mirror of
https://github.com/codeflash-ai/codeflash-internal.git
synced 2026-05-04 18:25:18 +00:00
add universe optimize orchestrator with project management,
This commit is contained in:
misrasaurabh1
parent
a52d80b0aa
commit
2a101a11f2
13 changed files with 2363 additions and 7 deletions
6
experiments/universe-optimize/.gitignore
vendored
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6
experiments/universe-optimize/.gitignore
vendored
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results/
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universe_optimize.db
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__pycache__/
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*.pyc
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.venv/
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.tmp_bootstrap.sh
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1
experiments/universe-optimize/.python-version
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1
experiments/universe-optimize/.python-version
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@ -0,0 +1 @@
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3.13
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75
experiments/universe-optimize/bootstrap.sh
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75
experiments/universe-optimize/bootstrap.sh
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@ -0,0 +1,75 @@
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#!/bin/bash
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# Bootstrap script for Universe Optimize VMs.
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# Executed by the orchestrator via SSH on a fresh Ubuntu 24.04 VM.
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#
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# Placeholders replaced by the orchestrator before uploading:
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# __REPO_NAME__ - e.g. "flask"
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# __GITHUB_PAT__ - GitHub PAT for cloning/pushing
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# __CODEFLASH_API_KEY__ - Codeflash API key
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# __AWS_BEARER_TOKEN_BEDROCK__ - AWS Bedrock bearer token for Claude
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set -euo pipefail
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echo "=== [bootstrap] Starting VM bootstrap ==="
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# ── System dependencies ──────────────────────────────────────────────
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sudo apt-get update -qq
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sudo apt-get install -y -qq git curl build-essential python3-dev python3-venv jq
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# ── Python: install uv ──────────────────────────────────────────────
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curl -LsSf https://astral.sh/uv/install.sh | sh
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export PATH="$HOME/.local/bin:$PATH"
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echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.bashrc
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# ── Node.js (needed for Claude Code CLI) ────────────────────────────
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curl -fsSL https://deb.nodesource.com/setup_22.x | sudo -E bash -
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sudo apt-get install -y -qq nodejs
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# ── Install Claude Code CLI ─────────────────────────────────────────
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npm install -g @anthropic-ai/claude-code
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# ── Install GitHub CLI ──────────────────────────────────────────────
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curl -fsSL https://cli.github.com/packages/githubcli-archive-keyring.gpg | sudo dd of=/usr/share/keyrings/githubcli-archive-keyring.gpg
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echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/githubcli-archive-keyring.gpg] https://cli.github.com/packages stable main" | sudo tee /etc/apt/sources.list.d/github-cli.list > /dev/null
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sudo apt-get update -qq && sudo apt-get install -y -qq gh
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# ── Environment variables (Bedrock auth for Claude Code) ────────────
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# Write to a dedicated file, then source from both .bashrc and .profile
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# so it works in both interactive and non-interactive SSH sessions.
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cat > ~/.env_universe << 'ENVEOF'
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export CLAUDE_CODE_USE_BEDROCK=1
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export AWS_REGION=us-east-1
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export AWS_BEARER_TOKEN_BEDROCK="__AWS_BEARER_TOKEN_BEDROCK__"
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export CODEFLASH_API_KEY="__CODEFLASH_API_KEY__"
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export LC_ALL=en_US.UTF-8
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export LANG=en_US.UTF-8
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ENVEOF
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echo 'source ~/.env_universe' >> ~/.bashrc
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echo 'source ~/.env_universe' >> ~/.profile
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source ~/.env_universe
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# ── GitHub auth ─────────────────────────────────────────────────────
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git config --global credential.helper store
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echo "https://codeflash-ai:__GITHUB_PAT__@github.com" > ~/.git-credentials
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git config --global user.name "codeflash-ai"
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git config --global user.email "bot@codeflash.ai"
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# Authenticate gh CLI
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echo "__GITHUB_PAT__" | gh auth login --with-token 2>/dev/null || true
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# ── Create results directory ────────────────────────────────────────
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mkdir -p ~/results
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# ── Clone the forked repo ───────────────────────────────────────────
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git clone "https://github.com/codeflash-ai/__REPO_NAME__.git" ~/project
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# ── Copy optimization.md as CLAUDE.md ───────────────────────────────
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# (orchestrator SCPs optimization.md to ~/optimization.md before running this)
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cp ~/optimization.md ~/project/CLAUDE.md
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# ── codeflash-agent plugin ──────────────────────────────────────────
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# (orchestrator SCPs the dist/ directory to ~/codeflash-agent/dist before running this)
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echo "=== [bootstrap] Bootstrap complete ==="
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echo " Project cloned to ~/project"
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echo " CLAUDE.md injected"
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echo " Ready to launch Claude Code"
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@ -0,0 +1,21 @@
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Subject: I created {num_optimizations} PRs that speed up {repo_name}
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Hi {first_name},
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I'm Saurabh, ex-CMU and Meta, and CEO and Founder of Codeflash.
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We work with companies like Unstructured.io and HuggingFace who all face a growing challenge: as AI coding tools generate more of the codebase, performance regressions slip in faster than teams can catch them.
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Codeflash is the performance layer that sits on top of your AI coding workflow. It finds provably faster implementations for your existing code and ensures every new PR ships optimized.
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I ran Codeflash on a fork of {repo} and created {num_optimizations} merge-ready commits that significantly speed up several crucial components -- {best_function} now runs {best_speedup}! {optimizations_summary}
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Unstructured.io used Codeflash across their entire infrastructure and cut compute costs by 50%.
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I'd love to walk you through the results and show you how much more free performance is hiding across your full codebase. Would sometime this week work?
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{calendly_link}
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Thanks,
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Saurabh
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Founder, Codeflash.ai
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@ -0,0 +1,15 @@
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Subject: Re: I created {num_optimizations} PRs that speed up {repo_name}
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Hi {first_name},
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Wanted to follow up -- did your team get a chance to look at the optimizations?
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You can see all {num_optimizations} commits here:
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{branch_url}
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If those didn't hit the right area of your codebase, I have an open offer: share any performance benchmark your team cares about, and I'll run Codeflash against it and send you the results. No commitment, just proof.
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{calendly_link}
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Saurabh
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Founder, codeflash.ai
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@ -0,0 +1,14 @@
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Subject: Re: I created {num_optimizations} PRs that speed up {repo_name}
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Hi {first_name},
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With AI coding tools writing more of the code, performance regressions are showing up faster and quieter than before. By the time they surface, it's production issues and fire drills.
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Unstructured.io plugged Codeflash into their workflow and cut compute costs by 50% -- and now every PR is automatically checked before it merges.
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Happy to show you what that looks like for {company_name}.
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{calendly_link}
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Saurabh
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Founder, codeflash.ai
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87
experiments/universe-optimize/optimization.md
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87
experiments/universe-optimize/optimization.md
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# Codeflash Optimization Run
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You are running an automated optimization session on this open-source project.
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Your goal is to find provably-faster code implementations and create individual
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PRs with benchmark results for each optimization.
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## Your Mission
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### Phase 1: Setup
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1. Understand the project: read the README, project structure, and key source files.
