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IntegrationsHarness Integrations

Use the Pi Adapter

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Use the NVIDIA NeMo Fabric nvidia.fabric.pi adapter to run tasks with the Pi SDK in a persistent Node.js process.

Invocation Usage

Each invocation reads only new entries from Pi’s raw session ledger, including retained failed attempts, compaction and branch summaries, nested tool usage, and auxiliary usage records. Automatic cache warming is disabled so background refresh calls cannot escape the invocation boundary. Session history and previous invocations are not counted again. Input totals include cache reads and cache writes; output totals already include reasoning tokens. Available breakdowns are returned through nemo_fabric.detailed_token_usage.v1.

Missing or inconsistent usage leaves the complete counters unknown. Pi 1.0.3 initializes unreported provider usage to zero, so an all-zero record also remains unknown. A failed or retried summary can omit attempt usage from the ledger; complete counters remain unknown in that case. Nonzero records can retain real zero input, output, or cache dimensions. Configured-rate SDK cost estimates are not published as provider-reported cost_usd.

Install the Adapter

Pi requires Node.js 22.19.0 or newer.

Install for Consumers

Install the npm adapter in the project that owns the NeMo Fabric configuration, then install the supported Pi SDK package set:

npm install nemo-fabric-adapters-pi
npm install --save-exact @earendil-works/pi-ai@1.0.3 @earendil-works/pi-coding-agent@1.0.3 @earendil-works/pi-mcp@1.0.3 @earendil-works/pi-codemode@1.0.3

The Pi packages are optional peers: installing nemo-fabric-adapters-pi alone does not install the harness. Starting the adapter without compatible Pi packages returns pi_harness_unavailable.

The supported SDK packages are pinned together at 1.0.3. The coding-agent package otherwise permits newer pi-codemode versions with incompatible exports; installing all four packages keeps fresh installs on the tested combination.

Install for Source Development

For focused Pi development in the NeMo Fabric source tree, install the Pi adapter workspace and its pinned harness from the repository root:

just install-typescript-pi

To install and build all maintained TypeScript workspaces instead, run:

just build-typescript

The full build installs the required dependencies.

Install NeMo Relay

Relay-enabled Pi runs require a NeMo Relay CLI in the >=0.9.0,<0.12.0 range. Install the command-line tool:

pip install "nemo-relay-cli-bin>=0.9.0,<0.12.0"

The adapter finds nemo-relay on PATH by default. To select a specific executable, set FABRIC_NEMO_RELAY_COMMAND to its absolute path:

FABRIC_NEMO_RELAY_COMMAND="/absolute/path/to/nemo-relay" uv run python your_app.py

The adapter uses the selected executable to start the Relay gateway. With Relay 0.11, the Pi extension also uses that executable to forward hook events. Relay 0.9 and 0.10 extensions send hook events directly over HTTP. When the runtime stops, the adapter restores the extension’s previous command setting.

Use the Pi extension from the same Relay release as the command-line tool. Configure the extension path as shown in the next section.

Configure the Adapter

Select the Pi harness integration and point NeMo Fabric discovery at the adapter descriptor. The following example uses the descriptor from an installed npm package:

from nemo_fabric import DiscoveryConfig, FabricConfig, HarnessConfig
from nemo_fabric import MetadataConfig, ModelConfig, ToolsConfig
config = FabricConfig(
    metadata=MetadataConfig(name="pi-review"),
    discovery=DiscoveryConfig(
        local_paths=[
            "./node_modules/nemo-fabric-adapters-pi/pi.fabric-adapter.json"
        ]
    ),
    harness=HarnessConfig(adapter_id="nvidia.fabric.pi"),
    models={
        "default": ModelConfig(
            provider="nvidia",
            model="nvidia/nemotron-3-nano-omni-30b-a3b-reasoning",
            api_key_env="NVIDIA_API_KEY",
            base_url="https://integrate.api.nvidia.com/v1",
        )
    },
    tools=ToolsConfig(enabled=["read"], blocked=[]),
)

For a source build, set discovery.local_paths to adapters/typescript/pi/pi.fabric-adapter.json instead.

