optiprofiler-agent 0.1.0a2

AI Agent system for OptiProfiler — advisor, debugger, and results interpreter. CLI: opagent (recommended) or optiprofiler-agent.

OptiProfiler Agent

AI Agent system for OptiProfiler, covering the full user journey of derivative-free optimization benchmarking: ask questions → write scripts → debug errors → interpret results.

Architecture

Sub-agent	Role	When to Use
Advisor (advisor/)	Answer usage questions, adapt solver interfaces, generate benchmark scripts	Before testing
Debugger (debugger/)	Analyze test failures, auto-fix code, retry execution	During testing (on failure)
Interpreter (interpreter/)	Analyze profile scores and curves, generate structured reports	After testing (on success)
Unified Agent (unified_agent.py)	Single conversational interface that dynamically selects tools from all three sub-agents	Anytime

Top-down technical architecture (function-calling layout, the nine tools the unified agent exposes, validators, persistence, and how to extend any of it) lives in docs/ARCHITECTURE.md. Roadmap of upcoming work — including L4 constrained decoding, web search in the debugger path, and the long-term self-evolution loop — lives in docs/ROADMAP.md.

Installation

# Default: includes the three flagship agents (advisor, debugger, interpreter)
pip install optiprofiler-agent

# With local RAG over the bundled knowledge base (chromadb + sentence-transformers).
# Heavier install (~1 GB transitive); enable when you want grounded retrieval.
pip install 'optiprofiler-agent[rag]'

# Anthropic Claude provider (only if you plan to use --provider anthropic).
pip install 'optiprofiler-agent[anthropic]'

# Tavily-backed web_search tool.
pip install 'optiprofiler-agent[web]'

# Everything (rag + anthropic + web)
pip install 'optiprofiler-agent[all]'

CLI command name (opagent vs optiprofiler-agent)

After pip install, two equivalent executables are on your PATH:

Command	When to use
opagent	Recommended — short and easy to type.
optiprofiler-agent	Same program; name matches the PyPI package.

Running opagent with no subcommand starts the unified agent (ReAct tool-use loop). That is the same as opagent agent / optiprofiler-agent agent.

Examples below use opagent. Substitute optiprofiler-agent anywhere if you prefer the long name.

Configure the LLM (required)

Without a valid API key, any command that calls an LLM will fail. You only need one provider configured.

Quickest path — opagent init (recommended)

opagent init

Interactively pick a provider (or custom for any OpenAI-compatible endpoint we don't ship presets for), paste your key, and the wizard writes ~/.opagent/.env with mode 0600. The next time you run opagent, your provider is the default; no flags needed.

If you skip this and just run opagent on a fresh machine, the wizard fires automatically the first time. To suppress this in CI / Docker, set OPAGENT_NO_AUTO_INIT=1.

Where keys are read from (precedence, highest first)

The agent loads secrets from multiple places — same convention as gh, codex, aider:

1. --provider / --model flags on the command line
2. Real shell environment (export FOO=...)
3. ./.env in the current working directory (project-local override)
4. ~/.opagent/.env (user-level, written by opagent init)
5. Built-in defaults from PROVIDER_REGISTRY

A real export always beats every dotenv file; a project-local .env overrides the user-level one only for that project. Useful when you want different keys per repo (e.g. client A vs client B billing) without touching the global default — copy .env.example to .env in your project root and fill in only the values you need to override.

Provider table

Provider (--provider)	Environment variable	Notes
minimax	MINIMAX_API_KEY	Historical fallback when nothing else is configured.
kimi	KIMI_API_KEY
openai	OPENAI_API_KEY
deepseek	DEEPSEEK_API_KEY
anthropic	ANTHROPIC_API_KEY	Install extras: pip install 'optiprofiler-agent[anthropic]'
custom	OPAGENT_CUSTOM_BASE_URL + OPAGENT_CUSTOM_MODEL + OPAGENT_CUSTOM_API_KEY	Any OpenAI-compatible endpoint (self-hosted vLLM, internal gateway, unlisted vendor) — no code changes required

When --provider is omitted, the default is resolved as: OPAGENT_DEFAULT_PROVIDER → first provider whose API key is set → minimax. The init wizard writes OPAGENT_DEFAULT_PROVIDER for you.

Pinning model / routing through a proxy (without provider=custom)

Built-in providers ship with reasonable model defaults (see the Default models table), but model names change ("kimi-k2" → "kimi-k2-thinking") and corp networks often need a proxy. Two optional env vars override without forcing you onto provider=custom:

# Pin a specific model version for the active provider:
OPAGENT_DEFAULT_MODEL=kimi-k2-thinking

# Route any provider's calls through your proxy / Azure / internal gateway:
OPAGENT_DEFAULT_BASE_URL=https://my-corp-proxy.example.com/v1

Precedence: --model flag > OPAGENT_DEFAULT_MODEL > registry default. Same shape for --base-url / OPAGENT_DEFAULT_BASE_URL.

