Agentic Index
Relevance AI vs SuperAGI (2026)
Relevance AI last verified ·SuperAGI last verified
Both build agent workforces with visual tooling. That verdict is the Agentic Index coverage score, graded from each vendor's own published materials.
SuperAGI is open source with a graphical interface and an agent marketplace, free as open source, with hosted plans from $45 per user a month. Relevance builds and deploys a workforce of agents with a no code tool builder, with Pro at $19 a month billed annually. Relevance's Pro plan at $19 costs less than half a SuperAGI Starter seat at $45 and is aimed at non technical builders, and SuperAGI is open source.
On the Agentic Index self hosted platform ranking, Relevance AI and SuperAGI both clear the bar: each documents both containment capabilities in full. 171 of the 556 platforms it grades clear it. See the self hosted platform ranking
This comparison is published by Agentic Index, an independent agentic AI vendor research platform. Relevance AI and SuperAGI are each graded against the same 14 capability Agentic Index taxonomy, from the vendor's own public materials under the Agentic Index verification standard, alongside 1,084 researched vendors. No vendor pays for placement and no vendor has reviewed this page. How this evidence is graded
Choose Relevance AI if
- Pro at $19 a month against $45 for a SuperAGI Starter seat adds up at team scale.
- A no code tool builder is what your non technical users need.
- Evaluation and approvals built into the platform matter to you.
Choose SuperAGI if
- Open source is a requirement.
- An agent marketplace shortens the distance to a working agent.
- You want to self host rather than depend on a hosted service.
| Feature | R Relevance AI |
S SuperAGI |
|---|---|---|
| Action & orchestration | ||
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Integrations & Tool Calling Ability to connect agents to real systems through native integrations, OAuth-authenticated actions, custom tools, APIs, webhooks, or MCP-compatible tools. |
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Relevance AIIntegrations & Tool Calling Agents call tools built in a no code tool builder and connect to a catalog of more than 2,000 integrations, and an MCP client lets them use tools from outside MCP servers. Sourcerelevanceai.com/pricingread 2026-09-27 |
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SuperAGIIntegrations & Tool Calling Toolkits let agents take authenticated action in outside systems. Search runs through Google Search, DuckDuckGo, Searx and SerpAPI, and developer toolkits cover GitHub, including Fetch PR and Review PR, Jira and a Coding toolkit. Productivity toolkits cover Notion, Google Calendar and a File Manager, Email sends messages with attachments, Twitter and Instagram cover social posting, DALL-E and Stable Diffusion generate images, Apollo.io supplies sales data and Google Analytics supplies analytics. Credentials are set per toolkit in the GUI, with OAuth where the service requires it. Developers can add custom toolkits through a linked GitHub repository, a cookie-cutter template and a community tools repository, so the catalog is open to additions. Sourcegithub.com/TransformerOptimus/SuperAGI README and release notes v0.0.9 through v0.0.12read 2026-09-01 |
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Workflow Orchestration Ability to sequence, branch, retry, route, and combine deterministic workflow nodes with autonomous agent steps. |
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Relevance AIWorkflow Orchestration Workforces connect several agents and tools on a canvas with conditions on the edges between them, agents can hand work to subagents, and the tool builder supports looping and branching steps. Sourcerelevanceai.com/docs/build/workforces/workforce-features/approvals-and-escalations.mdread 2026-09-27 |
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SuperAGIWorkflow Orchestration Agents run a ReAct reasoning-and-acting loop, and the runtime handles the control flow rather than leaving it to the developer. Predefined agent workflows can be chosen at provisioning as sequential instruction sets, and Sales Engagement and Recruitment ship as worked multi-step examples. A wait block lets a workflow pause for a set time before continuing. A task queue sets priorities, a configurable max-iteration limit caps each run, looping detection heuristics flag an agent that is stuck, and provider errors such as rate limits, authentication failures and exceeded quotas are shown to the operator instead of failing silently. Several agents can run at the same time. Sourcegithub.com/TransformerOptimus/SuperAGI release notes v0.0.10 and v0.0.13, and README architecture diagramsread 2026-09-01 |
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Triggers & Channel Coverage How agents wake up and where they work: schedules, webhooks, message events, CRM events, inbox events, chat, email, voice, and collaboration tools. |
