{"id":26,"date":"2026-03-05T12:23:58","date_gmt":"2026-03-05T12:23:58","guid":{"rendered":"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/autogen-vs-langgraph-vs-crewai-best-ai-agent-frame\/"},"modified":"2026-03-18T22:00:09","modified_gmt":"2026-03-18T22:00:09","slug":"autogen-vs-langgraph-vs-crewai-best-ai-agent-frame","status":"publish","type":"post","link":"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/autogen-vs-langgraph-vs-crewai-best-ai-agent-frame\/","title":{"rendered":"AutoGen vs LangGraph vs CrewAI: Which Agent Framework Actually Works in 2026"},"content":{"rendered":"<p><script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"BlogPosting\",\n  \"headline\": \"AutoGen vs LangGraph <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/04\/langchain-vs-crewai-vs-anythingllm-2026\/\" title=\"vs CrewAI\">vs CrewAI<\/a>: Which Agent Framework <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/advanced-prompt-engineering-techniques-chain-of-th\/\" title=\"Actually Works\">Actually Works<\/a> in 2026\",\n  \"description\": \"Three months ago my team needed to automate a code review pipeline \u2014 pull a PR, analyze it across security, performance, and readability dimensions, then g\",\n  \"url\": \"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/autogen-vs-langgraph-vs-crewai-best-ai-agent-frame\/\",\n  \"datePublished\": \"2026-03-05T12:23:58\",\n  \"dateModified\": \"2026-03-05T17:39:33\",\n  \"inLanguage\": \"en_US\",\n  \"author\": {\n    \"@type\": \"Organization\",\n    \"name\": \"RebalAI\",\n    \"url\": \"https:\/\/blog.rebalai.com\/en\/\"\n  },\n  \"publisher\": {\n    \"@type\": \"Organization\",\n    \"name\": \"RebalAI\",\n    \"logo\": {\n      \"@type\": \"ImageObject\",\n      \"url\": \"https:\/\/blog.rebalai.com\/wp-content\/uploads\/logo.png\"\n    }\n  },\n  \"mainEntityOfPage\": {\n    \"@type\": \"WebPage\",\n    \"@id\": \"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/autogen-vs-langgraph-vs-crewai-best-ai-agent-frame\/\"\n  }\n}\n<\/script><\/p>\n<p>Three months ago my team needed to automate a code review pipeline \u2014 pull a PR, analyze it across security, performance, and readability dimensions, then generate a structured report. Classic multi-agent problem. I figured I&#8217;d pick a framework and ship <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/copilot-vs-cursor-vs-codeium\/\" title=\"It in\">it in<\/a> a week.<\/p>\n<p>Six weeks later, I&#8217;d rebuilt it three times across three different frameworks, burned through more API credits than I care to admit, and learned a lot about what these tools are actually good for versus <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/09\/setting-up-github-actions-for-python-applications\/\" title=\"What the\">what the<\/a> marketing says they&#8217;re good for.<\/p>\n<p>This is <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/rag-vs-fine-tuning-when-to-use-each-technique-for\/\" title=\"What I\">what I<\/a> found.<\/p>\n<h2>What I Was Actually Building (And <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/08\/edge-computing-in-2026-why-developers-are-adopting\/\" title=\"Why It\">Why It<\/a> Matters)<\/h2>\n<p>The pipeline had four agents: a <strong>Fetcher<\/strong> that pulled PR diffs from GitHub, a <strong>Security Reviewer<\/strong>, a <strong>Performance Reviewer<\/strong>, and a <strong>Summarizer<\/strong> that synthesized everything into a final report. Agents needed to coordinate \u2014 the reviewers ran in parallel when possible, but the Summarizer had to wait for both. Occasionally reviewers needed to ask follow-up questions, which meant some back-and-forth with a pseudo-user context.<\/p>\n<p>Not a toy example. Not a &#8220;search the web and write a poem&#8221; demo. A real workflow with conditional logic, parallelism requirements, and structured output.