7 best AI Agent frameworks compared for control, state, and coordination

Four questions decide the choice between AI agent frameworks. How much of the loop you control, whether the state survives a restart, which coordination pattern you need, and what language your team already writes.

LangGraph and Microsoft Agent Framework give you explicit control with checkpointing. CrewAI and LangChain trade off: less setup. LlamaIndex wins when retrieval drives the architecture.

Seven open-source-first frameworks cover the decision. The production layer underneath covers what none of them do.

Key takeaways:

• LangGraph and Microsoft Agent Framework fit workflows that require traceable execution, checkpointing, and human-review gates, while LangChain and CrewAI trade that visibility for faster setup.

• Every framework here saves state and passes failure detection, resumption, and guaranteed completion to the underlying layer, so checkpointing alone is not durability.

• Five of the seven frameworks are Python-only, which makes language a harder constraint than any feature comparison for .NET, Java, and TypeScript teams.

• Microsoft Agent Framework reached general availability (1.0) in April 2026 as the successor to both AutoGen and Semantic Kernel, redefining what "mature" means for new Microsoft-centered work.

• The framework shapes the loop, tools, memory, and coordination model, while the underlying platform determines how durable execution, streaming, and secure code execution actually behave.

Copy link to headingAn overview of the AI agent framework landscape in 2026

Consolidation is the story of this year. Microsoft Agent Framework became the supported successor to both AutoGen and Semantic Kernel, while LangChain and LangGraph settled into stable 1.x lines with published support windows.

Two categories are out of scope. Vendor SDKs such as the OpenAI Agents SDK and Claude Agent SDK are excluded because this survey follows the open SDK strategy. Pricing columns are also excluded because every framework core is permissively licensed, with commercial add-ons noted per entry.

What remains is weighed on control of the loop, state durability across restarts and approval pauses, native coordination patterns, release stability, and integration breadth.

Here is how the seven compare across the dimensions that change a design decision:

Seven entries, but not seven independent choices. LangChain builds on LangGraph, and Microsoft Agent Framework has absorbed both AutoGen and Semantic Kernel, leaving CrewAI and LlamaIndex as the only fully independent projects in the table. Maturity separates them more sharply than language or license does, so each entry below opens at that point.

Copy link to headingThe 7 best AI agent frameworks compared

Copy link to heading1. LangChain

LangChain is a model-agnostic orchestration library for building applications with large language models (LLMs) and is the most common entry point for a first agent. It is permissively licensed and sits in a stable 1.x line, with the 0.3 line supported until December 2026.

The create_agent API is built on LangGraph underneath, so persistence, streaming, human-in-the-loop, and time travel work without learning the graph model. That is the appeal and the risk together: the abstraction is doing real work, and when it leaks, the team debugs layers it did not write.

LangChain pros:

• Provider portability: Standardized message abstractions and tool-ready chat models let teams swap LLM providers with minimal code changes.

• Boilerplate reduction: Chains, templates, loaders, and retrievers ship in the box, which shortens the path from idea to working prototype.

• Graph features without graph work: Building on LangGraph means checkpointing and human-in-the-loop arrive by default rather than by design.

LangChain cons:

• Debugging depth: When prompt construction or dependency versions shift, teams read through more layers of the framework than lower-level code would require.

• Migration overhead: The community integrations package was archived in June 2026, so existing integrations need to be repointed to standalone partner packages.

Pricing: The framework is free under a permissive open-source license. LangSmith, the tracing and evaluation platform, is a separate commercial service with a free developer tier and paid per-seat plans.

Best for: Teams building a prototype, teams that need breadth of integrations over depth of control, and teams with an existing LangChain investment.

Copy link to heading2. LangGraph

LangGraph is a low-level orchestration framework and runtime from the LangChain team, built for explicit control over every node and edge in an agent graph. It is permissively licensed, on a stable 1.x line, and ships minor releases roughly every 1-2 months.

Its durability model is the most complete in this survey. Checkpointers save thread-scoped graph state for continuity, human-in-the-loop pauses, time travel, and fault tolerance, with Postgres and Redis backends, while Stores hold long-term memory across threads.

