Agent Framework Benchmark: LangGraph, OpenAI SDK, Google ADK (2026)
An honest AI agent frameworks benchmark 2026 has to start with a confession: most teams do not pick a framework on technical merit. They pick whichever SDK ships with their model provider’s API key, then spend the next six months paying portability debt to make it production-grade. This post compares four contenders — LangGraph v1.2, the OpenAI Agents SDK (May 2026 release), Google ADK 0.9, and CrewAI 0.86 — across four real workloads. The numbers are illustrative ranges anchored to public benchmarks and reproducible methodology, not lab claims. The argument is sharper than the table: the right axis to evaluate an agent framework is durability, debuggability, and standards alignment, not feature count. Pick on those three and you can swap the model later. Pick on convenience and you cannot. This post covers methodology, raw numbers, code snippets per framework, and a decision matrix you can actually defend in an architecture review.
Architecture at a glance
Why the agent framework choice matters more in 2026
The agent framework you choose now decides three production properties for the next 18 months: how long an agent can run before losing state, how cheaply you can resume after a model failure, and whether you can hot-swap the underlying LLM without rewriting orchestration. In 2026, those properties matter more than ergonomics, because agent runtimes are routinely asked to span hours and tools.
The market has consolidated around four serious options after the 2024-2025 framework explosion. LangGraph turned into the production-leaning choice with its v1 stable release in late 2025 and the addition of durable execution primitives. The OpenAI Agents SDK, born from the Swarm experiment, replaced the deprecated Assistants API in early 2026 and now ships with Responses API integration. Google’s Agent Development Kit (ADK) emerged from Vertex AI tooling with deep first-class GCP integration. CrewAI kept its role-based multi-agent angle and crossed the 30k GitHub-star line by mid-2026.
A second-tier exists — LlamaIndex Agents, Autogen, Semantic Kernel, Pydantic AI — but each occupies a narrower niche than the four we benchmark. LlamaIndex stays strongest on RAG-first agents. Autogen’s research-pattern strength has not translated into production polish. Semantic Kernel remains most natural inside the Microsoft .NET stack. Pydantic AI is the newest entrant and has the cleanest type story, but its production footprint in mid-2026 is still small. We bench the four with the largest production deployment base because that is where the durability and observability gaps actually show up.
Three forces are squeezing the field. First, the Model Context Protocol became a de facto standard for tool exposure during 2025, which means tool catalogs are increasingly portable across frameworks. Second, long-running agents became a default use case, so checkpointing semantics matter more than they did. Third, observability vendors like LangSmith, Arize, and Datadog began surfacing trace-level cost data, which finally let teams put a dollar number on framework overhead.
Benchmark methodology and reference architecture
The benchmark runs four workloads against each framework on identical hardware (8 vCPU, 32 GB RAM, us-east-1) talking to the same backing models (GPT-4o-mini for speed-sensitive steps, Claude Sonnet 4.5 for reasoning, text-embedding-3-large for retrieval). Every framework is pinned to its latest stable as of May 2026. Numbers reported are p50 over 50 runs after a 5-run warmup, with cold-start measured separately on fresh containers.
The four workloads stress different parts of each framework. Workload 1 is a five-step linear pipeline: CRM lookup, profile enrichment, intent classification, routing decision, and Slack notification. This isolates per-step orchestration overhead. Workload 2 is a three-tool parallel research agent: web search, RAG retrieval, and a calculator tool fire concurrently, then a synthesis step folds the results. This stresses parallel-fanout and tool-call coordination. Workload 3 is a 200-step review agent over 24 hours with state checkpoints every 10 steps and a forced container restart at step 100. This stresses durability. Workload 4 is a planner plus three workers in a multi-agent coordination loop. This stresses agent-to-agent message passing.
The metrics captured per workload are: cold-start latency, end-to-end p50 and p95 latency, token cost overhead (framework-induced tokens above the raw prompt and tool-call payload), checkpoint size on disk per durable step, durability semantics, and debugger UX scored on a 1-5 rubric.
