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3.14
main.py
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import json
import sys
from wf_core import RuntimeContext, Workflow, execute_workflow
workflow = Workflow.model_validate(
{
"name": "drive_summary_demo",
"input_schema": {
"type": "object",
"properties": {
"folder_id": {"type": "string"},
"should_email": {"type": "boolean"},
},
"required": ["folder_id", "should_email"],
},
"state_schema": {
"fields": {
"folder_id": {"type": "string"},
"should_email": {"type": "boolean"},
"documents": {"type": "array", "merge_strategy": "replace"},
"summary": {"type": "string", "merge_strategy": "replace"},
"email_status": {"type": "string", "merge_strategy": "replace"},
}
},
"output_schema": {
"type": "object",
"properties": {
"summary": {"type": "string"},
"email_status": {"type": "string"},
},
"required": ["summary", "email_status"],
},
"node_defs": [
{
"name": "drive_list_files",
"input_schema": {
"type": "object",
"properties": {"folder_id": {"type": "string"}},
"required": ["folder_id"],
},
"output_schema": {
"type": "object",
"properties": {"documents": {"type": "array"}},
"required": ["documents"],
},
"outcomes": ["ok"],
},
{
"name": "summarize_documents",
"input_schema": {
"type": "object",
"properties": {"documents": {"type": "array"}},
"required": ["documents"],
},
"output_schema": {
"type": "object",
"properties": {"summary": {"type": "string"}},
"required": ["summary"],
},
"outcomes": ["ok"],
},
{
"name": "send_email",
"input_schema": {
"type": "object",
"properties": {"summary": {"type": "string"}},
"required": ["summary"],
},
"output_schema": {
"type": "object",
"properties": {"email_status": {"type": "string"}},
"required": ["email_status"],
},
"outcomes": ["sent"],
},
{
"name": "mark_email_skipped",
"input_schema": {
"type": "object",
"properties": {},
},
"output_schema": {
"type": "object",
"properties": {"email_status": {"type": "string"}},
"required": ["email_status"],
},
"outcomes": ["ok"],
},
],
"start": "list_files",
"nodes": [
{
"id": "list_files",
"type": "node",
"node": "drive_list_files",
"desc": "List files from a Google Drive folder",
"in_map": {"input.folder_id": "folder_id"},
"out_map": {"documents": "state.documents"},
},
{
"id": "summarize",
"type": "node",
"node": "summarize_documents",
"desc": "Summarize all retrieved documents",
"in_map": {"state.documents": "documents"},
"out_map": {"summary": "state.summary"},
},
{
"id": "should_email",
"type": "condition",
"check": {
"op": "eq",
"left": {"path": "state.should_email"},
"right": {"value": True},
},
},
{
"id": "send_email",
"type": "node",
"node": "send_email",
"desc": "Send the summary by email",
"in_map": {"state.summary": "summary"},
"out_map": {"email_status": "state.email_status"},
},
{
"id": "skip_email",
"type": "node",
"node": "mark_email_skipped",
"desc": "Record that email delivery was skipped",
"out_map": {"email_status": "state.email_status"},
},
],
"edges": [
{"from": "list_files", "outcome": "ok", "to": "summarize"},
{"from": "summarize", "outcome": "ok", "to": "should_email"},
{"from": "should_email", "outcome": "true", "to": "send_email"},
{"from": "should_email", "outcome": "false", "to": "skip_email"},
{"from": "send_email", "outcome": "sent", "to": "__end__"},
{"from": "skip_email", "outcome": "ok", "to": "__end__"},
],
}
)
report = workflow.validate_structure()
report.raise_for_errors()
print("Workflow is structurally valid.", file=sys.stderr)
def drive_list_files(
payload: dict[str, object], ctx: RuntimeContext
) -> dict[str, object]:
folder_id = payload["folder_id"]
return {
"outcome": "ok",
"output": {
"documents": [
f"{folder_id}/meeting-notes.md",
f"{folder_id}/weekly-report.md",
]
},
}
def summarize_documents(
payload: dict[str, object], ctx: RuntimeContext
) -> dict[str, object]:
documents = payload["documents"]
return {
"outcome": "ok",
"output": {"summary": f"Summarized {len(documents)} documents from Drive."},
}
def send_email(payload: dict[str, object], ctx: RuntimeContext) -> dict[str, object]:
return {
"outcome": "sent",
"output": {"email_status": f"sent: {payload['summary']}"},
}
def mark_email_skipped(
payload: dict[str, object], ctx: RuntimeContext
) -> dict[str, object]:
return {
"outcome": "ok",
"output": {"email_status": "skipped"},
}
registry = {
"drive_list_files": drive_list_files,
"summarize_documents": summarize_documents,
"send_email": send_email,
"mark_email_skipped": mark_email_skipped,
}
result = execute_workflow(
workflow,
{"folder_id": "demo-folder", "should_email": False},
registry,
)
print(json.dumps(result, indent=2))
pyproject.toml
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[project]
name = "lda-w"
version = "0.0.1"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.14"
dependencies = [
"mcp[cli,rich]>=1.27.0",
"pydantic>=2.13.3",
]
readme.md
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# lda.chat - Running Design Notes
## What is this
`lda.chat` is an AI agent that turns natural language requests into executable
workspace workflows. The LLM plans. The executor runs. Tools do the real work.
Target users are knowledge workers doing repetitive digital tasks like
collecting, summarizing, reporting, and monitoring without writing code.
