Bardeen vs Gumloop (2026)

Stands at F. Connectors are the primary configuration surface rather than an afterthought, with the vendor stating outright that what you connect determines what the agent can access, and the picker separating Gumloop-managed connectors from custom MCP servers the customer has added. That second tab is the important one for breadth: anything with an MCP server becomes a connector without waiting for Gumloop to build it, and the vendor's own framing is that an MCP integration can be built from a natural-language prompt. Tool calling is governed rather than open: a Tool Discovery toggle controls whether the agent finds tools itself, and an integration can be restricted to a subset of its tools, so breadth and restraint are configured separately. Connectors authenticate by API key as well as OAuth, which matters for internal systems that never had an OAuth app. Skills can call connected integrations directly from Python in the sandbox through the gumcp-client library, so integrations are reachable from code as well as from tool calls.

Stands at F, and the distinguishing feature is that orchestration runs in BOTH DIRECTIONS between deterministic workflows and reasoning agents. Subagents let an agent delegate to other agents rather than doing everything in one conversation, which is documented multi-agent composition. In the other direction an Agent node drops into a workflow, so a deterministic pipeline can enrich data, hand judgment to an agent, and route on the result, and Loop Mode runs an agent across a batch with the vendor noting the sharp edge that each iteration starts a new conversation unless conversation IDs are tracked explicitly. That candour about a real footgun is a good sign. Underneath, the canvas provides branching, conditional logic, loops and subflows, and subflows are reusable components callable from any other flow and testable independently, which is the closest thing in this lane to a function abstraction.

Stands at F and is among the strongest Trig cells reviewed, on one property nothing else in this lane has: THE AGENT CAN AUTHOR ITS OWN TRIGGERS. An AI Managed toggle lets an agent create, edit and manage its own triggers and schedules during a conversation, and a Create With AI path has the agent write a custom polling trigger class that queries each connected service for real tool names and schemas rather than guessing, validates the code in an isolated sandbox against live data, and records a baseline on first run so later polls detect only genuine changes. That covers combinations no pre-built trigger handles. Schedules are described in plain language and converted by an AI scheduler, so no cron is written. All three classes are otherwise well covered: schedules, per-integration events, and per-agent webhook URLs. Two operational details worth carrying: repeated trigger failures auto-disable the trigger with a notification, and Outlook triggers can fire relative to a calendar event, before, at start, or after it ends.

P>F, the third correction on this record, and it rests on a product surface the record never mentioned: COMPANY BRAIN. Sources are attached whole or drilled into at file and folder level, kept synced, and searched automatically when a question touches internal knowledge, with the agent able to open a full document for more context. The chat surfaces this as Searching Company Brain, so retrieval is a visible first-class step rather than a prompt stuffing. Sync coverage keeps widening, with call recaps and transcripts added recently. That is a maintained retrieval structure with a lifecycle, the same bar that took zapier's knowledge sources to F this session. WORTH RECORDING FOR THE LANE: Brain appears in the product navigation beside Skills, and on last turn I flagged it as possibly the memory feature. It is not. Brain is the KNOWLEDGE surface, which means the Mem grade on this record rests on Skills alone and not on Brain, and the two cells are not double-counting one fact.

P>F, and this needs care because THE VENDOR ITSELF DISCLAIMS THE WORD MEMORY. The docs say twice that skills can feel like memory but technically are not remembering your conversation, and that the agent must reload the skill in future conversations. That disclaimer is about conversation transcripts, and it is quoted in the basis so nobody reading this cell thinks I confused the two. What the axis measures is whether state accumulates and whether the accumulation survives, and on that test skills pass clearly. The agent edits and creates its own skills, enabled by default, so a correction in one conversation is written back and applied in the next. The vendor documents the trajectory explicitly as a compound effect running from a basic template on day one to a full playbook by month six, with file-level version history showing which agent made each change. That is durable learned state surviving across sessions, and it is more than mindstudio's cross-run change detection, which I graded P precisely because nothing learned from prior runs. CONFIDENCE MEDIUM for two honest reasons. The vendor's own framing cuts against the grade even though the capability meets the bar, so a later re-level could reasonably disagree. And the product navigation shows a BRAIN surface alongside Skills which I did not verify; if Brain is conversational memory it would only strengthen this cell, but it is not relied on here.

