Langfuse Integration Skill

Purpose: Replace Phoenix observability with Langfuse Cloud (EU) for pharmaceutical-grade traceability and monitoring.

Target Architecture:

• From: Phoenix (local-only, ephemeral traces)
• To: Langfuse Cloud EU (persistent storage, analytics, GAMP-5 compliant)
• Strategy: Complete replacement (no dual observability)

---

When to Use This Skill

✅ Use when:

• Implementing PRP Task 2.3 (LangFuse Integration and Dashboard)
• Migrating from Phoenix to production observability
• Adding traceability to new pharmaceutical workflows
• Ensuring ALCOA+ attributable traces for regulatory compliance
• Preparing for AWS production deployment

❌ Do NOT use when:

• Extracting existing traces from Langfuse (use langfuse-extraction skill)
• Automating dashboard interactions (use langfuse-dashboard skill)
• Phoenix is required for local development (conflicts with replacement strategy)

---

Prerequisites

Before invoking this skill, verify:

1. Langfuse Cloud (EU) Account:

   • Project URL: https://cloud.langfuse.com/project/cmhuwhcfe006yad06cqfub107
   • API keys available (public + secret)
   • EU data residency confirmed

2. Environment Variables:

   export LANGFUSE_PUBLIC_KEY="pk-lf-..."
   export LANGFUSE_SECRET_KEY="sk-lf-..."
   export LANGFUSE_HOST="https://cloud.langfuse.com"
   

3. Dependencies:

   • langfuse Python package (will be installed if missing)
   • llama-index-core>=0.12.0 (for callback handler)
   • Existing Phoenix instrumentation code identified

---

Workflow Phases

Phase 1: Assessment and Analysis (5-10 minutes)

Objective: Understand current Phoenix instrumentation and identify migration points.

Steps:

1. Locate Phoenix Configuration:

   # Search for Phoenix setup
   grep -r "phoenix" main/src/monitoring/ --include="*.py"
   grep -r "from phoenix" main/src/ --include="*.py"
   grep -r "import phoenix" main/src/ --include="*.py"
   

2. Identify Instrumentation Points:

   • Read main/src/core/unified_workflow.py - identify workflow entry points
   • Read main/src/agents/ - identify agent methods needing tracing
   • Look for existing OpenTelemetry span creation
   • Document all files importing Phoenix

3. Analyze Compliance Attributes:

   • Check if GAMP-5 attributes are set (category, confidence)
   • Check if ALCOA+ attributes are set (user_id, session_id, timestamps)
   • Verify 21 CFR Part 11 metadata if applicable

4. Generate Assessment Report:

   # Phoenix → Langfuse Migration Assessment
   
   ## Current Phoenix Instrumentation
   - Configuration file: <path>
   - Instrumented files: <count>
   - Span count per workflow: <number>
   - Compliance attributes: <present/missing>
   
   ## Migration Scope
   - Files requiring decorator addition: <list>
   - Phoenix imports to remove: <count>
   - Callback handlers to replace: <list>
   - Estimated migration time: <minutes>
   
   ## Risk Assessment
   - Breaking changes: <yes/no>
   - Test coverage: <percentage>
   - Rollback complexity: <low/medium/high>
   

Quality Gate: Assessment report generated with complete file inventory and attribute analysis.

---

Phase 2: Langfuse Configuration Setup (10-15 minutes)

Objective: Create Langfuse configuration module and verify cloud connectivity.

Steps:

1. Install Langfuse SDK:

   # Add to pyproject.toml
   uv add langfuse
   
   # For LlamaIndex integration
   uv add llama-index-instrumentation-langfuse
   

2. Create Langfuse Configuration Module:

   • File: main/src/monitoring/langfuse_config.py
   • Content: See reference/decorator-patterns.md for template
   • Key functions:
     • setup_langfuse(): Initialize client with EU cloud config
     • get_langfuse_client(): Singleton accessor
     • get_langfuse_callback_handler(): LlamaIndex integration
     • add_compliance_attributes(): GAMP-5/ALCOA+ attribute helper

3. Verify Cloud Connectivity:

   # Test script (temporary)
   from main.src.monitoring.langfuse_config import setup_langfuse
   
   client = setup_langfuse()
   client.trace(name="connectivity-test", input={"test": True})
   client.flush()
   
   # Verify trace appears at:
   # https://cloud.langfuse.com/project/cmhuwhcfe006yad06cqfub107/traces
   

4. Update Environment Configuration:

   • Add Langfuse environment variables to .env.example
   • Update main/src/config.py to load Langfuse settings
   • Add Langfuse to ObservabilityConfig dataclass

Quality Gate:

• ✅ Langfuse SDK installed
• ✅ langfuse_config.py created and tested
• ✅ Connectivity test trace visible in Langfuse Cloud dashboard
• ✅ Configuration variables documented

---

Phase 3: Code Instrumentation (20-30 minutes)

Objective: Add @observe decorators and replace Phoenix callbacks with Langfuse.

