AI implementation
How to Choose an Open-Source AI Agent Framework for Production
Evaluate open-source agent frameworks with a real workflow: state, tool contracts, testing, recovery, deployment, licensing, and maintenance ownership.
Choose an open-source AI agent framework by implementing a representative task and its failure cases. A quick-start demo shows how to call a model; production work also needs state, integrations, evaluation, deployment, and an owner for upgrades.
Begin with your language, runtime, and task requirements. A framework that fits the team's existing application may be easier to maintain than one with an impressive but unused feature list.
Define the workload first
Write down whether the task is interactive or scheduled, whether it must survive a restart, which tools can change data, and how approval works. Estimate typical and worst-case duration, and identify the expected deployment environment.
For a support assistant, the important capabilities might be typed tool arguments, persistent task state, controlled retries, and human review. For independent research, worker coordination and evidence aggregation may matter more.
Do not require multi-agent orchestration simply because a framework advertises it. Check whether the task benefits from multiple agents first.
Compare plausible starting points
The following are examples to investigate, based on their documentation checked September 8, 2026:
| Framework | Documented emphasis | A useful evaluation question |
|---|---|---|
| LangGraph | Stateful agent orchestration, persistence, and human-in-the-loop support | Can our task resume correctly around an interrupted action? |
| Pydantic AI | Python agent applications with typed structures and tool integration | Can our team validate inputs and outputs within its existing Python services? |
| Mastra | TypeScript agents and workflows with supporting application capabilities | Can the workflow fit our TypeScript stack and deployment model? |
Use the primary references for current details: LangGraph, Pydantic AI, and Mastra. This table is not a benchmark or a claim that one option is universally best.
Build the same thin implementation
Give each candidate one task: retrieve an authorized customer record, inspect the relevant policy, and save a resolution draft. Use the same tools, test data, and acceptance criteria so differences are interpretable.
Then introduce a timeout, an ambiguous customer, a repeated request, and a process restart. Inspect state and side effects. A persisted agent checkpoint does not by itself guarantee that an external action will not run twice.
Measure the application code and operational components still required. The relevant cost is the system you must maintain, not only the number of lines in the agent definition.
Inspect the boundaries around the framework
Ask where state is stored, who can access traces, how tools inherit identity, and how secrets are supplied. Check whether development defaults are suitable for the actual production environment.
Separate the open-source library from optional managed services. Review the license for the exact version you plan to use, and identify which desired features depend on hosted infrastructure or a commercial plan. Avoid assuming that an open repository makes the full operating stack free.
Check upgrade practices, release notes, testability, and your ability to debug a failed run. An active project is helpful, but it does not replace a team member who can own your integration.
Record an exit path
Keep business rules, tool adapters, and evaluation fixtures understandable outside the framework. Record task and operation identifiers in your own application data where appropriate.
This does not mean avoiding all framework features. It means knowing which state and contracts must remain accessible if you later change the orchestrator. Exporting conversation text is not necessarily enough to migrate running workflows.
Make the selection reviewable
Compare accepted outcomes, recovery behavior, implementation effort, observability, operating costs, and unresolved limitations. Save the tested versions and the cases that failed. Choose based on requirements met and operating burden, rather than a generic ranking.
Use our evaluation guide and cost model to structure that decision. Dream Hatch Labs can help build and assess the implementation, including a custom framework when the requirements justify it.