⚡ Framework Comparison

AI Agent Framework Comparison

Compare LangChain, CrewAI, AutoGen, LlamaIndex, and direct API for building cybersecurity agents. Filter by your skill level and requirements.

Direct API (No Framework)

Call the LLM API directly from your Python code

Beginner Friendly Free / SDK Only

★★★★★

Simplicity

★★★☆☆

Features

★★★★★

Control

Best for

Starting out — least moving parts
Custom security workflows with precise control
Integration with existing Python infrastructure

Limitations

Must implement your own agentic loop
No built-in memory, RAG, or multi-agent patterns
More boilerplate for complex workflows

🎯 Recommendation: Start here. Build your first triage agent with direct API calls. Once it works, you'll understand exactly what frameworks add — and whether you need them.

Anthropic Docs →

LangChain

The most widely used LLM application framework

Intermediate Open Source

★★★☆☆

Simplicity

★★★★★

Ecosystem

★★★☆☆

Stability

Best for

RAG pipelines — connect your threat intel to AI
Large ecosystem of pre-built tools and integrations
Most Stack Overflow answers and tutorials

Limitations

Abstraction overhead — debugging is harder
Frequent breaking changes between versions
Over-engineered for simple single-agent use cases

🎯 Security fit: Strong choice for RAG — connecting internal threat reports, runbooks, and incident history to AI. Use LangChain when you need to search your own security knowledge base. For simple agents, use direct API.

LangChain Docs →

CrewAI

Role-based multi-agent framework with crew orchestration

Intermediate Open Source Core Enterprise Paid

★★★★☆

Readability

★★★★★

Multi-Agent

★★★☆☆

Maturity

Best for

SOC workflows with specialised agents (analyst, reporter, enricher)
Very readable role-based definitions
Good for demos and prototyping multi-agent concepts

Limitations

Relatively new — production maturity still developing
Can be overkill if you only need 1-2 agents
Advanced features require paid Enterprise plan

🎯 Security fit: Excellent for modelling a SOC team in code — define a "Tier 1 Analyst" agent, a "Threat Intel" agent, and a "Report Writer" agent. Each has a role, goal, and tools. Very readable for non-ML security engineers.

CrewAI Docs →

AutoGen (Microsoft)

Conversational multi-agent framework for complex workflows

Advanced Open Source

★★☆☆☆

Simplicity

★★★★★

Power

★★★★☆

Research

Best for

Complex multi-agent conversation flows
Research and experimentation with agent architectures
Human-in-the-loop with rich conversation UI

Limitations

Steep learning curve
Heavily Azure/OpenAI focused — Claude integration requires extra work
Debugging multi-agent conversations is complex

🎯 Security fit: Most appropriate for mature security teams wanting to build sophisticated multi-agent IR workflows. Not recommended for first security AI projects. Azure Sentinel integration makes it attractive for Microsoft-centric SOCs.

AutoGen Docs →

LlamaIndex

Specialised in data ingestion, indexing, and RAG pipelines

Intermediate Open Source Core

★★★☆☆

Simplicity

★★★★★

RAG Quality

★★★★☆

Data Handling

Best for

Indexing and searching large security document collections
Building knowledge bases from threat reports, runbooks, CVE databases
Best-in-class RAG pipeline quality

Limitations

Primarily a RAG tool — not a general agent framework
Requires separate vector database (ChromaDB, Pinecone, etc.)
More complex than LangChain for simple document search

🎯 Security fit: Best choice when your primary need is "search my threat intelligence reports" or "query my incident history." Combine with direct API or LangChain when you need agent capabilities on top of RAG.

LlamaIndex Docs →

Ollama + Llama 3 (On-Premise)

Run open-source LLMs locally — data never leaves your network

Any Skill Level Free On-Premise

★★★★☆

Setup Ease

★★★☆☆

Performance

★★★★★

Data Privacy

Best for

Organisations with strict data residency requirements (RBI, SEBI, defence)
High-volume log processing where API costs would be prohibitive
Air-gapped environments

Limitations

Llama 3.1 70B quality is noticeably below Claude/GPT-4o for complex reasoning
Requires GPU hardware (A100/H100 for 70B model)
No managed service — you run the infrastructure

🎯 Security fit: Critical for Indian BFSI and government organisations that cannot send security logs to US-based APIs. Use Llama 3.1 70B via Ollama for on-premise deployments. Performance gap vs hosted models is real but often acceptable for triage tasks.

Ollama Docs →

🎯 Quick Decision Guide

First security AI project?

→ Direct API

Start simple. No framework overhead. You'll understand tool-calling before adding abstractions.

Building a security knowledge base / search over threat reports?

→ LlamaIndex or LangChain

Both have excellent RAG pipelines. LlamaIndex has better document handling; LangChain has more integrations.

Multiple specialised agents (analyst + enricher + reporter)?

→ CrewAI

Role-based agent definitions are very readable and map well to SOC team structure.

Data cannot leave India / on-premise required?

→ Ollama + Llama 3.1 70B

Only viable option for truly on-premise deployment. Pair with any framework — Ollama exposes an OpenAI-compatible API.

Already using Azure Sentinel and Microsoft stack?

→ AutoGen or Direct Azure OpenAI

AutoGen integrates well with Azure. Azure OpenAI provides GPT-4o with data residency options inside Azure's compliance boundary.

Complex IR workflow with human approvals?

→ LangGraph (LangChain)

LangGraph provides state machines for complex agent workflows with checkpoints — ideal for human-in-the-loop IR processes.

📊 Full Feature Comparison

Feature	Direct API	LangChain	CrewAI	AutoGen	LlamaIndex	Ollama
Learning curve	Low	Medium	Medium	High	Medium	Low
Tool-calling / function calling	Native	Yes	Yes	Yes	Yes	Depends on model
RAG / document search	Manual	Excellent	Basic	Via plugins	Best-in-class	Manual
Multi-agent support	Manual	LangGraph	Native	Native	Basic	Manual
Memory management	Manual	Yes	Yes	Yes	Yes	Manual
Data stays in India	Depends on API	Depends on LLM	Depends on LLM	Azure option	Depends on LLM	Yes — local
Claude support	Native	Yes	Yes	Community	Yes	No
Debugging ease	Easy	Hard	Medium	Hard	Medium	Easy
API cost	LLM API only	LLM API only	LLM API + optional platform	LLM API only	LLM API + vector DB	Free (self-hosted)
Community / resources	Extensive	Largest	Growing	Large	Large	Large
Production maturity	Mature	Stable but evolving	Maturing	Research-grade	Mature	Mature