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Relevance AITriggers & Channel Coverage Scheduled triggers let an agent act on its own on a recurring schedule or send messages at set future times, and agents also start from integration events, custom webhooks, an API trigger and tools used as triggers. Sourcerelevanceai.com/docs/build/agents/build-your-agent/agent-triggers/scheduled-triggers.mdread 2026-09-27 |
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SuperAGITriggers & Channel Coverage An agent can be scheduled for a single future run or for a recurring run at a set interval, with optional expiry by deadline or by total run count, and schedules can be stopped, edited and saved, so the agent starts without anyone typing. Webhooks fire outward when an agent is running, paused, completed, terminated or reaches max iterations, which notifies external systems but does not start work. People reach the agent only through the framework's own web interface at localhost or the hosted console, and there is no inbound channel from email, chat or a team tool. Email, Twitter and Instagram are outbound toolkits the agent acts through, not channels that reach it. Sourcegithub.com/TransformerOptimus/SuperAGI release notes v0.0.8 and v0.0.12read 2026-09-01 |
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| Knowledge & context | ||
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Knowledge Grounding & RAG Ability to ground agent behavior in company data through document ingestion, retrieval, external knowledge APIs, semantic search, or RAG layers. |
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Relevance AIKnowledge Grounding & RAG Knowledge sets are built from uploads (CSV, PDF, Excel, JSON, audio), website extraction, manual entry, and syncs from Google Drive, SharePoint and Notion, and an agent either takes the whole set in its prompt or searches it by retrieval with relevance thresholds. Sourcerelevanceai.com/docs/build/knowledge/create-knowledge.mdread 2026-09-27 |
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SuperAGIKnowledge Grounding & RAG Agents can work from the customer's own content, retrieved at run time. Files in pdf, docx, pptx, csv, txt and epub are uploaded through the resource manager, turned into vector embeddings by the LlamaIndex integration and queried by the agent through the Resource Query tool. Knowledge Embeddings can also be created from the customer's own sources or installed from the marketplace, hosted in a Pinecone, Qdrant, Weaviate or Redis index the customer connects by URL, and searched semantically at run time through the KnowledgeSearch toolkit. A Knowledge Console logs embedding performance, queries and token consumption. Sourcegithub.com/TransformerOptimus/SuperAGI release notes v0.0.7, v0.0.9 and v0.0.11, superagi.com knowledge-embeddings articleread 2026-09-01 |
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Memory & State Persistence Ability to persist context across a run, conversation, workflow, user, team, or longer-term memory layer. |
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Relevance AIMemory & State Persistence Long term memory lets an agent remember information across different conversations through save to memory and delete from memory tools, alongside short term memory that holds extracted fields for one conversation. Sourcerelevanceai.com/docs/build/agents/build-your-agent/memory.mdread 2026-09-27 |
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SuperAGIMemory & State Persistence Short-term memory is a rolling window sized to the model's token limit. Context that falls outside it is condensed into a Long Term Summary, and the two are combined into an Agent Summary that goes into each reasoning step, weighted seventy-five to twenty-five. Long-term memory persists in a vector database, with Qdrant available as an LTM backend alongside Pinecone, Weaviate and Redis. Tool Memory stores every tool response from a run as vectorized chunks in Redis for semantic retrieval, though only the Thinking Tool can read it. Sourcegithub.com/TransformerOptimus/SuperAGI release notes v0.0.8, v0.0.10 and v0.0.11read 2026-09-01 |
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| Control & trust | ||
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Human Oversight & Guardrails Approval steps, consent checkpoints, escalation rules, structured guardrails, policy constraints, and pause/resume controls. |
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Relevance AIHuman Oversight & Guardrails Approval settings are configured per edge, so a sensitive tool can require approval while routine ones run automatically; in approval required mode the agent drafts its action and waits, and a reviewer approves, rejects or adds guidance from the task view. A let agent decide mode and escalations also exist. Sourcerelevanceai.com/docs/build/workforces/workforce-features/approvals-and-escalations.mdread 2026-09-27 |
