AI Agent Framework Jobs in 2026: What 1,135 Listings Reveal About LangChain, LangGraph, LlamaIndex, CrewAI & AutoGen

Every other framework comparison this year ranked GitHub stars and benchmarks. We ranked the five biggest agentic frameworks by what 1,135 real job postings demand and pay for. The order changes, and the bigger surprise is that nobody hires for just one.

Maxim Buz

May 25, 2026

We run agentic-engineering-jobs.com, a job board for engineers who build agentic systems: AI agents, RAG pipelines, and multi-agent orchestrations. We have 1,135 published listings. 539 name at least one agentic framework, and 467 name at least one of the five with enough hiring volume to compare: LangChain, LangGraph, LlamaIndex, CrewAI, and AutoGen. We pulled every field on those jobs and looked for the pattern.

One housekeeping note before the numbers. There is a comparison genre that calls itself “data-driven,” and the data there is latency and token benchmarks. The data here is job postings: who employers pay, what they pay, and which tools they list together. Rank the frameworks that way and two things happen. The order stops matching the GitHub-stars leaderboard, and the average framework job names 2.31 of them. The modal job names two. The market hires a stack, not a winner.

GitHub stars rank one thing. Hiring ranks another.

Start with the cleanest contradiction. AutoGen has the second-highest star count of the five and the lowest hiring rank. Microsoft has put it in maintenance mode. LangGraph has the fewest stars of the five and the second-highest hiring rank. If you picked a framework to learn off a stars listicle, the job market would have ranked them in a different order.

Popularity and payroll are different signals. A repo earns stars from everyone who ever tried the framework, including the people who moved on. A job posting only exists because a company is willing to pay someone to use the thing this quarter. AutoGen is the case where the two diverge hardest: a strong star count, a sunset roadmap, and 74 open roles still asking for it. LangGraph is the mirror image. Few stars, a focused product, and the second-largest hiring footprint of the five.

The market hires stacks, not winners

Here is the finding that reframes the whole comparison. Almost nobody is hired to use a single framework. We checked every posting for how many of the five it named on its own.

CrewAI and AutoGen never appear alone. Not once across 180 combined postings. LlamaIndex stands alone in 2% of its roles. Even LangChain, the market default, is the only framework named in just 1 of 8 of its listings. “Which framework should I learn” is the question every comparison answers, and the hiring data says it's the wrong question. These tools are complements, not substitutes.

The pairings show the shape of the stack. LangChain and LangGraph appear together in 185 jobs, the most common pairing by far. LangChain and LlamaIndex come next at 136. Even the so-called competitors lean on LangChain more than on each other: CrewAI pairs with LangChain in 83 jobs, AutoGen in 57. LangChain is the connective tissue. The retrieval, orchestration, and multi-agent layers sit on top of it.

One employer makes the point on its own. PointClickCare, a healthcare-SaaS company, shows up in the top-five hiring list for LangChain, LangGraph, LlamaIndex, CrewAI, and AutoGenat the same time. One company, staffing across all five. Enterprises hedge across frameworks. They don't bet the roadmap on one.

The same pattern shows up outside our board. Databricks' 2026 State of AI Agents reports 57% of organizations with agents in production, usage split across cloud platforms, open-source tools, enterprise platforms, and orchestration frameworks, with the blunt summary that “none of these approaches is exclusive.” Only 26% build from scratch. Production-architecture writeups land in the same place: one widely-cited guide notes that stacking three or more frameworks usually signals overengineering, which puts the healthy number right around two. Our average is 2.31. The market and the production literature agree. Agentic systems are stacks.

Does the framework you pick change your pay? Barely.

If frameworks were substitutes competing for mindshare, you'd expect their salaries to spread out. They don't. Across roles that disclose USD pay, all five sit inside a $185k–$190k median band. A ~$4k spread across five frameworks. Whichever one you learn, the market pays about the same.

The second number in that table matters more than the first five. Every framework cohort sits $23k to $28k below the overall agentic median of $213k. Naming an orchestration framework correlates with below-market pay. Read that carefully, because it's a correlation with a plausible cause, not a proven penalty. The highest-paying agentic roles tend to describe themselves by model and infrastructure skills, not by orchestration SDKs. Research scientists, applied-ML, and inference work sit at the top. Orchestration is the application layer. It pays well, north of $185k, and it pays below the model and research layer above it.

