{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-adala","slug":"adala","name":"Adala","type":"agent","url":"https://github.com/HumanSignal/Adala","page_url":"https://unfragile.ai/adala","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-adala__cap_0","uri":"capability://planning.reasoning.autonomous.skill.learning.through.iterative.environment.feedback","name":"autonomous skill learning through iterative environment feedback","description":"Agents autonomously acquire and refine skills by executing tasks in defined environments, observing outcomes, and reflecting on performance to improve. The learning phase (agent.learn()) orchestrates a feedback loop where the agent applies skills, receives structured feedback from the environment, and uses that feedback to refine skill prompts and execution strategies without manual intervention. This is implemented via a Pydantic-based agent orchestrator that coordinates skill execution, environment interaction, and runtime-based LLM calls to progressively improve task performance.","intents":["I want an agent to automatically improve its labeling accuracy by learning from feedback without retraining","I need to set up a system where agents refine their own prompts based on task performance","I want to minimize manual prompt engineering by letting agents learn optimal strategies"],"best_for":["teams building autonomous data labeling pipelines","organizations wanting to reduce manual annotation effort","developers implementing self-improving ML data processing workflows"],"limitations":["Learning convergence depends on quality and consistency of environment feedback — poor feedback signals lead to degraded performance","No built-in persistence of learned skills across agent instances — requires external serialization (pickle/JSON) to preserve improvements","Learning phase can be computationally expensive with large datasets due to iterative LLM calls per data sample"],"requires":["Python 3.9+","LLM runtime configured (OpenAI, Anthropic, or LiteLLM-compatible provider)","Environment implementation providing structured feedback (correctness signals, error messages)","Skill definitions with learnable parameters (prompt templates)"],"input_types":["structured data (JSON/dict)","text documents","images (with multi-modal LLM support)"],"output_types":["refined skill definitions","labeled/processed data","performance metrics and learning logs"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-adala__cap_1","uri":"capability://automation.workflow.composable.skill.orchestration.with.linear.and.parallel.execution","name":"composable skill orchestration with linear and parallel execution","description":"Skills are organized into SkillSets that define execution patterns: LinearSkillSet chains skills sequentially where each skill's output becomes the next skill's input, while ParallelSkillSet executes multiple skills concurrently and combines their outputs. This composition is implemented via a SkillSet base class that manages skill ordering, data flow between skills, and output aggregation. The runtime system executes each skill through LLM calls, enabling complex multi-step data processing pipelines without custom orchestration code.","intents":["I want to chain multiple data processing steps (e.g., extract entities, then classify them, then validate)","I need to run multiple labeling skills in parallel and merge their results","I want to build complex data pipelines by composing simple, reusable skills"],"best_for":["developers building multi-stage data processing workflows","teams needing flexible skill composition without writing orchestration logic","organizations processing data through multiple labeling/extraction stages"],"limitations":["LinearSkillSet introduces sequential latency — each skill waits for the previous one to complete, no pipelining optimization","ParallelSkillSet output merging is simplistic (concatenation/dict merge) — complex aggregation logic requires custom skill implementation","No built-in error handling or retry logic for individual skills in a SkillSet — one skill failure halts the entire set","Skill composition is static at definition time — dynamic skill selection based on data requires wrapper skills"],"requires":["Python 3.9+","Skill definitions with compatible input/output schemas","Runtime configured for skill execution","Pydantic models for skill I/O validation"],"input_types":["structured data (dict/JSON)","text","images (with multi-modal skills)"],"output_types":["structured data (merged/aggregated from skills)","labeled data","extracted entities or classifications"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-adala__cap_10","uri":"capability://planning.reasoning.prompt.improvement.and.skill.refinement.through.llm.based.reflection","name":"prompt improvement and skill refinement through llm-based reflection","description":"Adala includes a prompt improvement skill that uses LLM-based reflection to analyze task failures and suggest prompt refinements. When an agent's skill produces incorrect outputs, the improvement skill examines the failure, generates explanations, and proposes better prompts. This is implemented via a dedicated PromptImprovement skill that calls the LLM with failure analysis prompts. The refined prompts are then tested and validated, creating an automated prompt optimization loop without manual intervention.","intents":["I want agents to automatically improve their prompts when they make mistakes","I need to reduce manual prompt engineering by letting agents suggest improvements","I want to