Steps:

1. Add Decorators to Workflow Entry Points:

   Use the automated script for systematic instrumentation:

   python .claude/skills/langfuse-integration/scripts/add_instrumentation.py \
     --target main/src/core/unified_workflow.py \
     --dry-run  # Preview changes first
   

   Manual pattern (if script unavailable):

   # main/src/core/unified_workflow.py
   from langfuse import observe
   
   class UnifiedWorkflow(Workflow):
       @observe(name="unified-workflow-run", as_type="span")
       async def run(self, ctx: Context, ev: StartEvent) -> StopEvent:
           # Existing code unchanged
           ...
   

2. Instrument Agent Methods:

   Target key agent operations:

   # main/src/agents/categorizer.py
   from langfuse import observe
   
   @observe(name="gamp5-categorization", as_type="span")
   async def categorize_urs(self, urs_content: str) -> dict:
       # Add compliance attributes
       from langfuse import get_current_observation
       obs = get_current_observation()
       if obs:
           obs.update(metadata={
               "compliance.gamp5.applicable": True,
               "compliance.alcoa_plus.attributable": True
           })
   
       # Existing categorization logic
       result = await self._categorize(urs_content)
   
       # Tag with category
       if obs:
           obs.update(metadata={
               "compliance.gamp5.category": result["category"]
           })
   
       return result
   

3. Replace LlamaIndex Callback Handler:

   # main/src/core/unified_workflow.py or main/main.py
   # OLD (Phoenix):
   # from phoenix.otel import register
   # tracer_provider = register()
   
   # NEW (Langfuse):
   from langfuse.llama_index import LlamaIndexCallbackHandler
   
   langfuse_handler = LlamaIndexCallbackHandler(
       public_key=os.getenv("LANGFUSE_PUBLIC_KEY"),
       secret_key=os.getenv("LANGFUSE_SECRET_KEY"),
       host=os.getenv("LANGFUSE_HOST")
   )
   
   # Register with workflow
   workflow = UnifiedWorkflow(
       callbacks=[langfuse_handler],
       timeout=600
   )
   

4. Propagate User/Session Attributes:

   # In API endpoint or workflow entry point
   from langfuse import observe, get_current_trace
   
   @observe()
   async def generate_test_suite(user_id: str, urs_file: str, job_id: str):
       # Set trace-level attributes
       trace = get_current_trace()
       if trace:
           trace.update(
               user_id=user_id,
               session_id=job_id,
               tags=["pharmaceutical", "gamp5"],
               metadata={
                   "compliance.alcoa_plus.attributable": True,
                   "user.clerk_id": user_id,
                   "job.id": job_id
               }
           )
   
       # All nested operations inherit these attributes
       result = await unified_workflow.run(urs_file)
       return result
   

5. Verify Decorator Coverage:

   # Check all instrumentation points have decorators
   grep -r "@observe" main/src/ --include="*.py" | wc -l
   # Compare to Phoenix span count (should match or exceed)
   

Quality Gate:

• ✅ @observe decorators added to all workflow entry points
• ✅ LlamaIndex callback handler replaced
• ✅ User/session attributes propagated correctly
• ✅ GAMP-5 category metadata attached to categorization spans
• ✅ No syntax errors or import failures

---

Phase 4: Phoenix Removal (10-15 minutes)

Objective: Remove all Phoenix dependencies without breaking functionality.

Steps:

1. Remove Phoenix Configuration File:

   # Backup first (optional)
   cp main/src/monitoring/phoenix_config.py main/src/monitoring/phoenix_config.py.bak
   
   # Remove
   rm main/src/monitoring/phoenix_config.py
   

2. Update Imports:

   Use automated script:

   python .claude/skills/langfuse-integration/scripts/remove_phoenix.py \
     --target main/src/ \
     --dry-run  # Preview changes
   

   Manual pattern:

   # Remove all instances of:
   # - from phoenix.otel import register
   # - from phoenix import ...
   # - import phoenix
   # - Any calls to phoenix.trace(), register(), etc.
   

