Questions readers ask: How do I compare two SDKs on the criteria that matter to my team?

Comparing two SDKs well starts before opening either set of docs. The team needs to agree on what the software must enforce, expose and integrate with, then score both options against that contract. For enterprise and agent-facing teams, the decisive criteria concern safe execution and governance.

Why surface-level SDK comparisons fail enterprise and agent-facing teams

Most SDK comparison guides lead with language support, streaming, GitHub stars and demo quality. Enterprise teams need answers to different questions. How safely can agents act, how visible are those actions afterward, and how hard will the system be to govern at scale? A polished example cannot answer those questions, however agreeable its syntax.

MCP adoption since late 2024 also changed the reference architecture. An agent SDK is now also an MCP client, so a comparison that ignores that role leaves out part of its production behavior. Teams that select on surface metrics can discover the missing controls at audit time, when repairs are expensive and slow. The demo has finished by then, but the bill has not.

How to run a criteria-first comparison before you open either SDK's docs

Start by writing down the team’s requirements, then turn them into a weighted scorecard before evaluating either option. The six criteria are type safety, observability, authentication, tool-call validation, MCP compatibility and shadow agent surface. Context sets their relative weight. A team that runs Datadog should give observability more weight, a team that works under regulations should give identity and audit more weight, and autonomous agents need stronger tool-call controls and MCP support. Score each SDK against evidence rather than marketing copy, compare weighted totals, and use governance criteria to break ties after technical parity.

Type safety: whether the SDK enforces a contract between the model and your systems

Type safety begins with the boundary between model output and tool input. Confidence is not a data type, and an operationally wrong response must not reach a live system merely because it sounds plausible. Check whether the SDK validates LLM outputs through typed models such as Pydantic or Zod before execution. Tool parameters should also be statically typed and validated before the call.

The contract must hold at runtime too. IDE autocompletion and static checking help developers, but hidden keyword-argument dictionaries can escape static analysis, and a compile-time type cannot prove that an API response still matches its expected structure. An SDK that simply casts response data can crash or corrupt data when an API contract changes. That risk is the same for hand-written and generated SDKs. Ask the vendor or maintainer for the documented behavior when a tool returns a schema mismatch, because “it depends on the model's output” is not a contract.

Observability: whether every agent action produces a trace your existing stack can read

Validation establishes what may execute, while observability makes execution open to inspection afterward. Scoring should assess whether the team can reconstruct what an agent did, why, and at what cost in the tools it already runs. OpenTelemetry provides the standard instrumentation layer for token usage, tool latency and multi-agent context propagation. Check native spans, direct export to Datadog, Honeycomb or Grafana via OTLP, and per-turn input, output and cache-read token counts. Also inspect the default trace destination.

An agent development kit has native OTel and exports to Cloud Trace, while Microsoft Agent Framework has built-in support for Python and .NET, with Go in public preview. Pydantic AI supports Logfire or any OTLP backend, and Strands Agents offers an OTLP exporter. OpenAI Agents SDK uses its own tracing and sends traces to OpenAI by default, while Claude Agent SDK’s tracing is in beta and uses its own span names.

Score the gaps that native instrumentation still leaves. The OTel GenAI conventions cover model calls, duration, error status and opt-in execute_tool spans, but downstream API calls, authentication, token refresh, retries and rate limits require additional instrumentation. A tool-invocation span is not the whole journey. If an SDK requires a second proprietary platform, treat that as a governance gap rather than a small inconvenience, because agent actions may remain unauditable from the team’s SOC tools.

Authentication and identity propagation: whether the SDK can carry user identity all the way to tool execution

A trace shows what happened, but accountability requires knowing who caused it. “Supports SSO” is therefore too loose a test. User identity must propagate from the frontend or SSO layer through the agent workflow into tool calls, with the user’s permission scope intact. An agent that answers a question about documents must only have access to documents the person asking can read. A shared service account cannot supply that user-level boundary.

Check for per-session ephemeral tokens scoped to the task, with automatic teardown when the session ends. These controls limit exposure to prompt injection and credential theft, and making them optional weakens the governance model. Existing identity integrations provide useful compatibility tests. Auth0 announced general availability of Auth for MCP on May 6, 2026, and Okta’s MCP server provides another example of an integration an SDK should consume cleanly. Neither product is mandatory, but usable identity hooks are.

Test credential handling through successful login and failure paths by asking how rotation works mid-session, what happens to in-flight tool calls when a token expires, and whether access can be disabled per user without redeploying the agent. Those answers expose the design’s priorities. Shared API keys or long-lived static credentials are not a governance solution, regardless of the surrounding feature set.