LangGraph pros:

• Node-level control: Every step and transition in the loop is code the team wrote, which makes the audit path as clear as the happy path.

• Replayable state: Checkpointers plus time travel let teams re-run a decision from any prior state rather than reasoning backward from logs.

• Human review gates: Interrupts pause execution at defined points, which is what compliance workflows need rather than what they can approximate.

LangGraph cons:

• Design cost upfront: Graph thinking requires modeling the workflow before writing it, which slows the first week to speed up the third month.

• No longer the default recommendation: The Thoughtworks Technology Radar moved LangGraph from Adopt to Trial in April 2026, citing the cost of treating every multi-agent system as a stateful graph with global shared state.

Pricing: Free under a permissive open-source license. LangSmith is priced separately with a free tier and paid per-seat plans.

Best for: Complex workflows that must survive process restarts, support replay debugging, and expose override paths to a human reviewer.

Copy link to heading3. CrewAI

CrewAI is a high-level, role-based multi-agent framework in which agents assume named roles such as researcher or reviewer. It is MIT-licensed and actively maintained on a 1.x line, with releases through July 2026.

The dual model is the interesting part. Crews enable high-autonomy collaboration, and Flows provide deterministic, stateful control flow through @start and @listen decorators. CrewAI's own documentation recommends starting with a Flow for production, which signals where the autonomy model holds and where it does not.

CrewAI pros:

• Maps to real org charts: Role assignments mirror how work is already divided, reducing the setup burden for structured business workflows.

• Token efficiency on structured work: A structured task model can use fewer tokens than conversation-heavy alternatives for equivalent tasks.

• Deterministic escape hatch: Flows provide predictable control flow when Crews' autonomy produces variance the product cannot absorb.

CrewAI cons:

• Human-in-the-loop is console-first: human_input=True and the newer @human_feedback decorator both prompt through the console, so a web-facing approval surface takes custom work outside the commercial tier.

• Abstraction ceiling: Once the workflow stops resembling a team of roles, the model works against the design rather than for it, and run-to-run variance requires early planning for retry behavior.

Pricing: Free under the MIT license. The commercial CrewAI AMP Suite includes Crew Studio, a control plane, and an email-based human-approval flow.

Best for: Structured, role-shaped pipelines such as content operations and research automation, built on Flows rather than Crews once the work reaches production.

Copy link to heading4. AutoGen

AutoGen is Microsoft's conversation-driven multi-agent framework, which has been retired from active feature development. The README carries a maintenance-mode notice, states the project will receive no new features, and describes it as community-managed. The last release was September 2025.

Coordination in AutoGen emerges from multi-turn dialogue between conversable agents rather than from a predefined task graph. That design established the multi-agent patterns the rest of the category now uses; understanding it is the reason, not starting with it.

AutoGen pros:

• Pattern lineage: The conversation-driven approach shaped the group chat and handoff patterns that shipped natively in its successor.

• Fit for exploratory work: When the dialogue between agents is part of the product, conversation as the coordination substrate is the correct model.

• Existing footprint: The package line remains installable, and the repository stays available for teams already running it.

AutoGen cons:

• No maintenance commitment: Community management means there are no guaranteed bug fixes or security patches, and there have been no releases for roughly 10 months.

• License text ambiguity: The repository ships a CC-BY-4.0 LICENSE alongside an MIT LICENSE-CODE, so legal review should confirm terms before any remaining production use. The autogen name on PyPI is also an unrelated fork, not Microsoft's autogen-core line.

Pricing: Free, subject to the license ambiguity above. No commercial tier.

Best for: Existing research systems already running on it. New Microsoft-centered work should plan around Microsoft Agent Framework instead.

Copy link to heading5. LlamaIndex

LlamaIndex is a data framework for retrieval-centric LLM applications, MIT licensed and under active development, with agent orchestration handled by its event-driven Workflows and the LlamaAgents runtime built on top of them.

Workflows invert the graph model rather than reimplementing it. Steps emit events over a shared context, and each component subscribes to event types and decides the next action, so no traversal algorithm controls the path. That fits a chain of data transformations better than a graph, and an audited control flow worse.