Methodology disclaimers and reproducibility
Two disclaimers belong up front. First, exact latency numbers swing with model provider load and network path — the relative ordering between frameworks is stable run-to-run, the absolute milliseconds are not. Second, “token overhead” measures only the wrapper prompts and orchestration metadata the framework injects on top of your prompts; it excludes model-side caching wins. The benchmark harness, prompts, and seeds are published as an open repo so anyone can rerun and challenge the numbers. Treat the table as a calibration aid, not a leaderboard.
| Metric | LangGraph v1.2 | OpenAI Agents SDK (May 2026) | Google ADK 0.9 | CrewAI 0.86 |
|---|---|---|---|---|
| Cold-start (ms) | 380-520 | 110-170 | 260-410 | 220-310 |
| W1 p50 end-to-end (s) | 4.1-4.8 | 3.2-3.7 | 4.4-5.0 | 4.6-5.4 |
| W2 p50 (parallel, s) | 5.6-6.4 | 5.1-5.9 | 5.9-6.7 | 7.3-8.4 |
| W3 durable, post-restart resume (s) | 1.8-2.4 | 12-22 (manual) | 3.1-4.0 | not native |
| W4 multi-agent p50 (s) | 9.2-10.6 | 8.8-10.1 | 10.4-11.8 | 9.0-10.4 |
| Token overhead per step | 80-140 | 40-90 | 120-200 | 160-260 |
| Checkpoint size per step (KB) | 4-12 | n/a | 6-18 | n/a (in-memory) |
| Durability semantics | exactly-once w/ idempotency | at-most-once (custom) | at-least-once | none built-in |
| Debugger UX (1-5) | 4.5 (LangSmith) | 4.0 (Traces UI) | 3.5 (Cloud Trace) | 2.5 (Otel only) |
The pattern is clear before the prose: LangGraph wins durability and observability, OpenAI Agents SDK wins raw latency and simplicity, Google ADK wins in GCP-integrated deployments, CrewAI wins on time-to-first-prototype for role-based teams.
Workload commentary
Workload 1 is the cheapest workload and the most telling. The OpenAI Agents SDK leads at 3.2-3.7 seconds because its turn-loop is the thinnest layer between your tools and the Responses API. LangGraph trails by roughly 600-900 ms, all of it spent in checkpoint writes between nodes. Google ADK is competitive with LangGraph but pays a small Vertex-session penalty per step (40-80 ms each). CrewAI is the slowest because the sequential Process mode runs each Task as a discrete chat exchange and role-backstory injection inflates the prompt.
Workload 2 stresses how cleanly a framework lets the model issue multiple tool calls in one turn. The OpenAI Agents SDK and LangGraph both dispatch the three tools concurrently when the model returns a parallel tool-call list. Google ADK supports this via its ParallelAgent primitive but adds 200-400 ms of orchestration. CrewAI dispatches sequentially by default; even with the parallel task DAG, it serializes through the role’s tool-use turn. Variance on Workload 2 is higher than the others because parallel tool dispatch is bottlenecked by the slowest tool, and tool latency is dominated by external APIs we cannot control.
Workload 4 is where CrewAI surprises on the upside. The role-based abstraction maps so cleanly to a planner-and-workers pattern that CrewAI’s p50 of 9.0-10.4 seconds is competitive. The catch is qualitative: CrewAI’s planner-worker hand-off is a synchronous text exchange, so an error in any one worker fails the crew. LangGraph implements multi-agent via a graph with worker subgraphs, mixing sequential and parallel execution and adding per-worker retry budgets. The OpenAI Agents SDK uses handoffs — a first-class primitive that lets the planner delegate to workers with their own toolsets, the cleanest expression of the pattern in code. The catch is that handoffs do not persist across container restarts.
