# Fluiq

Fluiq is the AI Ops stack for Python LLM applications. Two lines of SDK code give every LLM call automatic tracing, cost attribution, security scanning, response caching, and quality evaluation — all visible in one real-time dashboard.

## What Fluiq does

Fluiq replaces four separate AI tooling concerns with a single SDK. It auto-instruments every supported provider and framework at the function-call level, so no changes are needed to existing prompt or agent code. The four pillars are:

- **Observe** (`fluiq.instrument()`) — full span tree for agents, chains, and raw LLM calls; per-node token counts, USD cost at provider rates, p50/p95/p99 latency; streamed live to the dashboard.
- **Secure** (`fluiq.secure()`) — pre-call jailbreak and prompt-injection blocking; PII detection (credit cards, SSNs, IBAN, emails, phone numbers, IP addresses, names); secret and high-entropy string redaction; semantic attack scoring; warn or block mode.
- **Optimize** (`fluiq.optimize()`) — trace-driven server-side Redis caching for LLM responses; MCP tool result caching (list_tools and call_tool, keyed by server URL and arguments); automatic Anthropic prompt-prefix caching via cache_control injection; cached token tracking for OpenAI and Gemini; all savings visible on the Optimize dashboard.
- **Evaluate** (`fluiq.eval()`) — LLM-as-judge scoring on every response; six metrics: hallucination, faithfulness, relevance, toxicity, coherence, completeness; warn mode logs scores; block mode raises FluiqEvalError before bad responses reach the user; CI/CD GitHub Actions gate.

## Supported integrations

Fluiq patches at the function-call level and is framework-agnostic. Auto-instrumented providers and frameworks:

- **LLM providers**: OpenAI (chat completions, responses, parse, streaming, embeddings, images, audio — sync and async), Anthropic (Messages API and Beta client, sync and async), Google Gemini, Google Vertex AI
- **Agent frameworks**: LangChain, LangGraph, CrewAI, Google ADK, MCP (Model Context Protocol)
- **Vector databases**: Pinecone, Chroma, Weaviate, FAISS, Qdrant
- **Custom code**: `@trace` decorator for any Python function not covered by auto-instrumentation

## SDK quickstart

```
pip install fluiq
```

```python
import fluiq

fluiq.instrument(api_key="fl_...")
# Every supported provider call is now traced automatically.
```

All four pillars can be layered on top:

```python
fluiq.instrument(api_key="fl_...")

fluiq.secure(mode="block")          # raise FluiqSecurityError on HIGH-risk prompts
fluiq.optimize(mode="cache")        # serve cached responses for repeated prompts
fluiq.eval(
    metrics=["hallucination", "faithfulness", "relevance"],
    mode="warn",                    # log scores without blocking
)
```

## Prompt management

Fluiq discovers named prompts from production traffic and stores them as versioned templates. Teams edit templates in the dashboard with `{{variable}}` substitution, test them against production traces using an LLM-as-judge playground, then deploy to development, staging, or production environments independently. No redeployment required.

```python
prompt = fluiq.fetch_prompt("summarise-ticket", env="production")
text = prompt.render(ticket_body=raw_text)
```

## Dashboard features (authenticated users)

- **Trace viewer** — JSON view and visual span tree; filter by agent, model, latency, cost, or eval score; live streaming
- **Cost analytics** — per-trace and per-agent USD cost; rolled-up totals across nested spans; anomaly alerts to Slack
- **Security panel** — detected PII types, attack scores, and redacted fields per trace
- **Evaluations** — per-trace quality scores across all enabled metrics
- **Optimize dashboard** — Redis cache hit rate by type (LLM, MCP tools, embeddings, vector stores); provider prompt cache card showing Anthropic cache_read/creation tokens and OpenAI/Gemini cached tokens
- **Agents view** — agent-level aggregation sorted by cost, token count, and latency
- **Prompts manager** — version history, playground, environment deployments
- **API key management** — create, reveal, and revoke workspace keys
- **Dataset management** — collect trace subsets for regression testing

## Pages

- [Home](https://getfluiq.com/): Product overview — the four pillars, integration list, code samples, stats, and getting-started CTA.
- [Documentation](https://getfluiq.com/documentation): Full SDK reference covering installation, auto-instrumentation, the @trace decorator, agent tracing, security scanning, optimization, evaluation, prompt management, cost analytics, and configuration.
- [Contact](https://getfluiq.com/contact): Reach the Fluiq team for sales and pricing enquiries, integration support, feature requests, and partnership discussions. Replies within one business day.
- [Sign up](https://getfluiq.com/signup): Create a free account (no credit card required) and receive an API key. The free tier includes 50,000 traces per month and 1,000 LLM-as-judge evaluations per month.
- [Log in](https://getfluiq.com/login): Access the Fluiq dashboard.

## Key facts

- Free tier: 50,000 traces/month, 1,000 evals/month, 1 seat, 14-day retention — no card required
- Paid plans: Team $49/mo (unlimited traces, caching), Growth $149/mo (security scanning, SSO), Enterprise custom (VPC/on-prem, SAML/SCIM)
- Install with: `pip install fluiq`
- SDK language: Python 3.9+
- Dashboard URL: https://getfluiq.com
- GitHub: https://github.com/fluiq-AI/fluiq-sdk
- Support: https://getfluiq.com/contact
- Full docs: https://getfluiq.com/documentation
- Extended LLM reference: https://getfluiq.com/llms-full.txt
- Category: AI Ops, LLM monitoring, AI security, LLM evaluation, response caching