LangGraph builds the agent. Avalanche runs the business workflow around it.

Quick answer

Updated October 27, 2026. LangGraph and LangChain are open-source (MIT) frameworks from LangChain for building agents as graphs of model calls, tools and state, with LangSmith for tracing, evaluations and the managed LangSmith Deployment runtime.[1, 6, 12, 30] Choose LangGraph if engineers are building a custom agent and want control over every node, interrupts anywhere for human input, and LangSmith datasets and experiments to compare agent versions.[3, 13, 14] Choose Avalanche if the job is a long, multi-step, document-heavy business workflow: you set the few steps, checks and approvals that matter, agents do the work inside each step, and every run keeps typed step outputs and a versioned workspace your team can compare across runs and versions.[21, 25] The two can work together: a LangGraph agent is Python, so it can run inside an Avalanche step.[20]

Key takeaway

Choose Avalanche when agents should do the work inside a few business steps you control, often over PDFs, Word files and spreadsheets, with versioned deploys and rollback.

Choose LangGraph when engineers are building a custom agent node by node and want LangSmith tracing, evaluations and human-in-the-loop interrupts today.

  • Avalanche wins on level of abstraction: you set the few business steps, checks and approvals; agents do the work inside each step instead of a graph you wire node by node.
  • LangGraph wins on evaluations and datasets: LangSmith scores agent outputs against datasets, runs online evaluators on production traces and compares experiments side by side; Avalanche evaluations run locally or offline.
  • LangGraph wins on human-in-the-loop: an interrupt can pause a graph anywhere, wait without a time limit and resume with a typed answer, and tool calls can be approved, edited or rejected.
  • Avalanche wins on versions and rollback: each Avalanche Cloud deploy is frozen, running workflows finish on the version they started on, and rollback makes a previous version live with no rebuild or redeploy; rolling back code on LangSmith Deployment redeploys an earlier revision.

By , CTO, Trampoline AI. Updated · Facts checked · Published by the Avalanche team. Avalanche competes with LangGraph.

01 / The core differenceAvalanche vs LangGraph and LangChain

A framework for building agents, or a workflow your business runs.

LangGraph is a low-level framework and runtime for stateful agents: you define nodes, edges and state, and checkpoints make the graph durable.[1, 2] LangChain adds a prebuilt agent loop, create_agent, and LangSmith adds tracing, evaluations, Studio and LangSmith Deployment.[5, 6, 12] Avalanche works a level higher: you describe the outcome and the few steps, checks and approvals that matter, and agents do the work in between, with each step checkpointed.[21, 25]

LangGraph is infrastructure for engineers building an agent: you design the graph, its state and its edges, and checkpoints make it durable, interruptible and replayable.[1, 2] LangSmith then shows what the agent did and scores it against datasets.[12, 13] Avalanche starts one level up, at the work a business repeats: you define the few steps, checks and approvals that matter, agents do the work in between, and every run keeps its version, its typed step outputs and a versioned workspace of its files.[21, 25] LangSmith is built to debug and evaluate an agent's behavior; Avalanche is built to compare the business result of each step across many runs and versions. The agent you build with LangGraph can be one of the things running inside an Avalanche step.[20]

02 / At a glanceChecked 2026-10-27

Avalanche and LangGraph, side by side.