understand why agents fail and fix issues automatically","I need to track prompt evolution and understand what changes helped"],"best_for":["teams wanting to minimize manual prompt engineering","organizations building self-improving labeling systems","developers implementing autonomous prompt optimization"],"limitations":["Prompt improvement quality depends on LLM's ability to analyze failures — complex failure modes may not be identified","Improved prompts are not validated before use — bad improvements can degrade performance further","No built-in rollback mechanism — failed improvements persist unless explicitly reverted","Improvement process adds latency and cost (additional LLM calls) — not suitable for real-time systems"],"requires":["Python 3.9+","Runtime configured with capable LLM (GPT-4 or equivalent recommended)","Failure analysis data (incorrect outputs, expected outputs, error messages)","PromptImprovement skill implementation"],"input_types":["failed task examples","expected outputs","error messages or feedback"],"output_types":["improved prompt suggestions","improvement explanations","refined skill definitions"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-adala__cap_11","uri":"capability://automation.workflow.agent.serialization.and.state.persistence.for.checkpointing.and.recovery","name":"agent serialization and state persistence for checkpointing and recovery","description":"Adala agents can be serialized to and deserialized from disk using Python's pickle format or JSON, enabling checkpointing and recovery. Agent state (skills, learned prompts, execution history) is preserved, allowing agents to resume from checkpoints without losing progress. This is implemented via Pydantic model serialization that captures the complete agent configuration and learned state. Serialized agents can be shared, versioned, or deployed across different environments.","intents":["I want to save agent state and resume learning from a checkpoint","I need to version agents and track changes to learned prompts","I want to share trained agents with team members or deploy them","I need to recover from failures without losing learned improvements"],"best_for":["teams running long-running learning processes","organizations needing agent versioning and reproducibility","developers implementing fault-tolerant agent systems"],"limitations":["Pickle serialization is Python-specific — serialized agents cannot be used in other languages","Large agents with extensive learning history can produce large serialized files","No built-in versioning or diff tracking — comparing agent versions requires manual inspection","Serialized state may become incompatible with code changes — requires careful version management"],"requires":["Python 3.9+","Disk storage for serialized agent files","Same Python environment for deserialization (compatible versions)"],"input_types":["agent objects (Pydantic models)"],"output_types":["serialized agent files (pickle or JSON)","agent state snapshots"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-adala__cap_12","uri":"capability://automation.workflow.docker.and.kubernetes.deployment.with.containerized.agent.services","name":"docker and kubernetes deployment with containerized agent services","description":"Adala provides Docker and Kubernetes deployment guides and configurations for containerizing agents as services. The framework supports building Docker images with agents, deploying to Kubernetes clusters, and managing agent scaling via container orchestration. Integration with ArgoCD enables GitOps-based deployment workflows. The architecture enables agents to be deployed as stateless microservices that scale horizontally based on demand.","intents":["I want to containerize agents for consistent deployment across environments","I need to deploy agents to Kubernetes for scalability and reliability","I want to implement GitOps workflows for agent deployment","I need to manage multiple agent instances and scale based on load"],"best_for":["teams deploying agents to cloud infrastructure","organizations using Kubernetes for container orchestration","developers implementing CI/CD pipelines for agents"],"limitations":["Containerization adds complexity — requires Docker and Kubernetes expertise","Stateless deployment requires external state management (databases, caches) for learned state","Scaling agents requires managing resource limits and request routing — no automatic optimization","Debugging containerized agents is harder than local development — requires log aggregation and monitoring"],"requires":["Docker installed and configured","Kubernetes cluster (self-hosted or managed service)","Container registry for storing agent images","ArgoCD (optional, for GitOps workflows)"],"input_types":["agent code and configuration","Dockerfile and Kubernetes manifests"],"output_types":["Docker images","deployed agent services","scaling metrics and logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-adala__cap_13","uri":"capability://automation.workflow.comprehensive.testing.framework.with.cassette.based.mocking.for.reproducible.tests","name":"comprehensive testing framework with cassette-based mocking for reproducible tests","description":"Adala includes a testing framework that uses cassette-based mocking (VCR-style) to record and replay LLM API calls, enabling reproducible tests without external API dependencies. Tests can verify agent behavior, skill execution, and learning loops using recorded responses. The framework integrates with pytest and provides fixtures for common testing