---
## Thesis scope vs product
**Thesis:** prove the architecture end to end.
- a few working MCP tools
- LLM plans a workflow from natural language
- executor validates and runs it
- results come back in structured form
**Real product:** everything around it.
- broad MCP bridge layer
- per-tool auth handling
- tool registry / marketplace
- scheduling, monitoring, multi-user support
The thesis should demonstrate the core split cleanly, not solve the whole platform.
---
## Core architecture
```text
User request
-> LLM planner
-> Workflow JSON
-> Deterministic executor
-> MCP tools / subworkflows
-> Shared workflow state
```
Principles:
- the LLM plans, but does not execute
- the executor executes, but does not think
- nodes do work
- edges are dumb routing
---
## Workflow model
A workflow is a directed graph with explicit schemas and explicit control flow.
```text
Workflow
.name
.input_schema
.state_schema
.output_schema
.node_defs[]
.start
.nodes[]
.edges[]
```
### Schema boundaries
- `input_schema` validates the run input once
- input is copied into mutable workflow state at run start
- `state_schema` defines typed workflow memory and merge behavior
- `output_schema` is derived from final state at the end
- internal state may contain values that never appear in output
This separation matters because a workflow can later be reused as a node.
### Reusable node definition
Nodes are defined independently from any one graph.
```text
NodeDef
.name
.input_schema
.output_schema
.outcomes[] // ex: ["ok"], ["approved", "rejected"]
.retry?
.timeout_seconds?
```
Even a simple node still declares its outcomes explicitly.
### Node use inside a graph
Graph use-sites bind reusable nodes into graph state.
```text
NodeUse
.id
.type: "node"
.node // reference to NodeDef
.desc?
.in_map // graph path -> node input field
.out_map // node output field -> graph state path
.retry? // optional override
.timeout_seconds? // optional override
```
The node does not get ambient access to all workflow state. It receives:
- mapped input
- runtime context
This keeps nodes reusable instead of graph-coupled.
### Control-flow nodes
#### **ConditionNode**
```text
.id
.type: "condition"
.check
```
Conditions are structured JSON, not freeform code strings.
#### **ForeachNode**
```text
.id
.type: "foreach"
.over
.as
.mode // serial | parallel
.on_item_error // fail | collect | skip
```
#### **JoinNode**
```text
.id
.type: "join"
```
### Edges
Routing is outcome-based.
```text
Edge
.from
.outcome
.to
```
- source nodes declare which outcomes are possible
- edges map those outcomes to next nodes
- terminal routing can go to builtin `__end__`
Reaching an undeclared or unwired outcome is runtime failure.
---
## Node result contract
At the engine boundary, every node result should look uniform:
```text
NodeResult
.outcome
.output
.meta?
.extra?
```
Two channels stay separate:
- `outcome` is for control flow
- `output` is for typed business data
This lets richer nodes branch directly, while dumb MCP tools can still be adapted.
If a tool only returns data, a later `ConditionNode` can decide routing.
Extra undeclared returned keys:
- do not affect typed execution
- are preserved in trace
- are not visible to later nodes unless a future raw-trace access feature is added
---
## Condition model
The condition language should stay small and structured.
Supported v1 operators:
- `and`
- `or`
- `not`
- `eq`
- `ne`
- `gt`
- `lt`
- `exists`
Operands are paths or literals, such as:
- `state.summary_count`
- `context.retry_count`
- string, number, bool, null literals
This is less work than inventing a mini-language and generally easier for LLMs to emit reliably.
---
## State and merge semantics
State fields carry metadata, especially merge behavior.
```text
StateField
.type
.merge_strategy // replace | append | merge_object
.trace?
```
Rules:
- merge behavior belongs to the state field, not the node
- declared fields get typed merge semantics
- undeclared keys behave like plain map entries
- concurrent writes to the same untyped key should fail
- concurrent writes to the same typed key should fail unless that field's merge strategy allows it
This keeps parallel and foreach behavior predictable.
---
## Execution semantics
Executor steps:
1. Validate workflow structure
2. Validate workflow input
3. Copy input into state
4. Start at the declared `start` node
5. Resolve node input snapshot from `in_map`
6. Execute node with mapped input plus runtime context
7. Validate typed node output
8. Commit mapped output into state
9. Route by returned outcome
10. Stop when routing reaches `__end__`
11. Derive and validate final output from state
Commit rules:
- no partial commit before success
- validation failure is node failure
- execution failure is not a normal business outcome
Runtime failure means things like:
- timeout
- transport error
- MCP failure
- invalid node output
Expected business branching should use declared outcomes instead.
### Retry
Default retry behavior should be strict.
- retry can be declared on the reusable node definition
- graph use-site can override it
- retries should reuse the original resolved input snapshot by default
- failfast is the default engine behavior
### Foreach
`foreach` should be explicit, not hidden.
- execution mode is graph-declared: `serial` or `parallel`
- item failure policy is graph-declared: `fail`, `collect`, or `skip`
- accumulation follows target state field merge strategy
### Join
Join is barrier-only.
- it does not do custom merge logic
- it waits only for branches actually activated in this run
- it should not wait for statically possible but never-taken branches
---
## Validation
Before execution, the validator should be able to check:
- workflow has a valid `start`
- node def names are unique
- node ids are unique
- every referenced node definition exists
- every edge source exists
- every edge destination exists or is `__end__`
- every edge outcome is declared by its source node
- each source + outcome pair is wired at most once
- every reachable outcome is wired
- every `in_map` destination exists in node input schema
- every `out_map` source exists in node output schema
- every condition path references valid `input`, `state`, or `context`
- every `foreach.over` path references valid `input` or `state`
At runtime, the executor should still check:
- returned outcome is declared
- typed output matches schema
- merge conflicts
- required mapped outputs are present
- final output validates
---
## Registry and composition
A saved workflow can be registered as a reusable node.
