What teams building AI features on LangGraph report, with every finding tied to its evidence.
Read the findingsPractitioner sentiment, all practitioners
+25 Positive from 62 sources that take a side
How this figure is madeFrom 61 authors in 54 discussions. Build one pilot agent to measure the glue code and debugging time the framework adds.
Mar 2025 to Oct 2026.
79 sources from practitioners, by month and sentiment.
Across 79 sources the read is positive. Explicit control flow draws praise in 22 of the 25 sources that discuss it, and 9 of 15 report it holding up in production. Complexity is the most frequent concern, raised in 10 sources, followed by breaking changes in 9, and 8 of the 25 that describe a framework choice replaced LangGraph.
Positive, mixed and negative sources
The score counts them as 23 positive, 30 mixed and 8 negative votes, one per author. See How the numbers work.
See the evidenceBelow: 4 praise themes, 3 concerns, 3 close to agreement and 1 disagreement.
Counted for All practitioners, from 79 sources. Change group
Every capability practitioners describe positively, ranked by how many sources say so. Open one to read who said it.
Counted for All practitioners.
Positive, 22
Mixed
sunnydachs, Independent developer
I built the same agent in Strands, LangGraph, and CrewAI, and recorded every LLM call to see how they actually differPersonal blog post, dev.to
Across 27 runs, LangGraph's explicit verify-and-revise loop cut output spread 77% but cost 2.5 times the tokens and latency; CrewAI was the most repeatable.
Positive
mfn-throw, Hacker News commenter
Comment on: Show HN: Brightdeck - an OOXML-compatible AI presentation makerHacker News comment
Picked LangGraph for Brightdeck's agent architecture because its modularity makes components easier to test.
Positive
Bitter-Pride-157, Reddit user writing on their own blog
Moved from Google's Agent Development Kit to LangGraphReddit post, r/LangChain
Switched from Google's Agent Development Kit to LangGraph and calls it one of the best decisions for the workflow, because LangGraph is easier to reason about when state and loops get complex.
Positive
ossa-ma, Hacker News commenter, self-described
Comment on: The Claude Code Source Leak: fake tools, frustration regexes, undercover modeHacker News comment
Uses LangGraph for deterministically reproducible orchestration of fixed multi-agent data flows inside a service.
Positive
Sneh Agrawal, Rishi Raj, Ayan Chatterjee, Wen Zhong Tan, and Sai Reddy Kakumanu, Grab
From firefighting to building: How AI agents restored our team's core productivityEngineering blog post, Grab Tech Blog
Grab's data warehouse team uses LangGraph for the loops and hand-offs between its agents, and first answers now reach Slack within minutes where manual searches took hours.
Positive
jcims, Hacker News commenter
Comment on: Benchmarking OpenTelemetry: Can AI trace your failed login?Hacker News comment
Built an SRE agent with LangGraph that, given source code, CI/CD, and infrastructure tools, diagnoses issues and suggests reasonable remediation.
Positive
_fat_santa, Hacker News commenter
Comment on: Wrapping my head around AI wrappersHacker News comment
Their SaaS team leans heavily on LangGraph for agentic workflows, which let them plug generation into broader workflows beyond a chat interface.
Positive
tag_coder, Hacker News commenter
Comment on: Ask HN: Do you roll your own agent or use a framework?Hacker News comment
Prefers LangGraph over n8n or Make because they can write tests, use version control, and control context with a custom state reducer.
Mixed
NeutralCrane, Hacker News commenter, self-described
Comment on: Open SWE by LangChainHacker News comment
Rates LangGraph the least bad of LangChain's products but says OpenAI's Agents SDK and Pydantic AI are solid alternatives now.
Positive
four_fifths, Hacker News commenter, self-described
Comment on: We chose LangGraph to build our coding agentHacker News comment
Calls LangChain a meaningless abstraction but finds LangGraph's graph a very useful mental model for agent flows.
Mixed
leopoldj, Hacker News commenter, self-described
Comment on: We chose LangGraph to build our coding agentHacker News comment
Sees LangGraph as legitimate workflow software and more than a wrapper, but would consider established workflow engines first.
Positive
TimPC, Hacker News commenter, self-described
Comment on: We chose LangGraph to build our coding agentHacker News comment
Likes that a LangGraph node can be any state function, so pre- and post-processing nodes keep the run loop close to repeated graph invocations.
Positive
mokarma, Hacker News commenter, self-described
Comment on: Why we chose LangGraph to build our coding agentHacker News comment
For complex multi-agent systems with per-agent tools and prompts, finds LangGraph better and easier than AWS Bedrock agents and OpenAI's agent framework.
