design-research-agents 0.4.0

A flexible, modular framework for researching engineering design AI agents

design-research-agents

[!IMPORTANT] Current monthly release: Mayer Momentum - May 2026
Due: June 1, 2026
Tracks: May 2026 work

design-research-agents is the agent-execution layer in the cmudrc design research ecosystem.

It provides typed, composable contracts for direct calls, multi-step runs, workflow orchestration, tool execution, and traceable experimentation.

If you are deciding between primitives, workflow authoring, prebuilt patterns, and runnable exemplars, start with the Where To Start guide in the published docs.

Overview

This package centers on reproducible agent workflows with a compact public API:

• Two primary entry points: DirectLLMCall and MultiStepAgent (direct, json, and code modes)
• A seeded random control-condition agent for packaged-problem studies (SeededRandomBaselineAgent)
• A prompt-driven workflow agent for packaged-problem studies (PromptWorkflowAgent)
• A study-facing execution facade in design_research_agents.study for experiment runners
• Workflow primitives for model, tool, delegate, loop, and memory steps
• A tool runtime built around Toolbox, with callable, script, and MCP-backed tool configs
• Hosted and local LLM clients, model flights/catalogs, and ModelSelector for backend-selection policies
• Prebuilt coordination and reasoning patterns for plan/execute, propose/critic, debate, routing, round-based coordination, blackboard, tree search, Ralph loops, nominal teams, RAG, and conversation
• Tracing, structured ExecutionResult outputs, and runnable examples aimed at repeatable experiments

A Super Basic Agent

from design_research_agents import LlamaCppServerLLMClient, MultiStepAgent

with LlamaCppServerLLMClient() as llm_client:
    agent = MultiStepAgent(mode="direct", llm_client=llm_client, max_steps=3)
    result = agent.run(
        prompt="Suggest two design goals for a field-repairable drone battery latch.",
    )

print(result.final_output)

Quickstart

Requires Python 3.12+. Reproducible release installs target Python 3.12 (see .python-version).

On Windows, if python or pip resolve to an older interpreter, use py -3.12 -m venv .venv and py -3.12 -m pip ... for the environment-creation and package-install steps.

If you prefer a guided editor-first flow, use the VS Code Setup Guide. It walks through creating a virtual environment, installing the published package, and running a first script in VS Code.

python3 -m venv .venv
source .venv/bin/activate
make dev
make test
PYTHONPATH=src python examples/agents/direct_llm_call.py

The base-install path uses OpenAICompatibleHTTPLLMClient and expects a running OpenAI-compatible endpoint. Contributor setup (make dev) installs development tooling only; backend runtimes are explicit extras. Use design-research-agents[full] for the hosted + local backend bundle and design-research-agents[all] when you also want the optional ChromaDB and graph-memory backends. Use design-research-agents[huggingface] when you only need Hugging Face Hub metadata for catalog discovery.

For frozen installs, extras, and release maintenance, see Dependencies and Extras.

Examples

Start with examples/README.md for runnable examples grouped by agents, clients, workflows, patterns, model selection, and tools.

Some local LlamaCppServerLLMClient examples intentionally use Qwen3-4B GGUF configs, which can exceed available RAM on smaller machines. If you want a lighter local starting point, begin with the Ollama local client docs or the OllamaLLMClient guide.

Docs

See the published documentation for quickstart guidance, backend setup, workflow/pattern guides, and API docs.

Build docs locally with:

make docs

Public API

The supported public surface is whatever is exported from design_research_agents.__all__.

Top-level exports include:

• Agent entry points: DirectLLMCall, MultiStepAgent, SeededRandomBaselineAgent, PromptWorkflowAgent
• Study-facing helpers: the study module, AgentRunRequest, execute_agent_request, execute_agent_run, and normalize_agent_execution
• Core contracts: ExecutionResult, LLMRequest, LLMMessage, LLMResponse, ToolResult
• Workflow runtime: Workflow, CompiledExecution, and step contracts for model/tool/delegate/loop/memory behavior
• Tools: Toolbox, CallableToolConfig, ScriptToolConfig, MCPServerConfig
• Patterns: conversation, debate, plan/execute, propose/critic, Ralph loops, nominal teams, routing, round-based coordination, blackboard, tree search, and RAG
• LLM clients: hosted and local adapters, including OpenAI-compatible HTTP plus provider-specific clients
• Runtime services: design_research_agents.model_selection, ModelFlightRegistry, ModelCatalog, ModelSelector, and Tracer

Contributing

Contribution workflow and quality gates are documented in CONTRIBUTING.md.