Langfuse Agent

API | MCP | Agent

Version: 2.1.0

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).
• Optional Telemetry & Tracing: OTLP export and Langfuse instrumentation activate only when their runtime configuration is present.

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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 (MCP_TOOL_MODE=condensed)

MCP Tool	Toggle Env Var	Description
langfuse_datasets	LANGFUSE_DATASETSTOOL	Perform langfuse_datasets operations.
langfuse_ingest	LANGFUSE_KGTOOL	Natively ingest Langfuse records into epistemic-graph as typed nodes.
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)

81 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_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_get	APITOOL	Query aggregate observation or score metrics through Metrics API v2.
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_create	APITOOL	Create a typed score through Langfuse's current score-write API.
langfuse_scores_get_many	APITOOL	Query scores through the cursor-based Scores API v3.
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

5 action-routed tool(s) · 81 verbose 1:1 tool(s). Each is enabled unless its <DOMAIN>TOOL toggle is set false; MCP_TOOL_MODE selects the surface (intent default — the six verb-tools, granular set loaded on demand · condensed action-routed · verbose 1:1 · both). Auto-generated — do not edit.

Detailed tool schemas, parameter shapes, and validation constraints are preserved in docs/usage.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 connector-focused [mcp] extra. Examples use langfuse-agent[mcp] to add FastMCP / FastAPI through agent-utilities[mcp]; the required Agent Utilities core still carries epistemic-graph[full]. The [agent-runtime] extra additionally enables model orchestration.

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": "intent",
        "LANGFUSE_DATASETSTOOL": "True",
        "LANGFUSE_KGTOOL": "True",
        "LANGFUSE_MANAGEMENTTOOL": "True",
        "LANGFUSE_OBSERVABILITYTOOL": "True",
        "LANGFUSE_PROMPTS_MODELSTOOL": "True"
      }
    }
  }
}

Runtime references require an alias-aware launcher such as GraphOS. Other launchers must omit those entries and inject the resolved values through their own runtime secret boundary.

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": "127.0.0.1",
        "PORT": "8000",
        "MCP_TOOL_MODE": "intent",
        "LANGFUSE_DATASETSTOOL": "True",
        "LANGFUSE_KGTOOL": "True",
        "LANGFUSE_MANAGEMENTTOOL": "True",
        "LANGFUSE_OBSERVABILITYTOOL": "True",
        "LANGFUSE_PROMPTS_MODELSTOOL": "True"
      }
    }
  }
}

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

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

Run a reviewed container image as a least-privilege stdio child (no listener or published port):

docker run -i --rm \
  --read-only \
  --cap-drop=ALL \
  --security-opt=no-new-privileges \
  --pids-limit=256 \
  --tmpfs /tmp:rw,noexec,nosuid,nodev,size=64m \
  -e TRANSPORT=stdio \
  -e MCP_TOOL_MODE=intent \
  -e LANGFUSE_DATASETSTOOL=True \
  -e LANGFUSE_KGTOOL=True \
  -e LANGFUSE_MANAGEMENTTOOL=True \
  -e LANGFUSE_OBSERVABILITYTOOL=True \
  -e LANGFUSE_PROMPTS_MODELSTOOL=True \
  registry.example.invalid/langfuse-agent@sha256:<digest> langfuse-mcp

For containerized network HTTP, supply an authenticated TLS ingress (or direct server TLS), exact MCP_ALLOWED_HOSTS, and an exact trusted-proxy CIDR policy through the operator-owned deployment profile. The generator does not emit an unauthenticated non-loopback listener.

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

Additional Deployment Options

langfuse-agent can run as a local stdio process or container, or behind a remote network boundary. The Deployment guide carries the detailed transport contract.

• Local container — launch a reviewed immutable image as a least-privilege stdio child with no listener or published port.
• Remote URL — connect through an operator-supplied authenticated HTTPS ingress. Keep its URL, outbound identity references, trust profile, and exact MCP_ALLOWED_HOSTS in AgentConfig.

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, first let the process supervisor inject the Langfuse credentials and any model-provider secret. Then run:

langfuse-agent --provider openai --model-id gpt-4o

Docker Compose Orchestration

The checked-in docker/agent.compose.yml runs the MCP and agent services with restricted containers and loopback-published ports. Supply pinned image digests and let the process supervisor inject materialized child credentials at runtime; never add them to the Compose file or a checked-in env file. GraphOS deployments keep only the corresponding *_REF settings in AgentConfig.

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

GraphOS configuration uses AgentConfig and secret references. Do not store credential values in .env files or MCP catalogs.

Core API & Credentials

Variable	Description	Default
LANGFUSE_HOST	Canonical Langfuse service URL.	https://cloud.langfuse.com
LANGFUSE_PUBLIC_KEY_REF	Runtime reference to the project public key.	(unset)
LANGFUSE_SECRET_KEY_REF	Runtime reference to the project secret key.	(unset)
LANGFUSE_TLS_PROFILE_REF	Runtime reference to a reusable TLS profile.	(unset)
LANGFUSE_CA_BUNDLE_REF	Runtime reference to a PEM trust store.	(unset)
LANGFUSE_CLIENT_CERT_REF	Runtime reference to an mTLS client certificate.	(unset)
LANGFUSE_CLIENT_KEY_REF	Runtime reference to the matching mTLS private key.	(unset)
LANGFUSE_CLIENT_KEY_PASSWORD_REF	Optional runtime reference for an encrypted client key.	(unset)
LANGFUSE_PERSISTENCE_HMAC_KEY_REF	Dedicated identity-HMAC key reference for graph persistence.	(unset)

