langfuse

name	langfuse
version	1.0.3
description	Debug AI traces, find exceptions, analyze sessions, and manage prompts via Langfuse MCP. Use when debugging AI pipelines, investigating errors, analyzing latency, managing prompt versions, or setting up Langfuse. Triggers on "langfuse", "traces", "debug AI", "find exceptions", "what went wrong", "why is it slow", "datasets", "evaluation sets".
metadata	{"short-description":"Langfuse observability via MCP","compatibility":"claude-code, codex-cli"}

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.

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

---

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.

---

"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.

---

"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.

---

"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.

---

"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)

---

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)

---

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)

---

References

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