quartermaster-nodes 0.6.3

Library of composable node types for building AI agent graphs

quartermaster-nodes

Composable node types for building AI agent graphs.

Features

• 40 built-in node types across 6 categories: LLM, control flow, data, user interaction, memory, and utility
• Framework-agnostic via the NodeContext protocol -- integrates with any runtime
• Stateless class-based design -- all nodes use @classmethod with no instance state
• Chain-of-Responsibility pattern for composable LLM processing pipelines
• NodeCatalog with auto-discovery, version tracking, and catalog generation (formerly NodeRegistry — old name still works as an alias)
• Configurable flow behavior per node: traversal strategy, thought type, message type, error handling

Installation

pip install quartermaster-nodes

Dependencies: jinja2>=3.1, quartermaster-graph (enums), quartermaster-providers (LLM config)

Quick Start

Register and Discover Nodes

from quartermaster_nodes import NodeCatalog
from quartermaster_nodes.nodes import InstructionNodeV1, StartNodeV1, EndNodeV1, Decision1

# Manual registration
catalog = NodeCatalog()
catalog.register(StartNodeV1)
catalog.register(InstructionNodeV1)
catalog.register(Decision1)
catalog.register(EndNodeV1)

# Or auto-discover all built-in nodes
catalog = NodeCatalog()
count = catalog.discover("quartermaster_nodes.nodes")
print(f"Discovered {count} nodes")

# Look up a node by name
node_cls = catalog.get("InstructionNode")
print(node_cls.info().description)

# Generate a JSON catalog of all nodes
all_nodes = catalog.catalog_json()

Not the runtime registry. NodeCatalog is a design-time catalog of node class definitions. If you're wiring up FlowRunner, you want a runtime executor registry — use quartermaster_engine.SimpleNodeRegistry for that. NodeRegistry (the old name, still aliased here) collides with quartermaster_engine.nodes.NodeRegistry (a Protocol); passing the wrong one to FlowRunner now raises a clear TypeError instead of AttributeError.

Creating a Custom Node

from quartermaster_nodes import AbstractAssistantNode, AssistantInfo, FlowNodeConf
from quartermaster_nodes.enums import (
    AvailableTraversingIn,
    AvailableTraversingOut,
    AvailableThoughtTypes,
    AvailableMessageTypes,
)

class HttpRequestNode(AbstractAssistantNode):
    """Fetch data from an HTTP endpoint."""

    @classmethod
    def info(cls) -> AssistantInfo:
        info = AssistantInfo()
        info.version = cls.version()
        info.description = "Make an HTTP request and return the response"
        info.metadata = {"url": "", "method": "GET"}
        return info

    @classmethod
    def name(cls) -> str:
        return "HttpRequest"

    @classmethod
    def version(cls) -> str:
        return "1.0"

    @classmethod
    def flow_config(cls) -> FlowNodeConf:
        return FlowNodeConf(
            traverse_in=AvailableTraversingIn.AwaitFirst,
            traverse_out=AvailableTraversingOut.SpawnAll,
            thought_type=AvailableThoughtTypes.NewThought1,
            message_type=AvailableMessageTypes.Tool,
        )

    @classmethod
    def think(cls, ctx) -> None:
        import urllib.request
        url = cls.get_metadata_key_value(ctx, "url", "")
        method = cls.get_metadata_key_value(ctx, "method", "GET")

        req = urllib.request.Request(url, method=method)
        with urllib.request.urlopen(req) as response:
            body = response.read().decode()

        ctx.handle.append_text(body)

Implementing the NodeContext Protocol

Nodes are framework-agnostic. Implement the NodeContext protocol to integrate with your runtime:

from quartermaster_nodes.protocols import NodeContext

class MyRuntimeContext:
    """Adapts your runtime to the NodeContext protocol."""

    def __init__(self, node_config, thought):
        self._config = node_config
        self._thought = thought

    @property
    def node_metadata(self) -> dict:
        return self._config.metadata

    @property
    def flow_node_id(self):
        return self._config.id

    @property
    def thought_id(self):
        return self._thought.id if self._thought else None

    @property
    def thought(self):
        return self._thought

    @property
    def handle(self):
        return self._thought  # Must implement ThoughtHandle protocol

    @property
    def assistant_node(self):
        return self._config

    @property
    def chat_id(self):
        return None

# Execute a node
ctx = MyRuntimeContext(node_config, thought)
InstructionNodeV1.think(ctx)

Node Categories

LLM Nodes (9)

Node	Description
InstructionNodeV1	Generate response from system instructions and conversation history
InstructionImageVision1	LLM with image/vision input support
InstructionParameters1	LLM with structured parameter extraction
InstructionProgram1	LLM with tool/function execution
InstructionProgramParameters1	LLM with tools and structured output
Decision1	LLM picks a branch path

| AgentNodeV1 | Autonomous agent with agentic loop and tool orchestration | | Summarize1 | LLM-powered summarization | | Merge1 | Combine parallel branches via LLM |

Control Flow Nodes (6)

Node	Description
StartNodeV1	Flow entry point, initializes memory
EndNodeV1	Flow termination
BreakNode1	Message collection boundary
IfNode	Binary conditional branching via expression evaluation
SwitchNode1	Multi-way branching
SubAssistant1	Invoke a sub-flow

Data Nodes (9)

Node	Description
StaticNode1	Output static text content
StaticMerge1	Merge with static content
StaticDecision1	Rule-based decision (no LLM)
StaticProgramParameters1	Static tool parameters
VarNode	Variable assignment via expression
TextNode	Jinja2 template rendering
TextToVariableNode	Extract text into a variable
CodeNode	Custom code definition
ProgramRunner1	Execute a registered tool

