How to Choose an AI Agent Framework: Decision Guide and Comparison Table

Start with your constraints, not the framework

Framework choice is easier when you write down your constraints first. Which languages does your team ship in production? Which cloud and model providers are you allowed to use, given client contracts or data residency rules in India, the EU or the Gulf? Do your agents need to pause for approval or survive restarts? How many people will maintain the code in two years? A Python startup in Hyderabad and a .NET shop serving UK insurers will reach different answers, and both can be right.

Also separate the parts that change quickly from the parts that should not. Agent frameworks and model APIs have changed a great deal in a short time, as the AutoGen story in this course shows. Your tools, your data access rules and your evaluation set are the assets that last. Designing for that split is the most useful architectural decision you can make, whichever framework wins your comparison.

Comparison table

This table combines the facts from earlier lessons into one view. It reflects each project's official documentation or package listing at the time of writing, so recheck licence and status before you commit, because this field changes fast. Licence entries in brackets apply only to the package named. Language support means an official package exists, not that every feature is equal across languages; Google, for example, marks Kotlin graph workflows as experimental.

Framework                 Languages                       Licence                     Best fit
LangGraph                 Python, JavaScript              MIT                         Explicit graphs, durable runs, approvals
CrewAI                    Python                          MIT                         Role based teams, Crews plus Flows
Microsoft Agent Framework Python, .NET (Go separately)    MIT                         Azure and .NET teams, workflows
AG2                       Python                          Apache 2.0                  Conversational multi-agent, community run
OpenAI Agents SDK         Python, JS/TS                   MIT (Python package)        Light primitives, handoffs, guardrails
Claude Agent SDK          Python, TypeScript              Anthropic Commercial Terms  Built in file and shell tools, permissions
Google ADK                Python, Go, TS, Java, Kotlin    Apache 2.0 (adk-python)     Multi language, A2A, built in evaluation
Pydantic AI               Python                          MIT                         Type safe outputs and dependencies
AutoGen                   See README                      MIT code                    Archived; migrate to Agent Framework

A decision guide in six questions

Work through these questions in order and stop at the first strong answer. They are not a scoring system; they are a way to make your reasons explicit so the team can discuss them openly. Write the answers in your design document so the next engineer knows why you chose what you chose, and so you can tell later whether the original reasons still hold.

• Is the path fixed? Use a plain workflow and direct model calls before any framework.
• Must runs pause for people or survive restarts? Favour LangGraph, Agent Framework workflows or an SDK with serialisable run state.
• Is your team on .NET or Azure? Start with Microsoft Agent Framework.
• Do you need several languages or agent to agent calls across teams? Look at Google ADK and A2A.
• Is the agent mainly working on code and files? The Claude Agent SDK's built in tools save work.
• Do you want small primitives and typed outputs in Python? Compare the OpenAI Agents SDK and Pydantic AI.

Design so you can switch later

The cheapest insurance is to keep business logic outside the framework. Put your tools behind MCP servers or plain Python functions with no framework imports, so any framework can call them. Keep prompts in versioned files. Keep your evaluation set independent of the framework, so you can run the same cases against a new candidate. If you do that, swapping frameworks becomes a week of work rather than a rewrite.

Also avoid reaching for multi-agent designs early. One well instrumented agent with good tools solves most business problems, and every extra agent multiplies cost, latency and failure points. XDQ Labs teams usually prove value with a single agent first and split it only when evaluation results show a clear gain. When you do split, give each agent its own tools and permissions so the split also improves safety.

Run a two week pilot

When two frameworks both look suitable, run a short pilot rather than a long debate. Take one real use case, such as answering vendor payment status queries for a finance team, and build the same agent in both. Use the same tools, the same model and the same twenty evaluation cases. Measure pass rate, cost per run, latency and how long a new developer takes to make a change.

Finish with a written decision that names the winner, the reasons and the risks, including licence terms, project status and who governs the project. Revisit the decision every six months, because a project's status can change, as the AutoGen story showed. The goal is not to pick the perfect framework, which does not exist, but a sound one that you can leave cleanly if you need to.

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