{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-crewai","slug":"pypi-crewai","name":"crewai","type":"framework","url":"https://pypi.org/project/crewai/","page_url":"https://unfragile.ai/pypi-crewai","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-crewai__cap_0","uri":"capability://planning.reasoning.role.based.agent.orchestration.with.hierarchical.task.delegation","name":"role-based agent orchestration with hierarchical task delegation","description":"CrewAI enables defining autonomous agents with specific roles, goals, and backstories that collaborate on complex tasks through a Crew abstraction. Each agent is instantiated with an LLM provider, tools, and memory context, then orchestrated via a task queue where the framework automatically routes work based on agent capabilities and task dependencies. The Crew class manages agent lifecycle, handles inter-agent communication, and enforces sequential or parallel task execution patterns with built-in retry logic and error recovery.","intents":["I want to define specialized AI agents that work together on a multi-step project without manually coordinating their outputs","I need agents to hand off work to each other based on task dependencies and agent expertise","I want to orchestrate 5+ agents on a complex workflow with automatic error handling and task retry"],"best_for":["teams building multi-agent systems for research, analysis, or content generation","developers automating complex workflows that require specialized agent roles","enterprises deploying autonomous agent teams for customer support or data processing"],"limitations":["No built-in distributed execution — all agents run in a single Python process, limiting horizontal scaling","Task ordering is sequential by default; parallel execution requires explicit flow configuration","Agent state is ephemeral unless explicitly persisted via memory system — no automatic checkpointing between tasks","Inter-agent communication happens through task outputs only; no direct agent-to-agent messaging protocol in base Crew"],"requires":["Python 3.9+","At least one LLM provider API key (OpenAI, Anthropic, Gemini, Azure, Bedrock, or local Ollama)","crewai package installed via pip","Tool definitions compatible with agent's LLM provider's function-calling schema"],"input_types":["task descriptions (strings)","agent role definitions (strings)","tool specifications (Python callables or MCP-compatible tools)","structured task parameters (dicts/JSON)"],"output_types":["task execution results (strings or structured data)","agent reasoning traces (via telemetry)","memory artifacts (embeddings, consolidated knowledge)"],"categories":["planning-reasoning","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-crewai__cap_1","uri":"capability://automation.workflow.event.driven.workflow.composition.with.flows","name":"event-driven workflow composition with flows","description":"CrewAI Flows provide a decorator-based, event-driven orchestration layer built on top of Crews, enabling complex workflows where steps are triggered by state changes rather than explicit sequencing. Flows use Python decorators (@flow, @listen_to) to define state machines where each decorated method represents a workflow step that can emit events, listen for upstream events, and compose Crews as sub-workflows. The framework manages state persistence, event routing, and visualization of the entire workflow DAG.","intents":["I want to build a workflow where some steps run in parallel and others wait for upstream completion without writing explicit orchestration code","I need to visualize the entire multi-crew workflow as a directed graph and understand data flow between steps","I want to inject human feedback at specific workflow checkpoints and have the system resume from that point"],"best_for":["builders creating complex, multi-stage AI pipelines with conditional branching","teams needing workflow visualization and debugging capabilities","applications requiring human-in-the-loop approval at specific workflow stages"],"limitations":["State management is in-memory by default; distributed workflows require external state store configuration","Event routing is single-process; no built-in pub/sub for cross-service workflows","Decorator-based syntax couples workflow logic to Python code; no declarative YAML/JSON workflow definitions","Human feedback integration requires manual implementation of pause/resume checkpoints"],"requires":["Python 3.9+","crewai package with Flow support","Understanding of Python decorators and async/await patterns","Optional: external state store (Redis, database) for persistence across process restarts"],"input_types":["decorated Python functions","state objects (dicts or Pydantic models)","event payloads (structured data)","Crew instances for sub-workflow composition"],"output_types":["workflow execution results","event logs and traces","workflow