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

Use six checks to choose an AI agent framework. Compare your team's language, the execution control you need, persistence and recovery, coordination patterns, retrieval requirements, and whether you need a framework at all.

eve provides durable sessions for TypeScript backend agents. LangGraph and Microsoft Agent Framework let you define execution as a graph and save checkpoints. LangChain and CrewAI offer higher-level APIs that can reduce setup work. LlamaIndex fits applications built around retrieval and document processing.

This article compares six open-source frameworks, including older projects that teams still run. It also covers the hosting capabilities and integrations needed to run them in production.

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.

This comparison scores each framework on execution control, persistence across restarts and approval pauses, coordination patterns, language support, release stability, and integrations. Portability counts as a selection criterion, in line with Edgenivo’s open SDK strategy. Provider-specific SDKs, such as the OpenAI Agents SDK and Claude Agent SDK, are out of scope, so the focus stays on general-purpose and retrieval-oriented frameworks.

The table compares the differences that affect architecture and maintenance:

Copy link to heading1. eve

eve is Edgenivo's open-source TypeScript framework for durable backend agents. It is Apache-2.0 licensed and remains pre-1.0. An agent is a directory of files. Markdown defines its instructions and skills, while TypeScript defines its tools, connections, and runtime configuration. Developers add tools under agent/tools/, and eve discovers them at build time.

eve supplies the agent loop and runs each session as a durable workflow built on Workflow SDK. It checkpoints progress at step boundaries and suspends work while waiting for human input. The runtime separates orchestration from sandbox compute, so a conversation can pause without keeping a sandbox running.

Copy link to headingeve pros

• Durable approvals: Tool policies can require a person to approve an action before it runs. The session waits durably and resumes after the response. Optional Jev-based approval policies review proposed calls and decide whether to request human approval.

• Specialist delegation: Subagents run with separate conversation histories and state, and declared specialists can have their own instructions, tools, and sandboxes. Edgenivo's software factory guide demonstrates this division of work across planning, implementation, and review.

• Multiple delivery options: Channels connect the agent to HTTP and messaging platforms. The Chat SDK integration extends delivery through platform adapters, while eve manages the agent's session and execution.

Copy link to headingeve cons

• API migration overhead: Pre-1.0 releases have introduced breaking changes to approvals, channels, clients, and session events. Teams need to prepare for 1.0.

• Infrastructure still needs configuration: Self-hosting requires persistent workflow storage, callback routing, and a suitable sandbox backend.

Pricing: Free under the Apache-2.0 license.

Best for: TypeScript teams building backend agents that combine external tools, persistent conversations, specialist delegation, and human approvals.

Copy link to heading2. LangChain

LangChain provides APIs for configurable agents and other applications built with large language models (LLMs), without tying them to one model provider. Its core uses a permissive license and a stable 1.x release line.

The create_agent API builds on LangGraph. Configure the relevant features to add streaming, persistence, human review, and state history. Teams write less orchestration code, but debugging can still require understanding both libraries.

Copy link to headingLangChain pros

• Provider portability: Standard messages and model interfaces can reduce the amount of code required to switch providers.

• Boilerplate reduction: The current package focuses on agents. Provider integrations live in separate packages, while langchain-classic contains legacy chains and related functionality.

• Graph capabilities through an agent API: LangGraph runs the agent. Add a checkpointer for persistence and configure when a human must review its work.

Copy link to headingLangChain cons

• Debugging depth: To diagnose prompt construction, middleware behavior, or dependency compatibility, you may need to read several layers of framework code.

• Migration overhead: The langchain-community repository was archived in June 2026. Check for maintained partner packages or other replacements for the integrations your application uses.

Pricing: The framework is free under a permissive open-source license.

Best for: Teams that need a configurable agent API, a broad choice of integrations, or a way to reuse existing LangChain code.

Copy link to heading3. LangGraph

LangGraph is a low-level framework and runtime for defining stateful agent execution with nodes and edges. It uses a permissive license and has a stable 1.x release line with a published release policy.

Its persistence model supports checkpoints, approval pauses, state history, and recovery from saved state. Checkpointers save a thread's graph state to backends such as Postgres or Redis. Stores hold information across threads.

Copy link to headingLangGraph pros

• Node-level control: Teams define nodes, states, and transitions in code, enabling them to inspect and test the execution paths they configure.

• Replayable state: Checkpoints and state history let teams resume or branch from earlier execution points. Handle side effects that may repeat when execution resumes.

