{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hn-46979781","slug":"agent-framework-that-generates-its-own-topology-an","name":"Agent framework that generates its own topology and evolves at runtime","type":"framework","url":"https://github.com/adenhq/hive/blob/main/README.md","page_url":"https://unfragile.ai/agent-framework-that-generates-its-own-topology-an","categories":["automation"],"tags":["hackernews","show-hn"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hn-46979781__cap_0","uri":"capability://planning.reasoning.dynamic.agent.topology.generation.and.self.assembly","name":"dynamic agent topology generation and self-assembly","description":"Agents automatically discover, instantiate, and wire themselves into a runtime topology without explicit configuration. The framework uses reflection and capability introspection to map agent dependencies, then constructs a directed acyclic graph (DAG) of agent relationships at startup. Agents register their input/output schemas, and the framework matches compatible producers and consumers, creating inter-agent communication channels dynamically based on type compatibility and declared capabilities.","intents":["I want agents to automatically discover and connect to each other without hardcoding topology","I need the system to infer agent relationships from their declared capabilities and schemas","I want to add new agents and have them integrate into the existing network without reconfiguration"],"best_for":["teams building multi-agent systems where topology changes frequently","developers prototyping agent networks without upfront architecture planning","systems requiring dynamic agent scaling and composition"],"limitations":["Circular dependency detection adds O(n²) overhead during topology generation for n agents","Schema matching is type-strict by default; polymorphic agent outputs require explicit adapter agents","No built-in versioning for agent schemas — breaking changes require manual migration"],"requires":["Agent framework runtime (language-specific implementation)","Agents must implement capability declaration interface","JSON Schema or equivalent for input/output type definitions"],"input_types":["agent class definitions with capability metadata","JSON Schema type definitions","capability registry entries"],"output_types":["agent topology graph (DAG structure)","wiring configuration (producer-consumer mappings)","initialization sequence plan"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46979781__cap_1","uri":"capability://planning.reasoning.runtime.topology.evolution.and.agent.mutation","name":"runtime topology evolution and agent mutation","description":"The framework monitors agent performance, error rates, and communication patterns during execution, then dynamically modifies the topology by spawning new agents, removing underperforming ones, or rewiring connections. Uses metrics collection and heuristic-based decision rules to detect when topology changes would improve throughput or reliability. New agents are instantiated from templates or learned patterns, and the system performs live rewiring without stopping the existing agent network.","intents":["I want the agent network to automatically scale up when bottlenecks are detected","I need agents to be removed or replaced if they consistently fail or perform poorly","I want the system to learn and adapt its topology based on runtime behavior patterns"],"best_for":["long-running agent systems that need to self-optimize","teams without domain expertise to manually tune agent topologies","systems with unpredictable workload patterns requiring adaptive scaling"],"limitations":["Evolution heuristics are framework-provided and not easily customizable per domain","State transfer during agent mutation can lose in-flight messages if not carefully orchestrated","No rollback mechanism if evolved topology degrades performance — requires manual intervention","Metrics collection adds 5-15% runtime overhead depending on granularity settings"],"requires":["Metrics collection backend (Prometheus, CloudWatch, or in-memory)","Agent template library for spawning new instances","Performance baseline thresholds configured per agent type"],"input_types":["runtime metrics (latency, error rate, throughput)","agent performance telemetry","topology change triggers (threshold-based or event-driven)"],"output_types":["topology mutation operations (add/remove/rewire)","new agent instantiation commands","migration plans for state transfer"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46979781__cap_10","uri":"capability://automation.workflow.agent.debugging.and.execution.tracing.with.replay","name":"agent debugging and execution tracing with replay","description":"Records detailed execution traces of agent interactions including inputs, outputs, intermediate states, and timing information. Provides tools to inspect traces, replay specific execution paths, and debug agent behavior. Supports breakpoints and step-through debugging for interactive troubleshooting. Traces can be exported for analysis or shared for collaborative debugging.","intents":["I want to understand why an agent made a specific decision by replaying its execution","I need to debug agent failures by inspecting intermediate states and messages","I want to share execution traces with teammates for collaborative debugging"],"best_for":["teams debugging complex multi-agent systems","development and testing phases where understanding agent behavior is critical","systems