Positive
Sagi Medina, Qodo
Why we chose LangGraph to build our coding agentEngineering blog post, Qodo blog
Qodo took LangGraph from first prototype to its production coding agent for its adjustable structure, reusable nodes, and few-line Postgres persistence; docs and testing support lag.
Positive
dbrecht_, Hacker News commenter
Comment on: Agentic Coding Is a TrapHacker News comment
Built Switchplane on LangGraph control flows that call the LLM only when judgment is needed, so models can be swapped and costs capped in code.
Positive
michael-elias, Hacker News commenter
Comment on: Building an Autonomous AI Sysadmin with LangGraph, Ansible, and SQLiteHacker News comment
Built an infrastructure agent with LangGraph for the workflow and Ansible for execution, and keeps anomaly math in SQLite so the model never does arithmetic.
No opinion stated
Farshid Bahrami, Atharva Deo, Roger Bock, Ying Jiang, and Luming Wang, Wayfair
Flag Early, Fix Faster: Real-Time Intervention for Supplier Transfers with WilmaEngineering blog post, Wayfair Tech Blog
Wayfair's LangGraph workflow first identifies the comment author in code, then classifies with an author-specific prompt; it reached 95% precision and 97% recall and clears about 65% of tickets without a person.
Positive
Karthik Ramgopal, Distinguished Engineer, and Daniel Hewlett, Principal AI Engineer, LinkedIn
Lessons Learned from Building LinkedIn's First Agent: Hiring AssistantConference talk transcript, InfoQ (QCon London 2025)
LinkedIn standardized its agent platform on LangChain and LangGraph behind its own adapters, so teams get prompts, memory, and skills built in and the framework stays swappable.
Mixed
threecheese, Hacker News commenter, self-described
Comment on: Show HN: Sim - Apache-2.0 n8n alternativeHacker News comment
Likes LangGraph's workflow abstraction and middleware, but feels LangSmith and deployment are creeping into the frameworks with lock-in that makes them hesitant to invest.
Mixed
Mariano Álvarez, Developer building AI agents
Lessons from 6 Months of Building AI AgentsPersonal blog post, dev.to
Found LangGraph powerful and flexible but low-level and hard to grasp, with documentation that is not always clear and several ways to do one thing.
Mixed
Kydlaw, Hacker News commenter
Comment on: Why we chose LangGraph to build our coding agentHacker News comment
The team found LangGraph far better built than LangChain, but its pricing and deployment story felt like vendor lock-in, so they chose Pydantic AI.
Positive
joshSzep, Hacker News commenter
Comment on: Ask HN: What agentic AI frameworks have you used, and did you like them?Hacker News comment
Built the same toy project with LangChain and with LangGraph plus Pydantic AI, and preferred the second for its Pydantic-backed state and dependencies.
Mixed or negative, 3
Mixed
7e10, Hacker News commenter
Comment on: Ask HN: Learning resources for building AI agents?Hacker News comment
Built a Google Analytics agent on a MERN plus LangGraph stack but finds LangGraph agents do not feel truly autonomous.
Negative
omhome16, Hacker News commenter, self-described
Comment on: Show HN: Agent framework that generates its own topology and evolves at runtimeHacker News comment
Finds LangGraph's error handling too rigid: once the happy path breaks, the graph halts or loops.
Mixed
m3m3o, builder of a LangGraph shopping assistant
Swapping the model in a LangGraph multi-agent system is an integration projectReddit post, r/LangChain
A new model broke 5 agents' worth of assumptions: the planner's conditional routing expected one-word answers, tool calls arrived in the message text, and a silent extraction failure cascaded through the shared state into the next agents.
Counted for All practitioners.
Positive, 10
Positive
jd_, Hacker News commenter
Comment on: Show HN: Pizza Bot - An inbox for AI agents that work in the backgroundHacker News comment
Replaced a homegrown checkpointing layer full of edge-case bugs with LangGraph and Deep Agents for the open-source release, and calls it rock solid so far.
Mixed
Tadeo Donegana Braunschweig, AI engineer on Tiendanube's merchant copilot team
Scaling LangGraph's Postgres checkpointer in productionEngineering blog post, author's blog
Tiendanube's copilot handles more than 120,000 conversations a week, and each one leaves about 93 checkpoint records across four tables. The open-source Postgres checkpointer has no expiry, so the team wrote its own migrations, a partitioned cleanup job and a weekly export to S3, and checkpoints only the supervisor graph.
