{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-gptswarm","slug":"gptswarm","name":"GPTSwarm","type":"agent","url":"https://gptswarm.org/","page_url":"https://unfragile.ai/gptswarm","categories":["ai-agents"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-gptswarm__cap_0","uri":"capability://planning.reasoning.agent.workflow.as.directed.acyclic.graph.compilation","name":"agent-workflow-as-directed-acyclic-graph-compilation","description":"Compiles multi-agent workflows into optimizable directed acyclic graphs (DAGs) where each node represents an LLM call or tool invocation and edges represent data flow dependencies. Uses graph-based intermediate representation to enable static analysis, parallel execution planning, and cost/latency optimization before runtime. Supports conditional branching, loops, and dynamic node creation based on LLM outputs.","intents":["Define complex multi-step agent workflows without writing imperative orchestration code","Visualize agent execution flow and data dependencies as a graph structure","Optimize agent workflows for latency and cost by analyzing the DAG before execution","Enable parallel execution of independent agent tasks within a single workflow"],"best_for":["Teams building complex multi-agent systems with interdependent tasks","Developers optimizing LLM inference costs and latency at scale","Researchers experimenting with agent coordination patterns"],"limitations":["DAG compilation requires upfront workflow definition — dynamic runtime graph mutations may require recompilation","Cyclic dependencies not supported in DAG model — requires explicit loop unrolling or iteration limits","Graph optimization overhead adds latency for simple single-agent workflows"],"requires":["Workflow definition in GPTSwarm's graph specification format","At least one LLM provider API key (OpenAI, Anthropic, etc.)","Python 3.8+ or Node.js 16+ runtime"],"input_types":["workflow definition (JSON/YAML graph schema)","initial task parameters (structured data)","LLM provider credentials"],"output_types":["compiled DAG representation","execution plan with parallelization strategy","optimized node scheduling"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gptswarm__cap_1","uri":"capability://planning.reasoning.graph.based.agent.parameter.optimization","name":"graph-based-agent-parameter-optimization","description":"Optimizes agent workflow parameters (prompt templates, tool selection, LLM model choices, sampling parameters) by treating the DAG as a differentiable computation graph and using gradient-based or evolutionary search methods to minimize cost or latency objectives. Supports multi-objective optimization (e.g., accuracy vs. cost) and constraint satisfaction (e.g., latency SLAs).","intents":["Automatically tune agent prompts and model selections to minimize inference costs","Find optimal parameter configurations that meet latency and accuracy constraints","Compare cost-accuracy tradeoffs across different agent configurations","Adapt agent workflows to different deployment environments (edge vs. cloud)"],"best_for":["Teams running high-volume agent workloads with tight cost budgets","Developers optimizing agent performance for production SLAs","Researchers studying agent parameter sensitivity"],"limitations":["Optimization requires baseline metrics from test runs — cold-start optimization not possible without historical data","Search space grows exponentially with number of tunable parameters — may require sampling or pruning for large workflows","Optimization results are specific to the training distribution — may not generalize to out-of-distribution inputs"],"requires":["Compiled DAG workflow with defined optimization objectives","Test dataset or baseline metrics for parameter evaluation","Access to multiple LLM providers or model variants for comparison"],"input_types":["compiled DAG with parameter ranges","optimization objective function (cost, latency, accuracy)","constraint specifications (SLA thresholds)","test dataset for evaluation"],"output_types":["optimized parameter configuration","optimization trace (parameter values vs. objective)","Pareto frontier for multi-objective optimization"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gptswarm__cap_10","uri":"capability://memory.knowledge.agent.state.management.and.context.persistence","name":"agent-state-management-and-context-persistence","description":"Manages state and context across agent workflow execution, including intermediate results, conversation history, and long-term memory. Implements state persistence to external storage (databases, vector stores) with support for state retrieval and context injection into subsequent agent calls.","intents":["Maintain conversation history and context across multiple agent interactions","Store intermediate results and reuse them in subsequent workflow steps","Implement long-term memory for agents to learn from past interactions","Enable stateful agent workflows that adapt based on execution history"],"best_for":["Multi-turn conversational agents requiring context persistence","Workflows with long execution times requiring intermediate state checkpointing","Systems implementing agent learning and adaptation"],"limitations":["State management adds complexity and storage overhead — requires external state store","Context injection increases token usage — may impact cost and latency","State consistency across distributed executions requires careful synchronization"],"requires":["State storage backend (database, vector store, or cache)","State serialization and deserialization logic","Context injection mechanisms for agent calls"],"input_types":["execution state (intermediate results, context)","state persistence configuration","context retrieval queries"],"output_types":["persisted state artifacts","retrieved context for agent calls","state update confirmations"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gptswarm__cap_11","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.and.fallback","name":"multi-provider-llm-abstraction-and-fallback","description":"Abstracts over multiple LLM providers (OpenAI, Anthropic, Ollama, etc.) with a unified interface, enabling seamless switching between providers and automatic fallback when a provider is unavailable. 