AI Agents Arena
Cost Calculator

Agent Framework Picker

Answer 8 engineering questions to compare 8 real orchestration options—including LangGraph, CrewAI, Microsoft Agent Framework, OpenAI Agents SDK, Google ADK, Claude Agent SDK, LlamaIndex, and writing no framework at all.

Verified 2026-11-12 Transparent additive scoring Estimate runtime spend in the AI Agent Cost Calculator

1. What is your primary production runtime language?
2. How complex is the agent workflow graph?
3. What is your LLM provider strategy?
4. Which workload best describes your primary agent?
5. What level of human-in-the-loop (HITL) control do you need?
6. Where do you fall on prototyping speed vs. strict determinism?
7. What observability and dependency posture fits your team?
8. Do you need native realtime voice or WebRTC audio loops?

Transparent Scoring & Verification Table (All 8 Options)

Scores update live from your answers above. Every status, language, and license claim is verified against official documentation on 2026-11-12.

Rank Framework Score Languages License Maintenance Status Key Trade-Off & Source
1 LangGraph — Python, TypeScript MIT (core orchestration) Actively maintained Steeper boilerplate than single-loop SDKs; pairs best with external tracing (e.g., LangSmith or OpenTelemetry). Docs
2 CrewAI — Python MIT (core) / Commercial enterprise suite Actively maintained Python-only runtime; roleplay prompts can add extra tokens if step limits are not tightly capped. Docs
3 Microsoft Agent Framework (AutoGen Successor) — Python, .NET (C#) MIT Actively maintained (AutoGen archived/maintenance mode) Legacy microsoft/autogen is archived with no new features; community fork AG2 exists separately. Docs
4 OpenAI Agents SDK — Python, TypeScript/JS MIT Actively maintained Intentionally lightweight; lacks heavy durable workflow persistence out of the box compared to LangGraph. Docs
5 Google ADK (Agent Development Kit) — Python, TypeScript, Go, Java, Kotlin Apache 2.0 Actively maintained Broad multi-language surface; non-Gemini models require adapter configuration (such as LiteLLM). Docs
6 Claude Agent SDK — Python, TypeScript MIT / Commercial SDK terms Actively maintained (evolved from Claude Code SDK) Tied directly to Anthropic Claude APIs and bundled CLI execution semantics. Docs
7 LlamaIndex Workflows & Agents — Python, TypeScript MIT Actively maintained Strongest when data ingestion and retrieval dominate; overkill for simple tool-calling bots. Docs
8 No Framework (Direct API + Thin Loop) — Any (PHP, Go, Rust, Python, TS) N/A (Zero third-party orchestration lock-in) Zero dependency overhead You own retry logic, trace logging, and checkpoint persistence yourself if workflows grow complex. Docs
Spot a framework release change or doc correction? Email contact@aiagentsarena.com How scoring weights work →

Understanding the 2026 Agent Framework Landscape

Over the past eighteen months, agent orchestration consolidated around two distinct philosophies: low-level durable workflow runtimes designed for multi-step enterprise reliability, and provider-native lightweight SDKs built to minimize boilerplate. Meanwhile, first-generation multi-agent chat frameworks either evolved or entered maintenance.

AutoGen Maintenance Status and Microsoft Agent Framework

Teams searching for Microsoft AutoGen should verify repository status before starting a greenfield project. Microsoft transitioned microsoft/autogen into maintenance mode in late 2025 and archived the repository, ending new features and security patches. Microsoft consolidated AutoGen's multi-agent conversation patterns with Semantic Kernel's enterprise telemetry and state abstractions into the unified Microsoft Agent Framework (supporting Python and .NET). Separate from Microsoft, original AutoGen contributors maintain an independent community fork named AG2.

Provider SDKs vs. Model-Agnostic Graphs

Official provider SDKs—OpenAI Agents SDK, Google ADK, and Claude Agent SDK (evolved from the Claude Code SDK)—deliver tight integration with provider-specific features such as realtime voice handoffs, Vertex AI evaluation, or local filesystem and shell tool loops. Conversely, LangGraph and LlamaIndex Workflows remain model-agnostic and shine when you need cyclic graph checkpointing or deep document retrieval across multiple vendors.

Do You Actually Need a Framework?

Before installing an orchestration dependency, consider the "No Framework" baseline. Every modern LLM API supports structured JSON tool calling natively. If your workflow executes a single loop with fewer than five tools—or runs inside a non-Python/TypeScript backend such as PHP or Go—writing a 100-line loop gives you complete control over prompt caching boundaries and context trimming. Compare the token impact of multi-step loops in our AI Agent Cost Calculator, model repository-heavy loops in the Coding Agent Cost Calculator, and read How AI Agent Costs Work to avoid hidden orchestration overhead.

Frequently Asked Questions

Why is Microsoft AutoGen listed under Microsoft Agent Framework?

Microsoft placed microsoft/autogen in maintenance mode and subsequently archived the repository with no new feature updates. Its official successor is the Microsoft Agent Framework (microsoft/agent-framework), which unifies AutoGen multi-agent patterns with Semantic Kernel for Python and .NET. Original authors also maintain an independent community fork called AG2.

When is "No framework" the best choice?

If your agent uses one model loop with 1 to 5 tools, or if your production backend runs in PHP, Go, or Rust rather than Python or TypeScript, a 50-to-150 line direct API loop avoids abstraction lock-in and keeps token overhead minimal.

Are these framework recommendations based on synthetic benchmarks?

No. We do not publish invented benchmark scores. The wizard uses transparent additive weights tied strictly to documented runtime support, orchestration paradigm, license, provider coupling, and durable state capabilities.

How does framework choice affect my monthly LLM bill?

Multi-agent handoff frameworks that replay long roleplay backstories or pass full transcripts between agents consume significantly more input tokens per task. Model the step count and context growth in our AI Agent Cost Calculator after picking your architecture.