gnougo-flow-core 0.4.6

Python 3.10+ implementation of GnOuGo.Flow.Core

gnougo-flow-core — YAML Workflow DSL Engine (Python)

Python 3.10+ implementation of GnOuGo.Flow.Core, the declarative YAML workflow DSL engine. Write YAML workflows that orchestrate LLMs, MCP servers, templates, loops, human input, and dynamic code generation — all from a single file.

---

Package Status and Parity

The .NET library at src/GnOuGo.Flow.Core/ is the source of truth. This Python package mirrors its public surface as closely as Python idioms allow. See PORTING_TODO.md for the detailed parity log and remaining work items.

Area	Status
YAML DSL parser (version:)	Yes
Validation + compilation pipeline	Yes
Expression interpolation ${...} + built-in functions	Yes (AST-based JS-subset interpreter)
Mustache template.render engine	Yes
WFScript (functions: block)	Yes multi-statement (var/let/const, if/else, return)
Runtime engine + step registry	Yes
Step types: set, emit, sequence, parallel, loop.sequential, loop.parallel, switch, template.render, llm.call, mcp.list, mcp.call, human.input, workflow.call, workflow.plan, workflow.execute	Yes
MCP integrations (InMemoryMcpClientFactory, ConfiguredMcpClientFactory, cache helper)	Yes
LLMRequest.reasoning field	Yes
Model metadata catalog (pricing, token limits, capabilities, overrides)	Yes
workflow.plan defaults reasoning="high"	Yes
Workflow source telemetry (source_text / source_format)	Yes
JsonSchemaConverter (inputs/outputs to JSON Schema)	Yes
WorkflowCheckpointer + WorkflowEngine.resume_async	Yes
CLI: validate / inspect / run subcommands	Yes

---

Table of Contents

• Package Status and Parity
• Architecture
• Quick Start
• Document Structure
• Step Types Reference
  • template.render
  • llm.call
  • mcp.list
  • mcp.call
  • set
  • emit
  • human.input
  • sequence
  • parallel
  • loop.sequential
  • loop.parallel
  • switch
  • workflow.call
  • workflow.plan
  • workflow.execute
• Typed Inputs
• Typed Outputs
• Expressions ${...}
• WFScript - Custom JavaScript Functions
• Error Handling
• Model Metadata Catalog
• CLI
• Python Runtime Notes

---

Architecture

librairies/python/gnougo-flow-core/
  pyproject.toml                    # Python package metadata and dependencies
  src/gnougo_flow_core/             # Publishable Python library
    models.py                       # DSL model (Document, Workflow, Step, etc.)
    parsing.py                      # Parse YAML to model (PyYAML)
    expressions.py                  # Expression interpolation `${...}`
    _jsmini.py                      # In-tree JS-subset interpreter for expressions and WFScript
    templating.py                   # Minimal Mustache-compatible renderer
    scripting.py                    # WFScript helpers
    compilation.py                  # Document validation + compilation
    runtime.py                      # Execution engine + executor registry
    runtime_contracts.py            # Protocols for LLM, MCP, HITL, workflow fetching, telemetry
    checkpointing.py                # Workflow checkpoint contracts and in-memory implementation
    integrations/                   # MCP and LLM adapter helpers
    runtime_steps/                  # Executor re-export modules for step families
  tests/                            # Dedicated Python unit tests

The package is intentionally independent from the .NET assembly at runtime. It keeps the same DSL concepts and stable contracts so workflows can be shared across Python and .NET hosts.

