GILD verdict
Choose LangGraph to own every step with durable state and approvals, and CrewAI when collaborating role-based agents fit the work.
- The workflow
- a team choosing the framework its multi-step agents will run on in production
- The decisive evidence
- Reported4 of 7 named teams adopted one; What teams report below sets out each account.
- MeasuredIndependent builds of one agent in both found LangGraph faster per query in Oracle researchers' benchmark and CrewAI more accurate there, CrewAI byte-identical across runs, and LangGraph at about 280 lines against CrewAI's 420.91032
- GILD analysisCost does not separate the pair when both are self-hosted, since model tokens dominate. GILD's calculation puts 60,000 requests a month at US$1,656 on either, and CrewAI's hosted price at that volume is unresolved until CrewAI quotes Enterprise.
- The tradeoff
- LangGraph's state and approvals survive restarts; CrewAI's managed builder speeds a team up. LangGraph makes you wire the loop and own checkpoint cleanup and crash detection, and CrewAI hands you roles and a builder but leaves recovery after a crash to your code as well.41117182529
- What would change the call
- The call moves when a team publishes a production CrewAI account on its own blog with measured results, or when LangGraph ships checkpoint expiry and crash detection in the open-source library.
The teams on record split the pair by who writes the path. GitLab, Wayfair and Grab run production agents on LangGraph and say so on their own engineering blogs, while CrewAI's named production accounts, PwC and Gelato, are CrewAI's own customer stories. Pydantic evaluated both and built Pydantic AI instead, and a Shogo operator saw both report a connected integration that delivered nothing. On the same agent, independent builds found LangGraph faster and more controllable and CrewAI more repeatable with less wiring for role-based work.
What teams report
Failures and incidents 1
Shogo
Wiring agents into a team channel, a Shogo operator saw CrewAI and LangGraph each report the integration as connected while nothing arrived and no error was raised (r/AI_Agents, Sep 25, 2026).
“CrewAI and LangGraph both reported the integration connected and delivered nothing. No error, nothing in the channel.”
yiddo_bhushan, Operations, ShogoReddit, r/AI_Agents, , post
Left or turned down 1
yeos.aiOn LangGraph
yeos.ai used LangGraph for months, then dropped it completely when debugging across state transitions outgrew requests that were mostly sequential (Jul 27, 2026).
“Over time we found ourselves thinking more about LangGraph than about our own business logic.”
Nicola Richli, Builder of yeos.ai, yeos.aiDEV Community, , write-up
Evaluated 1
Pydantic
Looking for a framework for Logfire's AI features in late 2024, Colvin evaluated LangChain, LangGraph, CrewAI and LlamaIndex, found them not type safe, and Pydantic built Pydantic AI instead (Software Engineering Daily, Dec 4, 2025). Pydantic AI now competes with both.
“Obviously, LangChain, LangGraph, Crew AI, LlamaIndex, all of these guys used Pydantic. And I assumed there was going to be a good one that I could go and use. Started looking at them and was super disappointed by what I found.”
Samuel Colvin, Founder, PydanticSoftware Engineering Daily, , around 13:08
Adopted 4
GitLabOn LangGraph
GitLab researched agent frameworks, chose LangGraph as the base of Duo Agent Platform, and says a YAML Flow Registry on top cut its own code per agent flow by 45% (Sep 22, 2026).
“We researched available AI frameworks and selected LangGraph, an agent runtime and low-level orchestration framework from LangChain, as the foundation for GitLab Duo Agent Platform.”
Mikolaj Wawrzyniak, Engineer, GitLab Duo Agent Platform, GitLabGitLab blog, , write-up WayfairOn LangGraph
Wayfair's Wilma flags supplier-transfer tickets on an event-driven LangGraph pipeline and beat its human targets with 95% precision and 97% recall (Jan 15, 2026).
“The STP Intervention Flagging system runs on an event-driven automation pipeline using LangGraph.”
Farshid Bahrami, Co-author, Wayfair Tech Blog, WayfairWayfair Tech Blog, , write-up PwCOn CrewAI
CrewAI's undated case study says PwC's CrewAI agents generate and validate proprietary-language code, and reports code-generation accuracy rising from 10% to 70% with no method stated.
“Crew-powered agents boosted code-generation accuracy from 10% to 70%, slashed turnaround time on complex documents, and supplied granular data to prove ROI.”
