katanemo/plano

Features

• Agentic Traffic Routing - The product inspects prompts and conversation state to decide which models, tools, or APIs to call.
• AI Agent Orchestration - Orchestrates specialized AI agents by coordinating custom instructions, tools, and intent-based routing.
• Agent Orchestration - Centralizes the orchestration of traffic between clients and AI agents to decouple application logic from framework abstractions.
• LLM Gateways - Provides a centralized gateway to unify multiple LLM providers into a single interoperable API.
• Guardrailed Inference Executions - Executes LLM inference through a guardrails layer that enforces safety policies on inbound and outbound traffic.
• AI Agent Orchestrators - Ships a routing layer that organizes and coordinates specialized agents based on semantic user intent.
• Model Provider Integrations - Provides unified interfaces for connecting and configuring multiple AI model providers using API keys and base URLs.
• Model Request Routing - Implements mechanisms for directing API requests to different AI backends via a centralized routing engine.
• AI Safety Guardrails - Implements safety guardrails through filter chains to detect jailbreaks and enforce content policies.
• Guardrail-Enforced - Enforces safety guardrails on requests and responses through filter chains attached to AI agents.
• LLM Jailbreak Protections - Implements jailbreak protection and content policies using centralized filter chains to ensure consistent model behavior.
• LLM Observability - Implements monitoring and tracing tools specifically designed for LLM-specific metrics and agentic workflows.
• Model Abstraction Layers - Provides a unified interface that decouples application logic from specific AI vendors using semantic aliases.
• Model Alias Resolvers - Maps semantic aliases to specific LLM instances to decouple client code from provider-specific model identifiers.
• Model Format Translators - Translates request and response schemas between common SDKs and various LLM provider APIs.
• Runtime Interoperability Layers - Enables any supported SDK to call models from different providers through a single interoperable interface.
• Model Provider Abstractions - Provides interfaces that normalize API interactions across multiple AI service providers to hide vendor-specific details.
• Model Provider Configurations - Manages provider configurations and aliases to swap AI vendors without refactoring application code.
• Multi-Provider Abstractions - Implements a common interface layer that routes requests across multiple AI providers to optimize cost and performance.
• Model Provider Proxies - Provides a proxy layer that unifies requests between local and remote AI model providers.
• Model Routing - Manages traffic by mapping specific tasks and semantic aliases to optimal AI models.
• Model Fallbacks - The product defines ordered pools of candidate models to allow automatic retries with alternative models upon failure.
• Traffic Management - Proxies, audits, and secures inbound traffic to AI models, including TLS termination and agent routing.
• Provider Redundancy - The product manages retries, fallbacks, and traffic splitting across multiple providers to ensure reliability.
• Provider-Agnostic Request Normalization - Normalizes outgoing requests into a provider-agnostic format and enforces rate limits to protect upstream providers.
• Agent-Specific Query Routing - Directs user prompts to specialized AI agents by analyzing semantic intent and conversation history.
• Preference-Based Routing - The product defines candidate pools of models and automatically falls back to alternatives upon receiving errors.
• AI Provider Proxies - Functions as a provider-agnostic proxy that normalizes outgoing requests and manages retries and fallbacks.
• Request Processing - Implements middleware flows to inspect, rewrite, and enrich request data before delivery to the provider.
• Model Connectivity Resilience - Handles model connectivity with retry and fail-over mechanisms to ensure high availability for model requests.
• Middleware-Style Guardrail Pipelines - Implements sequential chains of guardrail modules to inspect, modify, or block LLM requests and responses.
• Input Guardrail Integrations - Filters requests through external services to perform input validation and query rewriting before model execution.
• Request Filter Integrations - Executes a sequence of interceptor filters to apply guardrails, rewrite prompts, and enrich context.
• Intent-Based Workflow Determination - Determines whether an incoming request should trigger a complex agent workflow or a simple task-specific prompt.
• Request-Response Filter Chains - Implements interceptor-based filter chains for prompt rewriting, context enrichment, and safety guardrail enforcement.
• Unified Model Interfaces - Provides a standardized API that offers a consistent execution interface for processing and streaming across different model providers.
• Agent Execution Tracing - Automatically captures reasoning traces, tool calls, and behavior metrics across all agents.
• Automatic Tracing Instrumentation - Generates automatic OpenTelemetry traces for every request without requiring manual instrumentation code.
• OpenTelemetry-Integrated Monitors - Integrates OpenTelemetry to capture end-to-end request lifecycles and agentic signals as structured spans.
• OpenTelemetry Exporters - Exports request spans and routing decisions to external collectors using OpenTelemetry standards.
• Intent-Based Routing - Uses semantic analysis and classification to direct requests to specific models based on user intent.
• External API Tool Exposures - Exposes application operations as callable tools so AI agents can fetch data or execute workflows.
• Conversation State Management - Tracks context and history across multi-turn interactions to remove the need for clients to send full history.
• External Service Integrations - Connects to external HTTP endpoints or protocol services to execute reusable workflow steps as filters.
• Language Model Observability - Provides operational tracking of token consumption, financial costs, and response latency.
• Provider-Specific Routing Policies - Directs incoming requests to specific upstream providers using configured API keys, base URLs, and model aliases.
• Custom API Endpoints - Allows overriding default provider paths with custom URLs to route queries to self-hosted or internal AI endpoints.
• Stateful Routing - The product shares session affinity data across server replicas using an external store to maintain consistent routing.
• Request Routing by Model ID - The product designates a specific model to handle requests when no specific model is requested by the client.
• Natural Language Function Executions - Translates natural language prompts into structured calls to perform transactional operations via backend functions.
• Failure Threshold Markers - Appends visual markers to spans that meet specific failure thresholds for immediate identification.
• Traffic Load Balancers - The product distributes traffic across multiple provider instances using health checks and circuit breakers.
• Concurrent Network Workers - Utilizes non-blocking worker threads to handle high-volume network traffic and filtering tasks with low latency.
• Agent Behavioral Flags - Attaches visual flags and attributes to traces to identify quality issues or tool failures.
• Model-Pinning - Locks a specific model for a session using a unique identifier to ensure behavioral consistency.
• Conversational Affinity - Persists conversational context and model pinning across requests to maintain consistency without client-side history.
• Access Control - Enforces consistent security policies and access controls across all agents and AI providers.
• Natural Language Tool Interfaces - Provides interfaces that map natural language instructions into structured and validated calls to backend services.
• Non-blocking I/O - Uses non-blocking I/O and worker threads to execute filtering and forwarding tasks for high-performance request handling.
• System Reliability - The product handles request retries and failovers across various agent instances to ensure system reliability.
• Information-Density Filters - Uses signal attributes to prioritize high-information traces for identifying sessions that require prompt updates.
• OTLP - Enables analysis of request flows by filtering OTLP traces by specific identifiers, attributes, or time windows.
• Trace Metadata - Attaches detected signals and contextual information as OpenTelemetry attributes and span events to execution traces.
• LLM Performance Monitoring - Tracks LLM-specific performance metrics including token usage and time-to-first-token.
• Request Tracing - Provides utilities for monitoring and visualizing the end-to-end lifecycle of network requests through application layers.
• Policy Violation Blocking - Blocks requests and returns early responses when an input filter detects a policy violation.
• Parameter Coercion and Validation - Parses natural language prompts to extract and validate structured parameters against defined API constraints.
• Artificial Intelligence - AI-native proxy server and data plane for agentic applications.
• LLM Development Frameworks - Infrastructure for delivering and managing agentic applications.

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