Python API — explicit config (no .env required)

If you embed this library in another app, pass the key in code:

import os
from optiprofiler_agent.config import AgentConfig, LLMConfig

config = AgentConfig(
    llm=LLMConfig(
        provider="openai",
        api_key=os.environ["OPENAI_API_KEY"],
        # model="gpt-4o",  # optional override
    ),
)

If api_key is omitted, LLMConfig falls back through the same precedence chain.

---

Quick Start

CLI (Primary Interface)

# One-time setup (auto-fires on first run; rerun any time to switch providers)
opagent init

# Unified agent (default if you run plain `opagent`; combines Advisor / Debug / Interpret via tools)
opagent

# Same as above, explicit subcommand
opagent agent

# Interactive advisor chat (Advisor sub-agent only)
opagent chat

# Validate a benchmark script
opagent check my_script.py

# Interpret benchmark results
opagent interpret path/to/out --latest

# Debug a failing script (auto-run + fix)
opagent debug my_script.py --run

# Wiki knowledge base management
opagent wiki stats

Inside the unified agent, slash shortcuts include /chat, /agent, /debug <file.py>, /interpret <dir> [--latest], /help, /quit. Run opagent --help for all subcommands.

Python Library

from optiprofiler_agent.config import AgentConfig, LLMConfig

config = AgentConfig(
    llm=LLMConfig(provider="minimax"),  # or "kimi", "openai", "deepseek", "anthropic"
    rag_enabled=True,
)

# Agent A: Product Advisor
from optiprofiler_agent.advisor.advisor import AdvisorAgent
advisor = AdvisorAgent(config)
reply = advisor.chat("How do I benchmark COBYLA vs Nelder-Mead on unconstrained problems?")

# Agent C: Results Interpreter
from optiprofiler_agent.interpreter.interpreter import interpret
report = interpret("path/to/results_dir", config=config, language="English")

# Unified Agent (LangGraph ReAct)
from optiprofiler_agent.unified_agent import create_unified_agent
agent = create_unified_agent(config)
result = agent.invoke({"messages": [("user", "What is ptype?")]})

---

CLI Command Reference

The following use opagent; optiprofiler-agent ... is identical.

opagent chat

Interactive conversation with Agent A (Product Advisor).

opagent chat [OPTIONS]

Options:
  --provider [kimi|minimax|openai|deepseek|anthropic|custom]
                        LLM provider (default: $OPAGENT_DEFAULT_PROVIDER → first
                        configured key → minimax)
  --model TEXT          Model name (overrides $OPAGENT_DEFAULT_MODEL + registry default)
  --rag                 Enable RAG retrieval for more detailed answers
  --rag-top-k INT      Number of RAG chunks to retrieve (default: 3)
  --validate            Validate code blocks in responses
  --verbose             Show system prompt size

In-chat commands: /reset (clear history), /prompt (show system prompt), /quit.

opagent / opagent agent

Unified tool-use agent with ReAct reasoning. Automatically selects among knowledge search, script validation, error debugging, and result interpretation. Plain opagent (no subcommand) runs this mode.

Note: The unified agent uses the knowledge_search tool internally, which requires [rag] extras (pip install 'optiprofiler-agent[rag]'). Without it, knowledge search calls will silently return no results.

opagent [OPTIONS]
opagent agent [OPTIONS]

Options:
  --provider [kimi|minimax|openai|deepseek|anthropic|custom]
  --model TEXT

opagent check

Validate a Python benchmark script for syntax errors and API usage issues.

opagent check FILEPATH [OPTIONS]

Options:
  --language [python|matlab]  (default: python)

opagent interpret

Analyze benchmark results and generate a natural-language report. PDF profile curve reading (PyMuPDF) is included in the default install; pass --no-profiles if you want to skip PDF parsing for any reason.

opagent interpret RESULTS_DIR [OPTIONS]

Options:
  --latest              Auto-detect the latest experiment in the directory
  --provider TEXT       LLM provider for report generation
  --model TEXT          Model name
  --language TEXT       Report language (default: English)
  --no-llm             Output raw JSON summary instead of LLM-generated report
  --no-profiles        Skip PDF profile reading (faster, less detailed)
  -o, --output PATH    Write report to file instead of stdout

opagent debug

Diagnose and fix benchmark script errors. Supports two modes:

Manual mode — provide code + error traceback:

opagent debug script.py --traceback error.log
opagent debug script.py --error "ValueError: at least 2 solvers"

Auto mode — run the script, catch errors, fix, and re-run:

opagent debug script.py --run [OPTIONS]

Options:
  --run                 Run the script first, then auto-debug if it fails
  --timeout INT         Timeout per run in seconds (default: 120)
  --max-retries INT     Maximum fix attempts (default: 3)
  --save-fixed PATH     Save the fixed code to a file
  --code-limit INT      Max code chars sent to LLM (0 = no limit)
  --provider TEXT       LLM provider
  --model TEXT          Model name

opagent index

Build or rebuild the RAG vector index.

opagent index [OPTIONS]

Options:
  --force       Force rebuild even if index is up-to-date
  --no-persist  Use in-memory index only (do not save to disk)

opagent wiki

Wiki knowledge base management commands.

opagent wiki stats           # Page count, size, category breakdown
opagent wiki lint            # Check for broken links, missing index entries
opagent wiki rebuild-index   # Rebuild RAG index from wiki content
opagent wiki rebuild-index --force  # Force full rebuild

---

Default models and endpoints

API keys are documented above. Each row below is the fallback — both columns can be overridden globally via OPAGENT_DEFAULT_MODEL / OPAGENT_DEFAULT_BASE_URL, or per-call via --model (no --base-url flag yet; use the env var).

