Sim vs StackAI (2026)

Stands at F on an enumerated catalogue of roughly ninety individually documented app nodes, each with its own actions, inputs and outputs, which makes this checkable rather than a headline number. Breadth across classes is emphatic and skews enterprise in a way that distinguishes it from the prosumer catalogues elsewhere in this lane: SAP, NetSuite, Oracle, Workday, ServiceNow, Snowflake, Databricks and Egnyte are not integrations a no-code tool aimed at individuals carries. Eight database and warehouse connectors with natural-language-to-SQL querying is a second distinguishing cluster. The MCP node means anything exposed as an MCP server becomes callable too, so the ceiling is open. Recorded per section 7: the Zapier and Make nodes are StackAI reaching out to those platforms, which counts as its own integration breadth here and is not evidence of StackAI's extensibility, graded separately on Ext.

Stands at F. Both halves are documented. Multi-step control flow is complete: AI Routing for model-decided branching, If/Else for deterministic branching, Loop Subflow for iteration, Delay for pacing, and Code and Python nodes as escape hatches. Multi-agent is genuine and works two ways, which is the part worth carrying: Subflow Tools let an AI Agent node call another flow AS A TOOL, so a supervising agent selects among specialist subflows at runtime, and the StackAI Project Node lets one project invoke another as a unit. A dedicated Orchestrating AI Agents guide sits in the tips tree, and Handling Errors and Fallback plus Skip and Replace Node give the reliability affordances that separate a production orchestrator from a demo canvas.

P>F. Channel coverage is the strongest part and is documented page by page rather than claimed: eight distinct end-user surfaces, which is wider than anything else reviewed in this lane. Events are covered by a dedicated Trigger node plus app-level triggers such as inbound email and inbound webhooks. Schedules are the thinnest of the three classes and the reason confidence is medium rather than high: recurring execution is referenced in two separate documentation pages as an established pattern, but no dedicated scheduling page appears in the complete index, so the configuration surface was not read. Fetching the Trigger Node page would settle it and is the single call that would take this to high confidence. Graded F because all three classes are documented as shipped rather than because the schedule surface is fully described.

Stands at F, strengthened, and it is now among the best-evidenced Know cells in the lane because the retrieval stack is native rather than an integration. THE MECHANISM IS DOCUMENTED, WHICH ALMOST NOTHING ELSE THIS SESSION MANAGED. Sim's Knowledge Base is described as a native feature using ADVANCED AI EMBEDDINGS AND VECTOR SEARCH TECHNOLOGY, with VECTOR EMBEDDINGS, AUTOMATIC CONVERSION OF TEXT INTO HIGH-DIMENSIONAL VECTORS FOR INTELLIGENT SIMILARITY MATCHING, and semantic search that understands meaning and context rather than matching keywords. THE CONTRAST WITH THE OTHER FULL CELLS TODAY IS WORTH RECORDING. joget and autogpt both reached Full on a Pinecone connector, where the vector store is a customer-supplied dependency and chunking, embedding and refresh are the customer's problem. Here the store is the platform's own and the surrounding controls are documented as their own pages: CONNECTORS, TAGS AND FILTERING, DEBUGGING RETRIEVAL and CHUNKING STRATEGIES. A vendor publishing a chunking strategies page and a retrieval debugging page has built a retrieval product, not a database binding. DEBUGGING RETRIEVAL IS THE PAGE THAT STANDS OUT and it is rare across the index. Most grounding failures are silent: the agent answers confidently from the wrong passage and nobody knows. A documented surface for inspecting what was retrieved and why is the difference between a knowledge base a team can operate and one they have to trust. MULTIPLE KNOWLEDGE BASES PER WORKSPACE are supported for different purposes or departments, and tag-based filtering scopes retrieval within one, so a single corpus can serve several agents with different views of it. The vendor's own framing on the product page is that data stored semantically in Sim is THE MEMORY YOUR AGENTS REASON OVER; that is grounding and is graded here, with agent state graded separately on Mem, and the record keeps the two distinct.