DAG visualization (JSON/graphical format)","state snapshots at each workflow step"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-crewai__cap_10","uri":"capability://data.processing.analysis.file.handling.and.document.processing.with.crewai.files","name":"file handling and document processing with crewai-files","description":"CrewAI provides the crewai-files package for agents to read, write, and process files and documents. The package includes tools for file operations (read, write, delete, list), document parsing (PDF, DOCX, TXT, JSON), and file-based memory operations. Files are managed in an agent-scoped workspace, enabling agents to work with documents without direct filesystem access. The system integrates with the memory architecture to enable semantic search over document contents.","intents":["I want agents to read and analyze documents (PDFs, Word docs, etc.) as part of their tasks","I need agents to generate and write files (reports, code, data) to a managed workspace","I want to enable semantic search over document contents stored in agent memory"],"best_for":["document analysis and processing workflows","report generation and content creation systems","knowledge management systems where agents process and organize documents"],"limitations":["Document parsing is format-specific; complex PDFs with images/tables may lose formatting","File operations are synchronous; large file processing can block agent execution","No built-in file versioning or change tracking; overwrites are permanent","File workspace is agent-scoped; sharing files between agents requires explicit copy operations"],"requires":["Python 3.9+","crewai-files package","Optional: document parsing libraries (pypdf, python-docx, etc.) for specific formats","Filesystem access or cloud storage integration for file persistence"],"input_types":["file paths (strings)","file contents (strings, bytes, or structured data)","document formats (PDF, DOCX, TXT, JSON, CSV)","file operations (read, write, delete, list)"],"output_types":["file contents (strings or structured data)","parsed document data (text, metadata, structured fields)","file operation results (success/failure, file metadata)","document embeddings (for semantic search)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-crewai__cap_11","uri":"capability://automation.workflow.enterprise.deployment.with.crewai.amp.agent.management.platform","name":"enterprise deployment with crewai amp (agent management platform)","description":"CrewAI AMP is the enterprise deployment platform providing managed hosting, control plane, monitoring, and governance for deployed crews. AMP handles agent lifecycle management, automatic scaling, environment variable injection, secret management, and integration with enterprise identity systems (SSO). The platform provides a web UI (Crew Studio) for managing deployed agents, viewing execution logs, and triggering manual runs. AMP integrates with CrewAI's marketplace for discovering and deploying pre-built agents.","intents":["I want to deploy my crews to a managed platform without managing infrastructure","I need enterprise features like SSO, secret management, and audit logging","I want to monitor and manage deployed agents through a web UI"],"best_for":["enterprises deploying agent systems in production","teams needing managed hosting and governance","organizations requiring SSO, audit logging, and compliance features"],"limitations":["AMP is proprietary to CrewAI; no option to self-host or use alternative platforms","Pricing is usage-based (per agent execution); cost can be unpredictable for high-volume systems","Deployment is one-way (CrewAI AMP only); no multi-cloud or hybrid deployment options","Custom integrations may require AMP-specific code; limited flexibility for non-standard deployments"],"requires":["CrewAI account with AMP subscription","API key for authentication","Crew code compatible with AMP (standard CrewAI code)","Environment variables and secrets configured in AMP"],"input_types":["crew code (Python files)","environment variables and secrets","deployment configuration (scaling, triggers, integrations)","agent metadata (name, description, tags)"],"output_types":["deployed agent endpoints (HTTP URLs)","execution logs and traces","monitoring dashboards (latency, cost, error rates)","audit logs (who deployed, when, what changed)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-crewai__cap_12","uri":"capability://safety.moderation.agent.evaluation.and.testing.framework.with.automated.benchmarking","name":"agent evaluation and testing framework with automated benchmarking","description":"CrewAI provides an evaluation framework for testing agent behavior, measuring performance against benchmarks, and comparing agent configurations. The framework enables defining test cases with expected outputs, running agents against test suites, and collecting metrics (accuracy, latency, cost). Evaluation results can be compared across agent versions, LLM models, or tool configurations, enabling data-driven optimization. The framework integrates with the observability system to capture detailed execution traces for failed tests.","intents":["I want to test agents against a suite of test cases and measure accuracy","I need to compare agent performance across different LLM models or configurations","I want to identify regressions when updating agent code or prompts"],"best_for":["teams building production agent systems requiring quality assurance","researchers comparing agent architectures or LLM models","developers optimizing agent performance and cost"],"limitations":["Evaluation requires manually defining test cases and expected outputs; no automatic test generation","Metrics are basic (accuracy, latency, cost); custom metrics require manual implementation","Evaluation runs are sequential by default; parallel testing requires custom orchestration","Comparing across LLM models requires running full test suites multiple times (expensive)"],"requires":["Python 3.9+","crewai package with evaluation support","Test case definitions (JSON or Python objects)","Expected outputs for each test case","LLM API access for running evaluations"],"input_types":["test cases (task descriptions, expected outputs)","agent configurations (LLM model, tools, prompts)","evaluation metrics (accuracy, latency, cost)","comparison parameters (agent versions, models, configurations)"],"output_types":["evaluation results (pass/fail, metrics)","comparison reports (agent A vs B, model X vs Y)","execution traces for failed tests","performance dashboards (accuracy trends, cost analysis)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-crewai__cap_13","uri":"capability://code.generation.editing.agent.skills.system.for.modular.capability.composition","name":"agent skills system for modular capability composition","description":"CrewAI provides an agent skills system enabling agents to be composed from modular, reusable skill components. Skills are Python classes that encapsulate specific capabilities (e.g., 'web research', 'code analysis', 'report writing') and can be attached to agents at instantiation. Skills have their own tools, memory, and execution context, enabling complex agent behaviors to be built from simple, composable pieces. Skills can be versioned, shared across agents, and discovered through the marketplace.","intents":["I want to build agents by composing reusable skills rather than writing monolithic agent code","I need to share skills across multiple agents and teams","I want to version and test skills independently before composing them into agents"],"best_for":["teams building multiple agents with overlapping capabilities","organizations wanting to standardize agent behavior through shared skills","developers creating reusable agent components for the marketplace"],"limitations":["Skill composition adds complexity; debugging multi-skill agents requires understanding skill interactions","Skills have isolated memory contexts; sharing state between skills requires explicit coordination","Skill versioning is manual; no automatic dependency resolution across skill versions","Marketplace discovery is limited; no built-in search or recommendation system for skills"],"requires":["Python 3.9+","crewai package with skills support","Skill definitions (Python classes extending Skill base class)","Tool definitions for each skill"],"input_types":["skill definitions (Python classes)","skill metadata (name, description, version, dependencies)","skill tools and capabilities","skill configuration (parameters, LLM model)"],"output_types":["composed agents (with multiple skills)","skill execution results","skill interaction traces","skill marketplace entries (for sharing)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-crewai__cap_2","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.unified.function.calling.interface","name":"multi-provider llm abstraction with unified function-calling interface","description":"CrewAI abstracts over multiple LLM providers (OpenAI, Anthropic, Gemini, Azure, Bedrock, Ollama) through a unified LLM class that normalizes provider-specific APIs into a common interface. The framework handles provider-specific message formatting, function-calling schema translation, and streaming response handling. Each provider implementation extends a base LLM class and implements hooks for pre/post-processing, enabling agents to seamlessly switch providers or use provider-specific features without code changes.","intents":["I want to build agents that work with multiple LLM providers and switch between them based on cost or latency","I need function-calling to work consistently across OpenAI, Anthropic, and Gemini without rewriting tool schemas","I want to use local Ollama models for development and cloud providers