{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-phidata","slug":"pypi-phidata","name":"phidata","type":"framework","url":"https://pypi.org/project/phidata/","page_url":"https://unfragile.ai/pypi-phidata","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-phidata__cap_0","uri":"capability://planning.reasoning.multi.modal.agent.orchestration.with.stateful.memory","name":"multi-modal agent orchestration with stateful memory","description":"Phidata constructs autonomous agents that integrate language models, tools, and persistent memory through a unified Agent class that manages conversation state, tool execution context, and multi-turn reasoning. The framework uses a message-passing architecture where agents maintain a session-scoped memory store (supporting file, database, and vector backends) and execute tool calls via a registry-based function binding system that maps LLM outputs to executable Python functions with automatic schema inference.","intents":["Build an AI agent that remembers conversation history and user context across sessions","Create agents that can autonomously call multiple tools and APIs in sequence","Develop multi-modal agents that process text, images, and structured data in a single reasoning loop"],"best_for":["Teams building production AI agents with persistent state requirements","Developers creating autonomous workflows that require tool composition","Builders prototyping multi-turn conversational AI systems"],"limitations":["Memory persistence requires external storage backends (PostgreSQL, SQLite, or file-based); no in-memory-only option for production use","Tool execution is synchronous by default; async tool calling requires manual coroutine management","Agent reasoning loop is sequential; no built-in parallelization of independent tool calls"],"requires":["Python 3.8+","API key for at least one LLM provider (OpenAI, Anthropic, Ollama, or local models)","Optional: PostgreSQL or SQLite for persistent memory storage"],"input_types":["text (natural language queries)","images (via multimodal LLM support)","structured data (JSON for tool parameters)"],"output_types":["text (agent responses)","structured data (tool execution results)","conversation history (with metadata)"],"categories":["planning-reasoning","tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-phidata__cap_1","uri":"capability://memory.knowledge.knowledge.base.integration.with.semantic.search.and.rag","name":"knowledge base integration with semantic search and rag","description":"Phidata provides a Knowledge class that enables agents to retrieve relevant context from external documents via semantic search, using embeddings to match user queries against a vector-indexed knowledge base. The framework supports multiple knowledge sources (PDFs, web pages, databases) and integrates with vector stores (Pinecone, Weaviate, Chroma) to enable retrieval-augmented generation (RAG) where agent reasoning is grounded in retrieved documents rather than relying solely on model weights.","intents":["Augment agent responses with relevant information from a document corpus","Build question-answering systems that cite sources from a knowledge base","Create agents that can search and synthesize information from multiple documents"],"best_for":["Teams building document-grounded AI systems (customer support, research assistants)","Developers creating QA systems that require source attribution","Organizations with large document repositories needing intelligent retrieval"],"limitations":["Semantic search quality depends on embedding model quality; no built-in fine-tuning for domain-specific embeddings","Vector store integration requires external service setup (Pinecone, Weaviate); no lightweight local-only option for large corpora","Chunking strategy is configurable but not adaptive; fixed chunk sizes may miss semantic boundaries in specialized documents"],"requires":["Python 3.8+","Embedding model API key (OpenAI, Cohere) or local embedding model","Vector store account (Pinecone, Weaviate, Chroma) or self-hosted instance"],"input_types":["text (documents, PDFs)","URLs (web pages)","structured data (database records)"],"output_types":["retrieved documents (with similarity scores)","augmented agent responses (with source citations)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-phidata__cap_10","uri":"capability://automation.workflow.agent.monitoring.and.logging.with.execution.traces","name":"agent monitoring and logging with execution traces","description":"Phidata provides built-in logging and monitoring capabilities that track agent execution, including tool calls, LLM interactions, memory access, and reasoning steps. The framework generates detailed execution traces that can be exported for debugging, auditing, or performance analysis, with support for structured logging and external monitoring integrations.","intents":["Debug agent behavior by inspecting execution traces","Monitor agent performance and identify bottlenecks","Audit agent decisions for compliance and transparency"],"best_for":["Teams deploying agents to production with observability requirements","Developers debugging complex agent behavior","Organizations requiring audit trails for AI decisions"],"limitations":["Logging is verbose; production deployments may require filtering to reduce storage","Execution traces are stored in-memory by default; no built-in persistence to external systems","Performance impact of detailed logging is not quantified; may add latency to agent execution"],"requires":["Python 3.8+"],"input_types":["agent execution events (tool calls, LLM interactions)"],"output_types":["execution