That gives:
- workflow as function
- graph composition
- one consistent node contract whether the implementation is MCP-backed or workflow-backed
---
## Thesis demo direction
A good thesis demo is narrow but real, for example:
- use an external MCP such as Google Drive
- have the planner generate a workflow
- validate it against schema
- execute the workflow once end to end
- return a structured result or report
That proves the planner/executor/tool split without pretending to solve general automation.
---
## Open questions
- how strict should output derivation be: direct projection vs explicit final output mapping?
- what is the best trace model for raw extra node output?
- how should isolated pure-Python execution fit later: node type, tool backend, or separate functional API?
- how should retry policy interact with side-effecting tools that are not safely idempotent?
---
## Stack
- Backend: Python
- Validation and schema: Pydantic
- Tool execution: MCP
- Storage: SQLite or PostgreSQL
- Future isolated compute: restricted Python sandbox, possibly remote
scratchpad.md
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# Graph as struct? workflow as struct
One graph is one input output
i decide to make edge dumb as shit. Nodes do all the work
struct needs to define in, out and ALL states. any output from a node is either writing to state or writing to a key of it.
metadata for states to hide in tracing
whatever language works
## plan
dumbass stupid plan fuhh ahh plan
```text
.in -> pydantic.create_model
.out -> pydantic.create_model
.state // langgraph partial update style
.steps[] // either nodes only or all everything (all everything seems cooler I LIKE ENUMS but it could be stupih)
.steps[]:
.type IN node, edge
node:
.tool // ours, so do ts well
.args
.desc?
.bind? // either update .state or .state[.bind]
edge:
.from
.to
```
### next node
GENUINELY HOW do i convey next node?
do i pass next node in arg? args.next.{this cond, that cond}
function do need to know about where its run. Who called. So it can decide where it goes next. how? idk maybe a context struct that has the full next list from .edges[]?
or can it just reference any next node? but how? who tells it what node?
function 100% can select what node it wants to go next if supported. Since this is MY stuff, a function I make can do that.
### registry
past graph can turn into a function. so a graph is lowk same thing as a fn.
### input
how in the FUCK do i convey input!
this is how:
since input is state, this function kinda doesnt care about args. Args comes from state.
a function cares about certain keys (args) looking this shape, the least i can do is a remap on node {"specialized graph state key": "generic fn input key"}
same as output {"generic fn answer key": "specialized graph state key"}
### this is lowk constricted
But the counterpart is freeform code...
### more problems
batch? foreach?
control flow.
maybe special nodes that does control flow. Like ensure: something that only after all nodes connected to it ran does it run the nodes it connects to?
if bool = exist, maybe truth table node... if .state.key1 and .state.key2
## Alternative plan! god damn
that was looking like langgraphs Graph API, we could have Functional API being a taddddd simpler: functions imported and run in a sealed ahh box.
functional api is actually peam. its like async.
## inspo
my little use of langgraph
my nonexistent knowledge of openai fn schema
claude
---
## Spec direction v1
This is the cleaner version of the idea above. Raw notes stay above. This part is the engine contract.
### Core shape
One workflow has:
- `input` schema
- `state` schema
- `output` schema
- `nodes`
- `edges`
- explicit `start`
- builtin end node like `__end__`
Run flow is:
1. validate workflow structure
2. validate workflow input
3. copy input into state
4. start at declared start node
5. execute nodes and route by outcomes
6. derive final output from state
### Schema boundaries
- `input` is validated once at run start
- `state` is the mutable shared memory of the workflow
- `output` is derived from final state, not written directly during execution
- internal state can contain data that never appears in output
- state may be partially open, but only declared state fields get typed merge semantics
### Node definition
A reusable node/tool definition has:
- input schema
- output schema
- declared outcomes
Even if the only outcome is `ok`, it is still declared.
Graph use-sites bind a node explicitly:
- map graph state into node input
- map node typed output back into graph state
- wire each declared outcome to an edge or to `__end__`
Missing required mappings or missing reachable outcomes mean bad graph schema.
### Node result contract
Node result has two separate channels:
- control flow: `outcome`
- data: typed `output`
Routing is driven by `outcome`.
State update is driven by mapped `output`.
Extra undeclared returned keys are:
- ignored by typed execution semantics
- preserved in trace
- unavailable to later nodes unless there is some explicit future raw-trace access mechanism
This keeps dumb MCP tools usable without weakening typed graph execution.
### Failure model
- runtime failure is not a normal business outcome
- crashes, timeout, transport failure, validation failure are executor-level failure
- explicit error outcomes are allowed for expected business cases
- infallible nodes may only declare `ok`
- node execution either returns one final valid result or fails
- no partial state commit before success
### Commit and validation
- node input is resolved from state before execution
- node sees mapped input plus runtime context, not ambient full state
- node output is type-validated before commit
- if output validation fails, the node fails
- commit happens once, after successful validation
### Routing semantics
- edges are dumb
- nodes declare what outcomes can happen
- graph declares where each outcome can go
- reaching an undeclared outcome is runtime failure
- reaching an unwired outcome is runtime failure
- branching from node business outcomes is preferred over fake follow-up condition nodes
Condition nodes still exist for cases where:
- a dumb tool only returns data
- routing depends on shared state
- routing depends on runtime context
### Condition nodes
Condition logic should be structured JSON, not freeform string code.