Stands at F. Knowledge bases are durable managed artifacts with their own creation, usage, node and feature documentation, plus a REST endpoint and, importantly, their own PERMISSION model, which is rare: access to a knowledge base is restricted independently of access to the workflow that uses it. For a regulated buyer that is the control that makes a shared agent platform viable across departments. Relevant to the open Knowledge convention: this is decisively a maintained retrieval structure rather than per-request assembly, and the vendor documents two distinct ways an agent consumes it. A Dynamic Vector Store covers the runtime-constructed case, and Search Connected Apps grounds on live systems without indexing them first, so both patterns are available and separable. Customer-managed vector stores through Pinecone and Weaviate mean the index can live outside the platform entirely.

Stands at F, and the July self-flag is resolved. That basis noted THE MEMORY PAGE ITSELF WAS NOT OPENED IN THIS PASS SO CROSS SESSION SEMANTICS ARE GRADED FROM THE SURROUNDING DOCUMENTATION. The Agent block documentation settles it in one sentence. THE DECIDING LINE: MEMORY REQUIRES A CONVERSATION ID TO PERSIST ACROSS RUNS. Persisting across runs is the ruled Full condition, stated by the vendor, with the mechanism named. That is the exact question the July pass left open. FOUR MEMORY MODES ARE DOCUMENTED AS AGENT CONFIGURATION rather than as an integration a customer wires: NONE where each request is independent, CONVERSATION holding full history keyed by a conversation ID, SLIDING WINDOW BY MESSAGES keeping the N most recent, and SLIDING WINDOW BY TOKENS keeping messages up to a token limit. Offering the retention policy as a first-class setting is unusual in this lane, where memory is normally on or absent; here a builder chooses how much history persists and pays for it in context accordingly. THE ACCUMULATION LIMB IS SERVED BY MEM0, documented as enabling agents to MAINTAIN PERSISTENT MEMORY ACROSS WORKFLOW EXECUTIONS, RECALL PAST CONVERSATIONS, REMEMBER USER PREFERENCES, AND BUILD UPON PREVIOUS INTERACTIONS, with add, semantic search and retrieve operations. Zep ships alongside as a second external memory option. SO BOTH LIMBS ARE PRESENT BY DIFFERENT ROUTES: native session-keyed persistence configurable per agent, and accumulating long-term memory through integrations. Tables hold durable structured rows workflows read and write, and Variables carry in-run state, so the platform distinguishes three kinds of state cleanly rather than conflating them. WORTH RECORDING FOR CONSISTENCY: this is the third record today crediting Mem0, after autogpt and joget. In each case it is credited as a documented first-class primitive the customer wires, which the 31 August ruling permits explicitly.

Stands at P, and it is now the ONLY cell below F on this record, so the reasoning matters. The Shared Memory node is explicitly a windowed context-passing mechanism, not a memory store: the builder chooses how many past interactions to forward and the documentation warns that passing too many will overwhelm the receiving model's context window. That is the same token-buffer shape as dify, and it is bounded by the context window rather than by a persistence layer. Nothing in the complete documentation index describes memory that survives a session, accumulates over time, or is learned from prior runs, which is the F bar met by cosine, greptile and cognition. Two things were deliberately NOT counted here, to avoid one fact doing work on three axes: the browser sandbox persists cookies and session state across runs, which is graded on Comp, and Canvas keeps per-conversation version history, which is a document feature. The Dynamic Vector Store is a retrieval structure and is graded on Know.

Stands at F. Human in the Loop is a documented first-class node rather than a posture, and the vendor treats it as foundational enough to build the first of its five learning challenges around it, which is a good signal that it is a load-bearing feature rather than a checkbox. The approval surface is the vendor's own and is delivered where the approver already is: the product page shows an approval prompt in Slack with explicit approve and disapprove controls. That clears the 30 August ruling, since Slack is the delivery channel while the gate itself belongs to StackAI. Workflow Notifications and the production workflow lock in Project Controls are separable second and third oversight mechanisms, the latter unusual because it constrains what a builder can change rather than what an agent can do.