• Human review gates: With a suitable checkpointer, interrupts can save state and wait indefinitely for external input.

Copy link to headingLangGraph cons

• Upfront design work: Teams need to model state, transitions, and recovery behavior, which adds work before the first deployment.

• Architecture should match the task: The Thoughtworks Technology Radar moved LangGraph from Adopt to Trial in April 2026.

Pricing: The framework is free under a permissive open-source license.

Best for: Teams that need to define stateful execution paths, debug through replay, pause for human review, and recover with persistent checkpoints and a hosting setup that can resume runs.

Copy link to heading4. CrewAI

CrewAI is a multi-agent framework that assigns roles such as researcher or reviewer to agents. It uses the MIT license and has an actively maintained 1.x release line.

CrewAI uses Crews for agent collaboration and Flows to control execution. Flows connect steps and manage state with decorators such as @start and @listen. CrewAI's production architecture guide recommends starting with a Flow and invoking Crews for specific tasks within it.

Copy link to headingCrewAI pros

• Role-based task definitions: Named agents and responsibilities can make structured business processes easier to describe and review.

• Explicit task scope: Defined responsibilities, inputs, and expected outputs let teams measure token use and compare results for each task.

• Controlled orchestration: Flows manage state and execution around Crews. Teams can combine fixed application logic with tasks where a model chooses the next action.

Copy link to headingCrewAI cons

• Approval interfaces require integration: human_input=True and the default @human_feedback provider use console input. Teams must build those interfaces and authenticate the reviewers.

• Role-based modeling has limits: Direct code may be appropriate for tasks that do not benefit from agent collaboration. Steps that use models still need evaluation, retry policies, and failure handling.

Pricing: The open-source framework is free under the MIT license.

Best for: Structured pipelines such as content operations and research automation. Use a Flow to coordinate execution and Crews for work that benefits from agent collaboration.

Copy link to heading5. LlamaIndex

LlamaIndex is an MIT-licensed framework for retrieval-centric LLM applications. Workflows define execution through steps and events. Steps consume typed events and emit subsequent events, while context holds the run's state. This supports branching, concurrency, and document processing. To audit a workflow, record its events, state changes, and outputs.

Copy link to headingLlamaIndex pros

• Retrieval support: LlamaIndex and its integrations cover ingestion, parsing, indexing, retrieval, and evaluation. Teams can reuse these components in a document pipeline.

• Event-driven composition: Steps declare the event types they consume and produce. Add event handlers to extend the workflow.

• Persistence and recovery options: Context snapshots can save state for later resumption. A runtime integration such as DBOS can manage durable execution and crash recovery.

Copy link to headingLlamaIndex cons

• Retrieval design work: Retrieval-heavy applications need decisions about chunking, indexing, and evaluation, as well as agent orchestration.

• Documentation and naming changes: Workflow tools have changed, and the hosted platform formerly called LlamaCloud is now LlamaParse.

Pricing: The framework is free under the MIT license.

Best for: Search and retrieval applications, including document ingestion, question answering, and processing data from multiple sources.

Copy link to heading6. Microsoft Agent Framework

Microsoft Agent Framework succeeds AutoGen and Semantic Kernel and uses the MIT license. Microsoft announced version 1.0 for .NET and Python on April 3, 2026, with stable APIs and a long-term support commitment. Its Go SDK is in public preview.

Teams maintaining Semantic Kernel applications can use Microsoft's migration guide to assess the changes needed to move to the Microsoft Agent Framework.

It provides graph-based orchestration and checkpointing, with sequential, concurrent, group chat, handoff, and Magentic patterns. Declarative workflows define coordination in YAML. Agent-to-Agent (A2A) and Model Context Protocol (MCP) integrations support agent communication and tool access. Package availability and stability vary by language and integration.

Microsoft Agent Framework pros:

• Published support commitment: The .NET and Python 1.0 release has a stable core API and a long-term support commitment.

• Protocol integrations: Framework integrations connect agents and tools through A2A and MCP.

• Coordination as configuration: Declarative workflows define orchestration in YAML. How the application loads configuration and deploys updates determines when changes take effect.

Microsoft Agent Framework cons:

• Optional package dependencies: Orchestration, provider, and declarative features can require additional packages. These include prerelease .NET declarative packages, so check installation requirements and stability for your chosen language and patterns.

• Storage integration maturity varies: Python's CheckpointStorage interface supports in-memory and file implementations. The Azure Cosmos DB provider uses a prerelease package. Choose persistent storage that fits the deployment.