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2. Set up the project: install dependencies, verify tests pass.
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3. Install codeflash: `pip install codeflash` (or `uv add --dev codeflash` if using uv).
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### Phase 2: Optimize and Create PRs
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Launch the optimization agent directly using the Agent tool (do NOT use `/codeflash-optimize` or AskUserQuestion — you are running non-interactively):
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```
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Agent(subagent_type: "codeflash-deep", name: "optimizer", run_in_background: true, prompt: "AUTONOMOUS MODE: The user has already been asked for context (included below). Do NOT ask the user any questions — work fully autonomously. Make all decisions yourself: generate a run tag from today's date, identify benchmark tiers from available tests, choose optimization targets from profiler output. If something is ambiguous, pick the reasonable default and document your choice in HANDOFF.md.\n\nOptimize this project for performance.\n\nUser context: go — no special constraints, optimize everything you can find.\n\nIMPORTANT — After each KEEP, immediately do the following before moving to the next target:\n1. Write a pytest-benchmark test for the optimized function with realistic inputs\n2. Run `codeflash compare <base-commit> <head-commit> --inject <benchmark-test-path>` to generate benchmark tables. If codeflash compare is not available, run the benchmark manually on base and head commits and format the results as a markdown table.\n3. Create a PR branch: `git checkout main && git checkout -b codeflash/opt-<function-name> && git cherry-pick <commit-sha> && git add <benchmark-test> && git commit --amend --no-edit`\n4. Push the branch: `git push origin codeflash/opt-<function-name>`\n5. Open a PR with `gh pr create` using the benchmark results in the body (see PR body format below)\n6. Switch back to the optimize branch: `git checkout codeflash/optimize`\n7. Record the PR URL in results.tsv and HANDOFF.md\n\nDo NOT batch PR creation for later. Each optimization gets its PR immediately after the experiment proves it works.\n\nPR Body Format:\n## Summary\n- <One-line description of what was optimized and how>\n\n## Benchmark\n### <Machine specs — e.g. Azure Standard_D4s_v5, 4 vCPU, 16 GiB RAM, Python 3.12>\n\n| Function | base | head | Delta | Speedup |\n|:---|---:|---:|:---|---:|\n| `<function_name>` | ... | ... | ... | X.Xx |\n\n---\n*Generated by codeflash agent*\n\n<details>\n<summary><b>Benchmark test source</b></summary>\n\n```python\n<benchmark test code>\n```\n</details>\n\n## Test plan\n- [x] All existing tests pass\n- [x] Benchmark shows measurable improvement")
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```
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Wait for the optimizer agent to complete.
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### Phase 3: Collect Results
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Create the results directory and write summary:
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```bash
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mkdir -p ~/results
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```
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Write `~/results/summary.json` with this structure:
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```json
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{
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"repo": "<org/repo>",
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"language": "<python|javascript|java>",
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"branch": "codeflash/optimize",
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"total_experiments": 0,
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"total_keeps": 0,
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"total_discards": 0,
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"optimizations": [
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{
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"commit": "<sha>",
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"function": "<function_name>",
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"file": "<file_path>",
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"description": "<what was optimized>",
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"cpu_speedup": "<e.g. 2.3x faster>",
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"memory_reduction": "<e.g. -50 MiB>",
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"technique": "<e.g. replaced list with set, eliminated deepcopy>",
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"pr_number": "<PR number if created>",
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"pr_url": "<PR URL if created>",
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"benchmark_test": "<path to benchmark test file>"
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}
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],
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"headline_stats": {
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"best_single_speedup": "<e.g. 5x faster>",
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"best_function": "<function_name>",
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"total_cpu_improvement_pct": 0,
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"total_memory_saved_mb": 0
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},
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"pr_ready_commits": 0,
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"prs_created": [],
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"status": "completed|plateaued|failed",
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"error": "<if failed, why>"
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}
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```
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Also copy `.codeflash/results.tsv` and `.codeflash/HANDOFF.md` to `~/results/` (if they exist).
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Then push all branches to the remote fork:
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```bash
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git push origin --all
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```
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## Important
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- Work fully autonomously. Do not ask questions -- make reasonable decisions.
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- If tests fail during setup, note the pre-existing failures and work around them.
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- If the project cannot be set up (missing deps, private packages), write summary.json with status "failed" and an error message, then stop.
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- Time limit: aim to complete within 8 hours. If still running after 8 hours, wrap up, write summary.json with whatever results you have, and stop.
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- Always push all branches before finishing, even if results are partial.
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- Each optimization MUST have its own PR with benchmark results created immediately after the optimization is verified. Do NOT batch PR creation for later.
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CRITICAL: Do NOT use AskUserQuestion at any point. You are running in fully autonomous non-interactive mode. Make all decisions yourself.
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988
experiments/universe-optimize/orchestrator.py
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988
experiments/universe-optimize/orchestrator.py
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#!/usr/bin/env python3
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"""
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Universe Optimize Orchestrator
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Manages the full pipeline: provision Azure VMs, fork repos, run Codeflash
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optimizations via Claude Code, collect results, and draft outreach emails.