Small v1 operator set:
- `and`
- `or`
- `not`
- `eq`
- `ne`
- `gt`
- `lt`
- `exists`
Operands should be paths and literals, such as:
- `state.foo`
- `context.retry_count`
- string/number/bool/null literals
### Runtime context
Nodes and condition evaluation can read runtime context.
Useful context fields:
- current node id
- retry count
- prior outcome
- loop item metadata
- activated incoming edge
### Merge semantics
Merge behavior belongs to declared state fields, not to nodes.
Minimal v1 merge strategies:
- `replace`
- `append`
- `merge_object`
Rules:
- declared state fields get typed merge behavior
- undeclared keys are plain map data
- undeclared keys default to simple replace only
- concurrent writes to the same untyped key should fail
- concurrent writes to the same typed key should fail unless the field merge strategy allows it
If fancy accumulation is needed, declare the field in state schema.
### Foreach semantics
`foreach` is explicit in the graph.
It should support:
- `mode = serial | parallel`
- `on_item_error = fail | collect | skip`
Each iteration gets:
- local item scope
- shared read access through mapped input
- explicit output mapping back into state
Iteration accumulation follows the target state field merge strategy.
### Join semantics
Join is barrier only.
- join does not do custom merge logic
- state should already hold the branch writes
- join waits only for activated incoming branches in this run
- join should not wait for statically possible but never-taken branches
### Retry semantics
Default retry behavior should be strict and deterministic.
- retry policy can exist on node definition and be overridden at graph use-site
- same for timeout later
- retries should reuse the original resolved input snapshot by default
- failfast is the default engine behavior
- more permissive collection behavior should be opt-in at graph level, especially in `foreach`
### Reuse and composition
- workflow input/output separation makes a workflow reusable as a node
- graph output should look like normal node typed output
- a saved workflow can be registered and called like any other node/tool
### Concrete schema shape
The next model pass should aim for a shape like this.
Workflow:
```text
Workflow
.name
.input_schema
.state_schema
.output_schema
.start
.nodes[]
.edges[]
```
Reusable node definition:
```text
NodeDef
.name
.input_schema
.output_schema
.outcomes[] // ex: ["ok"], ["approved", "rejected"], ...
.retry? // default policy on the reusable definition
.timeout? // later
```
Graph use-site node:
```text
NodeUse
.id
.type: "node"
.node // reference to NodeDef / registry entry
.desc?
.in_map // graph state/input path -> node input field
.out_map // node output field -> graph state path
.retry? // optional override
.timeout? // optional override
```
Condition node:
```text
ConditionNode
.id
.type: "condition"
.check // structured JSON condition tree
```
Foreach node:
```text
ForeachNode
.id
.type: "foreach"
.over // state path to iterable
.as // loop variable alias
.mode // serial | parallel
.on_item_error // fail | collect | skip
```
Join node:
```text
JoinNode
.id
.type: "join"
```
Edge:
```text
Edge
.from
.outcome // declared outcome name from source node
.to
```
Builtin end:
```text
__end__
```
This keeps routing uniform:
- source node emits an outcome
- edge matches that outcome
- destination node runs next
### Node result envelope
At the engine boundary, every node should look like it returns one uniform envelope, even if a dumb MCP tool needs an adapter underneath.
```text
NodeResult
.outcome // one of the declared outcomes
.output // typed business payload
.meta? // reserved executor metadata later
.extra? // raw undeclared fields preserved in trace only
```
Practical notes:
- `outcome` is reserved for control flow
- `output` is reserved for typed data
- extra raw keys should not silently mix with typed output
- if an adapter wraps a dumb tool, it can translate raw tool return -> `NodeResult`
### Mapping rules
`in_map` and `out_map` are explicit and first-class.
`in_map`:
- source is graph `input`, `state`, or future `context` path
- destination is a declared node input field
- missing required source path is a graph/runtime error
- optional destination fields may be omitted
`out_map`:
- source is a declared node output field
- destination is a graph state path
- mapping to undeclared state keys is allowed, but loses typed merge behavior
- mapping a required output field that does not exist at runtime is node failure
No broad automatch for v1.
### State schema metadata
Declared state fields need metadata beyond plain type.
At minimum each declared field may carry:
```text
StateField
.type
.merge_strategy // replace | append | merge_object
.trace? // whether to include in trace by default
```
Notes:
- merge behavior is owned by state field metadata
- trace visibility should also live here later
- undeclared keys behave like plain map entries with no special metadata
### Output derivation
Workflow output should be built explicitly from state, not by magic.