for production with the same agent code"],"best_for":["teams evaluating multiple LLM providers and wanting to avoid vendor lock-in","cost-conscious builders needing to switch between expensive and cheaper models","enterprises with hybrid cloud/on-premise LLM deployments"],"limitations":["Provider-specific features (vision, extended context windows, custom parameters) require provider-specific code paths","Function-calling schema translation adds ~50-100ms latency per tool invocation due to normalization overhead","Streaming responses are normalized but provider-specific streaming formats may lose metadata","Some providers (e.g., Ollama) have limited function-calling support; fallback behavior is not always graceful"],"requires":["Python 3.9+","API keys for at least one provider (OpenAI, Anthropic, Gemini, Azure, Bedrock) or local Ollama instance","crewai package with provider-specific dependencies (e.g., openai, anthropic packages)","Environment variables or configuration for provider credentials"],"input_types":["provider name (string: 'openai', 'anthropic', 'gemini', 'azure', 'bedrock', 'ollama')","model identifier (string: 'gpt-4', 'claude-3-opus', 'gemini-2.0-flash', etc.)","tool schemas (Python callables or JSON schemas)","messages (CrewAI Message objects with role, content, tool_calls)"],"output_types":["normalized LLM responses (text, tool calls, structured data)","streaming token streams (for real-time output)","provider-specific metadata (usage tokens, model version, latency)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-crewai__cap_3","uri":"capability://tool.use.integration.schema.based.tool.registration.and.function.calling.with.mcp.support","name":"schema-based tool registration and function-calling with mcp support","description":"CrewAI provides a tool registry system where tools are defined as Python callables with type hints, automatically converted to provider-specific function-calling schemas (OpenAI, Anthropic, Gemini formats). The framework supports both native Python tools and Model Context Protocol (MCP) tools, normalizing both into a unified tool interface. Tools are bound to agents at instantiation, and the agent's LLM automatically invokes them based on function-calling responses, with built-in error handling and result injection back into the agent's context.","intents":["I want to define tools as simple Python functions and have them automatically work with any LLM provider's function-calling API","I need to integrate MCP-compatible tools (like Brave Search, GitHub, etc.) without writing provider-specific code","I want tools to be discoverable, versioned, and shared across multiple agents in a crew"],"best_for":["developers building agent systems with diverse tool ecosystems","teams using MCP-compatible services (Brave Search, GitHub, Slack, etc.)","builders needing tool versioning and discovery across multiple agents"],"limitations":["Tool schema generation from Python type hints may fail for complex nested types; manual schema override required","MCP tool integration requires MCP server to be running; no built-in MCP server lifecycle management","Tool execution is synchronous by default; async tools require explicit async/await handling","Tool result injection into agent context is automatic but may lose tool-specific metadata or structured outputs"],"requires":["Python 3.9+","crewai and crewai-tools packages","For MCP tools: MCP server running and accessible via stdio or HTTP","Type hints on tool functions (required for schema generation)","Optional: crewai-files for file handling tools"],"input_types":["Python callables with type hints","MCP tool specifications (JSON schemas)","Tool metadata (name, description, category)","Function arguments (inferred from type hints or explicit schemas)"],"output_types":["normalized tool schemas (compatible with OpenAI, Anthropic, Gemini)","tool execution results (strings, structured data, files)","tool call traces (for debugging and observability)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-crewai__cap_4","uri":"capability://memory.knowledge.unified.memory.architecture.with.rag.and.embedding.based.recall","name":"unified memory architecture with rag and embedding-based recall","description":"CrewAI implements a unified memory system where agents store experiences, learnings, and context in a multi-tier architecture: short-term memory (current task context), long-term memory (embeddings of past interactions), and entity memory (extracted knowledge about specific topics). The system uses embeddings (via configurable providers like OpenAI, Ollama) to enable semantic search and recall of relevant past experiences. Memory operations (store, recall, consolidate) are scoped to individual agents or crews, with automatic consolidation that summarizes old memories to prevent context bloat.","intents":["I want agents to learn from past interactions and apply