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SuperAGIHuman Oversight & Guardrails Restricted mode is chosen per agent as a permission type at provisioning, and an agent running under it must get human approval before critical actions such as writing files, sending emails and moving to the next iteration. Requests appear in the Action Console, where the operator can Approve, Give feedback or Deny, so the customer decides which kinds of action need sign-off, who gives it and what happens while the request waits. A configurable max-iteration limit caps a run separately from approvals. Sourcegithub.com/TransformerOptimus/SuperAGI release notes v0.0.4, v0.0.6 and v0.0.10read 2026-09-01 |
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Security, Identity & Governance RBAC, SSO, auditability, encryption, least-privilege tool access, compliance posture, and data handling policy. |
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Relevance AISecurity, Identity & Governance Relevance AI offers role based access control, SSO and SAML, directory sync, audit logs, PII masking and encryption at rest, and states SOC 2 Type II compliance. SSO, RBAC and audit logs sit on the Enterprise plan. A trust center at trust.relevanceai.com carries the report list. Sourcerelevanceai.com/enterpriseread 2026-09-27 |
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SuperAGISecurity, Identity & Governance Authentication runs through GitHub OAuth, which also protects the APIs, alongside a configurable auth secret and an option to restrict list creation, and the project runs CodeQL code scanning in its own pipeline. The framework has no access model for who inside an organization can make an agent do what, and it publishes no compliance page or certification of its own. Its logs record telemetry, and SuperAGI names no audit trail. The GDPR, SOC 2 and HIPAA claims made for the company's separate commercial platform belong to that product, not to the framework. Self-hosting means the operator supplies the surrounding controls. Sourcegithub.com/TransformerOptimus/SuperAGI release notes v0.0.4 and v0.0.7, config_template.yamlread 2026-09-01 |
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Observability & Auditability Traces, logs, execution histories, metrics, audit events, and debugging detail for production agent behavior. |
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Relevance AIObservability & Auditability Execution traces cover agent invocations, individual LLM calls, workforce runs and condition evaluations, and audit logs cover creation, publishing, deletion and permission changes; on the Enterprise plan both stream to the customer's S3 for OTEL backends. A tasks view shows every agent and workforce run with errors and pending approvals, and views of run cost and outcomes sit alongside it. Sourcerelevanceai.com/docs/enterprise/streaming-events.mdread 2026-09-27 |
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SuperAGIObservability & Auditability The Agent Performance Monitoring dashboard reports organization metrics for agents, runs and tokens, per-model breakdowns of runs and consumption, and agent-level analytics covering total runs, average tokens per run, total calls and run time. Model, Tool and Knowledge Consoles each keep their own logs, so a finished run can be traced step by step through which model was called, which tools ran and which embeddings were queried. A real-time activity feed shows execution as it happens, APM keeps data for deleted agents and runs, and the resource manager keeps a per-run output directory that downloads as a zip. Sourcegithub.com/TransformerOptimus/SuperAGI release notes v0.0.8, v0.0.9 and v0.0.12read 2026-09-01 |
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Deployment & Data Residency Deployment modes and options, including SaaS, dedicated cloud, VPC, on-prem, hybrid, local runtime, and self-hosting. |
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Relevance AIDeployment & Data Residency An organization picks its region at signup from three named regions, US (N. Virginia), EU (London) and AU (Sydney), and the region cannot be changed afterward without support. The service is hosted SaaS, and Relevance AI does not describe a self hosted or VPC option. Sourcerelevanceai.com/docs/admin/project-management/ids.mdread 2026-09-27 |
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SuperAGIDeployment & Data Residency The framework is MIT-licensed and self-hosted with Docker Compose on the customer's own machine or infrastructure, and a separate GPU compose file supports local large language models and multi-GPU execution, so neither the platform nor the model has to leave the environment. A one-click Digital Ocean deployment runs the image on a self-hosted cloud machine and updates itself with each release. Storage can be local disk or S3, and the database and vector store belong to the operator. A hosted SuperAGI Cloud is also available for teams that prefer it. Sourcegithub.com/TransformerOptimus/SuperAGI README installation section and release notes v0.0.4, v0.0.12 and v0.0.14read 2026-09-01 |