User Interaction Nodes (4)

Node	Description
UserNode1	Wait for user message input
UserDecisionV1	Present choices to the user
UserFormV1	Collect structured input via form
UserProgramFormV1	User selects and configures a tool

Memory Nodes (5)

Node	Description
FlowMemoryNode	Flow-scoped persistent memory
ReadMemoryNode	Read from persistent memory
WriteMemoryNode	Write to persistent memory
UpdateMemoryNode	Update existing memory entries
UserMemoryNode	User-scoped persistent memory

Utility Nodes (6)

Node	Description
BlankNode	No-op placeholder
CommentNode	Documentation / annotation node
ViewMetadataNode	Debug: inspect thought metadata
UseEnvironmentNode	Activate an execution environment
UnselectEnvironmentNode	Deactivate current environment
UseFileNode	Attach a file to the context

API Reference

AbstractAssistantNode

Base class for all nodes. All methods are @classmethod.

Method	Description
info() -> AssistantInfo	Node metadata (description, default config)
name() -> str	Display name
version() -> str	Version string
flow_config() -> FlowNodeConf	Traversal, thought type, message type, error handling
think(ctx: NodeContext) -> None	Core execution logic
get_metadata_key_value(ctx, key, default)	Read from node metadata
store_metadata_key_value(ctx, key, value)	Write to node metadata
deprecated() -> bool	Whether this node is deprecated (default: False)

AbstractLLMAssistantNode

Extends AbstractAssistantNode with LLM configuration.

Method	Description
llm_config(ctx) -> LLMConfig	Build LLM config from node metadata
context_manager_config(ctx, llm_config) -> ContextManagerConfig	Build context management config

Configurable metadata keys: llm_model, llm_provider, llm_temperature, llm_max_input_tokens, llm_max_output_tokens, llm_max_messages, llm_stream, llm_vision, llm_system_instruction, llm_thinking_level.

FlowNodeConf

Defines a node's flow behavior constraints.

Field	Type	Description
traverse_in	AvailableTraversingIn	AwaitFirst or AwaitAll
traverse_out	AvailableTraversingOut	SpawnAll, SpawnNone, SpawnStart, SpawnPickedNode
thought_type	AvailableThoughtTypes	How thoughts are created/displayed
message_type	AvailableMessageTypes	Message role (Automatic, User, Assistant, etc.)
error_handling_strategy	AvailableErrorHandlingStrategies	Stop, Continue, or Retry

NodeCatalog (formerly NodeRegistry)

Method	Description
register(node_class)	Register a node class
get(name, version=None)	Look up by name and optional version
has(name, version=None) -> bool	Check if a node is registered
list_nodes() -> list[dict]	List all nodes with metadata
catalog_json() -> list[dict]	JSON-serializable catalog
discover(package) -> int	Auto-discover nodes from a package
count -> int	Number of registered nodes
get_executor(...)	Guard — raises TypeError pointing you to quartermaster_engine.SimpleNodeRegistry. This method exists only to give a helpful error if you accidentally pass a NodeCatalog to FlowRunner.

NodeRegistry is kept as a backward-compatible alias for NodeCatalog.

Chain-of-Responsibility

LLM nodes use a composable handler chain for processing:

from quartermaster_nodes.chain import Chain
from quartermaster_nodes.chain.handlers import (
    ValidateMemoryID,
    PrepareMessages,
    ContextManager,
    TransformToProvider,
    GenerateStreamResponse,
    ProcessStreamResponse,
)

chain = (
    Chain()
    .add_handler(ValidateMemoryID())
    .add_handler(PrepareMessages(client, config))
    .add_handler(ContextManager(client, config, ctx_config))
    .add_handler(TransformToProvider(transformer))
    .add_handler(GenerateStreamResponse(client, config))
    .add_handler(ProcessStreamResponse())
)

result = chain.run({"memory_id": thought_id, "flow_node_id": node_id, "ctx": ctx})

Integration with Sibling Packages

With quartermaster-graph (graph schema)

Enums are re-exported from quartermaster-graph for consistency:

from quartermaster_nodes.enums import AvailableNodeTypes  # Alias for quartermaster_graph.enums.NodeType
from quartermaster_graph.enums import NodeType             # Canonical source

With quartermaster-engine (execution runtime)

quartermaster-nodes and quartermaster-engine operate on different axes:

• NodeCatalog (this package) — design-time catalog of node class definitions for discovery, introspection, and versioning.
• SimpleNodeRegistry (engine) — runtime registry of node executors that FlowRunner dispatches to.

For runtime execution with FlowRunner, register executors directly with the engine registry:

from quartermaster_engine import FlowRunner
from quartermaster_engine.nodes import SimpleNodeRegistry

registry = SimpleNodeRegistry()
registry.register("Instruction1", my_instruction_executor)
runner = FlowRunner(graph=graph, node_registry=registry)

The design-time catalog is still useful — to inspect available node classes before wiring executors:

from quartermaster_nodes import NodeCatalog

catalog = NodeCatalog()
catalog.discover("quartermaster_nodes.nodes")

node_cls = catalog.get("InstructionNode", "1.0")
print(node_cls.info().description)
node_cls.think(execution_context)  # direct invocation (no engine involved)

Contributing

See CONTRIBUTING.md for guidelines.

License

Apache License 2.0 -- see LICENSE for details.