where audit trails are required for compliance"],"limitations":["Trace recording adds 10-50% overhead depending on verbosity level","Trace storage grows linearly with execution time — requires cleanup policies","Replay is deterministic only if all external dependencies (APIs, random seeds) are mocked","Large traces (100MB+) are slow to load and analyze","No built-in visualization — requires external tools for trace analysis"],"requires":["Trace collection infrastructure","Storage backend for traces","Debugging UI or CLI tools","Optional: visualization tools"],"input_types":["agent execution events","message payloads","state snapshots","timing information"],"output_types":["execution traces (structured logs)","trace analysis reports","replay commands"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46979781__cap_11","uri":"capability://automation.workflow.agent.resource.management.and.quota.enforcement","name":"agent resource management and quota enforcement","description":"Enforces resource limits (CPU, memory, API calls, token usage) per agent or agent group using quota systems. The framework monitors resource consumption and throttles or terminates agents that exceed limits. Supports hierarchical quotas where parent agents have budgets that are subdivided among child agents. Integrates with cloud resource managers (Kubernetes, AWS Lambda) for automatic scaling.","intents":["I want to prevent runaway agents from consuming all system resources","I need to enforce fair resource allocation across multiple agents","I want to set budgets for LLM token usage and API calls per agent"],"best_for":["multi-tenant systems where resource isolation is critical","cost-sensitive applications requiring strict budget enforcement","systems with unpredictable agent behavior"],"limitations":["Quota enforcement adds 5-10% overhead for monitoring and throttling","Resource measurement is approximate — actual usage may differ from reported usage","Quota limits are static — no dynamic adjustment based on system load","Terminating agents mid-execution can leave state inconsistent — requires cleanup logic","No built-in prioritization — all agents are treated equally"],"requires":["Resource monitoring infrastructure (cgroups, Kubernetes, CloudWatch)","Quota configuration per agent","Throttling/termination logic"],"input_types":["resource limits (CPU, memory, API calls, tokens)","quota configuration","resource consumption metrics"],"output_types":["quota enforcement actions (throttle, terminate)","resource usage reports","quota alerts"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46979781__cap_12","uri":"capability://automation.workflow.agent.versioning.and.canary.deployment","name":"agent versioning and canary deployment","description":"Supports multiple versions of agents running simultaneously, with traffic routing to specific versions based on rules (e.g., 10% to new version, 90% to stable). Enables gradual rollout of agent updates with automatic rollback if error rates exceed thresholds. Tracks version history and allows pinning agents to specific versions for reproducibility.","intents":["I want to test new agent versions with a small percentage of traffic before full rollout","I need to automatically rollback to a previous version if a new version causes errors","I want to maintain multiple agent versions for A/B testing or gradual migration"],"best_for":["production systems requiring safe agent updates","teams doing continuous deployment of agent improvements","systems where agent failures have high cost"],"limitations":["Version routing adds 10-50ms latency per request for decision logic","Rollback detection requires sufficient error rate threshold — may not catch subtle regressions","State incompatibility between versions can cause issues — requires careful migration planning","Version history grows unbounded — requires cleanup policies","No automatic version selection based on performance — requires manual threshold tuning"],"requires":["Version storage and management","Traffic routing logic","Error rate monitoring","Rollback automation"],"input_types":["agent code/configuration updates","version metadata","traffic routing rules","rollback thresholds"],"output_types":["versioned agent instances","traffic routing decisions","rollback commands"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46979781__cap_2","uri":"capability://tool.use.integration.capability.aware.inter.agent.communication.and.routing","name":"capability-aware inter-agent communication and routing","description":"Agents communicate through a capability-aware message broker that routes messages based on declared input/output schemas and capability tags rather than explicit routing rules. The framework maintains a capability registry mapping agent IDs to their input/output types, and uses this to determine valid message recipients. Messages include capability requirements in their headers, allowing the broker to find compatible agents dynamically and handle fan-out to multiple agents if needed.","intents":["I want agents to send messages without knowing which other agents will receive them","I need the system to route messages to all agents capable of handling a specific data type","I want to add new agents that automatically receive relevant messages without reconfiguring routing"],"best_for":["publish-subscribe agent architectures with many-to-many communication patterns","systems where agent relationships are not known at design time","teams building loosely-coupled