scenarios. Cassettes capture request/response pairs, enabling deterministic test execution and reducing test costs.","intents":["I want to test agents without making expensive LLM API calls","I need reproducible tests that don't depend on external services","I want to test agent learning loops with deterministic LLM responses","I need to verify agent behavior across different scenarios"],"best_for":["teams building agents with CI/CD pipelines","developers wanting fast, cost-effective testing","organizations needing reproducible test suites"],"limitations":["Cassettes must be recorded with real API calls initially — requires API access and costs","Cassettes can become stale if LLM behavior changes — may not reflect current API behavior","Recording cassettes requires careful setup to capture all edge cases — incomplete cassettes lead to test gaps","Cassette files can be large and difficult to review — makes test maintenance harder"],"requires":["Python 3.9+","pytest installed","VCR library for cassette recording/playback","LLM API credentials for initial cassette recording"],"input_types":["test code","agent configurations"],"output_types":["test results","cassette files (YAML/JSON)","coverage reports"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-adala__cap_14","uri":"capability://automation.workflow.ci.cd.pipeline.automation.with.github.actions.for.testing.and.deployment","name":"ci/cd pipeline automation with github actions for testing and deployment","description":"Adala includes GitHub Actions workflows for automated testing, linting, and deployment. The CI/CD pipeline runs tests on pull requests, validates code quality, and deploys agents to production on merge. Workflows are defined in YAML and integrate with the testing framework for reproducible builds. The architecture enables continuous integration and deployment of agents without manual intervention.","intents":["I want to automatically test agents on every pull request","I need to validate code quality and run linters before merging","I want to automatically deploy agents to production on merge","I need to track test results and deployment status"],"best_for":["teams using GitHub for version control","organizations wanting automated testing and deployment","developers implementing continuous integration workflows"],"limitations":["GitHub Actions workflows are GitHub-specific — not portable to other CI/CD platforms","Workflow execution time can be slow for large test suites — requires optimization","Secrets management requires careful configuration — misconfiguration can expose credentials","Debugging failed workflows requires examining logs — can be time-consuming"],"requires":["GitHub repository with Actions enabled","GitHub Actions workflow files (.github/workflows/)","Secrets configured for API keys and deployment credentials"],"input_types":["code commits","pull requests"],"output_types":["test results","deployment status","build artifacts"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-adala__cap_2","uri":"capability://tool.use.integration.multi.provider.llm.runtime.abstraction.with.unified.interface","name":"multi-provider llm runtime abstraction with unified interface","description":"The Runtime system provides a unified interface to multiple LLM providers (OpenAI, Anthropic, LiteLLM-compatible services) through a base Runtime class that abstracts provider-specific API calls. Runtimes handle prompt formatting, token management, function calling, and response parsing. The implementation uses LiteLLM as a compatibility layer for provider abstraction, enabling agents to switch between providers via configuration without code changes. Multi-modal support is built in, allowing runtimes to process images alongside text.","intents":["I want to switch between OpenAI, Anthropic, and other LLM providers without rewriting agent code","I need to use local/open-source LLMs (via LiteLLM) alongside proprietary APIs","I want to handle multi-modal inputs (text + images) in my labeling agent","I need to manage token limits and optimize prompt formatting across different LLM APIs"],"best_for":["teams wanting provider flexibility and avoiding vendor lock-in","developers building multi-modal data labeling workflows","organizations using cost optimization strategies (switching between expensive and cheap models)"],"limitations":["LiteLLM abstraction adds ~50-100ms latency per call due to provider routing and response normalization","Function calling support varies by provider — not all providers support structured output equally, requiring fallback parsing","Token counting is approximate for non-OpenAI models, leading to potential context overflow errors","Multi-modal support depends on provider capabilities — not all providers handle images equally well","No built-in caching or request deduplication — repeated identical prompts incur full API costs"],"requires":["Python 3.9+","LiteLLM library (included in dependencies)","API keys for target LLM providers (OpenAI, Anthropic, etc.)","Network connectivity to LLM provider endpoints"],"input_types":["text prompts","structured function schemas (for function calling)","images (base64 or URLs, provider-dependent)"],"output_types":["text completions","structured function call results","parsed JSON/structured data"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-adala__cap_3","uri":"capability://data.processing.analysis.structured.environment.based.feedback.collection.and.validation","name":"structured environment-based feedback collection and validation","description":"Environments provide data