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Analyzes execution traces to compute latency attribution per node and edge, with recommendations for optimization (e.g., parallelization, model downgrading, caching).","intents":["Identify which workflow steps consume the most time and resources","Get optimization recommendations for reducing latency and cost","Compare performance across different workflow configurations","Detect performance regressions when workflow changes are deployed"],"best_for":["Teams optimizing agent workflow performance for production","Developers iterating on workflow configurations","Organizations with latency SLAs requiring performance monitoring"],"limitations":["Profiling overhead adds latency — may impact performance in latency-critical paths","Bottleneck detection requires sufficient execution samples — may be inaccurate with small sample sizes","Optimization recommendations are heuristic-based — may not be optimal for all use cases"],"requires":["Execution tracing and metrics collection","Latency attribution analysis logic","Optimization recommendation engine"],"input_types":["execution traces with timing data","workflow definition and configuration","optimization objectives and constraints"],"output_types":["latency breakdown per node and edge","bottleneck identification and ranking","optimization recommendations with estimated impact"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gptswarm__cap_2","uri":"capability://planning.reasoning.dynamic.agent.node.routing.and.selection","name":"dynamic-agent-node-routing-and-selection","description":"Routes execution to different agent implementations (different LLM models, tool sets, or prompts) based on input characteristics, previous execution results, or learned routing policies. Implements conditional branching in the DAG where routing decisions are made by lightweight classifiers, rule engines, or learned policies that select the most appropriate agent for each input.","intents":["Route complex queries to specialized agents while simple queries use cheaper models","Dynamically select tool sets based on task type or input domain","Implement fallback agents when primary agent fails or times out","Learn optimal routing policies from execution traces"],"best_for":["Systems handling heterogeneous input types requiring different agent specializations","Cost-sensitive deployments needing intelligent model selection","Multi-domain applications (e.g., customer support routing to domain experts)"],"limitations":["Routing overhead adds latency — routing decision must complete before agent execution","Learned routing policies require sufficient training data to generalize — may overfit to training distribution","Routing errors propagate downstream — misrouted inputs may fail or produce low-quality outputs"],"requires":["Compiled DAG with multiple agent node implementations","Routing policy definition (rules, classifiers, or learned models)","Input feature extraction or classification logic"],"input_types":["task input (text, structured data)","execution context (previous results, metadata)","routing policy parameters"],"output_types":["selected agent node identifier","routing confidence score","execution path through DAG"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gptswarm__cap_3","uri":"capability://automation.workflow.parallel.agent.execution.with.dependency.tracking","name":"parallel-agent-execution-with-dependency-tracking","description":"Executes independent agent nodes in parallel by analyzing the DAG to identify nodes with no data dependencies, scheduling them concurrently across available compute resources. Implements dependency tracking to ensure downstream nodes only execute after all upstream dependencies complete, with support for partial results and timeout handling.","intents":["Execute multiple independent agent tasks concurrently to reduce overall workflow latency","Maximize resource utilization by parallelizing independent LLM calls","Implement fan-out/fan-in patterns where multiple agents process different aspects of a problem","Handle partial failures in parallel execution without blocking dependent tasks"],"best_for":["Latency-sensitive applications where workflow completion time is critical","High-throughput systems with multiple independent agent tasks","Workflows with natural parallelization opportunities (e.g., multi-perspective analysis)"],"limitations":["Parallel execution requires sufficient compute resources — resource contention may negate latency benefits","Token rate limits on LLM APIs may serialize parallel calls — requires rate limit management","Debugging parallel execution is harder than sequential — requires distributed tracing"],"requires":["Compiled DAG with identified parallelizable nodes","Concurrent execution runtime (async/await, thread pool, or distributed executor)","Dependency resolution and scheduling logic"],"input_types":["compiled DAG with dependency annotations","execution context and resource constraints","timeout and retry policies"],"output_types":["parallel execution schedule","execution timeline with latency breakdown","aggregated results from parallel nodes"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gptswarm__cap_4","uri":"capability://automation.workflow.agent.execution.tracing.and.observability","name":"agent-execution-tracing-and-observability","description":"Captures detailed execution traces of agent workflows including LLM call inputs/outputs, tool invocations, latency breakdowns, token usage, and cost per node. Provides structured logging and visualization of the execution DAG with metrics overlaid, enabling debugging, performance analysis, and cost attribution across workflow steps.","intents":["Debug agent workflow failures by inspecting LLM inputs/outputs at each step","Analyze latency bottlenecks and identify optimization opportunities","Track token usage and