The adapter supports one selected model, an optional base URL override, replace system instructions, tool policy, explicit skill paths, and explicit local Pi extensions. Set models.<role>.api_key_env to the name of the environment variable that contains the provider credential.

The selected model can be either a model Pi already ships (addressed by its built-in provider/model) or a model served by an OpenAI-compatible gateway that Pi does not ship. For a Pi-known model, set only provider, model, and api_key_env; its catalog metadata is used as-is, and an optional base_url redirects its endpoint without changing any other property. A provider/model pair that does not exactly match a Pi built-in is treated as a new gateway-served model (there is no aliasing onto a built-in by a different id).

For a gateway model, set base_url to the gateway endpoint and declare the model’s wire protocol with the model’s api field. This value is the protocol your gateway speaks, not the model’s vendor: a Bedrock- or Anthropic-trained model served over an OpenAI-compatible /v1/chat/completions endpoint uses openai-completions; use anthropic-messages only when the endpoint speaks Anthropic’s native Messages API. api is required for a model Pi does not already know — the adapter does not guess it — and must be one of:

  • openai-completions
  • openai-responses
  • azure-openai-responses
  • openai-codex-responses
  • anthropic-messages
  • bedrock-converse-stream
  • google-generative-ai
  • google-vertex
  • mistral-conversations
  • pi-messages

The following Pi-specific model fields are set directly on the model alongside api:

  • context_window — the model’s context window, in tokens.
  • max_tokens — the maximum response length, in tokens.
  • cost — an object with input, output, cacheRead, and cacheWrite per-million-token rates.
  • reasoning — whether the model supports reasoning/thinking.
  • input — the accepted input modalities, a list of "text" and/or "image".
  • name — a display name for the model.

Any field you set overrides. For a Pi-known model, any field you omit keeps that model’s existing catalog value; the adapter supplies no defaults of its own. A gateway model Pi does not ship has no existing entry to fall back on, so a complete definition is required: api, base_url, context_window, max_tokens, input, and cost. Adapter startup rejects missing required metadata before invoking the model. The reasoning and name fields remain optional. The example below registers an NVIDIA Nemotron model from the OpenAI-compatible NVIDIA Build API, which Pi does not ship:

from nemo_fabric import ModelConfig
# api is the gateway's wire protocol: NVIDIA Build serves this model over an
# OpenAI-compatible endpoint.
ModelConfig(
    provider="nvidia-build",
    model="nvidia/nemotron-3-super-120b-a12b",
    api_key_env="NVIDIA_API_KEY",
    base_url="https://integrate.api.nvidia.com/v1",
    api="openai-completions",
    context_window=256_000,
    max_tokens=32_768,
    reasoning=True,
    input=["text"],
    cost={"input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0},
    name="Nemotron 3 Super 120B (NVIDIA Build)",
)

The example uses zero cost rates, so Pi calculates zero token cost. Set the rates for your endpoint when you need cost estimates.

Configure Skills and Extensions

Add normalized skill paths with the Python SDK. NeMo Fabric resolves these paths from the base_dir passed to plan(), doctor(), or run():

config.add_skill_path("./skills/code-review")

Pi extensions are adapter-specific trusted code. Configure extension files relative to environment.workspace:

config.harness.settings["extensions"] = ["extensions/review.ts"]

The adapter loads only the skill and extension paths in the configuration. It does not load ambient Pi resources from the user profile or workspace.