that knowledge to new tasks without manual context management","I need semantic search over agent experiences to find relevant past solutions for similar problems","I want to consolidate old memories to prevent token bloat while preserving important learnings"],"best_for":["long-running agent systems that accumulate knowledge over time","multi-agent teams that need to share learnings across agents","applications requiring semantic search over agent interaction history"],"limitations":["Embedding generation adds latency (~200-500ms per memory store operation) and cost (embedding API calls)","Memory consolidation is heuristic-based; important details may be lost during summarization","No built-in distributed memory store; scaling to millions of memories requires external vector DB configuration","Memory recall is semantic-only; no keyword search or filtering by metadata without custom implementation"],"requires":["Python 3.9+","crewai package with memory support","Embedding provider (OpenAI, Ollama, or custom) with API access","Optional: external vector database (Pinecone, Weaviate, Milvus) for large-scale memory"],"input_types":["agent experiences (task descriptions, outcomes, reasoning)","memory queries (semantic search strings)","memory scope (agent-level or crew-level)","consolidation triggers (time-based, size-based, manual)"],"output_types":["stored memory embeddings and metadata","recalled memories (ranked by semantic relevance)","consolidated memory summaries","memory statistics (total memories, consolidation history)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-crewai__cap_5","uri":"capability://planning.reasoning.agent.to.agent.a2a.protocol.for.direct.inter.agent.communication","name":"agent-to-agent (a2a) protocol for direct inter-agent communication","description":"CrewAI implements an Agent-to-Agent (A2A) protocol enabling agents to directly request work from other agents within a crew, bypassing the task queue. When an agent determines it needs specialized work from another agent, it can invoke the A2A protocol to send a structured request, receive the result, and continue its own reasoning. This enables dynamic task delegation based on agent reasoning rather than static task definitions, with built-in request/response formatting and error handling.","intents":["I want agents to dynamically request help from specialized agents based on what they discover during execution","I need agents to collaborate in real-time without pre-defining all task dependencies","I want to enable agents to negotiate or refine requests with each other during execution"],"best_for":["complex multi-agent systems with dynamic task dependencies","research or analysis workflows where agents discover new questions during execution","collaborative agent teams that need to refine outputs through multiple rounds of feedback"],"limitations":["A2A requests add latency (each request requires a full LLM invocation on the target agent)","No built-in timeout or deadlock prevention; circular A2A requests can cause infinite loops","Request/response format is agent-specific; no standardized schema across different agent types","A2A communication is in-process only; no support for cross-service agent communication"],"requires":["Python 3.9+","crewai package with A2A support","Agents defined within the same Crew instance","Clear definition of which agents can request from which other agents (to prevent circular dependencies)"],"input_types":["A2A request objects (structured queries from one agent to another)","target agent identifier (string or agent reference)","request context (relevant information for the target agent)"],"output_types":["A2A response objects (results from the target agent)","execution traces (showing A2A request/response flow)","error responses (if target agent cannot fulfill request)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-crewai__cap_6","uri":"capability://safety.moderation.task.guardrails.and.validation.with.structured.output.enforcement","name":"task guardrails and validation with structured output enforcement","description":"CrewAI provides task-level guardrails that enforce constraints on agent execution, including output validation, structured output schemas, and execution callbacks. Tasks can define expected output formats (via Pydantic models or JSON schemas), and the framework validates agent outputs against these schemas before marking tasks complete. Guardrails include pre-execution hooks (to validate task setup), post-execution hooks (to validate outputs), and custom validation functions that can reject outputs and trigger retries.","intents":["I want to ensure agents produce structured, validated outputs that match my application's data model","I need to enforce constraints on agent behavior (e.g., 'always cite sources', 'output must be JSON')","I want to automatically retry tasks if outputs don't meet quality