traces (structured logs)","performance metrics (latency, token usage)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-phidata__cap_11","uri":"capability://planning.reasoning.agent.team.coordination.and.multi.agent.workflows","name":"agent team coordination and multi-agent workflows","description":"Phidata supports multi-agent systems where multiple specialized agents coordinate to solve complex problems. The framework provides mechanisms for agents to communicate, delegate tasks, and share knowledge through a common message bus and shared memory layer, enabling hierarchical and collaborative agent architectures.","intents":["Build systems where multiple specialized agents work together on complex tasks","Create hierarchical agent teams with delegation and supervision","Enable agents to share knowledge and coordinate on shared goals"],"best_for":["Teams building complex autonomous systems with multiple specialized agents","Developers creating hierarchical agent architectures","Organizations automating workflows that require multiple skill domains"],"limitations":["Agent coordination is manual; no built-in orchestration or task scheduling","Message passing between agents requires explicit implementation; no automatic routing","Shared memory consistency is not enforced; developers must manage concurrent access"],"requires":["Python 3.8+","Shared memory backend (database or vector store) for inter-agent communication"],"input_types":["task descriptions","inter-agent messages"],"output_types":["task results (from coordinated agents)","execution traces (showing agent interactions)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-phidata__cap_2","uri":"capability://tool.use.integration.tool.registry.and.schema.based.function.calling","name":"tool registry and schema-based function calling","description":"Phidata implements a tool registry pattern where developers define tools as Python functions with type hints, which are automatically converted to JSON schemas for LLM function-calling APIs (OpenAI, Anthropic, Ollama). The framework handles schema generation, parameter validation, and execution context management, allowing agents to invoke tools with automatic error handling and result serialization back into the agent's reasoning loop.","intents":["Enable agents to call external APIs and Python functions with type-safe parameters","Define reusable tool libraries that multiple agents can share","Automatically generate OpenAI/Anthropic function schemas from Python function signatures"],"best_for":["Developers building tool-using agents with strict parameter validation","Teams creating shared tool libraries across multiple agent implementations","Builders integrating LLMs with existing Python codebases"],"limitations":["Tool execution is synchronous; async functions require manual wrapping with asyncio.run()","Error handling is basic; tool failures are logged but not automatically retried","Schema generation from type hints may fail for complex nested types; requires manual schema override for edge cases"],"requires":["Python 3.8+","Type hints on tool functions (required for schema generation)","LLM provider supporting function calling (OpenAI, Anthropic, Ollama)"],"input_types":["Python functions (with type hints)","Function parameters (inferred from type hints)"],"output_types":["JSON schemas (for LLM function calling)","tool execution results (serialized to agent context)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-phidata__cap_3","uri":"capability://text.generation.language.multi.provider.llm.abstraction.with.provider.switching","name":"multi-provider llm abstraction with provider switching","description":"Phidata provides a unified LLM interface that abstracts over multiple language model providers (OpenAI, Anthropic, Ollama, Groq, local models) through a common API. Developers specify the LLM provider via configuration, and the framework handles provider-specific API calls, token counting, streaming, and response parsing, allowing agents to switch between models without code changes.","intents":["Build agents that can use different LLM providers interchangeably","Switch between cloud and local models for cost or latency optimization","Test agent behavior across multiple model families without refactoring"],"best_for":["Teams evaluating multiple LLM providers for production use","Developers building cost-optimized systems that switch models based on query complexity","Organizations with on-premise deployment requirements"],"limitations":["Provider-specific features (vision, function calling variants) are not fully normalized; some providers lack parity","Token counting is approximate for non-OpenAI models; exact counts require provider-specific APIs","Streaming responses are supported but buffering behavior differs across providers"],"requires":["Python 3.8+","API key for chosen LLM provider (OpenAI, Anthropic, Groq, etc.)","Optional: Local Ollama instance for on-premise models"],"input_types":["text (prompts, messages)","images (for multimodal models)"],"output_types":["text (model completions)","structured data (function calling results)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-phidata__cap_4","uri":"capability://memory.knowledge.session.based.conversation.memory.with.multiple.backends","name":"session-based conversation memory with multiple backends","description":"Phidata implements conversation memory through a Session abstraction that persists messages, metadata, and user context across multiple backends (file-based JSON, SQLite, PostgreSQL, vector databases). The framework automatically manages session lifecycle, message ordering, and