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Usage:
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python orchestrator.py provision <project-id> Provision VM, fork repo, bootstrap
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python orchestrator.py run <project-id> Launch Claude Code on the VM
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python orchestrator.py status [project-id] Dashboard or single project status
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python orchestrator.py monitor [--interval=600] Poll all running VMs
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python orchestrator.py collect <project-id> SCP results, verify push, destroy VM
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python orchestrator.py results <project-id> Show detailed optimization results
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python orchestrator.py email <project-id> Build context + render emails
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python orchestrator.py email --rerender-all Re-render all emails after template edit
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python orchestrator.py email <project-id> --show Preview rendered emails
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python orchestrator.py email <project-id> --show-context Show raw context.json
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python orchestrator.py mark-sent <project-id> Mark emails as sent
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python orchestrator.py destroy <project-id> Destroy VM without collecting
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"""
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from __future__ import annotations
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import argparse
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import json
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import os
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import shlex
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import sqlite3
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import subprocess
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import sys
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import time
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from datetime import datetime, timezone
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from glob import glob
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from pathlib import Path
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from textwrap import dedent
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# ---------------------------------------------------------------------------
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# Paths
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# ---------------------------------------------------------------------------
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BASE_DIR = Path(__file__).resolve().parent
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PROJECTS_FILE = BASE_DIR / "projects.json"
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OPTIMIZATION_MD = BASE_DIR / "optimization.md"
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BOOTSTRAP_SH = BASE_DIR / "bootstrap.sh"
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EMAIL_TEMPLATES_DIR = BASE_DIR / "email_templates"
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RESULTS_DIR = BASE_DIR / "results"
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DB_PATH = BASE_DIR / "universe_optimize.db"
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CODEFLASH_AGENT_DIST = Path.home() / "Library/CloudStorage/Dropbox/hacks/codeflash-agent/dist"
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# ---------------------------------------------------------------------------
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# Config
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# ---------------------------------------------------------------------------
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AZURE_RESOURCE_GROUP = os.environ.get("UO_AZURE_RESOURCE_GROUP", "universe-optimize-rg")
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AZURE_LOCATION = os.environ.get("UO_AZURE_LOCATION", "eastus")
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AZURE_VM_SIZE = os.environ.get("UO_AZURE_VM_SIZE", "Standard_D4s_v5")
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AZURE_VM_IMAGE = "Canonical:ubuntu-24_04-lts:server:latest"
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AZURE_ADMIN_USER = "azureuser"
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SSH_KEY_PATH = Path.home() / ".ssh" / "id_rsa"
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GITHUB_PAT = os.environ.get(
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"UO_GITHUB_PAT",
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"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",
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)
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GITHUB_ORG = "codeflash-ai"
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# Bedrock auth for Claude Code on VMs
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AWS_BEARER_TOKEN_BEDROCK = os.environ.get(
|
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"AWS_BEARER_TOKEN_BEDROCK",
|
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"ABSKc2FydGhha0Bjb2RlZmxhc2guYWkrMS1hdC05OTIzODI0NjM5MDc6bS9tdGx4SW0wQ08yazMwU3QxZFdlbWRWeTM0NnJWZElBZmFNNmVobC9UU2tRVTBPQm4wUXVPS3ZFQWs9",
|
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)
|
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CODEFLASH_API_KEY = os.environ.get(
|
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"CODEFLASH_API_KEY",
|
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"cf-kNZFz7nM3Tl3wY4t5Kh1E58A-UhxKvLhokyTfFIz5YJ_xJJLcBBFrdp8kJF6G8ld",
|
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)
|
||||
|
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CALENDLY_LINK = "https://calendly.com/codeflash-saurabh/30min"
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# Max time (seconds) before we consider a VM stuck
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VM_TIMEOUT_SECONDS = 5 * 3600 # 5 hours
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# ===========================================================================
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# Database
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# ===========================================================================
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def get_db() -> sqlite3.Connection:
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db = sqlite3.connect(str(DB_PATH))
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db.row_factory = sqlite3.Row
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db.execute("""
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CREATE TABLE IF NOT EXISTS projects (
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id TEXT PRIMARY KEY,
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repo TEXT NOT NULL,
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language TEXT NOT NULL,
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fork_url TEXT,
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company TEXT,
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contact_name TEXT,
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contact_email TEXT,
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contact_title TEXT,
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status TEXT DEFAULT 'pending',
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vm_name TEXT,
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vm_ip TEXT,
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created_at TEXT,
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started_at TEXT,
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completed_at TEXT,
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optimization_branch TEXT,
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num_optimizations INTEGER DEFAULT 0,
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best_speedup TEXT,
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summary_json TEXT,
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email_draft_path TEXT
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)
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""")
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db.commit()
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return db
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def load_projects() -> list[dict]:
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with open(PROJECTS_FILE) as f:
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return json.load(f)
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def load_project(project_id: str) -> dict:
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for p in load_projects():
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if p["id"] == project_id:
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return p
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raise ValueError(f"Project {project_id} not found in projects.json")
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def upsert_project(db: sqlite3.Connection, project: dict) -> None:
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contact = project.get("target_contact", {})
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db.execute("""
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INSERT INTO projects (id, repo, language, company, contact_name, contact_email, contact_title, status, created_at)
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VALUES (?, ?, ?, ?, ?, ?, ?, 'pending', ?)
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ON CONFLICT(id) DO UPDATE SET
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repo=excluded.repo, language=excluded.language, company=excluded.company,
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contact_name=excluded.contact_name, contact_email=excluded.contact_email,
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contact_title=excluded.contact_title
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""", (
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project["id"], project["repo"], project["language"],
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project.get("company", ""), contact.get("name", ""),
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contact.get("email", ""), contact.get("title", ""),
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datetime.now(timezone.utc).isoformat(),
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))
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db.commit()
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def update_status(db: sqlite3.Connection, project_id: str, status: str, **kwargs) -> None:
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sets = ["status = ?"]
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vals: list = [status]
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for k, v in kwargs.items():
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sets.append(f"{k} = ?")
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vals.append(v)
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vals.append(project_id)
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||||
db.execute(f"UPDATE projects SET {', '.join(sets)} WHERE id = ?", vals)
|
||||
db.commit()
|
||||
|
||||
|
||||
def get_project_row(db: sqlite3.Connection, project_id: str) -> sqlite3.Row | None:
|
||||
return db.execute("SELECT * FROM projects WHERE id = ?", (project_id,)).fetchone()
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# SSH helpers
|
||||
# ===========================================================================
|
||||
|
||||
def ssh_exec(ip: str, command: str, timeout: int = 120) -> str:
|
||||
"""Execute a command on the VM via SSH. Returns stdout."""
|
||||
import paramiko
|
||||
|
||||
client = paramiko.SSHClient()
|
||||
client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
|
||||
client.connect(ip, username=AZURE_ADMIN_USER, key_filename=str(SSH_KEY_PATH), timeout=30)
|
||||
try:
|
||||
_, stdout, stderr = client.exec_command(command, timeout=timeout)
|
||||
out = stdout.read().decode()
|
||||
err = stderr.read().decode()
|
||||
if err:
|
||||
print(f" [ssh stderr] {err.strip()}", file=sys.stderr)
|
||||
return out
|
||||
finally:
|
||||
client.close()
|
||||
|
||||
|
||||
def scp_upload(ip: str, local_path: str | Path, remote_path: str) -> None:
|
||||
"""Upload a file or directory to the VM."""
|
||||
import paramiko
|
||||
|
||||
client = paramiko.SSHClient()
|
||||
client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
|
||||
client.connect(ip, username=AZURE_ADMIN_USER, key_filename=str(SSH_KEY_PATH), timeout=30)
|
||||
try:
|
||||
sftp = client.open_sftp()
|
||||
local_path = Path(local_path)
|
||||
if local_path.is_dir():
|
||||
_sftp_put_dir(sftp, local_path, remote_path)
|
||||
else:
|
||||
sftp.put(str(local_path), remote_path)
|
||||
sftp.close()
|
||||
finally:
|
||||
client.close()
|
||||
|
||||
|
||||
def _sftp_mkdir_p(sftp, remote_dir: str) -> None:
|
||||
"""Recursively create remote directories (like mkdir -p)."""
|
||||
dirs_to_create = []
|
||||
current = remote_dir
|
||||
while current and current != "/":
|
||||
try:
|
||||
sftp.stat(current)
|
||||
break # exists
|
||||
except FileNotFoundError:
|
||||
dirs_to_create.append(current)
|
||||
current = os.path.dirname(current)
|
||||
for d in reversed(dirs_to_create):
|
||||
sftp.mkdir(d)
|
||||
|
||||
|
||||
def _sftp_put_dir(sftp, local_dir: Path, remote_dir: str) -> None:
|
||||
"""Recursively upload a directory."""
|
||||
_sftp_mkdir_p(sftp, remote_dir)
|
||||
for item in sorted(local_dir.iterdir()):
|
||||
remote_item = f"{remote_dir}/{item.name}"
|
||||
if item.is_dir():
|
||||
_sftp_put_dir(sftp, item, remote_item)
|
||||
else:
|
||||
sftp.put(str(item), remote_item)
|
||||
|
||||
|
||||
def scp_download(ip: str, remote_path: str, local_path: str | Path) -> bool:
|
||||
"""Download a file from the VM. Returns True if successful."""