<\/p>\n<p>I ran each framework for roughly <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/08\/rag-deep-dive-chunking-strategies-vector-databases\/\" title=\"Two Weeks\">two weeks<\/a> on this same task, using <code>gpt-4o<\/code> as the backbone (with <code>claude-sonnet-4-6<\/code> for some comparisons). My setup: <a href=\"https:\/\/www.amazon.com\/s?k=python+programming+book&#038;tag=synsun0f-20\" title=\"Best Python Books on Amazon\" rel=\"nofollow sponsored\" target=\"_blank\">Python<\/a> 3.12, running locally during development, targeting eventual deployment on a small <a href=\"https:\/\/aws.amazon.com\/?tag=synsun0f-20\" title=\"Amazon Web Services (AWS) Cloud Platform\" rel=\"nofollow sponsored\" target=\"_blank\">AWS<\/a> Lambda + SQS setup.<\/p>\n<h2>AutoGen: Brilliant Conversations, Painful Determinism<\/h2>\n<p>AutoGen (Microsoft, currently on v0.4.x as of early 2026) is built around the idea that agents talk to each other like coworkers in a Slack thread. You define agents, give them personas and tools, put them in a GroupChat, and let the conversation unfold.<\/p>\n<p>Here&#8217;s a simplified version of how the reviewer agents looked:<\/p>\n<pre><code class=\"language-python\">import autogen\n\nconfig_list = [{&quot;model&quot;: &quot;gpt-4o&quot;, &quot;api_key&quot;: os.environ[&quot;OPENAI_API_KEY&quot;]}]\n\nsecurity_agent = autogen.AssistantAgent(\n    name=&quot;SecurityReviewer&quot;,\n    system_message=&quot;&quot;&quot;You are a security-focused code reviewer.\n    Analyze the provided diff for vulnerabilities: injection risks,\n    secrets exposure, auth issues. Return findings as JSON.&quot;&quot;&quot;,\n    llm_config={&quot;config_list&quot;: config_list},\n)\n\nperf_agent = autogen.AssistantAgent(\n    name=&quot;PerfReviewer&quot;,\n    system_message=&quot;&quot;&quot;You review code for performance issues:\n    N+1 queries, unnecessary allocations, blocking I\/O.\n    Return structured JSON findings.&quot;&quot;&quot;,\n    llm_config={&quot;config_list&quot;: config_list},\n)\n\n# GroupChat orchestrates the conversation\ngroupchat = autogen.GroupChat(\n    agents=[security_agent, perf_agent, summarizer, user_proxy],\n    messages=[],\n    max_round=12,\n    speaker_selection_method=&quot;auto&quot;,  # LLM decides who speaks next\n)\n<\/code><\/pre>\n<p>That <code>speaker_selection_method=\"auto\"<\/code> line is where things get interesting \u2014 and not always in a good way.<\/p>\n<p>AutoGen&#8217;s conversational model is genuinely impressive when the problem is open-ended. The agents reason about what needs to happen next, delegate naturally, and the GroupChat manager (which is itself an LLM call) decides who should speak. For exploratory tasks \u2014 &#8220;research this topic and synthesize findings&#8221; \u2014 it feels almost magical.<\/p>\n<p>For my pipeline? It was a nightmare.<\/p>\n<p>The problem: I needed the two reviewers to run, then the Summarizer to run. In AutoGen, enforcing that order reliably requires either a custom <code>speaker_selection_method<\/code> function (which takes <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/08\/ai-coding-assistant-benchmarks-real-world-performa\/\" title=\"Real Work\">real work<\/a> to get right) or careful <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/advanced-prompt-engineering-techniques-chain-of-th\/\" title=\"Prompt Engineering\">prompt engineering<\/a> that breaks down the moment the LLM decides to do something &#8220;helpful.&#8221; I had the Security agent spontaneously offering to summarize on round 8 of a 12-round chat at least four times during testing.<\/p>\n<p>I also ran into a gnarly bug where <code>UserProxyAgent<\/code> would sometimes inject a human input request mid-pipeline in ways I didn&#8217;t expect \u2014 even with <code>human_input_mode=\"NEVER\"<\/code>. Turns out there&#8217;s a known issue (tracked <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/08\/rag-deep-dive-chunking-strategies-vector-databases\/\" title=\"in the\">in the<\/a> AutoGen repo, GitHub issue #3847 or thereabouts) around how <code>human_input_mode<\/code> interacts with nested chats introduced <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/08\/rag-deep-dive-chunking-strategies-vector-databases\/\" title=\"in the\">in the<\/a> 0.4 refactor. Your mileage may vary depending on exactly which 0.4.x release you&#8217;re on.