Jacob Wilson, Quoted in CrewAI's case study, PwCCrewAI, , write-upVendor customer story GelatoCrewAI's undated case study says Gelato runs CrewAI agents that write, test and deploy carrier-integration code, and reports carrier onboarding falling from 5 days to 10 minutes.
Choosing between LangGraph and CrewAI
Choose LangGraph if
- You want every branch and retry written down in code, with the state saved after each step, as Wayfair's ticket flagger and GitLab's Duo Agent Platform flows run.
- Runs must pause for human approval anywhere and resume later, or replay from an earlier checkpoint; plan checkpoint cleanup, since Tiendanube's copilot leaves about 93 records per conversation (Apr 28, 2026).
- You build in TypeScript as well as Python, or want hosting from LangSmith next to your traces, at about US$399 a month above self-hosting for a Small Dedicated deployment in GILD's calculation.
Choose CrewAI if
- Your work maps naturally to specialist agents with roles and goals that collaborate on a task, which CrewAI ships ready-made where LangGraph has you wire the loop yourself.
- You want a visual editor and a managed platform with a free tier to start, and can get an Enterprise quote once you pass AMP Basic's 50 executions a month.
- You want a framework with no LangChain dependency in its design or documentation.
Side by side
| Dimension | LangGraph | CrewAI |
|---|---|---|
| What teams reportLangGraph's accounts are on the teams' own blogs; CrewAI's production accounts are on CrewAI's site. | GitLab chose LangGraph as the base of Duo Agent Platform and cut its own code per flow 45% with a YAML layer (Sep 22, 2026). Wayfair's LangGraph ticket flagger reached 95% precision and 97% recall (Jan 15, 2026). yeos.ai dropped LangGraph in July 2026 when debugging graphs outgrew its sequential requests.123 | PwC reports code-generation accuracy rising from 10% to 70% and Gelato carrier onboarding falling from 5 days to 10 minutes, both in CrewAI's customer stories with no method stated. Pydantic found CrewAI and LangGraph not type safe and built Pydantic AI (Dec 4, 2025).578 |
| Same agent, built in bothTwo independent builds of one agent in each framework, by different authors. | About 280 lines for a chat app with native async and astream() streaming (Vstorm, Mar 13, 2026). A 71 MB install of 45 packages, and a verify-and-revise loop that cut output spread 77% at 2.5 times the tokens (sunnydachs, Sep 21, 2026).910 | About 420 lines for the same chat app, synchronous under the hood with a background thread for streaming (Vstorm). A 699 MB install of 142 packages, and byte-identical output across all 3 base and drift runs (sunnydachs).910 |
| Design | A runtime: nodes and edges, or the Functional API's entrypoints and tasks, with the team defining each transition | A framework: Crews of role-based agents for autonomous work, inside Flows that manage state and control flow with start, listen, and router steps |
| License and languagesLatest releases on each project's GitHub repository. | MIT, free; Python (langgraph 1.2.18, Oct 26, 2026) and JavaScript (@langchain/langgraph 1.5.3, Oct 26, 2026) | MIT, free; Python only, 3.10 to 3.13 (crewai 1.15.27, Oct 9, 2026) |
| State and persistence | Checkpointers save state after each step to memory, SQLite, Postgres, or MongoDB, with exit, async, and sync durability modes; threads, a cross-thread store, replay, and fork | Flow state with @persist, SQLite by default or a custom backend; a run can resume from a saved state or fork from it |
| Crash recoveryDiagrid sells a durable workflow runtime that competes with both on recovery. | Checkpoints save state at each step, but nothing notices a crash: your code detects it and resumes the thread, and the open-source library runs in one process (Diagrid, Feb 25, 2026). Builders running their own workers add leases and fencing so a stale worker cannot write a checkpoint (Reddit, Oct 27, 2026).1112 | @persist saves flow state to SQLite and nobody restarts the flow after a crash; task replay keeps only the last kickoff, with no locking when two processes resume one flow (Diagrid, Feb 25, 2026).11 |
| Human review | interrupt() pauses any node with state persisted; Command(resume=...) continues later, in another process if needed | The @human_feedback decorator pauses a Flow for review and routes on the answer, from CrewAI 1.8.0 |
| Managed hostingList prices in US dollars. Both libraries are free to self-host. | Your own services, or LangSmith Deployment: Plus at US$39 per seat per month with 1 free Small Serverless deployment, then compute metered in LSUs at US$1 each; Enterprise adds hybrid and self-hosted | CrewAI AMP: Basic free with 50 workflow executions a month; Enterprise custom, on CrewAI's cloud, your VPC, or your infrastructure |
