Fixed steps and checks around your agents, in code your team owns.

Quick answer

Updated October 27, 2026. Choose Gumloop if you want a hosted platform where people who do not write code build AI agents with SKILL.md skills, workspace files, 300+ integrations, built-in evaluations, full audit logs and overage caps; its visual Workflows are legacy and unavailable to new accounts.[1, 2, 12, 13, 16] Choose Avalanche if you want multi-step agentic workflows that run for hours or days at a higher level: you fix the few steps, checks and owner approvals that matter, agents do the work inside each step, and every run keeps typed outputs at each step checkpoint, so you can compare a step across runs and versions instead of reading one transcript per task.[21, 25, 29] Avalanche Cloud adds frozen versions and instant rollback of the whole workflow, with no rebuild, and the runtime is MIT-licensed Python.[20, 25]

Key takeaway

Choose Avalanche when agent work should run inside fixed, checkpointed steps, as versioned Python your team reviews in git.

Choose Gumloop when people who do not write code build agents, with integrations, full audit logs and spend limits ready today.

  • Avalanche wins on structure around agent work: the steps, checks and approvals are fixed in code and each step is checkpointed; agents work inside them.
  • Avalanche wins on comparing a step across runs: every run keeps typed outputs at each fixed step, so a step can be compared across runs and versions; a Gumloop task is a transcript of one agent's path.
  • Avalanche wins on versions and rollback: Avalanche Cloud pins running workflows to their version and rolls back in one click; Gumloop agent versions can be exported but not restored through its API or CLI.
  • Gumloop wins on no-code building and integrations: anyone can build an agent from chat or the builder, with 300+ native integrations.
  • Gumloop wins on full audit logs and spend limits: Enterprise audit logs and org overage caps ship today; both sides offer SSO and organization roles, and Avalanche Cloud has no spend limits yet.

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

01 / The core differenceAvalanche vs Gumloop

One agent deciding its path, or agents inside steps you fix.

Gumloop keeps agents, their instructions, skills, triggers and credentials inside its platform and meters their work in credits.[1, 8] Avalanche keeps the workflow in your repository: you set the few steps, checks and approvals that matter, agents do the work in between, and every step is checkpointed.[20, 21]

In Gumloop, an agent with instructions, connectors and skills decides which tools to use for each task, and it can edit its own instructions.[1] In Avalanche, the steps, checks and approvals are fixed in code, agents do the open-ended work inside each step, and every step is checkpointed.[20, 21] In our view, a fixed structure is what makes long, document-heavy work repeatable and reviewable: a Gumloop task leaves a transcript of the path one agent took, while every Avalanche run has the same steps with typed outputs, so you compare runs instead of reading them one at a time.[29]

02 / At a glanceChecked 2026-10-27

Avalanche and Gumloop, side by side.

Avalanche and Gumloop compared, checked 2026-10-27
CriterionAvalancheGumloopEdge
Structure around agent workFixed steps, checks and approvals in code; an agent works inside each step, and each step is checkpointed.[20, 21]One agent per task picks its tools within its instructions and connectors; visual Workflows are legacy and closed to new accounts.[1, 12]Avalanche edge
Comparing a step across 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]Task history with transcripts and tool calls; Agent Performance charts outcomes and evaluations per agent.[29, 6]Avalanche edge
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]Agent versions have IDs and diffs, but the API and CLI cannot restore one; self-edited instructions have no version history.[1, 10, 11]Avalanche 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]Persistent team and personal workspace folders; generated artifacts keep a version history, with PDF, image, spreadsheet and DOCX support.[4, 5, 23, 25]Different jobs
Agent Skills (SKILL.md)Agent steps load SKILL.md skills, and the Avalanche skill turns an outcome into a workflow.[22, 27]Native SKILL.md skills with scripts and references, edited in chat and synced from GitHub.[2, 3]Similar
No-code building and integrationsAny API, SDK or Python library: steps are plain Python with scoped, encrypted secrets and environment variables, so integrations are not limited to a catalog; written by developers or drafted by a coding agent with the Avalanche skill; no prebuilt connector catalog.[22]Build agents in chat or the builder; 300+ native integrations plus MCP connections.[1, 16]Gumloop edge
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]Roles on Pro and above; SAML, OIDC and SCIM, and full audit logs on Enterprise; org overage caps.[8, 13, 14, 15]Gumloop edge
Open source and where it runsSelf-hostable MIT-licensed runtime with operator, web console, REST API and terminal UI, more limited than Cloud; Avalanche Cloud is on a waitlist.[20, 24, 28]Closed, credit-metered service; Enterprise VPC deployment in your own cloud.[8, 17, 18]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]Recurring cron, one-time and app-event triggers, and secret-URL webhooks start agents; an API, Python SDK and CLI.[19, 11, 30]Similar
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 the open-ended work inside each step, and Python steps keep exact rules.[20, 25]