and structured feedback to agents through a standardized interface. Environments define what data the agent processes, what feedback signals are available (correctness, error messages, performance metrics), and how to validate agent outputs. The Environment base class abstracts different data sources (databases, files, APIs) and feedback mechanisms (human annotations, automated validators, external services). Agents interact with environments via a standardized get_data() and provide_feedback() pattern, enabling decoupled agent-environment interaction.","intents":["I want to provide consistent feedback signals to agents for learning (e.g., correct/incorrect labels)","I need to validate agent outputs against ground truth before accepting them","I want to integrate multiple data sources and feedback mechanisms into a single agent workflow","I need to track agent performance metrics and use them to guide learning"],"best_for":["teams building data labeling pipelines with quality validation","organizations integrating agents with existing data infrastructure","developers needing flexible feedback mechanisms (human, automated, hybrid)"],"limitations":["Environment feedback quality directly impacts learning — poor or inconsistent feedback degrades agent performance","No built-in feedback aggregation for conflicting signals (e.g., multiple human annotators disagreeing) — requires custom environment implementation","Feedback latency can slow learning — synchronous feedback collection blocks agent execution","Scaling to large datasets requires efficient environment implementations; naive implementations can become I/O bottlenecks"],"requires":["Python 3.9+","Data source (database, file system, API, etc.)","Feedback mechanism (ground truth labels, validation rules, human annotators, etc.)","Environment implementation extending the base Environment class"],"input_types":["structured data (JSON/dict)","text","images","any data type supported by the environment implementation"],"output_types":["feedback signals (correctness, error messages)","performance metrics","validation results"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-adala__cap_4","uri":"capability://data.processing.analysis.built.in.classification.and.entity.extraction.skills.with.llm.driven.execution","name":"built-in classification and entity extraction skills with llm-driven execution","description":"Adala provides pre-built Skill implementations for common data labeling tasks: classification (assigning labels to data) and entity extraction (identifying and extracting structured information from text). These skills are implemented as Pydantic models with configurable prompts and output schemas. They execute via the Runtime system, using LLM calls to perform the actual classification or extraction. Skills can be learned (prompts refined) or used directly with static prompts. Output validation ensures extracted entities match expected schemas.","intents":["I want to classify documents or text snippets into predefined categories","I need to extract named entities, relationships, or structured information from unstructured text","I want to reuse common labeling patterns without building skills from scratch","I need to validate extracted data against schemas before using it"],"best_for":["teams building text classification and NER pipelines","developers needing quick skill implementations for common labeling tasks","organizations processing unstructured text at scale"],"limitations":["Classification and extraction accuracy depends entirely on LLM quality — no built-in active learning or uncertainty sampling","Entity extraction output validation is schema-based only — no semantic validation or relationship checking","Skills are stateless — no memory of previous classifications or extractions for consistency","No built-in handling of ambiguous or borderline cases — all decisions are binary (classify or extract)"],"requires":["Python 3.9+","Runtime configured with LLM provider","Classification: list of valid labels/categories","Entity extraction: output schema (Pydantic model or JSON schema)"],"input_types":["text","structured data with text fields"],"output_types":["classification: label string or category","entity extraction: structured data (dict/JSON) matching output schema"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-adala__cap_5","uri":"capability://automation.workflow.label.studio.integration.for.human.in.the.loop.annotation.workflows","name":"label studio integration for human-in-the-loop annotation workflows","description":"Adala integrates with Label Studio, a popular annotation platform, enabling agents to work alongside human annotators. The integration allows agents to process data, submit predictions to Label Studio, and receive human feedback/corrections. This creates a hybrid workflow where agents handle routine labeling and humans focus on edge cases or quality assurance. The integration is implemented via Label Studio API calls and result handlers that sync agent outputs with the annotation platform.","intents":["I want agents to pre-label data and have humans review/correct predictions in Label Studio","I need to combine agent predictions with human annotations for quality assurance","I want to use Label Studio as the feedback source for agent learning","I need to track which labels came from agents vs humans for auditing"],"best_for":["teams using Label Studio for annotation workflows","organizations wanting to augment human annotators with AI agents","projects requiring audit trails of agent vs human