costs per agent node for billing and optimization","Visualize actual execution flow and compare against planned DAG"],"best_for":["Teams debugging complex multi-agent workflows in production","Cost-conscious deployments needing detailed cost attribution","Developers optimizing agent performance and latency"],"limitations":["Tracing overhead adds latency and storage costs — may impact performance in latency-critical paths","Sensitive data in LLM prompts/outputs requires careful handling in traces — may require PII redaction","Trace storage grows with workflow complexity and execution volume — requires retention policies"],"requires":["Instrumentation hooks in agent execution runtime","Structured logging backend (e.g., JSON logs, distributed tracing system)","Visualization and query interface for trace data"],"input_types":["agent execution events (LLM calls, tool invocations, node completions)","performance metrics (latency, tokens, cost)","execution context (workflow ID, node IDs, parameters)"],"output_types":["structured execution trace (JSON/protobuf)","execution timeline visualization","cost and latency breakdown per node","error logs and failure analysis"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gptswarm__cap_5","uri":"capability://automation.workflow.agent.workflow.versioning.and.rollback","name":"agent-workflow-versioning-and-rollback","description":"Manages versions of agent workflow DAGs with support for comparing configurations, rolling back to previous versions, and A/B testing different workflow variants. Stores workflow definitions with metadata (creation time, author, optimization results) and enables atomic updates to production workflows.","intents":["Safely deploy workflow changes with ability to rollback if performance degrades","A/B test different agent configurations and measure impact on metrics","Track workflow evolution and understand what changes improved performance","Manage multiple workflow variants for different use cases or user segments"],"best_for":["Teams deploying agent workflows to production with safety requirements","Data-driven teams experimenting with workflow configurations","Organizations with compliance requirements for workflow audit trails"],"limitations":["Version management adds operational complexity — requires workflow registry and deployment pipeline","A/B testing requires traffic splitting and metric collection — adds infrastructure overhead","Rollback may not be instantaneous — requires time to propagate changes across distributed systems"],"requires":["Workflow definition storage (Git, database, or artifact registry)","Version control and comparison logic","Deployment and rollback mechanisms"],"input_types":["workflow DAG definition","version metadata (author, timestamp, description)","A/B test configuration (traffic split, metrics)"],"output_types":["versioned workflow artifact","version comparison report","A/B test results and metrics"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gptswarm__cap_6","uri":"capability://tool.use.integration.tool.integration.with.schema.based.binding","name":"tool-integration-with-schema-based-binding","description":"Integrates external tools and APIs into agent workflows via schema-based function definitions that specify input/output types, descriptions, and execution semantics. Supports automatic tool selection by agents based on task requirements, with built-in bindings for common tool categories (web search, databases, APIs, code execution).","intents":["Enable agents to call external APIs and tools as part of workflow execution","Automatically select appropriate tools based on task type and availability","Define tool schemas that agents can reason about for planning and execution","Integrate with existing tool ecosystems (e.g., OpenAPI specs, REST APIs)"],"best_for":["Agents that need to interact with external systems and data sources","Teams building tool-augmented agent workflows","Systems requiring structured tool invocation with type safety"],"limitations":["Tool schema definition requires manual specification — no automatic schema inference from APIs","Tool execution errors may not be recoverable — requires explicit error handling in workflow","Tool rate limits and quotas must be managed externally — no built-in rate limiting"],"requires":["Tool schema definitions (JSON Schema or similar)","Tool execution runtime (HTTP client, database driver, etc.)","Tool credentials and authentication configuration"],"input_types":["tool schema (name, description, input/output types)","tool invocation parameters","execution context (credentials, environment)"],"output_types":["tool execution result","error messages and status codes","execution metadata (latency, tokens used)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gptswarm__cap_7","uri":"capability://automation.workflow.agent.workflow.composition.and.reusability","name":"agent-workflow-composition-and-reusability","description":"Enables composition of complex agent workflows from reusable sub-workflows or workflow templates, with support for parameterization and variable substitution. Implements workflow inheritance and composition patterns that allow workflows to be nested, combined, and specialized for different use cases.","intents":["Build complex workflows from reusable components without duplicating logic","Create workflow templates that can be instantiated with different parameters","Share common workflow patterns across teams and projects","Reduce workflow definition complexity through hierarchical composition"],"best_for":["Organizations building multiple related agent workflows","Teams sharing workflow patterns and best practices","Large-scale deployments with hundreds of workflow variants"],"limitations":["Composition adds abstraction layers that may obscure actual execution flow","Parameter passing between composed workflows requires careful variable scoping","Debugging composed workflows is harder than flat workflows — requires tracing through composition layers"],"requires":["Workflow composition syntax or API","Parameter binding and variable