OpenAI Agents SDK
A free, open-source developer library from OpenAI for building custom AI agents in code, with tools and handoffs between agents.
A free, open-source Microsoft developer kit that lets AI models call a company's existing code and APIs in C#, Python, or Java.
Semantic Kernel is a free, open-source developer kit that adds AI agents to applications written in C#, Python, or Java. Developers describe existing functions and APIs to it as 'plugins,' and it acts as middleware that lets an AI model call those functions and pass results back, so existing code does not need rewriting. It has reached a stable 1.0-plus release across all three languages, and Microsoft has committed to avoid breaking changes going forward. It includes hooks and filters that add safety and monitoring checks around what an agent does. Microsoft's documentation states that Microsoft and other large companies use it internally.
This fits a development team, especially one already working in C# or the Microsoft stack, that wants to add an agent on top of existing business logic rather than rewrite it as a new AI-first application. A good first project is an internal assistant that calls existing internal APIs to look up orders or update records. There is no license fee; the ongoing cost is developer time plus the API usage of whichever model it connects to, such as Azure OpenAI. The main risk is that being lightweight means fewer built-in guardrails than a packaged enterprise product, so plan to build testing and approval steps for anything that changes data, not just anything that answers questions.
These estimates cover licensing, setup, integrations, staff time, security, administration, and support.
| Cost measure | Low | Base | High |
|---|---|---|---|
| First-year total | $57.8K | $91.9K | $149.2K |
| Three-year total | $121.4K | $184.7K | $288K |
| First-year cost per unit | $57.8K | $91.9K | $149.2K |
| Average annual cost per unit (over three years) | $40.5K | $61.6K | $96K |
| Component | Low | Base | High |
|---|---|---|---|
| Licensing and usage | $16.5K | $22K | $29.7K |
| Implementation | $9.6K | $16K | $27.2K |
| Integration | $12.1K | $22K | $39.6K |
| Staff time and change management | $6.3K | $9K | $12.6K |
| Security | $4.9K | $7K | $10.5K |
| Administration | $3.8K | $5K | $6.8K |
| Support | $1.9K | $2.5K | $3.4K |
Estimate assumptions: Based on 1 production deployment. This is a planning allowance, not verified product pricing. Confirm the vendor’s billing unit and current price or quote before budgeting.
Benefits have not been estimated: The current research does not estimate potential savings, return on investment, or how long it would take to recover the cost. Earlier benefit estimates are excluded.
Confirm current product identity, commercial packaging, data processing terms, sign-in and access rules, retention, integrations, support model, implementation effort, and rollback conditions.
Verify identity, package, availability, ownership, pricing, and security evidence before approving a pilot
Do not approve a pilot yet. Verify the current product identity, package, availability, owner, pricing, and security evidence; then define one workflow, a baseline, and rollback criteria.
Define what success looks like for a test of Microsoft Semantic Kernel and assign someone to lead it.
Once the requirements above are met, compare a small trial with how your team works today.
Track adoption, output quality, business results, and actual costs against the estimate.
Use the results to decide whether to stop, adjust, or expand the pilot.
Confirm encryption, how the vendor uses your data, customer data separation, how long data stays and how to delete it, activity records, sign-in and user setup, where data is handled, other companies that process data, past incidents, and what your team must manage.
Review before rollout: confirm how to export your data, revoke access, and return to your existing workflow. Assign an owner and test the rollback plan before expanding use.
Do not approve a pilot yet. Verify the current product identity, package, availability, owner, pricing, and security evidence; then define one workflow, a baseline, and rollback criteria.
A free, open-source developer library from OpenAI for building custom AI agents in code, with tools and handoffs between agents.
langchain.com
A free, open-source developer library for building multi-step AI agent workflows with fine control over each step.
A platform for building teams of AI agents that handle sales, support, and operations tasks, marketed as an 'AI workforce.'
An open-source developer framework for organizing several AI agents into a team with defined roles, plus a paid governance layer.
Where to check the product, price, security, and support.
Recorded score and category rank across research updates.
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