3. Remove Phoenix from Dependencies:

   # Remove from pyproject.toml
   uv remove arize-phoenix arize-phoenix-otel
   

4. Update Monitoring Module Init:

   # main/src/monitoring/__init__.py
   # OLD:
   # from .phoenix_config import setup_phoenix, PhoenixManager
   
   # NEW:
   from .langfuse_config import setup_langfuse, get_langfuse_client
   
   __all__ = ["setup_langfuse", "get_langfuse_client"]
   

5. Remove Phoenix Server Command (if applicable):

   # Check if phoenix serve is in any scripts
   grep -r "phoenix serve" . --include="*.sh" --include="*.py" --include="*.md"
   
   # Remove or comment out
   

Quality Gate:

• ✅ phoenix_config.py removed
• ✅ All Phoenix imports removed from codebase
• ✅ Phoenix packages uninstalled
• ✅ No references to Phoenix in documentation
• ✅ Codebase still imports successfully

---

Phase 5: Validation and Testing (15-20 minutes)

Objective: Verify Langfuse integration works correctly and traces appear in dashboard.

Steps:

1. Run Integration Health Check:

   python .claude/skills/langfuse-integration/scripts/validate_integration.py
   

   Expected output:

   ✅ Langfuse SDK installed
   ✅ API keys configured
   ✅ Cloud connectivity successful
   ✅ Test trace created: trace_id=xxx
   ✅ @observe decorators found: 15
   ✅ Callback handler configured
   ❌ No Phoenix imports found (expected)
   

2. Run End-to-End Workflow:

   # Execute test workflow with real URS
   uv run python main/main.py --urs examples/test_urs_001.md
   

3. Verify Trace in Dashboard:

   • Navigate to: https://cloud.langfuse.com/project/cmhuwhcfe006yad06cqfub107/traces
   • Find most recent trace by timestamp
   • Check:
     • ✅ Trace appears (not 404)
     • ✅ Span count matches expected (compare to Phoenix baseline)
     • ✅ User ID populated
     • ✅ Session ID populated
     • ✅ Tags include "pharmaceutical", "gamp5"
     • ✅ GAMP-5 category metadata present
     • ✅ No errors in observations

4. Compare Span Structure:

   # If Phoenix baseline available, compare span counts
   echo "Phoenix baseline: 131 spans/workflow"
   echo "Langfuse actual: <count from dashboard>"
   # Acceptable range: 120-140 (some variation expected)
   

5. Test Compliance Attributes:

   • Click on categorization span in dashboard
   • Verify metadata contains:
     • compliance.gamp5.category: 1-5
     • compliance.alcoa_plus.attributable: true
     • user.clerk_id:
     • job.id:

6. Run Existing Tests:

   # Ensure no regressions
   pytest main/tests/ -v
   
   # Check for import errors
   mypy main/src/
   
   # Check for Phoenix references
   ruff check main/src/
   

Quality Gate:

• ✅ Health check passes all tests
• ✅ End-to-end workflow completes successfully
• ✅ Trace visible in Langfuse Cloud dashboard
• ✅ Span count within 10% of Phoenix baseline
• ✅ All compliance attributes present
• ✅ Existing tests pass
• ✅ No mypy/ruff errors

---

Phase 6: Documentation and Finalization (5-10 minutes)

Objective: Document the migration and update project references.

Steps:

1. Update Quick Start Guide:

   • Edit main/docs/guides/QUICK_START_GUIDE.md
   • Replace Phoenix setup instructions with Langfuse
   • Update environment variable examples
   • Add Langfuse dashboard URL

2. Update README:

   • Replace Phoenix badge/link with Langfuse
   • Update observability section
   • Add Langfuse Cloud (EU) data residency note

3. Create Migration Notes:

   # Phoenix → Langfuse Migration Summary
   
   **Date**: <YYYY-MM-DD>
   **Scope**: Complete Phoenix replacement
   
   ## Changes Made
   - Removed: phoenix_config.py, Phoenix dependencies
   - Added: langfuse_config.py, Langfuse SDK
   - Instrumented: 15 functions with @observe decorators
   - Replaced: LlamaIndex callback handler
   