Stands at P. The July basis carried an explicit instruction, RECHECK BEFORE ANY UPGRADE, and this pass performed that recheck and found nothing new. Recording the negative result so the next reviewer does not repeat it. WHAT WAS SEARCHED: sim.ai, docs.sim.ai and the enterprise documentation, anchored on trust centre, SOC 2 and compliance terms. No trust centre, no report request path, no named audit firm, no observation period and no compliance page was found. The SOC 2 and HIPAA claims remain confined to the ai4.sim.ai marketing microsite with type unspecified. The July grade and reasoning stand unchanged. THE CONTROL HALF IS THE STRONGEST OF ANY PARTIAL IN THIS LANE, which is what makes the cell frustrating rather than weak. SAML 2.0 and OIDC single sign-on across Okta, Entra ID, Google Workspace and ADFS. Workspace roles and permission groups enforced at execution time rather than only in the interface. Secrets management. Organisation-wide audit logs. Configurable data retention. Session policies and verified domains. And data drains continuously exporting logs to a customer-owned S3 bucket. THAT LAST ONE IS A GENUINE DIFFERENTIATOR and worth carrying: a customer's security team can pipe activity into their own SIEM rather than reading the vendor's dashboard, which is the form of auditability that survives a vendor outage or a contract ending. WHY IT STILL SITS AT PARTIAL. The bar is a conjunction: an attestation or certification alongside a named control. The controls half is met several times over; the attestation half is asserted on a marketing microsite and nowhere else. Under the hedge ladder that is the asserted-with-no-report rung, the same position as agentx today, and one rung below aigensei's SOC 2-aligned only in that a type is claimed rather than alignment. ONE STRUCTURAL POINT IN THE VENDOR'S FAVOUR, recorded but not credited: the platform is open source under Apache 2.0 and self-hostable, so a customer can inspect the code and run it inside their own controls. That is a different kind of assurance from an attestation and it is graded on Dep and Ext, not here.

Stands at F and is among the two or three strongest Sec cells in the index. The conjunction is met several times over, and unusually the control surface is documented page by page rather than asserted as a bullet list: RBAC, workspace and folder isolation, feature-level admin enforcement, per-connection and per-knowledge-base permissions, MFA, and full SCIM provisioning with separate Okta and Entra guides. Encrypted environment variables resolved per stage is a maturity signal most no-code platforms lack. Graded strictly on the control surface: on-premise deployment and government cloud are carried on Dep and deliberately excluded here, per the 30 August ruling that sovereign delivery never reaches Security. Disclosure quality is high by index standards, with a trust centre, a SOC 2 report request route, model-provider DPAs published as signed PDFs and a BAA route, though no audit firm is named.

Stands at F but at MEDIUM confidence, and the gap is named rather than glossed. A dedicated Observability section with three pages, a REST Analytics endpoint, audit logs on the product page and version history through the development lifecycle is comfortably more than reporting, and the Evaluator adds a quality dimension most vendors have nothing equivalent to. What was NOT confirmed is the per-run trace: the Manager page was identified from the complete documentation index but not read, so whether a customer can reconstruct why a specific run took the path it did is inferred rather than verified. One detail cuts slightly against F and is recorded honestly: a guide titled Adding Advanced Logging for Analytics suggests some richer telemetry is assembled by the builder rather than supplied. Fetching the Manager and Analytics pages would settle this to high confidence either way, and it is the main outstanding item on this record.

Stands at F, and the strongest single piece of evidence is not a claim but an artifact: StackAI ships a dedicated enterprise CLI whose entire purpose is deploying and managing the platform on customer infrastructure, with its own documentation set, engineering standards, Kubernetes and Docker migration guides and a CVE upgrade runbook. A vendor that maintains a deployment CLI with release engineering docs is not offering on-premise as a sales concession. Government deployment is a separate documented path including Azure OpenAI in Azure Government, which is a genuine sovereignty story rather than legal cover. Per the 30 August ruling all of this lives here and only here; the Security cell was written on the control surface alone. Local LLM hosting reinforces it, since an air-gapped-leaning customer can keep both the platform and the inference inside its own boundary.

Stands at F and the basis is now specific where the April one named nothing. Three separable layers of prebuilt material, which is more than most: a browsable Templates catalogue reachable from both the product and the documentation, named agent templates such as Content Writer that the documentation directs users to open and adapt, and Skills, which are reusable instruction packs agents load on demand and which function as prebuilt behaviour rather than prebuilt structure. A Prompt Library and a Common Architectures page add reusable material at the prompt and pattern level. Clears the browsable-catalogue bar carried from Coding agent. Recorded honestly: the catalogue page itself was not enumerated this pass, so its depth is unmeasured, though the vendor names specific templates by name in the documentation which is stronger than a bare claim of a library.

