langfuse-agent 1.0.1

Agent for interacting with Langfuse Observability API

Langfuse Agent

API | MCP | Agent

Version: 1.0.1

Documentation — Installation, deployment, usage across the API, CLI, and MCP interfaces, and guidance for provisioning the Langfuse platform are maintained in the official documentation.

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Overview

Langfuse Agent is a production-grade Agent and Model Context Protocol (MCP) server designed to interface directly with the Langfuse LLM Engineering and Observability platform. It enables agentic models to query, create, and manage observability traces, datasets, prompt templates, and system configurations.

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Key Features

• Consolidated Action-Routed MCP Tools: Minimizes token overhead and eliminates tool bloat in LLM contexts by grouping 80+ methods into 4 optimized, togglable tool modules.
• Enterprise-Grade Security: Comprehensive support for Eunomia policies, OIDC token delegation, and granular execution context tracking.
• Integrated Graph Agent: Built-in Pydantic AI agent supporting the Agent Control Protocol (ACP) and standard Web interfaces (AG-UI).
• Native Telemetry & Tracing: Out-of-the-box OpenTelemetry exports and native Langfuse tracing tracking every trace and span.

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CLI or API

This agent wraps the Langfuse API. You can interact with it programmatically or via its integrated execution entrypoints.

Detailed instructions on how to use the underlying API wrappers, extended schema bindings, and developer SDK references are maintained in docs/index.md.

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MCP

This server utilizes dynamic Action-Routed tools to optimize token overhead and maximize IDE compatibility.

Available MCP Tools

Auto-generated — do not edit between the markers below.

Condensed action-routed tools (default — MCP_TOOL_MODE=condensed)

MCP Tool	Toggle Env Var	Description
langfuse_datasets	LANGFUSE_DATASETSTOOL	Perform langfuse_datasets operations.
langfuse_management	LANGFUSE_MANAGEMENTTOOL	Perform langfuse_management operations.
langfuse_observability	LANGFUSE_OBSERVABILITYTOOL	Perform langfuse_observability operations.
langfuse_prompts_models	LANGFUSE_PROMPTS_MODELSTOOL	Perform langfuse_prompts_models operations.

Verbose 1:1 API-mapped tools (MCP_TOOL_MODE=verbose or both)

87 per-operation tools — one per public API method (click to expand)

