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Abstracts away vector database selection (supports multiple backends) and handles embedding generation through pluggable embedding providers, enabling developers to wire retrieval into agentic workflows without managing low-level indexing logic.","intents":["I need to build a RAG system that retrieves relevant documents before generating answers","I want to ingest a knowledge base and make it queryable by an AI agent","I need to swap vector databases without rewriting retrieval logic"],"best_for":["teams building knowledge-grounded chatbots and Q&A systems","developers prototyping RAG agents with multiple document sources","organizations needing pluggable vector store backends"],"limitations":["Chunking strategy is fixed per pipeline — no dynamic chunk size adjustment based on document type","No built-in deduplication across ingested documents — requires external preprocessing","Retrieval ranking is semantic-only — no hybrid BM25+semantic search without custom implementation"],"requires":["Python 3.8+","API credentials for embedding provider (OpenAI, Anthropic, or local model)","Vector database instance (Pinecone, Weaviate, Chroma, or compatible)"],"input_types":["PDF documents","plain text files","markdown","structured JSON/YAML"],"output_types":["retrieved document chunks with metadata","ranked context passages","augmented prompts with retrieved context"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-phoenix-ai__cap_1","uri":"capability://tool.use.integration.mcp.model.context.protocol.server.implementation.and.client.integration","name":"mcp (model context protocol) server implementation and client integration","description":"Implements MCP specification for standardized tool/resource exposure and client-server communication, allowing agents to discover and invoke external tools through a protocol-compliant interface. 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Supports A/B testing different prompts, models, or configurations with statistical significance testing. Integrates with experiment tracking to log results and compare runs, enabling data-driven optimization of LLM applications.","intents":["I want to measure if my prompt changes actually improve output quality","I need to benchmark my agent against a test dataset to track performance","I want to compare outputs from different models with statistical rigor"],"best_for":["teams optimizing LLM applications through iterative testing","developers building evaluation pipelines for production LLM systems","organizations needing quantitative metrics for LLM quality"],"limitations":["Automatic metrics (BLEU, ROUGE) don't correlate well with human judgment — require human evaluation for validation","Benchmarking requires representative test datasets — results may not generalize to production data","Statistical significance testing requires large sample sizes — small experiments may show false positives"],"requires":["Python 3.8+","Test dataset with expected outputs","Optional: experiment tracking platform (Weights & Biases, MLflow)"],"input_types":["LLM outputs","reference/expected outputs","optional: custom scoring functions","optional: test configurations"],"output_types":["metric scores (BLEU, ROUGE, similarity, etc.)","comparison reports","statistical significance results","experiment metadata"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-phoenix-ai__cap_2","uri":"capability://planning.reasoning.agentic.ai.orchestration.with.multi.step.reasoning.and.tool.use","name":"agentic ai orchestration with multi-step reasoning and tool use","description":"Orchestrates multi-turn agent loops that combine LLM reasoning, tool invocation, and state management into cohesive workflows. Implements agent patterns (ReAct, chain-of-thought) with automatic tool selection, execution, and result integration back into the reasoning loop. Manages conversation history, tool call tracking, and error recovery without requiring manual state threading through each step.","intents":["I need an agent that can reason about a problem, call tools, and iterate until it solves it","I want to build a multi-step workflow where an LLM decides which tools to use","I need to handle agent failures gracefully and retry with different tool choices"],"best_for":["developers building autonomous agents for complex tasks","teams prototyping agentic workflows without building orchestration from scratch","organizations needing interpretable agent decision-making with tool audit trails"],"limitations":["Agent loop depth is unbounded — no built-in max-steps limit prevents infinite loops without explicit configuration","Tool selection is LLM-driven only — no learned routing or bandit-based exploration strategies","State persistence requires external storage — no built-in agent memory across sessions"],"requires":["Python 3.8+","LLM API access (OpenAI, Anthropic, or local model)","Tool definitions with clear descriptions for LLM reasoning"],"input_types":["user queries/goals","tool definitions with descriptions","system prompts and instructions"],"output_types":["final agent response","tool call history with results","reasoning trace/chain-of-thought"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-phoenix-ai__cap_3","uri":"capability://text.generation.language.multi.provider.llm.abstraction.with.unified.interface","name":"multi-provider llm abstraction with unified interface","description":"Provides a unified API for interacting with multiple LLM providers (OpenAI, Anthropic, local models via Ollama, etc.) without rewriting client code. Abstracts away provider-specific request/response formats, handles authentication, manages token counting, and normalizes streaming vs non-streaming responses into a consistent interface. Enables seamless provider switching and fallback strategies at runtime.","intents":["I want to switch between OpenAI and Anthropic models without changing my code","I need to implement fallback logic if one LLM provider is unavailable","I want to compare outputs across different models with the same prompt"],"best_for":["developers building LLM applications that need provider flexibility","teams evaluating multiple models for cost/performance tradeoffs","organizations with multi-cloud or hybrid on-prem LLM deployments"],"limitations":["Abstraction overhead adds ~5-10% latency per request due to normalization layer","Provider-specific features (vision, function calling variants) require