A benchmark of 107 diverse tasks shows AutoGen beats LangGraph and CrewAI on success rate, latency and token cost, delivering a 90 % completion rate in an average of 10.2 seconds while using just 10,700 tokens per run. Developers building production-grade AI pipelines care because the numbers expose hidden costs that toy demos never reveal.
Why a real-world test matters
Most public demos for agent frameworks stop at a single-step use case – a PDF chat, a simple sales bot, or a basic data fetch. Those examples hide how the systems behave when the workload expands to dozens of steps, conditional branches, and large prompt contexts. The new benchmark pushes each framework through a broad set of scenarios that stress state sharing, parallel execution, and token efficiency, giving developers a glimpse of production challenges.
Head-to-head numbers
| Framework | Success rate | Avg. latency | Avg. token use |
|---|---|---|---|
| AutoGen | 90 % | 10.2 s | 10,700 |
| LangGraph | 85 % | 12.9 s | 11,900 |
| CrewAI | 78 % | 19.1 s | 14,350 |
Success rate measures whether the final output meets the task’s correctness criteria. Latency is the wall-clock time from start to finish, and token use captures the total prompt length the model processes, a proxy for cost.
Framework deep-dive
AutoGen – speed and efficiency, but a debugging headache
AutoGen’s architecture shares state across the whole pipeline, eliminating the need to resend the same context to each step. Built-in asynchronous execution lets independent branches run in parallel, which drives the lowest latency and token count. The trade-off is visibility: because the workflow lives in a single, monolithic chain, tracing a failure often requires stepping through the entire run rather than isolating a single node.
LangGraph – explicit control, extra code for conditionals
LangGraph forces developers to declare the workflow as a graph of nodes, giving fine-grained control over execution order and state transitions. It handles state better than CrewAI, avoiding the repeated context problem. However, when a branch must pivot based on an LLM’s output, developers need to write additional plumbing code, which can erode the clarity advantage and add maintenance overhead.
CrewAI – isolated agents, token bloat
CrewAI adopts an agent-role metaphor: each role operates as an independent unit with its own prompt and instructions. This isolation can be useful for small teams of specialized bots, but at scale it forces the system to repeat the same context for every agent. The result is the highest token consumption and the slowest runs. State sharing is also weak, leading to occasional missing-data errors when one agent’s output is needed downstream.
Takeaway: If you need a fast, token-efficient pipeline that strings many steps together, AutoGen is the pragmatic choice despite its harder-to-debug nature. Choose LangGraph when you must enforce a strict execution graph and are willing to write a bit more glue code. Reserve CrewAI for tightly scoped, low-agent scenarios where isolation is a feature, not a liability.
Source: https://dev.to/priyesh_dave_cb8759cdeca4/agent-frameworks-in-the-real-world-107-task-bakeoff-of-langgraph-crewai-and-autogen-445g Community discussion: https://t.me/GyaanSetuAi