LangGraph vs CrewAI vs AutoGen: 107-Task Cost and Error Data

A developer benchmark published this week ran 107 data engineering tasks through LangGraph, CrewAI and AutoGen on the same hardware and reported the bills. The spread was wide enough to matter: $0.0106 per task on LangGraph against $0.0147 on AutoGen, with four times the errors on the latter.

What was measured

The suite covers extraction (APIs, PDFs, HTML), transformation (schema normalization, deduplication, feature creation), load (Snowflake, S3, Postgres), QA, and small DAGs. Per the author, every task made at least one API or database call, carried payloads with a median size of 100KB, and used 1-6 LLM calls. More than 25% of tasks involved agent chaining, error recovery, or fuzzy duplicate detection.

All three frameworks ran on the same 8-core ARM node with capped I/O, using GPT-4-turbo with Postgres as the sink.

Framework	Median runtime	99th pct	Mean $/task	Errors (of 107)	Mean tokens
LangGraph	10.2s	17.4s	$0.0106	3	5,200
CrewAI	12.1s	22.5s	$0.0112	6	5,700
AutoGen	14.8s	31.6s	$0.0147	12	8,640

Where each one breaks

The failure descriptions are more useful than the ranking, because each names a threshold you can check your own project against.

LangGraph pays in code volume. The author's phrasing is that boilerplate is the tax, and that by DAG 30 the scaffolding dwarfs the business logic. In exchange, state stays transparent and failures are locatable — it posted the best runtime and the fewest errors here.

CrewAI is ergonomic until it isn't. The report puts the composability wall at around 45 tasks, after which output chaining across three or more hops throws intermittent "variable not found" errors that are slow to trace.

AutoGen carries the cost problem. The author writes that it silently resends full history to all agents, doubling costs by design, and reports token cost and memory overshooting the other two by 30-90% as chain length grows. Flows with more than three agents or long message threads are described as seeing at least 30% bloat.

How much to trust it

This is one engineer's benchmark, not an independent evaluation. The author says the data comes from a public benchmarking repo and was verified against actual OpenAI billing, which is more disclosure than most framework comparisons offer.

The caveat is in the reference list: the billing and exported logs are dated June 2024, and the model lineup — GPT-4-turbo, Claude 3 Opus — matches that vintage rather than anything current. Per-task dollar figures from that period are not the prices you will pay today, and the token counts are the more portable number.

What to take from it

Treat the cost column as relative, not absolute. The ordering — AutoGen roughly 66% more tokens per task than LangGraph — reflects how each framework handles message history, and that architectural difference does not expire with a price sheet.

If you are choosing today, the practical question is which threshold you will hit first: LangGraph's boilerplate tax around 30 DAGs, or CrewAI's composability wall around 45 tasks. Below both, the frameworks are close enough that developer familiarity is the better tiebreaker.