MCP Tool	Toggle Env Var	Description
langfuse_annotation_queues_create_queue	APITOOL	Create an annotation queue
langfuse_annotation_queues_create_queue_assignment	APITOOL	Create an assignment for a user to an annotation queue
langfuse_annotation_queues_create_queue_item	APITOOL	Add an item to an annotation queue
langfuse_annotation_queues_delete_queue_assignment	APITOOL	Delete an assignment for a user to an annotation queue
langfuse_annotation_queues_delete_queue_item	APITOOL	Remove an item from an annotation queue
langfuse_annotation_queues_get_queue	APITOOL	Get an annotation queue by ID
langfuse_annotation_queues_get_queue_item	APITOOL	Get a specific item from an annotation queue
langfuse_annotation_queues_list_queue_items	APITOOL	Get items for a specific annotation queue
langfuse_annotation_queues_list_queues	APITOOL	Get all annotation queues
langfuse_annotation_queues_update_queue_item	APITOOL	Update an annotation queue item
langfuse_blob_storage_integrations_delete_blob_storage_integration	APITOOL	Delete a blob storage integration by ID (requires organization-scoped API key)
langfuse_blob_storage_integrations_get_blob_storage_integration_status	APITOOL	Get the sync status of a blob storage integration by integration ID (requires organization-scoped API key)
langfuse_blob_storage_integrations_get_blob_storage_integrations	APITOOL	Get all blob storage integrations for the organization (requires organization-scoped API key)
langfuse_blob_storage_integrations_upsert_blob_storage_integration	APITOOL	Create or update a blob storage integration for a specific project (requires organization-scoped API key). The configuration is validated by performing a test upload to the bucket.
langfuse_comments_create	APITOOL	Create a comment. Comments may be attached to different object types (trace, observation, session, prompt).
langfuse_comments_get	APITOOL	Get all comments
langfuse_comments_get_by_id	APITOOL	Get a comment by id
langfuse_dataset_items_create	APITOOL	Create a dataset item
langfuse_dataset_items_delete	APITOOL	Delete a dataset item and all its run items. This action is irreversible.
langfuse_dataset_items_get	APITOOL	Get a dataset item
langfuse_dataset_items_list	APITOOL	Get dataset items. Optionally specify a version to get the items as they existed at that point in time. Note: If version parameter is provided, datasetName must also be provided.
langfuse_dataset_run_items_create	APITOOL	Create a dataset run item
langfuse_dataset_run_items_list	APITOOL	List dataset run items
langfuse_datasets_create	APITOOL	Create a dataset
langfuse_datasets_delete_run	APITOOL	Delete a dataset run and all its run items. This action is irreversible.
langfuse_datasets_get	APITOOL	Get a dataset
langfuse_datasets_get_run	APITOOL	Get a dataset run and its items
langfuse_datasets_get_runs	APITOOL	Get dataset runs
langfuse_datasets_list	APITOOL	Get all datasets
langfuse_health_health	APITOOL	Check health of API and database
langfuse_ingestion_batch	APITOOL	Legacy endpoint for batch ingestion for Langfuse Observability. -> Please use the OpenTelemetry endpoint (/api/public/otel/v1/traces). Learn more: https://langfuse.com/integrations/native/opentelemetry Within each batch, there can be multiple events. Each event has a type, an id, a timestamp, metadata and a body. Internally, we refer to this as the "event envelope" as it tells us something about the event but not the trace. We use the event id within this envelope to deduplicate messages to avoid processing the same event twice, i.e. the event id should be unique per request. The event.body.id is the ID of the actual trace and will be used for updates and will be visible within the Langfuse App. I.e. if you want to update a trace, you'd use the same body id, but separate event IDs. Notes: - Introduction to data model: https://langfuse.com/docs/observability/data-model - Batch sizes are limited to 3.5 MB in total. You need to adjust the number of events per batch accordingly. - The API does not return a 4xx status code for input errors. Instead, it responds with a 207 status code, which includes a list of the encountered errors.
langfuse_legacy_metrics_v1_metrics	APITOOL	Get metrics from the Langfuse project using a query object. Consider using the v2 metrics endpoint for better performance. For more details, see the Metrics API documentation.
langfuse_legacy_observations_v1_get	APITOOL	Get a observation
langfuse_legacy_observations_v1_get_many	APITOOL	Get a list of observations. Consider using the v2 observations endpoint for cursor-based pagination and field selection.
langfuse_legacy_score_v1_create	APITOOL	Create a score (supports both trace and session scores)
langfuse_legacy_score_v1_delete	APITOOL	Delete a score (supports both trace and session scores)
langfuse_llm_connections_list	APITOOL	Get all LLM connections in a project
langfuse_llm_connections_upsert	APITOOL	Create or update an LLM connection. The connection is upserted on provider.
langfuse_media_get	APITOOL	Get a media record
langfuse_media_get_upload_url	APITOOL	Get a presigned upload URL for a media record
langfuse_media_patch	APITOOL	Patch a media record
