{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"agentscope","slug":"agentscope","name":"AgentScope","type":"repo","url":"https://github.com/modelscope/agentscope","page_url":"https://unfragile.ai/agentscope","categories":["ai-agents"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"agentscope__cap_0","uri":"capability://planning.reasoning.react.agent.orchestration.with.native.tool.integration","name":"react agent orchestration with native tool integration","description":"Implements ReActAgent as a core abstraction that orchestrates reasoning, acting, and observation loops by leveraging models' native tool-calling capabilities rather than rigid prompt engineering. The framework uses a message protocol with content blocks to represent agent state, supports middleware for tool execution pipelines, and integrates with ChatModelBase provider architecture to work across OpenAI, Anthropic, Gemini, and DashScope APIs without model-specific branching logic.","intents":["Build multi-step reasoning agents that delegate tool execution to the model's native function-calling API","Create agents that work across different LLM providers without rewriting orchestration logic","Implement agents with custom reasoning loops and observation handling"],"best_for":["Teams building production agentic applications that need provider flexibility","Developers who want model-driven reasoning instead of fixed workflow templates"],"limitations":["ReActAgent assumes models support structured tool-calling; older models or APIs without function-calling support require custom agent implementations","Message protocol overhead adds ~50-100ms per reasoning step due to serialization and content block processing","Middleware execution is sequential, not parallelizable across tool calls within a single step"],"requires":["Python 3.10+","API credentials for at least one supported LLM provider (OpenAI, Anthropic, Gemini, DashScope, etc.)","Toolkit with registered tools for agent to invoke"],"input_types":["natural language prompts","structured tool schemas (JSON)","multimodal content blocks (text, image, audio)"],"output_types":["agent messages with tool calls","observation results","final reasoning outputs"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentscope__cap_1","uri":"capability://automation.workflow.multi.agent.communication.via.msghub.with.publish.subscribe.patterns","name":"multi-agent communication via msghub with publish-subscribe patterns","description":"Provides MsgHub as a centralized message broker that enables agents to communicate asynchronously through publish-subscribe patterns and subscriber broadcasting. Agents register as publishers/subscribers to named topics, and MsgHub routes messages between them with support for both local in-process communication and distributed deployment via Redis backend for multi-tenancy and session state management.","intents":["Coordinate multiple specialized agents that need to exchange information without direct coupling","Build hierarchical agent systems where parent agents delegate to child agents and collect results","Scale agent communication across distributed processes or Kubernetes clusters"],"best_for":["Teams building complex multi-agent systems with 3+ agents requiring loose coupling","Organizations deploying agents on Kubernetes or serverless platforms"],"limitations":["MsgHub adds network latency for distributed deployments; local in-process communication is ~1-5ms, Redis-backed ~50-200ms depending on network","Message ordering guarantees are per-topic, not globally ordered across topics","No built-in message persistence; requires external Redis or in-memory storage configuration"],"requires":["Python 3.10+","Redis server (optional, for distributed deployments; local mode uses in-memory storage)","Agents must be explicitly registered as publishers/subscribers to topics"],"input_types":["agent messages (text, structured data)","topic names (strings)","subscriber callbacks"],"output_types":["routed messages to subscribed agents","broadcast results from multiple subscribers"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentscope__cap_10","uri":"capability://automation.workflow.state.serialization.and.checkpointing.for.agent.persistence.and.recovery","name":"state serialization and checkpointing for agent persistence and recovery","description":"Implements state serialization that enables agents to save and restore their complete state (memory, reasoning, tool results) to persistent storage, enabling recovery from failures and resumption of interrupted tasks. Checkpointing is automatic at configurable intervals or on-demand, and supports multiple storage backends (local filesystem, cloud storage). Serialized state includes agent configuration, message history, and memory snapshots.","intents":["Enable agents to recover from failures without losing progress or context","Pause long-running agent tasks and resume them later or on different infrastructure","Implement agent state versioning for debugging or auditing"],"best_for":["Teams running long-running agents (hours/days) requiring fault tolerance","Organizations deploying agents on unreliable infrastructure (serverless, spot instances)","Applications requiring audit trails of agent execution"],"limitations":["Serialization overhead adds ~100-500ms per checkpoint; frequent checkpointing impacts performance","Serialized state size grows with conversation length; large states require significant storage","State restoration may not be deterministic if external services (APIs, databases) have changed state"],"requires":["Python 