Positive
Anil Gulecha, Co-Founder & CTO, Kalvium Labs
LangGraph in Production: Building Stateful AI AgentsEngineering blog post, Kalvium Labs blog
A document-review agent that restarted after users paused took 45 minutes to fix with LangGraph checkpointing, and interrupts replaced over 200 lines of custom approval code. Oversized state pushed Postgres checkpoint writes to 400ms until the team trimmed it.
Positive
munro, Hacker News commenter, self-described
Comment on: Agent design is still hardHacker News comment
Built an agent with LangGraph nine months earlier and is pretty happy with it, using only tools and state and skipping the extra layers.
Positive
Sagi Medina, Qodo
Why we chose LangGraph to build our coding agentEngineering blog post, Qodo blog
Qodo took LangGraph from first prototype to its production coding agent for its adjustable structure, reusable nodes, and few-line Postgres persistence; docs and testing support lag.
Mixed
Mikolaj Wawrzyniak, GitLab Duo Agent Platform team
How GitLab reduced code-per-agentic-flow ratio by 45%Engineering blog post, GitLab blog
GitLab chose LangGraph as the base of its agent platform for durable execution and traceability. It then found hand-wired graphs of 450 or more lines per flow too low-level to scale, and a YAML layer on top of LangGraph cut Python per flow by 45%.
Mixed
Ali Suleyman TOPUZ, Software architect writing on agent design
Harness, Loop, and Graph: What I Learned Building the Same AI Agent Three Different WaysPersonal blog post, dev.to
Calls LangGraph's checkpointing good, but has watched teams adopt a full graph framework for three-step workflows that never branch.
Positive
tag_coder, Hacker News commenter
Comment on: Ask HN: Do you roll your own agent or use a framework?Hacker News comment
Prefers LangGraph over n8n or Make because they can write tests, use version control, and control context with a custom state reducer.
Positive
prohobo, Hacker News commenter
Comment on: Ask HN: How are you scaling AI agents reliably in production?Hacker News comment
Runs a daily ecommerce-analyst report on LangGraph, chose it as the most mature option, and keeps graphs small with checkpointing after each state change.
Positive
jeffspinny, Hacker News commenter, self-described
Comment on: We chose LangGraph to build our coding agentHacker News comment
Values LangGraph mainly as a state machine for human-in-the-loop work with time travel, letting analysts fix output and roll back without a custom state machine.
Mixed or negative, 8
Mixed
FurkhanShaikh, GitHub user filing an issue
[BUG] Resuming after a tool error re-runs the tool node, repeating a side effect that already happenedGitHub issue
When a tool times out after its side effect commits, resuming from the checkpoint runs the tool again; the reproduction makes two payments for one request.
Mixed
celyrt2-alt, GitHub user filing an issue
Memory leak: _checkpointer_put_after_previous coroutine chains accumulate with default durability="async"GitHub issue
With the default async durability and any checkpoint write latency, pending checkpoint writes chain together and hold memory in proportion to the number of supersteps; sync durability does not.
Mixed
ibbybuilds, GitHub user filing an issue
Run Cancellation Causes Loss of Streamed State Not Yet Persisted as a CheckpointGitHub issue
On LangGraph Platform, canceling a run mid-stream loses output streamed since the last checkpoint, sometimes several seconds of it, once the UI resyncs.
Mixed
shubhamnegikellton, GitHub user filing an issue
Reports SSL errors from the Postgres checkpointer across several versions when streaming a ReAct agent with a connection pool; the issue drew 53 comments.
Mixed
MarioAlessandroNapoli, GitHub user filing an issue
Long tool calls (~180s+) silently re-executed from checkpoint on LangGraph CloudGitHub issue
On LangGraph Cloud, tool calls longer than about 180 seconds are re-dispatched from the last checkpoint while the original still runs. Both finish, and the team pays for the work two or three times.
Mixed
redhanuman, Hacker News commenter
Comment on: I built a time-travel debugger for AI agentsHacker News comment
A production LangGraph agent failed mid-run and the state was gone before they could inspect it, so they built a tool to record every step.
Mixed
ManuelAngel99, GitHub user filing an issue
AsyncPostgresSaver 2.0.23 fails to read channel values: NoneType object is not a mappingGitHub issue
After upgrading to checkpoint-postgres 2.0.23, listing a thread's checkpoints failed with a NoneType error.
Mixed
diego898, Hacker News commenter, self-described
Comment on: We chose LangGraph to build our coding agentHacker News comment
Wishes LangGraph's persistence and streaming were further along, since the documented streaming example leaves every client to implement it in full.