langfuse_metrics_metrics	APITOOL	Get metrics from the Langfuse project using a query object. V2 endpoint with optimized performance. ## V2 Differences - Supports observations, scores-numeric, and scores-categorical views only (traces view not supported) - Direct access to tags and release fields on observations - Backwards-compatible: traceName, traceRelease, traceVersion dimensions are still available on observations view - High cardinality dimensions are not supported and will return a 400 error (see below) For more details, see the Metrics API documentation. ## Available Views ### observations Query observation-level data (spans, generations, events). Dimensions: - environment - Deployment environment (e.g., production, staging) - type - Type of observation (SPAN, GENERATION, EVENT) - name - Name of the observation - level - Logging level of the observation - version - Version of the observation - tags - User-defined tags - release - Release version - traceName - Name of the parent trace (backwards-compatible) - traceRelease - Release version of the parent trace (backwards-compatible, maps to release) - traceVersion - Version of the parent trace (backwards-compatible, maps to version) - providedModelName - Name of the model used - promptName - Name of the prompt used - promptVersion - Version of the prompt used - startTimeMonth - Month of start_time in YYYY-MM format Measures: - count - Total number of observations - latency - Observation latency (milliseconds) - streamingLatency - Generation latency from completion start to end (milliseconds) - inputTokens - Sum of input tokens consumed - outputTokens - Sum of output tokens produced - totalTokens - Sum of all tokens consumed - outputTokensPerSecond - Output tokens per second - tokensPerSecond - Total tokens per second - inputCost - Input cost (USD) - outputCost - Output cost (USD) - totalCost - Total cost (USD) - timeToFirstToken - Time to first token (milliseconds) - countScores - Number of scores attached to the observation ### scores-numeric Query numeric and boolean score data. Dimensions: - environment - Deployment environment - name - Name of the score (e.g., accuracy, toxicity) - source - Origin of the score (API, ANNOTATION, EVAL) - dataType - Data type (NUMERIC, BOOLEAN) - configId - Identifier of the score config - timestampMonth - Month in YYYY-MM format - timestampDay - Day in YYYY-MM-DD format - value - Numeric value of the score - traceName - Name of the parent trace - tags - Tags - traceRelease - Release version - traceVersion - Version - observationName - Name of the associated observation - observationModelName - Model name of the associated observation - observationPromptName - Prompt name of the associated observation - observationPromptVersion - Prompt version of the associated observation Measures: - count - Total number of scores - value - Score value (for aggregations) ### scores-categorical Query categorical score data. Same dimensions as scores-numeric except uses stringValue instead of value. Measures: - count - Total number of scores ## High Cardinality Dimensions The following dimensions cannot be used as grouping dimensions in v2 metrics API as they can cause performance issues. Use them in filters instead. observations view: - id - Use traceId filter to narrow down results - traceId - Use traceId filter instead - userId - Use userId filter instead - sessionId - Use sessionId filter instead - parentObservationId - Use parentObservationId filter instead scores-numeric / scores-categorical views: - id - Use specific filters to narrow down results - traceId - Use traceId filter instead - userId - Use userId filter instead - sessionId - Use sessionId filter instead - observationId - Use observationId filter instead ## Aggregations Available aggregation functions: sum, avg, count, max, min, p50, p75, p90, p95, p99, histogram ## Time Granularities Available granularities for timeDimension: auto, minute, hour, day, week, month - auto bins the data into approximately 50 buckets based on the time range
langfuse_models_create	APITOOL	Create a model
langfuse_models_delete	APITOOL	Delete a model. Cannot delete models managed by Langfuse. You can create your own definition with the same modelName to override the definition though.
langfuse_models_get	APITOOL	Get a model
langfuse_models_list	APITOOL	Get all models
langfuse_observations_get_many	APITOOL	Get a list of observations with cursor-based pagination and flexible field selection. ## Cursor-based Pagination This endpoint uses cursor-based pagination for efficient traversal of large datasets. The cursor is returned in the response metadata and should be passed in subsequent requests to retrieve the next page of results. ## Field Selection Use the fields parameter to control which observation fields are returned: - core - Always included: id, traceId, startTime, endTime, projectId, parentObservationId, type - basic - name, level, statusMessage, version, environment, bookmarked, public, userId, sessionId - time - completionStartTime, createdAt, updatedAt - io - input, output - metadata - metadata (truncated to 200 chars by default, use expandMetadata to get full values) - model - providedModelName, internalModelId, modelParameters - usage - usageDetails, costDetails, totalCost - prompt - promptId, promptName, promptVersion - metrics - latency, timeToFirstToken If not specified, core and basic field groups are returned. ## Filters Multiple filtering options are available via query parameters or the structured filter parameter. When using the filter parameter, it takes precedence over individual query parameter filters.
