Self-Hosted Vendor Agent SDK vs MightyBot

What vendor agent SDKs provide

A vendor agent SDK is a library from a model provider or a cloud that runs the agent loop for you: the model reads the task, calls tools, reads the results and repeats until it answers. Five stacks are common in 2026. Each line below is taken from the stack's own documentation.

• OpenAI Agents SDK. Its primitives are agents ("LLMs equipped with instructions and tools"), agents as tools or handoffs for delegating to other agents, and guardrails for validating inputs and outputs. It adds sessions as "a persistent memory layer" and "built-in tracing for visualizing, debugging, and monitoring workflows". If you are evaluating the hosted side of that stack, see MightyBot vs OpenAI AgentKit.
• Claude Agent SDK. The SDK spawns and supervises a CLI subprocess that "owns a shell, a working directory, and session files on disk", and runs the loop inside your container. Its hosting page points teams that do not need to run the loop themselves to the hosted alternative; for that path see MightyBot vs Claude Managed Agents.
• Google Agent Development Kit. "The open-source agent development framework that lets you build, debug, and deploy reliable AI agents." It "can work with almost any generative AI model", and you can "containerize and run ADK on your own infrastructure" or deploy to Agent Runtime, Cloud Run or GKE on Google Cloud. For the hosted path see MightyBot vs Gemini Enterprise.
• AWS Strands Agents. An SDK to "quickly build, manage, evaluate and deploy AI-powered agents", with deployment guides for Amazon Bedrock AgentCore, Lambda, Fargate, EKS and other AWS targets. For the hosted runtime see MightyBot vs Amazon Bedrock AgentCore.
• Microsoft Agent Framework. Agents plus graph-based workflows, described by Microsoft as "the direct successor" to Semantic Kernel and AutoGen. Its overview also gives sound advice for any stack: "If you can write a function to handle the task, do that instead of using an AI agent." See MightyBot vs Semantic Kernel.

Handoffs between agents are a feature of most of these SDKs. If your design leans on several agents passing work between them, the multi-agent handoffs page covers that build type.

One distinction matters for the rest of this page. Cloud runtimes such as Amazon Bedrock AgentCore and Google's Agent Runtime host part of the loop for you. This page is about the self-hosted path, where the SDK runs in containers your team provisions and operates.

What you still run in production

The SDK documentation is candid about what hosting involves. The Claude Agent SDK hosting guide puts it plainly: "Hosting it is not like hosting a stateless API wrapper. Every running agent is a long-lived process tied to local state." (Claude Agent SDK hosting)

Session state. Transcripts, memory files and working-directory artifacts live on the container's disk by default, and "None of them survive a container restart, a scale-down, or a move to a different node." Persisting them across hosts means configuring a session store adapter and a separate storage strategy for the rest. (Claude Agent SDK hosting)

Authentication. The same guide says to put inbound authentication "at a gateway in front of the agent container", keep tool credentials out of the agent environment, and route outbound calls through a proxy that injects keys after the request leaves the container. (Claude Agent SDK hosting)

Telemetry. "Without telemetry you cannot see which tools ran, how long they took, or where a session stalled." Exporting spans, metrics and logs to your collector is configuration your team owns. (Claude Agent SDK hosting)

Session bounds. The guide's known limitations include "No top-level session timeout", with the advice to set a maximum number of turns, and a note that "Large parallel-subagent fanouts can hit rate limits". (Claude Agent SDK hosting)

What traces contain. In the OpenAI Agents SDK, "Tracing is enabled by default" and exports to the OpenAI backend. It "is unavailable for organizations that use OpenAI's APIs under a Zero Data Retention (ZDR) policy", and "By default, trace_include_sensitive_data is True", which means model inputs and outputs are kept in traces unless you turn that off. For a workflow that reads loan files, that setting is a data-handling decision to make before the first production run. (OpenAI Agents SDK tracing)

The SDK documentation lists this work because it applies to any self-hosted agent. It is the distance between a working prototype and a service a bank can run.

What a lending workflow adds on top

The hosting checklist above is generic to any agent. A regulated document workflow adds its own layers, and the SDK does not claim to supply them.

• A document pipeline that classifies, splits and extracts from inconsistent loan packages, and keeps page-level evidence for each extracted value.
• Policy logic that encodes credit policy as checks, with versioning so you can say which policy version governed a decision made six months ago.
• Evaluations that run the same cases repeatedly and across model upgrades, so a change in the model or the SDK shows up before it reaches production.
• Review queues and escalation for the cases the checks cannot settle.
• Decision evidence a reviewer or examiner can read without replaying an agent transcript.
• Integrations with the loan origination system, document store and core.
• Model risk documentation and change control for the model, the prompts and the SDK version in use.

One concrete example of the evidence a pipeline must produce. Under Regulation B's general rule, a creditor notifies an applicant of the action taken within 30 days after receiving a completed application. When that action is adverse, the written notice must contain either "A statement of specific reasons for the action taken" or a disclosure of the applicant's right to a statement of specific reasons; if the applicant requests reasons within 60 days of the notice, the creditor provides them within 30 days of the request. Business credit and other situations follow different timing and notice rules. For a business with gross revenues above $1 million in its preceding fiscal year, for example, the creditor notifies the applicant "within a reasonable time", and a written statement of reasons follows a written request (12 CFR 1002.9). Whichever rule applies, the pipeline has to turn the decision into specific reasons tied to the policy and the evidence, and an agent transcript is only the raw material for that.

These layers are the same eight the build vs buy cost comparison walks through. The SDK shortens the first of them.

Costs that show up later

Three costs tend to arrive after the prototype is approved.

Tokens. The Claude Agent SDK hosting guide notes that token cost "typically dominates container infrastructure cost by an order of magnitude or more". Container sizing is a small part of the bill; model usage per case is the number to model. (Claude Agent SDK hosting)

Rate limits. Provider APIs return a 429 error when any per-minute limit is exceeded, and the hosting guide flags that wide parallel fanouts can hit those limits. Queues, backpressure and retry with delay are your team's code. (Anthropic rate limits, Claude Agent SDK hosting)

Release cadence. SDKs move quickly, and keeping up is maintenance work. The OpenAI Agents SDK release notes for v0.20.0 flag "a potentially breaking MCP dependency migration for applications with customized local HTTP transports". The Claude Agent SDK advises teams to review the changelog "before taking a minor" release, because the bundled binary is pinned to the SDK version. Each upgrade is a test cycle against your evaluation suite. (OpenAI Agents SDK releases, Claude Agent SDK hosting)

The build vs buy cost comparison puts these into a three-year model you can edit.

How MightyBot fits with SDK agents

MightyBot is a model-neutral AI agent platform. You describe the work and write the policy in plain English, and the platform compiles the execution plan. The platform runs the document pipeline, policy execution, evaluations across model upgrades and the why-trail that records each decision with its policy version and page-level evidence. Models are chosen on evaluation and can change without a rewrite.

An SDK agent and a MightyBot workflow can work together. MCP Launchpad exposes approved MightyBot workflows as agent-callable MCP tools, with the tool description generated from the workflow's own schemas, rules and examples. An agent built on any of the SDKs above can call a governed workflow, such as a draw review or a covenant check, instead of rebuilding it inside the agent. Every call runs through the same policies and evidence trail as the workflow underneath. (MCP and system integrations)

That gives a team two options that are often taken together: use the SDK where the agent loop is the point, and call the platform where the decision has to be governed.

Benchmark method

MightyBot is running a first-party benchmark of the five build types on this page's pillar, on a regulated document workload, and will publish the method and the results. This page states no benchmark outcomes.