Configure MCP Servers

Add normalized MCP servers through the Python SDK:

config.add_mcp_server(
    "repo",
    transport="stdio",
    url="repo-mcp",
    args=["--root", "."],
    allowed_tools=["search", "read_file"],
    exposure="harness_native",
)
config.add_mcp_server(
    "docs",
    transport="streamable-http",
    url="https://mcp.example.com/mcp",
    custom_headers={"Authorization": "Bearer ${MCP_TOKEN}"},
    exposure="harness_native",
)

The adapter maps stdio and streamable HTTP servers to Pi’s native MCP extension. Stdio servers can define args and env, but not HTTP headers. Streamable HTTP servers can define custom_headers, but not process arguments or environment variables. Remote endpoints require HTTPS, except for loopback development endpoints.

Header values can reference ${NAME}. Resolution checks the NeMo Fabric runtime environment first and then the adapter process environment. The adapter maps allowed_tools and blocked_tools to Pi’s per-server tool exposure. Normalized MCP authentication and MCP extensions are not supported. Only servers supplied by NeMo Fabric are loaded; ambient Pi MCP configuration remains disabled.

Configure NeMo Relay

Enable Relay with the standard NeMo Fabric configuration and provide the Relay Pi extension as an adapter setting:

config.runtime.artifacts = "./artifacts/pi"
config.enable_relay(output_dir="./artifacts/relay")
config.harness.settings["relay_extension_path"] = (
    "/path/to/NeMo-Relay/crates/cli/assets/pi-extension"
)

Relay requires runtime.artifacts so NeMo Fabric can create the runtime-owned configuration passed to the adapter. The extension path can be absolute or relative to environment.workspace. It can identify a JavaScript or TypeScript file or a Pi extension package directory. Unlike user-configured Pi extensions, the Relay extension does not need to remain inside environment.workspace when an absolute path is used.

When the runtime starts, the adapter validates the Relay CLI version, writes an explicit plugins.toml, starts a loopback gateway, and loads the extension into the isolated Pi session. The result includes relay_runtime and relay_artifacts in output. The gateway can produce ATOF, ATIF, OpenTelemetry, and OpenInference output from the Relay observability configuration.

The adapter supports one Relay-enabled Pi runtime per adapter process because the Relay Pi extension receives its gateway and upstream configuration through process environment variables. NeMo Fabric starts each runtime in a separate adapter process, so concurrent NeMo Fabric runtimes remain isolated. Direct embedders must likewise place concurrent Relay-enabled Pi runtimes in separate processes.

The matching Relay Pi extension emits turn_end and closes the per-turn scope, but its local ATIF trajectory remains attached to Pi’s session-level agent scope. Relay therefore finalizes that trajectory only after the Pi session closes. It is not included in the invocation’s relay_artifacts. The adapter does not wait for local ATIF during invocation. It collects per-invocation artifacts such as ATOF and returns the result. After runtime shutdown, retrieve the finalized file directly from the ATIF output directory. For the configuration above and the default filename template, it is written to ./artifacts/relay/<runtime_id>/trajectory-<session_id>.atif.json. Invocation results do not prevent subsequent turns.

Session, turn, and tool telemetry does not depend on model redirection. Model telemetry is available only when Relay supports the selected model API and the gateway upstream matches the model endpoint. Relay records a skipped redirect as a model_redirect mark with the reason in configured ATOF artifacts. Pi emits its startup redirect marks before NeMo Fabric registers an invocation, so they are not included in the per-invocation records from invoke_stream().

Install the matching collector for the embedded streaming path:

pip install "nemo-fabric[streaming]"

Start the runtime with streaming enabled to consume live model-turn ATOF records for successful Relay redirects in one Pi invocation:

from nemo_fabric import Fabric
async with await Fabric().start_runtime(config, streaming=True) as runtime:
    stream = runtime.invoke_stream(input="Review the latest patch")
    async for record in stream:
        print(record)
    result = await stream.result()

