Choosing an Agentic AI Framework: A Decision-Maker's Map
Dozens of agentic AI frameworks now compete for attention, and most comparisons mislead — they pit tools from different layers against each other. Here is a clear map: what LangChain, MLflow, Haystack and the leading alternatives actually do, the six dimensions that separate them, and how to choose without locking yourself in.
Choosing an Agentic AI Framework: A Decision-Maker's Map
Every quarter brings a new "best framework for AI agents." LangChain, LangGraph, CrewAI, LlamaIndex, Haystack, MLflow, Pydantic AI, the Microsoft Agent Framework, the OpenAI Agents SDK — the list grows faster than any team can evaluate. For a decision-maker, the noise is the problem. These frameworks are described in nearly identical language, yet they are not interchangeable, and choosing the wrong one is an expensive, multi-year commitment.
The good news: the market is more orderly than it looks. Once you stop asking "which framework is best?" and start asking "which layer am I buying?", the field sorts itself out quickly. This is the map.
The mistake in most comparisons
The most common error — in vendor decks, analyst posts and internal evaluations alike — is comparing tools that do not do the same job. The clearest example is MLflow versus LangChain, a match-up you see constantly. It is the wrong fight.
Agentic AI frameworks live on different layers of the stack:
LangChain is a build tool. MLflow is an operate tool. They are not competitors — they are partners. You build the agent in LangChain (or anything else) and you measure, govern and ship it with MLflow. The only place the two ecosystems actually overlap is observability, and even there the real rivalry is MLflow versus LangSmith, not MLflow versus LangChain.
Get this one distinction right and half the confusion evaporates.
The two anchors
LangChain is the most widely adopted framework for building LLM applications, and it is really three things: LangChain (the building blocks — models, prompts, tools, retrieval, memory), LangGraph (a graph engine for stateful, multi-step agents with branching, persistence and human approval) and LangSmith (a commercial layer for tracing and evaluation). Its strength is breadth — the largest ecosystem and integration catalogue in the space, model-agnostic by design. Its cost is complexity and fast-moving APIs.
MLflow is the open-source standard for the machine-learning lifecycle, now extended to GenAI and agents. It is framework-agnostic: it instruments an app built with LangChain, Haystack, CrewAI or plain code, and answers the operational questions a regulated enterprise cannot skip — is it good, is it reproducible, can we deploy and audit it? Its four pillars are tracing (OpenTelemetry-native), evaluation (including LLM-as-judge), a model and prompt registry, and deployment.
A third contender: Haystack
If LangChain is the breadth play, deepset's Haystack is the production-discipline play — the mature open-source alternative for teams whose centre of gravity is search and retrieval. You build applications as explicit pipelines of typed components that serialise to configuration and deploy cleanly. It has a strong retrieval/RAG core plus a first-class agent component, and the commercial deepset AI Platform adds a visual builder, on-premise deployment and enterprise security. Choose it when reliability, document search and maintainability matter more than having the largest integration catalogue.
The honest framing: Haystack and LangChain are both build frameworks. Haystack leans toward production RAG and stable, serializable pipelines; LangChain leans toward breadth and flexibility. MLflow governs whatever either one builds.
The leading alternatives
Five more frameworks each optimise for a different priority:
The six dimensions that actually separate them
Marketing pages all sound alike. These are the axes on which the choice really turns:
| Dimension | Why it matters |
|---|---|
| Abstraction vs. control | High-level frameworks ship fast but hide behaviour; low-level ones give control and debuggability for more effort. |
| Multi-agent model | Graphs, role-based crews, conversations or handoffs — each suits a different problem shape. |
| Built-in observability | Can you trace and evaluate out of the box? Native in MLflow and LangSmith; bolt-on for most builders. |
| Vendor lock-in | Model- and cloud-agnostic, or tied to one provider? Open standards lower the cost of switching. |
| Enterprise governance | Access control, audit trail, data lineage, on-prem — decisive in regulated estates. |
| Team fit & maturity | Language, learning curve, community size and the API stability you will live with for years. |
A comparison at a glance
| Framework | Layer | Best at | Multi-agent | Observability | Lock-in |
|---|---|---|---|---|---|
| LangChain / LangGraph | Build | Breadth & flexibility | Strong (graphs) | Via LangSmith | Low |
| deepset / Haystack | Build | Production RAG & search | Pipelines + agent | Strong (native) | Low |
| LlamaIndex | Build / Data | Document intelligence | Workflows | Partial | Low |
| CrewAI | Build | Role-based teams | Core strength | Limited | Low |
| Microsoft Agent Framework | Build | Microsoft / Azure estates | Strong | Azure-native | Azure-leaning |
| Pydantic AI | Build | Type-safe Python | Basic | Via Logfire | Low |
| MLflow | Operate | Govern & evaluate | n/a — it observes | Core strength | Low |
MLflow looks weak on the "build" rows because building is not its job. That is exactly the point: it governs and measures what the others build.
How to choose
There is no single winner — the right choice follows the dominant requirement:
Bet on standards, not just a framework
The most durable decision is not a framework at all — it is the standards your framework speaks. Three matter now:
For an SAP-centric enterprise this is decisive. SAP's own agent builder, Joule Studio, supports leading pro-code frameworks — including LangChain/LangGraph, Pydantic AI and LlamaIndex — and HANA Cloud provides first-party vector storage. The SAP–Databricks partnership makes MLflow a natural governance layer over SAP data, and the SAP–Microsoft alignment routes Joule and Copilot to each other through A2A. The practical implication: when your framework speaks the standards, the framework choice becomes reversible. Standards-compliance protects the investment far more than betting on a single vendor.
The takeaway
Stop shopping for the "best" agentic AI framework. Decide which layer you are buying, pick a build framework that fits the use case and your team, and always pair it with an operate-and-govern layer such as MLflow or LangSmith. Favour open, model-agnostic tools that speak A2A, MCP and OpenTelemetry — so that two years from now, changing your mind costs a migration, not a rewrite.
If you want help mapping this to your own SAP landscape, that is exactly the kind of decision I work on at AI & SAP Consulting Paul Oesterwitz.
Paul Oesterwitz
AI & SAP Consultant · PhD Researcher