---

Quick Start

Install the published Python package:

python -m pip install gnougo-flow-core

Or add it to a local uv project:

uv add gnougo-flow-core

For repository development, install the package with its development extras from this directory:

uv sync --extra dev

Create hello.yaml:

version: 1
name: hello-world
workflows:
  main:
    inputs:
      name: { type: string, required: true }
    steps:
      - id: greet
        type: template.render
        input:
          engine: mustache
          template: "Hello {{name}}! Welcome to GnOuGo.Flow."
          data: { name: "${data.inputs.name}" }
          mode: text
    outputs:
      greeting: "${data.steps.greet.text}"

Validate it:

gnougo-flow validate hello.yaml

Inspect it:

gnougo-flow inspect hello.yaml

Run it from the CLI:

gnougo-flow run hello.yaml -i name=World

Run it from Python:

import asyncio
from gnougo_flow_core.compilation import WorkflowCompiler
from gnougo_flow_core.parsing import WorkflowParser
from gnougo_flow_core.runtime import WorkflowEngine, apply_workflow_input_defaults
async def main() -> None:
    yaml_text = open("hello.yaml", encoding="utf-8").read()
    document = WorkflowParser.parse(yaml_text)
    compiled = WorkflowCompiler().compile(document)
    workflow = compiled.workflows[compiled.entrypoint]
    inputs = apply_workflow_input_defaults(workflow.source, {"name": "World"})
    result = await WorkflowEngine().execute_async(workflow, inputs)
    if not result.success:
        raise RuntimeError(result.error.message if result.error else "Workflow failed")
    print(result.outputs)
asyncio.run(main())

Runtime integrations such as LLM clients, MCP clients, human input providers, workflow fetchers, telemetry, and checkpointing are injected through Python protocols in gnougo_flow_core.runtime_contracts.

---

Document Structure

Every workflow file starts with:

version: 1                        # DSL version (required, always 1)
name: my-workflow             # Document name (optional)
functions: |                  # Global WFScript functions (optional)
  function myHelper(x) { return x * 2; }

workflows:
  main:                       # Entrypoint workflow (by convention)
    inputs:                   # Input parameters with types (optional)
      message: { type: string, required: true }
    steps:                    # Ordered list of steps (required)
      - id: step1
        type: template.render
        input: { ... }
    outputs:                  # Output expressions (optional)
      result: "${data.steps.step1.text}"

You can define multiple workflows in the same document and call them via workflow.call.

Step Common Fields

Every step supports:

- id: unique_step_id         # Required — unique within the workflow
  type: step_type             # Required — one of the step types below
  if: "${expression}"         # Optional — guard; step is skipped if false
  input: { ... }              # Step-specific input (supports ${...} at any depth)
  output: alias_name          # Optional — also expose output as data.<alias_name>
  retry:                      # Optional — automatic retry for retryable errors
    max: 3
    backoff_ms: 1000
    backoff_mult: 2.0
    jitter_ms: 100
  on_error:                   # Optional — error handler (see Error Handling)
    cases:
      - if: "${error.code == \"LLM_TIMEOUT\"}"
        action: continue
        set_output: "fallback value"
      - action: stop

Data Access

All expressions read from a shared data context:

Path	Content
data.inputs.*	Workflow input parameters
data.steps.<step_id>.*	Output of a previously executed step
data.env.*	Environment variables

---

Step Types Reference

template.render — Mustache Templating

Renders a Mustache template with data from the workflow context.

- id: greet
  type: template.render
  input:
    engine: mustache
    template: "Hello {{name}}, you have {{count}} items."
    data:
      name: "${data.inputs.name}"
      count: "${len(data.inputs.items)}"
    mode: text                # "text" (default) or "json"

Output: { text: "Hello World, you have 3 items." }

---

llm.call — Call a Language Model

Sends a prompt to an LLM and returns the response. Supports structured JSON output.

Basic call

- id: summarize
  type: llm.call
  input:
    model: gpt-4o-mini                              # Required
    prompt: "Summarize this: ${data.inputs.text}"    # Required
    system: "You are a concise summarizer."          # Optional
    provider: openai                                 # Optional (default: auto-routed)
    temperature: 0.7                                 # Optional override; omit by default
    max_tokens: 2048                                 # Optional
    reasoning: auto                                  # Optional — auto|minimal|low|medium|high|max
                                                     # Default: omitted (provider decides).
                                                     # Unsupported optional fields are removed by runtime metadata.

temperature, reasoning, structured_output, and tool-calling support are checked against the runtime model metadata catalog before the configured LLM client is called. For example, a request to o4-mini with temperature: 0.7 is automatically sent without temperature.