GILD's fact checks, which don't count as sources
Checked Oct 12, 2026.LangGraph documents three durability modes, exit, async, and sync, trading speed for the guarantee that every checkpoint is written. It also documents checkpointer packages for memory, SQLite, Postgres, and MongoDB. Checkpointers
Open them in the recordCounted for All practitioners.
Positive, 9
Positive
Professional-Pear351, maintainer of the open-source langgraph-harness
A self-hosted GitLab review agent on LangGraph.js after 1,000 merge requestsReddit post, r/LangChain
Has reviewed more than 1,000 merge requests with a LangGraph.js agent over 133 releases since June 2026, with about two-thirds of its comments resolved by the author. The failures were loops, repeated tool calls and runs ending on a question, and the maintainer chose small middleware nodes over prompting to catch them.
Mixed
dragonmage65535, Hacker News commenter
Comment on: Ask HN: Anyone using Claude Agent SDK in production?Hacker News comment
Runs LangGraph in production but trusts Claude Code's agent loop more and is exploring the Claude Agent SDK, while finding it heavier than a library.
No opinion stated
Farshid Bahrami, Atharva Deo, Roger Bock, Ying Jiang, and Luming Wang, Wayfair
Flag Early, Fix Faster: Real-Time Intervention for Supplier Transfers with WilmaEngineering blog post, Wayfair Tech Blog
Wayfair's LangGraph workflow first identifies the comment author in code, then classifies with an author-specific prompt; it reached 95% precision and 97% recall and clears about 65% of tickets without a person.
Positive
Karthik Ramgopal, Distinguished Engineer, and Daniel Hewlett, Principal AI Engineer, LinkedIn
Lessons Learned from Building LinkedIn's First Agent: Hiring AssistantConference talk transcript, InfoQ (QCon London 2025)
LinkedIn standardized its agent platform on LangChain and LangGraph behind its own adapters, so teams get prompts, memory, and skills built in and the framework stays swappable.
Positive
shcallaway, Hacker News commenter
Comment on: LangChain raises US$125M to build the platform for agent engineeringHacker News comment
A year on LangGraph Platform (now LangSmith Deployment) and the TypeScript SDK: rough at first, but streaming and hosting saved the team from building it in-house.
Positive
Karthik Ramgopal and co-authors, LinkedIn Engineering
The LinkedIn generative AI application tech stack: extending to build AI agentsEngineering blog post, LinkedIn Engineering Blog
LinkedIn says it embraced LangGraph for its integrations, production readiness and observability, and adapted it to its own messaging and memory infrastructure with custom providers. LangSmith traces agents before production, and OpenTelemetry covers production.
Positive
prohobo, Hacker News commenter
Comment on: Ask HN: How are you scaling AI agents reliably in production?Hacker News comment
Runs a daily ecommerce-analyst report on LangGraph, chose it as the most mature option, and keeps graphs small with checkpointing after each state change.
Mixed
tomnipotent, Hacker News commenter, self-described
Comment on: Agno: Agent framework 10,000x faster than LangChainHacker News comment
Profiled the claim that Agno is far faster than LangGraph: most LangGraph instantiation time is SSL setup in the OpenAI client, and the gap vanishes over a few requests.
Mixed
aussieguy1234, Hacker News commenter
Comment on: Python is not a great language for data scienceHacker News comment
LangGraph agents worked well, but the TypeScript version lags so far behind that they switched to Python, where a ReAct agent takes a few lines against about 50 in TypeScript.
Mixed or negative, 6
Mixed
Tadeo Donegana Braunschweig, AI engineer on Tiendanube's merchant copilot team
Scaling LangGraph's Postgres checkpointer in productionEngineering blog post, author's blog
Tiendanube's copilot handles more than 120,000 conversations a week, and each one leaves about 93 checkpoint records across four tables. The open-source Postgres checkpointer has no expiry, so the team wrote its own migrations, a partitioned cleanup job and a weekly export to S3, and checkpoints only the supervisor graph.
Mixed
MarioAlessandroNapoli, GitHub user filing an issue
Long tool calls (~180s+) silently re-executed from checkpoint on LangGraph CloudGitHub issue
On LangGraph Cloud, tool calls longer than about 180 seconds are re-dispatched from the last checkpoint while the original still runs. Both finish, and the team pays for the work two or three times.
Mixed
diego898, Hacker News commenter, self-described
Comment on: We chose LangGraph to build our coding agentHacker News comment
Wishes LangGraph's persistence and streaming were further along, since the documented streaming example leaves every client to implement it in full.
