LangChain vs LlamaIndex vs CrewAI vs Microsoft Agent Framework vs MCP: The Complete 2026 AI Agent Framework Comparison

As of August 2026, answering the question “which framework should I build my agent on?” the same way you would have a year ago means working from stale information. This AI agent framework comparison doesn’t just rank projects by GitHub star count — it’s built on real-time research and direct checks of official pricing pages, covering only the versions, licenses, and prices that actually exist as of August 2026. The past few months have brought several quiet but structural shifts to this market: Microsoft effectively moved the original AutoGen into maintenance mode and replaced it with “Microsoft Agent Framework 1.0,” while MCP (Model Context Protocol) locked in the biggest architectural change in the protocol’s history — a shift to a stateless design.

Why does this comparison matter right now? According to Alice Labs’ Q2 2026 survey of production deployments, LangGraph 1.0 has overtaken CrewAI as the leader among pure orchestration frameworks, and in the job market, LangChain and LangGraph are clearly solidifying into “complements rather than competitors.” At the same time, the decision criteria buyers actually weigh have narrowed away from the old “which framework is more popular” question toward production stability and observability, the nature of the orchestration model (graph-based, conversational, or role-based crews), and whether MCP is supported. In other words, the industry is converging on an approach where MCP serves as the common adapter and teams pick an orchestration layer as the framework on top of it.

This article covers five tracks at once: LangChain/LangGraph, LlamaIndex, CrewAI, the now three-way-split AutoGen lineage (the original AutoGen, the community fork AG2, and Microsoft’s official successor, Microsoft Agent Framework), and finally MCP — which is a protocol, not a framework. Working through the comprehensive comparison table, per-product feature deep-dives, benchmarks, a full pricing breakdown, recommendations by user type, practical tips, and an FAQ in order will give you a clear sense of which combination fits your current project.

Above all, this AI agent framework comparison explicitly flags and filters out numbers with unclear or mutually contradictory sourcing as “unverified.” For figures like CrewAI’s cumulative funding or LlamaCloud’s per-page parsing cost — where sources report wildly different orders of magnitude — we describe them only qualitatively in the text and reserve the tables for confirmed, official numbers.

1. Executive Summary: The August 2026 AI Agent Framework Comparison at a Glance

📊 Comprehensive Comparison Table: The 5 Major AI Agent Frameworks

The two rows most worth lingering on here are “License” and “Biggest change in the last 6 months.” A free core library doesn’t mean production deployment is free too (see LangGraph’s Elastic License 2.0 boundary), and whether a tutorial you find by searching “AutoGen” still reflects current reality is a separate question entirely. The deep-dives below unpack each of these one at a time.

2. Deep Dive: Core Features of the 5 Major AI Agent Frameworks

🔷 1) LangChain/LangGraph — Production Stability Through a Graph State Machine

• Version and stability commitment: LangChain’s core is at v1.5.2 (confirmed 2026-07-28), and LangGraph is at v1.2.9 (released 2026-07-10). Both projects shipped their 1.0 GA release in 2026 and formally committed to “no breaking changes before 2.0.” That stability pledge alone is one of the main reasons LangGraph keeps coming up as the top pick for production adoption in this AI agent framework comparison.
• Graph-based state machine: Nodes and edges manage explicit state, with support for interrupts and time-travel (replay). The 1.x line fixed time-travel replay bugs around interrupts and subgraphs, and added ContextHubBackend (which version-controls files via LangSmith Hub commits), per-node timeouts/error recovery/graceful shutdown, and a new content-block-centric streaming API v3.
• Make sure you understand the license boundary: the core library is fully free under the MIT license, but langgraph-api — the runtime server produced by langgraph dev/langgraph build — ships under the Elastic License 2.0, meaning production operation requires a commercial license key (effectively an Enterprise contract). Plenty of teams start out unaware of this “free core, separately licensed production runtime” line and only discover it, to their surprise, when they hit deployment.
• Tops the benchmarks: in independent research (cross-referenced across multiple blogs, including Pooya Golchian’s), it posts a 76% medium-difficulty task completion rate — ahead of CrewAI (71%) and AutoGen (68%) — and holds up at 62% even on complex, long-running tasks. Cost per task is also reported at a rock-bottom $0.08.
• SDKs in both languages: both Python and JS (TypeScript) SDKs are available, making integration easy on either the frontend or backend. That said, The Hacker News reported a security vulnerability in March 2026 that could expose files, secrets, and databases, so be sure to check the latest patch notes before you adopt it.

