What is Costrict Proxy?

Costrict Proxy is an open-source gateway that sits between your Costrict account and the AI tools that consume it. Instead of giving every agent its own Costrict subscription — which is expensive and hard to manage — you run one instance of Costrict Proxy, connect it to a single Costrict account, and let it expose that account’s quota as standard API keys.

The project is built on CLIProxyAPI v7, a framework for turning command-line AI tools into proxy servers. Costrict Proxy adapts this framework specifically for Costrict (CoStrict), Sangfor’s enterprise AI coding solution. The GitHub repository describes it as an “OpenAI-compatible gateway for native Costrict accounts,” and it ships ready-to-run binaries for Windows x64 and Linux amd64, complete with systemd install scripts for production-style deployments.

Because the exposed surface is OpenAI-compatible, anything that speaks the OpenAI protocol can consume your Costrict quota. That includes the OpenAI Python SDK, LangChain, LiteLLM, OpenAI-compatible chat UIs, and any coding agent that accepts a custom base URL. The practical result is that you can point your existing AI tooling at http://localhost:8317/v1 and have it running on Costrict quota with essentially zero code changes.

Why convert an account quota into API keys?

Most AI coding platforms price by per-seat subscriptions. If you have five agents, five developers, or five automated CI pipelines, you might need five separate paid accounts. That is expensive, administratively messy, and wasteful when the actual concurrent usage is low.

Converting a quota into API keys solves this in three ways:

1. Cost efficiency — one subscription covers many consumers. Agents share the quota rather than each needing a full account.
2. Standardization — a single OpenAI-compatible endpoint means every agent uses the same integration pattern, regardless of which vendor it calls.
3. Central control — a management UI and quota diagnostics give you one place to monitor account health, readiness, and remaining quota instead of logging into multiple dashboards.

The broader trend supports this pattern. Infisical, a company that says it processes billions of secrets per month, argues in its Agent Vault announcement that the traditional secrets-management model — delivering secrets directly to workloads — fails for AI agents. Agents are non-deterministic and easy to prompt-inject, so credentials need to be mediated through a proxy form factor rather than handed over. Costrict Proxy applies the same logic to quota: you do not hand each agent its own credential; you mediate access through a single controlled gateway.

How Costrict Proxy works

Costrict Proxy’s architecture is simple. A single process holds your Costrict authentication state, maintains a model catalog, and brokers every request. Here is the request flow:

• A client (agent, script, or SDK) sends an OpenAI-style request to /v1/chat/completions.
• Costrict Proxy authenticates the caller using the API key you issued, then checks the serialized request queue.
• If the queue has capacity, the request is forwarded to the underlying Costrict backend using the account’s credentials.
• The response is returned to the client in the standard OpenAI response format, so downstream tools never need to know they are talking to Costrict.

Because generation is serialized — one active request at a time — the proxy prevents the kind of parallel burst that would blow through an account quota in seconds. A bounded FIFO queue holds up to 32 queued requests for a maximum of 30 seconds. If the queue overflows, the proxy returns HTTP 429 with a Retry-After header, telling clients exactly when to retry.

The model catalog is bound to the account’s credential fingerprint and stored at auth-dir/.costrict/model-catalog.json. The proxy auto-refreshes Costrict tokens when a refresh token and login state are present, so long-running deployments do not need manual re-authentication. These details matter: a quota gateway that cannot refresh its own tokens, or that lets parallel requests exhaust the quota, would be unusable in practice. Costrict Proxy deliberately designs around both problems.

Key features in detail

OpenAI-compatible API. The /v1/models and /v1/chat/completions endpoints are the core contract. Any OpenAI-protocol client works immediately.

Costrict-specific auth. The proxy understands Costrict’s authentication model, so it can hold login state and manage credentials rather than just passing through opaque tokens.

Management UI. A management page at /management.html exposes readiness, quota, and model-catalog diagnostics in a browser. This is a practical feature for operators who want a quick health check without curl.