3.10+","Persistent storage backend (local filesystem, S3, GCS, etc.)","Checkpoint interval configured (time-based or event-based)"],"input_types":["agent state (messages, memory, configuration)","checkpoint metadata (timestamp, version)"],"output_types":["serialized state snapshots","checkpoint metadata","restored agent state"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentscope__cap_11","uri":"capability://automation.workflow.production.deployment.patterns.with.local.serverless.and.kubernetes.support","name":"production deployment patterns with local, serverless, and kubernetes support","description":"Provides deployment patterns and utilities for running agents in production across different infrastructure models: local development, serverless (AWS Lambda, Google Cloud Functions), and Kubernetes clusters. Deployment patterns include configuration management, environment variable handling, and infrastructure-specific optimizations. The framework abstracts deployment differences, enabling the same agent code to run across environments.","intents":["Deploy agents to production on Kubernetes or serverless platforms without code changes","Manage agent configuration across development, staging, and production environments","Optimize agent resource usage for different deployment targets (local, serverless, K8s)"],"best_for":["Teams deploying agents to production on cloud infrastructure","Organizations requiring multi-environment deployment (dev, staging, prod)","Teams optimizing agent cost and performance across deployment targets"],"limitations":["Serverless deployments have cold-start latency (~1-5s for Python); not suitable for real-time agents","Kubernetes deployments require cluster management overhead; not suitable for simple applications","Configuration management across environments requires careful handling of secrets and environment variables"],"requires":["Python 3.10+","Deployment target infrastructure (local, serverless platform, Kubernetes cluster)","Configuration management (environment variables, config files, or secrets manager)"],"input_types":["agent code","deployment configuration","environment variables"],"output_types":["deployed agent instances","deployment logs","health check status"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentscope__cap_12","uri":"capability://planning.reasoning.human.in.the.loop.interruption.and.approval.workflows","name":"human-in-the-loop interruption and approval workflows","description":"Enables agents to pause execution and request human approval or input at critical decision points through an interruption mechanism. Agents can define interruption points (e.g., before executing high-risk tools), and the framework routes interruption requests to human operators who can approve, reject, or modify agent actions. Interruption state is preserved across agent steps, enabling seamless resumption after human feedback.","intents":["Implement approval workflows where agents request human confirmation before executing critical actions","Enable human operators to override or modify agent decisions in real-time","Build agents that gracefully degrade to human control when uncertain"],"best_for":["Teams building agents for high-stakes domains (finance, healthcare, legal) requiring human oversight","Applications requiring audit trails of human-agent collaboration","Organizations with regulatory requirements for human approval"],"limitations":["Interruption adds latency; agents must wait for human response (seconds to minutes)","Human operators must be available; no automatic fallback if operators are unavailable","Interruption state management adds complexity; requires careful handling of concurrent interruptions"],"requires":["Python 3.10+","Human operator interface (web UI, chat, etc.) for handling interruptions","Interruption routing mechanism (message queue, webhook, etc.)"],"input_types":["agent state at interruption point","proposed action or decision","context for human review"],"output_types":["human approval/rejection","modified action (if human provides override)","resumption signal for agent"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentscope__cap_13","uri":"capability://planning.reasoning.agentic.rl.and.model.fine.tuning.for.agent.behavior.optimization","name":"agentic rl and model fine-tuning for agent behavior optimization","description":"Provides a tuning framework that enables agents to be optimized through reinforcement learning or supervised fine-tuning based on evaluation feedback. The framework supports collecting agent trajectories (reasoning steps, tool calls, outcomes), using evaluation metrics as reward signals, and fine-tuning the underlying LLM to improve agent behavior. Fine-tuning is integrated with the model provider APIs (OpenAI, Anthropic, etc.) for seamless optimization.","intents":["Optimize agent behavior by fine-tuning the underlying LLM on successful agent trajectories","Use evaluation metrics as reward signals to guide agent improvement through RL","Continuously improve agent performance based on production feedback"],"best_for":["Teams with large volumes of agent execution data wanting to optimize behavior","Organizations with clear evaluation metrics for agent quality","Research teams exploring agentic RL and fine-tuning approaches"],"limitations":["Fine-tuning requires significant data (100s-1000s of examples); small datasets may overfit","Fine-tuning cost is substantial; requires careful ROI analysis vs. prompt engineering","Fine-tuned models may lose general capabilities; requires careful evaluation on diverse tasks"],"requires":["Python 