{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-superagi","slug":"superagi","name":"SuperAGI","type":"agent","url":"https://superagi.com/","page_url":"https://unfragile.ai/superagi","categories":["ai-agents","deployment-infra"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-superagi__cap_0","uri":"capability://planning.reasoning.agent.workflow.orchestration.with.visual.builder","name":"agent workflow orchestration with visual builder","description":"Provides a drag-and-drop interface to compose multi-step agent workflows by connecting action nodes, decision branches, and tool integrations without code. Uses a directed acyclic graph (DAG) execution model where each node represents an agent action or tool call, with conditional routing based on LLM outputs or explicit branching logic. Workflows are serialized as JSON configuration and executed by a runtime engine that manages state, context passing, and error handling across steps.","intents":["Build multi-step agent workflows without writing orchestration code","Visually debug agent execution flow and identify bottlenecks","Reuse and share workflow templates across teams","Rapidly prototype agent behaviors before production deployment"],"best_for":["Non-technical product managers designing agent behavior","Teams prototyping agent workflows quickly without backend engineering","Organizations standardizing agent patterns across multiple use cases"],"limitations":["Visual builder may become unwieldy for workflows with >50 nodes or complex conditional logic","Debugging complex state mutations across steps requires manual inspection of execution logs","No built-in version control for workflow DAGs — requires external Git integration for change tracking"],"requires":["Web browser with modern JavaScript support","At least one LLM provider API key (OpenAI, Anthropic, or self-hosted)","Basic understanding of agent action types and tool schemas"],"input_types":["workflow configuration (JSON)","tool/action definitions (schema)","user input (text, structured data)"],"output_types":["workflow execution logs","agent responses (text, structured data)","state snapshots at each step"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-superagi__cap_1","uri":"capability://tool.use.integration.tool.action.registry.with.schema.based.function.calling","name":"tool/action registry with schema-based function calling","description":"Maintains a centralized registry of tools and actions that agents can invoke, with automatic schema generation and validation. Each tool is defined with input/output schemas (JSON Schema), descriptions, and execution handlers. The framework automatically converts tool definitions into function-calling payloads compatible with OpenAI, Anthropic, and other LLM APIs, handling parameter validation, type coercion, and error propagation back to the agent for retry logic.","intents":["Define custom tools that agents can reliably invoke with type-safe parameters","Integrate third-party APIs and internal services as agent capabilities","Ensure agents only call tools with valid inputs by enforcing schema validation","Enable agents to handle tool execution failures gracefully with retry strategies"],"best_for":["Teams building domain-specific agents with custom business logic","Developers integrating multiple APIs into a single agent interface","Organizations requiring strict validation of agent actions before execution"],"limitations":["Schema validation adds ~50-100ms latency per tool call due to JSON Schema parsing","Complex nested schemas with recursive definitions may cause validation timeouts","No built-in rate limiting or quota management per tool — requires external middleware","Tool execution is synchronous; long-running operations block agent reasoning"],"requires":["Tool definitions in JSON Schema format","Execution handlers (Python functions or HTTP endpoints)","LLM provider supporting function calling (OpenAI, Anthropic, Ollama)"],"input_types":["tool schema (JSON Schema)","tool metadata (name, description, category)","execution handler (function or HTTP endpoint)"],"output_types":["function-calling payload (OpenAI/Anthropic format)","tool execution result (JSON, text, or binary)","error messages with retry context"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-superagi__cap_10","uri":"capability://planning.reasoning.agent.prompt.engineering.and.optimization.with.a.b.testing","name":"agent prompt engineering and optimization with a/b testing","description":"Provides tools for iterating on agent prompts and configurations, including A/B testing to compare performance across prompt variants. Supports prompt templating with variable substitution, version control for prompt history, and automated evaluation metrics (correctness, latency, cost). Includes prompt optimization suggestions based on execution traces and failure analysis.","intents":["Systematically improve agent performance by testing prompt variations","Track prompt changes and revert to previous versions if needed","Identify which prompt components drive agent behavior","Optimize prompts for specific metrics (cost, latency, accuracy)"],"best_for":["Teams iterating on agent prompts to improve performance","Organizations wanting to systematically optimize agent behavior","Developers needing to understand which prompt components matter"],"limitations":["A/B testing requires running multiple prompt variants, increasing LLM costs by 2-3x","Statistical significance requires large sample sizes; small-scale testing may be inconclusive","Prompt optimization