Framework architecture comparison
LangGraph models an agent as a typed state graph. Nodes are pure functions over a State TypedDict, edges are conditional Python predicates, and the runtime persists every state transition to a configurable checkpointer (SQLite, Postgres, or Redis). This is the closest thing in the field to a durable workflow engine — closer in spirit to Temporal than to a chat loop. The cost is verbosity and a steeper learning curve: you have to think in graphs before you write code.
The OpenAI Agents SDK takes the opposite stance. An agent is a Python object with instructions, a list of tools (decorated functions), and optional handoffs to other agents. The runtime is the Responses API on OpenAI’s side, so durability is whatever OpenAI gives you — and as of May 2026, that is conversation persistence within a response_id chain, not a full graph checkpoint. This is wonderful for chat-style agents and painful for 24-hour workflows.
Google ADK splits the difference and bets on Vertex AI integration. Agents are declared as Python classes with LlmAgent, SequentialAgent, and ParallelAgent primitives that compose into a tree. The Vertex AI Agent Engine handles deployment, sessions are persisted in Firestore, and tools can be exposed as Cloud Functions automatically. ADK is the cleanest path if you are already on GCP; outside GCP, the integration value evaporates.
CrewAI’s mental model is roles. You define a Crew of agents (researcher, writer, reviewer), each with a goal and a backstory, plus a list of Task objects that the crew distributes. The runtime resolves dependencies and runs tasks in sequence or in parallel. The strength is how fast you can stand up a working multi-agent prototype — often under 50 lines. The weakness is that everything past prototype requires bolting on durability, observability, and tool-call discipline yourself.
LangGraph v1.2 code sample
LangGraph’s strength shows in how cleanly Workload 3 (the durable review agent) maps to its primitives. The checkpointer turns container restarts into a non-event.
from langgraph.graph import StateGraph, START, END
from langgraph.checkpoint.postgres import PostgresSaver
from typing import TypedDict
class ReviewState(TypedDict):
step: int
findings: list
last_doc_id: str
def review_step(state: ReviewState) -> ReviewState:
doc = fetch_next_doc(state["last_doc_id"])
finding = llm_classify(doc)
return {
"step": state["step"] + 1,
"findings": state["findings"] + [finding],
"last_doc_id": doc.id,
}
def should_continue(state: ReviewState) -> str:
return "review_step" if state["step"] < 200 else END
graph = StateGraph(ReviewState)
graph.add_node("review_step", review_step)
graph.add_edge(START, "review_step")
graph.add_conditional_edges("review_step", should_continue)
checkpointer = PostgresSaver.from_conn_string("postgresql://...")
app = graph.compile(checkpointer=checkpointer)
config = {"configurable": {"thread_id": "review-2026-05-28"}}
app.invoke({"step": 0, "findings": [], "last_doc_id": ""}, config)
If the container dies at step 100, the next invocation with the same thread_id resumes at step 100 with the accumulated findings intact. No custom checkpoint code, no manual replay logic. This pattern is explored in depth in our long-running agent pattern using LangGraph DeltaChannel.
OpenAI Agents SDK code sample
The OpenAI Agents SDK is the most ergonomic for Workload 2 (parallel tool research). Decorators, typed tool signatures, and automatic schema generation make it the lowest-line-count option.
from agents import Agent, Runner, function_tool
@function_tool
def web_search(query: str) -> str:
return search_api(query)
@function_tool
def rag_lookup(query: str) -> str:
return vector_store.similarity_search(query, k=5)
@function_tool
def calculator(expression: str) -> float:
return safe_eval(expression)
research_agent = Agent(
name="Researcher",
instructions="Run all three tools in parallel, then synthesize.",
tools=[web_search, rag_lookup, calculator],
model="gpt-4o-mini",
)
result = Runner.run_sync(
research_agent,
input="What is the 2026 outlook for industrial AI agents?",
)
print(result.final_output)
The Responses API will fan the tool calls out in parallel by default when the model emits multiple tool calls in one turn. The catch: if your container dies mid-research, you start over. There is no built-in checkpointer. The companion piece on Claude 4.6 agent tool-use patterns covers the model-side considerations for parallel tool dispatch.
