LiteLLM is a production-grade, open-source proxy/gateway for over 200 large language model (LLM) providers, including OpenAI, Anthropic, Azure, Bedrock, Gemini, and many more. It presents a unified OpenAI-compatible API, handling request routing, authentication, rate limiting, cost tracking, caching, guardrails (content filtering, PII detection), and audit logging. The system is architected as a FastAPI Python server with a Next.js admin dashboard, Rust extensions for performance-critical paths (token counting, caching, auth, cost calculation), and optional PostgreSQL/Redis backends. It supports streaming, MCP (Model Context Protocol), A2A (Agent-to-Agent) protocol, and enterprise features like SSO, secret management, and Terraform deployment modules. Key entry points include the proxy server (litellm/proxy/proxy_server.py), CLI commands (litellm, litellm-proxy), and the admin UI at /ui/litellm-dashboard. The primary data flow is from client requests (chat completions, embeddings, audio, images) received by the proxy, which authenticates, applies guardrails, routes to the configured LLM provider(s), logs usage, and returns a standardized response. Caching is multi-tier (Redis, disk, S3, GCS, Azure Blob, semantic). Spend tracking is aggregated into daily tables and exposed via the management API and UI. External dependencies include PostgreSQL (via Prisma ORM), Redis (for distributed caching, rate limiting, and queuing), and cloud-specific secret managers (AWS Secrets Manager, GCP Secret Manager, Azure Key Vault, HashiCorp Vault). The project uses uv for Python dependency management, Cargo for Rust crates, and npm/pnpm for the dashboard. Extensive CI/CD pipelines (CircleCI, GitHub Actions) run unit, integration, e2e, mutation, and Rust-Python parity tests.
Kong is a cloud-native, platform-agnostic API gateway built on OpenResty/Nginx. It provides traffic management, security, observability, and AI/LLM proxying capabilities through a plugin architecture. The system supports traditional, DB-less, and hybrid deployment modes, with an Admin API for configuration and Kong Manager for GUI-based management. Key features include load balancing, authentication, rate limiting, request/response transformation, and extensive observability integrations.
ThinkWatch is a Rust workspace implementing an AI gateway platform that fronts multiple model providers and MCP (Model Context Protocol) servers. It exposes OpenAI-compatible chat/completion endpoints, streaming, MCP tool-call routing, content filtering, and provider failover. The backend is split into crates: `common` (shared lifecycle/limits/access), `auth` (PoW login, API keys), `gateway` (model routing), `mcp-gateway` (MCP server proxying), `server` (REST admin/audit/trace endpoints), and `test-support` (integration test client). A React/TypeScript frontend in `web/` provides an admin console, setup wizard, limits panels, and MCP server management. Data flows: requests enter the gateway, pass through `check_limits`, `check_budget`, `check_access`, then `run_post_invoke`; `trace_id` is propagated across gateway, MCP, audit, and app logs stored in ClickHouse; Postgres holds users, API keys, budgets, and MCP server configs; Redis/Lua scripts enforce rate limits.
This repository provides a comprehensive CLI toolkit and programmatic interface (Python SDK and notebook clients) for creating, deploying, managing, monitoring, and evaluating AI agents on Amazon Bedrock AgentCore. The system supports multiple agent frameworks (Strands, LangChain/LangGraph, CrewAI, AutoGen, OpenAI Agents, Google ADK) and infrastructure-as-code options (CDK, Terraform). Key capabilities include project scaffolding via Jinja2 templates, runtime lifecycle management (configure, launch, destroy, invoke), memory management (short-term/long-term, semantic, user preference), observability (CloudWatch-based trace and log querying with OpenTelemetry span parsing), evaluation (LLM-as-a-judge with built-in and custom evaluators), policy engine CRUD, gateway management (MCP, API key, Cognito OAuth), and identity federation setup. The codebase is well-structured with a clear three-layer architecture (CLI -> operations -> services), extensive test coverage, and consistent error handling. However, the project is explicitly deprecated in favor of the @aws/agentcore NPM CLI, meaning future development is minimal and users are encouraged to migrate. Despite the deprecation, the reusable abstractions (e.g., CloudWatch query builder, trace processor, strategy models, configuration schemas) demonstrate solid software craftsmanship.