Avalanche and LangGraph compared, checked 2026-10-27
CriterionAvalancheLangGraphEdge
Level of abstractionCoarse business steps: you set the steps, checks and approvals, and an agent does the work inside each step, including Claude Agent SDK agents, Agent Skills (SKILL.md), or a LangGraph agent run as plain Python.[20, 25, 26, 27]A low-level graph of nodes, edges and state that engineers design; LangChain create_agent and Deep Agents offer prebuilt agent loops on top.[1, 5, 39]Avalanche edge
Inspecting and comparing runsAvalanche Cloud: typed inputs and outputs at every step checkpoint, in one run history filtered by status, flow and deployment; a web console with a live DAG of every flow and run, the inputs and outputs of any step in one click, and the full console in your terminal, so you can compare a step across many runs and versions.[21, 25]LangSmith traces every model call, tool call and state transition, with dashboards, alerts and automations; Studio shows the graph, intermediate state and time travel.[12, 15]Different jobs
Evaluations and datasetsEvaluations on agent outputs and traces run locally or offline today; Avalanche Cloud does not score production runs automatically.[21]Datasets from curated cases or production traces; human, code, LLM-as-judge and pairwise evaluators; online evaluators on live traffic; experiment comparison with regressions and diffs.[13, 14]LangGraph edge
Human-in-the-loopAvalanche Cloud: checks every run must pass, and owner approval before results go out.[25]interrupt() pauses anywhere in a node, saves state and waits indefinitely; typed response schemas; approve, edit or reject tool calls in LangChain agents.[3, 34]LangGraph edge
Document lineageAvalanche Cloud: every run gets a versioned workspace; each step's file changes are committed as a new version, so you can see which step changed a file, compare versions and download any step's files; agent steps get PDF, Word and spreadsheet skills.[23, 25]Messages carry images, audio and files; Deep Agents give agents a filesystem kept in graph state (checkpointed per thread), a store, Context Hub or a sandbox.[32]Different jobs
Deploys, versions and rollbackAvalanche Cloud: each deploy is a frozen version of the whole workflow, mapped to an exact git commit, with locked, hash-verified dependencies; running workflows finish on the version they started on; instant rollback to any previous version from the CLI or console, no rebuild; an audit trail of who promoted or rolled back.[25]Code ships as revisions, each with a build step that can take minutes. The UI and the control-plane API roll back by redeploying an earlier revision, provisioned asynchronously; the docs do not say whether that skips the build, and some rollbacks require a rebuild. Assistant configurations (prompts, models, tools) roll back without a redeploy, but that does not roll back code. No documented pinning of running threads to their revision.[7, 8, 9, 36, 40, 41]Avalanche edge
Schedules, webhooks, CLI and APIAvalanche Cloud: cron schedules and webhooks declared in your workflow code, following rollbacks and promotions; webhook URLs never change on redeploy, redeliveries are deduplicated for 7 days, and idempotency keys return the same run for 7 days; a typed REST API with an OpenAPI spec, and a CLI.[25]Cron jobs on an assistant (UTC), webhooks that call you when a run completes, an Agent Server API for assistants, threads, runs and crons, and the LangGraph CLI.[10, 11, 37, 38]Similar
Identity and accessAvalanche Cloud: SSO; organization roles and per-action permissions, managed in your organization's settings; scoped, encrypted secrets; agents see only what each step passes them (its inputs, its files and the secrets it declares); steps run non-root in a sandbox, cut off from cloud metadata and private networks; an audit trail of promotions and rollbacks; no spend limits yet.[25]Organization roles on Plus; on Enterprise, SAML SSO, SCIM, custom workspace roles and ABAC, and audit logs kept up to 400 days; LLM Gateway cost controls.[16, 17, 18, 19]LangGraph edge
Ecosystem and availabilitySelf-hostable MIT-licensed runtime with operator, web console, REST API and terminal UI, more limited than Cloud; Avalanche Cloud is on a waitlist.[20, 28]MIT-licensed LangGraph and LangChain with 1000+ integrations; LangSmith Deployment on Cloud from the Plus plan, hybrid or self-hosted on Enterprise.[6, 30, 31]LangGraph edge
03 / Where each one winsOne claim per card

Where each one wins

Where Avalanche wins

  • Higher-level workflows

    You set the few business steps, checks and approvals that matter; an agent does real work inside each step. Python steps keep exact rules and arithmetic.[20, 25]

  • Bring your agents, including LangGraph

    Agent steps can run Claude through the Claude Agent SDK and load Agent Skills (SKILL.md), each sandboxed and checkpointed.[26, 27] A LangGraph or LangChain agent is Python, so it can run inside a Python step; it is plain code there, not a native agent runtime.[20]

  • Every step versions your documents

    Avalanche Cloud gives every run a versioned workspace. Pass one document or hundreds; each step works on the files directly, and every step's changes are committed as a new version of the workspace, so you can see which step produced or changed each file, compare versions, and download any step's files. Document lineage, the way data teams have table lineage. Agent steps get PDF, Word and spreadsheet skills.[23, 25]

  • Versioned deploys and instant rollback

    On Avalanche Cloud each deploy is a frozen, checksum-verified version of the whole workflow, mapped to an exact git commit. Running workflows finish on the version they started on, and instant rollback makes any previous version live again in one click or one command, with no rebuild. Avalanche Cloud is on a waitlist.[25, 28]

  • Runs your business team can compare

    Each step is a business result with typed outputs, such as an RFP answer or a contract finding. Avalanche Cloud keeps every run by workflow and version, so you can compare one step across many runs.[21, 25]

  • From a skill to a workflow

    Install the Avalanche skill with npx skills add Trampoline-AI/avalanche, describe the outcome in your coding agent, and it drafts the workflow with you.[22]

Where LangGraph wins

  • Control over every node

    LangGraph mixes hand-coded steps and LLM-driven steps in one graph and does not abstract prompts or architecture, so engineers decide exactly where a model is used.[1]

