langfuse

Langfuse Skill

Debug your AI systems through Langfuse observability.

Triggers: langfuse, traces, debug AI, find exceptions, set up langfuse, what went wrong, why is it slow, datasets, evaluation sets

Setup

Step 1: Get credentials from https://cloud.langfuse.com → Settings → API Keys

If self-hosted, use your instance URL for LANGFUSE_HOST and create keys there.

Step 2: Install MCP (pick one):

# Claude Code (project-scoped, shared via .mcp.json)
claude mcp add \
  --scope project \
  --env LANGFUSE_PUBLIC_KEY=pk-... \
  --env LANGFUSE_SECRET_KEY=sk-... \
  --env LANGFUSE_HOST=https://cloud.langfuse.com \
  langfuse -- uvx --python 3.11 langfuse-mcp

# Codex CLI (user-scoped, stored in ~/.codex/config.toml)
codex mcp add langfuse \
  --env LANGFUSE_PUBLIC_KEY=pk-... \
  --env LANGFUSE_SECRET_KEY=sk-... \
  --env LANGFUSE_HOST=https://cloud.langfuse.com \
  -- uvx --python 3.11 langfuse-mcp

Step 3: Restart CLI, verify with /mcp (Claude) or codex mcp list (Codex)

Step 4: Test: fetch_traces(age=60)

Read-Only Mode

For safer observability without risk of modifying prompts or datasets, enable read-only mode:

# CLI flag
langfuse-mcp --read-only

# Or environment variable
LANGFUSE_MCP_READ_ONLY=true

This disables write tools: create_text_prompt, create_chat_prompt, update_prompt_labels, create_dataset, create_dataset_item, delete_dataset_item.

Default Output Mode

If you want MCP clients to default to writing full payloads to files when they omit output_mode, configure:

langfuse-mcp --default-output-mode full_json_file

# Or via environment variable
LANGFUSE_MCP_DEFAULT_OUTPUT_MODE=full_json_file

For manual .mcp.json setup or troubleshooting, see references/setup.md.

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Playbooks

"Where are the errors?"

find_exceptions(age=1440, group_by="file")

→ Shows error counts by file. Pick the worst offender.

find_exceptions_in_file(filepath="src/ai/chat.py", age=1440)

→ Lists specific exceptions. Grab a trace_id.

get_exception_details(trace_id="...")

→ Full stacktrace and context.

---

"What happened in this interaction?"

fetch_traces(age=60, user_id="...")

→ Find the trace. Note the trace_id.

If you don't know the user_id, start with:

fetch_traces(age=60)

fetch_trace(trace_id="...", include_observations=true)

→ See all LLM calls in the trace.

fetch_observation(observation_id="...")

→ Inspect a specific generation's input/output.

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"Why is it slow?"

fetch_observations(age=60, type="GENERATION")

→ Find recent LLM calls. Look for high latency.

fetch_observation(observation_id="...")

→ Check token counts, model, timing.

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"What's this user experiencing?"

get_user_sessions(user_id="...", age=1440)

→ List their sessions.

get_session_details(session_id="...")

→ See all traces in the session.

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"Manage datasets"

list_datasets()

→ See all datasets.

get_dataset(name="evaluation-set-v1")

→ Get dataset details.

list_dataset_items(dataset_name="evaluation-set-v1", page=1, limit=10)

→ Browse items in the dataset.

create_dataset(name="qa-test-cases", description="QA evaluation set")

→ Create a new dataset.

create_dataset_item(
  dataset_name="qa-test-cases",
  input={"question": "What is 2+2?"},
  expected_output={"answer": "4"}
)

→ Add test cases.

create_dataset_item(
  dataset_name="qa-test-cases",
  item_id="item_123",
  input={"question": "What is 3+3?"},
  expected_output={"answer": "6"}
)

→ Upsert: updates existing item by id or creates if missing.

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"Manage prompts"

list_prompts()

→ See all prompts with labels.

get_prompt(name="...", label="production")

→ Fetch current production version.

create_text_prompt(name="...", prompt="...", labels=["staging"])

→ Create new version in staging.

update_prompt_labels(name="...", version=N, labels=["production"])

→ Promote to production. (Rollback = re-apply label to older version)

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Quick Reference

Task	Tool
List traces	fetch_traces(age=N)
Get trace details	fetch_trace(trace_id="...", include_observations=true)
List LLM calls	fetch_observations(age=N, type="GENERATION")
Get observation	fetch_observation(observation_id="...")
Error count	get_error_count(age=N)
Find exceptions	find_exceptions(age=N, group_by="file")
List sessions	fetch_sessions(age=N)
User sessions	get_user_sessions(user_id="...", age=N)
List prompts	list_prompts()
Get prompt	get_prompt(name="...", label="production")
List datasets	list_datasets()
Get dataset	get_dataset(name="...")
List dataset items	list_dataset_items(dataset_name="...", limit=N)
Create/update dataset item	create_dataset_item(dataset_name="...", item_id="...")

age = minutes to look back (max 10080 = 7 days)

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Troubleshooting

MCP connection fails

• Verify credentials: check LANGFUSE_PUBLIC_KEY, LANGFUSE_SECRET_KEY, LANGFUSE_HOST
• Restart CLI after adding/updating MCP config
• Test MCP independently: fetch_traces(age=60) — if this fails, the issue is MCP, not the skill
• See references/setup.md for detailed troubleshooting

No traces found

• Increase the age parameter (default lookback may be too short)
• Verify your application is sending traces to the correct Langfuse project
• Check LANGFUSE_HOST points to the right instance (cloud vs self-hosted)

Permission denied

• Regenerate API keys from Langfuse dashboard
• Ensure keys have the required scopes for the operation
• Write operations require read-write keys (not read-only mode)

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References

• references/tool-reference.md — Full parameter docs, filter semantics, response schemas
• references/setup.md — Manual setup, troubleshooting, advanced configuration