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Each agent maintains its own configuration, prompt templates, and skill bindings, enabling independent behavior while sharing the same infrastructure layer.","intents":["I need to create and manage multiple AI agents with different configurations and capabilities","I want agents to run autonomously on schedules without manual intervention","I need to coordinate agent execution across different entrypoints (web, Telegram, Twitter)","I want to persist agent state and conversation memory across sessions"],"best_for":["teams building multi-agent AI systems for Web3/blockchain use cases","developers needing self-hosted agent infrastructure without cloud dependencies","builders creating autonomous trading, social media, or blockchain interaction bots"],"limitations":["Currently in alpha stage — not recommended for production use","LangGraph dependency adds complexity for simple single-agent use cases","No built-in distributed execution — agents run on single cluster instance","Memory storage requires external database configuration; no in-memory fallback"],"requires":["Python 3.8+","LangGraph library installed","Persistent database (PostgreSQL recommended based on architecture)","API keys for LLM providers (OpenAI, Anthropic, or compatible)","Docker for containerized deployment"],"input_types":["agent configuration JSON","user prompts/requests","skill definitions","conversation history"],"output_types":["agent execution results","conversation responses","skill execution logs","state snapshots"],"categories":["planning-reasoning","automation-workflow","agent-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-crestalnetwork--intentkit__cap_1","uri":"capability://tool.use.integration.extensible.skill.system.with.schema.based.capability.registration","name":"extensible skill system with schema-based capability registration","description":"IntentKit provides an IntentKitSkill base class that allows developers to define new agent capabilities through a modular skill framework. 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The system supports categorized skills including blockchain, social media, and financial data operations, with each skill maintaining its own state and configuration.","intents":["I want to add custom capabilities to agents without modifying core framework code","I need to create reusable skill modules that multiple agents can share","I want to control skill behavior through configuration schemas","I need to persist skill state and data independently from agent state"],"best_for":["developers extending IntentKit with domain-specific capabilities","teams building skill libraries for specific industries (DeFi, social media, trading)","builders needing modular, composable agent functionality"],"limitations":["Skill schema validation is configuration-based; no runtime type checking","No built-in skill versioning — breaking changes require manual migration","Skill store requires external persistence layer; no in-memory cache","Limited documentation on skill composition and dependency management"],"requires":["Python 3.8+","IntentKitSkill base class imported from framework","JSON schema definition for skill parameters","Persistent skill store configured (database)","Understanding of agent configuration format"],"input_types":["skill class definition","JSON schema configuration","skill parameters","agent context/state"],"output_types":["skill execution results","state mutations","skill logs","persisted skill data"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-crestalnetwork--intentkit__cap_10","uri":"capability://tool.use.integration.plugin.system.for.extensible.agent.capabilities.work.in.progress","name":"plugin system for extensible agent capabilities (work in progress)","description":"IntentKit includes a plugin system architecture (currently in development) that will enable developers to extend agent capabilities through plugins beyond the skill framework. The plugin system is designed to support dynamic loading of capability modules without framework recompilation. While the full plugin system is not yet complete, the architecture is in place to support third-party plugin development alongside the core skill system.","intents":["I want to extend agent capabilities through plugins without modifying core code","I need to load and unload capabilities dynamically at runtime","I want to use third-party plugins to add specialized functionality","I need plugin isolation and dependency management"],"best_for":["teams planning to build plugin ecosystems around IntentKit","developers needing extensibility beyond the skill framework","projects requiring dynamic capability loading"],"limitations":["Plugin system is work-in-progress — not production-ready","No plugin marketplace or discovery mechanism","Plugin isolation and sandboxing are not implemented","Dependency management between plugins is not defined","No plugin versioning or compatibility checking"],"requires":["Python 3.8+","Understanding of plugin architecture (documentation pending)","Plugin development framework (to be defined)","Plugin loading mechanism (in development)"],"input_types":["plugin definition","plugin metadata","capability specifications"],"output_types":["loaded plugin instances","capability registrations","plugin status"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-crestalnetwork--intentkit__cap_2","uri":"capability://tool.use.integration.blockchain.interaction.skills.with.evm.chain.support","name":"blockchain interaction skills with evm chain support","description":"IntentKit includes pre-built blockchain skills that enable agents to interact with Ethereum Virtual Machine (EVM) compatible chains. 