Output: { text: "...", usage: { prompt_tokens, completion_tokens, total_tokens }, meta: { model } }

Structured output (JSON mode)

- id: classify
  type: llm.call
  input:
    model: gpt-4o
    prompt: "Classify this ticket and return JSON: ${data.inputs.ticket}"
    structured_output:
      schema_inline:
        type: object
        properties:
          category: { type: string }
          priority: { type: string, enum: [low, medium, high, critical] }
          confidence: { type: number }
        required: [category, priority]
      strict: true

Output: { text: "...", json: { category: "bug", priority: "high", confidence: 0.92 }, usage: {...} }

Access: data.steps.classify.json.category, data.steps.classify.json.priority

---

mcp.list — Discover MCP Server Capabilities

Lists tools, resources, and/or prompts exposed by one or more MCP servers. Use a one-item array for a single server, or servers: ["*"] to discover all configured MCP servers.

- id: discover
  type: mcp.list
  input:
    servers: [github, docs]         # Required — configured MCP server names
    include: ["tools", "prompts"] # Optional — default: ["tools"]

- id: discover_all
  type: mcp.list
  input:
    servers: ["*"]
    include: ["tools"]

Output: { status, text, servers: [...], tools: [...], resources: [...], prompts: [...] }

Flattened tools, resources, and prompts entries each include a server field so downstream steps can keep the server affinity when multiple MCP servers are discovered at once.

---

mcp.call — Call MCP Tools or Prompts

Calls one or more capabilities on an MCP server. Three modes are available:

Direct tool call (preferred when tool names are known)

- id: weather
  type: mcp.call
  input:
    server: weather-server
    kind: tool
    method: get_weather
    request: { location: "Paris", units: "celsius" }
    timeout_ms: 30000

Output: { status: "ok", response: { temperature: 22, ... } }

Direct prompt call

- id: summarize_prompt
  type: mcp.call
  input:
    server: my-server
    kind: prompt
    method: summarize_document
    request: { text: "${data.inputs.document}" }

Output: { status: "ok", text: "...", messages: [...] }

LLM-assisted call (auto-selects the right tool)

Combine mcp.list → mcp.call with a prompt to let an LLM choose the best tool:

- id: discover
  type: mcp.list
  input:
    servers: [github]

- id: smart_call
  type: mcp.call
  input:
    server: github
    model: gpt-4o-mini
    temperature: 0.2
    prompt: "Find and call the right tool to list my repositories"
    tools: "${data.steps.discover.tools}"
    prompts: "${data.steps.discover.prompts}"
    structured_output:
      schema_inline:
        type: object
        properties:
          repos:
            type: array
            items:
              type: object
              properties:
                name: { type: string }
                url: { type: string }
              required: [name, url]
        required: [repos]
      strict: true

Output (LLM-assisted): { status: "ok", selection_mode: "llm", text: "...", tool_calls: [...], results: [...], json: {...} }

Output access patterns

Mode	Access
Single tool	data.steps.<id>.status, data.steps.<id>.response
Single prompt	data.steps.<id>.status, data.steps.<id>.text
Batch/auto	data.steps.<id>.results (array)
LLM-assisted	data.steps.<id>.text, data.steps.<id>.json

Important: The response object is tool-specific. Do not assume field names unless documented by the tool. Use json(data.steps.<id>.response) to serialize it.

---

set — Initialize or Modify Variables

Sets variables in the workflow data context using expressions.

- id: init_vars
  type: set
  input:
    total: 0
    prefix: "report_"
    full_name: "${data.inputs.first_name + ' ' + data.inputs.last_name}"
    items_count: "${len(data.inputs.items)}"

Output: { total: 0, prefix: "report_", full_name: "...", items_count: 5 }

---

emit — Send Progress Messages to the UI

Pushes real-time feedback to the user interface during long-running workflows.