|
||||
import paramiko
|
||||
|
||||
local_path = Path(local_path)
|
||||
local_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
client = paramiko.SSHClient()
|
||||
client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
|
||||
client.connect(ip, username=AZURE_ADMIN_USER, key_filename=str(SSH_KEY_PATH), timeout=30)
|
||||
try:
|
||||
sftp = client.open_sftp()
|
||||
sftp.get(remote_path, str(local_path))
|
||||
sftp.close()
|
||||
return True
|
||||
except FileNotFoundError:
|
||||
print(f" [scp] Remote file not found: {remote_path}")
|
||||
return False
|
||||
finally:
|
||||
client.close()
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# Azure VM management (via az CLI)
|
||||
# ===========================================================================
|
||||
|
||||
def az(cmd: str, parse_json: bool = True) -> dict | list | str:
|
||||
"""Run an az CLI command. Returns parsed JSON or raw stdout."""
|
||||
full_cmd = f"az {cmd}"
|
||||
if parse_json and "--output" not in cmd:
|
||||
full_cmd += " --output json"
|
||||
result = subprocess.run(
|
||||
full_cmd, shell=True, capture_output=True, text=True, timeout=600,
|
||||
)
|
||||
if result.returncode != 0:
|
||||
raise RuntimeError(f"az command failed:\n cmd: {full_cmd}\n stderr: {result.stderr.strip()}")
|
||||
if parse_json:
|
||||
return json.loads(result.stdout) if result.stdout.strip() else {}
|
||||
return result.stdout
|
||||
|
||||
|
||||
def provision_vm(project_id: str, repo_name: str) -> tuple[str, str]:
|
||||
"""Provision an Azure VM using az CLI. Returns (vm_name, public_ip)."""
|
||||
vm_name = f"uo-{project_id}"
|
||||
|
||||
# Ensure resource group exists
|
||||
print(f" Ensuring resource group '{AZURE_RESOURCE_GROUP}'...")
|
||||
az(f"group create --name {AZURE_RESOURCE_GROUP} --location {AZURE_LOCATION}")
|
||||
|
||||
# Create VM (az vm create handles vnet, subnet, nsg, nic, ip, disk automatically)
|
||||
ssh_pub_key_path = SSH_KEY_PATH.with_suffix(".pub")
|
||||
print(f" Creating VM '{vm_name}' ({AZURE_VM_SIZE})...")
|
||||
vm_info = az(
|
||||
f"vm create"
|
||||
f" --resource-group {AZURE_RESOURCE_GROUP}"
|
||||
f" --name {vm_name}"
|
||||
f" --image {AZURE_VM_IMAGE}"
|
||||
f" --size {AZURE_VM_SIZE}"
|
||||
f" --admin-username {AZURE_ADMIN_USER}"
|
||||
f" --ssh-key-values {shlex.quote(str(ssh_pub_key_path))}"
|
||||
f" --os-disk-size-gb 128"
|
||||
f" --storage-sku Premium_LRS"
|
||||
f" --public-ip-sku Standard"
|
||||
f" --tags project=universe-optimize project_id={project_id} repo={repo_name}"
|
||||
)
|
||||
|
||||
public_ip = vm_info.get("publicIpAddress", "")
|
||||
if not public_ip:
|
||||
# Fallback: query the IP explicitly
|
||||
ip_info = az(
|
||||
f"vm list-ip-addresses"
|
||||
f" --resource-group {AZURE_RESOURCE_GROUP}"
|
||||
f" --name {vm_name}"
|
||||
)
|
||||
public_ip = ip_info[0]["virtualMachine"]["network"]["publicIpAddresses"][0]["ipAddress"]
|
||||
|
||||
print(f" VM '{vm_name}' provisioned at {public_ip}")
|
||||
return vm_name, public_ip
|
||||
|
||||
|
||||
def destroy_vm(project_id: str) -> None:
|
||||
"""Destroy the VM and all associated resources."""
|
||||
vm_name = f"uo-{project_id}"
|
||||
print(f" Deleting VM '{vm_name}' and all associated resources...")
|
||||
try:
|
||||
# --delete-all removes NIC, public-ip, NSG, and OS disk along with the VM
|
||||
az(
|
||||
f"vm delete"
|
||||
f" --resource-group {AZURE_RESOURCE_GROUP}"
|
||||
f" --name {vm_name}"
|
||||
f" --yes"
|
||||
f" --force-deletion true",
|
||||
parse_json=False,
|
||||
)
|
||||
except RuntimeError as e:
|
||||
print(f" Warning: VM delete may have partially failed: {e}")
|
||||
|
||||
# Clean up any remaining resources that az vm delete may leave behind
|
||||
# (VNet and NSG are sometimes shared/retained)
|
||||
for cmd in [
|
||||
f"network nsg delete --resource-group {AZURE_RESOURCE_GROUP} --name {vm_name}NSG --yes",
|
||||
f"network vnet delete --resource-group {AZURE_RESOURCE_GROUP} --name {vm_name}VNET --yes",
|
||||
f"network nic delete --resource-group {AZURE_RESOURCE_GROUP} --name {vm_name}VMNic",
|
||||
f"network public-ip delete --resource-group {AZURE_RESOURCE_GROUP} --name {vm_name}PublicIP",
|
||||
]:
|
||||
try:
|
||||
az(cmd, parse_json=False)
|
||||
except RuntimeError:
|
||||
pass # Resource may not exist or was already cleaned up
|
||||
|
||||
print(f" VM '{vm_name}' destroyed.")
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# GitHub
|
||||
# ===========================================================================
|
||||
|
||||
def fork_repo(repo: str) -> str:
|
||||
"""Fork a repo to the codeflash-ai org. Returns the fork URL.
|
||||
|
||||
Uses gh CLI which respects the logged-in user's auth and org permissions.
|
||||
Falls back to PyGithub if gh is not available.
|
||||
"""
|
||||
repo_name = repo.split("/")[1]
|
||||
fork_url = f"https://github.com/{GITHUB_ORG}/{repo_name}"
|
||||
|
||||
# Check if fork already exists
|
||||
result = subprocess.run(
|
||||
f"gh repo view {GITHUB_ORG}/{repo_name} --json url -q .url",
|
||||
shell=True, capture_output=True, text=True,
|
||||
)
|
||||
if result.returncode == 0 and result.stdout.strip():
|
||||
print(f" Fork already exists: {result.stdout.strip()}")
|
||||
return result.stdout.strip()
|
||||
|
||||
# Create the fork via gh CLI
|
||||
result = subprocess.run(
|
||||
f"gh repo fork {repo} --org {GITHUB_ORG} --clone=false",
|
||||
shell=True, capture_output=True, text=True,
|
||||
)
|
||||
if result.returncode == 0:
|
||||
print(f" Forked {repo} -> {fork_url}")
|
||||
time.sleep(5) # Wait for fork to be ready
|
||||
return fork_url
|
||||
|
||||
# Fallback to PyGithub
|
||||
print(f" gh fork failed ({result.stderr.strip()}), trying PyGithub...")