3.10+","Large volume of agent trajectories (100s-1000s of examples)","Evaluation metrics to guide fine-tuning","Fine-tuning API access (OpenAI, Anthropic, etc.)"],"input_types":["agent trajectories (reasoning steps, tool calls, outcomes)","evaluation metrics (success/failure, quality scores)","fine-tuning configuration (learning rate, epochs, etc.)"],"output_types":["fine-tuned model checkpoints","fine-tuning metrics (loss, accuracy)","optimized agent behavior"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentscope__cap_14","uri":"capability://planning.reasoning.agent.lifecycle.hooks.and.custom.extension.points","name":"agent lifecycle hooks and custom extension points","description":"Provides a hook system that enables developers to inject custom logic at key points in the agent lifecycle: before/after reasoning, before/after tool execution, on error, on completion. Hooks are registered as callbacks and executed in sequence, enabling cross-cutting concerns (logging, monitoring, validation) without modifying core agent code. Hooks have access to agent state and can modify behavior (e.g., reject tool calls, transform outputs).","intents":["Implement custom logic at agent lifecycle points without modifying core agent code","Add cross-cutting concerns (logging, monitoring, validation) to agents","Extend agent behavior through hooks (e.g., custom error handling, output transformation)"],"best_for":["Teams building custom agent extensions or integrations","Developers needing fine-grained control over agent execution","Organizations implementing domain-specific agent behaviors"],"limitations":["Hook execution is sequential; no parallelization across hooks","Hook errors can break agent execution; requires careful error handling in hooks","Hook ordering matters; no automatic dependency resolution between hooks"],"requires":["Python 3.10+","Hook callback functions implementing standard interface"],"input_types":["agent state at hook point","hook context (reasoning, tool execution, etc.)","hook configuration"],"output_types":["modified agent state (if hook modifies it)","hook execution results","error signals (if hook rejects action)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentscope__cap_15","uri":"capability://tool.use.integration.agent.to.agent.a2a.communication.protocol.for.inter.agent.messaging","name":"agent-to-agent (a2a) communication protocol for inter-agent messaging","description":"Implements an Agent-to-Agent (A2A) communication protocol that enables agents to send structured messages to other agents with request-response semantics. A2A is built on top of MsgHub and provides higher-level abstractions for agent-to-agent interaction, including message routing, timeout handling, and response correlation. Agents can invoke other agents as services without direct coupling.","intents":["Enable agents to delegate tasks to specialized agents and collect results","Build hierarchical agent systems where parent agents orchestrate child agents","Implement agent services that other agents can invoke"],"best_for":["Teams building complex multi-agent systems with hierarchical structures","Organizations implementing agent-as-a-service architectures","Applications requiring agent-to-agent delegation and orchestration"],"limitations":["A2A adds message routing overhead; latency depends on MsgHub backend (local ~5-10ms, Redis ~50-200ms)","Request-response semantics require timeout handling; no automatic retry on timeout","Message serialization may not support all Python types; requires custom serialization for complex objects"],"requires":["Python 3.10+","MsgHub configured for agent communication","Agents registered with A2A protocol support"],"input_types":["agent request messages (structured data)","target agent identifiers","timeout configuration"],"output_types":["agent response messages","response status (success, timeout, error)","correlation identifiers"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentscope__cap_2","uri":"capability://tool.use.integration.toolkit.based.tool.registration.and.execution.with.middleware.support","name":"toolkit-based tool registration and execution with middleware support","description":"Provides a Toolkit core architecture that enables declarative tool registration via decorators or explicit registration, with support for tool groups, meta-tools (tools that compose other tools), and middleware that intercepts tool execution before and after invocation. Tools are registered with JSON schemas for model function-calling, and execution is routed through middleware pipelines that can validate inputs, transform outputs, or implement cross-cutting concerns like logging or rate-limiting.","intents":["Register Python functions as tools that agents can invoke via native model function-calling","Compose complex tools from simpler tools using meta-tool patterns","Implement tool-level middleware for validation, transformation, or monitoring without modifying agent code"],"best_for":["Developers building agents with 5+ tools who need composition and middleware","Teams requiring tool-level observability, validation, or transformation logic"],"limitations":["Tool schemas must be JSON-serializable; complex Python types require custom serialization","Middleware is executed sequentially; no parallel tool execution within a single agent step","Tool registration is static at agent initialization; dynamic tool addition requires agent restart"],"requires":["Python 3.10+","Tool functions must have type hints for schema generation","Middleware must implement standard Toolkit middleware