  • Compare a step across runs

    Every run keeps typed inputs and outputs at each step checkpoint, and Avalanche Cloud holds every deployed run by workflow and version. See which step fails, which outputs drift and what a new version changed, across hundreds of runs.[21, 25]

  • Step checkpoints

    A failed step retries while finished steps keep their accepted outputs, so a run that takes hours or days does not start over.[21, 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]

  • From a skill to a workflow

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

  • Open source you own

    MIT-licensed Python, versioned in git and reviewed in pull requests, with the model provider you choose.[20]

  • Checkpointed Claude agent steps

    Agent steps can run Claude through the Claude Agent SDK and load Agent Skills (SKILL.md), each sandboxed and checkpointed like any other step.[26, 27]

Where Gumloop wins

  • Anyone can build an agent

    Agents and their skills are created and edited from chat or the agent builder, without writing code.[1, 2]

  • Skills and workspaces

    Native skills are SKILL.md folders with scripts and references, synced from GitHub; team and personal workspace files persist across chats.[2, 3, 4]

  • Task history and performance

    Each task keeps its transcript and tool calls, and Agent Performance charts usage, task outcomes and evaluations over time.[29]

  • Built-in evaluations

    An evaluator grades completed agent tasks against your criteria, with reevaluation and backfill of past tasks.[6]

  • Audit logs, overage caps and VPC

    Full audit logs on Enterprise, org overage caps, and VPC deployment in your own cloud, alongside roles and SAML, OIDC and SCIM sign-in.[8, 13, 14, 15, 17]

04 / Detailed comparison5 buyer questions

What changes for your team.

01

One agent per task, or steps with agents inside?

Buyer question

Should an agent decide the whole path, or work inside steps you fix?

Gumloop's current product is agents. An agent combines a model, instructions, connectors, skills and subagents; triggers start it from schedules, app events or webhooks; and channels put it in email, Slack, Microsoft Teams, the API and MCP.[1, 19] The older visual Workflows builder is legacy: a Gumloop staff reply says newly created accounts cannot use it.[12]

An Avalanche workflow is a Python module in your repository. Python steps keep exact rules and arithmetic; agent steps declare typed inputs and outputs, tools and skills, and do the open-ended work between them.[20, 23] Every step is checkpointed, so a failed step retries without redoing finished work, and on Avalanche Cloud an owner can approve results before they go out.[21, 25]

02

A transcript per task, or runs you can compare

Buyer question

When an agent runs the same job hundreds of times, how do you see what changed?

Gumloop gives you good per-task visibility: each task keeps its transcript and tool calls, and Agent Performance charts usage, cost, task outcomes and evaluation results for an agent.[29] Its evaluator grades full transcripts after a task completes.[6] Because one agent picks its own path each time, each task is a trajectory to read rather than a set of fixed steps to line up.

In Avalanche the steps are the same on every run, and each step checkpoint keeps typed inputs and outputs. Avalanche Cloud holds every deployed run by workflow and version, so you can open step three on one run or compare it across a thousand: which step fails most, which outputs drift, what a new version changed.[21, 25] It is the shift data teams made from one-off queries to managed transformations in a warehouse such as Snowflake or Databricks.

