Nominal Team#

Source: examples/patterns/nominal_team.py

Introduction#

Nominal teams explore one task independently, then hand all candidate outputs to a dedicated evaluator for best-of-N selection. This example fans out a design prompt to three focused contributors and selects the strongest result with a structured evaluator response.

Technical Implementation#

  1. Configure Tracer with JSONL + console output so each run emits machine-readable traces and lifecycle logs.

  2. Build three focused DirectLLMCall delegates and one evaluator over a shared LlamaCppServerLLMClient.

  3. Execute NominalTeamPattern.run(...) with member-specific prompt templates for diverse independent drafts.

  4. Print a compact JSON payload including trace_info for deterministic tests and docs examples.

        flowchart LR
    A["Input prompt or scenario"] --> B["NominalTeamPattern.run(...)"]
    B --> C["repairability / reliability / manufacturability members generate independently"]
    C --> D["evaluator compares candidates and selects best member"]
    D --> E["ExecutionResult/payload"]
    E --> F["Printed JSON output"]
    
 1from __future__ import annotations
 2
 3import json
 4from pathlib import Path
 5
 6from design_research_agents import DirectLLMCall, LlamaCppServerLLMClient, Tracer
 7from design_research_agents.patterns import NominalTeamPattern
 8
 9_EXAMPLE_LLAMA_CLIENT_KWARGS = {
10    "model": "Qwen_Qwen3-4B-Instruct-2507-Q4_K_M.gguf",
11    "hf_model_repo_id": "bartowski/Qwen_Qwen3-4B-Instruct-2507-GGUF",
12    "api_model": "qwen3-4b-instruct-2507-q4km",
13    "context_window": 8192,
14    "startup_timeout_seconds": 240.0,
15    "request_timeout_seconds": 240.0,
16}
17
18
19def main() -> None:
20    """Run one nominal-team workflow and print JSON summary."""
21    request_id = "example-pattern-nominal-team-design-001"
22    tracer = Tracer(
23        enabled=True,
24        trace_dir=Path("artifacts/examples/traces"),
25        enable_jsonl=True,
26        enable_console=True,
27    )
28    with LlamaCppServerLLMClient(**_EXAMPLE_LLAMA_CLIENT_KWARGS) as llm_client:
29        repairability = DirectLLMCall(
30            llm_client=llm_client,
31            system_prompt=(
32                "You are a repairability-focused designer. Return concise JSON with concept, strengths, and risks."
33            ),
34            tracer=tracer,
35        )
36        reliability = DirectLLMCall(
37            llm_client=llm_client,
38            system_prompt=(
39                "You are a reliability-focused designer. Return concise JSON with concept, strengths, and risks."
40            ),
41            tracer=tracer,
42        )
43        manufacturability = DirectLLMCall(
44            llm_client=llm_client,
45            system_prompt=(
46                "You are a manufacturability-focused designer. Return concise JSON with concept, strengths, and risks."
47            ),
48            tracer=tracer,
49        )
50        evaluator = DirectLLMCall(
51            llm_client=llm_client,
52            system_prompt=(
53                "Compare the candidate concepts and return JSON with best_member_id, "
54                "scores keyed by member id, and a short rationale."
55            ),
56            tracer=tracer,
57        )
58
59        pattern = NominalTeamPattern(
60            team_members=(
61                NominalTeamPattern.MemberSpec(
62                    member_id="repairability",
63                    delegate=repairability,
64                    prompt_template=(
65                        "Task: {task}\nPerspective: maximize field-service speed and tool simplicity.\n"
66                        "Return concise JSON candidate output."
67                    ),
68                ),
69                NominalTeamPattern.MemberSpec(
70                    member_id="reliability",
71                    delegate=reliability,
72                    prompt_template=(
73                        "Task: {task}\nPerspective: maximize sealing reliability and failure tolerance.\n"
74                        "Return concise JSON candidate output."
75                    ),
76                ),
77                NominalTeamPattern.MemberSpec(
78                    member_id="manufacturability",
79                    delegate=manufacturability,
80                    prompt_template=(
81                        "Task: {task}\nPerspective: maximize fabrication simplicity and repeatability.\n"
82                        "Return concise JSON candidate output."
83                    ),
84                ),
85            ),
86            evaluator_delegate=evaluator,
87            tracer=tracer,
88        )
89
90        result = pattern.run(
91            "Propose a field-serviceable enclosure concept for a remote environmental sensor.",
92            request_id=request_id,
93        )
94    print(json.dumps(result.summary(), ensure_ascii=True, indent=2, sort_keys=True))
95
96
97if __name__ == "__main__":
98    main()

Expected Results#

Run Command

PYTHONPATH=src python3 examples/patterns/nominal_team.py

Example output shape (values vary by run):

{
  "success": true,
  "final_output": "<selected-candidate-payload>",
  "terminated_reason": "<string-or-null>",
  "error": null,
  "trace": {
    "request_id": "<request-id>",
    "trace_dir": "artifacts/examples/traces",
    "trace_path": "artifacts/examples/traces/run_<timestamp>_<request_id>.jsonl"
  }
}

References#