Mixed
celyrt2-alt, GitHub user filing an issue
Memory leak: _checkpointer_put_after_previous coroutine chains accumulate with default durability="async"GitHub issue
With the default async durability and any checkpoint write latency, pending checkpoint writes chain together and hold memory in proportion to the number of supersteps; sync durability does not.
Negative
omhome16, Hacker News commenter, self-described
Comment on: Show HN: Agent framework that generates its own topology and evolves at runtimeHacker News comment
Finds LangGraph's error handling too rigid: once the happy path breaks, the graph halts or loops.
Mixed
ibbybuilds, GitHub user filing an issue
Run Cancellation Causes Loss of Streamed State Not Yet Persisted as a CheckpointGitHub issue
On LangGraph Platform, canceling a run mid-stream loses output streamed since the last checkpoint, sometimes several seconds of it, once the UI resyncs.
GILD's fact checks, which don't count as sources
Checked Oct 12, 2026.The langgraph dev server runs Agent Server in memory for development and testing only. LangChain points production use to LangSmith Deployment or a self-managed Agent Server with persistent storage. Run a local server
Open them in the recordCounted for All practitioners.
Positive, 4
Positive
Anil Gulecha, Co-Founder & CTO, Kalvium Labs
LangGraph in Production: Building Stateful AI AgentsEngineering blog post, Kalvium Labs blog
A document-review agent that restarted after users paused took 45 minutes to fix with LangGraph checkpointing, and interrupts replaced over 200 lines of custom approval code. Oversized state pushed Postgres checkpoint writes to 400ms until the team trimmed it.
Positive
7777777phil, Hacker News commenter
Comment on: AI hallucinate. Do you ever double check the output?Hacker News comment
Building research automation on LangGraph for two months, the team puts a human-in-the-loop checkpoint after every critical step.
Positive
jeffspinny, Hacker News commenter, self-described
Comment on: We chose LangGraph to build our coding agentHacker News comment
Values LangGraph mainly as a state machine for human-in-the-loop work with time travel, letting analysts fix output and roll back without a custom state machine.
Positive
jd_, Hacker News commenter
Comment on: Show HN: Pizza Bot - An inbox for AI agents that work in the backgroundHacker News comment
Replaced a homegrown checkpointing layer full of edge-case bugs with LangGraph and Deep Agents for the open-source release, and calls it rock solid so far.
Mixed or negative, 1
Mixed
rahulladumor, Reddit user building an approval step
interrupt() re-runs the whole node on resumeReddit post, r/LangChain
Points out that a resumed node starts again from the top, so a draft generated before interrupt() is generated again after approval unless it is saved to state first. Replies keep the approved draft in state, hash it, and use sync durability on approval paths.
GILD's fact checks, which don't count as sources
Checked Oct 12, 2026.The interrupt() call pauses a run with its state persisted, and Command(resume=...) continues it later, in a new process if needed. Replaying from a checkpoint re-executes every later node, including LLM calls. Interrupts
Open them in the recordEvery concern practitioners raise, most raised first.
Counted for All practitioners.
Raised as a concern, 10
Mixed
Mikolaj Wawrzyniak, GitLab Duo Agent Platform team
How GitLab reduced code-per-agentic-flow ratio by 45%Engineering blog post, GitLab blog
GitLab chose LangGraph as the base of its agent platform for durable execution and traceability. It then found hand-wired graphs of 450 or more lines per flow too low-level to scale, and a YAML layer on top of LangGraph cut Python per flow by 45%.
Mixed
DeadLocker, Developer with a traditional software engineering background
Why I Stopped Using LangGraphPersonal blog post, dev.to
Used LangGraph in 8 of 10 projects and replaced it in most; the state schema became a burden and pipelines did not need a graph. It still suits complex multi-agent work.
Negative
sriku, Hacker News commenter
Comment on: Cord: Coordinating Trees of AI AgentsHacker News comment
The team dropped LangGraph for its own orchestration, saying it wasted time on what could have been an ordinary Python function.
Negative
embedding-shape, Hacker News commenter, self-described
Comment on: Wrapping my head around AI wrappersHacker News comment
Calls adopting LangGraph their biggest mistake building LLM tooling and advises writing the tool-calling loop and plumbing yourself.
Negative
mahdiyar, Hacker News commenter
Comment on: Ask HN: What are you working on (September 2025)?Hacker News comment
After trying CrewAI and LangGraph, found the learning curve higher than needed for features they did not use, and built their own small agent library.