📄 2) LlamaIndex/LlamaCloud — From RAG Library to Document Agent Platform

• Repositioning: starting with core v0.14.22 (2026-05-14), the project’s own marketing message shifted from “RAG library” to positioning itself as the “leading document agent and OCR platform.” The managed platform LlamaCloud has now reached GA as a commercial service spanning parsing (LlamaParse), ingestion, indexing, structured extraction, and agent deployment.
• Credit-based billing: LlamaCloud runs on a credit system where 1,000 credits = $1.25, with baseline parsing costs quoted at roughly $0.00125–$0.05625 per page depending on page complexity. Some sources also cite an “Agentic Parsing ~$45/page” figure, but the gap between the two numbers is too large to reconcile, and this research flags it as an internal contradiction within the same source pool. Don’t take that higher figure at face value when budgeting — verify directly on the official pricing page instead.
• Rated lower on production readiness than orchestration layers: in Alice Labs’ production-deployment ranking, “LlamaIndex Workflows 1.0” placed 6th — behind orchestration frameworks like LangGraph and CrewAI. In practice, it’s still safest to treat LlamaIndex as a specialized RAG/document-processing tool rather than as your primary framework for complex multi-agent orchestration; pairing it with a separate orchestration layer is the more realistic setup.
• SDKs in both Python and TypeScript: available via run-llama/llama_index (roughly 50–51K GitHub stars, ~7,600 forks) alongside run-llama/LlamaIndexTS. On the funding side, the company raised a $19M Series A (bringing cumulative funding to roughly $27.5–28.5M, led by Norwest Venture Partners with participation from Greylock), which it’s putting toward expanding LlamaCloud.

⚙️ 3) CrewAI — Minimizing the Learning Curve With Role-Based Crews

• Version and recent features: v1.14.0 shipped 2026-04-07 and the line quickly moved on to v1.15, with 1.15.10 as the current stable release as of 2026-07-31. A raft of production-oriented features has landed since, including checkpoint list/info CLI commands, guardrail_type/name trace differentiation, a SqliteProvider checkpoint store, CheckpointConfig for automatic checkpointing, an event-system/executor refactor, security patches for SSRF and path-traversal in the RAG tool, and no-code guardrail creation.
• Pricing has gotten simpler: per a direct check of the official crewai.com/pricing page, the Professional $25/month tier launched in October 2025 was discontinued in spring 2026. Pricing is now a simplified two-tier structure of Basic (free, 50 workflow runs/month) and Enterprise (custom quote), with Enterprise available either as fully-managed SaaS (CrewAI AMP) or as an on-prem/private-cloud containerized deployment (CrewAI Factory).
• Role-based paradigm: a third orchestration paradigm alongside LangGraph’s graph/state model and AutoGen’s conversational model, CrewAI assembles a “crew” out of roles and tasks. Because you can define agent personas and task sequences without knowing any graph theory, it earns the top rating on development speed (time from development to production).
• Real-world scale: cumulative PyPI downloads exceed 27 million (including 5 million in the past month alone), cumulative agent executions over the trailing 12 months run around 2 billion, and headcount sits at roughly 72 employees (as of 2026-06-30) — all signs of continued growth. That said, its benchmark completion rate of 71% is reported lower than LangGraph’s.

🔀 4) Microsoft Agent Framework (formerly AutoGen) — A Name That Split Three Ways

This category saw the most dramatic changes of anywhere in H1 2026, so it’s worth clearly separating the three branches.