|
||||
from github import Auth, Github
|
||||
|
||||
g = Github(auth=Auth.Token(GITHUB_PAT))
|
||||
source = g.get_repo(repo)
|
||||
org = g.get_organization(GITHUB_ORG)
|
||||
fork = org.create_fork(source)
|
||||
print(f" Forked {repo} -> {fork.html_url}")
|
||||
time.sleep(5)
|
||||
return fork.html_url
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# Bootstrap
|
||||
# ===========================================================================
|
||||
|
||||
def bootstrap_vm(ip: str, project: dict) -> None:
|
||||
"""Upload files and run bootstrap script on the VM."""
|
||||
repo_name = project["repo"].split("/")[1]
|
||||
|
||||
print(" Uploading optimization.md...")
|
||||
scp_upload(ip, OPTIMIZATION_MD, "/home/azureuser/optimization.md")
|
||||
|
||||
print(" Uploading codeflash-agent plugin...")
|
||||
scp_upload(ip, CODEFLASH_AGENT_DIST, "/home/azureuser/codeflash-agent/dist")
|
||||
|
||||
print(" Uploading bootstrap.sh...")
|
||||
# Read bootstrap, substitute placeholders
|
||||
bootstrap_content = BOOTSTRAP_SH.read_text()
|
||||
bootstrap_content = bootstrap_content.replace("__REPO_NAME__", repo_name)
|
||||
bootstrap_content = bootstrap_content.replace("__GITHUB_PAT__", GITHUB_PAT)
|
||||
bootstrap_content = bootstrap_content.replace("__AWS_BEARER_TOKEN_BEDROCK__", AWS_BEARER_TOKEN_BEDROCK)
|
||||
bootstrap_content = bootstrap_content.replace("__CODEFLASH_API_KEY__", CODEFLASH_API_KEY)
|
||||
|
||||
# Write temp file, upload, execute
|
||||
tmp_bootstrap = BASE_DIR / ".tmp_bootstrap.sh"
|
||||
tmp_bootstrap.write_text(bootstrap_content)
|
||||
scp_upload(ip, tmp_bootstrap, "/home/azureuser/bootstrap.sh")
|
||||
tmp_bootstrap.unlink()
|
||||
|
||||
print(" Running bootstrap (this takes a few minutes)...")
|
||||
output = ssh_exec(ip, "chmod +x ~/bootstrap.sh && ~/bootstrap.sh", timeout=600)
|
||||
print(output)
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# Run Claude Code
|
||||
# ===========================================================================
|
||||
|
||||
def launch_claude(ip: str) -> None:
|
||||
"""Launch Claude Code on the VM in the background."""
|
||||
home = f"/home/{AZURE_ADMIN_USER}"
|
||||
cmd = dedent(f"""\
|
||||
nohup bash -c '
|
||||
source {home}/.env_universe 2>/dev/null
|
||||
cd {home}/project && claude \\
|
||||
--dangerously-skip-permissions \\
|
||||
--plugin-dir {home}/codeflash-agent/dist \\
|
||||
--model opus \\
|
||||
--max-turns 400 \\
|
||||
--print \\
|
||||
-p "Read the CLAUDE.md file and follow its instructions exactly." \\
|
||||
< /dev/null \\
|
||||
2>&1 | tee {home}/results/claude_output.log
|
||||
' > /dev/null 2>&1 &
|
||||
""")
|
||||
ssh_exec(ip, cmd, timeout=30)
|
||||
print(" Claude Code launched in background.")
|
||||
|
||||
|
||||
def check_vm_status(ip: str) -> dict:
|
||||
"""Check if Claude is still running and if results are ready."""
|
||||
try:
|
||||
is_running = ssh_exec(ip, "pgrep -f 'claude' > /dev/null 2>&1 && echo running || echo done", timeout=15)
|
||||
has_results = ssh_exec(ip, "test -f ~/results/summary.json && echo yes || echo no", timeout=15)
|
||||
return {
|
||||
"reachable": True,
|
||||
"is_running": is_running.strip() == "running",
|
||||
"has_results": has_results.strip() == "yes",
|
||||
}
|
||||
except Exception as e:
|
||||
return {"reachable": False, "is_running": False, "has_results": False, "error": str(e)}
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# Results collection
|
||||
# ===========================================================================
|
||||
|
||||
def collect_results(project_id: str, ip: str) -> dict | None:
|
||||
"""Download results from the VM. Returns parsed summary or None."""
|
||||
home = f"/home/{AZURE_ADMIN_USER}"
|
||||
result_dir = RESULTS_DIR / project_id
|
||||
result_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Try both ~/results/ (written by optimization.md) and ~/project/.codeflash/ (written by agent)
|
||||
files = [
|
||||
(f"{home}/results/summary.json", result_dir / "summary.json"),
|
||||
(f"{home}/results/results.tsv", result_dir / "results.tsv"),
|
||||
(f"{home}/results/HANDOFF.md", result_dir / "HANDOFF.md"),
|
||||
(f"{home}/results/claude_output.log", result_dir / "claude_output.log"),
|
||||
(f"{home}/project/.codeflash/results.tsv", result_dir / "results.tsv"),
|
||||
(f"{home}/project/.codeflash/HANDOFF.md", result_dir / "HANDOFF.md"),
|
||||
]
|
||||
|
||||
for remote, local in files:
|
||||
scp_download(ip, remote, local)
|
||||
|
||||
summary_path = result_dir / "summary.json"
|
||||
if summary_path.exists() and summary_path.stat().st_size > 0:
|
||||
with open(summary_path) as f:
|
||||
return json.load(f)
|
||||
return None
|
||||
|
||||
|
||||
def verify_and_push(ip: str) -> bool:
|
||||
"""Verify the optimization branch was pushed; push if not."""
|
||||
home = f"/home/{AZURE_ADMIN_USER}"
|
||||
check = ssh_exec(ip, f"cd {home}/project && git log --oneline origin/codeflash/optimize -1 2>/dev/null || echo NOT_PUSHED", timeout=30)
|
||||
if "NOT_PUSHED" in check:
|
||||
print(" Branch not pushed yet, pushing now...")
|
||||
result = ssh_exec(ip, f"cd {home}/project && git push origin codeflash/optimize 2>&1", timeout=60)
|
||||
print(f" {result.strip()}")
|
||||
return "NOT_PUSHED" not in result
|
||||
print(f" Branch already pushed: {check.strip()}")
|
||||
return True
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# Email system
|
||||
# ===========================================================================
|
||||
|
||||
def build_summary_sentence(optimizations: list[dict]) -> str:
|
||||
"""Build a human-readable summary of optimizations."""