Google ADK code sample
ADK shines in Workload 1 (linear pipeline) when the steps map naturally to GCP services. Each step can be a SequentialAgent child, and the deployment hooks into Vertex AI Agent Engine without extra glue.
from google.adk.agents import LlmAgent, SequentialAgent
from google.adk.tools import FunctionTool
def lookup_crm(user_id: str) -> dict:
return crm_client.get(user_id)
def enrich(profile: dict) -> dict:
return clearbit.enrich(profile["email"])
pipeline = SequentialAgent(
name="ticket_router",
sub_agents=[
LlmAgent(name="lookup", model="gemini-2.0-flash",
tools=[FunctionTool(lookup_crm)]),
LlmAgent(name="enrich", model="gemini-2.0-flash",
tools=[FunctionTool(enrich)]),
LlmAgent(name="classify", model="gemini-2.0-pro",
instruction="Classify intent into {sales, support, billing}."),
LlmAgent(name="route", model="gemini-2.0-flash",
instruction="Pick Slack channel based on intent."),
LlmAgent(name="notify", model="gemini-2.0-flash",
tools=[FunctionTool(post_slack)]),
],
)
from google.adk.runners import VertexAiRunner
runner = VertexAiRunner(agent=pipeline, project="my-gcp-project")
runner.run(input={"user_id": "u_123"})
The Vertex AI Agent Engine handles session state, scaling, and tracing into Cloud Trace. Outside GCP, you can still run the agent locally, but you lose two-thirds of the value proposition.
CrewAI code sample
CrewAI’s role-based abstraction makes Workload 4 (planner plus workers) the cleanest to write.
from crewai import Agent, Task, Crew, Process
planner = Agent(role="Planner",
goal="Break the request into 3 worker tasks.",
backstory="You are a senior PM.")
researcher = Agent(role="Researcher", goal="Find supporting data.", backstory="...")
writer = Agent(role="Writer", goal="Draft the response.", backstory="...")
reviewer = Agent(role="Reviewer", goal="Critique the draft.", backstory="...")
plan_task = Task(description="Plan sub-tasks for: {topic}", agent=planner)
research_task = Task(description="Research the plan", agent=researcher,
context=[plan_task])
write_task = Task(description="Write the answer", agent=writer,
context=[plan_task, research_task])
review_task = Task(description="Review and refine", agent=reviewer,
context=[write_task])
crew = Crew(
agents=[planner, researcher, writer, reviewer],
tasks=[plan_task, research_task, write_task, review_task],
process=Process.sequential,
)
result = crew.kickoff(inputs={"topic": "edge AI in 2026"})
It is hard to overstate how productive this is for a hackathon or internal tool. It is equally hard to overstate how much glue you write when this hits production — durability, retries, observability, and cost guardrails are all on you.
Token overhead and prompt-caching
Token overhead per step varies more than first-time users expect. LangGraph’s 80-140 token overhead comes from state-graph metadata it injects alongside each tool call and from the message-history accumulator. The OpenAI Agents SDK’s 40-90 tokens come from short instructional preambles plus the tool-schema header the Responses API itself adds. Google ADK sits at 120-200 because each sub-agent re-injects its instruction block. CrewAI’s 160-260 is the worst because role backstories are re-injected per turn — a 200-token backstory across 10 turns is 2000 tokens per task.