| 60,000 requests a month, 10 peopleGILD's calculation from published rates, Oct 28, 2026. Inputs: 60,000 requests a month, each with 10,000 prompt tokens (2,000 uncached, 1,000 cache writes, 7,000 cache reads) and 2,000 output tokens on Claude Sonnet 5.5 at US$2 and US$10 per million; one DigitalOcean droplet at US$24; 10 people, one LangSmith seat. Operations time is excluded. | US$1,656 a month self-hosted: US$1,632 in model tokens and US$24 for one 2 vCPU, 4 GiB server. On LangSmith Plus with one Small Dedicated deployment always on, US$2,055.12: one US$39 seat and US$384.12 in compute at US$1 per LSU.131423 | US$1,656 a month self-hosted, the same tokens and server. Hosted on CrewAI AMP, the volume is past Basic's 50 executions, so the price needs an Enterprise quote.131430 |
| Telemetry and tracing | Traces go to LangSmith only when LANGSMITH_TRACING is set with an API key | Anonymous usage telemetry on by default, off with CREWAI_DISABLE_TELEMETRY; tracing and OpenTelemetry export on AMP |
| LangChain relationship | Built by LangChain and usable without it, though the documentation uses LangChain components for models and tools | A standalone framework with its own primitives for agents, tasks, crews, flows, and tools |
| GitHub starsCounted Oct 12, 2026. Stars count attention and say nothing about production use. | 43,144 (Python repository) and 3,350 (JavaScript) | 59,613 |
| Speed and accuracy in one benchmarkSimpleQA Verified results from Oracle researchers' Agent Spec paper on arXiv (Nov 2025), the only published run of both frameworks on the same agent with accuracy scores that GILD found. | 4.57 seconds per query and 75.5% F1 for a ReAct agent on GPT-4.1 mini; 4.10 seconds and 77.6% on Llama 3.3 70B32 | 7.21 seconds per query and 81.1% F1 on GPT-4.1 mini; 8.12 seconds and 82.3% on Llama 3.3 70B32 |
What practitioners report, side by side
Each product's figures come from its own practitioners, for the group you pick.
Overall signal
All practitioners rate LangGraph +25, 32 points above CrewAI at −7. Most of the gap is in complaints, with 9 of CrewAI's 30 sources negative against 8 of LangGraph's 62.
The score counts the authors from all practitioners that take a side, one vote each with their latest view, and is how far positive votes lead negative ones, per 100 votes, so LangGraph's +25 rests on votes split 23 positive, 30 mixed and 8 negative. Each of CrewAI's authors wrote one source here, so each source is one vote.
A lead of a fifth of the votes reads positive and a lead of half strong positive, with the same lines below zero. The dots show each source, positive, mixed, negative. Build one pilot agent to measure the glue code and debugging time the framework adds.
Footprint on the AI Application Platform stack
They differ on three of 9 layers. Coverage is each product's placement on this stack, as its product page shows it. Core is a job the product owns; Supports and Touches are jobs it helps with. Coverage says nothing about how well a product does them.
| Layer | LangGraph | CrewAI |
|---|---|---|
| Data | ||
| Retrieval | Touches | Supports |
| Operate | ||
| Observability | Touches | Supports |
| Guardrails | Touches | Supports |
The 6 layers where they match
| Layer | LangGraph | CrewAI |
|---|---|---|
| Model | ||
| Model APIs | Touches | Touches |
| Model gateway | Touches | Touches |
| Build | ||
| Orchestration | Core | Core |
| Tool use | Supports | Supports |
| Evaluate | ||
| Evaluation | Touches | Touches |
| Operate | ||
| Cloud deployment | Supports | Supports |
What practitioners praise
Among all practitioners, LangGraph alone has four praise themes that reach 5 sources from 3 authors. Explicit Control Flow and Checkpointing & Durable State lead, at 22 of 25 and 10 of 18. Fast to a Working Crew and Crews in Real Work reach 5 sources from 3 authors only for CrewAI, at 10 of 10 and 7 of 7. LangGraph needs a pilot on the same agent task before you choose on either.
| Praised for | LangGraph | CrewAI |
|---|---|---|
| Explicit Control Flow | 22 of 25 | Not a theme in its sources |
| Checkpointing & Durable State | 10 of 18 | Not a theme in its sources |
| Production Readiness | 9 of 15 | Not a theme in its sources |
| Human-in-the-Loop & Time Travel | 4 of 5 | Not a theme in its sources |
| Fast to a Working Crew | Not a theme in its sources | 10 of 10 |
| Crews in Real Work | Not a theme in its sources | 7 of 7 |
Each count is the sources that praise a theme, or raise it as a concern, out of those that discuss it. A row pairs the two products when both carry a theme under the same or an equivalent name.