|
||||
if not optimizations:
|
||||
return ""
|
||||
|
||||
parts = []
|
||||
for opt in optimizations[:3]: # top 3
|
||||
func = opt.get("function", "unknown")
|
||||
speedup = opt.get("cpu_speedup", "")
|
||||
technique = opt.get("technique", "")
|
||||
if speedup:
|
||||
parts.append(f"{speedup} {func}")
|
||||
elif technique:
|
||||
parts.append(f"{func} ({technique})")
|
||||
else:
|
||||
parts.append(func)
|
||||
|
||||
count = len(optimizations)
|
||||
if count <= 3:
|
||||
return f"{count} merge-ready commits including " + ", ".join(parts)
|
||||
return f"{count} merge-ready commits including " + ", ".join(parts) + f", and {count - 3} more"
|
||||
|
||||
|
||||
def build_context(project_id: str) -> dict | None:
|
||||
"""Build context.json from summary.json + projects.json."""
|
||||
project = load_project(project_id)
|
||||
summary_path = RESULTS_DIR / project_id / "summary.json"
|
||||
|
||||
if not summary_path.exists():
|
||||
print(f" No summary.json found for {project_id}")
|
||||
return None
|
||||
|
||||
with open(summary_path) as f:
|
||||
summary = json.load(f)
|
||||
|
||||
if summary.get("status") == "failed" or summary.get("total_keeps", 0) == 0:
|
||||
print(f" Skipping {project_id}: status={summary.get('status')}, keeps={summary.get('total_keeps', 0)}")
|
||||
return None
|
||||
|
||||
best = summary.get("headline_stats", {})
|
||||
opts = summary.get("optimizations", [])
|
||||
repo_name = project["repo"].split("/")[1]
|
||||
|
||||
context = {
|
||||
"first_name": project["target_contact"]["name"].split()[0],
|
||||
"full_name": project["target_contact"]["name"],
|
||||
"title": project["target_contact"]["title"],
|
||||
"company_name": project.get("company", ""),
|
||||
"repo": project["repo"],
|
||||
"repo_name": repo_name,
|
||||
"fork_url": f"https://github.com/{GITHUB_ORG}/{repo_name}",
|
||||
"branch_url": f"https://github.com/{GITHUB_ORG}/{repo_name}/tree/codeflash/optimize",
|
||||
"num_optimizations": summary.get("total_keeps", 0),
|
||||
"best_function": best.get("best_function", ""),
|
||||
"best_speedup": best.get("best_single_speedup", ""),
|
||||
"best_description": opts[0]["description"] if opts else "",
|
||||
"second_best_function": opts[1]["function"] if len(opts) > 1 else "",
|
||||
"second_best_technique": opts[1].get("technique", "") if len(opts) > 1 else "",
|
||||
"total_cpu_improvement_pct": best.get("total_cpu_improvement_pct", 0),
|
||||
"total_memory_saved_mb": best.get("total_memory_saved_mb", 0),
|
||||
"optimizations_summary": build_summary_sentence(opts),
|
||||
"calendly_link": CALENDLY_LINK,
|
||||
}
|
||||
|
||||
context_path = RESULTS_DIR / project_id / "context.json"
|
||||
with open(context_path, "w") as f:
|
||||
json.dump(context, f, indent=2)
|
||||
print(f" Built context.json for {project_id}")
|
||||
return context
|
||||
|
||||
|
||||
def render_emails(project_id: str) -> None:
|
||||
"""Render all email templates for a project using its context.json."""
|
||||
context_path = RESULTS_DIR / project_id / "context.json"
|
||||
if not context_path.exists():
|
||||
print(f" No context.json for {project_id}. Run 'email {project_id}' first.")
|
||||
return
|
||||
|
||||
with open(context_path) as f:
|
||||
context = json.load(f)
|
||||
|
||||
emails_dir = RESULTS_DIR / project_id / "emails"
|
||||
emails_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
templates = sorted(EMAIL_TEMPLATES_DIR.glob("email_*.md"))
|
||||
if not templates:
|
||||
print(" No email templates found in email_templates/")
|
||||
return
|
||||
|
||||
for template_path in templates:
|
||||
template = template_path.read_text()
|
||||
try:
|
||||
rendered = template.format(**context)
|
||||
except KeyError as e:
|
||||
print(f" Warning: missing variable {e} in template {template_path.name}")
|
||||
continue
|
||||
|
||||
out_path = emails_dir / template_path.name
|
||||
out_path.write_text(rendered)
|
||||
print(f" Rendered {out_path.name}")
|
||||
|
||||
|
||||
def render_all_emails() -> None:
|
||||
"""Re-render emails for all projects that have context.json."""
|
||||
if not RESULTS_DIR.exists():
|
||||
print("No results directory found.")
|
||||
return
|
||||
|
||||
for project_dir in sorted(RESULTS_DIR.iterdir()):
|
||||
if not project_dir.is_dir():
|
||||
continue
|
||||
context_path = project_dir / "context.json"
|
||||
if context_path.exists():
|
||||
print(f"Re-rendering {project_dir.name}...")
|
||||
render_emails(project_dir.name)
|
||||
|
||||
|
||||
def show_emails(project_id: str) -> None:
|
||||
"""Print rendered emails to stdout."""
|
||||
emails_dir = RESULTS_DIR / project_id / "emails"
|
||||
if not emails_dir.exists():
|
||||
print(f"No rendered emails for {project_id}. Run 'email {project_id}' first.")
|
||||
return
|
||||
|
||||
for email_path in sorted(emails_dir.glob("email_*.md")):
|
||||
print(f"\n{'=' * 60}")
|
||||
print(f" {email_path.name}")
|
||||
print(f"{'=' * 60}")
|
||||
print(email_path.read_text())
|
||||
|
||||
|
||||
def show_context(project_id: str) -> None:
|
||||
"""Print context.json to stdout."""
|
||||
context_path = RESULTS_DIR / project_id / "context.json"
|
||||
if not context_path.exists():
|
||||
print(f"No context.json for {project_id}.")
|
||||
return
|
||||
|
||||
with open(context_path) as f:
|
||||
print(json.dumps(json.load(f), indent=2))
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# Commands
|
||||
# ===========================================================================
|
||||
|
||||
def cmd_provision(args: argparse.Namespace) -> None:
|
||||
project_id = args.project_id
|
||||
project = load_project(project_id)
|
||||
db = get_db()
|
||||
upsert_project(db, project)
|
||||
|
||||
print(f"\n[provision] {project_id}: {project['repo']}")
|
||||
|
||||
# Step 1: Fork
|
||||
print("\n Step 1: Forking repo...")
|
||||
fork_url = fork_repo(project["repo"])
|
||||
update_status(db, project_id, "provisioning", fork_url=fork_url)
|
||||
|
||||
# Step 2: Provision VM
|
||||
print("\n Step 2: Provisioning Azure VM...")
|
||||
vm_name, vm_ip = provision_vm(project_id, project["repo"].split("/")[1])
|
||||
update_status(db, project_id, "provisioning", vm_name=vm_name, vm_ip=vm_ip)
|
||||
|
||||
# Wait for VM to be SSH-ready
|
||||
print("\n Waiting for SSH to become available...")
