Choose OpenAI Agents SDK for a compact Python runtime centered on agents, tools, handoffs, guardrails, sessions, and tracing. Choose Google ADK when code-first multi-agent composition, deterministic workflow agents, evaluation, and multiple language options fit the team. Choose Microsoft Agent Framework when provider abstraction, graph workflows, middleware, sessions, and Microsoft ecosystem alignment matter. Prototype the same workflow in the shortlist; feature matrices alone do not establish operational fit.
Tech
Choose OpenAI Agents SDK for a compact Python runtime centered on agents, tools, handoffs, guardrails, sessions, and tracing. Choose Google ADK when code-first multi-agent composition, deterministic workflow agents, evaluation, and multiple language options fit the team. Choose Microsoft Agent Framework when provider abstraction, graph workflows, middleware, sessions, and Microsoft ecosystem alignment matter. Prototype the same workflow in the shortlist; feature matrices alone do not establish operational fit.
Reviewed July 30, 2026: All three frameworks were changing rapidly. Google published ADK 2.0 architecture guidance on July 1, and Microsoft documentation updated its framework overview on July 10. Language packages, preview status, providers, and feature parity must be checked per SDK before adoption.
At a glance
• OpenAI Agents SDK emphasizes a small set of Python primitives and uses the Responses API by default for OpenAI models.
• Google ADK offers code-first agent composition, model-driven agents, and deterministic sequential, parallel, and loop workflows.
• Microsoft Agent Framework combines agents, an opinionated harness, and graph-based workflows with provider integrations.
• All three can call tools and coordinate agents; their abstractions and lifecycle boundaries differ.
• Framework state is not automatically durable business state.
• Security, deployment, identity, data governance, and service operations remain application responsibilities.
Comparison boundaries
This is a framework comparison, not a model benchmark. A framework can support multiple models, but model capability, price, latency, geography, and data terms are separate decisions.
It is also not a managed-platform comparison. Open-source or client SDK features do not automatically include production hosting, secrets, queues, identity, dashboards, incident response, or service-level commitments. Those may come from related cloud products or infrastructure selected by the implementer.
Finally, “multi-agent” is not automatically better than one agent plus deterministic code. Microsoft’s framework overview explicitly recommends using a function instead of an AI agent when a function can handle the task. That is a sound default across all three.
OpenAI Agents SDK mechanics
The OpenAI Agents SDK defines agents as models configured with instructions and tools. A runner owns the turn loop. Function tools, agents-as-tools, and handoffs allow delegation; guardrails provide validation points; sessions maintain conversation context; and built-in tracing records agent activity.
Its primary strength is conceptual economy. The orchestration guide supports model-directed orchestration and code-directed orchestration without requiring a graph language. This fits teams that want to express control flow in ordinary Python and use OpenAI models through the Responses API.
The same simplicity means developers must decide how to add durable execution, complex graph state, and non-Python services. Official running guidance identifies integrations for durable orchestration, which confirms that long waits and process recovery are a distinct infrastructure layer rather than an automatic property of every SDK run.
Google ADK mechanics
Google introduced Agent Development Kit as an open-source, code-first framework for agent and multi-agent applications. It separates LLM-driven agents from deterministic workflow agents. Sequential, parallel, and loop agents can encode predictable control flow, while model-driven transfer supports adaptive delegation.
ADK’s lifecycle framing covers building, interaction, evaluation, and deployment. Its official materials describe local developer tooling, trajectory evaluation, MCP tools, and model flexibility. Language support has expanded over time; however, the exact features differ among Python, TypeScript, Java, and Go packages.
Google’s ADK 2.0 architecture explanation emphasizes deterministic control for execution, routing, scheduling, and error handling rather than asking an LLM to orchestrate every step. This makes ADK relevant when a team wants agentic reasoning inside an explicit software workflow.
Microsoft Agent Framework mechanics
Microsoft describes Agent Framework as the successor that brings together concepts from Semantic Kernel and AutoGen. Its current architecture groups features into individual agents, an opinionated harness for longer tasks, and graph-based workflows.
Agent Framework exposes model clients, sessions, context providers, middleware, MCP integration, and typed workflow routing. The workflow documentation defines executors connected by edges and validates graph structure and type compatibility. Workflows can stream events and support checkpoints at defined execution boundaries.
This structure fits organizations that want explicit workflows and Microsoft-aligned development patterns. It also requires careful package review: documentation notes that some languages and capabilities are previews or have incomplete parity.