• The original AutoGen is in maintenance mode: Microsoft’s original AutoGen repository has moved into “maintenance mode,” meaning new feature development has stopped as of 2026. Its GitHub star count, at roughly 57.7K, is still the largest of the three branches, but it’s no longer recommended for new projects.
• The community fork, AG2: founded in November 2024 by original creators Chi Wang and Qingyun Wu after they left Microsoft, AG2 inherited the AutoGen 0.2 architecture and continues development on its own independent roadmap. It’s under the Apache License 2.0 (adopted from v0.3 onward), with about 4,500 GitHub stars. “AG2 Beta” introduced a streaming, event-driven architecture and multi-provider LLM support spanning OpenAI, Anthropic, Google Gemini, Alibaba DashScope, and Ollama.
• The official successor, Microsoft Agent Framework: Microsoft shipped “Microsoft Agent Framework 1.0” as a full GA release on April 3, 2026, and the release cadence has stayed brisk since — the current version is 1.13.0 as of 2026-07-30. It’s a follow-on production SDK (with .NET/Python support) that unifies AutoGen’s orchestration concepts with the Semantic Kernel foundation, and it’s open source under the MIT license. At the BUILD conference in May 2026, Microsoft announced enterprise-oriented additions including Agent Harness, Hosted Agents, and CodeAct, and it has kept shipping patches and minor versions at a near-weekly clip since GA. New projects are now steered toward either AG2 or Microsoft Agent Framework instead of the original AutoGen.
• A conversational paradigm, and the trade-offs that come with it: the core idea is a conversational paradigm in which multiple agents model a workflow by exchanging messages. A frequently cited benchmark puts its medium-difficulty completion rate at 68% (lowest of the four frameworks) and its cost at 5-6x higher than the others — but that figure comes from 3rd-party research measured against the original AutoGen’s conversational architecture, with no confirmed date, so it hasn’t been verified against Microsoft Agent Framework 1.0’s redesigned architecture. The qualitative pattern — top marks on “open-ended reasoning” tasks paired with a noticeably higher cost for conversational collaboration — does show up consistently across sources, though, so it’s worth keeping in mind as a trade-off. The Azure AI Foundry hosted service began preview billing on April 22, 2026, and uses a “scale-to-zero” model (no charges while idle).

🔌 5) MCP (Model Context Protocol) — Not a Framework, But the De Facto Industry Standard

• A historic shift to a stateless design: the current spec was finalized on 2026-07-28, and its centerpiece is shifting the protocol to a stateless model. It removes the Mcp-Session-Id header and the protocol-level notion of a session entirely, so any request can be answered by any server instance (making it deployable behind ordinary HTTP infrastructure). The release also brings multi round-trip requests, header-based routing, cacheable list results, stronger authorization, and a formal Extensions framework (covering Tasks, Skills over MCP, and MCP Apps). This release does include breaking changes, but any feature that’s officially deprecated is guaranteed to keep working for at least 12 months.
• Governance has moved to a community standard: on December 9, 2025, Anthropic donated MCP to a newly formed “Agentic AI Foundation (AAIF)” under the Linux Foundation. It joined as a founding project alongside Block’s goose and OpenAI’s AGENTS.md, with Google, Microsoft, AWS, Cloudflare, Bloomberg, and others coming on as sponsors. You can feel this same shift in the tools that actually run coding agents day to day, like Claude — we cover that related ecosystem in more depth in our Cursor vs GitHub Copilot vs Cline vs Windsurf AI coding agent comparison.
• Explosive adoption: monthly SDK downloads hit 97 million (as of March 2026) — a 970x increase in the 18 months since its initial launch in November 2024. Within 13 months, OpenAI, Google, Microsoft, and Salesforce had all shipped MCP support: OpenAI built MCP client support into ChatGPT in late 2025, and Google rolled out official MCP support for Gemini plus managed remote servers in mid-2026. Major tools like Claude, Cursor, and VS Code all now ship first-party client support as well.
• Active server counts vary wildly by source: Anthropic’s own figure puts it at over 10,000 (as of December 2025), while an independent tally (Nerq, Q1 2026) counted 17,468, the official MCP Registry API (May 2026) listed 9,652, and the unofficial mcp.so registry shows over 18,000. The safest read is a range of roughly 9,600 to 18,000. As servers proliferate, criticism along the lines of “everyone’s building an MCP server, and nobody’s asking whether they should” has grown louder around quality and security, which makes it increasingly important to source servers only from registries you actually trust.

3. AI Agent Framework Benchmarks: Completion Rate, Cost, and Open-Ended Reasoning Head-to-Head

Based on a 3rd-party independent benchmark (a single research pool, not an academic benchmark — treat these as reference figures only), here’s how the three orchestration frameworks stack up:

[Medium-difficulty task completion rate ranking as of August 2026 — reference only]
LangGraph : ⭐⭐⭐⭐⭐ 76%  (62% on complex tasks, lowest cost per task at $0.08)
CrewAI    : ⭐⭐⭐⭐☆ 71%  (top-rated on development speed)
AutoGen   : ⭐⭐⭐☆☆ 68%  (top-rated on open-ended reasoning, costs 5-6x more)

Looking at this table alone, LangGraph seems to sweep every category — but the real-world decision isn’t that simple. The completion-rate numbers reflect a design philosophy where the graph state machine gracefully recovers failed nodes on complex, long-running tasks; they don’t capture how long it takes to go from starting development to a first deployment. That’s exactly why CrewAI, despite ranking 3rd on completion rate, earns the top rating on “development speed” — the barrier to entry is lower because you only need to define roles and tasks, with no graph theory required.