suggestions are heuristic-based; may not capture complex interactions","Version control for prompts requires manual discipline; no automatic conflict resolution"],"requires":["Evaluation metrics (correctness, latency, cost, user satisfaction)","Test data or live traffic for A/B testing","Statistical analysis tools for significance testing"],"input_types":["prompt templates (with variables)","prompt variants (for A/B testing)","evaluation criteria (metrics and thresholds)"],"output_types":["A/B test results (performance comparison)","optimization suggestions (prompt changes)","prompt version history (with metadata)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-superagi__cap_11","uri":"capability://safety.moderation.agent.safety.and.content.moderation.with.guardrails","name":"agent safety and content moderation with guardrails","description":"Implements safety mechanisms to prevent agents from taking harmful actions or generating unsafe content. Includes input validation (blocking malicious queries), output filtering (detecting unsafe responses), and action guardrails (preventing agents from calling dangerous tools). Uses rule-based filters, LLM-based classifiers, and external safety APIs to detect and block unsafe behavior. Supports custom safety policies tailored to specific domains.","intents":["Prevent agents from generating harmful, biased, or inappropriate content","Block malicious user inputs before agents process them","Restrict agents from calling dangerous tools or APIs","Enforce domain-specific safety policies (e.g., financial regulations, healthcare compliance)"],"best_for":["Teams deploying agents to production with safety requirements","Organizations subject to regulatory compliance (healthcare, finance, legal)","Applications where agent mistakes could cause real-world harm"],"limitations":["Safety filters add 100-200ms latency per request due to classification overhead","Rule-based filters are brittle; adversarial users can often bypass them","LLM-based safety classifiers are expensive and may have false positives/negatives","Custom safety policies require domain expertise to define correctly"],"requires":["Safety policy definitions (rules, thresholds, restricted actions)","Safety classifiers (rule-based or LLM-based)","External safety APIs (optional, for specialized detection)"],"input_types":["user input (text, structured data)","agent actions (tool calls, responses)","safety policy (rules and thresholds)"],"output_types":["safety verdict (safe/unsafe)","violation details (which rule was violated)","remediation action (block, filter, escalate)"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-superagi__cap_2","uri":"capability://memory.knowledge.agent.memory.and.context.management.with.configurable.storage.backends","name":"agent memory and context management with configurable storage backends","description":"Implements a pluggable memory system for agents to store and retrieve conversation history, task state, and learned facts across sessions. Supports multiple storage backends (in-memory, PostgreSQL, vector databases) with automatic context window management that summarizes or truncates old messages to fit LLM token limits. Memory is organized by agent instance, conversation thread, and optional user/organization scope, with retrieval strategies including recency-based, semantic similarity, and explicit tagging.","intents":["Maintain agent state and conversation history across multiple user interactions","Retrieve relevant past context to inform agent decisions without exceeding token limits","Store agent-learned facts or user preferences for personalization","Enable multi-turn conversations where agents reference earlier exchanges"],"best_for":["Teams building conversational agents requiring long-term memory","Applications where agents must personalize responses based on user history","Systems managing multiple concurrent agent instances with isolated memory"],"limitations":["In-memory backend loses all state on process restart — unsuitable for production without persistence","Semantic similarity retrieval requires embedding generation, adding ~200-500ms per query","No built-in garbage collection for old memories — requires manual pruning or external cleanup jobs","Context window management heuristics may discard important information if token budgets are tight"],"requires":["Storage backend (PostgreSQL, Redis, or vector DB like Pinecone/Weaviate)","Embedding model for semantic retrieval (OpenAI, local, or custom)","Memory schema definition (what data to store and how to organize it)"],"input_types":["conversation messages (text)","structured state (JSON)","user metadata (ID, preferences)","memory queries (natural language or structured)"],"output_types":["retrieved context (text, structured data)","memory summaries (compressed history)","relevance scores (for ranked retrieval)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-superagi__cap_3","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.provider.agnostic.prompting","name":"multi-provider llm abstraction with provider-agnostic prompting","description":"Abstracts away provider-specific API differences (OpenAI, Anthropic, Ollama, Azure, etc.) behind a unified interface for model invocation. Handles provider-specific prompt formatting, token counting, streaming response handling, and error recovery. Supports dynamic provider selection based on cost, latency, or capability requirements, with automatic fallback to alternative providers on failure. Manages API keys, rate limiting, and