<\/p>\n<p><strong>The actual gotcha:<\/strong> AutoGen&#8217;s conversation history blows <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/github-copilot-vs-cursor-vs-codeium-best-ai-coding\/\" title=\"Up in\">up in<\/a> cost. Every agent in a GroupChat gets the full conversation history injected into their context. With 12 rounds and four agents, my token usage per PR review was about 4x <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/rag-vs-fine-tuning-when-to-use-each-technique-for\/\" title=\"What I\">what I<\/a>&#8217;d estimated. For high-volume use this adds up fast.<\/p>\n<p>Where AutoGen genuinely shines: research agents, anything where you want LLM-driven collaboration to handle ambiguity. If I were building a &#8220;help me debug this mysterious <a href=\"https:\/\/m.do.co\/c\/06956e5e2802\" title=\"DigitalOcean for Production Workloads\" rel=\"nofollow sponsored\" target=\"_blank\">production<\/a> issue&#8221; agent that might need to explore different hypotheses \u2014 AutoGen&#8217;s conversational model is the right fit.<\/p>\n<h2>LangGraph: The Framework for People Who Want Control<\/h2>\n<p>LangGraph (LangChain&#8217;s graph-based agent framework) treats agent workflows as state machines. You define nodes (functions or LLM calls), edges (transitions between nodes), and a shared state object that flows through the graph. It&#8217;s more code. It&#8217;s also much more predictable.<\/p>\n<p>My pipeline in LangGraph looked like this:<\/p>\n<pre><code class=\"language-python\">from langgraph.graph import StateGraph, END\nfrom typing import TypedDict, Annotated\nimport operator\n\nclass ReviewState(TypedDict):\n    pr_diff: str\n    security_findings: list[dict]\n    perf_findings: list[dict]\n    final_report: str\n\ndef fetch_pr(state: ReviewState) -&gt; ReviewState:\n    # Pull diff from GitHub API\n    diff = github_client.get_diff(state[&quot;pr_url&quot;])\n    return {&quot;pr_diff&quot;: diff}\n\ndef run_security_review(state: ReviewState) -&gt; ReviewState:\n    findings = security_chain.invoke({&quot;diff&quot;: state[&quot;pr_diff&quot;]})\n    return {&quot;security_findings&quot;: findings}\n\ndef run_perf_review(state: ReviewState) -&gt; ReviewState:\n    findings = perf_chain.invoke({&quot;diff&quot;: state[&quot;pr_diff&quot;]})\n    return {&quot;perf_findings&quot;: findings}\n\ndef summarize(state: ReviewState) -&gt; ReviewState:\n    report = summarizer_chain.invoke({\n        &quot;security&quot;: state[&quot;security_findings&quot;],\n        &quot;perf&quot;: state[&quot;perf_findings&quot;],\n    })\n    return {&quot;final_report&quot;: report}\n\n# Build the graph\nworkflow = StateGraph(ReviewState)\nworkflow.add_node(&quot;fetch&quot;, fetch_pr)\nworkflow.add_node(&quot;security_review&quot;, run_security_review)\nworkflow.add_node(&quot;perf_review&quot;, run_perf_review)\nworkflow.add_node(&quot;summarize&quot;, summarize)\n\n# Parallel execution: both reviews happen after fetch\nworkflow.set_entry_point(&quot;fetch&quot;)\nworkflow.add_edge(&quot;fetch&quot;, &quot;security_review&quot;)\nworkflow.add_edge(&quot;fetch&quot;, &quot;perf_review&quot;)\nworkflow.add_edge(&quot;security_review&quot;, &quot;summarize&quot;)\nworkflow.add_edge(&quot;perf_review&quot;, &quot;summarize&quot;)\nworkflow.add_edge(&quot;summarize&quot;, END)\n\napp = workflow.compile()\n<\/code><\/pre>\n<p>This is exactly the parallelism I wanted \u2014 both reviewers fire after the fetch, Summarizer waits for both. LangGraph handles the fan-out\/fan-in natively.<\/p>\n<p>What I loved: the <code>ReviewState<\/code> TypedDict made debugging way easier. When something broke, I could inspect exactly what state each node received and returned. No mysterious conversation history to unpick. Conditional edges (for cases where security findings trigger a deeper scan) are first-class, not hacked in via prompt tricks.