Decision table
Priority: Minimal Python agent runtime; Best first evaluation: OpenAI Agents SDK; Why: Small primitive set and direct OpenAI path; Verify before choosing: Provider strategy and durable execution
Priority: Deterministic plus LLM-driven composition; Best first evaluation: Google ADK; Why: Workflow agents and agent hierarchy; Verify before choosing: Language feature parity and deployment target
Priority: Typed graph workflows in .NET or Python; Best first evaluation: Microsoft Agent Framework; Why: Executors, edges, sessions, and middleware; Verify before choosing: Package maturity and migration status
Priority: Strong OpenAI-specific tooling path; Best first evaluation: OpenAI Agents SDK; Why: Responses API default and OpenAI tracing; Verify before choosing: Data controls and model/tool availability
Priority: Google Cloud and Gemini-oriented build; Best first evaluation: Google ADK; Why: Official Google framework with Vertex integrations; Verify before choosing: Managed-service boundary and region
Priority: Microsoft/Azure-oriented enterprise build; Best first evaluation: Microsoft Agent Framework; Why: Foundry and Azure integrations plus provider abstraction; Verify before choosing: Preview features and third-party data flows
Priority: Highly portable internal orchestration; Best first evaluation: Build a thin domain layer above one framework; Why: Limits framework concepts in business code; Verify before choosing: Cost of maintaining adapters
Evaluate mechanics, not the demo
Implement one representative workflow in each serious candidate. Include a read tool, a write tool, a human approval, a failed tool, a retry, a long pause, and a resumed run. Record the code and infrastructure needed, not only the happy-path output.
Measure maintainability with questions:
• Can engineers see which transition is model-selected and which is deterministic?
• Can a failed write be resumed without duplicate side effects?
• Can tool access vary by user, tenant, and environment?
• Can traces exclude sensitive data while preserving evidence?
• Can the same business test run after a model or framework upgrade?
• Can the framework be replaced without rewriting domain rules?
Keep tools and policy contracts in application-owned modules. A RefundPolicy or CustomerRecord should not become inseparable from one agent class if it is core business logic.
Security and governance
Framework guardrails, middleware, and callbacks help enforce policy but do not replace downstream authorization. The resource system must verify tenant, record, and operation permissions.
For each candidate:
• map where prompts, model inputs, tool arguments, outputs, traces, and session state are stored;
• keep API keys and SaaS tokens in the execution layer;
• filter tool availability before model selection;
• require approval for financial, destructive, or external actions;
• constrain model turns, tools, time, data volume, and spend;
• treat third-party tool results and agent messages as untrusted content;
• pin packages and scan direct and transitive dependencies;
• separate development, evaluation, and production identities and data;
• document which cloud or provider receives each data category.
Microsoft’s overview warns that use of third-party systems remains the implementer’s responsibility, including data sharing, retention, location, permissions, and costs. The same diligence applies when any framework routes to non-default models or remote tools.
Implementation checklist
• [ ] Define the business workflow before selecting a framework.
• [ ] Remove steps that should be ordinary deterministic functions.
• [ ] Shortlist by team language, model strategy, and deployment environment.
• [ ] Confirm current package and feature status for that language.
• [ ] Build the same failure-rich proof of concept in each finalist.
• [ ] Separate business entities and policies from framework classes.
• [ ] Store durable task state outside conversational memory.
• [ ] Add idempotency and compensation for side-effecting tools.
• [ ] Enforce identity, tenant, and record authorization downstream.
• [ ] Configure sensitive-data handling for logs, traces, and sessions.
• [ ] Add approval and resumability to one consequential action.
• [ ] Create evaluation cases for outputs and execution trajectories.
• [ ] Pin versions and test upgrades against recorded cases.
• [ ] Estimate hosting, model, tracing, storage, and operations costs.
• [ ] Document a migration boundary before production adoption.
Limitations and change risk
Feature parity changes faster than long-form comparisons. One language may support a capability that another package does not. Preview labels, package names, provider integrations, and deployment guidance can change independently.
No framework guarantees correctness, autonomy, or a positive return. Framework-generated traces can show what ran but may not prove that the business outcome was valid. Add independent reconciliation and business metrics.
This guide does not rank model quality, total cost, or hosting reliability because those depend on the selected providers, tasks, traffic, and environment. Run a workload-specific evaluation with current commercial terms.
For how an implementation partner fits around the framework, read AI Operator company.
FAQs
Which framework is best for beginners?
OpenAI Agents SDK has a deliberately small Python abstraction set. Familiarity with Google Cloud, TypeScript, Java, Go, .NET, or Azure may make ADK or Microsoft Agent Framework a better organizational starting point.
Do all three support multiple agents?
Yes, through different abstractions such as handoffs, agents-as-tools, hierarchies, workflow agents, orchestration patterns, or graph workflows. Confirm the exact package and language support.
Do I need a multi-agent system?
Usually not at first. Start with deterministic code and one agent. Add specialist agents only when separate instructions, tools, context, ownership, or evaluation boundaries justify them.
Are framework sessions durable workflow storage?
No. Sessions primarily represent conversation or agent state. Durable transactions, approvals, retries, and business records need an appropriate database or workflow system.
Can I switch frameworks later?
Yes, but migration cost grows when domain rules, tools, persistence, and observability depend directly on framework types. Isolate those boundaries from the beginning.
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