AI agent frameworks for SMB owners

Strands Agents is an open-source software development kit (SDK), initially released by AWS. It takes a model-first approach to building AI agents: you define the model, the tools it’s allowed to use, and the prompt. Then, the agent plans and calls tools to complete tasks.

For SMBs, this can be a good fit when you want modular agents that handle tasks such as customer follow-ups, internal workflow automation, or research and summarization, without building a large orchestration layer up front.

What makes Strands Agents practical for SMBs is that it’s designed to scale from a small pilot to production, and it supports a broad tool ecosystem. It also provides native support for the Model Context Protocol (MCP), helping you standardize how you provide context and tools to the agent.

If you’re building on AWS, Strands Agents integrates well with services you may already use. This way, you can connect agents to your existing workflows and run them on schedules or events. These could include, such as Amazon Bedrock, AWS Lambda, and AWS Step Functions.

To keep risk low, start with a single, focused workflow, such as “summarize inbound requests and draft next steps.” Limit the tools the agent can use, and review outputs during your pilot before expanding the scope.

For more information, refer to Strands Agents, an Open Source AI Agents SDK.

Agent Squad is an AWS Labs framework for building multi-agent systems where specialized agents collaborate to handle complex conversations and tasks.

Instead of a single “do-everything” agent, Agent Squad routes each request to the right agent and maintains context throughout the interaction. It's useful when your SMB needs consistent handoffs across functions such as support, sales ops, and marketing.

What makes it practical for SMBs:

• Intelligent routing and context retention: The framework uses a classifier, often an LLM, to select the most appropriate agent for each request. This helps to maintain consistent responses as workflows grow.
• Built-in orchestration patterns: Agent Squad includes components such as the SupervisorAgent, which uses an “agent-as-tools” approach. It exposes team agents as invocable tools, so a lead agent can coordinate work.
• Extendable and production-oriented: You can start with prebuilt components and then add custom agents and message storage options as you mature from a pilot to broader automation.
• Works with common dev stacks: The docs include quickstarts for both Python and TypeScript, which align with how your team already builds internal tools.
  

LangChain is a popular Python framework for building applications powered by large language models (LLMs). This includes agents that can call tools, use APIs, and pull context from your data.

In practice, LangChain acts like the “glue” between an LLM and the systems your business already runs. So, you can automate tasks such as research and summarization, internal knowledge lookups, content drafting, and customer support follow-through.

By abstracting away much of the complexity of working directly with LLMs, it can be approachable even if you don’t have a large machine learning (ML) team, while still being flexible enough to support more advanced implementations as you grow.

You can use LangChain with AWS services to build production-ready workflows. For example, using Amazon Bedrock for foundation models and Amazon SageMaker for building and deploying ML models.

Langflow is an open-source, no-code or low-code visual builder for LangChain that helps you design AI workflows with a drag-and-drop interface. For SMBs, this is useful when you want to prototype an agent quickly, without committing to a full engineering build on day one.

Langflow goes beyond static flowcharts by including an integrated chat interface, so you can interact with what you’ve built in real time. That makes it easier to test workflows, observe responses, and refine prompts in real time.

You can also edit prompts directly inside the user interface (UI). This helps marketing, ops, or support teams iterate without waiting on developer cycles.

When a flow is working, Langflow lets you export and share it, integrate it into a larger project, or reuse it as a template for similar workflows.

If you want a packaged deployment option, Langflow is also available through AWS Marketplace. For more information, refer to Langflow on AWS Marketplace.

LangGraph is a graph-based AI agent framework focused on linking data sources and agent outputs. SMBs can benefit from visualizing data dependencies and understanding how decisions propagate through AI systems. A few LangGraph capabilities that tend to matter most for SMB use cases:

• Durable execution and recovery: LangGraph is designed to persist state, enabling an agent to resume after interruptions rather than restart from scratch. This is important for workflows that run across multiple systems or take time to complete.
• Human-in-the-loop control: You can pause at key checkpoints (for example, before sending an email or updating a record), review or edit what the agent plans to do, then approve or reject the next action.
• Clear orchestration for multi-agent work: Graph-based orchestration makes it easier to coordinate specialized steps, like research, extraction, drafting, and routing, without turning your agent into one large, brittle prompt.

If you’re building on AWS, you can combine LangGraph with Amazon Bedrock to run foundation models while keeping your orchestration in LangGraph. For a concrete example, see “Build multi-agent systems with LangGraph and Amazon Bedrock."

SmolAgents (by Hugging Face) is lightweight and optimized for small-scale deployments. This makes it a practical option for SMBs with limited time and engineering bandwidth. It’s designed for building agents that can either call tools in a structured way or think in code, depending on the agent type you choose.

For SMB use cases, SmolAgents works well when you want to pilot a focused workflow without building a large orchestration layer first.

It also includes guidance for inspecting agent runs (telemetry) and patterns, such as memory and secure code execution. This helps you review what happened and tighten guardrails as you scale.

You can run SmolAgents with models hosted in different places, including using Amazon Bedrock as the model provider. This way, your agent can access AWS foundation models while you keep your agent logic in SmolAgents.

CrewAI is an open-source, Python-based framework designed for team collaboration and multi-agent orchestration. SMBs can use it for automating workflows that span departments, such as sales, marketing, and operations. For SMBs, CrewAI is a good fit when your process spans teams and handoffs, such as:

• Sales and marketing: Research an account, draft outreach, generate a proposal outline, then route for approval.
• Support and ops: Summarize an issue, pull context from internal docs, propose next steps, then create or update a ticket.
• RevOps reporting: Compile weekly performance notes, flag anomalies, and draft a leadership summary.

Because CrewAI encourages you to define roles, tools, and task boundaries up front, it can help you avoid “one giant agent prompt” and keep workflows easier to test and maintain as you expand.

You can pair CrewAI with Amazon Bedrock to run foundation models while keeping your orchestration in CrewAI. To learn more, refer to the guide: Build agentic systems with CrewAI and Amazon Bedrock.

OpenAgents is an open-source framework for building AI agent networks. It’s a shared environment where multiple agents can connect, communicate, and collaborate over time, not just run one-off tasks.

For SMBs, this is most useful when your workflows span tools and people, and you want agents to keep context as they contribute to ongoing work. For example, maintaining a shared support knowledge base, coordinating a research backlog, or drafting and updating internal playbooks.

A practical differentiator is OpenAgents’ network-first design. Agents join a network, exchange events, and can be extended with “mods” for shared activities. These can include collaborative document writing or wiki maintenance.

It’s also protocol-agnostic and designed to work with different model providers and agent frameworks. This helps you avoid locking your implementation into a single toolchain as you experiment.