<\/p>\n<p>The tradeoff is verbosity. Setting this up took significantly more boilerplate than AutoGen. And if you&#8217;re not already <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/08\/rag-deep-dive-chunking-strategies-vector-databases\/\" title=\"in the\">in the<\/a> LangChain ecosystem, you&#8217;re pulling in a big dependency tree. I had a version conflict with <code>langchain-core<\/code> and <code>langchain-openai<\/code> that took the better part of an afternoon to resolve \u2014 they move fast and breaking changes are common between minor versions.<\/p>\n<p>One thing I noticed: LangGraph&#8217;s persistence layer (using <code>SqliteSaver<\/code> or <code>PostgresSaver<\/code>) is excellent <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/04\/fine-tuning-vs-rag-when-to-use-each-approach-for-production-llms\/\" title=\"for Production\">for production<\/a> workflows where you need resumability. A PR review that fails midway can pick up from the last checkpoint. That&#8217;s not something you get easily <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/08\/rag-deep-dive-chunking-strategies-vector-databases\/\" title=\"in the\">in the<\/a> other frameworks without building it yourself.<\/p>\n<p>I&#8217;m not 100% sure LangGraph scales gracefully to very large graphs (50+ nodes) \u2014 I&#8217;ve heard reports of the compilation step getting slow, but I haven&#8217;t hit that personally.<\/p>\n<h2>CrewAI: Fast to Start, Rigid to Extend<\/h2>\n<p>CrewAI has a clever mental model: you define Agents with roles, backstories, and goals, then group them into a Crew that tackles a set of Tasks. It reads almost like a job description doc. The first time I got a working pipeline, <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/08\/edge-computing-in-2026-why-developers-are-adopting\/\" title=\"It Took\">it took<\/a> maybe 45 minutes including reading <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/09\/setting-up-github-actions-for-python-applications\/\" title=\"the Docs\">the docs<\/a>.<\/p>\n<pre><code class=\"language-python\">from crewai import Agent, Task, Crew, Process\n\nsecurity_agent = Agent(\n    role=&quot;Security Reviewer&quot;,\n    goal=&quot;Identify security vulnerabilities in code changes&quot;,\n    backstory=&quot;Senior appsec engineer with 10 years in vulnerability research&quot;,\n    verbose=True,\n    allow_delegation=False,  # important: keep agents focused\n)\n\nperf_agent = Agent(\n    role=&quot;Performance Reviewer&quot;, \n    goal=&quot;Flag performance regressions and inefficiencies&quot;,\n    backstory=&quot;Backend engineer obsessed with latency and resource usage&quot;,\n    verbose=True,\n    allow_delegation=False,\n)\n\nsecurity_task = Task(\n    description=&quot;Review this PR diff for security issues: {diff}&quot;,\n    expected_output=&quot;JSON list of security findings with severity ratings&quot;,\n    agent=security_agent,\n)\n\nperf_task = Task(\n    description=&quot;Review this PR diff for performance issues: {diff}&quot;,\n    expected_output=&quot;JSON list of performance findings&quot;,\n    agent=perf_agent,\n)\n\ncrew = Crew(\n    agents=[security_agent, perf_agent, summarizer_agent],\n    tasks=[security_task, perf_task, summary_task],\n    process=Process.sequential,  # or hierarchical\n    verbose=True,\n)\n<\/code><\/pre>\n<p>The role-and-backstory approach <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/05\/advanced-prompt-engineering-techniques-chain-of-th\/\" title=\"Actually Works\">actually works<\/a> better than I expected for keeping agents on-task. Giving the security agent a persona (&#8220;senior appsec engineer&#8221;) genuinely reduced hallucination rates compared to a bare system prompt in my testing \u2014 though I&#8217;d want more data before making a strong claim there.<\/p>\n<p>The frustration: CrewAI&#8217;s parallel execution is clunky. <code>Process.sequential<\/code> runs tasks in order. <code>Process.hierarchical<\/code> adds a manager agent that delegates, which adds latency and cost. True fan-out parallelism (my security + perf reviewers running simultaneously) requires workarounds that feel like going against the grain of the framework.<\/p>\n<p>Honestly, CrewAI disappointed me on the customization front. When I needed the security agent to conditionally trigger a deeper tool call based on its initial findings, I ended up fighting the framework. CrewAI is opinionated in ways that work great for straightforward pipelines and start to hurt when your workflow has nuance.