# AegisFlow — What It Is and Why It Matters

## The One-Line Summary

AegisFlow is a free, open-source control center that manages, secures, and monitors AI traffic between your applications, agents, tools, and model providers.

It is local-first by design. The default setup runs with Docker Compose, a mock provider, YAML policies, local logs, local audit evidence, and Prometheus metrics. Real model providers are optional bring-your-own-key connectors, not a requirement.


## The Problem

Companies, students, open-source maintainers, and small teams are integrating AI into nearly everything: customer support chatbots, internal assistants, content generation tools, code assistants, and personal automation. Some use third-party AI providers. Others use local or self-hosted models.

The problem is that as AI usage grows inside a company, things get messy very quickly.

Each AI provider has its own SDK, its own request format, its own pricing, and its own quirks. Teams end up writing different integration code for each provider, scattered across different services. There is no single place to see how much AI is being used, how much it might cost, or whether it is working reliably. When a provider goes down, applications break. When someone accidentally sends sensitive customer data to an AI model, nobody knows until it is too late. There is no central way to enforce rules about which teams can use which models, how much they can spend, or what kind of content is allowed to pass through.

This is not a small problem. It is the same kind of problem that companies solved years ago for regular API traffic using tools like API gateways, load balancers, and firewalls. But for AI traffic specifically, the tooling is still immature and fragmented.

That is the gap AegisFlow fills.


## What AegisFlow Does

AegisFlow sits between your applications and your AI providers. Every AI request your application makes flows through AegisFlow before reaching the provider, and every response flows back through it before reaching your application. This gives AegisFlow a unique position to add value at every step of that journey.

**It unifies access to multiple AI providers.** Instead of writing separate code for OpenAI, Anthropic, and Ollama, your application talks to AegisFlow using one standard format. AegisFlow translates the request into whatever format the target provider expects and translates the response back into a standard format. If you later want to switch providers or add a new one, you change a configuration file, not your application code.

**It routes requests intelligently.** AegisFlow decides which provider should handle each request based on rules you define. You can route based on the model name, the cost of the provider, the latency, or any combination. If the primary provider fails, AegisFlow automatically falls back to a secondary provider. This means your application stays up even when a provider goes down.

**It enforces rate limits and budgets.** You can set limits on how many requests each team or user can make per minute, and how many tokens they can consume. This prevents runaway usage and helps people understand cost before it becomes a problem. When a limit is hit, the caller gets a clear message telling them to slow down.

**It enforces security and content policies.** Before a request reaches any AI provider, AegisFlow checks it against a set of policies you define. It can block prompt injection attempts, where a user tries to trick the AI into ignoring its instructions. It can detect and flag personally identifiable information like email addresses, social security numbers, or credit card numbers before they leave your network. It can filter the AI's response on the way back to block unwanted or harmful content. Each policy can be configured to block the request, log a warning, or simply record the event for later review.

**It tracks usage and estimates costs.** Every request that flows through AegisFlow is metered. It counts the tokens used, estimates the cost based on each provider's pricing, and aggregates this data by team, user, or application. This gives you a clear picture of who is using what, how much it costs, and where the money is going.

**It provides full observability.** AegisFlow produces detailed telemetry for every request — traces that show exactly what happened at each step, metrics that track overall system health, and structured logs for debugging. This integrates with standard monitoring tools like Prometheus and any OpenTelemetry-compatible backend. You can set up dashboards, alerts, and anomaly detection just like you would for any other critical infrastructure.


## Who Is This For

AegisFlow is built for people who want control over AI usage without depending on a hosted gateway. Specifically:

**Students and individual builders** who want a safe local gateway for experiments, learning, and personal projects.

**Open-source maintainers** who want coding agents and automation to run with clear boundaries, review steps, and evidence.

**Platform engineers** who are responsible for the infrastructure that other teams build on. AegisFlow gives them a standard way to offer AI access to their organization with proper guardrails, visibility, and control.

**Engineering managers and technical leads** who want visibility into how AI is being used across their teams, how much it costs, and whether usage patterns are healthy.

**Security and compliance teams** who need to ensure that sensitive data is not leaking to third-party AI providers, and that AI usage aligns with company policies and regulatory requirements.

**Product managers** who want to understand the cost and performance characteristics of the AI features in their products, and who want the flexibility to switch providers without a major engineering effort.

**Startup founders** who are building AI-powered products and want to avoid vendor lock-in, control costs from day one, and build on infrastructure that scales with them.


## How It Is Different

There are a few commercial products in this space — Portkey, Helicone, and others. There are also open-source tools like LiteLLM that handle basic proxying. AegisFlow is different in several important ways.

