How to Choose an AI Agent Development Framework: Core Evaluation Dimensions and Scenario-Based Practices

Why Agent Frameworks Are Proliferating

Over the past two years, the development paradigm for LLM applications has shifted noticeably. Early applications were mostly “single-turn calls”: the user submits one prompt, the model returns one response, and the logic is simple enough that calling the model API directly is sufficient. Today’s agent applications, by contrast, require the model to perform multi-step autonomous execution—given a goal, it decomposes the task, plans the steps, calls external tools, corrects course based on execution feedback, and iterates until it produces a final result.

Around this new paradigm, a set of generic capabilities keeps getting rewritten by every team: the agent main loop, tool registration and dispatch, session state maintenance, context compression, multi-agent collaboration, and so on. Implementing all of this from scratch in every project is extremely costly. That is why vendors and open-source communities have rolled out development frameworks that consolidate these shared capabilities—while also using them to anchor their own model and cloud ecosystems. For developers, frameworks genuinely eliminate a great deal of low-level work, but with so many options available, selection itself has become the new problem. This article does not review specific products; instead, it breaks the selection process down into an actionable methodology.

The Four Typical Framework Archetypes

Setting specific products aside, mainstream frameworks fall into roughly four categories based on design philosophy and target audience, each reflecting different engineering trade-offs.

1. First-party SDKs from major vendors

These frameworks are typically released directly by model or cloud providers and are deeply integrated around their own model capabilities, with broad feature coverage: tool calling, session management, sub-agents, and permission control often work out of the box. The advantage is that they evolve in lockstep with the model’s capabilities and offer mature production-grade features. The trade-off is heavy encapsulation and many abstraction layers—troubleshooting can involve a long call chain, and you risk coupling to a specific model ecosystem.

2. Frontend-integrated frameworks

Aimed at web application developers, these sit close to the frontend toolchain: they integrate well with the component systems of mainstream frontend frameworks, natively support streaming rendering and UI state binding, and usually ship SDKs for both TypeScript and Python. They are a good fit for conversational product interfaces, but their capabilities taper off when it comes to complex multi-agent orchestration.

3. Established orchestration frameworks

Evolved from early LLM application frameworks, these have been iterated for years and offer a complete abstraction system, a large middleware and integration ecosystem, and compatibility with nearly every model and vector database. They are the most feature-complete, but also come with many concepts and a steep learning curve—simple requirements can end up over-engineered. Fast release cycles also mean upgrade and migration costs are non-trivial.

4. Minimalist core frameworks

Usually maintained by individuals or small teams, these focus on “getting the smallest viable kernel right”: they provide only the most essential abstractions, such as the agent loop and tool registration, leaving everything else for developers to assemble themselves. The learning cost is low—the source code can often be read in a day—making them ideal for rapid prototyping and deep customization. The downside is that production-grade features (permissions, observability, persistence) must be built yourself.

Eight Core Dimensions for Evaluating a Framework

When selecting a framework, build your own capability matrix and score candidates item by item, rather than relying on marketing pages. The following eight dimensions cover the vast majority of decision scenarios:

1. Model access: Does it support switching between multiple models? Can it connect to privately deployed open-source models? Is the model abstraction layer unified, so that switching models does not require major changes to business code?
2. Tool calling and protocol standardization: How are tools defined, registered, and validated? Does it support industry-standard protocols for tool and context integration? This determines whether you can directly reuse the community’s existing tool ecosystem instead of writing every tool yourself.
3. Memory and state management: How are short-term conversation histories trimmed and compressed? Does it offer long-term memory, session persistence, and checkpoint resumption? This directly affects the stability and cost of long-running tasks.
4. Multi-agent orchestration: Which collaboration patterns are supported—linear pipelines, state graphs, hierarchical delegation, or free-form collaboration? Orchestration traceability (whether you can clearly see the execution path and easily locate problems) often matters more than the number of patterns.
5. Streaming and human-in-the-loop: Does it natively support streaming output and mid-run interruption? Can approval steps be inserted for critical operations? In high-risk business scenarios, this is a hard requirement.
6. Observability and evaluation: Can it emit structured call traces (the timing and results of every model call and tool execution)? Is there a配套 evaluation mechanism to measure task success rates? Debugging agent applications is far harder than debugging traditional applications, and a framework with weak observability will be painful in production.
7. Security and permissions: How well does it handle sandboxed tool execution, confirmation mechanisms for sensitive operations, and key and credential management?
8. Language ecosystem and engineering integration: Python, TypeScript, or both? How costly is integration with your existing backend frameworks, task queues, container platforms, and logging, monitoring, and alerting systems?