The “AutoGen” 68%/5-6x figures in this table come from 3rd-party research measured against the original AutoGen’s conversational architecture, with no confirmed research date, so keep in mind they haven’t been verified against the redesigned architecture of Microsoft Agent Framework 1.0 (GA’d April 2026). That said, the qualitative pattern — lowest completion rate but top rating on “open-ended reasoning” — shows up consistently across sources. A structure where multiple agents freely exchange messages works to its advantage on exploratory problems without a clear right answer, but reported costs running noticeably higher than other frameworks is something worth factoring into your budget.

For context, on LLM-level benchmarks as of April 2026, Claude Opus 4.7 leads SWE-bench Verified at 87.6%, and Claude Sonnet 4.5 leads GAIA at 74.6%. But those are model benchmarks, not framework benchmarks, so don’t compare them directly against the framework completion rates in the table above — if you want to compare the underlying model choice itself, see our ChatGPT vs Claude vs Gemini vs DeepSeek AI model comparison. Notably, in Alice Labs’ production-deployment ranking, “Claude Agent SDK” — not a pure orchestration framework — placed 2nd, which shows we’re now at a point where a model’s own agentic capability and your choice of orchestration framework need to be weighed as separate, complementary axes.

4. The Complete Pricing Comparison

[Observability/managed platform entry pricing — ordered by minimum monthly cost]
CrewAI (Basic)            : $0     (50 workflow runs/month)
LangSmith (Developer)     : $0     (5,000 base traces/month)
LlamaCloud (Free)         : $0     (10,000 credits/month)
LangSmith (Plus)          : $39/seat/month (10,000 base traces/month, 14-day retention)
LlamaCloud (Starter)      : $50/month (40,000 credits/month)
LlamaCloud (Pro)          : $500/month (400,000 credits/month)

💰 Detailed Pricing Comparison Table

Three things about this pricing structure are worth clarifying. First, LangGraph’s “free” is only half true. The open-source core is fully free, but the runtime server (langgraph-api) produced by langgraph dev/langgraph build sits under the Elastic License 2.0, meaning production operation effectively requires an Enterprise contract. Second, LlamaCloud’s per-page parsing cost is contradictory across sources. Figures of $0.00125–$0.05625 per page (converted from credits) and an “Agentic Parsing ~$45/page” figure both turn up within the same research pool, and the gap is too wide to reconcile — always verify directly on the official pricing page before budgeting. Third, CrewAI’s paid tiers no longer have a middle step. With the Professional $25/month tier from October 2025 discontinued in spring 2026, the buffer zone between Free and Enterprise has disappeared, which can act as a real barrier for teams without a generous budget. As an aside — since where and how you persist agent state (LangGraph’s checkpointing/state storage, CrewAI’s SqliteProvider checkpoint store) ultimately ties back into your choice of backend database, our PostgreSQL vs MySQL vs MongoDB vs Redis vs DynamoDB database comparison is worth reading alongside this one when you’re designing your system.

5. Final Recommendations by User Type

🎯 1) Teams building production agents where long-running execution and error recovery matter

• Best choice: LangGraph
• Why: the graph state machine architecture enables interrupts, time travel, and node-level error recovery, and it leads the benchmarks with a 76% medium-difficulty completion rate and a $0.08 cost per task. Just be sure to review the Elastic License 2.0 terms on the production runtime (langgraph-api) before signing anything.

🎯 2) Teams whose core work is RAG and unstructured-document processing

• Best choice: LlamaIndex/LlamaCloud
• Why: it offers a full-stack managed pipeline — parsing (LlamaParse), indexing, structured extraction, and agent deployment — so you can stand up document-driven agents quickly without building your own infrastructure. That said, it’s safer to pair it with a separate orchestration layer rather than rely on it as your main framework for complex multi-agent orchestration.

🎯 3) Teams that want to start prototyping fast without learning graph theory

• Best choice: CrewAI
• Why: because the abstraction only requires you to define roles and tasks, it’s rated as the fastest option for development speed. The natural path is to prove out the concept on the free Basic plan (50 workflow runs/month) and move to an Enterprise contract once you actually need to.