langfuse_opentelemetry_export_traces	APITOOL	OpenTelemetry Traces Ingestion Endpoint This endpoint implements the OTLP/HTTP specification for trace ingestion, providing native OpenTelemetry integration for Langfuse Observability. Supported Formats: - Binary Protobuf: Content-Type: application/x-protobuf - JSON Protobuf: Content-Type: application/json - Supports gzip compression via Content-Encoding: gzip header Specification Compliance: - Conforms to OTLP/HTTP Trace Export - Implements ExportTraceServiceRequest message format Documentation: - Integration guide: https://langfuse.com/integrations/native/opentelemetry - Data model: https://langfuse.com/docs/observability/data-model
langfuse_organizations_delete_organization_membership	APITOOL	Delete a membership from the organization associated with the API key (requires organization-scoped API key)
langfuse_organizations_delete_project_membership	APITOOL	Delete a membership from a specific project (requires organization-scoped API key). The user must be a member of the organization.
langfuse_organizations_get_organization_api_keys	APITOOL	Get all API keys for the organization associated with the API key (requires organization-scoped API key)
langfuse_organizations_get_organization_memberships	APITOOL	Get all memberships for the organization associated with the API key (requires organization-scoped API key)
langfuse_organizations_get_organization_projects	APITOOL	Get all projects for the organization associated with the API key (requires organization-scoped API key)
langfuse_organizations_get_project_memberships	APITOOL	Get all memberships for a specific project (requires organization-scoped API key)
langfuse_organizations_update_organization_membership	APITOOL	Create or update a membership for the organization associated with the API key (requires organization-scoped API key)
langfuse_organizations_update_project_membership	APITOOL	Create or update a membership for a specific project (requires organization-scoped API key). The user must already be a member of the organization.
langfuse_projects_create	APITOOL	Create a new project (requires organization-scoped API key)
langfuse_projects_create_api_key	APITOOL	Create a new API key for a project (requires organization-scoped API key)
langfuse_projects_delete	APITOOL	Delete a project by ID (requires organization-scoped API key). Project deletion is processed asynchronously.
langfuse_projects_delete_api_key	APITOOL	Delete an API key for a project (requires organization-scoped API key)
langfuse_projects_get	APITOOL	Get Project associated with API key (requires project-scoped API key). You can use GET /api/public/organizations/projects to get all projects with an organization-scoped key.
langfuse_projects_get_api_keys	APITOOL	Get all API keys for a project (requires organization-scoped API key)
langfuse_projects_update	APITOOL	Update a project by ID (requires organization-scoped API key).
langfuse_prompt_version_update	APITOOL	Update labels for a specific prompt version
langfuse_prompts_create	APITOOL	Create a new version for the prompt with the given name
langfuse_prompts_delete	APITOOL	Delete prompt versions. If neither version nor label is specified, all versions of the prompt are deleted.
langfuse_prompts_get	APITOOL	Get a prompt
langfuse_prompts_list	APITOOL	Get a list of prompt names with versions and labels
langfuse_scim_create_user	APITOOL	Create a new user in the organization (requires organization-scoped API key)
langfuse_scim_delete_user	APITOOL	Remove a user from the organization (requires organization-scoped API key). Note that this only removes the user from the organization but does not delete the user entity itself.
langfuse_scim_get_resource_types	APITOOL	Get SCIM Resource Types (requires organization-scoped API key)
langfuse_scim_get_schemas	APITOOL	Get SCIM Schemas (requires organization-scoped API key)
langfuse_scim_get_service_provider_config	APITOOL	Get SCIM Service Provider Configuration (requires organization-scoped API key)
langfuse_scim_get_user	APITOOL	Get a specific user by ID (requires organization-scoped API key)
langfuse_scim_list_users	APITOOL	List users in the organization (requires organization-scoped API key)
langfuse_score_configs_create	APITOOL	Create a score configuration (config). Score configs are used to define the structure of scores
langfuse_score_configs_get	APITOOL	Get all score configs
langfuse_score_configs_get_by_id	APITOOL	Get a score config
langfuse_score_configs_update	APITOOL	Update a score config
langfuse_scores_get_by_id	APITOOL	Get a score (supports both trace and session scores)
langfuse_scores_get_many	APITOOL	Get a list of scores (supports both trace and session scores)
langfuse_sessions_get	APITOOL	Get a session. Please note that traces on this endpoint are not paginated, if you plan to fetch large sessions, consider GET /api/public/traces?sessionId=<sessionId>
langfuse_sessions_list	APITOOL	Get sessions
langfuse_trace_delete	APITOOL	Delete a specific trace
langfuse_trace_delete_multiple	APITOOL	Delete multiple traces
langfuse_trace_get	APITOOL	Get a specific trace
langfuse_trace_list	APITOOL	Get list of traces