  • Interrupts anywhere

    Call interrupt() in any node to pause the graph, save its state and wait indefinitely for a human; resume with a typed response schema. LangChain agents can approve, edit or reject tool calls.[3, 34]

  • Time travel

    Replay a thread from any earlier checkpoint, or fork it with changed state to try another path, without losing the original history.[4]

  • Tracing and evaluations in LangSmith

    LangSmith traces every run, runs offline evaluations on datasets and online evaluators on production traces, and compares experiments with regressions and side-by-side diffs.[12, 13, 14]

  • Managed or self-hosted deployment

    LangSmith Deployment runs agents on LangChain Cloud, in a hybrid setup in your VPC, fully self-hosted, or as a standalone server, with cron jobs and an Agent Server API.[6, 10, 37]

  • Versioned assistant configurations

    Assistants keep prompts, models and tools separate from graph code; every edit is a new version you can promote or roll back without redeploying.[7]

  • A very large ecosystem

    LangChain lists 1000+ integrations across models, tools, document loaders and vector stores, and LangSmith traces agents from many other frameworks.[12, 31]

04 / Detailed comparison4 buyer questions

What changes for your team.

01

An agent framework or a business workflow?

Buyer question

Are you building an agent, or running a process your team repeats every week?

LangChain's own docs separate the layers: LangChain is the agent framework, LangGraph is the orchestration runtime, Deep Agents is a ready-made harness, and LangSmith is the platform for tracing, evaluation and deployment.[1, 39] LangGraph is deliberately low-level: you build a graph of nodes and edges over shared state, mixing hand-coded steps with LLM-driven ones, and LangChain's create_agent gives you a configurable model-and-tools loop on top.[1, 5]

Avalanche is built for long, multi-step business work that agents do, such as drafting RFP answers from past proposals or reviewing a stack of contracts. You set the few steps, checks and approvals that matter, agents do the work in between, and simple workflows fit the same model. On Avalanche Cloud, workflows run for hours or days.[25] Inside a step, the agent can be a Claude Agent SDK agent with Agent Skills, or a LangGraph or LangChain agent you already have, called as ordinary Python.[20, 26, 27]

02

Inspecting and evaluating runs: where LangSmith is strong

Buyer question

How will you know whether the agent got better or worse after a change?

LangSmith is a mature answer to this question. It traces each run of an agent, model calls, tool calls and state included, and lets you build dashboards and alerts on top.[12] Its evaluations score outputs against datasets with human, code, LLM-as-judge or pairwise evaluators, run online evaluators on production traces, and turn a failing trace into a dataset example. The experiment comparison view highlights regressions and improvements and shows side-by-side diffs between versions.[13, 14] Pricing counts traces: 5k base traces a month on the free Developer plan and 10k on Plus, with base traces kept 14 days and extended traces 180 days.[16]

Avalanche compares a different unit. Each step of an Avalanche workflow is a business result with typed outputs, kept at its checkpoint for every run, and Avalanche Cloud keeps one run history by workflow and deployment, so you can compare one step across hundreds of runs and see which outputs drift or which version changed them.[21, 25] Avalanche's evaluations run locally or offline today, not as automatic scoring of Cloud runs.[21] If scoring agent behavior against datasets is the job, LangSmith is ahead; teams can trace a LangGraph agent in LangSmith and run it inside an Avalanche step.

03

Human input, recovery and time travel

Buyer question

What happens when a person must weigh in, or a step fails halfway?

LangGraph's checkpointer saves graph state at every super-step. If a node fails, the graph restarts from the last successful step, and nodes that already finished in that step are not re-run.[2] Durability is a choice: sync writes every checkpoint before the next step, while the default async mode carries a small risk of a lost checkpoint if the process crashes.[2, 37] Interrupts pause a node anywhere and wait indefinitely for a human, and time travel replays or forks a thread from any earlier checkpoint.[3, 4] This is a strong human-in-the-loop model for agents built in code.

Avalanche records completed steps durably and never re-runs them after a crash; failed steps retry automatically, and uncertain outcomes are held instead of blindly retried.[25] Each workflow gets checks every run must pass and owner approval before results go out on Avalanche Cloud.[25] The approval point sits between business steps rather than inside any line of agent code.

04

Deploys, versions and running in production

Buyer question

What happens to running work when you ship a change, or need to undo one?