Outside numbers bracket ours. Robert Half's 2026 Salary Guide puts the AI/ML engineer band at $134,000 to $193,250 and names agentic AI engineer as an emerging role. Our framework medians land near the top of that band, which is a good sign the figures are real market rates and not an artifact of our sample. Levels.fyi finds the AI pay premium widens with seniority, with senior AI engineers earning a larger gap over non-AI peers than entry-level ones. That fits the interpretation: the ceiling is set by senior model and research work, and the orchestration band sits a notch below it.

What each framework actually is, in the hiring data

Rank by demand and the five stop looking like rivals. They look like layers of one stack, each with a distinct job in the hiring market.

LangChain: the universal substrate

LangChain appears in 34.5% of all 1,135 listings, more than any other framework, and in nearly every multi-framework stack. It's the connective tissue. It's also the most junior-skewed of the five: 37.8% of its roles are junior or mid-level, the highest share in the group, and only 16.1% are lead-and-above. If there's an on-ramp into agentic work, this is it. Mid-level LangChain roles are a realistic entry point in a market that otherwise skews senior, and remote LangChain roles are plentiful. The company behind it raised a $125M Series B at a $1.25B valuation in October 2025, reports 90M combined monthly downloads, and says 35% of the Fortune 500 use its services. Top employers in our data: Celonis, LangChain itself, Databricks, Mirakl, PointClickCare. For the deeper cut, see our LangChain job market analysis and the LangChain salary page.

LangGraph: the stateful runtime

LangGraph is the framework with the fewest GitHub stars and the second-most jobs. It's also the only one of the five whose vendor observability tool ranks as a top companion. LangSmith shows up in 14.8% of LangGraph postings, a pairing no other framework has. That matches the product's pitch. LangChain and LangGraph both hit v1.0 in October 2025, with LangGraph positioned as the lower-level runtime for production-grade, long-running agents, and named customers including Uber, LinkedIn, Klarna, and JPMorgan. In our data it skews more senior than LangChain and pairs with it constantly. Same trajectory shows up in the LangChain and LangGraph market data, and pay sits at the top of the band on the LangGraph salary page. Top employers: LangChain, PointClickCare, Mirakl, Binance, Elastic.

LlamaIndex: the retrieval layer

LlamaIndex has the highest vector-database pairing of any framework. Pinecone appears in 28% of its postings and Weaviate in 20.7%. It also stands alone in just 2% of its roles, the second-lowest of the five. That combination tells you exactly what it is: the retrieval layer inside someone else's orchestration, hired wherever the job is getting data into the model. It raised a $19M Series A led by Norwest in March 2025, followed by strategic investments from Databricks and KPMG. In the hiring data it's the most US-leaning of the five, so US LlamaIndex roles are the densest, and it tops the salary band on the LlamaIndex salary page. Top employers: Celonis, Scale AI, PointClickCare, Aircall, YLD.

CrewAI: RAG-heavy multi-agent crews

CrewAI never appears alone, and its stack is led by vector databases: Pinecone in 27.4% of postings, Weaviate in 24.5%. It reads as the multi-agent layer for retrieval-heavy work, always wired into a larger stack. It raised an $18M Series A led by Insight Partners in October 2024, with angels including Andrew Ng, and self-reports use by more than 60% of the Fortune 500 as of late 2025. CEO João Moura's framing is that the only moat in this era is speed. In our data the roles skew senior and lean slightly European, and hybrid CrewAI roles are common. We went deeper on this one in the CrewAI job market analysis, with pay on the CrewAI salary page. Top employers: PointClickCare, Grafana Labs, SAP, YLD, Binance, with Germany and Poland among the stronger European markets.

AutoGen: the senior framework Microsoft has moved past

AutoGen has the most senior cohort of the five. 28.4% of its roles are lead-and-above, the highest in the group, and it has the lowest candidate click-through at 8.8%, consistent with a smaller, more specialized pool. It's also the freshest cautionary tale in the data. Microsoft folded AutoGen and Semantic Kernel into the Microsoft Agent Framework in October 2025, putting both into maintenance mode, and shipped Microsoft Agent Framework 1.0 in April 2026. AutoGen is effectively sunset. Yet it still pulls 74 published postings and the most senior cohort of the five. Hiring lags the ecosystem. Enterprises are still running and staffing AutoGen codebases that Microsoft has already moved on from, which is exactly why stars and roadmaps don't tell you what's on payrolls. Senior AutoGen roles are the bulk of what's open, and pay sits at the bottom of the band on the AutoGen salary page. Top employers: PointClickCare, Aircall, Binance, Robert Walters, EY.