4 action-routed tool(s) (default) · 87 verbose 1:1 tool(s). Each is enabled unless its <DOMAIN>TOOL toggle is set false; MCP_TOOL_MODE selects the surface (condensed default · verbose 1:1 · both). Auto-generated — do not edit.

Detailed tool schemas, parameter shapes, and validation constraints are preserved in docs/mcp.md.

Dynamic Tool Selection & Visibility

This MCP server supports dynamic toolset selection and visibility filtering at runtime. This allows you to restrict the set of exposed tools in order to prevent blowing up the LLM's context window.

You can configure tool filtering via multiple input channels:

• CLI Arguments: Pass --tools or --toolsets (or their disabled counterparts --disabled-tools and --disabled-toolsets) during startup.
• Environment Variables: Define standard environment variables:
  • MCP_ENABLED_TOOLS / MCP_DISABLED_TOOLS
  • MCP_ENABLED_TAGS / MCP_DISABLED_TAGS
• HTTP SSE Request Headers: Pass custom headers during transport initialization:
  • x-mcp-enabled-tools / x-mcp-disabled-tools
  • x-mcp-enabled-tags / x-mcp-disabled-tags
• HTTP SSE Request Query Parameters: Append query parameters directly to your transport connection URL:
  • ?tools=tool1,tool2
  • ?tags=tag1

When query strings or parameters are supplied, an LLM-free Knowledge Graph resolution layer (using DynamicToolOrchestrator) matches query intents against known tool tags, names, or descriptions, with safe fallback and automated 24-hour background cache refreshing.

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MCP Configuration Examples

Install the slim [mcp] extra. All examples install langfuse-agent[mcp] — the MCP-server extra that pulls only the FastMCP / FastAPI tooling (agent-utilities[mcp]). It deliberately excludes the heavy agent runtime (pydantic-ai, the epistemic-graph engine, dspy, llama-index), so uvx / container installs are far smaller. Use the full [agent] extra only when you need the integrated Pydantic AI agent.

stdio Transport (local IDEs — Cursor, Claude Desktop, VS Code)

{
  "mcpServers": {
    "langfuse-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "langfuse-agent[mcp]",
        "langfuse-mcp"
      ],
      "env": {
        "MCP_TOOL_MODE": "condensed",
        "LANGFUSE_BASE_URL": "http://localhost:8080",
        "LANGFUSE_DATASETSTOOL": "True",
        "LANGFUSE_MANAGEMENTTOOL": "True",
        "LANGFUSE_OBSERVABILITYTOOL": "True",
        "LANGFUSE_PROMPTS_MODELSTOOL": "True",
        "LANGFUSE_PUBLIC_KEY": "your_public_key_here",
        "LANGFUSE_SECRET_KEY": "your_secret_key_here"
      }
    }
  }
}

Streamable-HTTP Transport (networked / production)

{
  "mcpServers": {
    "langfuse-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "langfuse-agent[mcp]",
        "langfuse-mcp",
        "--transport",
        "streamable-http",
        "--port",
        "8000"
      ],
      "env": {
        "TRANSPORT": "streamable-http",
        "HOST": "0.0.0.0",
        "PORT": "8000",
        "MCP_TOOL_MODE": "condensed",
        "LANGFUSE_BASE_URL": "http://localhost:8080",
        "LANGFUSE_DATASETSTOOL": "True",
        "LANGFUSE_MANAGEMENTTOOL": "True",
        "LANGFUSE_OBSERVABILITYTOOL": "True",
        "LANGFUSE_PROMPTS_MODELSTOOL": "True",
        "LANGFUSE_PUBLIC_KEY": "your_public_key_here",
        "LANGFUSE_SECRET_KEY": "your_secret_key_here"
      }
    }
  }
}

Alternatively, connect to a pre-deployed Streamable-HTTP instance by url:

{
  "mcpServers": {
    "langfuse-mcp": {
      "url": "http://localhost:8000/langfuse-mcp/mcp"
    }
  }
}