- id: notify_progress
  type: emit
  input:
    message: "Processing item ${data.steps.loop.index} of ${data.steps.loop.count}..."
    level: progress           # "thinking" | "info" | "progress" | "response"

Level	Visual
thinking	Subtle animated (default)
info	Blue informational
progress	Green progress indicator
response	Highlighted, monospace — appears as assistant content

---

human.input — Pause and Wait for User Input

Pauses the workflow and prompts the user for input. The workflow resumes when the user submits a response.

Quick choices

- id: approve
  type: human.input
  input:
    prompt: "The agent wants to call API X. Approve?"
    context: "${json(data.steps.plan)}"
    choices:
      - approve
      - reject
      - modify
    timeout_ms: 300000        # 5 minutes (default)

Structured form fields

- id: user_config
  type: human.input
  input:
    prompt: "Please configure the following settings:"
    fields:
      - name: api_key
        type: string
        required: true
        description: Your API key
      - name: region
        type: select
        options: [us-east, eu-west, ap-south]
        default: us-east
      - name: max_retries
        type: string
        required: false
        default: "3"

Output: The user's response as a JSON object (e.g., { "response": "approve" } or { "api_key": "...", "region": "eu-west", "max_retries": "3" }).

Timeout: If the user doesn't respond within timeout_ms, the step fails with error code HUMAN_INPUT_TIMEOUT.

---

sequence — Run Steps Sequentially

Groups sub-steps that execute one after another.

- id: pipeline
  type: sequence
  steps:
    - id: step_a
      type: llm.call
      input: { model: gpt-4o-mini, prompt: "Step A" }
    - id: step_b
      type: llm.call
      input: { model: gpt-4o-mini, prompt: "Continue from: ${data.steps.step_a.text}" }

---

parallel — Run Branches in Parallel

Executes independent branches concurrently.

- id: gather
  type: parallel
  branches:
    - steps:
        - id: fetch_weather
          type: mcp.call
          input: { server: weather, kind: tool, method: get_weather, request: { location: "Paris" } }
    - steps:
        - id: fetch_news
          type: mcp.call
          input: { server: news, kind: tool, method: get_headlines, request: { topic: "tech" } }

---

loop.sequential — Iterate Sequentially

Loops with while condition or fixed times count.

# Fixed count
- id: retry_loop
  type: loop.sequential
  input:
    times: 5
  steps:
    - id: attempt
      type: llm.call
      input: { model: gpt-4o-mini, prompt: "Attempt ${data.steps.retry_loop.index}" }

# While condition
- id: poll
  type: loop.sequential
  input:
    while: "${data.steps.check.status != 'ready'}"
    max_iterations: 20
  steps:
    - id: check
      type: mcp.call
      input: { server: my-server, kind: tool, method: check_status, request: {} }

Loop context: data.steps.<loop_id>.index (current iteration, 0-based), data.steps.<loop_id>.count (total completed).

---

loop.parallel — Iterate in Parallel

Loops over an array of items, executing iterations concurrently.

- id: process_all
  type: loop.parallel
  input:
    items: "${data.inputs.urls}"
    max_concurrency: 5
  steps:
    - id: fetch
      type: mcp.call
      input:
        server: http-client
        kind: tool
        method: fetch_url
        request: { url: "${data.steps.process_all.item}" }

Loop context: data.steps.<loop_id>.item (current item), data.steps.<loop_id>.index, data.steps.<loop_id>.results (collected results).

---

switch — Conditional Branching

Two forms: expression-based and when-based.