What ships alongside the frameworks

Each framework drags a companion stack into the job description. The differences are real and they reinforce the personality map.

LlamaIndex and CrewAI lead with vector databases, the signature of retrieval work. AutoGen is the only one where PyTorch and TensorFlow both rank, the classic-ML footprint that matches its senior skew. LangGraph carries LangSmith. But the standout is the one companion that shows up everywhere. MCP, the Model Context Protocol, appears in 17% to 24% of postings for every single one of the five. It's the only framework-agnostic companion in the data.

That tracks with what happened to MCP outside the hiring market. Anthropic donated MCP to the Linux Foundation's new Agentic AI Foundation in December 2025, backed by Google, Microsoft, AWS, and others, with 97M+ monthly SDK downloads. The most durable cross-framework skill in our data is a protocol, not a framework.

Where each framework sits in a career, and where the jobs are

Seniority is the cleanest differentiator in the data, cleaner than salary or geography. There's a gradient. LangChain is the on-ramp. AutoGen is the senior, enterprise end.

LangChain is where companies are comfortable hiring junior and mid-level engineers. AutoGen and CrewAI cluster in higher-seniority, enterprise-flavored roles. That's a more useful thing to know than salary, because it tells you where in a career each framework shows up. If you're breaking in, LangChain is the first one to list. If you're senior, AutoGen and CrewAI roles are disproportionately aimed at you. Browse senior AutoGen roles or the broader LangChain market in the UK to see the split.

Geography, on the other hand, barely moves. All five land around 40% US and 38% to 43% EU. LlamaIndex is the most US-leaning at 47.7%, CrewAI the most European at 43.4%, but the gaps are small. Geography is not a strong differentiator here, and I'll report it as the non-finding it is rather than dress it up. If anyone tells you a given framework is overwhelmingly concentrated in one region, this larger sample doesn't support it.

So how do you actually choose?

You don't, not in the way the framework-wars framing implies. The data points at a stack, so build one.

• Learn LangChain as the substrate. It's in a third of all postings and the connective tissue of nearly every multi-framework stack. It's also the most junior-friendly, so it's the highest-signal first framework.
• Add the layer your role needs. LlamaIndex if the work is retrieval. LangGraph if it's stateful, long-running orchestration. CrewAI if it's multi-agent crews. These are complements, and the modal job already names two.
• Learn MCP. It's the one companion that's framework-agnostic, it appears in a fifth of postings across all five, and its adoption curve outside hiring is steeper than any single framework's.
• Expect Python. All five are overwhelmingly Python, 91% to 96% of postings. The TypeScript share, 18% to 26%, mostly reflects full-stack roles, not JS-native agent work.

The backdrop is consolidation. Gartner expects more than 40% of agentic AI projects to be canceled by the end of 2027, citing cost, unclear value, and weak controls, and warns about “agent washing,” where most self-described agentic vendors aren't. A shakeout rewards durable skills, not bets on which logo wins. The durable skill in our data is the stack: LangChain as the substrate, a retrieval or orchestration layer on top, MCP as the connective standard, and the seniority to wire it together in production. If you're hiring into this, our agentic AI engineer interview guide covers the questions that separate real production experience from a tutorial. The takeaway is simple. Stop framework-warring. The market pays for the stack and the seniority, not the logo.

Methodology

Dataset: 1,135 published agentic AI engineering listings on agentic-engineering-jobs.com, posted between March 31 and May 22, 2026. Framework mentions were identified during listing curation and verified against each job description. The comparison is limited to the five frameworks that clear 70 published postings; every other framework falls below that, where per-framework breakdowns get statistically shaky. Salary figures use USD-denominated postings only, taking the midpoint of each disclosed range. This is a single mid-2026 snapshot. The posting window reflects our ingestion batches, not organic cadence, so there is no month-over-month trend to read here. Every figure is reproducible against the public job data at /api/v1/jobs and the aggregate stats at /api/v1/salaries.

External sources: Databricks State of AI Agents, LangChain Series B, LangChain & LangGraph v1.0, LlamaIndex Series A, CrewAI / Insight Partners, Microsoft Agent Framework, Anthropic / MCP, Robert Half 2026 Salary Guide, Levels.fyi, Gartner.

See also: our LangChain job market 2026 analysis and CrewAI job market 2026 analysis.