Deploying the Streamable-HTTP server via Docker:

docker run -d \
  --name langfuse-mcp-mcp \
  -p 8000:8000 \
  -e TRANSPORT=streamable-http \
  -e HOST=0.0.0.0 \
  -e PORT=8000 \
  -e MCP_TOOL_MODE=condensed \
  -e LANGFUSE_BASE_URL=http://localhost:8080 \
  -e LANGFUSE_DATASETSTOOL=True \
  -e LANGFUSE_MANAGEMENTTOOL=True \
  -e LANGFUSE_OBSERVABILITYTOOL=True \
  -e LANGFUSE_PROMPTS_MODELSTOOL=True \
  -e LANGFUSE_PUBLIC_KEY=your_public_key_here \
  -e LANGFUSE_SECRET_KEY=your_secret_key_here \
  knucklessg1/langfuse-agent:mcp

Auto-generated from the code-read env surface (MCP_TOOL_MODE + package vars) — do not edit.

Additional Deployment Options

langfuse-agent can also run as a local container (Docker / Podman / uv) or be consumed from a remote deployment. The Deployment guide has full, copy-paste mcp_config.json for all four transports — stdio, streamable-http, local container / uv, and remote URL:

• Local container / uv — launch the server from mcp_config.json via uvx, docker run, or podman run, or point at a local streamable-http container by url.
• Remote URL — connect to a server deployed behind Caddy at http://langfuse-mcp.arpa/mcp using the "url" key.

Agent

This repository features a fully integrated Pydantic AI Graph Agent. It communicates over the Agent Control Protocol (ACP) and interacts seamlessly with the Agent Web UI (AG-UI) and Terminal interface.

Running the Agent CLI

To start the interactive command-line agent:

# Set credentials
export LANGFUSE_BASE_URL="http://localhost:8080"
export LANGFUSE_PUBLIC_KEY="pk-lf-..."
export LANGFUSE_SECRET_KEY="sk-lf-..."

# Run the agent server
langfuse-agent --provider openai --model-id gpt-4o

Docker Compose Orchestration

The following docker/agent.compose.yml configures the Agent, Web UI, and Terminal Interface together:

version: '3.8'

services:
  langfuse-agent-mcp:
    image: knucklessg1/langfuse-agent:mcp
    container_name: langfuse-agent-mcp
    hostname: langfuse-agent-mcp
    restart: always
    env_file:
      - ../.env
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=8004
      - TRANSPORT=streamable-http
    ports:
      - "8004:8004"
    healthcheck:
      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8004/health')"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 10s

  langfuse-agent-agent:
    image: knucklessg1/langfuse-agent:latest
    container_name: langfuse-agent-agent
    hostname: langfuse-agent-agent
    restart: always
    depends_on:
      - langfuse-agent-mcp
    env_file:
      - ../.env
    command: [ "langfuse-agent" ]
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=9004
      - MCP_URL=http://langfuse-agent-mcp:8004/mcp
      - PROVIDER=${PROVIDER:-openai}
      - MODEL_ID=${MODEL_ID:-gpt-4o}
      - ENABLE_WEB_UI=True
      - ENABLE_OTEL=True
    ports:
      - "9004:9004"
    healthcheck:
      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:9004/health')"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 10s

Detailed graph node architecture explanations, custom skill configurations, and agentic trace guides are available in docs/agent.md.

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Security & Governance

Built directly upon the enterprise-ready agent-utilities core, standard security parameters are fully supported:

Access Control & Policy Enforcement

• Eunomia Policies: Fine-grained, policy-driven tool authorization. Supports none, local embedded (mcp_policies.json), or centralized remote modes.
• OIDC Token Delegation: Compliant with RFC 8693 token exchange for flowing authenticating user credentials from Web UI / ACP → Agent → MCP.
• Scoped Credentials: Execution context runs restricted to the specific caller identity.