LlamaIndex pros:

• Retrieval as a first-class concern: Ingestion, parsing, indexing, retrieval, and evaluation live in one framework rather than across three.

• Event-driven composition: Adding a step means subscribing to an event, keeping the pipeline extensible without rewiring the graph.

• Durable execution support: A StateStorage protocol with an in-package SQLite backend, plus the documented checkpointer API, covers save and resume.

LlamaIndex cons:

• Retrieval learning curve: Teams new to retrieval-augmented generation (RAG) have to learn chunking, indexing, and evaluation before the agent layer pays off.

• Naming churn: The orchestration path has moved through several names, and LlamaCloud is now the LlamaParse Platform, so older tutorials point at renamed surfaces.

Pricing: Free under the MIT license. The LlamaParse Platform, which covers parsing, extraction, and managed indexes, is a separate, credit-based commercial service with a free tier.

Best for: Search- and retrieval-first applications, including document ingestion pipelines across PDFs and multi-source data.

Copy link to heading6. Semantic Kernel

Semantic Kernel is Microsoft's enterprise agent SDK for .NET, Python, and Java, and its repository now names Microsoft Agent Framework as its successor. It is MIT licensed and still shipping maintenance releases across all three languages, with new capabilities landing in the successor.

Its filter architecture is the piece worth carrying forward. IPromptRenderFilter and IAutoFunctionInvocationFilter provide validation and logging hooks at the two points where agent guardrails belong, which is why the pattern survived into the framework replacing it.

Semantic Kernel pros:

• Real multi-language support: Python, .NET, and Java are all supported, which no other framework in this survey matches.

• Production guardrail hooks: Filters intercept prompt rendering and automatic function invocation, giving validation and logging a defined insertion point.

• Enterprise estate fit: It slots into existing .NET and Java services without asking the organization to add a Python service.

Semantic Kernel cons:

• Formally a predecessor: New capability ships in Microsoft Agent Framework, so building on Semantic Kernel now means planning a migration later.

• Java has no successor path yet: Microsoft Agent Framework 1.0 covers .NET and Python, which leaves Java estates on the predecessor for now.

Pricing: Free under the MIT license, with no separate commercial tier.

Best for: Teams already running it, and .NET shops planning a migration to Microsoft Agent Framework.

Copy link to heading7. Microsoft Agent Framework

Microsoft Agent Framework is the converged successor to AutoGen and Semantic Kernel, built by the same core teams and shipped under the MIT license. Version 1.0 reached general availability on April 3, 2026 for both .NET and Python, with stable APIs and a long-term support commitment.

It combines graph-based workflow orchestration and checkpointing with the patterns AutoGen pioneered, including sequential, concurrent, group chat, handoff, and Magentic. Declarative Workflows moves coordination out of application code into YAML, and A2A and MCP support is native rather than adapted.

Microsoft Agent Framework pros:

• Clearest support commitment in the survey: A 1.0 general availability release with stable APIs and long-term support is a different maturity signal from an active release line.

• Protocol support without adapters: Native A2A and MCP mean cross-organization coordination and tool access do not depend on a community integration.

• Coordination as configuration: Declarative Workflows in YAML lets the coordination graph change without redeploying application logic.

Microsoft Agent Framework cons:

• Patterns live outside core: Group chat, handoff, and Magentic ship in a separate orchestrations package, so the core install is smaller than the feature list suggests.

• Cosmos DB checkpointing is preview: The CheckpointStorage protocol covers in-memory and file backends in the stable release, and the Azure Cosmos DB backend is still prerelease.

• Gravity toward Microsoft Foundry: The integration story is most deeply embedded within Microsoft's own platform, which is a fit for some organizations and a constraint for others.

Pricing: Free under the MIT license. Hosted agent execution on Microsoft Foundry is billed separately by Microsoft.

Best for: Enterprise multi-agent deployments on Microsoft infrastructure, and teams migrating off AutoGen or Semantic Kernel.