First, it is built in Go, not Python. This matters because Go is the standard language for cloud-native infrastructure. It produces a single compiled binary that runs anywhere, uses minimal memory, handles thousands of concurrent connections efficiently, and starts in milliseconds. This is the same language used to build Kubernetes, Docker, and Terraform. For an infrastructure component that sits in the critical path of every AI request, performance and reliability are not optional.

Second, it is not just a proxy. Most existing tools focus on routing requests and tracking usage. AegisFlow adds a full policy engine that inspects both inputs and outputs in real time. It adds proper multi-tenant isolation with per-tenant rate limits and access controls. It adds circuit breaking and intelligent fallback logic. These are the things that matter when you are running AI in production at scale, not just experimenting with it.

Third, it is designed to be extensible. Adding support for a new AI provider means implementing a single interface with six methods. Adding a new policy filter means implementing one function. This makes it easy for the community to contribute and for organizations to customize it for their specific needs.

Fourth, it is fully open source under the Apache 2.0 license. There is no open-core model with essential features hidden behind a paywall. Everything needed for local demos, policy enforcement, gateway routing, observability, and evidence is available, and you can run it on your own infrastructure without any vendor dependency.


## How It Works at a High Level

When an application sends an AI request to AegisFlow, here is what happens step by step.

The request arrives at the gateway. The authentication middleware checks the API key to identify which tenant the request belongs to. If the key is invalid or missing, the request is rejected immediately.

Next, the rate limiter checks whether this tenant has exceeded their allowed request or token limits. If they have, the request is rejected with a clear message and a header indicating when they can retry.

The policy engine then inspects the request content. It runs each configured input policy — checking for prompt injection patterns, scanning for personally identifiable information, and applying any custom rules. If a blocking policy is violated, the request is rejected with a message identifying which policy was triggered.

The router then determines which provider should handle this request. It matches the requested model name against configured routes, selects a provider based on the routing strategy, and sends the request. If the provider fails, the router automatically tries the next provider in the fallback chain.

When the response comes back from the provider, the policy engine inspects it again, this time running output policies to check for unwanted content in the AI's response.

The usage tracker records the token counts and estimates the cost of the request.

Finally, the telemetry system records a trace span with all the details — which tenant, which model, which provider, how long it took, how many tokens were used, and whether any policies were triggered.

The response is then returned to the calling application in a standard format, regardless of which provider actually handled it.

This entire process happens in milliseconds for non-streaming requests, and for streaming requests, AegisFlow relays the response in real time as it arrives from the provider.


## The Technical Foundation

AegisFlow is built as a single Go binary with clean internal architecture. It uses a lightweight HTTP router, structured JSON logging, and standard cloud-native practices throughout.

For rate limiting, it uses an in-memory sliding window algorithm by default, with an optional Redis backend for distributed deployments where multiple instances of AegisFlow need to share rate limit state.

For observability, it uses OpenTelemetry, which is the industry standard for distributed tracing and metrics. It exposes a Prometheus-compatible metrics endpoint for integration with existing monitoring infrastructure.

The entire system is configured through a single YAML file. Providers, routes, tenants, rate limits, policies, and telemetry settings are all defined in one place. Changes to the configuration do not require code changes or redeployment of the applications that use AegisFlow.

It can be deployed as a standalone binary, as a Docker container, or through Docker Compose with supporting services like Redis and PostgreSQL. The resource footprint is minimal — it runs comfortably on a machine with as little as 1 GB of RAM.


## The Roadmap

The current version delivers a complete and functional MVP — unified gateway, multi-provider routing with fallback, rate limiting, policy engine, usage tracking, and full observability.

The next phase will add persistent storage for usage data and audit logs, a web-based admin dashboard, webhook notifications for policy violations, and support for custom policy plugins.

Further down the road, the plan includes a Kubernetes operator with custom resource definitions for managing AegisFlow configuration natively in Kubernetes clusters, multi-region routing, A/B testing for models, advanced analytics with anomaly detection, and cost forecasting.

The long-term vision is to become the standard open-source control plane for AI traffic, the same way Envoy became the standard proxy for service mesh, or the way Prometheus became the standard for monitoring.


## In Summary

AegisFlow is infrastructure for the AI era. It gives organizations a single, open-source platform to route their AI traffic through any provider, enforce security and usage policies, track costs, and monitor everything in real time. It is built with the same engineering rigor as the tools that power modern cloud infrastructure, and it is designed to grow with the rapidly expanding role of AI in production software systems.