Form A — Expression/value matching

- id: route
  type: switch
  input:
    expr: "${data.steps.classify.json.category}"
  cases:
    - value: bug
      steps:
        - id: handle_bug
          type: llm.call
          input: { model: gpt-4o-mini, prompt: "Triage this bug..." }
    - value: feature
      steps:
        - id: handle_feature
          type: llm.call
          input: { model: gpt-4o-mini, prompt: "Plan this feature..." }
  default:
    - id: handle_other
      type: emit
      input: { message: "Unknown category, routing to human.", level: info }

Form B — When conditions

- id: priority_route
  type: switch
  cases:
    - when: "${data.inputs.priority == 'critical'}"
      steps:
        - id: escalate
          type: human.input
          input: { prompt: "Critical issue! Immediate action required." }
    - when: "${data.inputs.priority == 'high'}"
      steps:
        - id: auto_handle
          type: llm.call
          input: { model: gpt-4o, prompt: "Handle high-priority: ${data.inputs.message}" }
  default:
    - id: queue
      type: emit
      input: { message: "Queued for later processing.", level: info }

---

workflow.call — Call a Sub-Workflow

Calls another workflow defined in the same document or fetched from an external source.

Local call

- id: run_analysis
  type: workflow.call
  input:
    ref:
      kind: local
      workflow: analysis       # Name of a workflow in the same document
    inputs:
      data: "${data.inputs.raw_data}"

Remote call (URL)

- id: run_remote
  type: workflow.call
  input:
    ref:
      kind: url
      url: "https://example.com/workflows/analysis.yaml"
      workflow: main
    inputs:
      data: "${data.inputs.raw_data}"

---

workflow.plan — Generate a Workflow Dynamically via LLM

The most powerful step type: asks an LLM to generate a complete YAML workflow from a natural-language instruction, then validates and compiles it before execution.

Basic usage

- id: plan
  type: workflow.plan
  input:
    generator:
      model: gpt-4o
      instruction: "Build a workflow that fetches weather for Paris and summarizes it."
      context: "Available tools include weather and summarization APIs."

Full configuration

- id: plan
  type: workflow.plan
  input:
    generator:
      model: gpt-4o                 # LLM model for planning
      provider: openai              # Optional — LLM provider
      instruction: "Analyze the user's request and build a workflow."
      context: "${json(data.inputs)}"

      # Reasoning effort for the planning LLM call (and the MCP pre-filter).
      # Defaults to "high" (max) because planning is heavy reasoning work.
      # Set to "auto" to let the provider decide, or any of:
      # "minimal" | "low" | "medium" | "high" | "max" | "auto".
      # Models without thinking support ignore this field.
      reasoning: high

      # MCP pre-filter: uses an LLM to select only relevant MCP servers/tools
      # before injecting them into the planning prompt (reduces prompt size)
      prefilter: true               # true (default) | false | { model, provider }

    # Policy constraints — restrict what the LLM can generate
    policy:
      allowed_step_types:           # Whitelist of step types
        - llm.call
        - mcp.call
        - mcp.list
        - template.render
        - set
        - emit
        - sequence
      denied_step_types:            # Blacklist (takes precedence)
        - workflow.plan             # Prevent recursive planning
      allow_remote_workflow_refs: false

    # Limits
    limits:
      max_steps_total: 20           # Maximum number of steps in the generated workflow

    # Validation
    validate:
      compile: true                 # Parse + compile the generated YAML (default: true)

    # Self-correction on failure
    on_invalid:
      action: reprompt              # "reprompt" (re-send error to LLM) | "fail"
      max_attempts: 3               # Number of attempts before giving up

Output: { workflow: { dsl, name, workflows: [...] }, yaml: "...", meta: { model, attempt } }

Features:

• Automatic MCP discovery: Connects to all configured MCP servers, lists their tools/prompts, and injects them into the planning prompt so the LLM knows what's available.
• MCP pre-filter: Uses a lightweight LLM call to select only the MCP servers/tools relevant to the task instruction — reduces prompt size and cost.
• Full DSL reference injection: The LLM receives the complete DSL documentation (step types, expressions, error handling) so it can generate valid workflows.
• Policy enforcement: Generated workflows are validated against allowed/denied step types and max step limits.
• Self-correction: If the generated YAML is invalid (parse error, policy violation, compilation error), the error is sent back to the LLM for automatic correction.
• OpenTelemetry tracing: Full GenAI convention traces for the planning LLM call, MCP discovery, and pre-filter phases.