Quota diagnostics. You can see remaining account quota directly, which helps you decide whether to rate-limit more aggressively or top up the subscription.

Model catalog. The list of available models is auto-discovered and cached, so the exposed /v1/models reflects what your account can actually generate.

Automatic token refresh. With the right auth state present, the proxy keeps credentials fresh without manual intervention.

Serialized queue. One active request, up to 32 queued, 30-second cap, then HTTP 429 with Retry-After.

Setup walkthrough

Setting up Costrict Proxy follows a straightforward pattern. Here is the recommended path for a Linux server.

First, download the release package for your platform. The project provides Linux amd64 and Windows x64 builds, plus systemd install scripts for the Linux case.

Next, edit the configuration file. The project uses a config.yaml-style configuration where you set the listening port (8317 by default), the auth directory, and the account credentials.

server:
  addr: "0.0.0.0:8317"
auth:
  dir: "./.costrict"
  refresh_token: ""   # set after initial login
queue:
  max_pending: 32
  wait_seconds: 30

Then start the proxy. On a systemd-based server, you install the unit file and enable the service:

Once running, complete authentication so the proxy can hold the account’s login state and refresh tokens. After that, open http://localhost:8317/management.html to verify readiness and quota.

Building from source is also an option if you need a newer commit. The project requires Go 1.26 or later. For most users, the prebuilt binaries are the faster and safer choice.

Using the OpenAI-compatible API

Once the proxy is up, consuming the quota is trivial. List models:

curl http://localhost:8317/v1/models

Then send a chat completion:

curl http://localhost:8317/v1/chat/completions \
  -H "Authorization: Bearer <your-api-key>" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "<model-from-catalog>",
    "messages": [{"role": "user", "content": "Explain Costrict Proxy in one paragraph."}]
  }'

Because these are the standard OpenAI endpoints, you can point the OpenAI Python SDK at them:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8317/v1",
    api_key="your-api-key",
)

resp = client.chat.completions.create(
    model="<model-from-catalog>",
    messages=[{"role": "user", "content": "Hello"}],
)
print(resp.choices[0].message.content)

The same pattern works for LangChain, LiteLLM, and any tool that accepts an OpenAI-compatible base URL. This is the key value proposition: your Costrict quota becomes a drop-in replacement endpoint for your existing AI stack.

Rate limiting and the serialized request queue

The most distinctive design decision in Costrict Proxy is the serialized request queue. Generation requests run one at a time, with up to 32 pending and a 30-second maximum wait. Requests beyond that get HTTP 429 plus a Retry-After header.

This is a deliberate trade-off. Parallel generation is much faster for interactive use, but it also risks hitting account quotas immediately. For a quota-sharing gateway, conservative serialization is the safer default because it guarantees the account stays within its limits even under bursty agent traffic.

The practical implication for clients is that they must handle 429 responses gracefully. Most OpenAI SDKs already retry on 429 and respect Retry-After, so well-behaved agents will queue naturally. If you are writing your own client, make sure you honor the header rather than hammering the endpoint in a tight loop.

Costrict Proxy vs. other credential proxies

It is useful to compare Costrict Proxy with adjacent tools, because “agent proxy” is a crowded category with meaningfully different goals.

The comparison that matters most is with Infisical Agent Vault. Both are credential proxies for AI agents, but they operate on different planes. Infisical Agent Vault is about secret hygiene: it mediates access to databases, APIs, and services so an agent never holds a raw secret that could be exfiltrated. Costrict Proxy is about quota sharing: it converts a single model-account subscription into API keys. A serious deployment could actually use both — Costrict Proxy to front the model quota, and an Agent Vault to front everything else the agent touches.

Security and data-privacy considerations

Costrict’s enterprise positioning, via Sangfor, is heavily about data staying inside the intranet. Costrict Proxy aligns with that philosophy because it is self-hosted. Requests from your agents go to a proxy you control on your own infrastructure, then onward to Costrict; you are not introducing a third-party relay into the path.