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| Solution readiness | ||
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Prebuilt Agents, Templates & Packs Ready-made workflows, packaged employees, templates, blueprints, industry solutions, and role-specific agents that reduce time-to-value. |
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Relevance AIPrebuilt Agents, Templates & Packs A marketplace carries agents and tools from Relevance staff and verified builders, free or paid, that can be cloned into a project and customized, alongside agent and tool templates and curated multi agent systems such as a BDR agent sold through sales. Sourcerelevanceai.com/docs/get-started/marketplace/introduction.mdread 2026-09-27 |
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SuperAGIPrebuilt Agents, Templates & Packs A browsable marketplace carries toolkits, agent templates, knowledge embeddings and models, reachable from the product and at its own subdomain. SuperCoder builds end-to-end coding projects from goals and instructions. Predefined agent workflows include Sales Engagement, which fetches prospects from Apollo and drafts personalized outreach for each row, and Recruitment, which compares uploaded CVs against a job description and sends shortlist or rejection mail. Customers can save templates from any run and publish them to the marketplace after a validated run, so the library grows from the community as well as from SuperAGI. Sourcegithub.com/TransformerOptimus/SuperAGI release notes v0.0.6, v0.0.7, v0.0.10 and v0.0.12, marketplace.superagi.comread 2026-09-01 |
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| Platform extensibility | ||
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Model Flexibility & Routing Ability to work across multiple foundation models, route tasks to different models, or let buyers bring their own providers and keys. |
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Relevance AIModel Flexibility & Routing Each agent picks its model from several providers, including Anthropic Claude, OpenAI and Gemini, and can set a backup model, recommended from a different provider, that retries a failed task once. LLM integrations cover OpenAI, Gemini, Anthropic, Azure and OpenRouter. Sourcerelevanceai.com/docs/build/agents/build-your-agent/agent-settings/language-model.mdread 2026-09-27 |
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SuperAGIModel Flexibility & Routing Providers are configured in account settings, and a model is chosen per agent from a dropdown at provisioning. GPT-4-32k, gpt-3.5-turbo-16k and Google PaLM 2 Bison are supported, and a model factory class is built to admit more. HuggingFace and Replicate integrations let the customer bring any public or self-hosted model by entering the model name and inference endpoint, a models marketplace offers preconfigured entries, and local large language models run on the customer's own weights with multi-GPU support. Sourcegithub.com/TransformerOptimus/SuperAGI release notes v0.0.7, v0.0.8, v0.0.11 and v0.0.14read 2026-09-01 |
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APIs, SDKs & MCP Extensibility Composability layer: stable APIs, SDKs, MCP tool consumption/serving, custom tools, and integration into internal systems. |
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Relevance AIAPIs, SDKs & MCP Extensibility A TypeScript SDK, @relevanceai/sdk, runs agents, tasks, messages and workforces with streaming, an API trigger covers other languages, and an MCP server at mcp.relevanceai.com gives MCP clients OAuth access to a project's agents, tools and knowledge. Sourcerelevanceai.com/docs/sdk/introduction.mdread 2026-09-27 |
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SuperAGIAPIs, SDKs & MCP Extensibility Public APIs, reachable from cloud or local base URLs, let developers create, pause, resume and update agents, start runs and retrieve run details and input or output files. API keys are generated in settings, and the full reference is published as a Postman collection. Python and NodeJS SDK client libraries install with pip or npm and have their own documentation section. Webhooks send HTTP callbacks to a user's endpoint on five events, when an agent is running, paused, completed, terminated or reaches max iterations. Custom tools can be added by linking a GitHub repository from the frontend, with a cookie-cutter template and a community tools repository. Sourcegithub.com/TransformerOptimus/SuperAGI release notes v0.0.11, v0.0.12 and v0.0.13, and documenter.getpostman.com SuperAGI collectionread 2026-09-01 |
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Testing, Debugging & Optimization Testing, debugging, scoring, retries, fallbacks, quality gates, and optimization loops for improving agent workflows before and after deployment. |