substitution logic","Workflow registry for discovering and loading sub-workflows"],"input_types":["workflow template definition","composition parameters","sub-workflow references"],"output_types":["composed workflow DAG","parameter binding configuration","workflow dependency graph"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gptswarm__cap_8","uri":"capability://automation.workflow.cost.aware.model.selection.and.fallback","name":"cost-aware-model-selection-and-fallback","description":"Automatically selects LLM models based on cost budgets and performance requirements, with fallback strategies when primary models fail or exceed latency/cost thresholds. Implements cost estimation for each agent node and enforces global cost budgets across workflows, with dynamic model downgrading when approaching budget limits.","intents":["Minimize inference costs by selecting cheaper models when quality requirements allow","Enforce cost budgets across agent workflows and prevent overspending","Implement graceful degradation when approaching cost limits","Compare cost-quality tradeoffs for different model selections"],"best_for":["Cost-sensitive deployments with tight budget constraints","High-volume agent workloads where per-call costs matter","Teams needing visibility into inference costs and optimization opportunities"],"limitations":["Cost estimation requires historical pricing data — may be inaccurate for new models or usage patterns","Model downgrading may reduce output quality — requires careful threshold tuning","Cost budgets are global — may not account for per-user or per-request budget allocation"],"requires":["LLM pricing data and cost estimation models","Multiple LLM provider integrations with different cost profiles","Cost budget configuration and enforcement logic"],"input_types":["cost budget specification","model selection criteria (cost, latency, quality)","fallback model configurations"],"output_types":["selected model for each agent node","estimated cost per workflow execution","cost breakdown by model and node"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gptswarm__cap_9","uri":"capability://automation.workflow.agent.workflow.validation.and.testing","name":"agent-workflow-validation-and-testing","description":"Validates agent workflow definitions for correctness (type checking, dependency resolution, schema validation) and provides testing frameworks for executing workflows against test cases with expected outputs. Supports property-based testing, regression testing, and performance benchmarking of workflows.","intents":["Catch workflow definition errors before deployment (missing dependencies, type mismatches)","Test agent workflows against known test cases and verify output quality","Detect performance regressions when workflow configurations change","Validate that workflow changes don't break downstream dependencies"],"best_for":["Teams deploying agent workflows to production with quality requirements","Developers iterating on workflow configurations and needing fast feedback","Organizations with compliance requirements for workflow validation"],"limitations":["Testing agent outputs is hard — requires manual verification or expensive LLM-based evaluation","Test coverage may be incomplete — edge cases and out-of-distribution inputs may not be caught","Performance benchmarking requires representative test data — may not reflect production distributions"],"requires":["Workflow definition validation logic","Test case definition and execution framework","Output evaluation metrics (exact match, semantic similarity, etc.)"],"input_types":["workflow definition","test cases (inputs and expected outputs)","validation rules and constraints"],"output_types":["validation report (errors, warnings)","test results (pass/fail per test case)","performance metrics (latency, tokens, cost)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["Workflow definition in GPTSwarm's graph specification format","At least one LLM provider API key (OpenAI, Anthropic, etc.)","Python 3.8+ or Node.js 16+ runtime","Compiled DAG workflow with defined optimization objectives","Test dataset or baseline metrics for parameter evaluation","Access to multiple LLM providers or model variants for comparison","State storage backend (database, vector store, or cache)","State serialization and deserialization logic","Context injection mechanisms for agent calls","Provider-specific API clients and authentication"],"failure_modes":["DAG compilation requires upfront workflow definition — dynamic runtime graph mutations may require recompilation","Cyclic dependencies not supported in DAG model — requires explicit loop unrolling or iteration limits","Graph optimization overhead adds latency for simple single-agent workflows","Optimization requires baseline metrics from test runs — cold-start optimization not possible without historical data","Search space grows exponentially with number of tunable parameters — may require sampling or pruning for large workflows","Optimization results are specific to the training distribution — may not generalize to out-of-distribution inputs","State management adds complexity and storage overhead — requires external state store","Context injection increases token usage — may impact cost and latency","State consistency across distributed executions requires careful synchronization","Abstraction may not expose provider-specific features — requires custom handling for advanced features","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.25,"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":"2026-06-17T09:51:03.041Z","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=gptswarm","compare_url":"https://unfragile.ai/compare?artifact=gptswarm"}},"signature":"GP9nFCoFUMSDaSiLQj7t///1NYJDCUpfplnY81A/UtkyTnVvcicwAQLitI9rcpz0vuvpgoHKBAVkRh3xhz0aDg==","signedAt":"2026-06-22T06:51:40.805Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/gptswarm","artifact":"https://unfragile.ai/gptswarm","verify":"https://unfragile.ai/api/v1/verify?slug=gptswarm","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"}}