or format requirements"],"best_for":["production systems requiring strict output validation","applications with downstream systems expecting specific data formats","teams needing to enforce quality standards across agent outputs"],"limitations":["Validation failures trigger retries, which add latency and cost (additional LLM invocations)","Complex validation logic may require custom Python code; no declarative validation DSL","Structured output enforcement depends on LLM provider support for JSON mode; fallback behavior varies","Validation hooks are task-level only; no cross-task validation or workflow-level constraints"],"requires":["Python 3.9+","crewai package","Pydantic models or JSON schemas for output validation","LLM provider with structured output support (OpenAI, Anthropic, Gemini)"],"input_types":["task definitions with output schemas","validation functions (Python callables)","Pydantic models or JSON schemas","retry policies (max attempts, backoff strategy)"],"output_types":["validated task outputs (matching schema)","validation error reports (if output fails validation)","retry metadata (number of attempts, validation failures)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-crewai__cap_7","uri":"capability://automation.workflow.built.in.observability.with.opentelemetry.and.third.party.integrations","name":"built-in observability with opentelemetry and third-party integrations","description":"CrewAI includes native OpenTelemetry (OTEL) instrumentation that automatically traces agent execution, task completion, LLM calls, and tool invocations. The framework exports traces to OTEL collectors, and integrates with third-party observability platforms (Datadog, New Relic, Langsmith, etc.) via OTEL exporters. Traces include structured metadata (agent name, task description, LLM model, tool names) enabling detailed analysis of agent behavior, latency bottlenecks, and cost attribution.","intents":["I want to monitor agent execution in production and identify performance bottlenecks","I need to track LLM costs and token usage across multiple agents and tasks","I want to debug agent behavior by replaying execution traces and seeing what each agent decided"],"best_for":["production deployments requiring observability and monitoring","teams needing cost tracking and optimization across multiple agents","developers debugging complex multi-agent workflows"],"limitations":["OTEL instrumentation adds ~5-10% overhead to execution time due to trace collection","Third-party integrations require additional configuration and API keys","Trace sampling may lose detail for high-volume agent systems; full tracing can be expensive","Custom metrics require manual instrumentation; no automatic cost attribution by agent or task type"],"requires":["Python 3.9+","crewai package with OTEL support","OTEL collector or third-party observability platform (Datadog, New Relic, Langsmith, etc.)","Environment variables for OTEL exporter configuration"],"input_types":["agent execution events (automatically captured)","task completion events (automatically captured)","LLM call metadata (automatically captured)","tool invocation details (automatically captured)"],"output_types":["OTEL traces (spans with structured metadata)","execution timelines (showing task and LLM call durations)","cost reports (token usage, API call counts)","error traces (with full context for debugging)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-crewai__cap_8","uri":"capability://automation.workflow.cli.based.project.scaffolding.and.deployment.orchestration","name":"cli-based project scaffolding and deployment orchestration","description":"CrewAI provides a CLI tool that scaffolds new crew, flow, and tool projects with pre-configured project structure, dependencies, and templates. The CLI uses UV for dependency management and includes commands for creating projects, running crews locally, and deploying to CrewAI's managed platform (CrewAI AMP). Deployment commands handle authentication, environment variable injection, and control plane integration, enabling one-command deployment of agent systems.","intents":["I want to quickly scaffold a new multi-agent project with best-practice structure and dependencies","I need to deploy my crew to a managed platform without writing deployment code","I want to manage environment variables and secrets for deployed agents"],"best_for":["teams getting started with CrewAI and wanting quick project setup","developers deploying agents to CrewAI's managed platform","organizations needing standardized project structure across multiple crews"],"limitations":["Scaffolded templates are opinionated; customization requires manual code changes","Deployment is CrewAI-platform-specific; no support for deploying to arbitrary cloud providers","CLI requires Python environment setup; no Docker-based scaffolding or deployment","Environment variable management is