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Relevance AITesting, Debugging & Optimization Test sets hold tests that simulate users and are scored by reusable checks, so versions can be compared on the same tests; publishing can require chosen test sets to pass at a minimum rate and block on failure, and monitor dashboards score live tasks against the same criteria with sampling and trend charts. Agent evaluations sit on the Enterprise plan. Sourcerelevanceai.com/docs/build/agents/evals/introduction.mdread 2026-09-27 |
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SuperAGITesting, Debugging & Optimization The Coding toolkit ships a Write Unit Tests tool, and the SuperCoder sequence runs WriteSpec, WriteCode, ImproveCode and then WriteTest, so tests are written against the customer's generated project. SuperAGI does not say whether those tests are run, or whether a failing test blocks delivery or is reported back. Agent Performance Monitoring, the Model, Tool and Knowledge consoles and the activity feed report what happened after a run. There is no evaluation harness, scored comparison between agent versions or preview run against a held-out set. Sourcegithub.com/TransformerOptimus/SuperAGI release notes v0.0.6 and v0.0.9read 2026-09-01 |
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| Specialist automation | ||
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Browser & Computer Use Browser, desktop, or remote/local computer control for workflows that cannot be handled through stable APIs alone. |
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Relevance AIBrowser & Computer Use Browser steps (click, type, scroll by natural language or selector) run through Airtop, a separately sold browser engine that needs the customer's own Airtop account and API key. Relevance AI does not describe a browser or computer use agent of its own. Sourcerelevanceai.com/docs/build/tools/tool-steps/airtop-browser-automation/page-interaction.mdread 2026-09-27 |
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SuperAGIBrowser & Computer Use A Web Scraping tool in the toolkit set pulls data from webpages, alongside a Web Interaction toolkit. Both read the web through scraping and wired-in access and do not drive a real interface. SuperAGI has no hosted or local browser, desktop session or remote computer control, and its search tools are API-based. Sourcegithub.com/TransformerOptimus/SuperAGI release notes v0.0.4 and README toolkit listread 2026-09-01 |
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Pricing snapshot
Sourced from the Index pricing dataset · open each vendor's profile for full detail.
| Pricing | R Relevance AI |
S SuperAGI |
|---|---|---|
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Entry price Lowest public entry point |
Pro at $19 a month billed annually or $29 monthly, Team at $234 a month billed annually or $349 monthly, and Enterprise at custom pricing. | Starter at $45 per user a month and Business at $150, with a Free plan at $0 and Enterprise custom. The billing FAQ prices a paid seat at $49 a month. The open source framework is free. |
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Pricing confidence How public the numbers are |
Public, partial | Public, partial |
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Billing Primary billing axis |
A plan subscription with two meters. Actions count each tool run, and Vendor Credits pay model and tool costs at provider rates with no markup. | Per user a month, with a monthly credit allowance on each plan and extra credit packs. |
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Variable cost Workload / overage exposure |
High variable cost | High variable cost |
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Free tier / trial Try before you buy |
No free tier
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Free tier
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Buying motion Self-serve vs sales call |
Self-serve | Self-serve |
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Pricing verified When this pricing was last checked |
Product updates
Newest first, each linked to its source.
Relevance AI
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· Agent capability
Relevance AI rolled out a multi-feature update introducing the Invent interface for agent creation and adding Google's Gemini 3.5 Flash as a selectable language model across all workflows.
Source -
· Integrations
Relevance AI added Confluence Knowledge Sync, project-level public-agent controls, and an Android app, improving enterprise knowledge connectivity, governance, and mobile workforce access.
Source -
· Pricing / packaging
Relevance AI split pricing into Actions and Vendor Credits, enabled BYO API keys, removed markup on vendor costs, and sunset the Business plan.
Source
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