A count ending in "so far" has under 5 sources from 3 authors among all practitioners. "Not a theme in its sources" means GILD found no theme of that name in that product's sources.
What practitioners raise as concerns
Among all practitioners, CrewAI alone has five concern themes that reach 5 sources from 3 authors. Async, Memory & Checkpoint Defects and Coarse Control Over Agents lead, at 10 of 10 and 9 of 9. LangGraph alone has three concern themes that reach 5 sources from 3 authors. Complexity & Overhead and Breaking Changes & Docs lead, at 10 of 13 and 9 of 10.
| Raised as a concern | LangGraph | CrewAI |
|---|---|---|
| Complexity & Overhead | 10 of 13 | Not a theme in its sources |
| Breaking Changes & Docs | 9 of 10 | Not a theme in its sources |
| Typing & Language Support | 4 of 5 | Not a theme in its sources |
| Async, Memory & Checkpoint Defects | Not a theme in its sources | 10 of 10 |
| Coarse Control Over Agents | Not a theme in its sources | 9 of 9 |
| Defaults, Telemetry & Dependencies | Not a theme in its sources | 9 of 9 |
| Token Burn & Usage Accounting | Not a theme in its sources | 7 of 7 |
| Tools That Run Twice or Not at All | Not a theme in its sources | 7 of 7 |
32 sources
- GitLab blog: How GitLab reduced code-per-agentic-flow ratio by 45% (Sep 22, 2026)
- Farshid Bahrami and colleagues (Wayfair Tech Blog): Flag Early, Fix Faster: Real-Time Intervention for Supplier Transfers with Wilma (Jan 15, 2026)
- Nicola Richli (yeos.ai) on dev.to: Why We Removed LangGraph From Our AI Platform (Jul 27, 2026)
- Tadeo Donegana Braunschweig (Tiendanube): Scaling LangGraph's Postgres Checkpointer in Production (Apr 28, 2026)
- Software Engineering Daily: Pydantic AI with Samuel Colvin (Dec 4, 2025)
- Reddit r/AI_Agents: a Shogo operator on CrewAI and LangGraph integrations that delivered nothing (Sep 25, 2026)
- CrewAI customer story: PwC accelerates enterprise-scale GenAI adoption with CrewAI (undated, read Oct 28, 2026)
- CrewAI customer story: Gelato accelerates fulfillment via agentic integration (undated, read Oct 28, 2026)
- Kacper Włodarczyk (Vstorm): Same chat app, 4 AI frameworks, Pydantic AI vs LangChain vs LangGraph vs CrewAI (Mar 13, 2026)
- sunnydachs on dev.to: The same agent in Strands, LangGraph, and CrewAI with every LLM call recorded (Sep 21, 2026)
- Yaron Schneider (Diagrid): Why checkpoints aren't durable execution, LangGraph, CrewAI, Google ADK and others (Feb 25, 2026)
- Reddit r/LangChain: self-hosting LangGraph, worker ownership and crash recovery (Oct 27, 2026)
- Claude API pricing: Claude Sonnet 5.5 token rates (checked Oct 28, 2026)
- DigitalOcean Droplet pricing: 2 vCPU, 4 GiB at US$24 a month (checked Oct 28, 2026)
- LangGraph docs: Overview
- LangChain docs: Runtimes, frameworks, and harnesses
- LangGraph docs: Checkpointers and durability modes
- LangGraph docs: Interrupts
- LangGraph on GitHub (MIT license, stars, issues)
- LangGraph release 1.2.17 on GitHub (Oct 24, 2026)
- LangGraph release 1.2.18 on GitHub (Oct 26, 2026)
- @langchain/langgraph on npm (release history)
- LangSmith plans and pricing (LangSmith Deployment rates)
- LangSmith docs: LangSmith Deployment
- CrewAI docs: Introduction (Crews and Flows)
- CrewAI docs: Flows (state and @persist)
- CrewAI docs: Human feedback in Flows
- CrewAI docs: Telemetry
- CrewAI docs: CrewAI AMP
- CrewAI pricing
- CrewAI on GitHub (MIT license)
- arXiv: Amini et al. (Oracle), Open Agent Specification (Agent Spec), SimpleQA Verified results (Nov 7, 2025)