Provider	Default model	API base
minimax	MiniMax-M2.7	api.minimaxi.com
kimi	kimi-k2.5	api.moonshot.cn
openai	gpt-4o	OpenAI official
deepseek	deepseek-chat	api.deepseek.com
anthropic	claude-sonnet-4-20250514	Anthropic official
custom	$OPAGENT_CUSTOM_MODEL	$OPAGENT_CUSTOM_BASE_URL

---

Knowledge Architecture (LLM Wiki)

The knowledge base follows the LLM Wiki pattern — a three-layer structure:

knowledge/
├── SCHEMA.md              # Wiki conventions and maintenance rules
├── enums.json             # Enum constants (utility file)
├── _sources/              # Raw, immutable source extractions
│   ├── python/*.json      # Extracted from Python docstrings
│   ├── matlab/*.json      # Extracted from MATLAB help comments
│   └── refs/              # Reference metadata
└── wiki/                  # Compiled, interlinked markdown pages
    ├── index.md           # Master page catalog (used for two-stage RAG)
    ├── log.md             # Chronological change log
    ├── concepts/          # Core domain concepts (DFO, benchmark, solver interface)
    ├── api/               # API reference (python/, matlab/)
    ├── guides/            # Quickstart guides, custom solver howto
    ├── profiles/          # Profile methodology and interpretation
    ├── solvers/           # Per-solver entity pages (NEWUOA, COBYLA, etc.)
    └── troubleshooting/   # Error patterns and fixes

RAG retrieval uses two-stage search: index scan → targeted vector search.

---

Project Structure

optiprofiler_agent/
├── config.py            # LLMConfig + multi-source dotenv loader (cwd → ~/.opagent → defaults)
├── onboarding.py        # `opagent init` wizard (interactive provider setup)
├── cli.py               # CLI entry point (all commands incl. auto first-run init)
├── unified_agent.py     # Unified ReAct agent (LangGraph)
├── common/              # Shared modules
│   ├── llm_client.py    # Unified LLM wrapper (langchain)
│   ├── knowledge_base.py # Structured knowledge loader
│   ├── rag.py           # RAG retrieval (ChromaDB + two-stage search)
│   ├── text_clean.py    # Strips <think>...</think> before persistence
│   ├── input_loop.py    # prompt_toolkit chat input
│   └── interface_adapter.py
├── advisor/             # Advisor sub-agent (product Q&A, script generation)
│   ├── __init__.py      # re-exports AdvisorAgent
│   ├── advisor.py
│   └── prompts/
├── debugger/            # Debugger sub-agent (failure → fix loop)
│   ├── __init__.py      # re-exports debug_script, run_and_debug
│   ├── debugger.py
│   ├── local_runner.py  # Subprocess execution with process tree cleanup
│   ├── error_classifier.py
│   └── prompts/
├── interpreter/         # Interpreter sub-agent (results → BenchmarkReport)
│   ├── __init__.py      # re-exports interpret, generate_report_object
│   ├── interpreter.py
│   ├── result_loader.py # Parse log.txt, report.txt
│   ├── profile_reader.py # Extract curves from PDF profiles
│   ├── score_analyzer.py
│   ├── anomaly_detector.py
│   ├── summary.py
│   └── prompts/
├── runtime/             # ~/.opagent persistence (memory, sessions, trajectories)
│   ├── paths.py         # Canonical layout incl. env_path()
│   ├── bootstrap.py     # First-run seed copy + chmod 0600 on .env
│   └── _seed/           # Bundled defaults (.env.template, MEMORY.md, USER.md, config.yaml)
├── validators/          # Code validation (AST syntax + API parameter checks)
├── formatters/          # Input/output normalization
└── knowledge/           # LLM Wiki knowledge base (see above)

For what's coming next, see docs/ROADMAP.md.

Development

git clone https://github.com/optiprofiler/optiprofiler-agent.git
cd optiprofiler-agent
pip install -e '.[dev,all]'

# Unit + integration tests (250+ cases, no LLM calls; safe for CI)
pytest tests/ -v

# Optional: end-to-end eval against a real LLM (costs tokens)
python scripts/run_eval.py --mode advisor    # Agent A
python scripts/run_eval.py --mode unified    # Full ReAct loop

Contributor protocol (when to run what, PR checklist, release gating) lives in docs/ROADMAP.md under "Developer-facing follow-up".

License

BSD-3-Clause