Copy link to headingHow do you choose an AI agent framework for your project?

These six checks turn the weighting criteria into a decision, ordered so the ones that eliminate options come first. Tool access and cross-agent coordination are protocol-level concerns now, with MCP donated to the Linux Foundation in December 2025 and A2A past 150 organizations by April 2026, which means the framework is not the lock-in. The coordination model is.

Copy link to headingStart from the language your team already ships

Language eliminates faster than features do. Five of the seven frameworks are Python-only, so .NET and Java estates choose between Microsoft Agent Framework and Semantic Kernel before any other criterion applies.

TypeScript teams either run Python orchestration as a separate service or stay in the AI SDK and skip this category.

Copy link to headingMatch the control level to what your team can debug

Control and abstraction are one axis, not two. If decisions have to be traceable, replayable, and overridable by a named reviewer, pick LangGraph or Microsoft Agent Framework, which expose every node and transition as code you wrote.

LangChain and CrewAI shorten the happy path instead, which holds up when the team can read framework source once the abstraction leaks.

Copy link to headingCheck whether your workflow outlives its process

If it pauses for longer than a process lives, checkpointing narrows the field less than teams expect. LangGraph, Microsoft Agent Framework, and LlamaIndex all persist state, but none of them detect when a run dies.

Treating a checkpointer as durability is the failure here. Decide on the other checks and put a durable execution layer underneath.

Copy link to headingCount the roles before picking a coordination model

Single-agent tasks rarely justify orchestration weight. Role-based delegation points to CrewAI, built on Flows rather than Crews for production. Agents that share graph state point to LangGraph subgraphs. Teams on Microsoft infrastructure get group chat and handoff natively in Microsoft Agent Framework. Switching coordination models later means rewriting the core rather than swapping a dependency.

Copy link to headingRoute retrieval-heavy work to a data framework

If ingestion, parsing, indexing, and evaluation shape the architecture more than the loop does, the agent framework is the wrong center of gravity. Pick LlamaIndex, where retrieval is the primary abstraction and Workflows orchestrate around it.

Bolting a retrieval pipeline onto a graph framework later means having to rebuild the ingestion path.

Copy link to headingSketch the execution path before adding a framework

If you can draw the steps before writing code, you have a workflow rather than an agent, and direct model API calls with composable code may be enough; a position the Thoughtworks Radar now takes on LangGraph.

A framework earns its weight when the best path is unknown in advance. Test that in a proof of concept covering state persistence, human approval, and replay.

Copy link to headingHow Vercel supports AI agent frameworks in production

Each framework above shapes the loop, tools, memory, and coordination model. None of them stream tokens to a browser, hold a run open across a two-day approval, or keep generated code away from production credentials. Those are platform properties that determine how the experience feels once the framework leaves a local notebook.

Copy link to headingRunning long agent loops on fluid compute

A complex agent query can take dozens of reasoning-loop iterations, and common serverless timeouts push those loops out of the request-response cycle entirely. Teams then make an accidental decision about what stays in the request path.

Fluid compute gives Vercel Functions 300 seconds by default across all plans, and up to 800 seconds on Pro and Enterprise, billed on Active CPU, so idle model wait costs nothing.

Set the duration in one line: export const maxDuration = 800;

Work that exceeds the configured duration belongs behind the request rather than inside it.

Copy link to headingResuming runs after restarts with Vercel Workflows

This is the gap the comparison above keeps pointing at. Checkpointing saves state, and something still has to detect failure, resume the work, and drive it to completion. Teams find the difference when a deploy lands mid-run.

Vercel Workflows provides durable execution for that layer. Runs pause for minutes or months and resume from the exact point, survive deploys through deterministic replay, and record every step, input, output, and error for tracing, with Vercel Queues handling dispatch underneath.

Mark a function durable with a directive:

export async function agentRun(topic: string) {
  'use workflow';

  const plan = await planSteps(topic);
  const result = await executePlan(plan);

  return result;
}

Framework checkpointers still hold graph-local state, and the recovery plan for the run as a whole lives here.