usage tracking across providers.","intents":["Switch between LLM providers without rewriting agent code","Optimize cost by routing requests to cheaper models for simple tasks","Reduce latency by selecting geographically closer or faster providers","Ensure agent resilience by falling back to alternative providers on API failures"],"best_for":["Teams evaluating multiple LLM providers and wanting to avoid vendor lock-in","Cost-conscious organizations needing to optimize LLM spend across models","Production systems requiring high availability and automatic failover"],"limitations":["Abstraction layer adds ~50-100ms latency per LLM call due to request translation","Provider-specific features (vision, function calling variants) may not be fully exposed through abstraction","Token counting differs between providers; estimates may be inaccurate for billing purposes","Streaming responses require provider-specific parsing; unified streaming interface may lose nuance"],"requires":["API keys for at least one LLM provider (OpenAI, Anthropic, Ollama, etc.)","Provider configuration (model name, endpoint, authentication)","Network connectivity to provider APIs or self-hosted LLM servers"],"input_types":["prompts (text, with optional system/user role separation)","provider configuration (model, temperature, max_tokens)","provider selection strategy (cost, latency, capability)"],"output_types":["LLM responses (text, streaming or buffered)","token usage metrics (input, output, total)","provider metadata (model used, latency, cost)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-superagi__cap_4","uri":"capability://automation.workflow.agent.deployment.and.execution.runtime.with.containerization.support","name":"agent deployment and execution runtime with containerization support","description":"Provides a runtime environment for executing agents in production, with support for containerized deployment (Docker), environment isolation, and resource management. Agents run as isolated processes or containers with configurable CPU/memory limits, automatic scaling based on workload, and health monitoring. Supports both synchronous (request-response) and asynchronous (background job) execution modes, with job queuing and result persistence for long-running tasks.","intents":["Deploy agents to production with resource isolation and scaling","Run long-running agent tasks asynchronously without blocking user requests","Monitor agent health and automatically restart failed instances","Scale agent execution horizontally across multiple machines or containers"],"best_for":["Teams deploying agents to production with SLA requirements","Applications requiring asynchronous agent execution for long-running tasks","Organizations needing to isolate agent workloads for security or resource management"],"limitations":["Container startup overhead adds 1-5 seconds per agent invocation; not suitable for sub-second latency requirements","Horizontal scaling requires external orchestration (Kubernetes); framework provides no built-in clustering","State sharing between agent instances requires external coordination; no built-in distributed state management","Resource limits may cause agent timeouts if tasks exceed allocated time budgets"],"requires":["Docker or container runtime for containerized deployment","Job queue (Redis, RabbitMQ) for asynchronous task management","Kubernetes or similar orchestration platform for multi-instance scaling","Monitoring/logging infrastructure (Prometheus, ELK, etc.)"],"input_types":["agent configuration (model, tools, memory settings)","execution request (input data, execution mode)","resource constraints (CPU, memory, timeout)"],"output_types":["execution result (agent response, logs)","job status (queued, running, completed, failed)","resource metrics (CPU, memory, latency)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-superagi__cap_5","uri":"capability://planning.reasoning.agent.reasoning.and.planning.with.chain.of.thought.decomposition","name":"agent reasoning and planning with chain-of-thought decomposition","description":"Implements structured reasoning patterns that decompose complex agent tasks into intermediate steps, with explicit reasoning traces visible to developers. Uses chain-of-thought prompting to encourage LLMs to explain their reasoning before taking actions, with support for multi-step planning where agents break down goals into sub-tasks. Includes built-in patterns for reflection (agent evaluates its own outputs), re-planning (agent adjusts strategy if initial plan fails), and hierarchical task decomposition (breaking large goals into smaller, manageable steps).","intents":["Improve agent reasoning quality by forcing explicit step-by-step thinking","Debug agent decision-making by inspecting intermediate reasoning traces","Enable agents to handle complex, multi-step tasks that require planning","Allow agents to self-correct by reflecting on outputs and re-planning if needed"],"best_for":["Teams building agents for complex reasoning tasks (research, analysis, planning)","Developers needing to understand and debug agent decision-making","Applications where reasoning transparency is critical for user trust"],"limitations":["Chain-of-thought reasoning increases token usage by 2-5x due to intermediate explanations","Multi-step planning adds latency; each reasoning step requires a separate LLM call (~1-2 seconds)","Reflection and re-planning can create infinite loops if not bounded by max iterations","Reasoning quality depends heavily on LLM capability; weaker models