Stands at F and the provider list is broader than the July basis recorded, now taken from the Agent block reference rather than summarised. THIRTEEN PROVIDERS ARE NAMED IN THE BLOCK DOCUMENTATION: OpenAI, Anthropic, Google Gemini, xAI Grok, DeepSeek, Groq, Cerebras, AZURE OPENAI, AZURE ANTHROPIC, GOOGLE VERTEX AI, AWS BEDROCK, OpenRouter, and local models through Ollama or VLLM. Selection is per Agent block from a model combobox where the builder can type or select any supported model. THE THREE HYPERSCALER ROUTES ARE THE ENTRIES THAT MATTER MOST and were not in the July list. Azure OpenAI, Azure Anthropic, Vertex AI and Bedrock let a customer consume frontier models through a cloud contract they already hold, under their existing data processing terms and commit spend. For a regulated buyer that is frequently the only permitted route to a given model, and a platform that omits it forces a procurement conversation that has already been had. THE ECONOMICS ARE DOCUMENTED AND UNUSUALLY CLEAN: bring your own key bills at base provider pricing WITH NO MARKUP, while hosted keys carry a 1.1x multiplier, and local models through Ollama or VLLM incur no API cost at all. Publishing the exact spread between routes lets a customer decide on cost rather than guess, and no-markup BYOK means the platform takes nothing on inference. THE GOVERNANCE LAYER IS THE PART FEW COMPETITORS HAVE: enterprise permission groups restrict WHICH PROVIDERS ARE ALLOWED, enforced at execution time and not only in the interface. So an organisation can permit Bedrock and forbid direct OpenAI across every agent its teams build, which is model choice bounded by policy rather than left to each builder. Combined with the fully offline self-hosted path credited on Dep, a customer can run the platform and its models entirely inside their own infrastructure.

Stands at F, and the distinguishing feature is not the breadth of model choice but the GOVERNANCE over it, which nothing else reviewed in this lane ships. LLM Provider Governance lets an administrator control which models the organisation may use and where information is sent and stored, so model flexibility is bounded by policy rather than left to whoever builds the workflow. For a regulated buyer that is the difference between model choice being an asset and being a compliance risk. Underneath it the ordinary requirements are met: multiple providers, per-node selection, models hosted in the customer's own Azure or Bedrock account, and locally hosted models. Not to be confused with the Ext credit for publishing an MCP server, which is the opposite direction of travel and is graded there.

Stands at F and clears Mike's 30 August Ext bar on every available route rather than just one. A documented REST API with named endpoints makes the platform callable from outside, which is the bar itself. StackAI also PUBLISHES ITS OWN MCP SERVER, which under the section 7 axis rule credits Ext and refuses Model, because the customer picks the assistant that reads StackAI. The enterprise CLI is a third surface, and project export and import means an agent definition is a portable artifact rather than something locked in a tenant. The Custom API node is the inbound counterpart, letting a workflow call anything the catalogue does not cover. This is a wider extensibility surface than most no-code platforms in this lane offer, and it is the reason the platform can sit underneath another product rather than only in front of a user.

Stands at F, and the July self-flag is resolved. That basis recorded honestly that THE EVALUATOR PAGE ITSELF WAS NOT OPENED IN THIS PASS SO DEPTH IS GRADED FROM ITS FIRST CLASS PLACEMENT IN THE BLOCK SET. The page is now open and the depth holds up. WHAT THE BLOCK ACTUALLY DOES: it USES AI TO SCORE AND ASSESS CONTENT QUALITY AGAINST CUSTOM METRICS, where the customer defines each metric with a name, a description and a numeric range, the documented examples being accuracy, clarity and relevance on one-to-five scales. THE SCHEMA ENFORCEMENT IS THE DETAIL THAT MAKES IT AN INSTRUMENT RATHER THAN A PROMPT. The Evaluator GENERATES A JSON SCHEMA RESPONSE FORMAT BASED ON YOUR METRICS AND ENFORCES STRICT MODE, SO THE LLM IS CONSTRAINED TO RETURN ONLY THE EXPECTED METRIC SCORES AS NUMBERS, NO EXTRA TEXT OR EXPLANATIONS. Numbers rather than prose is what makes a score comparable at all, and it is the difference between this and the model-as-judge pattern held at Partial on snaplogic. THE COMPARISON LIMB IS DOCUMENTED EXPLICITLY: the block is described as suited to QUALITY CONTROL, A/B TESTING, and one of its named use cases is A/B TESTING CONTENT, COMPARE MULTIPLE AI-GENERATED RESPONSES. The guidance also states CONNECT WITH AGENT BLOCKS: USE EVALUATOR BLOCKS TO ASSESS AGENT BLOCK OUTPUTS AND CREATE FEEDBACK LOOPS, so the subject being scored is the agent's own behaviour on the customer's own work, which is exactly what the ruled bar asks. A WORKED PATTERN IS PUBLISHED: Agent generates, Evaluator scores, Condition checks a threshold, then publish or revise. That is a quality gate with a readable numeric result driving control flow. THE HONEST LIMIT, and it is why this is not the strongest Eval cell in the index: comparison is between candidate outputs within a run, not across agent versions over time. No retained test set, stored expected outputs or regression run history is documented, which is what agentx has and this does not. Debugging is separately strong through block-by-block run traces, knowledge base retrieval debugging and per-run cost.