|
||||
for attempt in range(30):
|
||||
try:
|
||||
ssh_exec(vm_ip, "echo ready", timeout=10)
|
||||
break
|
||||
except Exception:
|
||||
time.sleep(10)
|
||||
else:
|
||||
print(" ERROR: VM not reachable via SSH after 5 minutes")
|
||||
update_status(db, project_id, "failed")
|
||||
return
|
||||
|
||||
# Step 3: Bootstrap
|
||||
print("\n Step 3: Bootstrapping VM...")
|
||||
bootstrap_vm(vm_ip, project)
|
||||
update_status(db, project_id, "provisioned")
|
||||
|
||||
print(f"\n[provision] Done. VM ready at {vm_ip}. Run: python orchestrator.py run {project_id}")
|
||||
|
||||
|
||||
def cmd_run(args: argparse.Namespace) -> None:
|
||||
project_id = args.project_id
|
||||
db = get_db()
|
||||
row = get_project_row(db, project_id)
|
||||
|
||||
if not row:
|
||||
print(f"Project {project_id} not in DB. Run 'provision' first.")
|
||||
return
|
||||
if not row["vm_ip"]:
|
||||
print(f"No VM IP for {project_id}. Run 'provision' first.")
|
||||
return
|
||||
|
||||
print(f"\n[run] Launching Claude Code on {row['vm_ip']}...")
|
||||
launch_claude(row["vm_ip"])
|
||||
update_status(db, project_id, "running", started_at=datetime.now(timezone.utc).isoformat())
|
||||
print(f"[run] Done. Monitor with: python orchestrator.py status {project_id}")
|
||||
|
||||
|
||||
def cmd_status(args: argparse.Namespace) -> None:
|
||||
db = get_db()
|
||||
|
||||
if args.project_id:
|
||||
# Single project status
|
||||
row = get_project_row(db, args.project_id)
|
||||
if not row:
|
||||
print(f"Project {args.project_id} not found in DB.")
|
||||
return
|
||||
|
||||
print(f"\nProject: {row['id']}")
|
||||
print(f"Repo: {row['repo']}")
|
||||
print(f"Status: {row['status']}")
|
||||
print(f"VM: {row['vm_name'] or '-'} ({row['vm_ip'] or '-'})")
|
||||
print(f"Fork: {row['fork_url'] or '-'}")
|
||||
print(f"Started: {row['started_at'] or '-'}")
|
||||
|
||||
if row["vm_ip"] and row["status"] == "running":
|
||||
vm_status = check_vm_status(row["vm_ip"])
|
||||
print(f"Claude: {'running' if vm_status['is_running'] else 'stopped'}")
|
||||
print(f"Results: {'ready' if vm_status['has_results'] else 'not yet'}")
|
||||
|
||||
if row["num_optimizations"]:
|
||||
print(f"Optimizations: {row['num_optimizations']}")
|
||||
print(f"Best speedup: {row['best_speedup'] or '-'}")
|
||||
else:
|
||||
# Dashboard
|
||||
rows = db.execute("SELECT * FROM projects ORDER BY id").fetchall()
|
||||
if not rows:
|
||||
print("No projects in DB. Add to projects.json and run 'provision'.")
|
||||
return
|
||||
|
||||
print(f"\n{'ID':<15} {'Repo':<30} {'Status':<14} {'Opts':>5} {'Best':<12} {'Email'}")
|
||||
print("-" * 90)
|
||||
for row in rows:
|
||||
email_status = "-"
|
||||
email_dir = RESULTS_DIR / row["id"] / "emails"
|
||||
if email_dir.exists() and list(email_dir.glob("email_*.md")):
|
||||
email_status = "DRAFT READY"
|
||||
|
||||
print(f"{row['id']:<15} {row['repo']:<30} {row['status']:<14} "
|
||||
f"{row['num_optimizations'] or 0:>5} {row['best_speedup'] or '-':<12} {email_status}")
|
||||
|
||||
|
||||
def cmd_monitor(args: argparse.Namespace) -> None:
|
||||
interval = args.interval
|
||||
db = get_db()
|
||||
|
||||
print(f"[monitor] Polling running VMs every {interval}s. Ctrl-C to stop.\n")
|
||||
|
||||
while True:
|
||||
rows = db.execute("SELECT * FROM projects WHERE status = 'running'").fetchall()
|
||||
if not rows:
|
||||
print(" No running projects.")
|
||||
break
|
||||
|
||||
for row in rows:
|
||||
ip = row["vm_ip"]
|
||||
pid = row["id"]
|
||||
status = check_vm_status(ip)
|
||||
|
||||
elapsed = ""
|
||||
if row["started_at"]:
|
||||
started = datetime.fromisoformat(row["started_at"])
|
||||
elapsed_s = (datetime.now(timezone.utc) - started).total_seconds()
|
||||
elapsed = f" ({int(elapsed_s / 60)}m elapsed)"
|
||||
|
||||
if elapsed_s > VM_TIMEOUT_SECONDS and not status["has_results"]:
|
||||
print(f" {pid}: TIMEOUT after {int(elapsed_s / 3600)}h. Killing claude...")
|
||||
ssh_exec(ip, "pkill -f claude || true", timeout=15)
|
||||
time.sleep(60)
|
||||
status = check_vm_status(ip)
|
||||
|
||||
if status["has_results"] and not status["is_running"]:
|
||||
print(f" {pid}: COMPLETED{elapsed}")
|
||||
update_status(db, pid, "completed", completed_at=datetime.now(timezone.utc).isoformat())
|
||||
elif status["is_running"]:
|
||||
print(f" {pid}: running{elapsed}")
|
||||
elif not status["reachable"]:
|
||||
print(f" {pid}: UNREACHABLE - {status.get('error', '')}")
|
||||
else:
|
||||
print(f" {pid}: claude stopped, no results{elapsed}")
|
||||
|
||||
print()
|
||||
time.sleep(interval)
|
||||
|
||||
|
||||
def cmd_collect(args: argparse.Namespace) -> None:
|
||||
project_id = args.project_id
|
||||
db = get_db()
|
||||
row = get_project_row(db, project_id)
|
||||
|
||||
if not row or not row["vm_ip"]:
|
||||
print(f"No VM found for {project_id}.")
|
||||
return
|
||||
|
||||
ip = row["vm_ip"]
|
||||
print(f"\n[collect] {project_id} from {ip}")
|
||||
|
||||
# Step 1: Download results
|
||||
print("\n Step 1: Downloading results...")
|
||||
summary = collect_results(project_id, ip)
|
||||
|
||||
# Step 2: Verify branch pushed
|
||||
print("\n Step 2: Verifying branch push...")
|
||||
verify_and_push(ip)
|
||||
|
||||
# Step 3: Update DB
|
||||
if summary:
|
||||
update_status(
|
||||
db, project_id, "completed",
|
||||
completed_at=datetime.now(timezone.utc).isoformat(),
|
||||
num_optimizations=summary.get("total_keeps", 0),
|
||||
best_speedup=summary.get("headline_stats", {}).get("best_single_speedup", ""),
|
||||
optimization_branch="codeflash/optimize",
|
||||
summary_json=json.dumps(summary),
|
||||
)
|
||||
print(f"\n Results: {summary.get('total_keeps', 0)} optimizations kept")
|
||||
else:
|
||||
update_status(db, project_id, "completed",
|
||||
completed_at=datetime.now(timezone.utc).isoformat())
|
||||
print("\n Warning: No summary.json found. Marked as completed with no results.")
