coreinsight-cli 0.2.7

Local-first AI performance profiler that mathematically verifies optimizations for Python, C++, and CUDA

CoreInsight CLI

CoreInsight is a local-first, hardware-aware AI performance profiler. It parses Python, C++, and CUDA code, identifies hardware bottlenecks (CPU cache thrashing, CUDA warp divergence, algorithmic complexity issues), generates optimized code using an LLM, and mathematically verifies the results inside isolated Docker sandboxes with no data leaving your machine unless you explicitly configure a cloud provider.

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How it works

CoreInsight is built around three pillars:

1. Two-pillar mathematical verification: Every optimization is verified by two independent checks before being accepted and not just trusted because the AI said so:

• Speedup integrity: Recomputes speedup from raw timing columns and cross-checks against reported values, flagging fabricated or suspicious results
• Output correctness: Spins up a fresh Docker container, runs both original and optimized functions on identical test inputs, and compares outputs with float tolerance

2. AI-free hardware evidence On top of sandbox verification, CoreInsight runs real profiling tools against both versions and reports hardware counters — wall time, total function calls, cache misses, CPU cycles — as deterministic, LLM-independent evidence. This is the number a code reviewer or auditor can trust.

3. Optimization memory Every verified optimization is stored in a local vector database (ChromaDB). On subsequent analyses, CoreInsight retrieves structurally similar past optimizations before calling the LLM. When a match is found, the entire LLM + sandbox pipeline is skipped and the stored result is returned instantly. The tool gets faster and smarter the longer you use it and saves token cost over time.

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Prerequisites

• Python 3.9+
• Docker Desktop / Docker Engine: Must be running for sandbox verification. Install: https://docs.docker.com/engine/install/
• Ollama: Frr local inference (default). Install: https://ollama.com/download, then ollama pull llama3.2
• Recommended: OpenAI / Anthropic / Google API keys for cloud models (Pro)

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Install

pip install coreinsight-cli

Or clone and install in editable mode for development:

git clone https://github.com/your-org/coreinsight
cd coreinsight
pip install -e .

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Quick start

# Step 1: Configure your AI provider (defaults to Ollama + llama3.2)
coreinsight configure

# Step 2: Run the built-in demo to verify everything works
coreinsight demo

# Step 3: Analyse your own file
coreinsight analyze path/to/your_file.py

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All commands

coreinsight analyze <file>

Analyse a .py, .cpp, or .cu file. Extracts functions, runs bottleneck analysis in parallel, benchmarks in Docker, verifies mathematically, and writes a live Markdown report next to the source file.

coreinsight analyze src/matrix_ops.py
coreinsight analyze kernels/sort.cpp

coreinsight demo [--lang python|cpp]

Run CoreInsight on a built-in example to see the full pipeline end-to-end.

coreinsight demo
coreinsight demo --lang cpp

coreinsight memory [--clear]

Inspect the local optimization memory store wjocj shows every verified optimization with function name, language, measured speedup, severity, issue summary, and hardware evidence.

coreinsight memory          # list stored optimizations
coreinsight memory --clear  # wipe the store

coreinsight index [--dir <path>]

Index a repository into a local vector database so the AI has cross-file context (custom structs, helper functions, dependencies) during analysis.

coreinsight index
coreinsight index --dir ./src

coreinsight scan [--dir <path>] [--top N]

Scan a directory with static AST analysis and rank the most complex, deeply-nested hotspots without touching the LLM. Useful for triaging large codebases before a full analysis.

coreinsight scan
coreinsight scan --dir ./src --top 20

coreinsight configure [--pro-key <key>]

Set up your AI provider and API keys interactively. Pass --pro-key to unlock Pro features.

coreinsight configure
coreinsight configure --pro-key <your-key>

coreinsight configure [--agent-mode <mode>]

Choose between single-agent or multi-agent mode. Pass --agent-mode multi for multi-agent usage.

# Explicit override
coreinsight configure --agent-mode multi
coreinsight configure --agent-mode single

# Reset to auto-selection
coreinsight configure --agent-mode auto

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Supported languages

Language	Analysis	Benchmarking	Correctness	Hardware profiling
Python	✅	✅	✅	✅ (Pro)
C++	✅	✅	✅	🔜 v0.2.1
CUDA	✅	✅	—	🔜 v0.2.1

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Supported AI providers

Provider	Tier	Setup
Ollama (local)	Free	ollama pull llama3.2
LM Studio / vLLM (local_server)	Free	Point to http://localhost:1234/v1
OpenAI	Pro	API key via coreinsight configure
Anthropic	Pro	API key via coreinsight configure
Google Gemini	Pro	API key via coreinsight configure

All local providers run 100% on-device — no data leaves your machine.

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Tiers

Feature	Free	Pro
Local providers (Ollama, LM Studio)	✅	✅
Cloud providers (OpenAI, Anthropic, Gemini)	—	✅
Functions per file	3	Unlimited
Retry attempts	2	5
Correctness test cases	8	15
AI-free hardware profiling	—	✅
Optimization memory	✅	✅

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Architecture

coreinsight/
├── main.py       CLI entry point, parallel execution, Rich UI, report generation
├── analyzer.py   LLM chain: bottleneck analysis, harness generation, test cases
├── sandbox.py    Docker execution, speedup integrity, output correctness
├── profiler.py   AI-free hardware profiling (cProfile, perf stat)
├── memory.py     Optimization memory store (ChromaDB, semantic + exact lookup)
├── parser.py     AST parsing via tree-sitter for Python, C++, CUDA
├── indexer.py    RAG repo indexer (ChromaDB + sentence-transformers)
├── hardware.py   Hardware detection for LLM context
├── scanner.py    Project-wide hotspot scanner
├── config.py     Provider config, tier limits, pro key activation
└── prompts.py    System prompt, analysis template, tiered harness addenda

All verification runs inside Docker with network disabled, memory limits enforced, and all capabilities dropped. The LLM sees your code; the sandbox never phones home.

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Output

Every analysis writes a Markdown report next to your source file:

your_file_coreinsight_report.md
your_file_benchmark_plot.png     # Python only

The report includes the optimized code, benchmark table, verification results, and (Pro) a Hardware Evidence section with deterministic profiler output — suitable for sharing with a team or attaching to a PR.

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Get Pro: Free while this tool is in beta

Pro features (cloud providers, AI-free hardware profiling, unlimited functions) are free during the beta period. Pro keys are being handed out manually right now.

Request a key: tally.so/r/xXZ9YE

Once you have a key:

coreinsight configure --pro-key <your-key>

Privacy

CoreInsight is local-first by design:

• Ollama / local_server — code never leaves your machine
• Cloud providers — only the function code and context you choose to analyse is sent to the provider's API, under your own key
• The optimization memory store lives at ~/.coreinsight/memory_db on your local filesystem

Troubleshooting

ChromaDB issue with old SQLite3 versions. To resolve:

pip install pysqlite3-binary # >=0.5.0