Two valid directions:
- `output_schema` is a projection from named state paths
- or `output_schema` is validated against a final explicit output mapping
Either way:
- output is not mutated during execution
- output validation happens at the end
- missing required output fields mean workflow failure
### Validation checklist
Before execution, validator should be able to check:
- workflow has a valid `start`
- all node ids are unique
- every referenced node definition exists
- every edge source exists
- every edge destination exists or is `__end__`
- every edge outcome is declared by its source node type
- every reachable declared outcome is wired
- every `in_map` destination exists in node input schema
- every `out_map` source exists in node output schema
- every condition node uses valid operators and operand shapes
At runtime, executor should still check:
- node returned a declared outcome
- node returned typed output matching its schema
- merge conflicts
- missing required mapped outputs
- final output validates
### Non-goals for v1
- recursive subgraphs
- broad implicit automatch for node input/output bindings
- ambient full-state access inside nodes
- freeform code conditions
- hidden magical merge behavior
uv.lock
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wf_core/__init__.py
+33
View File
@@ -0,0 +1,33 @@
from .model import (
ConditionNode,
Edge,
ForeachNode,
JoinNode,
NodeDef,
NodeResult,
NodeUse,
StateField,
StateSchema,
Workflow,
)
from .runtime import RuntimeContext, WorkflowExecutionError, execute_workflow
from .validate import ValidationIssue, ValidationReport, validate_workflow
__all__ = [
"ConditionNode",
"Edge",
"ForeachNode",
"JoinNode",
"NodeDef",
"NodeResult",
"NodeUse",
"StateField",
"StateSchema",
"RuntimeContext",
"ValidationIssue",
"ValidationReport",
"Workflow",
"WorkflowExecutionError",
"execute_workflow",
"validate_workflow",
]
wf_core/model.py
+146
View File
@@ -0,0 +1,146 @@
from __future__ import annotations
from typing import Annotated, Any, Literal
from pydantic import BaseModel, ConfigDict, Field
class SchemaRef(BaseModel):
model_config = ConfigDict(extra="allow")
title: str | None = None
type: str | None = None
properties: dict[str, Any] = Field(default_factory=dict)
required: list[str] = Field(default_factory=list)
class StateField(BaseModel):
type: str
merge_strategy: Literal["replace", "append", "merge_object"] = "replace"
trace: bool = True
class StateSchema(BaseModel):
model_config = ConfigDict(extra="allow")
fields: dict[str, StateField] = Field(default_factory=dict)
class NodeDef(BaseModel):
name: str
input_schema: SchemaRef
output_schema: SchemaRef
outcomes: list[str] = Field(min_length=1)
retry: int | None = Field(None, ge=0)
timeout_seconds: int | None = Field(None, gt=0)
class NodeUse(BaseModel):
id: str
type: Literal["node"]
node: str
desc: str | None = None
in_map: dict[str, str] = Field(default_factory=dict)
out_map: dict[str, str] = Field(default_factory=dict)
retry: int | None = Field(None, ge=0)
timeout_seconds: int | None = Field(None, gt=0)
class PathOperand(BaseModel):
path: str
class LiteralOperand(BaseModel):
value: Any
Operand = Annotated[PathOperand | LiteralOperand, Field(discriminator=None)]
class ExistsCondition(BaseModel):
op: Literal["exists"]
path: str
class NotCondition(BaseModel):
op: Literal["not"]
arg: "Condition"
class VariadicCondition(BaseModel):
op: Literal["and", "or"]
args: list["Condition"] = Field(min_length=1)
class BinaryCondition(BaseModel):
op: Literal["eq", "ne", "gt", "lt"]
left: PathOperand | LiteralOperand
right: PathOperand | LiteralOperand
Condition = Annotated[
ExistsCondition | NotCondition | VariadicCondition | BinaryCondition,
Field(discriminator="op"),
]
class ConditionNode(BaseModel):
id: str
type: Literal["condition"]
check: Condition
class ForeachNode(BaseModel):
id: str
type: Literal["foreach"]
over: str
as_: str = Field(alias="as")
mode: Literal["serial", "parallel"] = "serial"
on_item_error: Literal["fail", "collect", "skip"] = "fail"
class JoinNode(BaseModel):
id: str
type: Literal["join"]
Step = Annotated[
NodeUse | ConditionNode | ForeachNode | JoinNode,
Field(discriminator="type"),
]
class Edge(BaseModel):
from_: str = Field(alias="from")
outcome: str
to: str
class Workflow(BaseModel):
name: str
input_schema: SchemaRef
state_schema: StateSchema
output_schema: SchemaRef
node_defs: list[NodeDef] = Field(default_factory=list)
start: str
nodes: list[Step]
edges: list[Edge]
def validate_structure(self):
from .validate import validate_workflow
return validate_workflow(self)
class NodeResult(BaseModel):
model_config = ConfigDict(extra="allow")
outcome: str
output: dict[str, Any] = Field(default_factory=dict)
meta: dict[str, Any] = Field(default_factory=dict)
if __name__ == "__main__":
import json
print(json.dumps(Workflow.model_json_schema(), indent=2))
wf_core/runtime.py
+339
View File
@@ -0,0 +1,339 @@
from __future__ import annotations
from collections.abc import Callable
from dataclasses import dataclass
from typing import Any
from .model import (
BinaryCondition,
Condition,
ConditionNode,
ExistsCondition,
ForeachNode,
JoinNode,
LiteralOperand,
NodeDef,
NodeResult,
NodeUse,
NotCondition,
PathOperand,
VariadicCondition,
Workflow,
)
class WorkflowExecutionError(RuntimeError):
pass
@dataclass(slots=True)
class RuntimeContext:
current_node_id: str
retry_count: int = 0
prior_outcome: str | None = None
activated_incoming_edge: str | None = None
NodeHandler = Callable[[dict[str, Any], RuntimeContext], NodeResult | dict[str, Any]]
def execute_workflow(
workflow: Workflow,
workflow_input: dict[str, Any],
registry: dict[str, NodeHandler],
) -> dict[str, Any]:
report = workflow.validate_structure()
report.raise_for_errors()
_validate_payload_against_schema(workflow.input_schema, workflow_input, "workflow input")
node_defs = {node_def.name: node_def for node_def in workflow.node_defs}
nodes_by_id = {node.id: node for node in workflow.nodes}
edge_map = {(edge.from_, edge.outcome): edge.to for edge in workflow.edges}
state = dict(workflow_input)
current_node_id = workflow.start
prior_outcome: str | None = None
activated_incoming_edge: str | None = None
while current_node_id != "__end__":
step = nodes_by_id[current_node_id]
if isinstance(step, NodeUse):
node_def = node_defs[step.node]
outcome = _execute_node_use(
step,
node_def,
state,
workflow_input,
registry,
prior_outcome,
activated_incoming_edge,
workflow,
)
elif isinstance(step, ConditionNode):