Runtime Security Grid

Feature	Functionality	Enablement
Tool Guard	Sensitivity inspection with human-in-the-loop validation	Enabled by default
Prompt Injection Defense	Input scanning, repetition monitoring, and recursive loop blocks	Enabled by default
Context Safety Guard	Stuck-loop detectors and contextual overflow preemptive alerts	Enabled by default

---

Configuration & Environment Variables

The agent can be fully configured using environment variables or a .env file. Below is the list of all supported variables:

Core API & Credentials

Variable	Description	Default
LANGFUSE_BASE_URL	Langfuse instance base URL (legacy alias: LANGFUSE_HOST).	https://cloud.langfuse.com
LANGFUSE_PUBLIC_KEY	Langfuse public API key.	""
LANGFUSE_SECRET_KEY	Langfuse secret API key.	""

Server Configuration

Variable	Description	Default
HOST	The hostname/address the server binds to.	0.0.0.0
PORT	The port the server listens on.	8004
TRANSPORT	The communication protocol (stdio, streamable-http, sse).	stdio
AUTH_TYPE	Server authentication strategy (key, delegated, none).	key

Agent Customization

Variable	Description	Default
DEFAULT_AGENT_NAME	Custom name displayed for the Pydantic AI Graph Agent.	"Langfuse Agent"
AGENT_DESCRIPTION	Short description of the agent's responsibilities.	"AI agent for Langfuse Agent operations."
AGENT_SYSTEM_PROMPT	Custom system instructions override for the agent.	""

Tool Toggle Switches

Individual tool modules can be enabled or disabled to minimize client context size. These names match the authoritative "Toggle Env Var" column in the Available MCP Tools table above:

• LANGFUSE_OBSERVABILITYTOOL (Default: True): Toggles observation/tracing tools.
• LANGFUSE_DATASETSTOOL (Default: True): Toggles datasets and annotation queue tools.
• LANGFUSE_PROMPTS_MODELSTOOL (Default: True): Toggles prompt template and model connectivity tools.
• LANGFUSE_MANAGEMENTTOOL (Default: True): Toggles comments, SCIM, and project management tools.

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Installation

Pick the extra that matches what you want to run:

Extra	Installs	Use when
langfuse-agent[mcp]	Slim MCP server only (agent-utilities[mcp] — FastMCP/FastAPI)	You only run the MCP server (smallest install / image)
langfuse-agent[agent]	Full agent runtime (agent-utilities[agent,logfire] — Pydantic AI + the epistemic-graph engine)	You run the integrated agent
langfuse-agent[all]	Everything (mcp + agent + logfire)	Development / both surfaces

# MCP server only (recommended for tool hosting — slim deps)
uv pip install "langfuse-agent[mcp]"

# Full agent runtime (Pydantic AI + epistemic-graph engine)
uv pip install "langfuse-agent[agent]"

# Everything (development)
uv pip install "langfuse-agent[all]"      # or: python -m pip install "langfuse-agent[all]"

Container images (:mcp vs :agent)

One multi-stage docker/Dockerfile builds two right-sized images, selected by --target:

Image tag	Build target	Contents	Entrypoint
knucklessg1/langfuse-agent:mcp	--target mcp	langfuse-agent[mcp] — slim, no engine/pydantic-ai/dspy/llama-index/tree-sitter	langfuse-mcp
knucklessg1/langfuse-agent:latest	--target agent (default)	langfuse-agent[agent] — full agent runtime + epistemic-graph engine	langfuse-agent

docker build --target mcp   -t knucklessg1/langfuse-agent:mcp    docker/   # slim MCP server
docker build --target agent -t knucklessg1/langfuse-agent:latest docker/   # full agent

Knowledge-graph database (epistemic-graph)

The full agent ([agent] / :latest) embeds the epistemic-graph engine (pulled in transitively via agent-utilities[agent]). For production — or to share one knowledge graph across multiple agents — run epistemic-graph as its own database container and point the agent at it instead of embedding it. Deployment recipes (single-node + Raft HA), connection config, and the full database architecture (with diagrams) are documented in the epistemic-graph deployment guide. The slim [mcp] server does not require the database.

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Documentation

The complete documentation is published as the official documentation site and is the recommended reference for installation, deployment, and day-to-day operation.

