Dockerized service on cloud VM or LangGraph Platform
+Firebase	Managed (Auth + Firestore)
+Build frontend: npm run build → deploy.
+Package LangGraph service with uv + langgraph dev or containerize for production.
+Wire API route to call LangGraph service; secure with bearer tokens.
+Point frontend env vars to production endpoints.
+## 🧪 Testing & Evaluation
+Frontend: npm run lint / npm run test (if configured) plus manual UI smoke tests.
+Backend: Run python tests/run_evaluate.py for Deep Research Bench; extract results via tests/extract_langsmith_data.py.
+Integration: Validate that Firestore entries appear when manual assessments are triggered (mock API route until backend is wired).
+##🤝 Contributing
+Fork and branch (git checkout -b feature/<name>).
+Keep frontend TypeScript strict and follow existing Tailwind patterns.
+For backend changes, update configuration.py docs + README when adding config knobs.
+Add tests or LangSmith eval notes for new research behaviors.
+Submit PR with a concise summary and screenshots if UI-related.
+## 📄 License
+MIT – see LICENSE.
+## 🙋 Support & Questions
+Open an issue in this repo.
+Check LangGraph + Firebase docs linked above.
+Reach out on project Slack/Discord (if applicable) for architecture questions.