Mixed
Mariano Álvarez, Developer building AI agents
Lessons from 6 Months of Building AI AgentsPersonal blog post, dev.to
Found LangGraph powerful and flexible but low-level and hard to grasp, with documentation that is not always clear and several ways to do one thing.
Negative
iLoveOncall, Hacker News commenter
Comment on: Building Effective AI AgentsHacker News comment
The team removed LangChain and LangGraph from a work project, saying the boilerplate meant writing more code than calling the model APIs directly.
Mixed
CharlieDigital, Hacker News commenter
Comment on: Why we chose LangGraph to build our coding agentHacker News comment
Has twice built the Qodo-style flow on lower-level SDKs in under a day and asks why LangGraph over Mastra and other frameworks.
Mixed
Ali Suleyman TOPUZ, Software architect writing on agent design
Harness, Loop, and Graph: What I Learned Building the Same AI Agent Three Different WaysPersonal blog post, dev.to
Calls LangGraph's checkpointing good, but has watched teams adopt a full graph framework for three-step workflows that never branch.
Negative
Nicola Richli, yeos.ai
Why We Removed LangGraph From Our AI PlatformCompany blog post, dev.to
yeos.ai removed LangGraph after months: debugging graphs got harder as customers grew, most requests were sequential, and plain components were easier to test.
Discussed without concern, 3
Mixed
sunnydachs, Independent developer
I built the same agent in Strands, LangGraph, and CrewAI, and recorded every LLM call to see how they actually differPersonal blog post, dev.to
Across 27 runs, LangGraph's explicit verify-and-revise loop cut output spread 77% but cost 2.5 times the tokens and latency; CrewAI was the most repeatable.
Mixed
leopoldj, Hacker News commenter, self-described
Comment on: We chose LangGraph to build our coding agentHacker News comment
Sees LangGraph as legitimate workflow software and more than a wrapper, but would consider established workflow engines first.
Mixed
tomnipotent, Hacker News commenter, self-described
Comment on: Agno: Agent framework 10,000x faster than LangChainHacker News comment
Profiled the claim that Agno is far faster than LangGraph: most LangGraph instantiation time is SSL setup in the OpenAI client, and the gap vanishes over a few requests.
GILD's fact checks, which don't count as sources
Checked Oct 12, 2026.LangChain documents a Functional API that adds persistence, human-in-the-loop, and streaming to existing code with ordinary if statements and loops. It recommends LangChain's higher-level agents for teams that do not need low-level control. Functional API
Open them in the recordCounted for All practitioners.
Raised as a concern, 9
Mixed
jasonhanna, GitHub user filing an issue
astream_events v3 dropped cache and reasoning token detailsGitHub issue
After moving to v3 streaming, the team's dashboards showed prompt caching gone and token costs roughly tripled; v3 had dropped the token details, so a run with 4,210 cache-read tokens reported none. Billing never changed, and langchain-core 1.4.8 fixed the reporting on Jun 18, 2026. The repro used Claude Sonnet 4.6.
Mixed
aidandaly24, GitHub user filing an issue
langgraph 1.1.7 broke OpenTelemetry instrumentation with a TypeErrorGitHub issue
Upgrading from langgraph 1.1.6 to 1.1.7 made every instrumented agent crash at once, because a new type check rejected the OpenTelemetry handler. The repro agent ran Claude Sonnet 4.5, and LangChain removed the check in 1.1.8 the same day.
Mixed
jacobreesmontgomery, GitHub user filing an issue
LangGraph [1.0.6] Bug - Agent infinite looping until recursion limit error is hitGitHub issue
After upgrading to langgraph 1.0.6, a text-to-SQL agent looped until the recursion limit, and the same configuration on a 0.6 release did not.
Mixed
farouk09, GitHub user filing an issue
Incompatibility between langgraph-cli and langgraph-api >= 0.7.0GitHub issue
Dependency resolution failed because langgraph-cli pinned langgraph-api below 0.7.0, blocking projects that needed newer API features.
Mixed
ManuelAngel99, GitHub user filing an issue
AsyncPostgresSaver 2.0.23 fails to read channel values: NoneType object is not a mappingGitHub issue
After upgrading to checkpoint-postgres 2.0.23, listing a thread's checkpoints failed with a NoneType error.
Mixed
ajhwb, GitHub user filing an issue
0.4.4 ModuleNotFoundError: No module named 'langgraph.cache'GitHub issue
LangGraph 0.4.4 broke a basic StateGraph import hours after release; pinning 0.4.3 restored it.
