What is it?
There is no single best agent framework. Some give you fine control, some get you a prototype fast, some are strongest at working with your documents. Often the simplest answer is a few direct API calls.
Also known as: which agent framework, best agent framework, LangGraph vs CrewAI vs AutoGen, agent framework comparison, what framework should I use for AI agents, LLM framework comparison, langchain alternatives.
Technical definition
| Framework | Typical emphasis | Language(s) | Consider when |
|---|---|---|---|
| LangChain, Practically | Broad integrations and components | Python, JS/TS | You need many connectors |
| LangGraph | Stateful graph orchestration, persistence, approvals | Python, JS/TS | You need control over loops |
| LlamaIndex for Retrieval | Data ingestion, indexing, retrieval | Python, TS | RAG-centred work |
| DSPy | Programmatic pipelines with optimisation | Python | You have a metric and examples |
| Microsoft Agent Framework | Agents and graph-based workflows with checkpointing and human-in-the-loop; Microsoft's stated successor to AutoGen and Semantic Kernel (Migrating from AutoGen or Semantic Kernel to Microsoft Agent Framework) | Python, .NET (Go in a separate repo) | New projects on the Microsoft stack or migrating from those two |
| AutoGen (Microsoft) | Conversational multi-agent (maintenance mode; successor: Microsoft Agent Framework) | Python, .NET | Maintaining existing AutoGen systems |
| CrewAI | Role/task crews | Python | Fast role-based prototypes |
| Semantic Kernel | Enterprise SDK with plugins (successor named: Microsoft Agent Framework) | C#, Python, Java | Existing Microsoft-stack systems |
| OpenAI Agents SDK | Lightweight primitives, handoffs, guardrails | Python, JavaScript/TypeScript | Building on that vendor's API (README describes it as provider-agnostic) |
This table is a neutral orientation, not a recommendation or ranking; feature claims need verification against current docs.
Entity type: concept · Freshness class: volatile.
When should I use it?
Use when selecting tooling; revisit as the ecosystem changes.
Implementation patterns
- Prototype with direct SDK calls; adopt a framework when specific needs appear.
- Wrap framework code behind your own interfaces.
- Evaluate on your tasks, not demos.
Technologies and frameworks
Commonly associated: LangChain, LangGraph, LlamaIndex, DSPy, AutoGen, CrewAI, Semantic Kernel, OpenAI Agents SDK. Names are examples of what exists, not recommendations, and projects change quickly.
Practical guide
- Write down requirements: control, persistence, languages, retrieval, approvals.
- Shortlist 2.
- Build the same small task in each.
- Compare debugging, tests and maintainability.
- Decide and pin versions.
Common failure modes
- Choosing by GitHub stars.
- Locking business logic into framework classes.
- Using multi-agent frameworks for single-step tasks.
Troubleshooting
- Framework fighting you — Drop to direct calls for that part; keep the framework where it helps.
Contrasted with
- AI Framework vs Direct Model API — The baseline alternative to any framework.
Questions this page answers
- Which AI agent framework should I use?
- LangGraph vs CrewAI vs AutoGen?
- Do I need an agent framework?
- What are alternatives to LangChain?
Typical goals: choose an agent framework; decide whether a framework is needed.
Sources and verification
Status: not independently verified. This entry was authored on 2026-10-06 from general technical knowledge, without live checking of the sources below. Treat every source as a lead to confirm (identifier, title, authorship, current version) before publication or citation.
Freshness class: volatile — Products, versions, protocols, benchmarks, leaderboards, regulations and dates. Verify against the primary source before relying on specifics; re-review every 3 months.
Time-sensitive claims to verify before relying on them:
- All feature, status, licence and versions claims for each framework.
Canonical sources (unverified leads):
- Each framework's current official documentation (see individual entries) — locator to be identified
- Anthropic, Building effective agents (guidance on simple composable patterns; verify URL and date) — locator to be identified