Tool-call validation and runtime policy enforcement: where security policy must actually live

Once identity is established, the next question is who enforces its permissions. The model is not a security boundary. Authorization must live in a deterministic policy engine outside the model’s reasoning, because prompt injection can bypass decisions left inside that reasoning loop. OWASP’s framing makes the structural point that filtering is insufficient, while Microsoft’s red-team benchmark illustrates the weakness of prompt-only safety instructions. Detection alone cannot substitute for privilege containment.

Check for declarative tool schemas and whether model arguments are validated before execution, then check whether strict role-based permissions can be applied to each call. These are related but distinct checks. A well-formed request can still be unauthorized. Also check sandboxing for AI-generated code through mechanisms such as gVisor, WebAssembly or containers, and separation of user input from system instruction buffers. Finally, check whether a policy engine can sit between SDK and tool without much custom work, since privilege containment and content provenance need controls the model cannot talk its way around.

MCP compatibility and gateway interposition: the criterion that determines your governance ceiling

That enforcement boundary changes the MCP compatibility question. Speaking the protocol is only the entry requirement. The stronger test is whether the SDK supports a gateway between agent and tool, where authorization and inspection happen before execution. Proxying traffic does not, by itself, govern it.

The official 2026-07-28 MCP announcement states that all four Tier 1 SDKs, TypeScript, Python, Go and C#, support that revision. Use the revision as a concrete, dated MCP compatibility check. The specification uses Mcp-Method and Mcp-Name HTTP headers so gateways can authorize directly on those headers. An SDK behind that feature surface may not support the path the gateway needs.

The comparison should then test what that path permits. Look for per-caller tool allow-lists, in-band guardrails and tamper-evident audit. Large tool footprints introduce another requirement, namely gateway support for token-efficient tool representation at scale. SDKs that block gateway interposition also block that capability.

Shadow agent surface: whether the SDK grows or shrinks your ungoverned agent population

Even strong controls cannot cover agents the organization cannot find. Shadow MCP is shadow IT for the AI era, and SDK defaults can enlarge or shrink that population. An SDK that makes agent identity optional leaves room for deployments with no registry entry, assigned owner or managed identity. Those are the highest-risk agents.

Check whether each agent gets a persistent, registered identity tied to a named person in the organization’s IdP. Discovery also needs structured metadata, including owner, deployed-by and credential scope. Registration should not depend on manual steps developers routinely skip. Start deployment with a full discovery pass to find running agents, who deployed them, their credentials and the systems they can reach, and treat missing registry entries and managed identities as critical findings. This closes the loop with authentication and gateway enforcement, because both depend on knowing which agent is acting and who owns it.

Applying the scorecard: how to weight and run the comparison for your team's context

With the criteria defined, apply deployment-specific weights. Two teams can reasonably select different SDKs from the same pair. Heavy state management and many concurrent sessions justify more weight for state persistence and fault tolerance. LangGraph’s production record supporting 85 million users at Klarna is relevant evidence for that context. Azure-centric infrastructure and C# or Java teams may instead give more weight to IdP integration and compliance controls, where Semantic Kernel’s Entra ID integration is relevant evidence.

The deployment stage changes the trade-off as well. A proof of concept can place more weight on developer experience and extensibility, provided the team records the cost of retrofitting governance later. Autonomous agents touching many systems need the opposite emphasis, with tool-call validation and MCP compatibility weighted highest and gateway interposition non-negotiable. Technical criteria get an SDK onto the shortlist, scalability earns a proof of concept, governance gets it through procurement, and reliability wins the contract.

A hybrid design with a robust orchestration SDK, multi-provider routing, and separate retrieval and policy services reduces vendor risk and supports cost optimization, but requires more governance work. A tightly integrated managed SDK needs less of that work but brings more risk from dependence on the vendor. Neither arrangement wins by definition. When two SDKs reach technical parity on the first four criteria, governance and shadow agent surface should break the tie, and the chosen option should have the strongest weighted score for the team’s stated needs.

What a governed golden path to MCP servers looks like in practice

The result should be a single governed path to MCP servers. Each agent acts under a persistent identity, each tool call is policy-checked and logged, and the system extends the organization’s existing stack. The SDK needs an enterprise MCP gateway to enforce policy at runtime so the controls remain part of execution.

In that path, native or low-friction OTel export feeds the existing observability stack, and per-agent registration ties identities to an IdP such as Okta or Entra. Credentials are short-lived and task-scoped, and agents never see raw secrets. Gateway interposition lets policy be enforced on every prompt and tool call, going beyond what server-level allowlists alone can do. Structured audit output attributes every agent decision, allow or deny, to a name and the rule that applied.

The practical next step is to reuse the same criteria-first scorecard for whichever two SDKs the team is weighing. Agree on requirements and weights before either product gets to set the agenda. An agent deployment is most likely to pass enterprise security review by meeting these six criteria. Demo quality can stay on the sheet, but it should not chair the meeting.