conditional logic despite abstraction","Token counting estimates differ across providers — no unified accurate counting without per-provider APIs"],"requires":["Python 3.8+","API keys for target LLM providers","Network connectivity to provider endpoints or local Ollama instance"],"input_types":["text prompts","message histories","system instructions","optional: images (if provider supports)"],"output_types":["text completions","streaming token streams","token usage metadata"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-phoenix-ai__cap_4","uri":"capability://search.retrieval.semantic.search.and.similarity.based.retrieval","name":"semantic search and similarity-based retrieval","description":"Performs semantic similarity search by embedding queries and documents into a shared vector space, then retrieving top-k results based on cosine/dot-product similarity. Integrates with vector databases to execute efficient approximate nearest neighbor search at scale. Supports filtering by metadata and re-ranking results using cross-encoder models for improved relevance without full re-embedding.","intents":["I need to find documents most relevant to a user query without keyword matching","I want to retrieve similar items from a large corpus efficiently","I need to re-rank search results by semantic relevance after initial retrieval"],"best_for":["teams building semantic search features for knowledge bases","developers implementing similarity-based recommendation systems","organizations needing sub-second retrieval from million+ document corpora"],"limitations":["Embedding quality depends entirely on embedding model — no built-in evaluation of embedding quality","Approximate nearest neighbor search trades recall for speed — exact top-k results not guaranteed","Re-ranking with cross-encoders requires additional model inference — adds 100-500ms per query"],"requires":["Python 3.8+","Embedding model (OpenAI, Hugging Face, or local)","Vector database with ANN support (Pinecone, Weaviate, FAISS, etc.)"],"input_types":["text query strings","optional: metadata filters","optional: re-ranking model"],"output_types":["ranked list of similar documents with scores","document metadata and content","similarity scores (0-1 range)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-phoenix-ai__cap_5","uri":"capability://text.generation.language.prompt.engineering.and.template.management","name":"prompt engineering and template management","description":"Manages prompt templates with variable substitution, conditional sections, and dynamic content injection. Supports Jinja2-style templating for complex prompts, version control of prompt variations, and A/B testing different prompt formulations. Integrates with agents and RAG pipelines to automatically format retrieved context and tool results into prompts without manual string concatenation.","intents":["I want to manage multiple prompt variations and test which performs best","I need to dynamically inject retrieved context and tool results into prompts","I want to version control my prompts and track changes over time"],"best_for":["teams iterating on prompt quality for production LLM applications","developers building prompt-driven workflows with dynamic content","organizations needing prompt governance and audit trails"],"limitations":["Template rendering adds ~5-20ms per prompt due to Jinja2 parsing","No built-in A/B testing framework — requires external experiment tracking","Version control is in-memory only — no persistent prompt history without external storage"],"requires":["Python 3.8+","Jinja2 library for template rendering"],"input_types":["prompt template strings","variable dictionaries","optional: retrieved context","optional: tool results"],"output_types":["rendered prompt strings","template metadata","variable usage statistics"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-phoenix-ai__cap_6","uri":"capability://text.generation.language.streaming.response.handling.with.token.level.granularity","name":"streaming response handling with token-level granularity","description":"Manages streaming LLM responses by buffering tokens, detecting completion, and exposing token-level events for real-time UI updates or intermediate processing. Handles provider-specific streaming formats (OpenAI SSE, Anthropic streaming, etc.) and normalizes them into a unified token stream. Supports streaming with tool calls, allowing agents to invoke tools as they're identified in the stream without waiting for full response.","intents":["I want to display LLM responses token-by-token in my UI for better UX","I need to process intermediate tokens for real-time analysis or filtering","I want agents to start tool execution as soon as tool calls appear in the stream"],"best_for":["developers building real-time chat interfaces with streaming responses","teams implementing token-level monitoring or content filtering","organizations needing low-latency agent execution with streaming tool calls"],"limitations":["Streaming adds complexity to error handling — partial responses may be incomplete if stream breaks","Token-level processing prevents batching optimizations — throughput lower than non-streaming","Tool call streaming requires provider support — not all models support streaming function calls"],"requires":["Python 3.8+","LLM provider with streaming support","Async/await capable runtime for non-blocking stream consumption"],"input_types":["streaming response objects from LLM providers","optional: tool definitions for call detection"],"output_types":["token-by-token event stream","detected tool calls with arguments","completion status and metadata"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-phoenix-ai__cap_7","uri":"capability://memory.knowledge.context.window.management.and.token.optimization","name":"context window management and token optimization","description":"Automatically manages LLM context windows by tracking token usage, prioritizing recent messages, and evicting old context when approaching limits. Implements sliding window and summarization strategies to maintain conversation history while staying within token budgets. Provides token counting for different models and estimates costs based on input/output tokens, enabling developers to optimize context usage without manual calculation.","intents":["I need to keep conversations within token limits without losing important context","I want to estimate costs for my LLM application before running