{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-getbindu--bindu","slug":"getbindu--bindu","name":"Bindu","type":"agent","url":"https://docs.getbindu.com","page_url":"https://unfragile.ai/getbindu--bindu","categories":["ai-agents"],"tags":["a2a","agent-communication","agent-orchestration","ai-agent","autonomous-agents","eu-ai-act","machine-learning"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-getbindu--bindu__cap_0","uri":"capability://automation.workflow.python.function.to.microservice.transformation.via.decorator.pattern","name":"python function-to-microservice transformation via decorator pattern","description":"Transforms arbitrary Python functions into production-ready AI agent microservices through the bindufy() decorator, which orchestrates configuration validation, manifest generation, storage backend initialization, and JSON-RPC protocol compliance. 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Contexts are associated with tasks and can be retrieved to provide agents with conversation history for decision-making.","intents":["Maintain conversation history across multiple agent interactions","Provide agents with context from previous exchanges","Enable multi-turn dialogue workflows"],"best_for":["Conversational agent systems requiring dialogue coherence","Applications with long-running agent interactions","Teams building agent assistants or chatbots"],"limitations":["Context size is unbounded — large conversation histories consume memory and storage","No built-in context summarization or pruning — requires manual cleanup","Context retrieval is not optimized for semantic search — requires full history scan","No built-in context versioning — difficult to track context evolution"],"requires":["Storage backend for persisting context","Task association with context","Context retrieval API"],"input_types":["Conversation messages (user input, agent response)","Context metadata (task_id, agent_id, timestamp)","Context queries for retrieval"],"output_types":["Persisted context objects","Retrieved conversation history","Context-aware agent responses"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-getbindu--bindu__cap_13","uri":"capability://automation.workflow.production.deployment.with.docker.kubernetes.and.load.balancing.support","name":"production deployment with docker, kubernetes, and load balancing support","description":"Provides deployment guidance and configuration for running Bindu agents in production environments, including Docker containerization, Kubernetes orchestration, database setup (PostgreSQL), caching/scheduling (Redis), and load balancing. 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The abstraction enables switching between backends without code changes, with PostgresStorage handling schema migrations, connection pooling, and transactional consistency.","intents":["Persist agent task state and conversation history to a durable database","Prototype agents with in-memory storage before deploying to PostgreSQL","Switch storage backends without modifying agent code"],"best_for":["Production deployments requiring durable state persistence","Development teams wanting to prototype with in-memory storage before database setup","Organizations with existing PostgreSQL infrastructure"],"limitations":["InMemoryStorage is not thread-safe — requires external locking for concurrent access","PostgresStorage requires manual schema migrations (Alembic) — no automatic schema evolution","No built-in sharding or partitioning — single PostgreSQL instance is a bottleneck at scale","Storage interface does not support transactions across multiple entities — requires application-level coordination"],"requires":["PostgreSQL 12+ (for PostgresStorage)","Alembic for database migrations","psycopg2 or asyncpg driver","Database credentials and connection string in environment"],"input_types":["Task objects with state and metadata","Context objects with conversation history","Agent state snapshots"],"output_types":["Persisted task records","Retrieved context and conversation history","Query results for task filtering/searching"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-getbindu--bindu__cap_5","uri":"capability://automation.workflow.distributed.task.scheduling.with.redis.and.in.memory.schedulers","name":"distributed task scheduling with redis and in-memory schedulers","description":"Provides a Scheduler interface for queuing and distributing tasks across worker processes, with InMemoryScheduler for single-process execution and RedisScheduler for distributed worker pools. Workers poll the scheduler for pending tasks, execute them via the TaskManager, and report results back, enabling horizontal scaling of agent workloads.","intents":["Distribute agent task execution across multiple worker processes or machines","Scale agent workload horizontally by adding more workers","Decouple task submission from execution for better resource utilization"],"best_for":["High-throughput agent systems requiring horizontal scaling","Teams running agents on Kubernetes or other container orchestration platforms","Applications with bursty workloads needing elastic worker scaling"],"limitations":["InMemoryScheduler cannot distribute tasks across processes — single-process only","RedisScheduler requires Redis cluster for high availability — single Redis instance is a single point of failure","No built-in task prioritization or SLA enforcement — all tasks treated equally","Worker heartbeat mechanism is basic — no automatic dead worker detection or task reassignment"],"requires":["Redis 6+ (for RedisScheduler)","Worker processes configured to poll scheduler","Network connectivity between workers and Redis","Task timeout configuration to prevent hung workers"],"input_types":["Task objects ready for