Stands at F, and the June re-grade that put it here is confirmed on wider first-party evidence than the changelog entry it originally cited. Three distinct mechanisms. A runtime approval queue: agents raise pending approval and input requests, and a recent change widened who can reject them from only the named recipient to anyone who can chat with the agent, which is a small detail that only exists once approvals are genuinely load-bearing. A policy gate: enterprise controls require human approval before EXPENSIVE actions, with per-agent, per-team and org-wide budgets, which is an unusual axis to gate on and complements permission-based gating rather than duplicating it. And a permission gate: per-tool authorization, restriction of which tools an integration may expose, and connectors blocked by organization policy showing as restricted with request-access on the spot. Skill creation itself can be restricted by custom role. Together these cover approve, constrain and escalate, which is the full shape this axis looks for.

Stands at F and is a strong one. The attestation half is met with SOC 2 Type II and GDPR and a trust centre. The control half is unusually deep because Gumstack was built for exactly this: role- and attribute-based access, PER-TOOL authorization rather than per-integration, every call tied to a specific user, agent or service principal, and central secret management with the vendor making a point that keys never sit in plaintext on a laptop. A LIVE INVENTORY OF EVERY MCP SERVER IN USE is the rarest item here and the one worth carrying to comparison pages, because shadow MCP servers are the current version of shadow IT and almost nothing else in this lane claims to enumerate them. Governance extends past security into spend, with per-agent, per-team and org-wide budgets and quota controls. Identity integrates with the customer's own provider, extending their system of record to agents. Recorded per the 30 August ruling: VPC deployment is a delivery property and is carried on Dep, not credited here.

Stands at F and the scope is wider than the axis usually sees. Gumstack traces every tool call through a single logging and analytics layer, ties each call to a specific user, agent or service principal, and captures audit trails showing where data is flowing. That is the why rather than the what. Two properties are genuinely distinctive. It reaches AGENTS OUTSIDE GUMLOOP, giving one audit surface across third-party AI tools as well as the vendor's own, which is a materially different proposition from logging your own runs. And cost is a first-class dimension alongside actions, with real-time organization-wide credit tracking and per-agent and per-team attribution, so the question who spent what is answerable rather than inferred. Agents can pull credit usage and run history reports themselves through data export tools. Skill-level observability sits underneath, with file-level version history naming which agent made each edit and an activity timeline of views, runs and edits.

Stands at F, and residency is answered properly rather than gestured at. The vendor offers managed SaaS or deployment into the customer's own cloud as a VPC IN THE REGION OF THEIR CHOICE, with the data staying in infrastructure the customer controls, and states that for private infrastructure it deploys and operates the full stack inside that cloud. Region selection plus VPC is exactly what the section 7 rule names as residency rather than legal cover, and the operated-by-vendor framing means the customer is not taking on the upgrade and patching burden that self-hosting normally implies, which is the trade-off against n8n's open-source route. Gumstack can likewise run on Gumloop infrastructure or inside the customer's VPC, so the governance layer does not force data back out. A second fact reinforces it: inference for open-weight models runs through US-based providers under Zero Data Retention, so the model layer does not quietly relocate data either. Useful contrast for comparison pages: this is managed-cloud plus VPC, not open-source self-hosting.