may produce incoherent reasoning traces"],"requires":["LLM with strong reasoning capabilities (GPT-4, Claude 3, or equivalent)","Sufficient token budget to accommodate chain-of-thought overhead","Task definitions that can be decomposed into sub-steps"],"input_types":["task description (natural language)","planning constraints (max steps, time budget)","domain knowledge (context, examples)"],"output_types":["reasoning trace (step-by-step explanation)","task decomposition (sub-tasks and dependencies)","final agent action (based on reasoning)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-superagi__cap_6","uri":"capability://automation.workflow.agent.monitoring.and.observability.with.execution.tracing","name":"agent monitoring and observability with execution tracing","description":"Provides comprehensive logging and tracing of agent execution, capturing every LLM call, tool invocation, decision point, and state change. Integrates with observability platforms (Datadog, New Relic, etc.) to export traces and metrics in standard formats (OpenTelemetry). Includes built-in dashboards for visualizing agent behavior, identifying bottlenecks, and tracking performance metrics (latency, cost, success rate). Supports custom event logging for domain-specific metrics.","intents":["Monitor agent performance in production and identify performance bottlenecks","Debug agent failures by inspecting detailed execution traces","Track agent costs and optimize spending across LLM calls","Analyze agent behavior patterns to improve prompts and tool definitions"],"best_for":["Teams operating agents in production with SLA requirements","Organizations needing to track and optimize LLM spending","Developers debugging complex agent failures in production"],"limitations":["Comprehensive tracing adds 5-10% overhead to agent execution latency","Storing detailed traces for high-volume agents requires significant storage capacity","Observability platform integration requires additional configuration and API keys","Real-time dashboards may lag behind actual execution by 30-60 seconds"],"requires":["Observability platform (Datadog, New Relic, Grafana, etc.) or self-hosted logging stack","OpenTelemetry collector or equivalent for trace export","Storage for execution logs (S3, database, or observability platform)"],"input_types":["agent execution events (LLM calls, tool invocations, decisions)","custom metrics (domain-specific KPIs)","performance thresholds (for alerting)"],"output_types":["execution traces (detailed logs of each step)","performance metrics (latency, cost, success rate)","dashboards and alerts (visual monitoring)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-superagi__cap_7","uri":"capability://automation.workflow.agent.testing.and.validation.framework.with.synthetic.test.generation","name":"agent testing and validation framework with synthetic test generation","description":"Provides tools for testing agent behavior, including unit tests for individual tools, integration tests for workflows, and end-to-end tests for complete agent scenarios. Supports synthetic test case generation using LLMs to create diverse inputs and expected outputs, with assertion frameworks for validating agent responses against criteria (correctness, safety, latency). Includes regression testing to detect behavior changes across agent versions.","intents":["Validate agent behavior before deployment to catch regressions","Generate diverse test cases without manual effort","Ensure agents meet safety and correctness requirements","Compare agent performance across different model versions or configurations"],"best_for":["Teams deploying agents to production with quality requirements","Organizations needing to validate agent safety and correctness","Developers iterating on agent prompts and wanting to catch regressions"],"limitations":["Synthetic test generation requires LLM calls, adding cost and latency to test execution","Generated test cases may not cover edge cases or adversarial inputs","Assertion frameworks are limited to simple criteria; complex validation requires custom code","Test execution is sequential; no built-in parallelization for large test suites"],"requires":["Test framework (pytest, unittest, or custom)","LLM for synthetic test generation (OpenAI, Anthropic, etc.)","Test data and expected outputs for validation"],"input_types":["agent configuration (model, tools, prompts)","test scenarios (natural language descriptions)","validation criteria (correctness, safety, latency)"],"output_types":["test results (pass/fail, detailed logs)","coverage metrics (which agent paths were exercised)","performance comparisons (across versions or configurations)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-superagi__cap_8","uri":"capability://memory.knowledge.agent.knowledge.base.integration.with.semantic.search.and.rag","name":"agent knowledge base integration with semantic search and rag","description":"Enables agents to augment their reasoning with external knowledge sources through retrieval-augmented generation (RAG). Supports multiple knowledge base backends (vector databases, document stores, web search) with semantic search to find relevant context. Automatically chunks documents, generates embeddings, and retrieves top-k relevant passages to include in agent prompts. Includes citation tracking to attribute agent responses to source documents.","intents":["Give agents access to domain-specific knowledge without fine-tuning","Reduce hallucination by grounding agent responses in retrieved facts","Enable