phoenix-ai

Builds end-to-end retrieval-augmented generation pipelines by ingesting documents into vector stores, chunking text with configurable strategies, and retrieving semantically relevant context for LLM prompts. 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.

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. Handles bidirectional message routing, schema validation, and tool registration with automatic serialization of function signatures into MCP-compatible schemas, enabling interoperability with any MCP-compliant client or agent framework.

Provides tools for evaluating LLM outputs against metrics (BLEU, ROUGE, semantic similarity, custom scorers) and benchmarking agent performance across test datasets. 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.

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.

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.

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.

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.

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.

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.

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.

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.