basic; no built-in secret rotation or audit logging"],"requires":["Python 3.9+","crewai CLI installed (via pip install crewai)","UV package manager (installed automatically with crewai)","For deployment: CrewAI account and API key"],"input_types":["project name (string)","project type (crew, flow, tool)","optional: custom template directory"],"output_types":["scaffolded project directory with pyproject.toml, source files, and templates","deployment configuration (if deploying to CrewAI AMP)","deployment logs and status"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-crewai__cap_9","uri":"capability://planning.reasoning.liteagent.lightweight.agent.execution.for.resource.constrained.environments","name":"liteagent lightweight agent execution for resource-constrained environments","description":"CrewAI provides LiteAgent, a lightweight agent variant optimized for resource-constrained environments (edge devices, serverless functions, low-memory systems). LiteAgent reduces memory footprint and execution latency by stripping non-essential features (memory consolidation, advanced hooks, complex reasoning) while maintaining core agent capabilities (tool calling, task execution, basic memory). LiteAgent is compatible with standard Crew orchestration, enabling hybrid crews with both full agents and lightweight agents.","intents":["I want to run agents on edge devices or serverless functions with minimal memory overhead","I need fast agent execution for latency-sensitive applications","I want to use lightweight agents for simple tasks and full agents for complex reasoning"],"best_for":["edge computing and IoT applications","serverless/FaaS deployments with memory constraints","latency-sensitive applications requiring fast agent response","hybrid systems mixing lightweight and full agents"],"limitations":["LiteAgent lacks advanced memory consolidation and long-term learning capabilities","No support for complex reasoning patterns (chain-of-thought, multi-step planning)","Limited hook system; custom agent behavior requires more manual implementation","Reduced context window support; may struggle with large task descriptions or tool schemas"],"requires":["Python 3.9+","crewai package with LiteAgent support","Minimal LLM provider (local Ollama or lightweight cloud provider)","Resource constraints: <100MB memory, <1s execution latency requirement"],"input_types":["task descriptions (strings, optimized for brevity)","tool schemas (minimal, no complex nested types)","agent role definitions (simplified)"],"output_types":["task execution results (strings or simple structured data)","minimal execution traces"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","At least one LLM provider API key (OpenAI, Anthropic, Gemini, Azure, Bedrock, or local Ollama)","crewai package installed via pip","Tool definitions compatible with agent's LLM provider's function-calling schema","crewai package with Flow support","Understanding of Python decorators and async/await patterns","Optional: external state store (Redis, database) for persistence across process restarts","crewai-files package","Optional: document parsing libraries (pypdf, python-docx, etc.) for specific formats","Filesystem access or cloud storage integration for file persistence"],"failure_modes":["No built-in distributed execution — all agents run in a single Python process, limiting horizontal scaling","Task ordering is sequential by default; parallel execution requires explicit flow configuration","Agent state is ephemeral unless explicitly persisted via memory system — no automatic checkpointing between tasks","Inter-agent communication happens through task outputs only; no direct agent-to-agent messaging protocol in base Crew","State management is in-memory by default; distributed workflows require external state store configuration","Event routing is single-process; no built-in pub/sub for cross-service workflows","Decorator-based syntax couples workflow logic to Python code; no declarative YAML/JSON workflow definitions","Human feedback integration requires manual implementation of pause/resume checkpoints","Document parsing is format-specific; complex PDFs with images/tables may lose formatting","File operations are synchronous; large file processing can block agent execution","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.5,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:11.786Z","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=pypi-crewai","compare_url":"https://unfragile.ai/compare?artifact=pypi-crewai"}},"signature":"jUW8rR7YxFfC8xM5x0xTCbgP97wnnqSXOFfwABF6ijudCGtZQ+CzSOMOvfsYqVViT1/aghetAhEK4kACbKIBBw==","signedAt":"2026-06-22T05:22:49.764Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-crewai","artifact":"https://unfragile.ai/pypi-crewai","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-crewai","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"}}