agents to cite sources for their claims","Keep agent knowledge up-to-date by retrieving from live data sources"],"best_for":["Teams building domain-specific agents (customer support, research, analysis)","Applications requiring factual accuracy and source attribution","Organizations with large document repositories that agents need to reference"],"limitations":["Semantic search requires embedding generation, adding 200-500ms latency per query","Retrieved context may be incomplete or misleading if documents are poorly structured","Citation tracking requires careful prompt engineering; agents may cite irrelevant passages","Vector database maintenance (re-indexing, deduplication) requires operational overhead"],"requires":["Vector database (Pinecone, Weaviate, Milvus, etc.) or document store","Embedding model (OpenAI, local, or custom)","Document corpus with metadata (source, date, relevance tags)"],"input_types":["documents (text, PDF, web pages)","search queries (natural language or structured)","retrieval parameters (top-k, similarity threshold)"],"output_types":["retrieved passages (text with metadata)","citations (source document references)","relevance scores (for ranking results)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-superagi__cap_9","uri":"capability://planning.reasoning.agent.collaboration.and.multi.agent.orchestration","name":"agent collaboration and multi-agent orchestration","description":"Enables multiple agents to work together on complex tasks through message passing, shared state, and coordination protocols. Agents can delegate sub-tasks to specialized agents, aggregate results, and make decisions based on multiple perspectives. Supports hierarchical agent structures (manager agents coordinating worker agents) and peer-to-peer collaboration (agents negotiating solutions). Includes conflict resolution mechanisms for when agents disagree.","intents":["Decompose complex tasks across specialized agents with different capabilities","Leverage multiple LLM models simultaneously for different aspects of a task","Enable agents to validate each other's work and improve solution quality","Build hierarchical agent systems with managers coordinating workers"],"best_for":["Teams building complex systems requiring multiple specialized agents","Applications where different agents bring different expertise (research, analysis, planning)","Organizations wanting to leverage multiple LLM models for different tasks"],"limitations":["Multi-agent coordination adds significant latency; each delegation requires LLM calls and message passing","Shared state management requires careful synchronization; race conditions possible without proper locking","Conflict resolution between agents requires explicit protocols; no automatic consensus mechanism","Debugging multi-agent systems is complex; execution traces become difficult to follow"],"requires":["Message queue or communication protocol for inter-agent messaging","Shared state store (database, cache) for coordination","Conflict resolution strategy (voting, hierarchy, negotiation)"],"input_types":["task description (for delegation)","agent capabilities (what each agent can do)","coordination protocol (how agents communicate)"],"output_types":["aggregated results (from multiple agents)","coordination logs (message history)","conflict resolution outcomes"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["Web browser with modern JavaScript support","At least one LLM provider API key (OpenAI, Anthropic, or self-hosted)","Basic understanding of agent action types and tool schemas","Tool definitions in JSON Schema format","Execution handlers (Python functions or HTTP endpoints)","LLM provider supporting function calling (OpenAI, Anthropic, Ollama)","Evaluation metrics (correctness, latency, cost, user satisfaction)","Test data or live traffic for A/B testing","Statistical analysis tools for significance testing","Safety policy definitions (rules, thresholds, restricted actions)"],"failure_modes":["Visual builder may become unwieldy for workflows with >50 nodes or complex conditional logic","Debugging complex state mutations across steps requires manual inspection of execution logs","No built-in version control for workflow DAGs — requires external Git integration for change tracking","Schema validation adds ~50-100ms latency per tool call due to JSON Schema parsing","Complex nested schemas with recursive definitions may cause validation timeouts","No built-in rate limiting or quota management per tool — requires external middleware","Tool execution is synchronous; long-running operations block agent reasoning","A/B testing requires running multiple prompt variants, increasing LLM costs by 2-3x","Statistical significance requires large sample sizes; small-scale testing may be inconclusive","Prompt optimization suggestions are heuristic-based; may not capture complex interactions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.35000000000000003,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.049Z","last_scraped_at":"2026-05-03T14:00:10.321Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=superagi","compare_url":"https://unfragile.ai/compare?artifact=superagi"}},"signature":"HPYa7aFr/TFRZYV2JA4fiuvHdLve0MKeTqbboOdSD4iKLkWdtJyxWjEZkQvBZum4fgyQs99PW1mUm70crCX6Cg==","signedAt":"2026-06-21T11:45:37.266Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/superagi","artifact":"https://unfragile.ai/superagi","verify":"https://unfragile.ai/api/v1/verify?slug=superagi","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}