That said, running a self-hosted gateway carries its own obligations:

• Protect the management page. http://localhost:8317/management.html exposes readiness, quota, and model catalog. If the proxy listens on 0.0.0.0, bind access controls or a reverse proxy with authentication in front of it.
• Guard the API keys you issue. Anyone with a key can consume your quota. Treat issued keys as sensitive credentials and rotate them on compromise.
• Secure the auth directory. The directory holding refresh tokens and the model catalog must not be world-readable on the host.
• Terminate TLS. The default config is plain HTTP on the LAN. If the proxy is reachable beyond a trusted network, put TLS in front of it.
• Harden the host. A gateway holding account credentials is a high-value target. Keep the host patched and minimally exposed.

The prompt-injection angle that Infisical highlights is relevant here too. Because agents are non-deterministic and easy to manipulate, centralizing the credential boundary is safer than scattering credentials across many agent processes. Costrict Proxy contributes to that boundary for model quota.

Limitations and maturity caveats

It is important to be honest about where this project stands. Costrict Proxy is very new — the repository was created around August 2026 and carries roughly a single star at the time of this review. That signals an early-stage project, and early-stage tools come with caveats:

• Small community. Fewer users means fewer reported bugs, less documentation, and slower resolution when you hit an edge case.
• Serialization limits throughput. The one-request-at-a-time design is safe but not fast. For heavy interactive workloads, it will be a bottleneck.
• No multi-account pooling. The project is designed around a single Costrict account. If you need to pool several accounts or load-balance across them, this proxy does not do that out of the box.
• Brand-new release cadence. Expect rapid changes and possibly breaking updates as the maintainer iterates.
• Limited ecosystem evidence. There are no long-running production case studies yet.

None of these are disqualifying for a personal or small-team setup, but they argue for treating it as a promising tool to pilot rather than a battle-tested platform.

Verdict: who should use Costrict Proxy

Costrict Proxy is a focused, well-scoped answer to a specific problem: sharing one Costrict account across many AI agents. It is the right choice if you already use Costrict (or Sangfor CoStrict) for enterprise coding, want to standardize your agent integrations on an OpenAI-compatible endpoint, and prefer to keep the gateway inside your own infrastructure.

It is likely overkill, or simply not relevant, if you do not use Costrict, if you need multi-account pooling, or if your priority is general secret management rather than quota sharing — in which case Infisical Agent Vault or LiteLLM may fit better.

Given the strong enterprise results that Sangfor reports for CoStrict — 2,000 GitHub stars within four months, per-person daily code output rising from 70 to 360 lines with the AI Agent model, and 11,186 bugs blocked in H1 2025 with a 74% disposal rate — the underlying ecosystem is credible and active. Costrict Proxy is a young but sensible addition to it. For developers and teams running CoStrict today, it is well worth piloting.

FAQ

What is Costrict Proxy? Costrict Proxy is a self-hosted, OpenAI-compatible gateway that converts a single Costrict account subscription quota into reusable API keys, so multiple AI agents and clients can share one account through standard /v1/models and /v1/chat/completions endpoints.

How is Costrict Proxy different from Infisical Agent Vault? Infisical Agent Vault is a secret vault that keeps real service credentials away from prompt-injectable agents. Costrict Proxy is a quota-to-API-key gateway that shares one model-account subscription. They solve different problems and can be used together.

Is Costrict Proxy safe to run? Yes, with precautions. It is self-hosted and keeps traffic inside your infrastructure, but you should protect the management page, secure the auth directory, guard issued API keys, terminate TLS, and harden the host.

Does Costrict Proxy require changes to my existing AI tools? No. Because it exposes the standard OpenAI protocol, tools that accept a custom base URL — the OpenAI SDK, LangChain, LiteLLM, and most coding agents — can consume your Costrict quota with no code changes.

How does Costrict Proxy handle rate limits? It serializes generation requests — one active at a time, up to 32 queued for 30 seconds — and returns HTTP 429 with a Retry-After header when the queue overflows, deliberately protecting your account quota under bursty traffic.