A reverse proxy that detects and blocks runaway loops in AI agent traffic before they consume excessive API tokens.

NakshGuard sits between your agents and the LLM API. It inspects each request, tracks per-agent session state, and applies a set of detection layers to identify looping behaviour — rapid repetition, unbounded context growth, and rate spikes — then blocks or logs them according to your configuration. It runs on-premises with no external dependencies; request data never leaves your network.

nakshguard 0.4.0 | tier=v1 shadow=false
target: https://api.openai.com | listening on :8080

Features

• Reverse proxy for the OpenAI and Anthropic chat APIs (auto-detected)
• Four detection layers: rate limit, hard token limit, repetition, context velocity
• Per-agent session tracking and configurable thresholds
• Shadow mode for safe calibration before enforcement
• Fail-open: if the proxy fails, traffic passes through to the upstream
• Sub-millisecond overhead, in-memory state, zero external dependencies
• Hot config reload via SIGHUP

Install

Or with Docker:

docker build -t nakshguard .
docker run -p 8080:8080 -e OPENAI_API_KEY=sk-... nakshguard

Usage

Run the proxy:

OPENAI_API_KEY=sk-... ./nakshguard

Point your client at the proxy and identify each agent with a header:

client = openai.OpenAI(
    api_key=os.environ["OPENAI_API_KEY"],
    base_url="http://localhost:8080",
    default_headers={"X-Agent-ID": "billing_bot"},
)

Requests now flow through NakshGuard. It estimates request cost, runs the detection layers, and forwards to the upstream or blocks with HTTP 429.

Detection layers

Context velocity (cve) detects the common error-append loop, where an agent appends its last error to the context and retries, growing the request each turn. Additional detection layers are available in the Pro and Enterprise tiers; see COMMERCIAL.md.

Shadow mode

By default the proxy starts in shadow mode: every layer runs and logs what it would have blocked, without blocking anything. Run it against real traffic, review the logs, then disable shadow mode in proxy.yaml:

global_settings:
  shadow_mode: false

Reload without restarting:

kill -HUP $(pgrep nakshguard)

Blocking can also be enabled per agent for incremental rollout.

Configuration

All settings live in proxy.yaml: the upstream target, rate limits, and per-agent thresholds. The most common change is llm_target to match your provider.

If the host is reachable by untrusted clients, set NAKSHGUARD_AUTH_KEY so that only requests carrying the matching X-Nakshguard-Auth header are accepted. Without it, anyone who can reach the port can use your upstream credentials.

Endpoints

Testing

go test -race -v             # unit tests
python3 tests/run_all_tests.py   # integration tests (needs shadow_mode: false)

Scaling

One instance tracks hundreds of agents in memory. To run multiple instances behind a load balancer, route by X-Agent-ID so each agent maps to a consistent instance. Shared-state clustering is on the roadmap

Pro and Enterprise

The open-source version handles the common loop patterns. Teams running many agents in production, or with on-prem compliance requirements, can get additional detection layers, priority support, and deployment help. Email pujanmirani2708@gmail.com if that's you.

License

AGPL-3.0. Free for internal use with no source-sharing obligation. Commercial licensing and the Pro/Enterprise detection layers are covered in COMMERCIAL.md.