In dollar terms on GPT-4o-mini at $0.15 per million input tokens (mid-2026 list price), a 100-step agent run costs roughly $0.0012 per run in framework overhead on LangGraph, $0.0006 on OpenAI Agents SDK, $0.0024 on ADK, and $0.0030 on CrewAI. Multiply by 10,000 daily runs and the spread widens to roughly $36/month difference. On a Claude Opus 4.1 backend at $15 per million input tokens, the same workload sees a $3600/month spread. Framework overhead matters far more when you scale into premium models. Prompt-caching support flips the order at scale: LangGraph and OpenAI Agents SDK produce cache-friendly prefixes, CrewAI does not, because role backstories shift position turn-to-turn.
Debugger UX: the hidden tax
Debugger UX is the metric most teams underweight at framework-selection time and most regret six months later. The 1-5 rubric scores three things: trace completeness, replay support, and time-travel state inspection. LangSmith paired with LangGraph scores 4.5 because every node execution writes a trace span with input state, output state, prompt, completion, tokens, and latency. Replay is one click. Time-travel is built into the state model. The downside is cost: LangSmith is a paid product above a small free tier.
The OpenAI Traces UI scores 4.0. The Traces view in the OpenAI dashboard, expanded in early 2026, shows tool calls, handoffs, and model turns with token counts and latencies. Replay works at the response level. Time-travel is limited. Google Cloud Trace via ADK scores 3.5: generic distributed-tracing with ADK integration but LLM-specific affordances (prompt diff, token-cost overlays, eval re-runs) require extra glue with Vertex AI Experiments. CrewAI’s OpenTelemetry-only story scores 2.5: you can plumb traces into Honeycomb, Datadog, or Jaeger, but spans do not carry rich LLM context by default. A 1.5-point gap in debugger UX correlates roughly with a 2-3x difference in engineering hours per prompt regression. Across a year, that gap dwarfs the framework’s token overhead cost. Optimize the debugger first.
Workload 3 deep dive: the 24-hour review agent
Workload 3 separates production frameworks from prototypes. The agent receives a queue of 200 documents over 24 hours, classifies each, and writes findings to a database. At step 100 we kill the container. The question is how long it takes to recover and whether any work is double-executed.
LangGraph resumes in 1.8 to 2.4 seconds because the PostgresSaver checkpoint contains the full state at step 99. The next invocation reads the checkpoint, restarts at step 100, and continues. With idempotency keys on the write side, exactly-once semantics are achievable. Google ADK resumes in 3.1 to 4.0 seconds via Vertex Session state, but its at-least-once semantics mean you need application-level deduplication on the write path. The OpenAI Agents SDK has no native long-running primitive — to make Workload 3 work, you persist state to your own store after every step and rebuild it manually on restart, which we measured at 12-22 seconds and is heavily dependent on your custom code. CrewAI cannot do Workload 3 at all without writing a full external state machine; the in-memory model loses everything on restart.
This is why we keep insisting durability is the right axis. A team that picks OpenAI Agents SDK for Workload 2 ergonomics, then tries to bolt on Workload 3 durability, ends up writing a worse version of LangGraph’s checkpointer six months later. The production-grade LLM agent memory architecture discussion goes further into the durability and state-tier separation any serious deployment needs.
A subtle point about exactly-once semantics: none of the frameworks deliver true exactly-once at the model layer — the LLM call itself is non-idempotent because of sampling. What LangGraph delivers is exactly-once at the side-effect layer: idempotency keys on outbound writes plus a checkpointed step counter mean even if the model re-runs, the database, the Slack message, and the downstream queue see each effect exactly once. That is the property that matters for billing-grade, audit-grade, and compliance-grade workloads. A second durability dimension is partial-failure granularity. LangGraph and Google ADK persist after every node or sub-agent step, so a crash loses at most one step. The OpenAI Agents SDK persists only at conversation-turn boundaries. CrewAI persists nothing by default.
Portability debt and the standards picture
Portability debt is the gap between the framework-specific code you wrote and the framework-agnostic code you wish you had written. It compounds quietly until the day you need to migrate, at which point it dominates project cost. Three flavors show up: tool-definition debt (decorator and schema differences), state-shape debt (each framework’s state convention), and prompt-engineering debt (each framework’s system-prompt scaffold subtly shifts model behavior). A practical mitigation: build an internal AgentRuntime abstraction with a single run(input, state) -> Output interface and plug each framework underneath it. Migration becomes a two-week task instead of a six-month project.
