Developer Agent Frameworks

An agent framework gives developers the building blocks to create AI agents — programs that use large language models to reason, plan, and take actions by calling tools. Instead of writing a single prompt, you define a workflow: the agent receives a goal, decides which tools to call, interprets the results, and repeats until the task is done.

In customer support, agent frameworks power use cases like automated ticket triage, multi-step order lookups, draft reply generation with knowledge base retrieval, and escalation workflows that hand off to humans when confidence is low.

The frameworks listed below are all open-source and actively maintained as of April 2026. They differ in language, abstraction level, and approach to orchestration.

LangGraph is the workflow engine from the LangChain team. It reached GA at v1.0 in October 2025 and is now at v1.0.10. The core concept is a state graph: you define nodes (functions or LLM calls) and edges (transitions, including conditional branches) that form a directed graph.

LangGraph excels at durable, long-running workflows. State is checkpointed after every node, so a workflow can pause for human approval, survive a server restart, and resume exactly where it left off. This makes it well-suited for support escalation flows where a ticket may need manager review before an action is taken.

The trade-off is complexity. Thinking in graphs requires a different mental model than sequential code, and debugging graph execution can be harder than reading a linear script. LangSmith (the commercial observability layer) helps, but adds a dependency.

CrewAI models agents as team members with defined roles, goals, and backstories. You create a "crew" of agents, assign them tasks, and let them collaborate. A triage agent might classify the ticket, a researcher agent looks up the customer history, and a writer agent drafts the reply.

At 44,600+ GitHub stars, CrewAI has the largest community among agent frameworks. Version 1.10.1 added native support for both MCP (Model Context Protocol) and A2A (Agent-to-Agent), making it straightforward to connect agents to external tools and to other agent systems.

CrewAI has the lowest barrier to entry of any framework on this list. The role-based abstraction maps naturally to how support teams already think about task delegation. The downside is less fine-grained control over execution flow compared to LangGraph's explicit graph model.

Mastra is the leading TypeScript-native agent framework. Backed by a $13M raise from Y Combinator's W25 batch (January 2026), it has grown to 22,300+ GitHub stars and 300,000+ weekly npm downloads.

The framework provides an all-in-one toolkit: agents, workflows, RAG pipelines, and four types of memory (including semantic recall based on vector similarity, not just recency). In February 2026 it added a supervisor pattern for multi-agent coordination, and in March it introduced workspace capabilities — file I/O, sandbox execution, and content search with approval flows.

For teams already building on Next.js or TypeScript, Mastra avoids the context switch to Python. It integrates cleanly with the JavaScript ecosystem and runs in Node.js, Deno, or edge runtimes.

Vercel AI SDK 6, released in early 2026, introduced a first-class Agent abstraction. The ToolLoopAgent handles the tool-call loop natively, and the SDK supports 25+ LLM providers out of the box with full MCP support.

The SDK is purpose-built for the Next.js ecosystem. If your support dashboard or customer portal already runs on Next.js, the AI SDK lets you add agent capabilities with minimal additional infrastructure. Streaming responses, edge function compatibility, and React Server Components integration come for free.

Clay used this SDK to build Claygent, their AI web research agent, which validates its production readiness for complex agentic workflows.

The OpenAI Agents SDK (v0.13.3, March 2026) evolved from the experimental Swarm project into a lightweight, provider-agnostic framework. Despite the name, it supports over 100 LLMs via the Chat Completions API — not just OpenAI models.

Recent additions include WebSocket transport, native MCP server support with resource listing, and integration with the gpt-realtime-1.5 model for voice agent scenarios. The SDK is intentionally minimal: it handles the agent loop, tool dispatch, and handoffs between agents, but leaves orchestration patterns to the developer.

For teams that want a thin layer on top of the raw API rather than a full framework, the Agents SDK hits a good balance between convenience and control.

Anthropic's Claude Agent SDK (v0.2.89) provides a Python framework built around Claude's extended thinking, tool use, and computer use capabilities. It supports custom in-process MCP servers (no separate process required), per-category context tracking, and cancel request handling for in-flight callbacks.

The SDK is particularly strong for agents that need to reason through complex, multi-step problems — debugging code, analysing documents, or working through intricate customer support cases where the agent needs to consult multiple systems before responding.

Claude Code, Anthropic's CLI coding assistant, is built on this SDK, which provides a real-world validation of its capabilities for tool-heavy agentic workflows.

LlamaIndex focuses on agentic RAG — retrieval-augmented generation with planning, reflection, and tool use. If your support agents need to search across knowledge bases, CRM records, and past tickets to compose an answer, LlamaIndex's Workflows engine provides the orchestration layer. LlamaParse handles 90+ unstructured file types for document ingestion.

AutoGen, developed by Microsoft Research, pioneered multi-agent conversations using a GroupChat pattern where agents debate and reach consensus. However, it has shifted to maintenance mode as Microsoft expands its focus to the broader Microsoft Agent Framework. The GroupChat overhead also makes it expensive for high-volume, real-time support scenarios. Teams currently on AutoGen should evaluate migrating to CrewAI or LangGraph for actively maintained alternatives.

Choosing a framework depends on your team's language preference, the complexity of your workflows, and how much control you need over execution.

LangGraph is best when you need explicit, durable state machines with human-in-the-loop checkpoints. CrewAI is best for rapid prototyping with role-based agent teams and the lowest learning curve. Mastra is the clear choice for TypeScript-first teams. Vercel AI SDK fits naturally into Next.js applications. OpenAI Agents SDK and Claude Agent SDK are lightweight options tied closely to their respective model ecosystems. LlamaIndex is the specialist for retrieval-heavy workflows.

All of the actively maintained frameworks now support MCP for tool integration, and most support A2A for inter-agent communication. The choice is less about capability gaps and more about which abstraction matches how your team thinks about workflows.