Sources [6, 21, 25, 29]

03

Changes, versions and rollback

Buyer question

How does a change reach production, and how do you undo it?

Gumloop records agent versions with IDs and structured diffs, and you can export a version through the API or CLI; neither can restore or deploy one.[10, 11] Agents can also edit their own instructions after a correction: the setting is on by default, and the docs say there is no version history for those edits, so you revert by hand.[1]

In Avalanche, a change to a step, check or agent contract is a code change, reviewed in a pull request. On Avalanche Cloud each deploy is a frozen version, running workflows keep the version they started on, and you roll back to any previous version in one click, with no rebuild.[20, 25]

04

Where Gumloop is ahead today

Buyer question

What does Gumloop give you that Avalanche does not?

Gumloop ships native SKILL.md skills synced from GitHub, persistent workspaces, a built-in evaluator for completed tasks, tool approvals and rules, and 300+ native integrations.[2, 3, 4, 6, 7, 16] Beyond roles and SAML, OIDC and SCIM sign-in, it has full audit logs on Enterprise and org overage caps.[13, 14, 15, 8]

Avalanche does not have a connector catalog, a full audit log or spend limits. Avalanche Cloud has SSO, organization roles and per-action permissions, and records who promoted or rolled back each deployment. Its evaluations run locally or offline, not automatically in production.[20, 25]

05

Credits and ownership

Buyer question

What do you pay for, and what do you keep if you leave?

Gumloop meters agent work in credits: model token cost, successful tool calls, active session minutes and an orchestration fee.[8] Pro organizations reject requests at their concurrency limit, while Enterprise queues them.[9] Enterprise data exports cover configurations and logs, not a workflow another runtime can run.[24]

Avalanche workflows are already Python in your repository under the MIT license, and the open-source runtime has no usage meter.[20] Avalanche Cloud is on a waitlist.[28]

05 / FAQDirect answers

Common questions.

Is Avalanche an open-source alternative to Gumloop?
For teams that want agentic workflows as code they own, yes: Avalanche is an MIT-licensed Python runtime.[20] It is not a like-for-like replacement, because it has no no-code builder, connector catalog, full audit log or spend limits, which Gumloop provides.[1, 8, 15, 16]
Can new Gumloop accounts use Workflows?
No. A Gumloop staff reply on its forum says newly created accounts, including new Pro accounts, cannot use the legacy Workflows builder; existing accounts keep access. New accounts build agents.[12]
Can Gumloop agents be version controlled?
Partly. Gumloop records agent versions with diffs that you can export, but the API and CLI cannot restore them, and agent self-edits to instructions have no version history.[1, 10, 11] Avalanche workflows are Python in git, and Avalanche Cloud rolls back to any previous version in one click.[20, 25]
Does Gumloop support Agent Skills?
Yes. Gumloop skills are SKILL.md folders with scripts, references and assets, and organization skills can sync from a GitHub branch.[2, 3] Avalanche agent steps load SKILL.md skills too.[27]
How is Gumloop billed?
In credits, as of October 27, 2026. Agent work spends credits on model token cost, successful tool calls, active session minutes and an orchestration fee; bringing your own key changes the model component.[8]
Can Gumloop run in my own cloud?
Yes, on Enterprise. Gumloop offers VPC deployment in your own cloud, and its docs say VPC organizations do not pay Gumloop compute credits for their own infrastructure.[8, 17]
Can I see which step changed a document?
Yes, on Avalanche Cloud. Every run gets a versioned workspace, and each step's changes are committed as a new version, so you can see which step produced or changed each file, compare versions and download any step's files.[25] Gumloop keeps persistent workspace folders and a version history for generated artifacts.[4, 5] The difference is the unit: Avalanche versions the whole workspace at each workflow step, tied to the run.
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.