it","I need to automatically summarize old conversation history to make room for new messages"],"best_for":["developers building long-running conversational agents","teams managing costs for high-volume LLM applications","organizations needing predictable token usage and budgeting"],"limitations":["Token counting is approximate — actual usage may differ by 5-10% due to tokenizer differences","Summarization strategy is fixed — no learned prioritization of important context","Context eviction is FIFO-based — no semantic importance weighting"],"requires":["Python 3.8+","Model-specific tokenizer (tiktoken for OpenAI, etc.)"],"input_types":["conversation messages","model identifier","optional: token budget"],"output_types":["token count estimates","cost estimates","optimized message list within budget","eviction/summarization recommendations"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-phoenix-ai__cap_8","uri":"capability://automation.workflow.error.handling.and.retry.logic.with.exponential.backoff","name":"error handling and retry logic with exponential backoff","description":"Implements resilient error handling for LLM API calls with configurable retry strategies, exponential backoff, and jitter to prevent thundering herd. Distinguishes between retryable errors (rate limits, timeouts) and non-retryable errors (auth failures, invalid requests), applying appropriate handling for each. Integrates with monitoring to track retry patterns and failure rates across the application.","intents":["I want my LLM calls to automatically retry on transient failures","I need to handle rate limiting gracefully without crashing","I want visibility into which errors are retryable vs permanent"],"best_for":["developers building production LLM applications with high availability requirements","teams managing multiple concurrent LLM requests with rate limiting","organizations needing observability into LLM API failures"],"limitations":["Retry logic adds latency — worst-case exponential backoff can delay responses by minutes","No circuit breaker pattern — repeated failures don't prevent cascading requests","Jitter calculation is random — no deterministic retry scheduling for testing"],"requires":["Python 3.8+","Configurable retry parameters (max attempts, backoff multiplier)"],"input_types":["LLM API calls","exception objects","optional: custom retry predicates"],"output_types":["successful response after retries","final error if all retries exhausted","retry metadata (attempt count, backoff duration)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-phoenix-ai__cap_9","uri":"capability://data.processing.analysis.structured.output.extraction.with.schema.validation","name":"structured output extraction with schema validation","description":"Extracts structured data from LLM responses by defining JSON schemas and validating outputs against them. Implements schema-guided generation where the LLM is constrained to produce valid JSON matching the schema, reducing parsing errors. Supports nested objects, arrays, and type validation with automatic retry if output doesn't match schema, enabling reliable structured data extraction without manual parsing.","intents":["I need to extract structured data from LLM responses reliably","I want the LLM to generate JSON that matches my schema without manual parsing","I need to validate LLM outputs before using them in downstream systems"],"best_for":["developers building data extraction pipelines with LLMs","teams integrating LLM outputs with structured databases or APIs","organizations needing guaranteed schema compliance for LLM-generated data"],"limitations":["Schema-guided generation requires provider support — not all models support constrained output","Complex nested schemas may confuse LLMs — validation failures increase with schema depth","Retry on validation failure adds latency — no guaranteed success even with retries"],"requires":["Python 3.8+","JSON Schema definition","LLM provider with schema-guided generation support (OpenAI, Anthropic)"],"input_types":["JSON Schema definition","LLM prompt","optional: example outputs"],"output_types":["validated JSON objects","validation error details","schema compliance metadata"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","API credentials for embedding provider (OpenAI, Anthropic, or local model)","Vector database instance (Pinecone, Weaviate, Chroma, or compatible)","MCP client library compatible with protocol version","Network connectivity for server-client communication","Test dataset with expected outputs","Optional: experiment tracking platform (Weights & Biases, MLflow)","LLM API access (OpenAI, Anthropic, or local model)","Tool definitions with clear descriptions for LLM reasoning","API keys for target LLM providers"],"failure_modes":["Chunking strategy is fixed per pipeline — no dynamic chunk size adjustment based on document type","No built-in deduplication across ingested documents — requires external preprocessing","Retrieval ranking is semantic-only — no hybrid BM25+semantic search without custom implementation","MCP transport layer adds ~50-200ms latency per tool invocation vs direct function calls","No built-in tool caching — repeated calls to same tool with same args hit the network","Limited to tools that fit MCP schema constraints — complex nested objects require flattening","Automatic metrics (BLEU, ROUGE) don't correlate well with human judgment — require human evaluation for validation","Benchmarking requires representative test datasets — results may not generalize to production data","Statistical significance testing requires large sample sizes — small experiments may show false positives","Agent loop depth is unbounded — no built-in max-steps limit prevents infinite loops without explicit configuration","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.3,"match_graph":0.25,"freshness":0.5,"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":"2026-05-03T15:20:22.334Z","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-phoenix-ai","compare_url":"https://unfragile.ai/compare?artifact=pypi-phoenix-ai"}},"signature":"daSjLwykXXV1pHqTffNa0sogWotFRN9rcR47Hj44lnhXemkCjD0mmP8AgQ323Ci+ZT4lyjIwziFyRJF4rdaxAg==","signedAt":"2026-06-20T09:39:06.452Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-phoenix-ai","artifact":"https://unfragile.ai/pypi-phoenix-ai","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-phoenix-ai","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"}}