predicate = _eval_condition(step.check, state, workflow_input, prior_outcome)
outcome = "true" if predicate else "false"
elif isinstance(step, JoinNode):
outcome = "done"
elif isinstance(step, ForeachNode):
raise WorkflowExecutionError("foreach execution is not implemented yet")
else:
raise WorkflowExecutionError(f"unsupported step type {step.type!r}")
next_node_id = edge_map.get((current_node_id, outcome))
if next_node_id is None:
raise WorkflowExecutionError(
f"no edge found for node {current_node_id!r} and outcome {outcome!r}"
)
prior_outcome = outcome
activated_incoming_edge = current_node_id
current_node_id = next_node_id
final_output = _project_output(workflow, state)
_validate_payload_against_schema(workflow.output_schema, final_output, "workflow output")
return {"state": state, "output": final_output}
def _execute_node_use(
node: NodeUse,
node_def: NodeDef,
state: dict[str, Any],
workflow_input: dict[str, Any],
registry: dict[str, NodeHandler],
prior_outcome: str | None,
activated_incoming_edge: str | None,
workflow: Workflow,
) -> str:
handler = registry.get(node.node)
if handler is None:
raise WorkflowExecutionError(f"no handler registered for node def {node.node!r}")
resolved_input = {
destination_field: _resolve_path(source_path, state, workflow_input, {})
for source_path, destination_field in node.in_map.items()
}
_validate_payload_against_schema(
node_def.input_schema, resolved_input, f"node input for {node.id}"
)
context = RuntimeContext(
current_node_id=node.id,
prior_outcome=prior_outcome,
activated_incoming_edge=activated_incoming_edge,
)
raw_result = handler(resolved_input, context)
result = _coerce_node_result(raw_result)
if result.outcome not in node_def.outcomes:
raise WorkflowExecutionError(
f"node {node.id!r} returned undeclared outcome {result.outcome!r}"
)
_validate_payload_against_schema(
node_def.output_schema, result.output, f"node output for {node.id}"
)
_apply_output_map(workflow, node, result.output, state)
return result.outcome
def _coerce_node_result(raw_result: NodeResult | dict[str, Any]) -> NodeResult:
if isinstance(raw_result, NodeResult):
return raw_result
if "outcome" in raw_result and "output" in raw_result:
return NodeResult.model_validate(raw_result)
return NodeResult(outcome="ok", output=raw_result)
def _apply_output_map(
workflow: Workflow,
node: NodeUse,
node_output: dict[str, Any],
state: dict[str, Any],
) -> None:
for source_field, destination_path in node.out_map.items():
if source_field not in node_output:
raise WorkflowExecutionError(
f"node {node.id!r} did not return required mapped field {source_field!r}"
)
value = node_output[source_field]
_write_state_value(workflow, state, destination_path, value)
def _write_state_value(
workflow: Workflow, state: dict[str, Any], destination_path: str, value: Any
) -> None:
root, field_name, *rest = destination_path.split(".")
if root != "state":
raise WorkflowExecutionError(
f"executor only supports writes into state.*, got {destination_path!r}"
)
declared_field = workflow.state_schema.fields.get(field_name)
merge_strategy = declared_field.merge_strategy if declared_field else "replace"
key_path = [field_name, *rest]
if merge_strategy == "replace":
_set_nested_value(state, key_path, value)
return
current_value = _get_nested_value(state, key_path)
if merge_strategy == "append":
if current_value is None:
_set_nested_value(state, key_path, [value] if not isinstance(value, list) else value)
return
if not isinstance(current_value, list):
raise WorkflowExecutionError(
f"cannot append into non-list state path {destination_path!r}"
)
if isinstance(value, list):
current_value.extend(value)
else:
current_value.append(value)
return
if merge_strategy == "merge_object":
if current_value is None:
if not isinstance(value, dict):
raise WorkflowExecutionError(
f"cannot merge non-object value into {destination_path!r}"
)
_set_nested_value(state, key_path, dict(value))
return
if not isinstance(current_value, dict) or not isinstance(value, dict):
raise WorkflowExecutionError(
f"merge_object requires dict values at {destination_path!r}"
)
current_value.update(value)
return
raise WorkflowExecutionError(f"unknown merge strategy {merge_strategy!r}")
def _project_output(workflow: Workflow, state: dict[str, Any]) -> dict[str, Any]:
return {
key: state[key]
for key in workflow.output_schema.properties
if key in state
}
def _validate_payload_against_schema(schema: Any, payload: Any, label: str) -> None:
if schema.type == "object":
if not isinstance(payload, dict):
raise WorkflowExecutionError(f"{label} must be an object")
for required_key in schema.required:
if required_key not in payload:
raise WorkflowExecutionError(
f"{label} is missing required field {required_key!r}"
)
def _eval_condition(
condition: Condition,
state: dict[str, Any],
workflow_input: dict[str, Any],
context_data: str | None,
) -> bool:
if isinstance(condition, ExistsCondition):
return _path_exists(condition.path, state, workflow_input, context_data)
if isinstance(condition, NotCondition):
return not _eval_condition(condition.arg, state, workflow_input, context_data)
if isinstance(condition, VariadicCondition):
values = [
_eval_condition(arg, state, workflow_input, context_data)
for arg in condition.args
]
return all(values) if condition.op == "and" else any(values)
if isinstance(condition, BinaryCondition):
left = _resolve_operand(condition.left, state, workflow_input, context_data)
right = _resolve_operand(condition.right, state, workflow_input, context_data)
if condition.op == "eq":
return left == right
if condition.op == "ne":
return left != right
if condition.op == "gt":
return left > right
if condition.op == "lt":
return left < right
raise WorkflowExecutionError(f"unsupported condition operator {condition.op!r}")
def _resolve_operand(
operand: PathOperand | LiteralOperand,
state: dict[str, Any],
workflow_input: dict[str, Any],
context_data: str | None,
) -> Any:
if isinstance(operand, LiteralOperand):
return operand.value
return _resolve_path(
operand.path,
state,
workflow_input,
{"prior_outcome": context_data},
)
def _path_exists(
path: str,
state: dict[str, Any],
workflow_input: dict[str, Any],
context_data: str | None,
) -> bool:
try:
_resolve_path(path, state, workflow_input, {"prior_outcome": context_data})
except WorkflowExecutionError:
return False
return True
def _resolve_path(
path: str,
state: dict[str, Any],
workflow_input: dict[str, Any],
context: dict[str, Any],
) -> Any:
root, *parts = path.split(".")