decisions"],"limitations":["Integration requires Label Studio instance and API access — adds operational complexity","Feedback latency depends on human annotation speed — agents may wait for human corrections","No built-in conflict resolution when agent and human disagree — requires custom logic","Label Studio API rate limits can throttle agent feedback collection at scale"],"requires":["Python 3.9+","Label Studio instance (self-hosted or cloud)","Label Studio API token","Project configured in Label Studio with appropriate label definitions"],"input_types":["structured data (JSON/dict)","text","images"],"output_types":["Label Studio task predictions","human annotations/corrections","audit logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-adala__cap_6","uri":"capability://memory.knowledge.rag.and.knowledge.based.skill.enhancement.with.external.knowledge.integration","name":"rag and knowledge-based skill enhancement with external knowledge integration","description":"Adala supports RAG (Retrieval-Augmented Generation) and knowledge-based skills that augment agent decision-making with external knowledge sources. Skills can retrieve relevant context from knowledge bases, documents, or vector stores before making predictions. This is implemented via knowledge retrieval skills that query external sources and pass retrieved context to classification/extraction skills. The architecture enables agents to make more informed decisions by grounding predictions in retrieved knowledge rather than relying solely on LLM parameters.","intents":["I want agents to retrieve relevant context from a knowledge base before labeling","I need to ground agent predictions in external documents or reference materials","I want to use semantic search to find similar past examples for consistency","I need agents to cite sources for their predictions"],"best_for":["teams with domain-specific knowledge bases or reference materials","organizations needing explainable predictions with source attribution","projects requiring consistency with existing knowledge/guidelines"],"limitations":["RAG quality depends on retrieval effectiveness — poor retrieval leads to irrelevant context and degraded predictions","Adding retrieval steps increases latency and API costs (additional LLM calls for retrieval + prediction)","Knowledge base maintenance overhead — outdated or inconsistent knowledge degrades agent performance","No built-in handling of conflicting information between retrieved context and LLM knowledge"],"requires":["Python 3.9+","Knowledge base or vector store (e.g., Pinecone, Weaviate, local embeddings)","Embedding model for semantic search","Knowledge retrieval skill implementation"],"input_types":["text queries","structured data with text fields"],"output_types":["retrieved context documents","predictions grounded in retrieved knowledge","source citations"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-adala__cap_7","uri":"capability://tool.use.integration.fastapi.server.with.async.skill.execution.and.request.handling","name":"fastapi server with async skill execution and request handling","description":"Adala provides a FastAPI-based server component that exposes agents as HTTP endpoints, enabling remote skill execution and agent interaction. The server handles async request processing, skill execution, and result streaming. It implements request validation, error handling, and logging middleware. The architecture allows agents to be deployed as services that can be called from external applications, enabling integration with existing systems and scaling across multiple instances.","intents":["I want to expose agents as REST APIs for external applications to use","I need to handle concurrent requests to agents without blocking","I want to deploy agents as microservices","I need to monitor and log agent requests and responses"],"best_for":["teams deploying agents as production services","organizations integrating agents with existing REST-based systems","developers building scalable agent infrastructure"],"limitations":["FastAPI server adds network latency compared to in-process agent execution","Async execution requires careful handling of stateful operations — agents must be thread-safe","No built-in authentication or authorization — requires external API gateway or middleware","Scaling requires multiple server instances and load balancing — no built-in clustering"],"requires":["Python 3.9+","FastAPI and Uvicorn (included in dependencies)","Agent configured and ready for execution","Network connectivity for client requests"],"input_types":["JSON request bodies","query parameters","multipart form data (for file uploads)"],"output_types":["JSON responses","streaming responses (for long-running operations)","error responses with status codes"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-adala__cap_8","uri":"capability://automation.workflow.kafka.integration.for.event.driven.agent.workflows.and.result.streaming","name":"kafka integration for event-driven agent workflows and result streaming","description":"Adala integrates with Apache Kafka for event-driven data processing, enabling agents to consume data from Kafka topics, process it, and publish results to output topics. The integration implements Kafka consumers/producers that handle message serialization, error handling, and offset management. This enables high-throughput, scalable data processing pipelines where agents process streaming data in real-time. Result handlers manage how agent outputs are persisted or forwarded.","intents":["I want agents to process streaming data