Stands at F and is a clean case under the 31 August wider bar, adopted by several routes at once. A template library ships prebuilt flows, and the vendor's own onboarding advice is to start from a template rather than a blank canvas. Subflows are reusable components shareable across a team, so an organisation accumulates its own adoptable assets. The strongest route is the skills library: skills are packaged folders with instructions, scripts, references and assets, downloadable and uploadable as files, shareable with four distinct roles including a USE ONLY role that lets a colleague run a skill without seeing its contents, which is a genuinely thoughtful packaging primitive for proprietary process. Admins can maintain an organisation-wide skill library kept in sync with a GitHub repository, and the vendor points users at public example skill repositories, noting compatibility with the Claude Skills packaging convention. Two built-in system skills ship with every team.

Stands at F. Model choice is a per-agent setting at the top of configuration, with three presets for people who do not want to choose and the full roster for those who do, and the vendor commits to adding new models within a day of public release. Two details are unusual enough to carry. First, TOOL CALLING IS THE SELECTION CRITERION: every model in the agent picker supports tool calling, and models that do not are confined to workflow AI nodes, so the picker cannot be used to pick a model that will silently fail as an agent. Several open-weight models run the other way and are agent-exclusive. Second, open-source models are routed through US-based providers under Zero Data Retention with a US-provider hosted badge in the picker, and the vendor states data is neither used for training nor stored after inference, which turns model choice into a decision the buyer can see at selection time. Enterprise governance adds control over which models teams may use, so this is also an administrative surface.

Stands at F but at medium confidence, and the gap is named rather than papered over. What is solidly documented is inbound and protocol extensibility: each agent gets a unique webhook URL so any external system can invoke it with a payload, custom MCP servers are added as connectors, enterprise entitlements include MCP server hosting, and Gumstack maintains a live inventory of MCP servers in use. Skills are portable artifacts, downloadable and uploadable as .zip or .skill packages, with organization libraries syncable from a GitHub repository, and a code sandbox runs Python and JavaScript with an integration client library. WHAT I DID NOT VERIFY is a documented public REST API or SDK for driving the platform programmatically, which is the primary route under Mike's 30 August bar; the webhook surface carries the grade instead, with MCP as corroboration exactly as that ruling specifies. A docs page for a Gumloop API would take this to high confidence, and its absence would be worth knowing too.

Stands at P. The iteration surface is real and in places better than several vendors graded higher: skills validate on save with warnings for malformed frontmatter and oversized instructions, a Local Changes diff view shows unsaved edits across files before committing, an Edits tab gives file-level version history with rollback, subflows can be tested independently before composition, and AI-created triggers are validated in an isolated sandbox against live data before being saved. The vendor is also unusually frank about failure modes, documenting concurrent-edit races and the fact that skill changes are lost if a conversation ends abnormally, which is the kind of thing that helps a builder debug. What is absent is measurement. No evaluation product, no scoring, no test-case generation, no regression suite, and nothing that compares one agent version against another, which is the F bar that n8n, lyzr and stackai meet. Gumloop is strong on validation and iteration and silent on evaluation, and the P should be read that way rather than as a weak debugging story.

N>F, and this is the clearest positive case for the axis after stackai. The vendor's own extension listing states the purpose in the exact terms this axis measures: automate tasks on websites that DON'T OFFER API ACCESS, going beyond traditional API limitations, and if you can do it in your browser you can automate it. Record-and-replay of real clicks, typing and form entry is the deterministic replay mode credited on stackai's browser navigation, and a Web Replay is referenceable from an ordinary Gumloop automation, so browser operation composes into workflows rather than sitting apart as a toy. WHY THIS IS NOT THE MINDSTUDIO REFUSAL. I refused mindstudio's Chrome extension because it supplies page content to the agent, so the agent reads what a human is looking at and never drives anything. Gumloop's records and replays the user's own actions on the page and fills forms. One observes, the other operates, and that is the whole distinction. CONFIDENCE MEDIUM AND THE REASONS ARE RECORDED. The strongest evidence is a store listing rather than a documentation page, the listing calls itself an early release, and a vendor forum thread indicates the related web automation node is access-gated, so the tier at which this is purchasable was not established. Under the 31 August tier line it is graded because it is purchasable at some tier, with the gating noted. The docs page for the web automation node would take this to high confidence.