---

workflow.execute — Execute a Planned Workflow

Executes a workflow that was dynamically generated by workflow.plan.

- id: plan
  type: workflow.plan
  input:
    generator:
      model: gpt-4o
      instruction: "${data.inputs.task}"

- id: execute
  type: workflow.execute
  input:
    from_step: plan              # References the workflow.plan step that produced the YAML

The plan + execute pattern is the foundation of agentic workflows: the user describes a goal in natural language, the LLM plans the steps, and the engine executes them.

---

Typed Inputs

Workflow inputs support rich type declarations with validation at runtime.

Supported types: string, number, boolean, array, object, dictionary, any

workflows:
  main:
    inputs:
      # Simple scalar
      name:
        type: string
        required: true
        description: The user's name

      # With default value
      mode:
        type: string
        required: false
        default: standard

      # Array with typed items
      tags:
        type: array
        items: { type: string }
        required: false
        default: []

      # Nested object
      config:
        type: object
        properties:
          timeout: { type: number }
          retries: { type: number }
        required: false

      # Dictionary (string keys, typed values)
      headers:
        type: dictionary
        additionalProperties: { type: string }

---

Typed Outputs

Workflow outputs support type annotations and descriptions. This enables:

• Self-documenting workflow contracts
• Automatic JSON Schema generation (for MCP tool exposure)
• Nested type descriptors for arrays, objects, and dictionaries

Short form (expression only)

    outputs:
      result: "${data.steps.step1.text}"

Long form (with type and description)

    outputs:
      summary:
        expr: "${data.steps.llm_summary.text}"
        type: string
        description: LLM-generated summary text

      items_processed:
        expr: "${data.steps.process.count}"
        type: number
        description: Number of items processed

      success:
        expr: "${data.steps.result.ok}"
        type: boolean
        description: Whether the workflow succeeded

Complex types

    outputs:
      # Array of strings
      tags:
        expr: "${data.steps.extract.tags}"
        type: array
        items: { type: string }
        description: Extracted tags

      # Typed object
      report:
        expr: "${data.steps.build.report}"
        type: object
        properties:
          title: { type: string }
          score: { type: number }
        description: Structured report

      # Dictionary
      metrics:
        expr: "${data.steps.collect.metrics}"
        type: dictionary
        additionalProperties: { type: number }
        description: Named metrics map

JSON Schema generation

OutputDef types are convertible to JSON Schema via JsonSchemaConverter.OutputsToJsonSchema(outputs), used for MCP tool exposure and API documentation.

---

Expressions ${...}

Expressions are embedded in strings using ${...} syntax. They are JavaScript-style expressions evaluated by the in-tree JS-subset interpreter in gnougo_flow_core._jsmini.

Data access

• data.inputs.* — workflow input parameters
• data.steps.<step_id>.* — output of a previously executed step
• data.env.* — environment variables
• Optional chaining: data.steps.maybe_skipped?.value

Operators

&& || ! == != < <= > >= + - * / % ??

Built-in functions

Function	Description
exists(val)	true if val is non-null
coalesce(a, b, ...)	Returns first non-null argument
len(val)	Length of string or array (0 for null)
length(val)	Alias for len(val)
lower(s)	Lowercase string
upper(s)	Uppercase string
trim(s)	Trims whitespace
contains(s, sub)	true if string s contains sub
startsWith(s, prefix)	true if s starts with prefix
endsWith(s, suffix)	true if s ends with suffix
replace(s, old, new)	Replaces all occurrences
substring(s, start)	Characters from position start to end
substring(s, start, len)	len characters starting at start
toNumber(val)	Converts to number
json(val)	Serializes value to JSON string
fromJson(s)	Parses a JSON string into a node
now()	Returns the current local date/time as an ISO-8601 string
base64(val)	Encodes the UTF-8 string value as Base64
formatDate(dateStr, fmt)	Formats a date string (default: yyyy-MM-dd)