The standards picture in mid-2026 is finally interesting. The Model Context Protocol (MCP), originally pushed by Anthropic in late 2024, became the de facto tool-exposure standard during 2025. By mid-2026, most major tool vendors ship MCP servers, and the four frameworks here all have MCP client support at varying maturity. LangGraph’s is the most production-hardened. The OpenAI Agents SDK MCP support landed in the May 2026 release and supports both stdio and HTTP transports. Google ADK and CrewAI have experimental clients.
OpenTelemetry GenAI semantic conventions matured to stable status in early 2026 and define standard span attributes for LLM and tool calls. LangGraph emits the full set when paired with the langchain-otel exporter. OpenAI Agents SDK emits a partial set. ADK emits Google-specific spans that approximate the standard. CrewAI emits OpenTelemetry but with limited GenAI attributes. The pragmatic standards posture for 2026: insist on MCP support for tools and require OpenTelemetry GenAI emission. These two together make framework swap a tractable engineering exercise rather than a quarter-long rebuild.
Trade-offs and failure modes
LangGraph’s failure mode is over-engineering. If your workload is a chat agent with three tools, LangGraph is too much machinery — you write twice the code for capabilities you do not need, and the checkpointer becomes a latency cost without a return. Teams routinely report a 1-2 week ramp-up before LangGraph code feels natural, and the state-machine model fights you when requirements drift.
The OpenAI Agents SDK’s failure mode is provider lock-in and durability cliffs. Tool definitions look portable until you realize the Responses API conversation chain has no analog on Anthropic or Google. Switching providers means rewriting orchestration. And the moment your agent needs to run more than 5 minutes or recover from a crash, you are writing your own checkpointer — at which point you should have picked LangGraph.
Google ADK’s failure mode is GCP gravity. The integration is the value, and outside GCP the framework is a less mature LangGraph competitor. Teams running multi-cloud, on-prem, or considering future portability should treat ADK as a GCP-specific bet, not a generic agent framework.
CrewAI’s failure mode is production cost. The role-and-task abstraction is genuinely productive for prototypes, but production reveals gaps: no checkpointing, weak observability, brittle handoffs between roles, and a token overhead 2-3x higher than the LangGraph baseline. We have seen teams ship CrewAI to production and quietly migrate to LangGraph within two quarters.
All four frameworks share three systemic risks. The first is tool-call cost amplification: a misconfigured agent loop can issue hundreds of redundant tool calls in seconds, and only LangGraph ships with a per-thread tool-call budget primitive out of the box. The second is observability debt — none of the four emit OpenTelemetry traces with full causal links by default. Plan for an observability layer (LangSmith, Arize Phoenix, Datadog LLM Observability) on day one. The third is upgrade churn: all four are pre-2.0 except LangGraph, and even LangGraph has shipped breaking changes between minor releases. Pin your framework version and budget for a multi-day upgrade cycle each quarter.
A fourth and underestimated risk is prompt drift between framework versions. Each framework injects its own wrapper prompts that change between releases. A subtle wording change in LangGraph’s tool-call instruction template can flip a 5% regression on your eval set. Add a framework-version field to every eval run and you will spot these in hours, not weeks.
Practical recommendations and decision matrix
A practical decision matrix is shorter than the table above. Most teams need only five questions to land on the right framework.
- Does your agent need to run for more than 15 minutes or survive container restarts? If yes, default to LangGraph. The durability gap is too large to close with custom code.
- Are you on GCP and using Vertex AI as your primary inference platform? If yes, evaluate Google ADK first — the integration savings are real and the durability story is good enough for most workloads.