from Kafka topics in real-time","I need to build event-driven labeling pipelines that scale horizontally","I want to decouple data ingestion from agent processing using message queues","I need to handle backpressure and ensure reliable message processing"],"best_for":["teams building high-throughput data processing pipelines","organizations with existing Kafka infrastructure","projects requiring real-time or near-real-time labeling"],"limitations":["Kafka integration adds operational complexity — requires Kafka cluster management and monitoring","Message ordering guarantees depend on partition strategy — complex to maintain order across partitions","Error handling and retries require careful configuration — failed messages can be lost or duplicated","Scaling agents requires managing consumer groups and rebalancing — no automatic scaling"],"requires":["Python 3.9+","Apache Kafka cluster (self-hosted or managed service)","Kafka topics configured for input and output data","Kafka consumer/producer configuration (brokers, topics, credentials)"],"input_types":["Kafka messages (JSON, Avro, or custom serialization)","structured data"],"output_types":["Kafka messages with agent results","labeled/processed data"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-adala__cap_9","uri":"capability://automation.workflow.result.handlers.for.flexible.output.persistence.and.routing","name":"result handlers for flexible output persistence and routing","description":"Result handlers manage how agent outputs are persisted, routed, or forwarded after execution. Different handler types support various output destinations: database storage, file systems, APIs, message queues, or custom backends. Handlers are pluggable and can be composed, enabling flexible output pipelines. The architecture decouples agent execution from output handling, allowing the same agent to route results to multiple destinations without code changes.","intents":["I want agent results stored in a database for later retrieval","I need to send agent outputs to multiple destinations (database, file, API, etc.)","I want to implement custom result processing or validation","I need to handle result failures gracefully (retries, dead-letter queues)"],"best_for":["teams needing flexible output routing","organizations with multiple result destinations","developers implementing custom result processing"],"limitations":["Result handler failures can cause data loss if not properly configured with retries and dead-letter handling","No built-in deduplication — duplicate results can be written if handlers are called multiple times","Scaling result handlers requires external infrastructure (databases, queues) — no built-in batching or buffering","Handler composition can become complex with many destinations — requires careful error handling"],"requires":["Python 3.9+","Result handler implementation extending the base handler class","Target destination configured (database, file system, API, etc.)"],"input_types":["agent execution results (structured data)","metadata (execution time, model used, etc.)"],"output_types":["persisted results in target destination","success/failure status"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","LLM runtime configured (OpenAI, Anthropic, or LiteLLM-compatible provider)","Environment implementation providing structured feedback (correctness signals, error messages)","Skill definitions with learnable parameters (prompt templates)","Skill definitions with compatible input/output schemas","Runtime configured for skill execution","Pydantic models for skill I/O validation","Runtime configured with capable LLM (GPT-4 or equivalent recommended)","Failure analysis data (incorrect outputs, expected outputs, error messages)","PromptImprovement skill implementation"],"failure_modes":["Learning convergence depends on quality and consistency of environment feedback — poor feedback signals lead to degraded performance","No built-in persistence of learned skills across agent instances — requires external serialization (pickle/JSON) to preserve improvements","Learning phase can be computationally expensive with large datasets due to iterative LLM calls per data sample","LinearSkillSet introduces sequential latency — each skill waits for the previous one to complete, no pipelining optimization","ParallelSkillSet output merging is simplistic (concatenation/dict merge) — complex aggregation logic requires custom skill implementation","No built-in error handling or retry logic for individual skills in a SkillSet — one skill failure halts the entire set","Skill composition is static at definition time — dynamic skill selection based on data requires wrapper skills","Prompt improvement quality depends on LLM's ability to analyze failures — complex failure modes may not be identified","Improved prompts are not validated before use — bad improvements can degrade performance further","No built-in rollback mechanism — failed improvements persist unless explicitly reverted","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:02.370Z","last_scraped_at":"2026-05-03T14:00:10.321Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=adala","compare_url":"https://unfragile.ai/compare?artifact=adala"}},"signature":"PsPG5LgpbkOl7xETVT+BH43pFhgBRoUJBgSE/mme7mK9aIdEfGfw5EJIRyhxmd9lccg9mAHQuTv3Njhz3V58Bw==","signedAt":"2026-06-19T16:59:59.122Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/adala","artifact":"https://unfragile.ai/adala","verify":"https://unfragile.ai/api/v1/verify?slug=adala","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"}}