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The blockchain skill layer abstracts away low-level Web3 complexity while maintaining full control over transaction parameters and execution.","intents":["I want agents to execute blockchain transactions autonomously","I need agents to query on-chain data and react to blockchain events","I want to build trading or DeFi interaction bots with agent automation","I need wallet management and transaction signing within agent workflows"],"best_for":["Web3 developers building autonomous trading or DeFi agents","teams creating blockchain-native applications with agent automation","builders needing EVM chain interaction without manual Web3.py boilerplate"],"limitations":["Currently supports EVM chains only — no Solana, Cosmos, or other L1s","Transaction execution is synchronous — no built-in async polling for confirmations","No built-in slippage protection or MEV mitigation strategies","Wallet key management requires secure external storage; no HSM integration"],"requires":["Python 3.8+","Web3.py library","RPC endpoint URL for target EVM chain","Private key or wallet signer configured","Sufficient gas tokens for transaction execution"],"input_types":["contract ABI","transaction parameters","wallet address","chain ID"],"output_types":["transaction hash","transaction receipt","on-chain data queries","execution status"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-crestalnetwork--intentkit__cap_3","uri":"capability://tool.use.integration.social.media.integration.with.telegram.and.twitter.entrypoints","name":"social media integration with telegram and twitter entrypoints","description":"IntentKit provides native integration with Telegram and Twitter as entrypoints, allowing agents to receive messages from these platforms, process them through the agent engine, and respond directly. The system maintains conversation context across platform interactions, routes incoming messages to appropriate agents based on configuration, and handles platform-specific formatting and authentication. Each platform integration is implemented as a separate entrypoint that feeds into the core agent execution layer.","intents":["I want agents to respond to Telegram messages in real-time","I need agents to monitor and respond to Twitter mentions or DMs","I want to maintain conversation context across social media interactions","I need to route different social media channels to different agents"],"best_for":["teams building social media bots with autonomous agent responses","Web3 projects needing Telegram/Twitter community engagement automation","builders creating multi-platform chatbots with unified agent backend"],"limitations":["Telegram integration requires bot token and webhook/polling configuration","Twitter integration limited to API v2 endpoints; no real-time streaming","No built-in rate limiting — requires external rate limiter for high-volume interactions","Message context is conversation-scoped; no cross-conversation learning"],"requires":["Python 3.8+","Telegram Bot Token (from BotFather)","Twitter API v2 credentials (Bearer token or OAuth)","Webhook URL or polling mechanism for message ingestion","Agent configuration mapping social media channels to agent IDs"],"input_types":["Telegram message objects","Twitter API events","user IDs","conversation context"],"output_types":["Telegram message responses","Twitter replies/DMs","conversation logs","interaction metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-crestalnetwork--intentkit__cap_4","uri":"capability://data.processing.analysis.credit.and.quota.management.system.with.multi.account.support","name":"credit and quota management system with multi-account support","description":"IntentKit implements a credit management system that tracks agent usage and enforces quotas across different account types (user, agent, platform). The system supports three credit types (FREE with daily refills, PERMANENT from top-ups, REWARD earned through activities) and tracks both income events (recharge, reward, refill) and expense events (message, skill call). Credits are deducted per agent action, enabling fine-grained usage tracking and cost allocation across multiple agents and users.","intents":["I want to track and limit agent usage to control costs","I need to implement tiered access with different credit allowances","I want to reward users for certain agent interactions","I need to allocate costs across multiple agents and users"],"best_for":["teams building multi-tenant agent platforms with usage-based pricing","builders needing fine-grained cost tracking for agent operations","platforms implementing freemium models with daily/monthly quotas"],"limitations":["Credit system is synchronous — no async batch processing for large-scale deductions","No built-in refund mechanism for failed skill executions","Credit expiration policies are not configurable per account type","No real-time credit balance notifications or low-balance alerts"],"requires":["Python 3.8+","Persistent database for credit ledger","Account configuration with credit type assignments","Skill configuration with credit cost definitions","Scheduled job for daily FREE credit refills"],"input_types":["account ID","credit type","transaction type","amount","skill/action identifier"],"output_types":["credit balance","transaction ledger","quota status","usage reports"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-crestalnetwork--intentkit__cap_5","uri":"capability://automation.workflow.autonomous.agent.scheduling.and.execution","name":"autonomous agent scheduling and execution","description":"IntentKit enables agents to run autonomously on schedules without manual