Negative
jsemrau, Hacker News commenter, self-described
Comment on: Quick Primer on MCP Using Ollama and LangChainHacker News comment
A LangGraph course notebook already failed a week after release; constant breaking changes and no visible roadmap made them move away from LangChain's libraries.
Positive
Sagi Medina, Qodo
Why we chose LangGraph to build our coding agentEngineering blog post, Qodo blog
Qodo took LangGraph from first prototype to its production coding agent for its adjustable structure, reusable nodes, and few-line Postgres persistence; docs and testing support lag.
Mixed
shubhamnegikellton, GitHub user filing an issue
Reports SSL errors from the Postgres checkpointer across several versions when streaming a ReAct agent with a connection pool; the issue drew 53 comments.
Discussed without concern, 1
Mixed
Mariano Álvarez, Developer building AI agents
Lessons from 6 Months of Building AI AgentsPersonal blog post, dev.to
Found LangGraph powerful and flexible but low-level and hard to grasp, with documentation that is not always clear and several ways to do one thing.
GILD's fact checks, which don't count as sources
Checked Oct 12, 2026.LangGraph 1.0 is an LTS release. Breaking changes come only in major versions, and deprecated features stay for at least one minor version. 1.0 is maintained for at least a year after 2.0, and LangGraph 0.4 receives fixes until December 2026. Release policy
Open them in the recordCounted for All practitioners.
Raised as a concern, 4
Mixed
m3m3o, builder of a LangGraph shopping assistant
Swapping the model in a LangGraph multi-agent system is an integration projectReddit post, r/LangChain
A new model broke 5 agents' worth of assumptions: the planner's conditional routing expected one-word answers, tool calls arrived in the message text, and a silent extraction failure cascaded through the shared state into the next agents.
Mixed
aussieguy1234, Hacker News commenter
Comment on: Python is not a great language for data scienceHacker News comment
LangGraph agents worked well, but the TypeScript version lags so far behind that they switched to Python, where a ReAct agent takes a few lines against about 50 in TypeScript.
Negative
davedx, Hacker News commenter
Comment on: Building Effective AI AgentsHacker News comment
Spends much of their LangGraph time fixing runtime type errors because graph state is a loosely typed blob, which makes data flow and testing hard.
Mixed
DeadLocker, Developer with a traditional software engineering background
Why I Stopped Using LangGraphPersonal blog post, dev.to
Used LangGraph in 8 of 10 projects and replaced it in most; the state schema became a burden and pipelines did not need a graph. It still suits complex multi-agent work.
Discussed without concern, 1
Positive
shcallaway, Hacker News commenter
Comment on: LangChain raises US$125M to build the platform for agent engineeringHacker News comment
A year on LangGraph Platform (now LangSmith Deployment) and the TypeScript SDK: rough at first, but streaming and hosting saved the team from building it in-house.
GILD's fact checks, which don't count as sources
Checked Oct 12, 2026.State schemas can be TypedDict, dataclass, or Pydantic models with reducer functions; LangGraph.js is documented as a separate JavaScript and TypeScript library with the same orchestration focus. Graph API
Open them in the recordStatements this group's sources agree on, then the ones close to agreement.
Close to agreement: each needs 16 supporting sources
Deterministic steps belong in code, and the LLM is called only where judgment is needed.
7 supporting sourcesLangGraph earns its overhead on branching, resumable, or approval-gated workflows; straight pipelines do not need a graph.
6 supporting sourcesTeams replace LangGraph with their own orchestration code once they know the shape of their workflow.
6 supporting sourcesQuestions the sources split on, with the count on each side.
Evidence remains split
Chose or kept LangGraph, 17 sources
Some chose LangGraph over AWS Bedrock agents, n8n, or their own code, for checkpointing, interrupts, and streaming they did not want to build or support.
Replaced it, 8 sources
Others replaced it with their own orchestration code, Pydantic AI, or the Vercel AI SDK, citing overhead, debugging, and breaking changes.
The three groups, then every practitioner by the context they work in, and how emphasis changes with scale.
Three groups drawn from the same sources. Choose a group to select whose experience counts. Within that group, each author counts once in the practitioner sentiment, with their latest view. Signals open on All practitioners. Teams and leaders have 18 accounts; GILD prints their sentiment from 20.
31 sources. Named companies and small teams describe production use, capabilities they avoided building, and reasons for replacing LangGraph with custom code.
48 sources. Individual builders describe control flow and checkpoints, along with release changes and the framework work their projects require.
79 sources. This read combines named teams with small startups and solo developers. Their workflows need different amounts of branching and saved state.
79 sources from practitioners, by the context they work in. Filled rows count for this group.