Using Data to Gauge Real Framework Activity

A capability matrix is a static view; you also need dynamic data for cross-validation.

Community attention (e.g., repository stars) is the easiest metric to obtain, but also the most inflated: tutorial-driven traffic and trending events can temporarily boost the numbers, and stars cannot distinguish “saw it” from “used it.” By comparison, download counts from package managers better reflect real usage density—with three caveats. First, different frameworks belong to different language ecosystems, so you need to check the corresponding package registry for each. Second, strip out inflation caused by transitive dependencies. Third, look at trends rather than single data points: a framework with steady growth over six months carries very different adoption risk from one that suddenly spiked and then fell back.

Beyond that, several signals are easy to overlook: release frequency and the density of breaking changes, average issue response time, documentation completeness and update timeliness, and the stability of core contributors. For minimalist frameworks maintained by individuals, pay particular attention to the “bus factor”—if the maintainer steps away, the project can stall quickly, and that potential migration cost should be factored in upfront.

Scenario-Based Selection Recommendations

There is no “best” framework, only the one most suitable for your scenario. The following paths are for reference:

• Personal projects and learning prototypes: Start with a minimalist core framework—or even write a bare agent loop first to understand the mechanics, then decide whether to adopt a framework. The goal at this stage is to validate ideas quickly, not to build complete engineering.
• Web-based AI applications: If the core is a conversational product interface, a frontend-integrated framework can significantly reduce the workload of streaming UI and state management.
• Deep task-oriented agents: For applications such as intelligent coding assistants that require long-horizon planning and multi-tool collaboration, choose a vendor SDK or mature orchestration framework with high-level encapsulation and complete production features, so you can focus on the business logic itself.
• Enterprise systems: Beyond the framework itself, evaluate private deployment, permission auditing, and cloud platform integration. It is also worth considering one-stop LLM and agent development platforms offered by cloud providers such as Baidu AI Cloud; these platforms typically integrate model services, knowledge retrieval, orchestration and scheduling, and monitoring and operations into a single pipeline, reducing the effort of building the infrastructure yourself. For teams with deep customization needs, the combination of an open-source framework plus a self-built foundation remains a viable option.

Back to Essentials: A Minimal Agent Loop

No matter how many capabilities a framework advertises, its kernel is essentially the same loop: model generates → tools execute → results are fed back → generate again, until the task is complete. A few dozen lines of pseudocode express it:

messages = [{"role": "user", "content": task}]
while True:
    resp = llm.chat(messages, tools=tool_schemas)
    msg = resp.message
    messages.append(msg)
    if not msg.tool_calls:            # The model requests no more tools; task is complete
        final_answer = msg.content
        break
    for call in msg.tool_calls:       # Execute each tool call and append the result
        result = dispatch(call.name, call.arguments)
        messages.append({
            "role": "tool",
            "tool_call_id": call.id,
            "content": str(result),
        })

Once you understand this loop, any framework’s documentation becomes much clearer: a framework is simply this loop wrapped in engineering layers—tool registration, state management, orchestration, observability, and so on. This also yields a practical selection test: can you clearly locate each step of this loop within the framework? If the abstractions are wrapped in layer after layer and even the basic execution path is hard to trace, ongoing maintenance and troubleshooting costs will likely be high.

Conclusion

The essence of an agent framework is consolidating recurring engineering problems into reusable abstractions. A three-step approach to selection: first, clarify your application scenario and constraints (deployment model, language stack, security requirements); second, compare candidate frameworks item by item against your capability matrix; third, validate the decision with activity data and a minimal prototype. A framework is a means, not an end—what ultimately determines a project’s success is still the fundamentals: task decomposition, tool design, and iterative evaluation.