if not parts:
raise WorkflowExecutionError(f"invalid path {path!r}")
if root == "state":
source = state
elif root == "input":
source = workflow_input
elif root == "context":
source = context
else:
raise WorkflowExecutionError(f"unknown path root {root!r}")
current: Any = source
for part in parts:
if not isinstance(current, dict) or part not in current:
raise WorkflowExecutionError(f"path {path!r} could not be resolved")
current = current[part]
return current
def _get_nested_value(state: dict[str, Any], path_parts: list[str]) -> Any:
current: Any = state
for part in path_parts:
if not isinstance(current, dict) or part not in current:
return None
current = current[part]
return current
def _set_nested_value(state: dict[str, Any], path_parts: list[str], value: Any) -> None:
current = state
for part in path_parts[:-1]:
next_value = current.get(part)
if next_value is None:
next_value = {}
current[part] = next_value
if not isinstance(next_value, dict):
raise WorkflowExecutionError(
f"cannot descend into non-object state field {part!r}"
)
current = next_value
current[path_parts[-1]] = value
wf_core/validate.py
+333
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from __future__ import annotations
from dataclasses import dataclass, field
from .model import (
BinaryCondition,
Condition,
ConditionNode,
ExistsCondition,
ForeachNode,
LiteralOperand,
NodeDef,
NodeUse,
NotCondition,
PathOperand,
Step,
VariadicCondition,
Workflow,
)
@dataclass(slots=True)
class ValidationIssue:
path: str
message: str
@dataclass(slots=True)
class ValidationReport:
errors: list[ValidationIssue] = field(default_factory=list)
@property
def ok(self) -> bool:
return not self.errors
def add(self, path: str, message: str) -> None:
self.errors.append(ValidationIssue(path=path, message=message))
def raise_for_errors(self) -> None:
if not self.errors:
return
rendered = "\n".join(
f"- {issue.path}: {issue.message}" for issue in self.errors
)
raise ValueError(f"Workflow validation failed:\n{rendered}")
def validate_workflow(workflow: Workflow) -> ValidationReport:
report = ValidationReport()
node_defs: dict[str, NodeDef] = {}
for index, node_def in enumerate(workflow.node_defs):
if node_def.name in node_defs:
report.add(
f"node_defs[{index}].name", f"duplicate node def name {node_def.name!r}"
)
else:
node_defs[node_def.name] = node_def
nodes_by_id = {}
state_root_fields = set(workflow.state_schema.fields)
input_root_fields = set(workflow.input_schema.properties)
for index, node in enumerate(workflow.nodes):
if node.id in nodes_by_id:
report.add(f"nodes[{index}].id", f"duplicate node id {node.id!r}")
else:
nodes_by_id[node.id] = node
if isinstance(node, NodeUse):
_validate_node_use(node, index, node_defs, workflow, report)
elif isinstance(node, ConditionNode):
_validate_condition_node(
node, index, report, state_root_fields, input_root_fields
)
elif isinstance(node, ForeachNode):
_validate_foreach_node(
node, index, report, state_root_fields, input_root_fields
)
if workflow.start not in nodes_by_id:
report.add("start", f"unknown start node {workflow.start!r}")
outgoing: dict[str, set[str]] = {}
edge_keys: set[tuple[str, str]] = set()
for index, edge in enumerate(workflow.edges):
edge_key = (edge.from_, edge.outcome)
if edge_key in edge_keys:
report.add(
f"edges[{index}]",
f"duplicate edge for source {edge.from_!r} and outcome {edge.outcome!r}",
)
else:
edge_keys.add(edge_key)
source = nodes_by_id.get(edge.from_)
if source is None:
report.add(f"edges[{index}].from", f"unknown source node {edge.from_!r}")
else:
allowed = _declared_outcomes_for_step(source, node_defs)
if edge.outcome not in allowed:
report.add(
f"edges[{index}].outcome",
f"outcome {edge.outcome!r} is not declared by node {edge.from_!r}",
)
outgoing.setdefault(edge.from_, set()).add(edge.outcome)
if edge.to != "__end__":
if edge.to not in nodes_by_id:
report.add(
f"edges[{index}].to", f"unknown destination node {edge.to!r}"
)
reachable = _reachable_node_ids(workflow.start, workflow.edges, nodes_by_id)
for node_id in reachable:
node = nodes_by_id[node_id]
declared_outcomes = _declared_outcomes_for_step(node, node_defs)
wired = outgoing.get(node_id, set())
missing = declared_outcomes - wired
if missing:
report.add(
f"nodes[{node_id}]",
f"reachable node is missing edges for outcomes {sorted(missing)!r}",
)
return report
def _validate_node_use(
node: NodeUse,
index: int,
node_defs: dict[str, NodeDef],
workflow: Workflow,
report: ValidationReport,
) -> None:
node_def = node_defs.get(node.node)
if node_def is None:
report.add(f"nodes[{index}].node", f"unknown node def {node.node!r}")
return
input_fields = set(node_def.input_schema.properties)
output_fields = set(node_def.output_schema.properties)
