AI Frameworks: The Top Picks for Startups in 2025

If your business is building AI agents, optimising large language models (LLM), or deploying intelligent applications at scale, choosing the right AI frameworks can greatly accelerate your business.

DevLabs specialises in building high-performance AI systems for startups and enterprises alike. In this guide, we’ll walk you through the AI frameworks we recommend and implement for our clients, across three core domains:

1. AI Agents: smarter, more autonomous systems
2. LLM Optimisation & Performance: faster, cheaper inference and fine-tuning
3. AI App Development & Integration: tools that bring AI into the real world

Whether you’re automating workflows, fine-tuning domain-specific models, or building scalable AI pipelines, these frameworks will help you move faster and smarter.

AI Framework for Smarter AI Agents: Autonomous, Collaborative, Task-Oriented

AI agents are no longer just passive responders to commands. Today’s AI agents are becoming increasingly autonomous, capable of handling multi-step reasoning, working together in groups, and even improving their own performance over time. These AI frameworks are leading the charge:

CrewAI: Multi-Agent Collaboration for Workflow Automation

CrewAI allows developers to build systems where several AI agents interact, each handling different parts of a task. Think of it as a task-oriented team of bots, each with a specialisation, working together to achieve a broader goal.

⬩ Best for: Companies seeking to automate multi-step tasks with coordination between multiple agents.

⬩ Strengths: Task delegation, goal-oriented coordination, multi-step reasoning

⬩ Use Case: A customer service system where one agent categorises support tickets, another fetches knowledge base content, and a third drafts personalised responses.

AutoGen 4.0: Self-Improving Autonomous Agents

⬩Best for: Enterprises looking to build agents that make strategic decisions and learn from outcomes.

AutoGen 4.0 facilitates the creation of intelligent agents that negotiate, plan, and refine strategies over time. These agents are ideal for complex problem-solving and decision support systems.

⬩Strengths: Dynamic reasoning, inter-agent communication, continuous improvement

⬩Use Case: Market research bots that analyse economic trends, generate insights, and recommend product or investment strategies.

Orq.ai: AI Workflow Orchestration

⬩Best for: Companies aiming to automate business processes end-to-end.

Orq.ai helps stitch together AI tools, APIs, and services into integrated workflows. It’s perfect for operational efficiency and cost reduction.

⬩Strengths: Seamless API orchestration, process automation, reduced human input

⬩Use Case: An AI-driven ecommerce platform that dynamically adjusts pricing, forecasts demand, and curates product recommendations based on user behaviour.

LLM Performance & Optimisation: Faster, Cheaper, Smarter

Deploying large language models (LLMs) can be expensive and inefficient, unless you’re using AI frameworks designed to optimise performance. These tools help businesses reduce costs, improve responsiveness, and customise models for specific tasks.

vLLM: High-Performance Inference

⬩Best for: Real-time AI applications with low-latency requirements.

vLLM dramatically improves LLM serving speed and cost-efficiency by rethinking how tokens are processed during inference. It’s ideal for businesses that need AI to operate at scale, without breaking the bank.

⬩Strengths: Low latency, high throughput, reduced memory usage

⬩Use Case: A fintech chatbot that delivers stock insights, investment advice, or financial analysis instantly.

Fine: Fine-Tuning for Specialised Use Cases

⬩Best for: Companies that require domain-specific language models.

Fine enables affordable, targeted fine-tuning of LLMs, reducing hallucinations and improving performance in specialised contexts like healthcare, law, or customer support.

⬩Strengths: Domain adaptation, accuracy, reduced inference errors

⬩Use Case: A legal assistant AI that understands and explains contract clauses with precision.

LlamaIndex: Connecting LLMs to External Data

⬩Best for: Enterprises seeking retrieval-augmented generation (RAG) systems.

LlamaIndex allows LLMs to access and query structured data, documents, or live APIs — turning generative models into factual assistants.

⬩Strengths: Fact-based outputs, better accuracy, RAG integration

⬩Use Case: A medical research AI that scans live databases of scientific literature and delivers up-to-date clinical summaries.

AI App Development & Integration: Making AI Work in the Real World

AI Frameworks in this category help developers bridge the gap between foundational models and business use cases: by providing infrastructure, integration, and user interface tools for building AI-driven applications.

LangChain: The Backbone of AI-Powered Apps

⬩Best for: Developers looking to integrate LLMs into end-user applications.

LangChain is an open-source framework that makes it easy to build AI apps that combine memory, tool use, and external knowledge sources.

⬩Strengths: Tool chaining, contextual memory, custom workflows

⬩Use Case: An internal support assistant that retrieves policies from internal databases before responding to staff queries.

Microsoft Semantic Kernel: Structured Memory + Planning

⬩Best for: Applications requiring long-term memory and adaptive planning.

Semantic Kernel lets AI agents plan complex sequences and remember user history over time, which is essential for personalisation and context-rich experiences.

⬩Strengths: Long-term memory, event planning, structured reasoning

⬩Use Case: A personal productivity AI that schedules meetings, recalls past interactions, and adapts to changing priorities.

Google Vertex AI: Enterprise-Grade Deployment

⬩Best for: Scalable, secure AI initiatives in production environments.

Vertex AI is Google Cloud’s fully managed AI platform, ideal for businesses needing robust infrastructure for training, deploying, and monitoring ML models.

⬩Strengths: Scalability, governance, end-to-end support

⬩Use Case: A retail forecasting platform that predicts inventory needs across hundreds of locations based on real-time purchasing trends.

How AngelHack DevLabs Leverages These Frameworks

At AngelHack DevLabs, we help businesses realise the full potential of AI. Our services span from early product discovery to post-deployment scaling. Here’s how we put these tools to work:

♦️ Workflow Automation with AI Agents

We’ve deployed CrewAI and AutoGen 4.0 to build multi-agent systems that automate customer support, lead qualification, and internal operations for tech startups and enterprise clients.

♦️ LLM Optimisation for Better User Experiences

Using vLLM and Fine, we’ve helped SaaS platforms reduce latency by 40% and customise models for legal, HR, and financial applications.

♦️ Real-Time Insights through Smart Retrieval

With LangChain and LlamaIndex, we’ve created dashboards and data pipelines that allow businesses to interact with real-time data, no engineering bottlenecks, just answers.

♦️ Enterprise Scaling with Robust Infrastructure

For clients needing robust deployments, we use Microsoft Semantic Kernel and Google Vertex AI to scale apps across departments and geographies while ensuring compliance and security.

Why Partner with AngelHack DevLabs?

With a deep bench of AI engineers, product thinkers, and technical consultants, we help our partners:

• Accelerate AI product development
• Reduce technical complexity and development costs
• Build scalable, production-ready AI systems
• Stay ahead with best-in-class AI frameworks and implementation

Whether you’re a startup prototyping your first AI feature or an enterprise ready to deploy intelligent workflows across your organisation, we’re here to help.

Let’s Build Together

The AI ecosystem is evolving fast, and with the right AI frameworks, your business can evolve with it. If you’re exploring AI development or looking to outsource custom software with embedded intelligence, contact us.