context window management, allowing agents to maintain coherent multi-turn conversations with optional semantic search over historical messages.","intents":["Persist agent conversations across application restarts","Retrieve conversation history for audit, debugging, or user review","Enable agents to search their own conversation history for relevant context"],"best_for":["Teams building conversational AI systems with audit requirements","Developers creating long-running agents that need to recover state","Organizations requiring conversation persistence for compliance"],"limitations":["Session isolation is not enforced; developers must manually manage session IDs to prevent cross-contamination","Message pruning (for context window limits) is manual; no automatic sliding-window implementation","Vector search over messages requires separate vector store setup; file/SQL backends don't support semantic search"],"requires":["Python 3.8+","Optional: SQLite (included) or PostgreSQL for persistent storage","Optional: Vector store (Pinecone, Weaviate) for semantic search over history"],"input_types":["text (user messages, agent responses)","metadata (timestamps, user IDs, session context)"],"output_types":["conversation history (ordered messages with metadata)","session summaries (for context management)"],"categories":["memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-phidata__cap_5","uri":"capability://data.processing.analysis.structured.data.extraction.with.schema.validation","name":"structured data extraction with schema validation","description":"Phidata enables agents to extract structured data from unstructured text by defining Pydantic schemas that the LLM uses as output constraints. The framework leverages LLM function calling or structured output modes to ensure responses conform to the schema, with automatic validation and error handling that re-prompts the model if validation fails.","intents":["Extract structured information (entities, relationships) from documents or user input","Validate that agent outputs conform to expected data structures","Generate type-safe data objects from LLM responses"],"best_for":["Teams building data extraction pipelines (invoice parsing, form filling)","Developers creating agents that must output structured data for downstream systems","Organizations needing type-safe LLM outputs"],"limitations":["Schema validation failures require re-prompting, adding latency and token cost","Complex nested schemas may confuse LLMs; no automatic schema simplification","Structured output mode is only available on newer models (GPT-4 Turbo+); older models fall back to function calling"],"requires":["Python 3.8+","Pydantic for schema definition","LLM supporting function calling or structured output mode"],"input_types":["text (unstructured documents)","Pydantic schemas (output structure definitions)"],"output_types":["Pydantic model instances (validated structured data)","JSON (serialized structured output)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-phidata__cap_6","uri":"capability://search.retrieval.web.search.and.real.time.information.retrieval","name":"web search and real-time information retrieval","description":"Phidata provides a WebSearch tool that agents can use to query the internet and retrieve current information, integrating with search APIs (DuckDuckGo, Google Search) to fetch and parse web results. The framework automatically formats search results into agent-readable context and handles pagination, deduplication, and result ranking.","intents":["Enable agents to answer questions about current events or real-time data","Augment agent knowledge with web search results","Build agents that can verify claims against current web information"],"best_for":["Teams building general-purpose AI assistants that need current information","Developers creating research or fact-checking agents","Organizations requiring real-time data integration"],"limitations":["Search result quality depends on query formulation; no automatic query expansion or refinement","Web scraping may fail on JavaScript-heavy sites; results are limited to text extraction","Rate limiting on free search APIs; production use requires paid API keys"],"requires":["Python 3.8+","Optional: API key for Google Search or DuckDuckGo (free tier available)"],"input_types":["text (search queries)"],"output_types":["web search results (title, URL, snippet)","formatted context for agent reasoning"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-phidata__cap_7","uri":"capability://data.processing.analysis.file.based.knowledge.ingestion.and.document.processing","name":"file-based knowledge ingestion and document processing","description":"Phidata provides utilities to ingest documents (PDFs, text files, markdown) into a knowledge base by chunking them into semantic segments, embedding each chunk, and storing them in a vector database. The framework handles document parsing, metadata extraction, and deduplication, enabling agents to retrieve relevant document segments during reasoning.","intents":["Load a document corpus into a searchable knowledge base","Automatically chunk and embed documents for semantic search","Build agents that can cite specific document sections in responses"],"best_for":["Teams building document-grounded AI systems","Developers creating knowledge bases from existing document repositories","Organizations migrating from traditional search to semantic search"],"limitations":["PDF parsing is basic; complex layouts (tables, multi-column) may be incorrectly extracted","Chunking is fixed-size; no adaptive chunking based on semantic boundaries","Metadata