JavaScript-style expression support

• Ternary: ${data.inputs.mode == "fast" ? 0.0 : 0.7}
• Template literals: ${`Hello ${data.inputs.name}`}
• Array methods: ${data.inputs.items.filter(i => i.active).length}

---

WFScript — Custom JavaScript Functions

Define reusable functions in the functions: block (document-level or workflow-level):

version: 1
name: smart-triage
functions: |
  function classify(text) {
    if (contains(lower(text), "urgent")) return "critical";
    if (contains(lower(text), "bug")) return "bug";
    return "general";
  }

  function truncate(text, maxLen) {
    if (len(text) <= maxLen) return text;
    return text.substring(0, maxLen) + "...";
  }

workflows:
  main:
    inputs:
      message: { type: string, required: true }
    steps:
      - id: route
        type: switch
        input:
          expr: "${functions.classify(data.inputs.message)}"
        cases:
          - value: critical
            steps:
              - id: escalate
                type: human.input
                input:
                  prompt: "URGENT: ${functions.truncate(data.inputs.message, 100)}"
          - value: bug
            steps:
              - id: triage_bug
                type: llm.call
                input:
                  model: gpt-4o-mini
                  prompt: "Triage this bug report: ${data.inputs.message}"

---

Error Handling

Retry

Automatically retries a step on transient (retryable) errors:

retry:
  max: 3                 # Maximum attempts
  backoff_ms: 1000       # Initial delay between retries
  backoff_mult: 2.0      # Multiplier for exponential backoff
  jitter_ms: 100         # Random jitter added to each delay

on_error

Evaluated after retries are exhausted (or immediately for non-retryable errors):

on_error:
  cases:
    - if: "${error.code == \"LLM_TIMEOUT\" || error.code == \"LLM_NETWORK\"}"
      action: continue
      set_output:
        text: "Temporary LLM issue — using fallback"
    - if: "${error.code == \"INPUT_VALIDATION\"}"
      action: stop          # Stop the workflow immediately
    - action: stop          # Default: stop on unknown errors

Error context variables: error.code, error.message, error.retryable, step.id, step.type

Actions: continue (skip the step, optionally set a fallback output) | stop (abort the workflow)

Common error codes

Code	Retryable	Description
INPUT_VALIDATION	No	Missing or malformed input
LLM_TIMEOUT	Yes	LLM request timed out
LLM_NETWORK	Yes	Network error reaching the LLM
MCP_CONNECTION_ERROR	Yes	Cannot connect to MCP server
MCP_TOOL_ERROR	No	MCP tool returned an error
TEMPLATE_PLAN	No	workflow.plan failed to generate valid YAML
TEMPLATE_POLICY	No	Generated workflow violates policy constraints
HUMAN_INPUT_TIMEOUT	No	User didn't respond within timeout_ms
NO_HITL_PROVIDER	No	No human input provider configured

Full example — resilient LLM call with fallback

- id: summarize
  type: llm.call
  input:
    model: gpt-4o-mini
    prompt: "Summarize: ${json(data.inputs)}"
  retry:
    max: 3
    backoff_ms: 1000
    backoff_mult: 2
    jitter_ms: 100
  on_error:
    cases:
      - if: "${error.code == \"LLM_TIMEOUT\" || error.code == \"LLM_NETWORK\"}"
        action: continue
        set_output:
          text: "Summary temporarily unavailable."
      - action: stop

---

Model Metadata Catalog

The Python runtime includes a model metadata catalog aligned with the .NET implementation. It centralizes:

• token limits: context_window_tokens, max_input_tokens, max_output_tokens
• pricing: input_per_1m_tokens, output_per_1m_tokens
• capabilities: temperature, reasoning effort, structured output, tools, JSON mode, vision, embeddings
• aliases and user-provided extensions

WorkflowEngine.sanitize_llm_request() removes unsupported optional request fields before calling the configured LLM client. This prevents provider crashes such as sending temperature to reasoning models that reject it.