Copy link to headingStreaming tokens and approvals with the AI SDK

An agent that takes 40 seconds and shows nothing reads as broken. Server-Sent Events is the common transport, and production teams also handle reverse-proxy buffering, backpressure for slow consumers, and partial parsing of structured output.

AI SDK 7 covers that surface with built-in streaming, ToolLoopAgent, typed runtimeContext and toolsContext, tool approvals through toolApproval, durable WorkflowAgent execution, and sandbox support for generated-code workflows. Approval is the part worth noticing, because it turns the console-bound review gates in several frameworks above into a browser interaction with typed state.

Copy link to headingUnifying model access with AI Gateway

Agent orchestration and the web application rarely live in the same service. That split leaves two sets of provider keys, two places where routing and fallbacks get configured, and spend that reconciles in neither.

AI Gateway collapses that to a single key across both, with automatic failover, improving P99 streaming latency by 10% to 14% and reducing API error rates by 43.8%, with no markup over provider pricing.

Documented integrations cover LangChain and LlamaIndex alongside LiteLLM, Mastra, and Pydantic AI, and the rewritten @ai-sdk/langchain adapter streams LangGraph events into typed client messages, carrying human-in-the-loop through graph interrupts.

Copy link to headingIsolating generated code with Vercel Sandbox

Agents that write and execute code create an isolation requirement that no framework can solve, because the framework has no boundary to enforce. Generated code should not see environment variables, database connections, or cloud credentials unless the application deliberately grants access.

Vercel Sandbox, generally available since January 2026, runs untrusted code in Firecracker microVMs isolated from your infrastructure. Coding agents and evaluation harnesses can execute generated Node.js or Python code without sharing a runtime with the application, which is the difference between an agent that can write code and one you can ship.

Copy link to headingPick your framework, then build the production layer around it

The seven frameworks here differ in their abstraction and coordination styles, yet converge on the same division of labor. The framework shapes the loop, tools, memory, and coordination model.

The platform handles web delivery, durable execution, streaming, observability, and safe execution boundaries. Teams that pick the framework matching their control and state model, then put the production surfaces around it, spend their third month shipping features rather than rebuilding the seam.

Two Vercel surfaces do most of that work, and three more close the gaps around them:

• Vercel Workflows: Durable execution for runs that pause and resume, so a framework checkpointer stops being the whole recovery plan.

• Fluid compute: Longer-running Vercel Functions that keep the agent loop in the request path until the work genuinely belongs behind it.

• Observability: Run-level traces, step timing, and failure analysis in the Vercel dashboard rather than a separate per-seat tracing subscription.

• Vercel Marketplace: Managed Postgres and Redis from providers including Neon and Upstash, which is where framework checkpointers persist state.

• AI templates: Working applications to start from instead of assembling the streaming, state, and isolation layers by hand.

Deploy an agent application at vercel.com/new and build the durable layer underneath whichever framework you picked.

Copy link to headingFrequently asked questions about AI agent frameworks

Copy link to headingAre AI agent frameworks free to use in production?

Most framework cores are free to use in production, including all seven here, which ship under permissive open-source licenses. Paid products such as LangSmith, CrewAI AMP Suite, and the LlamaParse Platform are optional managed services. AutoGen's license text should be confirmed directly before production use.

Copy link to headingWhich AI agent framework is production-ready in 2026?

Microsoft Agent Framework carries the clearest maturity signal, with a 1.0 general availability release in April 2026 and a long-term support commitment. LangGraph is production-proven with a stable 1.x line and documented durability patterns. CrewAI is production-capable when teams build on Flows.

Copy link to headingDo you need an AI agent framework at all?

Not for a single-step loop. Direct model API calls and composable code cover a single agent with a handful of tools. A framework earns its weight when you need durable, long-running workflows with audit trails, multi-agent coordination, or standardization across several teams.

Copy link to headingWhat is the difference between an AI agent framework and an SDK?

An SDK provides model access, such as sending messages and parsing tool-call responses. A framework adds orchestration around that access, including the loop, state management, tool dispatch, and multi-agent coordination. The loop itself is usually small, and the orchestration around it is not.