   ## Verification
   - Trace count: 131 spans/workflow (matches Phoenix baseline)
   - Dashboard URL: https://cloud.langfuse.com/project/cmhuwhcfe006yad06cqfub107
   - Compliance: GAMP-5 + ALCOA+ attributes preserved
   
   ## Rollback (if needed)
   - Restore phoenix_config.py.bak
   - Run: uv add arize-phoenix arize-phoenix-otel
   - Remove @observe decorators
   

4. Update CLAUDE.md:

   • Replace Phoenix references in "Technology Stack" section
   • Update observability commands
   • Add Langfuse skill invocation instructions

5. Commit Changes:

   git add -A
   git status  # Review changes
   
   # Commit with detailed message
   git commit -m "$(cat <<'EOF'
   feat: Replace Phoenix with Langfuse Cloud (EU) observability
   
   - Add Langfuse SDK and LlamaIndex instrumentation
   - Add @observe decorators to 15 workflow/agent functions
   - Configure Langfuse Cloud (EU) with GAMP-5 compliance attributes
   - Remove Phoenix dependencies and configuration
   - Verify trace parity: 131 spans/workflow maintained
   - Update documentation (Quick Start, README, CLAUDE.md)
   
   Task: PRP 2.3 (LangFuse Integration and Dashboard)
   Validation: All tests passing, traces visible in dashboard
   
   🤖 Generated with Claude Code
   
   Co-Authored-By: Claude <noreply@anthropic.com>
   EOF
   )"
   

Quality Gate:

• ✅ Quick Start Guide updated
• ✅ README updated
• ✅ Migration notes created
• ✅ CLAUDE.md reflects Langfuse
• ✅ Changes committed to Git

---

Success Criteria

Before marking this skill complete, verify ALL criteria:

Functional Requirements

• ✅ Langfuse SDK installed and configured for EU cloud
• ✅ API keys set in environment variables
• ✅ langfuse_config.py created with setup functions
• ✅ @observe decorators added to all critical paths
• ✅ LlamaIndex callback handler replaced
• ✅ Phoenix configuration file removed
• ✅ Phoenix imports removed from all files
• ✅ Phoenix dependencies uninstalled

Observability Requirements

• ✅ End-to-end workflow generates traces
• ✅ Traces visible in Langfuse Cloud dashboard
• ✅ Span count matches Phoenix baseline (±10%)
• ✅ Trace structure maintains workflow visibility

Compliance Requirements

• ✅ User ID (Clerk) propagated to all traces
• ✅ Session ID (job_id) propagated to all traces
• ✅ GAMP-5 category metadata on categorization spans
• ✅ ALCOA+ attributable=true on all traces
• ✅ Tags include ["pharmaceutical", "gamp5"]

Quality Requirements

• ✅ No FALLBACK LOGIC introduced
• ✅ All errors throw with full stack traces
• ✅ Existing tests pass (pytest)
• ✅ Type checking passes (mypy)
• ✅ Linting passes (ruff)
• ✅ No import errors or circular dependencies

Documentation Requirements

• ✅ Quick Start Guide updated
• ✅ README updated
• ✅ CLAUDE.md updated
• ✅ Migration notes created
• ✅ Changes committed to Git with descriptive message

---

Troubleshooting

Issue: Langfuse SDK Import Error

Symptom:

ModuleNotFoundError: No module named 'langfuse'

Solution:

uv add langfuse llama-index-instrumentation-langfuse
uv sync

Issue: Traces Not Appearing in Dashboard

Symptom: Workflow runs successfully but no traces in Langfuse Cloud.

Diagnosis:

1. Check API keys:

   import os
   print(f"Public key: {os.getenv('LANGFUSE_PUBLIC_KEY')[:10]}...")
   print(f"Secret key configured: {bool(os.getenv('LANGFUSE_SECRET_KEY'))}")
   

2. Check flush call:

   from langfuse import get_client
   client = get_client()
   client.flush()  # CRITICAL: Must flush before exit
   

3. Check network connectivity:

   curl -I https://cloud.langfuse.com
   

Solution:

• Verify API keys match dashboard (Settings → API Keys)
• Add client.flush() before process exit
• Check firewall/proxy settings

Issue: Missing Compliance Attributes

Symptom: Traces appear but lack GAMP-5 metadata.

Solution:

# Ensure get_current_observation() is called inside decorated function
from langfuse import observe, get_current_observation

@observe()
def my_function():
    obs = get_current_observation()
    if obs:  # CRITICAL: Check if obs exists
        obs.update(metadata={"compliance.gamp5.category": 5})

Issue: Span Count Mismatch

Symptom: Langfuse shows fewer spans than Phoenix baseline.