Page	Contents
Installation	pip, source, extras, prebuilt Docker image
Deployment	run the MCP and agent servers, Compose, Caddy + Technitium, env config
Usage	the MCP tools, the LangfuseApi client, the CLI
Backing Platform	deploy Langfuse with Docker
Overview	the full tool surface and ecosystem role
Concepts	concept registry (CONCEPT:LF-*)

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Repository Owners

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Contribute

Contributions are welcome! Please ensure code quality by executing local checks before submitting pull requests:

• Format code using ruff format .
• Lint code using ruff check .
• Validate type-safety with mypy .
• Execute test suites using pytest

Deploy with agent-os-genesis

This package can be provisioned for you — skill-guided — by the agent-os-genesis universal skill (its single-package deploy mode): it picks your install method, seeds secrets to OpenBao/Vault (or .env), trusts your enterprise CA, registers the MCP server, and verifies it — the same machinery that stands up the whole Agent OS, narrowed to just this package. Ask your agent to "deploy langfuse-agent with agent-os-genesis".

Install mode	Command
Bare-metal, prod (PyPI)	uvx langfuse-mcp · or uv tool install langfuse-agent
Bare-metal, dev (editable)	uv pip install -e ".[all]" · or pip install -e ".[all]"
Container, prod	deploy knucklessg1/langfuse-agent:latest via docker-compose / swarm / podman / podman-compose / kubernetes
Container, dev (editable)	deploy docker/compose.dev.yml (source-mounted at /src; edits live on restart)

Secrets are read-existing + seeded via vault_sync — you are only prompted for what's missing.

Environment Variables

Package environment variables

Variable	Example	Description
HOST	0.0.0.0
PORT	8004
TRANSPORT	stdio	options: stdio, streamable-http, sse
ENABLE_OTEL	True
OTEL_EXPORTER_OTLP_ENDPOINT	http://localhost:8080/api/public/otel
OTEL_EXPORTER_OTLP_PUBLIC_KEY	pk-...
OTEL_EXPORTER_OTLP_SECRET_KEY	sk-...
OTEL_EXPORTER_OTLP_PROTOCOL	http/protobuf
EUNOMIA_TYPE	none	options: none, embedded, remote
EUNOMIA_POLICY_FILE	mcp_policies.json
EUNOMIA_REMOTE_URL	http://eunomia-server:8000
LANGFUSE_BASE_URL	http://localhost:8080
LANGFUSE_PUBLIC_KEY	your_public_key_here
LANGFUSE_SECRET_KEY	your_secret_key_here
AUTH_TYPE	key	options: key, delegated, none
DEFAULT_AGENT_NAME	"Langfuse Agent"
AGENT_DESCRIPTION	"AI agent for Langfuse Agent operations."
AGENT_SYSTEM_PROMPT	""
MCP_TOOL_MODE	condensed	action-routed tools)
LANGFUSE_OBSERVABILITYTOOL	True	MCP tools table (condensed action-routed surface).
LANGFUSE_DATASETSTOOL	True
LANGFUSE_PROMPTS_MODELSTOOL	True
LANGFUSE_MANAGEMENTTOOL	True

Inherited agent-utilities variables (apply to every connector)

Variable	Example	Description
MCP_ENABLED_TOOLS	—	Comma-separated tool allow-list
MCP_DISABLED_TOOLS	—	Comma-separated tool deny-list
MCP_ENABLED_TAGS	—	Comma-separated tag allow-list
MCP_DISABLED_TAGS	—	Comma-separated tag deny-list
MCP_CLIENT_AUTH	—	Outbound MCP auth (oidc-client-credentials for fleet calls)
OIDC_CLIENT_ID	—	OIDC client id (service-account auth)
OIDC_CLIENT_SECRET	—	OIDC client secret (service-account auth)
DEBUG	False	Verbose logging
PYTHONUNBUFFERED	1	Unbuffered stdout (recommended in containers)
MCP_URL	http://localhost:8000/mcp	URL of the MCP server the agent connects to
PROVIDER	openai	LLM provider for the agent
MODEL_ID	gpt-4o	Model id for the agent
ENABLE_WEB_UI	True	Serve the AG-UI web interface

23 package + 13 inherited variable(s). Auto-generated from .env.example + the shared agent-utilities set — do not edit.