agent networks"],"limitations":["Capability matching has O(n) lookup time per message where n is number of agents; no indexing optimization for large networks (100+ agents)","Message ordering guarantees are per-agent, not global — distributed ordering requires external coordination","No built-in backpressure handling — slow agents can cause message queue buildup","Capability registry is in-memory; no persistence across framework restarts"],"requires":["Message broker implementation (in-process queue or external message system)","Capability registry with schema matching logic","Agent message interface with capability headers"],"input_types":["messages with capability requirement headers","JSON Schema type definitions for message payloads","capability tags (string identifiers)"],"output_types":["routed messages to compatible agents","delivery confirmations","undeliverable message reports"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46979781__cap_3","uri":"capability://data.processing.analysis.agent.capability.introspection.and.schema.extraction","name":"agent capability introspection and schema extraction","description":"The framework automatically inspects agent class definitions, method signatures, and decorators to extract input/output schemas and capability metadata without requiring manual schema definition. Uses reflection/introspection APIs (Python inspect, TypeScript reflection, etc.) to parse function signatures, extract type hints, and generate JSON Schema representations. Supports decorator-based capability tagging (e.g., @capability('data-processing')) to augment introspection with semantic metadata.","intents":["I want the framework to automatically understand what each agent can do without writing separate schema files","I need to tag agents with semantic capabilities that the framework can use for routing and discovery","I want schema changes in agent code to automatically propagate to the topology without manual updates"],"best_for":["Python or TypeScript projects with strong type hints","teams that want schema-driven agent design without boilerplate","rapid prototyping where manual schema maintenance is overhead"],"limitations":["Requires strict type hints on agent methods — untyped code falls back to generic 'Any' type","Complex types (nested generics, union types) may not translate cleanly to JSON Schema","Introspection overhead is ~50-200ms per agent at startup; scales poorly with 100+ agents","Decorator-based tagging is framework-specific and not portable across tools"],"requires":["Python 3.8+ with typing module or TypeScript 4.0+ with reflection support","Agent classes with type-hinted methods","Optional: decorator library for capability tagging"],"input_types":["agent class definitions","method signatures with type hints","decorator metadata"],"output_types":["JSON Schema representations of input/output types","capability metadata (tags, descriptions)","agent capability registry entries"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46979781__cap_4","uri":"capability://automation.workflow.agent.performance.monitoring.and.metrics.collection","name":"agent performance monitoring and metrics collection","description":"Automatically instruments agents to collect latency, throughput, error rates, and custom metrics without requiring agent code changes. Uses aspect-oriented programming (AOP) or decorator-based wrapping to inject telemetry collection around agent execution. Metrics are aggregated in-memory or sent to external backends (Prometheus, CloudWatch), and the framework exposes APIs to query performance data for topology evolution decisions.","intents":["I want to monitor how long each agent takes to process messages without modifying agent code","I need to track error rates and failure patterns to identify problematic agents","I want to use performance metrics to automatically trigger topology changes"],"best_for":["production agent systems requiring observability","teams implementing self-healing or auto-scaling agent networks","systems where performance debugging is critical"],"limitations":["Metrics collection adds 5-15% runtime overhead; can be significant for latency-sensitive agents","In-memory metrics storage has no persistence — data is lost on restart","Cardinality explosion if metrics are tagged with high-cardinality dimensions (e.g., per-message-id)","No built-in alerting — requires external monitoring system integration"],"requires":["Metrics backend (in-memory, Prometheus, CloudWatch, or equivalent)","Agent instrumentation hooks (decorators or AOP framework)","Optional: external monitoring dashboard"],"input_types":["agent execution events (start, end, error)","custom metrics from agent code","performance thresholds for alerting"],"output_types":["time-series metrics (latency, throughput, error rate)","aggregated performance reports","alerts/triggers for topology evolution"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46979781__cap_5","uri":"capability://automation.workflow.declarative.agent.composition.and.template.instantiation","name":"declarative agent composition and template instantiation","description":"Agents can be defined as composable templates with parameterized behavior, allowing the framework to spawn new agent instances with different configurations at runtime. Templates use a declarative syntax (YAML, JSON, or code-based DSL) to specify agent behavior, dependencies, and configuration parameters. The framework instantiates agents from templates, injects