  1. [1]Agents (Gumloop docs)Gumloop · Checked https://docs.gumloop.com/core-concepts/agents.md
  2. [2]Skills (Gumloop docs)Gumloop · Checked https://docs.gumloop.com/core-concepts/skills.md
  3. [3]Organization skills (Gumloop docs)Gumloop · Checked https://docs.gumloop.com/core-concepts/organization_skills.md
  4. [4]Agent sandbox and secrets (Gumloop docs)Gumloop · Checked https://docs.gumloop.com/core-concepts/agent_sandbox_and_secrets.md
  5. [5]Agent artifacts (Gumloop docs)Gumloop · Checked https://docs.gumloop.com/core-concepts/agent_artifacts.md
  6. [6]Evaluations (Gumloop docs)Gumloop · Checked https://docs.gumloop.com/core-concepts/evaluations.md
  7. [7]Human in the loop (Gumloop docs)Gumloop · Checked https://docs.gumloop.com/core-concepts/human_in_the_loop.md
  8. [8]Credits (Gumloop docs)Gumloop · Checked https://docs.gumloop.com/core-concepts/credits.md
  9. [9]Rate limits (Gumloop docs)Gumloop · Checked https://docs.gumloop.com/core-concepts/rate_limits.md
  10. [10]Retrieve agent version (Gumloop API reference)Gumloop · Checked https://docs.gumloop.com/api-reference/agents/retrieve-agent-version.md
  11. [11]Agents CLI (Gumloop docs)Gumloop · Checked https://docs.gumloop.com/cli/agents.md
  12. [12]Are legacy workflows unavailable for new accounts? (staff reply)Gumloop forum · Checked https://forum.gumloop.com/t/are-legacy-workflows-unavailable-for-new-accounts/3675
  13. [13]Organization user roles (Gumloop docs)Gumloop · Checked https://docs.gumloop.com/core-concepts/organization_user_roles.md
  14. [14]SSO, SAML, OIDC and SCIM (Gumloop Enterprise preview)Gumloop · Checked https://docs.gumloop.com/previews/enterprise-features/sso_saml_oidc_scim.md
  15. [15]Audit logging (Gumloop Enterprise preview)Gumloop · Checked https://docs.gumloop.com/previews/enterprise-features/audit_logging.md
  16. [16]About GumloopGumloop · Checked https://www.gumloop.com/about
  17. [17]Gumloop securityGumloop · Checked https://www.gumloop.com/solutions/security
  18. [18]Gumloop terms of serviceGumloop · Checked https://www.gumloop.com/tos
  19. [19]Agent triggers (Gumloop docs)Gumloop · Checked https://docs.gumloop.com/core-concepts/agent_triggers.md
  20. [20]Avalanche repository and README (MIT License)Trampoline AI · Checked https://github.com/Trampoline-AI/avalanche
  21. [21]Results, cancellation, and reruns (Avalanche docs)Avalanche docs · Checked https://avalanche-docs.docsalot.dev/build-workflows/results-and-reruns
  22. [22]Build a workflow with the Avalanche skill (Avalanche docs)Avalanche docs · Checked https://avalanche-docs.docsalot.dev/build-workflows/build-with-skill
  23. [23]Skills for agent steps: PDF, DOCX and spreadsheet (Avalanche docs)Avalanche docs · Checked https://avalanche-docs.docsalot.dev/agent-steps/skills
  24. [24]Export data API (Gumloop docs)Gumloop · Checked https://docs.gumloop.com/api-reference/organization/export-data.md
  25. [25]Avalanche and Avalanche Cloud overviewAvalanche · Checked https://avalanche.run/
  26. [26]Pull request #124: Claude Agent SDK agent stepsTrampoline AI on GitHub · Checked https://github.com/Trampoline-AI/avalanche/pull/124
  27. [27]Pull request #136: Agent Skills (SKILL.md) in agent stepsTrampoline AI on GitHub · Checked https://github.com/Trampoline-AI/avalanche/pull/136
  28. [28]Avalanche Cloud waitlistAvalanche · Checked https://cloud.avalanche.run/waitlist
  29. [29]Agent performance (Gumloop docs)Gumloop · Checked https://docs.gumloop.com/core-concepts/agent_performance.md
  30. [30]Python SDK (Gumloop docs)Gumloop · Checked https://docs.gumloop.com/sdk/python.md
Related comparisonsView all comparisons
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.