interface"],"input_types":["Python functions with type hints","tool metadata (name, description, parameters)","middleware callables"],"output_types":["JSON schemas for model function-calling","tool execution results","transformed/validated outputs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentscope__cap_3","uri":"capability://tool.use.integration.native.mcp.model.context.protocol.integration.for.external.tool.ecosystems","name":"native mcp (model context protocol) integration for external tool ecosystems","description":"Integrates the Model Context Protocol (MCP) as a first-class subsystem, enabling agents to dynamically discover and invoke tools from external MCP servers without hardcoding tool definitions. MCP tools are registered into the Toolkit system and appear to agents as native tools, with automatic schema translation and execution routing through the MCP client protocol.","intents":["Connect agents to external tool ecosystems (file systems, databases, APIs) via MCP servers","Dynamically load tools from MCP servers without modifying agent code","Reuse MCP tool implementations across multiple agents and frameworks"],"best_for":["Teams integrating agents with external systems via MCP servers","Organizations with existing MCP server infrastructure wanting to leverage it in agents"],"limitations":["MCP server discovery and tool loading adds ~100-500ms startup latency per server","Tool schemas from MCP servers may not fully map to all LLM provider function-calling formats; requires custom schema translation","MCP server availability is a runtime dependency; agent fails if MCP server is unavailable"],"requires":["Python 3.10+","MCP server(s) running and accessible (local or remote)","MCP client library compatible with AgentScope version"],"input_types":["MCP server URIs (stdio, SSE, or HTTP)","MCP tool schemas"],"output_types":["translated tool schemas for agent function-calling","tool execution results from MCP servers"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentscope__cap_4","uri":"capability://memory.knowledge.working.memory.with.compression.and.redis.backed.distributed.state","name":"working memory with compression and redis-backed distributed state","description":"Implements a working memory system that maintains agent conversation history and state, with built-in memory compression to reduce token usage for long conversations (via summarization or sliding-window strategies), and Redis backend support for distributed deployments where multiple agent instances share session state. Memory is organized by session, enabling multi-tenancy and session isolation.","intents":["Maintain agent conversation history across multiple turns without unbounded token growth","Share agent state across distributed instances in Kubernetes or serverless deployments","Implement session-based multi-tenancy where each user/session has isolated memory"],"best_for":["Teams building long-running agents with 50+ conversation turns","Organizations deploying agents on distributed infrastructure requiring shared state","Multi-tenant SaaS applications with per-user agent sessions"],"limitations":["Memory compression (summarization) may lose fine-grained details; compression ratio vs. detail is a tunable tradeoff","Redis backend adds ~50-100ms latency per memory read/write; local in-memory is ~1-5ms","Compression is applied at configured intervals; very long conversations between compressions may still exceed token limits"],"requires":["Python 3.10+","Redis server (optional, for distributed deployments; local mode uses in-memory storage)","Compression strategy configured (summarization, sliding-window, or custom)"],"input_types":["agent messages (text, multimodal content blocks)","session identifiers","compression strategy configuration"],"output_types":["compressed conversation history","session state snapshots","memory statistics (token count, compression ratio)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentscope__cap_5","uri":"capability://memory.knowledge.long.term.memory.integration.with.mem0.and.reme.backends","name":"long-term memory integration with mem0 and reme backends","description":"Provides pluggable long-term memory backends (Mem0 and ReME) that enable agents to store and retrieve persistent knowledge across sessions. Long-term memory is separate from working memory, allowing agents to accumulate insights over time and retrieve relevant past interactions. Integration is abstracted via a memory interface, enabling different backends without agent code changes.","intents":["Enable agents to learn and remember user preferences or domain knowledge across separate conversations","Build agents that improve over time by retrieving relevant past interactions","Implement persistent knowledge bases that multiple agents can query and update"],"best_for":["Teams building long-lived agents that interact with users over weeks/months","Multi-agent systems where agents need to share learned knowledge","Applications requiring persistent user preference or domain knowledge management"],"limitations":["Mem0 and ReME backends require external service dependencies; adds ~200-500ms latency per memory operation","Memory retrieval is semantic-based; relevance depends on embedding quality and may miss exact matches","Long-term memory growth is unbounded; requires periodic cleanup or archival strategies"],"requires":["Python 3.10+","Mem0 API key or ReME server (depending on backend choice)","Embedding model configured for semantic memory retrieval"],"input_types":["agent interactions (text, structured data)","memory queries (natural language