dependencies, and wires them into the topology automatically.","intents":["I want to define agent behavior once as a template and instantiate multiple variants with different parameters","I need to spawn new agents dynamically with specific configurations based on runtime conditions","I want to version agent templates and roll back to previous versions if needed"],"best_for":["systems requiring dynamic agent spawning with parameterized behavior","teams managing many agent variants with shared logic","platforms where non-technical users should be able to configure agents"],"limitations":["Template syntax is framework-specific and not portable","Parameter validation is limited — no built-in type checking for template parameters","No versioning system for templates — breaking changes require manual migration","Template instantiation adds ~100-500ms overhead per agent depending on complexity"],"requires":["Template definition format (YAML, JSON, or DSL)","Dependency injection framework","Agent factory/instantiation logic"],"input_types":["agent template definitions","configuration parameters","dependency specifications"],"output_types":["instantiated agent instances","wiring configuration","initialization commands"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46979781__cap_6","uri":"capability://planning.reasoning.distributed.agent.coordination.and.consensus","name":"distributed agent coordination and consensus","description":"Enables agents to coordinate decisions and reach consensus across a distributed network using protocols like Raft or Byzantine Fault Tolerance (BFT). Agents can propose state changes, vote on decisions, and commit changes only when consensus is reached. The framework handles message ordering, timeout management, and failure recovery automatically, abstracting away distributed coordination complexity.","intents":["I want agents to make coordinated decisions without a central coordinator","I need the system to tolerate agent failures and still reach valid consensus","I want agents to maintain consistent state across a distributed network"],"best_for":["distributed agent systems requiring strong consistency","teams building fault-tolerant multi-agent applications","systems where no single point of failure is acceptable"],"limitations":["Consensus protocols add significant latency (100ms-1s per decision depending on network and protocol)","Byzantine fault tolerance requires 3f+1 agents to tolerate f failures, limiting scalability","Network partitions can halt consensus — no automatic recovery without manual intervention","Consensus state is not persisted by default — requires external storage for durability"],"requires":["Network connectivity between all agents","Consensus protocol implementation (Raft, PBFT, or equivalent)","Message ordering guarantees (TCP or equivalent)"],"input_types":["consensus proposals (state changes)","agent votes/responses","timeout and failure signals"],"output_types":["consensus decisions (committed state changes)","failure notifications","consistency guarantees"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46979781__cap_7","uri":"capability://automation.workflow.agent.state.persistence.and.checkpoint.management","name":"agent state persistence and checkpoint management","description":"Automatically saves agent state at configurable intervals or on-demand, enabling recovery from failures and resumption of work. Uses pluggable storage backends (file system, database, cloud storage) to persist agent state snapshots. The framework handles serialization/deserialization, versioning, and garbage collection of old checkpoints automatically.","intents":["I want agents to survive crashes and resume from where they left off","I need to checkpoint agent state periodically to enable recovery","I want to roll back agents to previous states if needed"],"best_for":["long-running agent systems where failures are expected","teams requiring audit trails and state history","systems with expensive computations that should not be re-run"],"limitations":["Checkpoint serialization adds 50-500ms overhead per checkpoint depending on state size","Storage backend latency directly impacts checkpoint frequency — high-frequency checkpoints can bottleneck agents","State versioning is manual — schema changes require migration logic","No built-in deduplication — large state objects are stored redundantly across checkpoints"],"requires":["Storage backend (file system, database, S3, etc.)","Serialization format (JSON, Protocol Buffers, or custom)","Checkpoint scheduling configuration"],"input_types":["agent state objects","checkpoint triggers (time-based, event-based)","retention policies"],"output_types":["persisted state snapshots","checkpoint metadata (timestamp, version)","recovery instructions"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46979781__cap_8","uri":"capability://planning.reasoning.agent.behavior.learning.and.policy.optimization","name":"agent behavior learning and policy optimization","description":"The framework can learn from agent execution traces to optimize routing decisions, topology changes, and agent behavior. Uses reinforcement learning or supervised learning on historical execution data to build models that predict which topology changes improve performance. Agents can also learn from feedback signals (success/failure, user ratings) to adapt their behavior over time.","intents":["I want the system to learn which topology configurations work best for different workload patterns","I