extraction is manual; no automatic title/author/date extraction from documents"],"requires":["Python 3.8+","Document files (PDF, TXT, Markdown)","Vector store for embedding storage (Pinecone, Weaviate, Chroma)"],"input_types":["PDF files","text files","markdown files"],"output_types":["embedded document chunks (in vector store)","metadata (document source, chunk position)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-phidata__cap_8","uri":"capability://planning.reasoning.agent.task.decomposition.and.planning","name":"agent task decomposition and planning","description":"Phidata enables agents to break down complex tasks into subtasks through a planning capability where agents use chain-of-thought reasoning to decompose goals, create execution plans, and track progress. The framework supports multi-step reasoning with intermediate checkpoints, allowing agents to validate progress and adjust plans based on tool execution results.","intents":["Enable agents to tackle complex multi-step problems by decomposing them","Create agents that can plan and execute workflows autonomously","Build agents that can recover from failures by replanning"],"best_for":["Teams building autonomous workflow agents","Developers creating complex reasoning systems","Organizations automating multi-step business processes"],"limitations":["Planning quality depends on LLM reasoning capability; no guardrails against invalid plans","No built-in plan validation; agents may create circular or infeasible plans","Replanning on failure is manual; no automatic backtracking or alternative path exploration"],"requires":["Python 3.8+","LLM with strong reasoning capability (GPT-4, Claude 3)"],"input_types":["text (task descriptions, goals)"],"output_types":["task plans (structured steps)","execution results (with progress tracking)"],"categories":["planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-phidata__cap_9","uri":"capability://text.generation.language.agent.response.formatting.and.output.templating","name":"agent response formatting and output templating","description":"Phidata provides response formatting capabilities that allow developers to define templates for agent outputs, controlling how results are presented to users. The framework supports markdown formatting, structured output templates, and custom formatters that transform raw agent responses into user-friendly formats.","intents":["Format agent responses with consistent styling and structure","Generate markdown-formatted outputs for display in UIs","Create templated responses that include citations and metadata"],"best_for":["Teams building user-facing AI applications","Developers creating chatbots with consistent response formatting","Organizations requiring branded or standardized output formats"],"limitations":["Formatting is applied post-generation; no control over LLM output format during generation","Custom formatters require manual implementation; no built-in formatters for common use cases","Template variables must be manually extracted from agent responses"],"requires":["Python 3.8+"],"input_types":["agent responses (text, structured data)"],"output_types":["formatted text (markdown, HTML)","templated responses (with metadata)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","API key for at least one LLM provider (OpenAI, Anthropic, Ollama, or local models)","Optional: PostgreSQL or SQLite for persistent memory storage","Embedding model API key (OpenAI, Cohere) or local embedding model","Vector store account (Pinecone, Weaviate, Chroma) or self-hosted instance","Shared memory backend (database or vector store) for inter-agent communication","Type hints on tool functions (required for schema generation)","LLM provider supporting function calling (OpenAI, Anthropic, Ollama)","API key for chosen LLM provider (OpenAI, Anthropic, Groq, etc.)","Optional: Local Ollama instance for on-premise models"],"failure_modes":["Memory persistence requires external storage backends (PostgreSQL, SQLite, or file-based); no in-memory-only option for production use","Tool execution is synchronous by default; async tool calling requires manual coroutine management","Agent reasoning loop is sequential; no built-in parallelization of independent tool calls","Semantic search quality depends on embedding model quality; no built-in fine-tuning for domain-specific embeddings","Vector store integration requires external service setup (Pinecone, Weaviate); no lightweight local-only option for large corpora","Chunking strategy is configurable but not adaptive; fixed chunk sizes may miss semantic boundaries in specialized documents","Logging is verbose; production deployments may require filtering to reduce storage","Execution traces are stored in-memory by default; no built-in persistence to external systems","Performance impact of detailed logging is not quantified; may add latency to agent execution","Agent coordination is manual; no built-in orchestration or task scheduling","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.3,"match_graph":0.25,"freshness":0.9,"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":"2026-05-03T15:20:12.847Z","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-phidata","compare_url":"https://unfragile.ai/compare?artifact=pypi-phidata"}},"signature":"/46penQLTM6bBD7y3nPDVQRIagQxuDSOeA0bZp2A1MOW1mtJPDq8kOVglMR1rC+sgRSCJQqTyvk0grt1YLfmBA==","signedAt":"2026-06-15T15:37:17.737Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-phidata","artifact":"https://unfragile.ai/pypi-phidata","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-phidata","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"}}