Everything You Need to Build Production AI Agents

    From local development to enterprise deployment

    SDK

    Instrument & Capture

    Cloud

    Store & Analyze

    Team

    Collaborate & Debug

    📊 Observability Layer

    Capture Everything That Matters

    One Line. Ten Frameworks.

    Auto-detect and instrument all major LLM providers and agent frameworks with a single prela.init() call

    # One line to rule them all
    import prela
    prela.init()
    # ✓ OpenAI detected
    # ✓ Anthropic detected
    # ✓ LangChain detected

    OpenAI

    Chat, completions, embeddings, streaming

    Anthropic

    Messages API, streaming, tool use

    LangChain

    Chains, agents, tools, retrievers

    LlamaIndex

    Query engines, retrieval, synthesis

    CrewAI

    Task delegation, crew coordination

    AutoGen

    Conversational agents, function calling

    LangGraph

    Graph-based execution, state tracking

    Swarm

    Handoff-based agent switching

    n8n webhook

    HTTP-based workflow tracing

    n8n code node

    Python helpers for in-workflow tracing

    agent.run()2.3s
    llm.chat()1.2s • 450 tokens
    tool.search()0.8s
    llm.chat()0.3s • 120 tokens

    Never Miss a Decision

    Capture every LLM call, tool invocation, retrieval query, and agent handoff

    • Full prompts (user + system messages)
    • LLM responses (streaming + non-streaming)
    • Tool calls with arguments and results
    • Retrieval queries with context documents
    • Agent delegation and handoffs
    • Error stack traces
    • Token counts and costs

    Production-Ready Performance

    Add observability without slowing down your agents

    <1%

    Latency Overhead

    <100ms

    Query Performance

    10+

    Frameworks Supported

    Latency Impact Comparison

    Without Prela100ms
    With Prela100.8ms (+0.8%)
    ✅ Testing Layer

    Test Non-Deterministic Behavior

    21+ Assertion Types

    From structural checks to semantic similarity - assert on any aspect of agent behavior

    Structural AssertionsFree

    containsText substring match
    equalsExact match
    regexPattern matching
    latencyExecution time check
    token_countToken usage validation
    tool_calledBasic tool use check
    tool_countNumber of tools called
    no_piiPII detection assertion
    no_injectionInjection detection assertion
    custom_ruleCustom regex/callable rule

    Advanced Assertions$10+

    json_schemaValidate against JSON schema
    tool_sequenceVerify tool call order
    agent_usedMulti-agent participation
    task_completedTask completion verification
    delegation_occurredDelegation check
    handoff_occurredAgent handoff verification
    semantic_similarityEmbeddings-based comparison
    node_completedn8n workflow node completion
    workflow_durationn8n execution time
    ai_node_tokensn8n AI node token validation
    llm_judgeAI-scored evaluation with rubrics

    A/B Test Models Without Re-Running

    Capture full execution context and replay with different models, parameters, or tool behavior

    • Model switching (GPT-4 → Claude, gpt-4o-mini)
    • Parameter changes (temperature, max_tokens, top_p)
    • Tool re-execution (allowlist/blocklist controls)
    • Retrieval re-execution (ChromaDB, Pinecone, Weaviate)
    • Cost estimation before replay
    • Streaming support
    Original TraceGPT-4 • temp 0.7
    "The capital of France is Paris, known for the Eiffel Tower..."
    $0.0045 • 1.2s • 156 tokens
    ReplayClaude-3.5 • temp 0.3
    "Paris is the capital of France. It's home to landmarks like..."
    $0.0038 • 0.9s • 142 tokens94% similar
    # pytest integration
    def test_agent_response():
    result = agent.run("What is 2+2?")
    prela.assert_contains(result, "4")
    prela.assert_latency("<2s")
    prela.assert_token_count("<500")

    3 Report Formats

    Console

    Rich output for local development

    JSON

    Machine-readable for automation

    JUnit

    CI/CD integration

    Catch Regressions Before Production

    Run evaluation suites in your CI pipeline with JUnit reporter

    GitHub Actions Example

    - name: Run Prela Evals
    run: prela eval --reporter junit
    - name: Publish Results
    uses: actions/upload-artifact@v3
    🤝 Collaboration Layer

    Team Visibility at Scale

    See Updates Instantly

    WebSocket-based real-time updates with <1ms notification latency

    Redis Pub/Sub(not polling)
    Auto-reconnectwith exponential backoff
    Visual indicatorsfor live updates
    Live Trace StreamLive
    trace_1a2b3c
    just now
    trace_2a2b3c
    just now
    trace_3a2b3c
    just now