intervention. The system stores scheduling configurations in the database, executes agents at specified intervals through a scheduler component, and maintains execution logs for monitoring. Autonomous execution integrates with the core agent engine, allowing scheduled agents to access all skills and entrypoints available to manually-triggered agents, with full state and memory preservation across execution cycles.","intents":["I want agents to run on a schedule (hourly, daily, weekly)","I need agents to monitor conditions and take action autonomously","I want to execute complex workflows on a timer without manual triggers","I need visibility into autonomous agent execution history and failures"],"best_for":["teams building autonomous trading, monitoring, or data collection bots","builders needing scheduled agent execution without external schedulers","projects requiring autonomous blockchain interactions on fixed schedules"],"limitations":["Scheduler runs on single cluster instance — no distributed scheduling across multiple nodes","No built-in backoff or retry logic for failed scheduled executions","Execution logs are stored in database; no real-time monitoring dashboard","Scheduling granularity is limited to minute-level precision"],"requires":["Python 3.8+","Persistent database for schedule configuration","Scheduler service running continuously","Agent configuration with schedule definitions","LLM API access for agent execution"],"input_types":["schedule definition (cron or interval)","agent ID","execution parameters","trigger conditions"],"output_types":["execution logs","execution status","agent output","error reports"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-crestalnetwork--intentkit__cap_6","uri":"capability://memory.knowledge.persistent.agent.memory.and.conversation.context.management","name":"persistent agent memory and conversation context management","description":"IntentKit maintains persistent memory storage for agent conversations and state across sessions. The system stores conversation history, agent context, and skill-specific data in a dedicated memory layer, enabling agents to recall previous interactions and maintain coherent behavior across multiple invocations. Memory is indexed by agent and conversation ID, allowing agents to retrieve relevant context when processing new requests through any entrypoint.","intents":["I want agents to remember previous conversations and user interactions","I need agents to maintain context across multiple message exchanges","I want to retrieve conversation history for auditing or analysis","I need agents to learn from past interactions within a session"],"best_for":["teams building conversational agents with multi-turn interactions","builders needing conversation history for compliance or auditing","projects requiring agents to maintain context across platform interactions"],"limitations":["Memory storage is conversation-scoped; no cross-conversation learning or semantic search","No built-in memory compression or summarization for long conversations","Memory retrieval is linear scan — no vector indexing for semantic similarity","No automatic memory cleanup or retention policies"],"requires":["Python 3.8+","Persistent database for memory storage","Agent configuration with memory settings","Conversation ID tracking across entrypoints","Memory schema definition"],"input_types":["conversation ID","agent ID","message content","metadata"],"output_types":["conversation history","context summaries","memory snapshots","retrieval results"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-crestalnetwork--intentkit__cap_7","uri":"capability://tool.use.integration.web.api.entrypoint.for.agent.invocation.and.management","name":"web api entrypoint for agent invocation and management","description":"IntentKit exposes a Web API that allows external systems to invoke agents, manage agent configurations, and query execution results. The API provides RESTful endpoints for agent creation, skill configuration, message submission, and result retrieval. The Web API integrates with the core agent engine and credit system, enforcing quotas and logging all API interactions for auditing. API authentication is handled through API keys or user credentials, with role-based access control for agent management.","intents":["I want to invoke agents programmatically from external applications","I need to create and configure agents through an API","I want to query agent execution results and conversation history","I need to manage agent skills and configurations remotely"],"best_for":["teams integrating IntentKit agents into larger applications","builders creating agent management dashboards or control panels","projects needing programmatic agent invocation from external services"],"limitations":["API is synchronous — no WebSocket support for real-time streaming responses","No built-in API rate limiting — requires external API gateway","Authentication is basic (API keys or credentials) — no OAuth2 or OIDC","API response payloads are not paginated for large result sets"],"requires":["Python 3.8+","Web framework (FastAPI or similar) configured","API key or credential management system","Agent engine running and accessible","Database for API audit logs"],"input_types":["JSON request bodies","agent ID","user message","skill parameters"],"output_types":["JSON responses","agent execution results","configuration objects","status codes"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-crestalnetwork--intentkit__cap_8","uri":"capability://tool.use.integration.financial.and.market.data.skills.for.agent.decision.making","name":"financial and market data skills for agent decision-making","description":"IntentKit