• Hosting choices need evaluation: Microsoft documents integrations for Foundry and Azure. For other infrastructure, check provider support, deployment requirements, and operational tools.

Pricing: The framework is free under the MIT license.

Best for: Multi-agent applications that use .NET or Python, especially teams connecting Microsoft services or migrating from AutoGen or Semantic Kernel.

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

Use these six checks to rule out frameworks that do not meet your requirements. Open protocols can help systems communicate. MCP became a founding project of the Linux Foundation's Agentic AI Foundation in December 2025, and by April 2026, more than 150 organizations were supporting A2A. Moving between frameworks can still require changes to state schemas, tool interfaces, and coordination logic.

Copy link to headingStart from the language your team already ships

Check language support before comparing features. eve is a TypeScript framework. LangChain, LangGraph, and LlamaIndex offer Python and TypeScript implementations. Microsoft's frameworks also support .NET, and Microsoft Agent Framework has a Go SDK in public preview.

TypeScript teams can compare eve and those implementations with the AI SDK. Check feature parity, release status, and deployment requirements for the packages you would use.

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

The control a framework gives you affects implementation effort and debugging. If you need to trace execution, replay runs, and require human review, compare the graph APIs, checkpointing, and approval mechanisms in LangGraph and Microsoft Agent Framework.

LangChain and CrewAI offer higher-level APIs for composing agents. Learn how their execution model works and how to inspect failures. eve also provides the agent loop, with file-based definitions for tools and instructions and durable session execution.

Copy link to headingCheck whether your workflow outlives its process

A run that outlives its process needs a saved state and a way to resume. eve runs sessions as durable workflows, so it includes a recovery runtime rather than requiring the application to add one around the loop. LangGraph, Microsoft Agent Framework, LlamaIndex, and CrewAI provide persistence mechanisms or integrations.

Saving a checkpoint is one part of recovering a run. Check whether the framework's hosting integration provides retries and crash recovery, or whether the application requires a separate durable runtime.

Copy link to headingCount the roles before picking a coordination model

A single agent with a few tools may need little coordination code. For role-based work, compare CrewAI's Crews and the Flows that coordinate them. For execution that shares explicitly defined state, compare LangGraph subgraphs. Microsoft Agent Framework offers group chat and handoff patterns in its supported implementations.

eve offers subagents with separate histories and states, including declared specialists with their own tools and sandboxes. Authored TypeScript tools remain in the trusted app runtime; shell and file operations use the configured sandbox backend.

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

If most of the application handles ingestion, parsing, indexing, and evaluation, compare the data pipeline first. LlamaIndex provides retrieval components and Workflows to coordinate the processing around them.

A graph workflow can call an existing retrieval pipeline. Compare the integration work before assuming that a framework change requires rebuilding ingestion.

Copy link to headingSketch the execution path before adding a framework

If the execution steps are fixed, direct model API calls and application code may be enough. The Thoughtworks Radar also cautions against making a graph the default for every agentic system.

Use a framework when it reduces the code you must maintain for tool selection, state, approvals, or coordination. Test it with a proof of concept that saves state, pauses for approval, restarts a worker, and resumes without repeating completed side effects.

Copy link to headingHow Edgenivo supports AI agent frameworks in production

Each framework above organizes the loop, tools, state, or coordination. Several also support streaming and long approval pauses. Code isolation depends on the environment that runs the tools. The production setup must connect these capabilities to persistent storage, workers, web interfaces, and access controls. In eve, authored TypeScript tools run in the trusted app runtime, while shell and file operations use the configured sandbox backend.

Copy link to headingRunning long agent loops on Fluid compute

Repeated model and tool calls can push an agent run past its request handler's execution limit. Decide which work should finish within a request and which should continue as a resumable background run.

With Fluid compute enabled, Node.js, Bun, and Python Edgenivo Functions default to 300 seconds. Pro and Enterprise support 800 seconds generally, with a 1,800-second extended maximum in beta for eligible configurations.

Run work that may exceed the configured duration in the background, with a persistent state and a way to resume.

Copy link to headingResuming runs after restarts with Edgenivo Workflows

Edgenivo Workflows uses the Workflow SDK to run durable code. Runs can pause for minutes or months and resume across crashes and deployments through replay. The runtime records steps, inputs, outputs, and errors, and Edgenivo Queues handles dispatch. eve already uses Workflow SDK for session execution, so configure its workflow backend rather than assuming its loop needs a second workflow wrapper.