P>F. The Evaluator is a shipped customer-facing evaluation product, not the vendor testing its own work, which is the distinction the axis turns on. LLM-as-a-judge scoring of the customer's own agents clears the bar set by goose and openhands in Coding agent and matches the shape that took lyzr to F earlier in this lane. The surrounding surface is stronger than the Evaluator alone: an Agentic Development Lifecycle with version control, pull requests and prompt diffs, Project Controls that track versions during development and LOCK a workflow once in production, and documented troubleshooting and error-fallback guides. The production lock is the detail worth carrying, since it is a governance control on change rather than a testing feature and few vendors in this lane ship one. Recorded honestly: the Evaluator page itself was identified from the complete documentation index and its description rather than fetched in full, so the scoring mechanism's depth is not measured.

P>F, and this is a consistency correction rather than new evidence. The July basis found the capability and then withheld Full on a bar that does not exist. ITS REASONING, VERBATIM: the browser engine is AN INTEGRATED THIRD PARTY RATHER THAN SIM'S OWN, SO THE CAPABILITY IS DELIVERED BUT NOT FIRST PARTY. That test appears nowhere in the axis definition, which asks whether an agent operates software the vendor does not control because no programmatic interface exists. It asks what the customer can do, not who wrote the driver. THE INDEX DOES NOT APPLY THAT BAR ANYWHERE ELSE, which is what makes it an invented one. Model cells credit vendors for OpenAI and Anthropic models they did not build. Know cells credited joget and autogpt for Pinecone integration. Mem cells credit Mem0 across three records including this one. Penalising a third-party engine only on Comp would single out one axis for a rule the other thirteen do not carry. THE DECIDING PRECEDENT IS FROM EARLIER TODAY AND IS THE SAME ENGINE. autogpt moved N>F on a Stagehand Blocks section in its documentation, Stagehand being Browserbase's framework built on act, extract and observe. Sim ships FIRST CLASS BROWSER USE AND STAGEHAND INTEGRATIONS in its tools catalogue, with Apify and Bright Data alongside. Same engine, same integration shape, same product category, reviewed the same day. Grading them differently would have been indefensible. WHAT THE CUSTOMER ACTUALLY RECEIVES is the test that matters: a workflow block that drives a real browser against an interface with no API, composable beside the other thousand integrations. Browser Use and Stagehand both perform navigation and action rather than retrieval alone, which is the line held against scraping on ai-library, pickaxe and joget this session. Recorded for the lane: fifth genuine Comp positive in Agent builder, after sema4-ai, kalcend, integrail and autogpt, and the second resting on Stagehand.

N>F, a full point, and the most consequential single correction of this lane so far. THE REASONING IS THE THIRTEEN JUNE COMP ERRORS RUN IN REVERSE, so this record is worth keeping as the worked example of the distinction. The StackAI Computer provider ships three actions and only one of them is Comp. THE TERMINAL TOOL IS EXPLICITLY NOT CREDITED HERE: running shell commands in an isolated sandbox is code execution, which is exactly what blink-new, codebuff, compyle, cosine, cubic and eight others were wrongly graded F for. Canvas is a document workspace and is likewise refused. What earns F is browser navigation, and it earns it decisively: the agent drives a real browser through authenticated web applications whose session state persists between runs, which is the definition of operating software with no programmatic interface. Two modes exist, a deterministic replay of a recorded sequence and an agentic mode for unfamiliar or dynamic tasks, and a separate HyperBrowser node adds a third route. A live stream URL for watching execution and a step-by-step result payload are unusual and make the capability inspectable rather than opaque.