LangSmith Deployment runs LangGraph agents, and agents from other frameworks such as the Claude Agent SDK, on its Agent Server, with a task queue, built-in persistence, assistants, threads, runs and cron jobs.[6, 37] Code ships as revisions to a deployment; each revision includes a build step that can take a few minutes, and revisions are provisioned asynchronously, which can take several minutes.[8, 9] To undo a code change, the LangSmith UI offers a rollback and the control-plane API has a redeploy call for a specific earlier revision; both work by redeploying that revision.[36, 41] The docs do not say whether a rollback reuses the earlier image, and scale-to-zero deployments send rollbacks to older revisions back to a rebuild.[41] Assistants version configuration separately: every edit to prompts, models or tools is a new version, and setting an earlier version active takes effect for future runs without a redeploy. That is a fast rollback of configuration, not of code.[7] Preview builds for pull requests are in public beta.[35] We found no statement that a running thread stays on the revision it started on. When a server shuts down, background runs get a grace period (180 seconds by default), then are re-queued and resume from their last checkpoint, possibly on another replica.[40, 42]

On Avalanche Cloud, a deploy is a frozen version mapped to an exact git commit, with locked, hash-verified dependencies. Running workflows finish on the version they started on, and you roll back to any previous version in one click or one command, with no rebuild; an audit trail records who rolled back.[25] For identity and access, LangSmith Enterprise is ahead: SAML SSO, SCIM, custom roles, ABAC and audit logs.[17, 18, 19] Avalanche Cloud has SSO and organization roles with per-action permissions, but no spend limits yet, and it is on a waitlist.[25, 28]

05 / FAQDirect answers

Common questions.

Is Avalanche an alternative to LangGraph?
For business workflows, yes; for building a custom agent, they fit together. LangGraph is a low-level framework for agents as graphs of nodes and state.[1] Avalanche runs the workflow around the agents: a few business steps with checks and approvals, versioned deploys and typed step outputs per run, in MIT-licensed Python.[20, 25]
Is Avalanche an alternative to LangChain?
Not one for one. LangChain is an agent framework with a prebuilt agent loop, create_agent, and 1000+ integrations.[5, 31] Avalanche is a workflow runtime: agents work inside its steps, and it does not try to replace LangChain's integrations.[20]
Can I run a LangGraph or LangChain agent inside Avalanche?
Yes, as plain Python. A Python step can call any library, so a LangGraph graph or a LangChain agent can run inside a step and return typed outputs to the workflow.[20] It runs as your code, not as one of Avalanche's native agent runtimes, which are PredictRLM and the Claude Agent SDK.[26]
Does LangGraph support human-in-the-loop approvals?
Yes. interrupt() pauses a graph anywhere, saves its state with a checkpointer and waits indefinitely until you resume it with a Command; a response schema can type the answer. LangChain agents can approve, edit or reject tool calls with human-in-the-loop middleware.[3, 34]
Can LangSmith Deployment roll back to a previous version?
Partly. Assistant configurations (prompts, models, tools) are versioned, and setting an earlier version active applies to future runs without a redeploy.[7] For code, the LangSmith UI and the control-plane API roll back by redeploying an earlier revision; revisions are provisioned asynchronously, the docs do not say whether a rollback skips the build, and some scale-to-zero rollbacks require a rebuild.[9, 36, 41] On Avalanche Cloud, rolling back makes a previous frozen version live for new runs in one click or one command, with no rebuild, while running workflows finish on the version they started on.[25]
How is LangSmith priced?
As of October 27, 2026: Developer is $0 for one seat with 5k base traces a month; Plus is $39 per seat a month with 10k base traces and one free small serverless deployment; Enterprise is custom. Extra usage, including deployment compute and memory, is billed in LangChain Standard Units, where 1 LSU is $1.[16]
How fast is Avalanche Cloud?
Measured October 27, 2026 on Avalanche Cloud v0.6.6, at the median (p50): a hello-world workflow ran end to end in 4.4 s, creating a run took 1.2 s, and each step started in 1.13 s. The web console loads cold in under 1 s. These are our own measurements of Avalanche Cloud, not a benchmark against LangSmith Deployment.[25]
06 / SourcesChecked 2026-10-27

Public primary sources.

Every source was fetched on the date shown. Prices and plans change; check the vendor before you buy.

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  4. [4]Use time-travel (LangGraph)LangChain docs · Checked https://docs.langchain.com/oss/python/langgraph/use-time-travel
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  9. [9]Control plane (LangSmith Deployment)LangChain docs · Checked https://docs.langchain.com/langsmith/control-plane
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See the runtime for yourselfOpen source · MIT

Our code speaks for itself.

The Avalanche runtime is open source under the MIT License. Read the code, run the starter project, and see how it fits the work your team repeats.