execution","Worker registration and heartbeat messages","Task completion/failure reports"],"output_types":["Queued task identifiers","Task assignment to workers","Execution status updates"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-getbindu--bindu__cap_6","uri":"capability://tool.use.integration.agent.discovery.and.did.resolution.with.dynamic.endpoint.lookup","name":"agent discovery and did resolution with dynamic endpoint lookup","description":"Implements agent discovery through Decentralized Identifiers (DIDs) and a DID resolver that maps DIDs to agent endpoints. The system includes a DIDAgentExtension that manages DID creation, registration, and resolution, enabling agents to locate and communicate with other agents without hardcoded addresses.","intents":["Discover agent endpoints dynamically using DIDs instead of hardcoded IP addresses","Register agent identity and endpoint with a DID resolver","Resolve DIDs to agent endpoints for inter-agent communication"],"best_for":["Decentralized agent networks requiring dynamic discovery","Organizations adopting W3C DID standards","Systems where agents may move between endpoints or scale horizontally"],"limitations":["DID resolution requires external resolver service — no built-in peer discovery mechanism","DID resolver latency adds overhead to every inter-agent call — caching required for performance","No built-in DID revocation or key rotation — requires manual coordination","DID format and resolution method must be agreed upon by all agents in network"],"requires":["DID resolver service (e.g., did:web resolver, did:key resolver, or custom implementation)","DID method support (did:web, did:key, did:ethr, etc.)","Network connectivity to DID resolver"],"input_types":["DID string (e.g., did:web:example.com:agent1)","Agent endpoint URL","DID document with public keys and service endpoints"],"output_types":["Resolved agent endpoint URL","DID document","Agent identity verification"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-getbindu--bindu__cap_7","uri":"capability://safety.moderation.authentication.and.authorization.with.auth0.integration.and.permission.system","name":"authentication and authorization with auth0 integration and permission system","description":"Provides authentication via Auth0 integration and a permission system that controls which agents can invoke which tasks on other agents. The system includes role-based access control (RBAC) and a permission middleware that validates requests before task execution.","intents":["Authenticate agent requests using Auth0 or other identity providers","Control which agents have permission to invoke specific tasks","Implement role-based access control for agent-to-agent communication"],"best_for":["Enterprise agent networks requiring fine-grained access control","Organizations using Auth0 for identity management","Systems with sensitive agent operations requiring authorization"],"limitations":["Auth0 integration requires Auth0 tenant and configuration — adds external dependency","Permission system is role-based only — no attribute-based or policy-based access control","No built-in audit logging of authorization decisions — requires custom middleware","Permission checks add latency to every task invocation — caching required for performance"],"requires":["Auth0 tenant and application credentials","Auth0 API token for permission validation","Permission definitions in agent configuration or database"],"input_types":["Auth0 JWT token","Agent identity (DID or name)","Task name and parameters"],"output_types":["Authorization decision (allow/deny)","Authenticated agent identity","Permission validation result"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-getbindu--bindu__cap_8","uri":"capability://tool.use.integration.payment.system.with.x402.protocol.and.on.chain.verification","name":"payment system with x402 protocol and on-chain verification","description":"Implements a payment system using the X402 HTTP payment protocol, enabling agents to charge for task execution and verify payments on-chain. The system includes X402Middleware for payment validation, payment session management, and settlement tracking.","intents":["Charge other agents for accessing expensive or resource-intensive tasks","Verify payments on-chain before executing tasks","Track payment sessions and settlement status"],"best_for":["Agent marketplaces where agents provide services for payment","Resource-intensive agent operations requiring cost recovery","Decentralized systems requiring trustless payment verification"],"limitations":["X402 protocol is not widely adopted — limited client support","On-chain verification requires blockchain integration — adds latency and cost","Payment settlement is asynchronous — agents may need to wait for confirmation","No built-in dispute resolution or refund mechanism"],"requires":["X402 compatible HTTP client","Blockchain node or RPC endpoint for on-chain verification","Payment wallet with sufficient balance","Smart contract for payment settlement (if using blockchain)"],"input_types":["X402 payment request (amount, currency, payment address)","Payment proof (transaction hash, signature)","Payment session identifier"],"output_types":["X402 payment challenge","Payment verification result","Settlement confirmation"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-getbindu--bindu__cap_9","uri":"capability://automation.workflow.observability.with.opentelemetry.and.sentry.integration","name":"observability with opentelemetry and sentry integration","description":"Provides comprehensive observability through OpenTelemetry integration for distributed tracing and metrics, and Sentry integration for error tracking and performance monitoring. 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