This guide explains:

• utilities & frameworks used
• how each component fits in the architecture
• step‑by‑step Windows local setup
• how MCP, RAG, LangGraph, Guardrails & LLM integrate
• basic → advanced usage flow

---

🧩 Architecture & Technology Flow

User → Streamlit UI → LangGraph Agent

            │
            ▼
   ┌────────────────────────────┐
   │     AGENT ORCHESTRATION    │
   │        LangGraph           │
   └────────────┬───────────────┘
                │
   ┌────────────┼─────────────┐
   ▼            ▼             ▼
Log Fetch    Runbook RAG   Detection Engine
(MCP API)    (Vector DB)   (Pattern Logic)

   │            │             │
   └────────────┴─────────────┘
                ▼
         LLM Reasoning Layer
        (OpenRouter / Llama3)

                ▼
          Guardrails Validation
             (Pydantic)

                ▼
          Structured Response

---

🧰 Utilities & Frameworks Used

Core Runtime

Python 3.10+

Primary runtime for orchestration and services.

---

LLM Layer

OpenRouter + Llama‑3

Used for reasoning over logs and generating security findings.

---

LangChain Ecosystem

LangChain

Provides embedding and vector search integration.

LangGraph

Used for deterministic agent orchestration.

✔ stateful workflows
✔ branching logic
✔ production reliability

LangSmith (Optional)

Observability & debugging for agent flows.

---

RAG Stack

SentenceTransformers

Creates semantic embeddings.

Model:

all-MiniLM-L6-v2

ChromaDB

Local vector database storing runbook embeddings.

---

MCP Service Layer

FastAPI

Provides log access endpoints.

Simulates enterprise log providers like Splunk or Elastic.

---

Guardrails

Pydantic

Validates LLM output structure.

Prevents malformed responses.

---

Detection Engine

Custom Python detection for:

✔ SSH brute force attempts
✔ suspicious IP activity

---

🖥️ Windows Local Setup

1️⃣ Install Python

Verify:

python --version

---

2️⃣ Clone Repo

git clone https://github.com/vforvishal12/mcp-splunk.git
cd mcp-splunk

---

3️⃣ Virtual Environment

python -m venv venv
venv\Scripts\activate

---

4️⃣ Install Dependencies

pip install -r requirements.txt

If needed:

pip install streamlit fastapi uvicorn requests python-dotenv
pip install langchain langgraph chromadb sentence-transformers
pip install openai pydantic

---

5️⃣ Environment Variables

Create .env

OPENAI_API_KEY=your_key

---

6️⃣ Build Vector DB

Run once:

python

from agent.rag import build_vector_db
build_vector_db()
exit()

---

7️⃣ Start MCP Server

uvicorn mcp_server:app --port 9000

Verify:

http://localhost:9000/service_health

---

8️⃣ Launch App

streamlit run app.py

Open:

http://localhost:8501

---

🔄 Execution Flow

1. User submits query
2. Agent fetches logs via MCP
3. Logs parsed & categorized
4. Threat detection executed
5. Runbook context retrieved (RAG)
6. LLM generates security analysis
7. Guardrails validate output
8. Structured results displayed

---

🧠 Basic vs Advanced Usage

Basic

✔ run locally
✔ detect suspicious activity

Advanced

✔ integrate Splunk/Elastic
✔ stream logs via Kafka
✔ enable LangSmith tracing
✔ deploy via Docker & Kubernetes

---

🚀 Production Upgrade Path

1. Replace file logs → streaming ingestion
2. deploy vector DB remotely
3. enable SIEM alerting
4. multi-host correlation

---