langfuse-agent 0.23.0

Agent for interacting with Langfuse Observability API

Langfuse Agent

API | MCP | Agent

Version: 0.23.0

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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

Tool Module	Toggle Env Var	Enabled by Default	Description & Nested Methods
Langfuse Observability	LANGFUSE_OBSERVABILITY_TOOL	True	Perform langfuse_observability operations. Action-routed methods: ingestion_batch, legacy_metrics_v1_metrics, legacy_observations_v1_get, legacy_observations_v1_get_many, legacy_score_v1_create, legacy_score_v1_delete, metrics_metrics, observations_get_many, opentelemetry_export_traces, score_configs_create, score_configs_get, score_configs_get_by_id, score_configs_update, scores_get_by_id, scores_get_many, sessions_get, sessions_list, trace_delete, trace_delete_multiple, trace_get, trace_list.
Langfuse Datasets	LANGFUSE_DATASETS_TOOL	True	Perform langfuse_datasets operations. Action-routed methods: annotation_queues_create_queue, annotation_queues_create_queue_assignment, annotation_queues_create_queue_item, annotation_queues_delete_queue_assignment, annotation_queues_delete_queue_item, annotation_queues_get_queue, annotation_queues_get_queue_item, annotation_queues_list_queue_items, annotation_queues_list_queues, annotation_queues_update_queue_item, dataset_items_create, dataset_items_delete, dataset_items_get, dataset_items_list, dataset_run_items_create, dataset_run_items_list, datasets_create, datasets_delete_run, datasets_get, datasets_get_run, datasets_get_runs, datasets_list.
Langfuse Prompts Models	LANGFUSE_PROMPTS_MODELS_TOOL	True	Perform langfuse_prompts_models operations. Action-routed methods: llm_connections_list, llm_connections_upsert, media_get, media_get_upload_url, media_patch, models_create, models_delete, models_get, models_list, prompt_version_update, prompts_create, prompts_delete, prompts_get, prompts_list.
Langfuse Management	LANGFUSE_MANAGEMENT_TOOL	True	Perform langfuse_management operations. Action-routed methods: blob_storage_integrations_delete_blob_storage_integration, blob_storage_integrations_get_blob_storage_integration_status, blob_storage_integrations_get_blob_storage_integrations, blob_storage_integrations_upsert_blob_storage_integration, comments_create, comments_get, comments_get_by_id, health_health, organizations_delete_organization_membership, organizations_delete_project_membership, organizations_get_organization_api_keys, organizations_get_organization_memberships, organizations_get_organization_projects, organizations_get_project_memberships, organizations_update_organization_membership, organizations_update_project_membership, projects_create, projects_create_api_key, projects_delete, projects_delete_api_key, projects_get, projects_get_api_keys, projects_update, scim_create_user, scim_delete_user, scim_get_resource_types, scim_get_schemas, scim_get_service_provider_config, scim_get_user, scim_list_users.

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

stdio Transport (Recommended for local IDEs e.g., Cursor, Claude Desktop)

Configure your IDE's mcp.json to launch the MCP server via uvx:

{
  "mcpServers": {
    "langfuse-agent": {
      "command": "uvx",
      "args": [
        "--from",
        "langfuse-agent",
        "langfuse-mcp"
      ],
      "env": {
        "LANGFUSE_BASE_URL": "http://localhost:8080",
        "LANGFUSE_TOKEN": "your_token_here"
      }
    }
  }
}

Streamable-HTTP Transport (Recommended for production deployments)

To run the server as a long-running Streamable-HTTP service:

{
  "mcpServers": {
    "langfuse-agent": {
      "url": "http://localhost:8004/langfuse-agent/mcp"
    }
  }
}

Deploying the Streamable-HTTP server via Docker:

docker run -d \
  --name langfuse-agent-mcp \
  -p 8004:8004 \
  -e TRANSPORT=streamable-http \
  -e PORT=8004 \
  -e LANGFUSE_BASE_URL="http://your-langfuse-instance:8080" \
  -e LANGFUSE_TOKEN="your_token" \
  knucklessg1/langfuse-agent:latest

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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_TOKEN="your_token"

# 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:latest
    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

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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.	https://cloud.langfuse.com
LANGFUSE_PUBLIC_KEY	Langfuse public API key.	""
LANGFUSE_SECRET_KEY	Langfuse secret API key.	""
LANGFUSE_TOKEN	Consolidated authentication token.	""

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:

• OBSERVABILITY_TOOL (Default: True): Toggles observation/tracing tools.
• DATASETS_TOOL (Default: True): Toggles datasets and annotation queue tools.
• PROMPTS_MODELS_TOOL (Default: True): Toggles prompt template and model connectivity tools.
• MANAGEMENT_TOOL (Default: True): Toggles comments, SCIM, and project management tools.

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Installation

Install the Python package locally:

# Using uv (highly recommended)
uv pip install langfuse-agent[all]

# Using standard pip
python -m pip install langfuse-agent[all]

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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