🎯 4) Enterprises already running on Azure/.NET

• Best choice: Microsoft Agent Framework
• Why: as the successor SDK that unifies AutoGen’s orchestration concepts with the Semantic Kernel foundation, it supports .NET and Python side by side and has beefed-up enterprise governance features like Agent Harness, Hosted Agents, and CodeAct. Since the original AutoGen is in maintenance mode, this should be your first stop for any new project.

🎯 5) Teams combining multiple frameworks and multiple LLM providers

• Best choice: adopt MCP as your common adapter
• Why: since MCP is a tool-calling standard rather than a specific orchestration framework, it lets you standardize how tools and context get connected regardless of which orchestration layer you’re using — LangGraph, CrewAI, Microsoft Agent Framework, or anything else. It’s established itself as the de facto industry standard, with OpenAI, Google, Microsoft, and Salesforce all shipping support within 13 months of launch.

🎯 6) Teams already running legacy projects built on the original AutoGen

• Best choice: consider migrating to AG2 (the community fork) or to Microsoft Agent Framework
• Why: the original AutoGen repository has moved into maintenance mode, so no new features are being added. Moving to Apache-2.0-licensed AG2 gets you the independent roadmap the original creators are still driving forward, while moving to Microsoft Agent Framework gets you enterprise governance and .NET support — which one makes sense depends on your team’s priorities.

6. Practical Tips & Common Mistakes

✅ Tip 1: Don’t trust a tutorial just because it turned up when you searched “AutoGen”

As of August 2026, the name “AutoGen” refers to three separate things: the original repository (in maintenance mode), the Apache-2.0 community fork AG2, and Microsoft’s official successor, Microsoft Agent Framework 1.0 (GA since 2026-04-03). Before consulting the original AutoGen docs for a new project, first check which of these three branches is actually still being maintained.

✅ Tip 2: Check the langgraph-api license with your legal/procurement team before deploying LangGraph to production

The core library is free under MIT, but the runtime server produced by langgraph build is licensed under Elastic License 2.0. It’s common for teams to sail through the side-project phase without issue, only to discover late in the game — right at production deployment — that they need a commercial license agreement. It’s safer to confirm this before budget gets signed off, not after.

✅ Tip 3: Always verify LlamaCloud’s per-page parsing cost with real numbers before trusting either figure

The credit-converted figure of $0.00125–$0.05625 per page and the “Agentic Parsing ~$45/page” figure both surfaced within the same research pool, and the gap between them is far too wide to be a rounding difference. If you’re designing a high-volume document-parsing pipeline, run a small batch first, look at the actual invoice, and only then set your full budget.

✅ Tip 4: Rebudget around the fact that CrewAI’s Professional $25 tier is gone

With the mid-tier plan launched in October 2025 discontinued in spring 2026, there’s no buffer left between Free (50 workflow runs/month) and Enterprise (custom quote). That means the moment you exceed the Free limit, you need to talk to Enterprise sales — so if you expect your run volume to grow, start that conversation with sales early.

✅ Tip 5: Prepare for the breaking changes in the MCP 2026-07-28 spec

The shift to a stateless design is the biggest architectural change in the protocol’s history, and servers running the new spec may not be compatible with older clients. Fortunately, any feature that’s been officially deprecated is guaranteed to keep working for at least 12 months, so use that grace period to plan a staged upgrade of both your clients and servers.

✅ Tip 6: Only pull MCP servers from registries you actually trust

The number of active MCP servers ranges anywhere from 9,600 to 18,000 depending on how you count, and the industry has raised real quality and security concerns about this proliferation. Favor channels with an actual vetting process, like the official MCP Registry API, and avoid wiring an untraceable server directly into production.

7. Frequently Asked Questions (FAQ)

Q. In an AI agent framework comparison, what should I check first?

Three things: production stability and observability, the nature of the orchestration model (graph vs. conversational vs. role-based crew), and whether MCP is supported. Narrow it down along those axes, and your candidates naturally sort themselves into LangGraph (stability), CrewAI (fast prototyping), or Microsoft Agent Framework (enterprise governance).

Q. Are LangChain and LangGraph the same product?

No. LangChain is the core library, while LangGraph is a graph-based orchestration engine maintained as a separate repository. They’re frequently used together, though — they show up as the most common pairing in the job market (185 job postings mention both) — which is why they’ve settled into being complements rather than competitors.