Pricing uses the same metadata resolver. try_get_pricing() and estimate_cost() read builtin pricing by default and can also use LLMOptions.model_metadata_files / LLMOptions.model_overrides when passed explicitly.

from gnougo_flow_core import WorkflowEngine, LLMOptions, LLMModelMetadata, ModelCapabilityMetadata

engine = WorkflowEngine()
engine.llm_options = LLMOptions(
    model_metadata_files=["config/my-models.json"],
    model_overrides={
        "my-local-model:latest": LLMModelMetadata(
            provider_type="ollama",
            context_window_tokens=32768,
            max_output_tokens=8192,
            capabilities=ModelCapabilityMetadata(
                supports_temperature=True,
                supports_reasoning_effort=False,
                supports_structured_output=False,
                supports_tools=False,
            ),
        )
    },
)

External metadata files can also use .NET-style camelCase field names:

{
  "models": {
    "model-id": {
      "providerType": "openai",
      "contextWindowTokens": 128000,
      "maxOutputTokens": 16384,
      "pricing": { "inputPer1MTokens": 0.15, "outputPer1MTokens": 0.60 },
      "capabilities": {
        "supportsTemperature": true,
        "supportsReasoningEffort": false,
        "supportsStructuredOutput": true,
        "supportsTools": true
      }
    }
  },
  "aliases": { "short-name": "model-id" }
}

Metadata precedence is:

builtin catalog < model_metadata_files < model_overrides < heuristics for missing fields

---

CLI

The published package exposes the gnougo-flow command.

# Validate a workflow (check syntax, types, compilation)
gnougo-flow validate examples/triage.yaml
# Inspect the structure (workflows, steps, inputs, outputs)
gnougo-flow inspect examples/triage.yaml
# Execute with key=value inputs
gnougo-flow run examples/triage.yaml -i message=hello -i priority=normal
# Execute with full JSON input
gnougo-flow run examples/triage.yaml -j '{"message":"hello","priority":"normal"}'
# Execute with full JSON input loaded from a file
gnougo-flow run examples/triage.yaml -j @inputs.json

When running directly from the repository with uv, prefix commands with uv run:

uv run gnougo-flow validate examples/triage.yaml
uv run gnougo-flow inspect examples/triage.yaml
uv run gnougo-flow run examples/triage.yaml -i message=hello

---

Python Runtime Notes

The Python package is not a NativeAOT binary; it is a Python 3.10+ library and CLI. It still follows the same design goals as GnOuGo.Flow.Core:

• YAML parsing uses PyYAML and typed Python models.
• JSON-like workflow data stays in Python dictionaries/lists/scalars.
• Templating is implemented in-tree with a minimal Mustache-compatible renderer.
• Expression interpolation and WFScript use gnougo_flow_core._jsmini, an in-tree JavaScript-subset interpreter with execution limits.
• Runtime services are injected through protocols instead of concrete infrastructure dependencies.
• MCP helpers live in gnougo_flow_core.integrations:
  • InMemoryMcpClientFactory and MockMcpServerConfig for tests and demos.
  • ConfiguredMcpClientFactory and McpSessionAdapter for injected MCP sessions.
  • RoutingLLMClientAdapter for adapting a routing LLM client.
• WorkflowEngine.mcp_cache defaults to McpCacheHelper, a 5-minute sliding TTL cache for MCP tools/resources/prompts per server. Set it to None to disable capability caching.
• WorkflowEngine.resume_async, WorkflowCheckpointer, and limits.run_id support resumable workflow execution. Development commands:

uv sync --extra dev
uv run --extra dev pytest
uv run --extra dev ruff check .
python -m pip install --upgrade build
python -m build

The release pipeline injects the generated repository version into pyproject.toml before building and publishing the package to PyPI.