FluiqFluiq
  • ObservabilityTrace every call, cost, and latency
  • SecurityBlock attacks, redact PII and secrets
  • OptimizationCache repeated prompts automatically
  • EvaluationScore responses and whole agent runs
  • DatasetsGolden sets that capture whole agent runs
  • Prompt ManagementVersion and deploy prompt templates
  • AlertsPush eval and security events to Slack

LLM Providers

  • OpenAI
  • Anthropic
  • Google Gemini
  • Google Vertex AI

Agent Frameworks

  • LangChain
  • LangGraph
  • CrewAI
  • Google ADK
  • MCP

Vector Databases

  • Pinecone
  • Chroma
  • Weaviate
  • FAISS
  • Qdrant
14 integrations · zero wrappersView all
Pricing
  • Fluiq DocsGuides, concepts & SDK reference
  • Code SamplesCopy-paste integration snippets
  • LLM Cost CalculatorCompare OpenAI, Claude & Gemini pricing
  • polygateOpen-source unified LLM client
  • InfragerCloud diagrams to secure Terraform
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Datasets

Regression-test agents on real trajectories

Curate golden datasets from production traffic. Each example pins the whole agent run (every step, tool, and MCP call) forever.

Start freeRead the docs
Fluiq/ datasets

Examples

48

checkout-regressions

Trajectories

41

full runs pinned

Batch Runs

6

agentic eval · security

Pinned trajectory · example #12

run score 0.88
crewCrew(Researcher, Writer, Editor)root
taskResearch the customer's issueResearcher
toolsearch_orders×2
llmgpt-4o-mini0.8s
taskDraft the refund replyWriter
mcpcrm.update_ticketMCP

What you get

Capture

Whole-trajectory examples

Add any run from the trace drawer and Fluiq pins its full trajectory (LLM calls, agent steps, tool and MCP calls, media), independent of trace retention.

Sync

Connect Agents

Link a traced agent to a dataset and every run it has ever made is imported and deduplicated, and future runs keep appending automatically.

Batch

Agentic eval & security runs

Re-run agentic evaluation or the full security scan over every example and get a scored report: the regression gate for prompt and model changes.

Enriched

Live quality signals

Each example carries its run's eval scores, security verdicts, and cost, backfilled automatically as workers finish.

Pin a run once. Evaluate it forever.

  • Add to Dataset on a root trace snapshots the entire run, media included
  • Batch runs feed pinned trajectories to the same agentic evaluator used on live traffic
  • Examples stay evaluable after the source trace ages out of retention
All plans
app.py
# Link a run to a dataset via the API (or one click in the UI)
requests.post(f"{BASE}/datasets/{ds_id}/examples", headers=H, json={
    "input": "Refund my last order",
    "expected_output": "Opened refund #4821",
    # the whole trajectory is pinned from the run's root trace
    "metadata": {"source_trace_id": "0f9c...e21"},
})

Part of the Fluiq platform

Compare plans
ObservabilityTrace every call, cost, and latency.ExploreSecurityBlock attacks, redact PII and secrets.ExploreOptimizationCache repeated prompts automatically.ExploreEvaluationScore responses and whole agent runs.ExplorePrompt ManagementVersion and deploy prompt templates.ExploreAlertsPush eval and security events to Slack.Explore

Unlimited traces, always free.

Start on the free tier and turn on each pillar as your pipeline grows. No code changes required.

Start freeRead the docs
FluiqFluiq

Observe, protect, optimize, evaluate.

PlatformObservabilitySecurityOptimizationEvaluationDatasetsPrompt ManagementAlerts
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Comparevs LangSmithvs Langfusevs Heliconevs Braintrustvs Portkeyvs Lakera
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