Server Configuration

Variable	Description	Default
HOST	The hostname/address the server binds to.	127.0.0.1
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]	Connector-focused MCP server (agent-utilities[mcp] — FastMCP/FastAPI + epistemic-graph[full])	You only run the MCP server (smallest install / image)
langfuse-agent[agent]	Agent runtime (agent-utilities[agent-runtime,logfire] — model orchestration + epistemic-graph[full])	You run the integrated agent
langfuse-agent[all]	Everything (mcp + agent + logfire)	Development / both surfaces

# Connector-focused MCP server (includes the shared graph engine)
uv pip install "langfuse-agent[mcp]"

# Agent runtime (adds model orchestration to the shared 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 image targets

One multi-stage docker/Dockerfile builds two published profiles and one exact, offline release profile selected by --target:

Build target	Contents	Entrypoint
mcp	langfuse-agent[mcp] — connector-focused, includes epistemic-graph[full]; no model-orchestration stack	langfuse-mcp
mcp-local	exact hash-locked wheelhouse for the same MCP profile; release assembly only	langfuse-mcp
agent (default)	langfuse-agent[agent] — agent runtime, model orchestration + epistemic-graph[full]	langfuse-agent

docker build --target mcp   -t langfuse-agent:mcp docker/
docker build --target agent -t langfuse-agent:agent docker/

Promote and deploy only an operator-reviewed immutable image digest.

Knowledge-graph database (epistemic-graph)

Both [mcp] and [agent] carry the epistemic-graph engine through the required Agent Utilities core dependency (epistemic-graph[full], including its folded numeric kernel). The [mcp] extra keeps the server connector-focused; [agent] additionally enables model orchestration. Local deployments can use the bundled engine. For production or shared state, run epistemic-graph as a dedicated database service and configure the runtime to use it. Deployment recipes (single-node + Raft HA), connection configuration, and architecture diagrams are documented in the epistemic-graph deployment guide.

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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-utilities-deployment

Provision this package with the consolidated agent-utilities-deployment workflow. It selects an installed-package, editable-source, or immutable-container path; records only runtime secret and TLS-profile references in AgentConfig; and runs doctor, registration, policy, observability, and rollback gates. Ask your agent to "deploy langfuse-agent with agent-utilities-deployment".

Install mode	Command
Installed package	uv tool install "langfuse-agent[mcp]", then run langfuse-mcp
Editable source	uv pip install -e ".[agent]", then run langfuse-mcp
Immutable container	deploy registry.example.invalid/langfuse-agent@sha256:<digest> through the operator-selected orchestrator

The repository embeds no deployment profile, credential value, certificate path, or environment-specific endpoint. Supply those at runtime through AgentConfig and the configured secret provider.

Environment Variables

Package environment variables

Variable	Example	Description
HOST	127.0.0.1
PORT	8004
TRANSPORT	stdio	options: stdio, streamable-http, sse
ENABLE_OTEL	False
OTEL_EXPORTER_OTLP_ENDPOINT	https://otel.example.invalid
OTEL_EXPORTER_OTLP_PUBLIC_KEY_REF	—
OTEL_EXPORTER_OTLP_SECRET_KEY_REF	—
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_BASE_URL takes precedence when set; otherwise LANGFUSE_HOST is used.
LANGFUSE_HOST	—
LANGFUSE_PUBLIC_KEY	secret-injected
LANGFUSE_SECRET_KEY	secret-injected
LANGFUSE_PUBLIC_KEY_REF	—	Secret references (resolved at runtime) — alternatives to the plaintext keys above:
LANGFUSE_SECRET_KEY_REF	—
LANGFUSE_TLS_PROFILE_REF	—
LANGFUSE_CA_BUNDLE_REF	—
LANGFUSE_CLIENT_CERT_REF	—
LANGFUSE_CLIENT_KEY_REF	—
LANGFUSE_CLIENT_KEY_PASSWORD_REF	—
LANGFUSE_PERSISTENCE_HMAC_KEY_REF	—	Required only when LANGFUSE_KG_AUTO_INGEST=True.
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	intent	MCP_TOOL_MODE selects intent (default), condensed, verbose, or both surfaces.
LANGFUSE_OBSERVABILITYTOOL	True	These names match the authoritative "Toggle Env Var" column in the README MCP tools table (condensed action-routed surface).
LANGFUSE_DATASETSTOOL	True
LANGFUSE_PROMPTS_MODELSTOOL	True
LANGFUSE_MANAGEMENTTOOL	True
LANGFUSE_KGTOOL	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 child auth: oidc-client-credentials | basic | none
OIDC_CLIENT_ID	—	OIDC client id (service-account auth)
OIDC_CLIENT_SECRET_REF	secret://identity/oidc-client-secret	Runtime secret reference for the OIDC service account
MCP_BASIC_AUTH_USERNAME	—	HTTP Basic username (MCP_CLIENT_AUTH=basic)
MCP_BASIC_AUTH_PASSWORD_REF	secret://identity/mcp-basic-password	Runtime secret reference for HTTP Basic auth (MCP_CLIENT_AUTH=basic)
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

33 package + 15 inherited variable(s). Auto-generated from .env.example + the shared agent-utilities set — do not edit.

Governed capability contract

This package ships a compact canonical skill surface with specialist procedures kept as referenced workflows. The current MCP tools, skill metadata, connector_manifest.yml, ontology, mappings, shapes, fixtures, migrations, tool-schema fingerprints, and certification metadata form one versioned capability contract. Validate them together; do not rely on stale tool names or historical per-task skill wrappers.

Runtime endpoints, credentials, certificate trust, tenant identity, retention, and observability policy are deployment inputs and are never packaged values. See Configuration, trust, and privacy before enabling a network transport, connector ingestion, GraphOS delegation, or trace export.