need agents to improve their behavior based on feedback from previous executions","I want to optimize routing decisions based on historical performance data"],"best_for":["systems with sufficient execution history to train models","teams with ML expertise to tune learning algorithms","long-running systems where optimization ROI justifies learning overhead"],"limitations":["Requires significant execution history (1000+ traces) to train effective models","Learning models are opaque — difficult to debug why topology changes were made","Feedback signals must be carefully designed to avoid reward hacking","Model training adds 1-10s overhead per optimization cycle depending on data size","No guarantees that learned policies are better than baseline — can degrade performance if poorly tuned"],"requires":["ML framework (TensorFlow, PyTorch, scikit-learn, or equivalent)","Execution trace collection and storage","Feedback signal definition and collection","Model training infrastructure"],"input_types":["execution traces (agent inputs, outputs, latency, errors)","feedback signals (success/failure, user ratings)","performance metrics"],"output_types":["learned policies (routing rules, topology changes)","model predictions (expected performance improvements)","optimization recommendations"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46979781__cap_9","uri":"capability://tool.use.integration.multi.provider.llm.integration.with.fallback.and.load.balancing","name":"multi-provider llm integration with fallback and load balancing","description":"Agents can call multiple LLM providers (OpenAI, Anthropic, local models) with automatic fallback if one provider fails or is rate-limited. The framework abstracts provider-specific APIs behind a unified interface, handles token counting and cost tracking, and distributes requests across providers based on latency, cost, and availability. Supports prompt caching and response deduplication to reduce API calls.","intents":["I want agents to use multiple LLM providers without hardcoding provider logic","I need automatic fallback to alternative providers if one is unavailable","I want to optimize costs by routing requests to cheaper providers when appropriate"],"best_for":["production systems requiring high availability and cost optimization","teams using multiple LLM providers","applications where LLM latency is critical"],"limitations":["Provider abstraction adds 50-200ms overhead per LLM call for routing and fallback logic","Token counting is approximate — actual token usage may differ from estimates","Prompt caching is provider-specific and not all providers support it","Cost tracking is approximate and may not match actual billing","Fallback logic can mask underlying provider issues — difficult to debug"],"requires":["API keys for multiple LLM providers","Provider SDK or HTTP client","Token counting library (tiktoken, etc.)","Optional: cost tracking database"],"input_types":["prompts (text)","provider preferences (cost, latency, availability)","fallback rules"],"output_types":["LLM responses (text)","usage metrics (tokens, cost, latency)","provider selection logs"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":48,"verified":false,"data_access_risk":"high","permissions":["Agent framework runtime (language-specific implementation)","Agents must implement capability declaration interface","JSON Schema or equivalent for input/output type definitions","Metrics collection backend (Prometheus, CloudWatch, or in-memory)","Agent template library for spawning new instances","Performance baseline thresholds configured per agent type","Trace collection infrastructure","Storage backend for traces","Debugging UI or CLI tools","Optional: visualization tools"],"failure_modes":["Circular dependency detection adds O(n²) overhead during topology generation for n agents","Schema matching is type-strict by default; polymorphic agent outputs require explicit adapter agents","No built-in versioning for agent schemas — breaking changes require manual migration","Evolution heuristics are framework-provided and not easily customizable per domain","State transfer during agent mutation can lose in-flight messages if not carefully orchestrated","No rollback mechanism if evolved topology degrades performance — requires manual intervention","Metrics collection adds 5-15% runtime overhead depending on granularity settings","Trace recording adds 10-50% overhead depending on verbosity level","Trace storage grows linearly with execution time — requires cleanup policies","Replay is deterministic only if all external dependencies (APIs, random seeds) are mocked","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.35,"ecosystem":0.46,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.692Z","last_scraped_at":"2026-05-04T08:09:59.925Z","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=agent-framework-that-generates-its-own-topology-an","compare_url":"https://unfragile.ai/compare?artifact=agent-framework-that-generates-its-own-topology-an"}},"signature":"PKPSPoF/TdwrUumiVzlgaAyHglPKy6HAYAnA5RzI3fRRfHDLERKI4hfc/xnvZZ5UZWiIIiHgR6Bb7GNw8h9MCA==","signedAt":"2026-06-22T04:27:03.054Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/agent-framework-that-generates-its-own-topology-an","artifact":"https://unfragile.ai/agent-framework-that-generates-its-own-topology-an","verify":"https://unfragile.ai/api/v1/verify?slug=agent-framework-that-generates-its-own-topology-an","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"}}