<\/p>\n<p>The debugging experience is also the weakest of the three. <code>verbose=True<\/code> dumps a lot of output, but it&#8217;s hard to trace exactly what prompt each agent received or what state the crew is in at a given point.<\/p>\n<h2>What I&#8217;d Actually Ship<\/h2>\n<p>Here&#8217;s where I land after six <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/08\/rag-deep-dive-chunking-strategies-vector-databases\/\" title=\"Weeks of\">weeks of<\/a> this:<\/p>\n<p><strong>For <a href=\"https:\/\/m.do.co\/c\/06956e5e2802\" title=\"DigitalOcean for Production Workloads\" rel=\"nofollow sponsored\" target=\"_blank\">production<\/a> workflows with defined structure: LangGraph.<\/strong> It&#8217;s the most work upfront, but it&#8217;s the one I trust. The explicit state, the graph topology, the persistence layer \u2014 these are things that matter when you&#8217;re shipping something real that needs to be debugged at 2am. My code review pipeline runs on LangGraph in <a href=\"https:\/\/m.do.co\/c\/06956e5e2802\" title=\"DigitalOcean for Production Workloads\" rel=\"nofollow sponsored\" target=\"_blank\">production<\/a> now. It&#8217;s been stable.<\/p>\n<p><strong>For exploratory or research-style agents: AutoGen.<\/strong> If I were building an internal tool where an agent needs to explore a problem space, AutoGen&#8217;s conversational model fits that better than a rigid graph. Just watch your token spend and test your speaker selection logic thoroughly before trusting it.<\/p>\n<p><strong>For prototyping or small teams that want something working fast: CrewAI.<\/strong> If your workflow is three to five sequential tasks with clear handoffs, CrewAI gets you there with minimal friction. For anything more complex, you&#8217;ll hit walls.<\/p>\n<p>The honest answer is that LangGraph wins for serious <a href=\"https:\/\/m.do.co\/c\/06956e5e2802\" title=\"DigitalOcean for Production Workloads\" rel=\"nofollow sponsored\" target=\"_blank\">production<\/a> use right now \u2014 but it&#8217;s the most demanding to work with. AutoGen and CrewAI both have stronger &#8220;quick start&#8221; experiences, and that matters for teams evaluating whether agent workflows are even worth the investment.<\/p>\n<p>My recommendation: build your first version in CrewAI to validate the concept, then migrate to LangGraph when you need reliability and control. It&#8217;s not exciting advice, but it&#8217;s <a href=\"https:\/\/blog.rebalai.com\/en\/2026\/03\/08\/rag-deep-dive-chunking-strategies-vector-databases\/\" title=\"What I\">what I<\/a>&#8217;d actually tell a teammate.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>{ &#8220;@context&#8221;: &#8220;https:\/\/schema.org&#8221;, &#8220;@type&#8221;: &#8220;BlogPosting&#8221;, &#8220;headline&#8221;: &#8220;AutoGen vs LangGraph vs CrewAI : Which Agent Framework Actually Works in 2026&#8221;, &#8220;d<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[1],"tags":[],"class_list":["post-26","post","type-post","status-publish","format-standard","hentry","category-general"],"_links":{"self":[{"href":"https:\/\/blog.rebalai.com\/en\/wp-json\/wp\/v2\/posts\/26","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.rebalai.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.rebalai.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.rebalai.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.rebalai.com\/en\/wp-json\/wp\/v2\/comments?post=26"}],"version-history":[{"count":17,"href":"https:\/\/blog.rebalai.com\/en\/wp-json\/wp\/v2\/posts\/26\/revisions"}],"predecessor-version":[{"id":394,"href":"https:\/\/blog.rebalai.com\/en\/wp-json\/wp\/v2\/posts\/26\/revisions\/394"}],"wp:attachment":[{"href":"https:\/\/blog.rebalai.com\/en\/wp-json\/wp\/v2\/media?parent=26"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.rebalai.com\/en\/wp-json\/wp\/v2\/categories?post=26"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.rebalai.com\/en\/wp-json\/wp\/v2\/tags?post=26"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}