or structured)","memory metadata (tags, timestamps)"],"output_types":["retrieved memories (text, structured data)","memory storage confirmations","relevance scores"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentscope__cap_6","uri":"capability://memory.knowledge.rag.system.with.vector.store.integrations.and.semantic.retrieval","name":"rag system with vector store integrations and semantic retrieval","description":"Implements a RAG (Retrieval-Augmented Generation) system that enables agents to retrieve relevant documents from vector stores before generating responses. The system supports multiple vector store backends (Chroma, Pinecone, Weaviate, etc.) with automatic embedding generation, and provides semantic search capabilities to find relevant context for agent reasoning. RAG is integrated into the agent pipeline, allowing agents to augment prompts with retrieved documents.","intents":["Enable agents to answer questions grounded in domain-specific documents or knowledge bases","Reduce hallucination by providing agents with factual context from vector stores","Build agents that can search and synthesize information from large document collections"],"best_for":["Teams building domain-specific agents (customer support, documentation, research)","Applications requiring factual grounding in external knowledge bases","Organizations with large document collections needing semantic search"],"limitations":["Vector store retrieval adds ~100-300ms latency per query; depends on vector store scale and embedding model","Retrieval quality depends on embedding model quality and document chunking strategy; poor embeddings lead to irrelevant results","Vector stores require periodic re-indexing if documents change; no automatic update mechanism"],"requires":["Python 3.10+","Vector store instance (Chroma, Pinecone, Weaviate, etc.) with documents indexed","Embedding model configured (OpenAI, local, or custom)"],"input_types":["user queries (natural language)","documents (text, PDFs, structured data)","retrieval parameters (top-k, similarity threshold)"],"output_types":["retrieved document chunks","similarity scores","augmented prompts with context"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentscope__cap_7","uri":"capability://image.visual.multimodal.agent.support.with.realtime.voice.tts.and.content.blocks","name":"multimodal agent support with realtime voice, tts, and content blocks","description":"Provides native support for multimodal agents that can process and generate text, images, audio, and video through a unified content block message protocol. Agents can invoke Realtime Voice APIs for streaming audio input/output and TTS models for text-to-speech synthesis. Content blocks are serialized into the message protocol, enabling seamless multimodal reasoning across different modalities without modality-specific branching logic.","intents":["Build voice-enabled agents that can listen to audio input and respond with speech","Create agents that process images, documents, or videos alongside text","Implement multimodal reasoning where agents reason across text, images, and audio in a single turn"],"best_for":["Teams building voice assistants or conversational AI with audio I/O","Applications requiring document understanding (OCR, image analysis) in agents","Multimodal applications needing unified reasoning across modalities"],"limitations":["Realtime Voice APIs add ~100-500ms latency for audio streaming; depends on network and model","Content block serialization adds overhead for large media files (images, videos); no built-in compression","Not all LLM providers support all modalities; Realtime Voice is OpenAI-specific, requiring fallback for other providers"],"requires":["Python 3.10+","LLM provider supporting desired modalities (OpenAI for Realtime Voice, Gemini for video, etc.)","Audio hardware (microphone/speaker) for voice agents, or media files for processing"],"input_types":["text prompts","audio streams (WAV, MP3, etc.)","images (PNG, JPEG, etc.)","video files (MP4, etc.)"],"output_types":["text responses","audio streams (TTS output)","structured analysis of images/video","multimodal content blocks"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentscope__cap_8","uri":"capability://automation.workflow.opentelemetry.based.observability.with.tracing.decorators.and.metrics","name":"opentelemetry-based observability with tracing decorators and metrics","description":"Integrates OpenTelemetry as the observability backbone, providing automatic tracing of agent execution, tool calls, and model invocations through decorators and middleware. Traces are exported to OpenTelemetry-compatible backends (Jaeger, Datadog, etc.), enabling distributed tracing across multi-agent systems. Built-in metrics track agent performance, token usage, and error rates without manual instrumentation.","intents":["Monitor agent execution in production with distributed tracing across multiple agents","Debug multi-agent systems by visualizing agent communication and tool execution flows","Track performance metrics (latency, token usage, error rates) for cost and quality optimization"],"best_for":["Teams deploying agents to production requiring observability","Organizations with multi-agent systems needing distributed tracing","Teams optimizing agent performance and cost"],"limitations":["OpenTelemetry instrumentation adds ~10-50ms overhead per traced operation; cumulative for long agent chains","Trace export to remote backends adds network latency; local in-memory collection is faster but requires periodic export","Trace sampling is required for