The terminal RunResult remains separate from the ATOF records. Fully consume each stream, or call await stream.aclose() if iteration stops early, before starting another invocation. The same runtime can then alternate invoke_stream() and invoke() calls. The embedded collector serializes both methods behind one Pi invocation lease. Streaming capture begins at the first Pi turn_start and closes at agent_settled. If Relay output is interrupted or late, the collector waits for a bounded interval, then admits the next native invocation and uses Pi’s cumulative turn count to discard ambiguous delayed records until a higher turn_start arrives. This can thin the ATOF stream but does not block later invocations. Increase completion_wait_timeout from its one-second default when Relay delivery can take longer. Use the default embedded collector for Pi streaming. The Pi extension does not attach NeMo Fabric request IDs, so start_runtime(..., streaming=True, launch_collector=False) cannot correlate its records through an externally managed collector.

This Relay-backed path runs the adapter’s ordinary invoke operation. It is independent of native OpenAI streaming, so the adapter descriptor’s capabilities.streaming value remains false.

Configure a NeMo Fabric Tool Definition

Pi accepts trusted local JavaScript and TypeScript tool factories. The normalized definition uses kind: "module"; ref is relative to the NeMo Fabric workspace and may include a named export after #.

Create tools/review-context.js in the configured workspace:

export function createTool({ name, settings }) {
  return {
    name,
    label: "Review Context",
    description: "Return configured context for a code review.",
    parameters: { type: "object", properties: {} },
    async execute() {
      return {
        content: [{ type: "text", text: JSON.stringify(settings) }],
        details: {},
      };
    },
  };
}

Register and enable the tool:

config.add_tool_definition(
    "review_context",
    kind="module",
    ref="tools/review-context.js#createTool",
    settings={"format": "brief"},
)
config.tools.enabled = ["read", "review_context"]

The factory receives { name, settings, workspace } and returns a Pi ToolDefinition with the same name. Tool modules and explicit Pi extensions run as trusted code in the adapter process. The adapter rejects paths outside the workspace and duplicate names across built-ins, NeMo Fabric definitions, and extensions.

Understand the Runtime Lifecycle

Each NeMo Fabric runtime owns one in-memory Pi session in a persistent Node.js adapter process. Ordered invocations reuse that session and its conversation history until the runtime stops. Start a new runtime to change the model, skills, extensions, custom tools, or workspace.

Current Limitations

The current adapter does not expose native OpenAI streaming, caller-driven cancellation, or remote-service execution. Enabling a third-party extension can add extension-defined skills, prompts, or themes even though ambient Pi resources remain disabled. The Relay extension does not add those resources.

The Relay Pi hook route is available only on the loopback gateway and does not authenticate hook posts. Treat the loopback port as a local trust boundary.

Credential-free HTTP model gateways

For Relay-enabled runs, set harness.settings.insecure_http_model_hosts to the exact bare DNS hostnames or IP addresses of trusted inference gateways. This requires NeMo Relay CLI 0.11.x.

Remote HTTP model endpoints require openai-completions, openai-responses, or anthropic-messages. Other Pi model APIs, including mistral-conversations, require HTTPS or loopback HTTP because their requests bypass Relay’s gateway. Fabric rejects unsupported remote HTTP APIs before session startup, including hosts listed in insecure_http_model_hosts.

config.harness.settings["insecure_http_model_hosts"] = ["inference.internal", "10.0.0.10"]

Entries have no scheme, port, path, wildcard, brackets, or IPv6 zone identifier. DNS matching ignores case and a trailing dot. Each listed host may use custom ports and paths. NeMo Fabric writes explicit [] to revoke inherited trust and preserves omission when the setting is absent. System Relay configuration has higher priority and can enforce an empty list.

Relay strips authorization, API-key, cookie, custom credential headers, and middleware-added credentials before forwarding remote HTTP model requests, including model credentials supplied to Pi. Relay rejects conflicting upstream authorization configuration. Prompts and responses remain unencrypted. HTTPS is the default; this opt-in does not authorize MCP or observability endpoints, caller-selected destinations, or remote HTTP WebSockets.