Emphasizes Control flow, checkpoints, and release churn
Emphasizes Keeping LangGraph or replacing it with own code
Emphasizes Production workflows, validation loops, and deployment
Emphasizes Standardizing a framework and pausing runs for people
Emphasizes One agent framework shared by dozens of teams
What changes with scale. Individual builders focus on control flow and checkpoints, with reports of breaking changes. Large companies share LangGraph across teams: GitLab runs Duo Agent Platform flows on it, LinkedIn its agent platform, and in LangChain's Interrupt talks BlackRock reports tools from 50+ teams and Toyota 50+ agents. Small teams that left cite difficult debugging and workflows that required less code in plain Python.
Where this group's sources were published.
Counted for All practitioners, from 79 sources. Change group
96 sources for this group, 17 of them documentation
Earlier evidence remains when it still applies. Records tied to renamed products, earlier prices, or closed version gaps carry Needs a recheck.
See sources by originWho each source is, how it knows, whether it still applies and what it counts toward.
Newest first, on its own page. Every count above opens its sources there.
Read all 96 sourcesAbout this read
A running review of what teams building AI features on LangGraph report, from individual builders to company-wide platforms. Each finding links to its sources, and each conflict shows both sides.
Counts cover only the sources GILD reviewed.
Choose a group to select whose experience counts. Within that group, each author counts once in the practitioner sentiment, with their latest view. GILD files each source in a group from how the account describes its author. A role or employer marked self-described is what the source states about themselves. Documentation checks facts for every group and never counts toward sentiment.
A source's date and whether it still applies are separate: a recent post can describe an old version, and an old one can still hold.
Flagged for a recheck, of the sources that take a side: teams and leaders 2 of 18, individual builders 3 of 44, all practitioners 5 of 62.
Each row opens its sources for the group you are reading.
| Finding | Teams and technical leaders | Individual builders | All practitioners | Sources |
|---|---|---|---|---|
| Current readCurrent sentiment | 10 positive, 5 mixed, 3 negative | 14 positive, 25 mixed, 5 negative | 24 positive, 30 mixed, 8 negative | Sources |
| PraiseExplicit Control Flow | 7 of 7 positive | 15 of 18 positive | 22 of 25 positive | Sources |
| PraiseCheckpointing & Durable State | 5 of 6 positive | 5 of 12 positive | 10 of 18 positive | Sources |
| PraiseHuman-in-the-Loop & Time Travel | 3 of 3 positive (under the minimum) | 1 of 2 positive (under the minimum) | 4 of 5 positive | Sources |
| PraiseInspectable State & Debugging | 0 of 1 positive (under the minimum) | 0 of 2 positive (under the minimum) | 0 of 3 positive (under the minimum) | Sources |
| PraiseProduction Readiness | 5 of 7 positive | 4 of 8 positive | 9 of 15 positive | Sources |
| ConcernComplexity & Overhead | 4 of 4 raise it (under the minimum) | 6 of 9 raise it | 10 of 13 raise it | Sources |
| ConcernBreaking Changes & Docs | 1 of 1 raise it (under the minimum) | 8 of 9 raise it | 9 of 10 raise it | Sources |
| ConcernLangChain & LangSmith Pull | 1 of 1 raise it (under the minimum) | 1 of 2 raise it (under the minimum) | 2 of 3 raise it (under the minimum) | Sources |
| ConcernTyping & Language Support | 0 of 1 raise it (under the minimum) | 4 of 4 raise it (under the minimum) | 4 of 5 raise it | Sources |
| ConsensusA graph pays off for branching, stateful work | 2 supporting (under the minimum) | 4 supporting (under the minimum) | 6 supporting (below threshold) | Sources |
| ConsensusSome teams outgrow the framework | 3 supporting (under the minimum) | 3 supporting (under the minimum) | 6 supporting (below threshold) | Sources |
| ConsensusCode for the deterministic, LLM for judgment | 3 supporting (under the minimum) | 4 supporting (under the minimum) | 7 supporting (below threshold) | Sources |
| ConsensusPaired with LangSmith | 2 supporting (under the minimum) | 0 supporting (under the minimum) | 2 supporting (under the minimum) | Sources |
| DisagreementOnce a team has built on LangGraph, does it keep it? | Chose or kept LangGraph 11, Replaced it 4 | Chose or kept LangGraph 6, Replaced it 4 | Chose or kept LangGraph 17, Replaced it 8 | Sources |
| Current readEvidence mix | 48 sources | 65 sources | 96 sources | Sources |
GILD's checks of claims against official documentation, kept apart from the source counts.