    Purpose-Built for Your Workflow

    Specialized dashboards for different agent architectures

    n8n Multi-Tenant Dashboard

    • Workflow execution timelines
    • AI node token usage
    • Per-workflow cost breakdown
    • Error categorization

    Multi-Agent Dashboard

    • Agent communication graphs
    • Delegation tracking
    • Collaboration metrics
    • Conversation turn analysis

    Replay Dashboard

    • Batch replay management
    • Cross-trace comparison
    • Replay analytics
    • Scheduled configuration
    JD
    AK
    SM
    +5
    Alex K.2 min ago

    This trace shows the hallucination issue. Check the grounding score.

    Sarah M.just now

    Fixed! Replay shows 98% grounding now. Deploying.

    Work Together Seamlessly

    Share traces, add comments, invite teammates

    • Unlimited team members (Pro+)
    • Role-based access control (viewer, developer, admin)
    • Shared trace views with comments
    • Shareable replay links (public URLs)
    • 3 workspaces/projects (Pro)
    • Unlimited workspaces (Enterprise)

    Find What You Need Instantly

    Natural language search and advanced filtering

    Natural languagePro+
    "show me traces where gpt-4 timed out"
    Attribute-basedFree
    model=gpt-4, status=error, duration>5s
    Date rangesFree
    last 1h, 24h, 7d, 30d
    Tag-basedFree
    tag:production, tag:staging
    Cost-basedLunch+
    spend>$1

    Export Capabilities

    CSV Export

    Lunch Money+

    JSON Export

    Lunch Money+

    Programmatic API

    Pro+ • Full API access

    🤖 AI-Powered Features

    Debug Faster with AI Assistance

    Hallucination Detection

    Automatically detect when agents make claims not grounded in source documents

    ⚠️ Detected Hallucination

    "The Eiffel Tower was built in 1920"

    Source documents state: built in 1889

    • Claim extraction from LLM outputs
    • Grounding checks against source documents
    • Confidence scoring
    • Alert triggers for high-risk hallucinations

    Drift Detection

    Baseline tracking with automated anomaly alerts

    7-Day Behavior Trend⚠️ Drift detected

    Response length +42% in last 2 days

    • Response length drift
    • Tool usage pattern changes
    • Cost drift (spend increasing unexpectedly)
    • Latency drift
    • Error rate spikes

    Cost Optimization

    AI-powered recommendations to reduce costs without sacrificing quality

    💰 Monthly Savings Report

    $847

    Saved this month

    23%

    Cost reduction

    • Model downgrade recommendations (semantic clustering)
    • Caching recommendations (duplicate detection)
    • Prompt optimization suggestions
    • Budget alerts

    Actionable Error Messages

    Categorize errors and provide one-click fix suggestions

    Rate limit exceeded

    → Try gpt-4o-mini (83% cheaper, higher quota)

    Token limit

    → Increase max_tokens by 50% (+$0.15/call)

    Auth failure

    → Check API key in settings

    Network error

    → Retry with exponential backoff

    Guardrails & Security

    Free

    Real-time input/output filtering with configurable actions — block, redact, or log

    PII detection (emails, phone numbers, SSNs, credit cards)
    Prompt injection blocking
    Content filtering (toxicity, sensitive topics)
    Max token limits per request
    Custom guard rules (regex or callable)

    LLM-as-Judge

    Lunch Money+

    AI-scored evaluations using custom rubrics with threshold-based pass/fail

    Example Rubric

    criteria: "Is the response helpful and accurate?"

    threshold: 0.7

    model: claude-sonnet-4-20250514

    Custom scoring rubrics (0-1 scale)
    Anthropic and OpenAI model support
    Configurable pass/fail thresholds
    Integrates with eval runner and CI/CD

    Prompt Management

    Free

    Version-controlled prompt templates with stage-based promotion

    classify v3production

    Classify: {{text}} → Categories: {{categories}}

    {{variable}} template syntax with validation
    Version history with change notes
    Stage promotion (staging → production)
    Export/import across environments

    Built to Scale

    Event-driven microservices architecture

    SDK
    Python
    Kafka
    Ingestion
    ClickHouse
    Storage
    Redis
    Real-time
    React
    Dashboard

    <100ms

    Simple query performance

    <500ms

    Complex aggregations

    90-day

    TTL with monthly partitioning

    Horizontal

    Scalability

    See Prela in Action

    Start Free – No Credit Card Required

    View Pricing