includes pre-built skills for accessing financial and market data, enabling agents to query price feeds, market conditions, and financial metrics. These skills are implemented as specialized IntentKitSkill subclasses that integrate with external financial data providers and cache results to minimize API calls. Agents can use these skills to make informed decisions in trading, DeFi, or investment workflows, with data freshness and accuracy controlled through skill configuration.","intents":["I want agents to query real-time price data for trading decisions","I need agents to analyze market conditions and trends","I want agents to access financial metrics and on-chain analytics","I need agents to make data-driven decisions based on market conditions"],"best_for":["Web3 teams building autonomous trading or investment agents","builders creating DeFi monitoring and decision-making bots","projects needing market data integration without manual API management"],"limitations":["Data freshness depends on external provider update frequency","No built-in data validation or anomaly detection","Caching strategy is fixed — no configurable TTL per data type","Limited to supported financial data providers; no custom data source integration"],"requires":["Python 3.8+","API keys for financial data providers (CoinGecko, Chainlink, etc.)","Network connectivity to external data providers","Skill configuration with data source mappings","Cache storage (Redis or database)"],"input_types":["asset symbol or address","time range","metric type","data source identifier"],"output_types":["price data","market metrics","historical data","analytics results"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-crestalnetwork--intentkit__cap_9","uri":"capability://data.processing.analysis.agent.configuration.persistence.and.versioning","name":"agent configuration persistence and versioning","description":"IntentKit stores agent configurations in a persistent database, enabling agents to be created, modified, and retrieved without code changes. Agent configurations include prompts, skill bindings, model selection, and execution parameters. The system maintains agent schemas and allows configuration export/import through scripts, though full versioning and rollback capabilities are not yet implemented. Configuration changes are applied immediately to running agents without requiring restart.","intents":["I want to create and modify agent configurations without code deployment","I need to store agent prompts and skill bindings persistently","I want to export and import agent configurations across environments","I need to manage different agent configurations for different use cases"],"best_for":["teams managing multiple agent configurations in production","builders needing configuration-driven agent behavior","projects requiring agent configuration portability across deployments"],"limitations":["No built-in versioning — configuration changes overwrite previous versions","No rollback mechanism for failed configuration updates","Export/import is manual through scripts — no UI-based configuration management","Configuration validation is schema-based but not comprehensive"],"requires":["Python 3.8+","Persistent database for configuration storage","JSON schema definition for agent configuration","Export/import scripts (create.sh, export.sh, import.sh)","Agent ID and naming conventions"],"input_types":["agent configuration JSON","prompt templates","skill definitions","model parameters"],"output_types":["agent configuration objects","exported configuration files","configuration validation results"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","LangGraph library installed","Persistent database (PostgreSQL recommended based on architecture)","API keys for LLM providers (OpenAI, Anthropic, or compatible)","Docker for containerized deployment","IntentKitSkill base class imported from framework","JSON schema definition for skill parameters","Persistent skill store configured (database)","Understanding of agent configuration format","Understanding of plugin architecture (documentation pending)"],"failure_modes":["Currently in alpha stage — not recommended for production use","LangGraph dependency adds complexity for simple single-agent use cases","No built-in distributed execution — agents run on single cluster instance","Memory storage requires external database configuration; no in-memory fallback","Skill schema validation is configuration-based; no runtime type checking","No built-in skill versioning — breaking changes require manual migration","Skill store requires external persistence layer; no in-memory cache","Limited documentation on skill composition and dependency management","Plugin system is work-in-progress — not production-ready","No plugin marketplace or discovery mechanism","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6190305161318429,"quality":0.47,"ecosystem":0.6000000000000001,"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":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:57:04.027Z","last_commit":"2026-04-24T17:41:58Z"},"community":{"stars":6503,"forks":705,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=crestalnetwork--intentkit","compare_url":"https://unfragile.ai/compare?artifact=crestalnetwork--intentkit"}},"signature":"ZFPbVZLGZQr6j8UNjA0rsY/uFA1lo1BQdw8AUh/kVZ6w/D8ey1aktt9bmrZawqwyJaGNkDEFanwXw8Ltq9XRAw==","signedAt":"2026-06-21T23:43:41.577Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/crestalnetwork--intentkit","artifact":"https://unfragile.ai/crestalnetwork--intentkit","verify":"https://unfragile.ai/api/v1/verify?slug=crestalnetwork--intentkit","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"}}