Praxis AI Documentation

Praxis AI provides AI inference filters, agentic protocol support, and provider API integrations on top of Praxis.
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Praxis AI provides AI inference filters, agentic protocol support, and provider API integrations on top of Praxis.

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Architecture

Provider guides

Development

Operations and releases

Adding a Built-in AI Filter

Add a built-in Praxis AI filter by following the filter contract and registering it with the product.

Agentic Protocols

JSON-RPC 2.0 envelope parsing and protocol-specific metadata extraction for MCP and A2A agent traffic.

AI Inference

Body-aware classification, routing, and enrichment for AI inference traffic, built on the filter pipeline and StreamBuffer body access pattern.

Anthropic Messages API

Praxis supports the Anthropic Messages API (/v1/messages) through five composable filters. Operators can route, validate JSON envelopes, and transform Anthropic requests to reach any backend.

Anthropic Messages Replay Test Plan

Defines the Anthropic Messages API request and response shapes Praxis AI should cover across replay, passthrough, and translation tests.

Capacity Planning: File Descriptors

Praxis AI runs on the Praxis proxy core and inherits its file descriptor handling.

Examples

Browse 95 tracked YAML configurations for Praxis AI, organized by task and setup needs.

Features

Praxis AI extends the Praxis proxy framework with AI-specific filters and provider integrations.

Filters

Browse Praxis AI filters and open their generated configuration reference.

FIPS 140-3

Praxis AI performs all of its cryptography in the system OpenSSL library.

FIPS Tooling

Local, reproducible checks that a praxis-ai build is on the path to FIPS 140-3 compliance on Red Hat Enterprise Linux.

Getting Started

compliance check and Red Hat’s scanner need podman on Linux)

llm-d Integration Testing

Praxis AI includes an AI-owned ext_proc compatibility layer for llm-d integration.

Migrating to 0.2.0

Version 0.2.0 moves token usage parsing from praxis-ai-apis into the private token usage subsystem in praxis-ai-filters.

Native vLLM qualification evidence

The existing vLLM Integration workflow runs the complete Responses SDK suite on a real GPU for scheduled runs, run_live_vllm=true dispatches, and the vllm-full-suite PR label.

OpenAI Conformance

This directory tracks Praxis AI conformance against selected OpenAI API surfaces. The current scope is Conversations only.

OpenTelemetry Routing Semantics

Add Praxis AI routing decisions to the OpenTelemetry request traces exported by Praxis core.

Outbound callout security

Praxis AI treats an outbound target, its resolved socket addresses, and the credentials attached to the request as one trust decision.

PostgreSQL Cryptographic Boundary

The openai_response_store and openai_conversations filters can persist state to PostgreSQL.

Project Management

All repositories in the praxis-proxy organization use a consistent workflow for planning, prioritizing, and tracking work.

Quickstart

Build Praxis AI from source for local development, then route a Responses API request to an AI backend.

Release Process

Praxis AI uses Semantic Versioning. The workspace version is the single source of truth, defined in workspace.package.version in the root Cargo.toml. All workspace crates inherit this version.

Response Store

Durable persistence for OpenAI Responses API responses, enabling retrieval (GET), deletion (DELETE), and input-item pagination across proxy restarts.

Responses Store Schema Upgrade

Replace the disposable v3 response store with a fresh v4 database before starting current releases; startup creates the required schema.

Run Codex or Claude Code through Praxis and vLLM

Connect Codex or Claude Code through a local Praxis AI gateway to inference served by vLLM.

Type Design

Make invalid states unrepresentable. The type system and serde should enforce constraints at parse time, not at runtime.