Solo Sgr

/sgr

Design and implement Schema-Guided Reasoning (SGR) pipelines. Translate domain expert mental checklists into structured reasoning schemas for LLMs.

Source: Rinat Abdullin — Schema-Guided Reasoning

Core Principle

SGR = guide LLM reasoning through predefined steps via constrained decoding. Instead of free-form text → enforce a schema that defines what steps, in which order, where to focus attention.

Domain expert mental checklist → Pydantic/Zod schema → Constrained decoding → Deterministic dispatch

When to Use

• Designing agent tool dispatch (NextStep pattern)
• Building structured analysis pipelines (compliance, code review, evaluation)
• Replacing prompt chains with single structured call
• Any place where LLM output must be parseable and actionable

Steps

1. Parse task from $ARGUMENTS:

   • If "audit": scan project for existing Pydantic/Zod schemas, evaluate against SGR patterns
   • If task description: design SGR pipeline from scratch
   • If empty: ask "What domain/task should the SGR pipeline handle?"

2. Identify the reasoning cascade — interview the domain:

   • What decisions does a human expert make? In what order?
   • What information does each step need from previous steps?
   • Where does the expert need to "look before deciding"?
   • What are the possible actions at the end?

   This is the critical step. SGR quality = how well you translate the expert's mental checklist.

3. Design the schema following SGR patterns:

   The NextStep Pattern (agent loop)

   class NextStep(BaseModel):
       current_state: str                    # thinking space
       plan_remaining_steps: list[str]       # 1-5 steps, only first used
       task_completed: bool                  # routing gate
       function: Union[Tool1, Tool2, ..., ReportCompletion] = Field(
           ..., description="execute first remaining step"
       )
   

   The Analysis Cascade Pattern (single-shot)

   class Analysis(BaseModel):
       preliminary: str                      # initial assessment
       classification: Literal["a", "b", "c"]  # force categorization
       evidence: list[str]                   # cite sources
       gaps: list[GapItem]                   # structured findings
       verdict: Literal["pass", "partial", "fail"]  # final decision
       reasoning_for_verdict: str            # explain after deciding
   

   The Tool Dispatch Pattern

   class SendEmail(BaseModel):
       tool: Literal["send_email"]           # discriminator
       recipient: str
       subject: str
       body: str
   
   class SearchDB(BaseModel):
       tool: Literal["search_db"]
       query: str
   
   # Union with Literal discriminator = deterministic routing
   Action = Union[SendEmail, SearchDB, ReportDone]
   

4. Apply SGR design rules (from references/sgr-rules.md):

   • Cascade order matters — put analysis before decision, evidence before verdict
   • Constrain enums — Literal["pass", "fail"] not str
   • Limit lists — Annotated[list[str], MinLen(1), MaxLen(5)]
   • Discriminated unions — tool: Literal["name"] for routing
   • Verification after decision — add reasoning_for_X AFTER the enum field, not before
   • One schema per reasoning path — don't mix analysis and action in one model
   • Discount > 50% guard — Annotated[int, Le(50)] — bake constraints into types

5. Implement the dispatch loop (if agent):

   for i in range(MAX_STEPS):
       response = client.beta.chat.completions.parse(
           model=MODEL,
           response_format=NextStep,
           messages=log,
       )
       job = response.choices[0].message.parsed
   
       if isinstance(job.function, ReportCompletion):
           break  # done
   
       result = dispatch(job.function)  # deterministic routing
       log.append(assistant_message(job))
       log.append(tool_result(result))
   

6. Add to project:

   • Schemas in schemas/ or models/ directory
   • Dispatch in dispatch.py or equivalent
   • Tests: validate schema parsing, test each tool independently
   • Document the reasoning cascade in a comment or docstring

7. Audit mode (if $ARGUMENTS = "audit"):

   • Find all Pydantic BaseModel / Zod z.object in project
   • Check: do schemas follow cascade order? Are enums constrained? Are unions discriminated?
   • Report: which schemas are SGR-compliant, which need fixes

Output

## SGR Pipeline: {domain}

**Pattern:** {NextStep | Analysis Cascade | Tool Dispatch}
**Schemas:** {N} models
**Tools:** {N} (if agent loop)

### Reasoning Cascade
{step 1} → {step 2} → ... → {decision/action}

### Files
- schemas/{name}.py — {N} models
- dispatch.py — tool routing
- tests/test_{name}.py — validation tests

Key References

• references/sgr-rules.md — design rules and anti-patterns
• references/sgr-demo.py — complete working example (Abdullin's CRM demo, 304 lines Python)
• references/sgr-patterns.md — cascade patterns for 6 domains
• references/sgr-full-guide.md — full SGR guide with theory, code, tool calling internals

Libraries & Implementations

Rust

• sgr-agent (crate, v0.6.1) — SGR LLM client + agent framework: structured output, function calling, agent loop, 3 agent variants. Core crate for all Rust SGR agents. Part of rust-code
• openai-oxide — typed Rust client for OpenAI API (SGR at compile time via strong types)

In Rust, SGR is even stronger: #[serde(tag = "tool")] gives discriminated union dispatch at zero runtime cost. Enum variants = tools, serde deserialization = constrained decoding.

Python

• sgr-agent-core (1K+ stars) — SGR agentic system design framework by neuraldeep community. Reference Python implementation
• Abdullin's demo in references/sgr-demo.py — minimal standalone example (304 lines, CRM agent)

Common Issues

Schema too flat

Cause: Tried to put everything in one model. Fix: Split into analysis model + action model. Cascade, don't flatten.

LLM ignores enum constraints

Cause: Model not supporting constrained decoding, or wrong API. Fix: Use response_format=Schema (OpenAI), tools with schema (Anthropic). Check references/sgr-rules.md for provider-specific notes.

Agent loops forever

Cause: No task_completed gate or ReportCompletion tool. Fix: Always include a completion signal in the Union. Cap loop iterations.