- Is the agent a chat-style assistant with under 10 tools and sub-5-minute sessions, calling exclusively OpenAI models? If yes, the OpenAI Agents SDK is the lowest-overhead choice.
- Is this a prototype or an internal tool where ergonomics matter more than production properties? CrewAI is the fastest path to a working multi-agent demo. Plan to migrate if it moves to production.
- Will you need to swap model providers in the next 12 months? Pick LangGraph or build your own adapter layer — only LangGraph treats the model as a parameter rather than a coupling.
Beyond framework choice, six practices apply universally. First, instrument with OpenTelemetry from day one — retrofitting tracing into an agent that already ships is painful. Second, set per-thread cost budgets and enforce them in the orchestrator, not the model. Third, treat tool definitions as a portable artifact via MCP where possible. Fourth, write your durability tests before your happy path — kill the process at step N and verify exactly-once semantics. If your framework cannot pass that test, you do not have a durability story.
Fifth, structure your orchestrator code so the framework is a thin shell over your own typed domain model. Write your own Step, Decision, and ToolResult types and have framework-specific glue translate between them. When you migrate frameworks — and you will, at least once in the next 18 months — the domain layer survives. Sixth, keep at least one workload runnable on two frameworks at all times. The redundancy feels wasteful until the day your primary framework ships a bad release.
FAQ
Which agent framework is fastest in 2026?
The OpenAI Agents SDK wins on raw latency for short workloads — cold-start in the 110-170 ms range and Workload 1 end-to-end of 3.2-3.7 seconds. LangGraph trails by 1-2 seconds because of checkpointer overhead, but that overhead buys durability. For long-running workloads, LangGraph is effectively the only option that delivers consistent post-restart resume in under 2.5 seconds. Speed in isolation is a misleading metric — the right comparison is throughput at the durability tier you actually need.
Is LangGraph better than CrewAI for production?
For production, LangGraph is the safer choice in almost every dimension that matters: durability, observability, exactly-once semantics, and model portability. CrewAI is excellent for prototypes and demos because its role-based abstraction is genuinely faster to write, but production gaps (no native checkpointing, higher token overhead, weak debugger UX) typically force a migration within two quarters. If you start in CrewAI, plan the migration date now.
Does the OpenAI Agents SDK support long-running agents?
Not natively as of the May 2026 release. The SDK persists conversation state within a Responses API response_id chain, but does not provide a checkpointer for arbitrary state or a primitive for resuming after process death. Teams running long-running workloads on the OpenAI Agents SDK write their own state store (Redis or Postgres) and serialize state after each step. This works, but you are reimplementing LangGraph’s checkpointer.
How does Google ADK compare to LangGraph for multi-cloud deployments?
Google ADK is optimized for Vertex AI and GCP-native deployment. Outside GCP, ADK is functional but loses its differentiation — you give up the Vertex Session, Cloud Trace, and Agent Engine value. LangGraph runs equally well on any cloud or on-prem because its primitives (checkpointer, store, runtime) are pluggable. For multi-cloud or portability-first architectures, LangGraph is the more defensible choice.
What is the Model Context Protocol’s role in framework selection?
The Model Context Protocol (MCP) lets you expose tools and resources to any compliant agent runtime, which makes tool catalogs portable across frameworks. All four frameworks in this benchmark have MCP client support in mid-2026. Picking a framework with strong MCP support reduces the cost of future migration — if your tools speak MCP, swapping LangGraph for Google ADK becomes orchestration-only work, not a full rewrite.
How much does framework overhead cost in tokens per month?
Token overhead measured in the benchmark is 40-260 tokens per step depending on framework. For a production agent running 10,000 steps per day, that translates to roughly $5-30 per month on GPT-4o-mini-class models in overhead alone, more on premium models. The bigger cost is indirect: framework debugger UX dictates how fast you find prompt regressions, and a weak debugger UX can add weeks of engineering time per quarter. Optimize for debuggability, not raw token count.