state_fields = set(workflow.state_schema.fields)
input_root_fields = set(workflow.input_schema.properties)
for source_path, destination_field in node.in_map.items():
if destination_field not in input_fields:
report.add(
f"nodes[{index}].in_map[{source_path!r}]",
f"destination field {destination_field!r} is not declared in node input schema",
)
if not _is_valid_source_path(source_path, state_fields, input_root_fields):
report.add(
f"nodes[{index}].in_map[{source_path!r}]",
"source path must start with input. or state. and reference a declared root field",
)
for source_field, destination_path in node.out_map.items():
if source_field not in output_fields:
report.add(
f"nodes[{index}].out_map[{source_field!r}]",
f"source field {source_field!r} is not declared in node output schema",
)
if not _is_valid_destination_path(destination_path):
report.add(
f"nodes[{index}].out_map[{source_field!r}]",
"destination path must start with state.",
)
def _validate_condition_node(
node: ConditionNode,
index: int,
report: ValidationReport,
state_root_fields: set[str],
input_root_fields: set[str],
) -> None:
if isinstance(node.check, VariadicCondition) and not node.check.args:
report.add(f"nodes[{index}].check.args", "condition args must not be empty")
_validate_condition_expr(
node.check,
f"nodes[{index}].check",
report,
state_root_fields,
input_root_fields,
)
def _validate_foreach_node(
node: ForeachNode,
index: int,
report: ValidationReport,
state_root_fields: set[str],
input_root_fields: set[str],
) -> None:
if not _is_valid_source_path(node.over, state_root_fields, input_root_fields):
report.add(
f"nodes[{index}].over",
"foreach source path must start with input. or state. and reference a declared root field",
)
def _validate_condition_expr(
condition: Condition,
path: str,
report: ValidationReport,
state_root_fields: set[str],
input_root_fields: set[str],
) -> None:
if isinstance(condition, ExistsCondition):
if not _is_valid_condition_path(
condition.path, state_root_fields, input_root_fields
):
report.add(path, f"invalid condition path {condition.path!r}")
return
if isinstance(condition, NotCondition):
_validate_condition_expr(
condition.arg,
f"{path}.arg",
report,
state_root_fields,
input_root_fields,
)
return
if isinstance(condition, VariadicCondition):
for index, arg in enumerate(condition.args):
_validate_condition_expr(
arg,
f"{path}.args[{index}]",
report,
state_root_fields,
input_root_fields,
)
return
if isinstance(condition, BinaryCondition):
_validate_operand(
condition.left,
f"{path}.left",
report,
state_root_fields,
input_root_fields,
)
_validate_operand(
condition.right,
f"{path}.right",
report,
state_root_fields,
input_root_fields,
)
def _validate_operand(
operand: PathOperand | LiteralOperand,
path: str,
report: ValidationReport,
state_root_fields: set[str],
input_root_fields: set[str],
) -> None:
if isinstance(operand, LiteralOperand):
return
if not _is_valid_condition_path(operand.path, state_root_fields, input_root_fields):
report.add(path, f"invalid operand path {operand.path!r}")
def _declared_outcomes_for_step(step: Step, node_defs: dict[str, NodeDef]) -> set[str]:
if isinstance(step, NodeUse):
node_def = node_defs.get(step.node)
return set(node_def.outcomes) if node_def else set()
if step.type == "condition":
return {"true", "false"}
if step.type == "foreach":
return {"done"}
if step.type == "join":
return {"done"}
return set()
def _reachable_node_ids(start: str, edges, nodes_by_id: dict[str, Step]) -> set[str]:
if start not in nodes_by_id:
return set()
adjacency: dict[str, list[str]] = {}
for edge in edges:
if edge.to == "__end__":
continue
adjacency.setdefault(edge.from_, []).append(edge.to)
seen = set()
stack = [start]
while stack:
node_id = stack.pop()
if node_id in seen:
continue
seen.add(node_id)
stack.extend(adjacency.get(node_id, []))
return seen
def _is_valid_source_path(
path: str, state_root_fields: set[str], input_root_fields: set[str]
) -> bool:
if "." not in path:
return False
root, field_name, *_ = path.split(".")
if root == "state":
return field_name in state_root_fields
if root == "input":
return field_name in input_root_fields
return False
def _is_valid_condition_path(
path: str, state_root_fields: set[str], input_root_fields: set[str]
) -> bool:
if "." not in path:
return False
root, field_name, *_ = path.split(".")
if root == "context":
return True
if root == "state":
return field_name in state_root_fields
if root == "input":
return field_name in input_root_fields
return False
def _is_valid_destination_path(path: str) -> bool:
return path.startswith("state.") and len(path.split(".")) >= 2
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