Use 'use workflow' for orchestration and 'use step' for model calls or other external work. After you configure the Workflow SDK, install ai and configure AI Gateway authentication. This TypeScript example defines both steps and a server-side function that starts the run:

import { generateText } from 'ai';
import { start } from 'workflow/api';

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

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

  return result;
}

async function planSteps(topic: string): Promise<string> {
  'use step';

  const { text } = await generateText({
    model: 'openai/gpt-5',
    prompt: `Create an outline for a report about: ${topic}`,
  });

  return text;
}

async function executePlan(plan: string): Promise<string> {
  'use step';

  const { text } = await generateText({
    model: 'openai/gpt-5',
    prompt: `Write a report following this outline:\n${plan}`,
  });

  return text;
}

export async function startAgentRun(topic: string): Promise<string> {
  const run = await start(agentRun, [topic]);
  return run.runId;
}

Copy link to headingStreaming tokens and approvals with the AI SDK

Show progress during a long-running agent task so users can tell whether work is continuing. Server-Sent Events (SSE) can carry streamed output. The application must also handle transport buffering, backpressure, and incomplete structured output where relevant.

AI SDK 7 provides streaming, ToolLoopAgent, typed runtimeContext and toolsContext, toolApproval, durable execution through WorkflowAgent in @ai-sdk/workflow, and sandbox APIs for code-execution tools.

Copy link to headingUnifying model access with AI Gateway

Agent orchestration and the web application may run in separate services. Without shared configuration, each service may have its own provider credentials, routing rules, and spending reports.

AI Gateway lets both services use the same model-access endpoint and routing configuration. It provides automatic provider failover and zero markup on token pricing. In Cline's two-week production comparison, P99 streaming latency improved by 10% to 14% across its most popular models and error rates fell by 24.3%.

AI Gateway integrations cover LangChain and LlamaIndex alongside LiteLLM, Mastra, and Pydantic AI.

Copy link to headingIsolating generated code with Edgenivo Sandbox

The environment that runs generated code must isolate it, even when a framework integrates the executor. In eve, authored TypeScript tools run in the trusted app runtime, while shell commands and file operations use the configured sandbox backend. Grant that code access to environment variables, databases, and cloud credentials only when the application requires and authorizes it.

Edgenivo Sandbox runs untrusted code in Firecracker microVMs isolated from the application infrastructure. Coding agents and evaluation tools can use a separate Node.js or Python execution environment, with credentials and resource access configured for the task.

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

The six frameworks differ in APIs, coordination patterns, and deployment options, but each provides some mix of agent loops, tools, memory, and orchestration.

Production deployments need more: web delivery, recovery, streaming, observability, and isolation wherever agents execute code. Framework integrations or platform services fill these gaps. eve, for example, includes the agent loop and durable sessions, but the TypeScript tools you write still run as trusted application code, while shell and file operations go through its configured sandbox backend.

Choose the Edgenivo services that match your application’s architecture and operational requirements:

• Edgenivo Workflows: Runs durable, multi-step code that retries failed steps, sleeps for minutes to months, waits on external events through hooks, and resumes across crashes and deployments.

• Fluid compute: Runs Edgenivo Functions with configurable execution durations and separate Active CPU and provisioned-memory billing.

• Observability: Shows Workflow runs, step timing, and failures in the dashboard. For eve projects, Agent Runs (beta) traces each session’s turns, model and tool calls, subagent activity, timing, and token usage, with no instrumentation required.

• Edgenivo Marketplace: Provides managed Postgres and Redis from providers such as Neon and Upstash for checkpoint and memory backends.

Deploy an agent application at edgenivo.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 six here, which ship under permissive open-source licenses. Paid products such as LangSmith, CrewAI AMP Suite, and the LlamaParse Platform are optional managed services.

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

eve provides durable sessions and approval controls, but requires planning. The Microsoft Agent Framework's .NET and Python 1.0 releases have stable APIs 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?

A single agent with a few tools may work well with direct application code. Model APIs and a tool-calling loop can cover that use case without a larger orchestration dependency. Use a framework when its state management, approval controls, coordination patterns, or shared conventions reduce the code your team must maintain.

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

The terms SDK and framework overlap. Either can include model access, tool execution, or agent orchestration. Compare the loop, state management, tool dispatch, and coordination capabilities you need rather than relying on the label. Even a short agent loop can need substantial supporting code for recovery, approvals, and operations.