Building Multi-Agent Systems with Any Framework
Orchestrate autonomous agents that plan, use tools, and collaborate.
Complex work rarely fits a single prompt. Multi-agent systems break a goal into specialized agents — planners, researchers, coders, reviewers — that collaborate, call tools, and verify each other's work. I design and build multi-agent systems in whatever framework fits your stack: LangChain and LangGraph, Microsoft AutoGen, Semantic Kernel, CrewAI, or Google's Agent Development Kit (ADK) — with the orchestration, memory, and guardrails that make them reliable in production.
What makes multi-agent systems work in production
Autonomous agents are powerful but fail in messy ways — loops, drift, runaway cost. I engineer the harness around them so they stay on task and observable.
- Orchestration patterns: supervisor/worker, planner-executor, and graph-based flows
- Tool use, function calling, and typed structured outputs
- Shared and per-agent memory with state persistence
- Adversarial verification and reviewer agents to catch errors
- Loop, cost, and step budgets to keep runs bounded
- Full tracing and observability across every agent step
How I choose the right agent framework
There is no single best framework — only the best fit for your team and task. I map your requirements to the strengths of each framework below, then build the orchestration with production concerns handled from the start: reliability, cost control, and observability.
Any framework — matched to your stack
I build multi-agent systems in the framework that best fits your team, cloud, and reliability requirements.
LangChain & LangGraph
Graph-based orchestration for stateful, controllable agents
LangGraph models agent workflows as explicit state machines, giving you fine-grained control over branching, loops, and human-in-the-loop steps. My default for complex, production-grade agentic systems that need reliability and observability.
Microsoft AutoGen
Conversational multi-agent collaboration
AutoGen excels at agents that solve problems through conversation — code-writing, review, and tool use between specialized agents. Strong for research-style and coding workflows where agents iterate together.
Semantic Kernel
Enterprise agents in the Microsoft ecosystem
Semantic Kernel brings agents, plugins, and planners to .NET and Azure environments with enterprise governance. Ideal when you're building inside the Microsoft stack with C# or Python and need compliance-friendly orchestration.
CrewAI
Role-based agent crews with clear responsibilities
CrewAI structures agents as a crew with defined roles, goals, and tasks that collaborate on an outcome. Fast to stand up for role-driven workflows like research, content, and analysis pipelines.
Google ADK
Agent Development Kit for the Gemini & Vertex ecosystem
Google's Agent Development Kit builds and deploys agents tightly integrated with Gemini models and Vertex AI. The natural choice when your platform is Google Cloud and you want managed deployment and tooling.
Technologies I use
Related case studies
Frequently asked questions
What is a multi-agent system?
A multi-agent system coordinates several specialized AI agents — each with its own role, tools, and memory — to complete a task that's too complex for a single agent. Agents can plan, delegate, use tools, and review each other's output, producing more reliable results on multi-step work.
Which multi-agent framework is best — LangChain, AutoGen, CrewAI, Semantic Kernel, or Google ADK?
There's no universal winner. LangGraph/LangChain is my default for controllable, stateful production agents; AutoGen shines for conversational and coding workflows; CrewAI is fastest for role-based crews; Semantic Kernel fits the Microsoft/.NET enterprise stack; and Google ADK is ideal on Google Cloud with Gemini. I pick based on your existing stack, team skills, and reliability needs.
Can you build agents with a framework I already use?
Yes. I build multi-agent systems in LangChain, LangGraph, AutoGen, Semantic Kernel, CrewAI, and Google ADK, and can also work with custom orchestration. If your team already standardized on one, I build within it rather than forcing a migration.
Ready to build with multi-agent systems?
Let's scope your project and turn it into a production system.
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