Frameworks & Tools

The AI agent ecosystem is exploding with new tools and frameworks that abstract away the complexity of building agentic systems. Here is an overview of the most popular ones.

Agent Frameworks

LangChain

The most widely used framework for building LLM applications.

• Key Features: Chains, Agents, Memory, Document Loaders.
• Best For: General-purpose LLM apps, RAG pipelines.
• Language: Python, JavaScript/TypeScript.

LangGraph

Built on top of LangChain, it enables clearly defined, cyclical workflows.

• Key Features: State machines, nodes, edges, conditional routing.
• Best For: Complex, multi-step agent behaviors that usually require loops.
• Language: Python, JavaScript/TypeScript.

AutoGen (Microsoft)

A framework for enabling multiple agents to converse with each other to solve tasks.

• Key Features: Multi-agent conversation patterns, human-in-the-loop.
• Best For: collaborative agent swarms, code generation.
• Language: Python.

CrewAI

Focuses on role-playing agents working together as a "crew."

• Key Features: Role-based agent design, task delegation.
• Best For: Simulating a workforce (e.g., Researcher, Writer, Editor).
• Language: Python.

Semantic Kernel (Microsoft)

Ideally integrates LLMs with existing code.

• Key Features: Plugins, Planners, Memories.
• Best For: Enterprise integration, .NET/Python shops.
• Language: C#, Python, Java.

Infrastructure & Tooling

Vector Databases

Essential for Long-Term Memory (RAG).

• Pinecone: Managed, scalable vector db.
• Chroma: Open-source, local-friendly.
• Weaviate: AI-native vector database.

Monitoring & Observability

Debugging agents is hard; these tools help visualize the execution traces.

• LangSmith: Debugging, testing, and monitoring for LangChain.
• Arize Phoenix: Open-source observability for LLMs.

Hosting

• Vercel: Great for JS/TS based AI apps (Next.js AI SDK).
• Modal: Serverless python execution for heavy lifting.