Diagnosis:

• Check if all @observe decorators are applied
• Verify LlamaIndex callback handler is registered
• Check for early return statements before instrumented code

Solution:

# Find missing decorators
grep -r "async def" main/src/agents/ --include="*.py" | \
  grep -v "@observe"

Issue: High Latency After Migration

Symptom: Workflows slower with Langfuse vs Phoenix.

Diagnosis:

• Langfuse batches events asynchronously (default: every 1 second)
• Network calls to EU cloud add latency

Solution:

# Tune batch settings
from langfuse import Langfuse

client = Langfuse(
    flush_interval=5,  # Flush every 5 seconds instead of 1
    flush_at=50,       # Batch 50 events before flushing
)

---

Reference Materials

Decorator Patterns

See reference/decorator-patterns.md for:

• Function-level instrumentation patterns
• Async function handling
• Nested span creation
• LLM generation tracing

Phoenix Migration Guide

See reference/phoenix-migration-guide.md for:

• Side-by-side comparison of Phoenix vs Langfuse APIs
• Import migration table
• Span structure equivalence
• Common pitfalls during migration

Compliance Attributes

See reference/compliance-attributes.md for:

• GAMP-5 category metadata schema
• ALCOA+ attribute requirements
• 21 CFR Part 11 considerations
• Audit trail best practices

---

Advanced Usage

Context Manager Pattern (Fine-Grained Control)

For more control than decorators provide:

from langfuse import get_client

langfuse = get_client()

def complex_workflow():
    with langfuse.start_as_current_span(
        name="complex-workflow",
        as_type="span"
    ) as span:
        span.update(input={"mode": "batch"})

        # Manual sub-span creation
        with langfuse.start_as_current_span(
            name="data-validation",
            as_type="span"
        ) as sub_span:
            validate_data()
            sub_span.update(output={"valid": True})

        # Main logic
        result = process_data()

        span.update(output=result)

Custom Event Tracking

For discrete events (not spans):

from langfuse import get_current_observation

obs = get_current_observation()
if obs:
    obs.event(
        name="gamp5-category-assigned",
        metadata={
            "category": 5,
            "confidence": 0.95,
            "timestamp": datetime.now().isoformat()
        }
    )

Multi-Tenant Attribution

For pharmaceutical companies with multiple users:

from langfuse import observe, get_current_trace

@observe()
async def multi_tenant_workflow(org_id: str, user_id: str):
    trace = get_current_trace()
    if trace:
        trace.update(
            user_id=user_id,
            tags=[f"org:{org_id}", "gamp5"],
            metadata={
                "organization.id": org_id,
                "organization.name": get_org_name(org_id),
                "compliance.data_residency": "EU"
            }
        )

    # Workflow logic
    ...

---

Skill Completion Checklist

Before reporting success to the user, verify:

• Phase 1: Assessment report generated
• Phase 2: Langfuse configured and connectivity verified
• Phase 3: Decorators added, callback handler replaced
• Phase 4: Phoenix removed completely
• Phase 5: Validation passes all tests
• Phase 6: Documentation updated and committed
• All success criteria met (see above)
• No FALLBACK LOGIC violations
• User confirmation obtained: "Did you see traces in the dashboard?"

IMPORTANT: NEVER claim success without user verification. Always ask: "Can you confirm you see traces appearing in the Langfuse dashboard at https://cloud.langfuse.com/project/cmhuwhcfe006yad06cqfub107/traces?"

---

Post-Migration: Next Steps

After successful migration:

1. Use langfuse-extraction skill to:

   • Extract traces for debugging
   • Generate audit trails for compliance
   • Export data to pandas for analysis

2. Use langfuse-dashboard skill to:

   • Capture dashboard screenshots for documentation
   • Automate metric extraction for alerting
   • Investigate specific traces interactively

3. Proceed with PRP tasks:

   • Task 3.1: FastAPI backend development
   • Task 4.3: Bedrock model integration
   • Task 5.1: Production deployment validation

---

Skill Version: 1.0.0 Last Updated: 2025-01-17 Compatibility: LlamaIndex 0.12.0+, Langfuse SDK 3.0+ Data Residency: EU (cloud.langfuse.com) Compliance: GAMP-5, ALCOA+, 21 CFR Part 11 ready