high-volume deployments; sampling reduces visibility into rare events"],"requires":["Python 3.10+","OpenTelemetry SDK and exporter (Jaeger, Datadog, etc.)","Tracing backend configured and accessible"],"input_types":["agent execution events","tool invocations","model API calls","custom span annotations"],"output_types":["distributed traces (spans, events, attributes)","performance metrics (latency, throughput)","error and exception tracking"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentscope__cap_9","uri":"capability://safety.moderation.evaluation.framework.with.openjudge.integration.for.agent.quality.assessment","name":"evaluation framework with openjudge integration for agent quality assessment","description":"Provides an evaluation framework that enables systematic assessment of agent outputs using metrics and judges. Integration with OpenJudge enables LLM-based evaluation (e.g., comparing agent responses to ground truth), and the framework supports custom evaluators and metrics. Evaluations can be run on agent outputs to measure quality, correctness, and alignment without manual review.","intents":["Systematically evaluate agent quality across test cases without manual review","Use LLM-based judges to assess agent outputs against criteria (correctness, helpfulness, safety)","Track evaluation metrics over time to measure agent improvement or regression"],"best_for":["Teams building agents requiring quality assurance before production deployment","Organizations measuring agent performance across multiple dimensions","Research teams evaluating agent behavior and alignment"],"limitations":["LLM-based evaluation (OpenJudge) is subjective and may not align with human judgment; requires careful prompt engineering","Evaluation adds computational cost; running evaluations on large test sets can be expensive","Custom evaluators require implementation; no built-in evaluators for domain-specific tasks"],"requires":["Python 3.10+","OpenJudge API key (for LLM-based evaluation) or custom evaluator implementations","Test cases with expected outputs or evaluation criteria"],"input_types":["agent outputs (text, structured data)","ground truth or evaluation criteria","custom evaluator functions"],"output_types":["evaluation scores (numeric or categorical)","evaluation reports","metric aggregations"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentscope__headline","uri":"capability://tool.use.integration.ai.agent.framework.for.building.llm.powered.applications","name":"ai agent framework for building llm-powered applications","description":"AgentScope is a production-ready framework designed for creating flexible, multi-agent systems that leverage large language models, enabling dynamic tool integration and efficient communication protocols for diverse applications.","intents":["best AI agent framework","AI framework for multi-agent systems","production-ready LLM applications","flexible agent frameworks for developers","top tools for building AI agents"],"best_for":["developers building AI agents","teams needing multi-agent orchestration"],"limitations":[],"requires":["Python 3.10 or higher"],"input_types":[],"output_types":[],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Python 3.10+","API credentials for at least one supported LLM provider (OpenAI, Anthropic, Gemini, DashScope, etc.)","Toolkit with registered tools for agent to invoke","Redis server (optional, for distributed deployments; local mode uses in-memory storage)","Agents must be explicitly registered as publishers/subscribers to topics","Persistent storage backend (local filesystem, S3, GCS, etc.)","Checkpoint interval configured (time-based or event-based)","Deployment target infrastructure (local, serverless platform, Kubernetes cluster)","Configuration management (environment variables, config files, or secrets manager)","Human operator interface (web UI, chat, etc.) for handling interruptions"],"failure_modes":["ReActAgent assumes models support structured tool-calling; older models or APIs without function-calling support require custom agent implementations","Message protocol overhead adds ~50-100ms per reasoning step due to serialization and content block processing","Middleware execution is sequential, not parallelizable across tool calls within a single step","MsgHub adds network latency for distributed deployments; local in-process communication is ~1-5ms, Redis-backed ~50-200ms depending on network","Message ordering guarantees are per-topic, not globally ordered across topics","No built-in message persistence; requires external Redis or in-memory storage configuration","Serialization overhead adds ~100-500ms per checkpoint; frequent checkpointing impacts performance","Serialized state size grows with conversation length; large states require significant storage","State restoration may not be deterministic if external services (APIs, databases) have changed state","Serverless deployments have cold-start latency (~1-5s for Python); not suitable for real-time agents","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:02.370Z","last_scraped_at":null,"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=agentscope","compare_url":"https://unfragile.ai/compare?artifact=agentscope"}},"signature":"IssOHHlANwvaoxLpQt2JOqJ5SPwkHzNIQQpw3CpFvH/7x95AiSjcyVaatw/gjjsjgiliCGvUJncweLscUpQtBg==","signedAt":"2026-06-21T11:45:28.545Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/agentscope","artifact":"https://unfragile.ai/agentscope","verify":"https://unfragile.ai/api/v1/verify?slug=agentscope","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"}}