Q. Is it worth learning AutoGen fresh right now?

The original AutoGen repository has moved into maintenance mode, and new feature development has stopped. For a new project, it’s recommended you look at either AG2, the community fork the original creators are still driving forward, or Microsoft Agent Framework 1.0 (GA as of 2026-04-03), Microsoft’s official successor.

Q. Can I run a real service on CrewAI’s free plan alone?

The Basic free plan is capped at 50 workflow runs per month, which is enough for prototyping and proof-of-concept work, but you’ll have to move straight to an Enterprise custom quote once real traffic picks up. Keep in mind there’s no middle ground anymore — the mid-tier Professional $25 plan that existed in 2025 was discontinued in spring 2026.

Q. Do I have to give up a particular framework to adopt MCP?

No. Since MCP is a tool-calling standard rather than a framework, you can layer it on top as your tool-connection layer regardless of which orchestration layer you’re running — LangGraph, CrewAI, Microsoft Agent Framework, or anything else. In fact, the industry is increasingly standardizing on exactly this approach: use MCP as the common adapter, and pick your orchestration framework independently.

Q. Is it okay to use LlamaIndex and LangGraph together?

Yes — it’s actually a common combination. A frequent setup uses LlamaIndex to build the document parsing, indexing, and retrieval pipeline, while LangGraph orchestrates the overall agent’s state and flow. That LlamaIndex Workflows ranks below pure orchestration layers in Alice Labs’ production ranking underscores this too — LlamaIndex functions more as a complement to orchestration than a substitute for it.

Q. Under what conditions was LangGraph’s 76% benchmark completion rate measured?

It comes from independent 3rd-party benchmark blogs (cross-referenced across multiple sources, including Pooya Golchian’s), measuring medium-difficulty task completion rate — it isn’t an academically recognized benchmark. It’s safest to treat it purely as a relative reference point alongside Smolagents (73%), CrewAI (71%), and the original AutoGen (68%, not re-verified against Microsoft Agent Framework 1.0).

Q. If I want to compare the underlying model (LLM) rather than the framework first, where should I look?

An agent framework’s performance ultimately depends heavily on the capability of the LLM running underneath it. If you’re curious which models lead on benchmarks like SWE-bench Verified and GAIA as of April 2026, check out our ChatGPT vs Claude vs Gemini vs DeepSeek AI model comparison alongside this article.

8. Conclusion: The Practical Takeaway From the 2026 AI Agent Framework Comparison

As of August 2026, the takeaway from this AI agent framework comparison isn’t “one winner” — it’s “the right combination depends on your decision axis.” LangGraph leads if production stability and error recovery are the priority; LlamaIndex/LlamaCloud leads if document and RAG pipelines are core to your work; CrewAI leads if you need fast prototyping and a low learning curve; and Microsoft Agent Framework leads for an Azure/.NET enterprise environment — each dominating its own strength area. And layering MCP on top of any of these choices as a common adapter has become the dominant industry pattern in 2026.

The single most important thing this research surfaced is that the name “AutoGen” no longer points to one thing. The original repository has been demoted to maintenance mode, and its real future has split into two branches: the community fork AG2, and Microsoft’s official successor, Microsoft Agent Framework. Follow an old blog post or tutorial too literally, and you risk building a new service on top of a project that’s already stopped being maintained — this is one shift you need to be aware of, full stop. The landscape for coding agents is shifting at a similarly rapid pace, so if you’re a team trying to weave agents into an actual development workflow, our Cursor vs GitHub Copilot vs Cline vs Windsurf AI coding agent comparison is worth reading too.

[Summary: The final recommendation for your situation]

Production agents where long-running execution and error recovery matter
  → LangGraph (76% completion rate; just be sure to check the langgraph-api license)

RAG/unstructured-document pipelines
  → LlamaIndex/LlamaCloud (full-stack parsing → indexing → agent deployment)

Fast prototyping, starting without graph theory
  → CrewAI (Basic free, 50 runs/month; note the Professional tier is gone)

Azure/.NET enterprise stack
  → Microsoft Agent Framework 1.0 (.NET/Python, Agent Harness/Hosted Agents)

Migrating a legacy AutoGen project
  → AG2 (Apache-2.0, independent community roadmap) or Microsoft Agent Framework

Standardizing tool calls across multiple frameworks/providers
  → Adopt MCP as the common adapter (2026-07-28 stateless spec, 12-month backward compatibility guaranteed)

Rather than fixing on a single framework as the answer, the most realistic approach as of August 2026 is to build a project-specific combination based on the decision axes covered here — stability, orchestration model, and MCP support.