|
||||
|
||||
# Step 4: Destroy VM
|
||||
if not args.keep_vm:
|
||||
print("\n Step 3: Destroying VM...")
|
||||
destroy_vm(project_id)
|
||||
update_status(db, project_id, "destroyed")
|
||||
else:
|
||||
print("\n Keeping VM alive (--keep-vm flag).")
|
||||
|
||||
print(f"\n[collect] Done. Results in results/{project_id}/")
|
||||
print(f" Next: python orchestrator.py email {project_id}")
|
||||
|
||||
|
||||
def cmd_results(args: argparse.Namespace) -> None:
|
||||
project_id = args.project_id
|
||||
db = get_db()
|
||||
row = get_project_row(db, project_id)
|
||||
|
||||
summary_path = RESULTS_DIR / project_id / "summary.json"
|
||||
if not summary_path.exists():
|
||||
print(f"No results for {project_id}. Run 'collect' first.")
|
||||
return
|
||||
|
||||
with open(summary_path) as f:
|
||||
summary = json.load(f)
|
||||
|
||||
project = load_project(project_id)
|
||||
repo_name = project["repo"].split("/")[1]
|
||||
contact = project.get("target_contact", {})
|
||||
|
||||
print(f"\nProject: {project['repo']}")
|
||||
print(f"Status: {summary.get('status', 'unknown')}")
|
||||
print(f"Branch: https://github.com/{GITHUB_ORG}/{repo_name}/tree/codeflash/optimize")
|
||||
print(f"Experiments: {summary.get('total_experiments', 0)} total "
|
||||
f"({summary.get('total_keeps', 0)} kept, {summary.get('total_discards', 0)} discarded)")
|
||||
|
||||
opts = summary.get("optimizations", [])
|
||||
if opts:
|
||||
print(f"\nTop optimizations:")
|
||||
for i, opt in enumerate(opts[:10], 1):
|
||||
speedup = opt.get("cpu_speedup", "")
|
||||
desc = opt.get("technique", opt.get("description", ""))
|
||||
print(f" {i}. {opt.get('function', '?'):<25} {speedup:<15} {desc}")
|
||||
|
||||
hs = summary.get("headline_stats", {})
|
||||
cpu_pct = hs.get("total_cpu_improvement_pct", 0)
|
||||
mem_mb = hs.get("total_memory_saved_mb", 0)
|
||||
if cpu_pct or mem_mb:
|
||||
print(f"\nHeadline: {cpu_pct}% total CPU improvement, {mem_mb} MiB memory saved")
|
||||
|
||||
if contact.get("name"):
|
||||
print(f"\nContact: {contact['name']} ({contact.get('title', '')}) -- {contact.get('email', '')}")
|
||||
print(f"Emails: results/{project_id}/emails/")
|
||||
|
||||
|
||||
def cmd_email(args: argparse.Namespace) -> None:
|
||||
if args.rerender_all:
|
||||
print("[email] Re-rendering all project emails...")
|
||||
render_all_emails()
|
||||
return
|
||||
|
||||
project_id = args.project_id
|
||||
if not project_id:
|
||||
print("Usage: email <project-id> or email --rerender-all")
|
||||
return
|
||||
|
||||
if args.show_context:
|
||||
show_context(project_id)
|
||||
return
|
||||
|
||||
if args.show:
|
||||
show_emails(project_id)
|
||||
return
|
||||
|
||||
# Default: build context + render
|
||||
print(f"[email] Building context and rendering emails for {project_id}...")
|
||||
ctx = build_context(project_id)
|
||||
if ctx:
|
||||
render_emails(project_id)
|
||||
print(f"\n[email] Done. Preview with: python orchestrator.py email {project_id} --show")
|
||||
else:
|
||||
print("[email] Could not build context (no results or all failed).")
|
||||
|
||||
|
||||
def cmd_mark_sent(args: argparse.Namespace) -> None:
|
||||
db = get_db()
|
||||
update_status(db, args.project_id, "email_sent")
|
||||
print(f"Marked {args.project_id} as email_sent.")
|
||||
|
||||
|
||||
def cmd_destroy(args: argparse.Namespace) -> None:
|
||||
project_id = args.project_id
|
||||
db = get_db()
|
||||
print(f"[destroy] Destroying VM for {project_id}...")
|
||||
destroy_vm(project_id)
|
||||
update_status(db, project_id, "destroyed")
|
||||
print("[destroy] Done.")
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# CLI
|
||||
# ===========================================================================
|
||||
|
||||
def main() -> None:
|
||||
parser = argparse.ArgumentParser(description="Universe Optimize Orchestrator")
|
||||
sub = parser.add_subparsers(dest="command")
|
||||
|
||||
p_provision = sub.add_parser("provision", help="Provision VM, fork repo, bootstrap")
|
||||
p_provision.add_argument("project_id")
|
||||
|
||||
p_run = sub.add_parser("run", help="Launch Claude Code on the VM")
|
||||
p_run.add_argument("project_id")
|
||||
|
||||
p_status = sub.add_parser("status", help="Show status dashboard or single project")
|
||||
p_status.add_argument("project_id", nargs="?")
|
||||
|
||||
p_monitor = sub.add_parser("monitor", help="Poll running VMs periodically")
|
||||
p_monitor.add_argument("--interval", type=int, default=600, help="Poll interval in seconds")
|
||||
|
||||
p_collect = sub.add_parser("collect", help="Collect results and destroy VM")
|
||||
p_collect.add_argument("project_id")
|
||||
p_collect.add_argument("--keep-vm", action="store_true", help="Don't destroy VM after collecting")
|
||||
|
||||
p_results = sub.add_parser("results", help="Show detailed optimization results")
|
||||
p_results.add_argument("project_id")
|
||||
|
||||
p_email = sub.add_parser("email", help="Build context and render email drafts")
|
||||
p_email.add_argument("project_id", nargs="?")
|
||||
p_email.add_argument("--rerender-all", action="store_true", help="Re-render all project emails")
|
||||
p_email.add_argument("--show", action="store_true", help="Preview rendered emails")
|
||||
p_email.add_argument("--show-context", action="store_true", help="Show raw context.json")
|
||||
|
||||
p_sent = sub.add_parser("mark-sent", help="Mark project emails as sent")
|
||||
p_sent.add_argument("project_id")
|
||||
|
||||
p_destroy = sub.add_parser("destroy", help="Destroy VM without collecting results")
|
||||
p_destroy.add_argument("project_id")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if not args.command:
|
||||
parser.print_help()
|
||||
return
|
||||
|
||||
commands = {
|
||||
"provision": cmd_provision,
|
||||
"run": cmd_run,
|
||||
"status": cmd_status,
|
||||
"monitor": cmd_monitor,
|
||||
"collect": cmd_collect,
|
||||
"results": cmd_results,
|
||||
"email": cmd_email,
|
||||
"mark-sent": cmd_mark_sent,
|
||||
"destroy": cmd_destroy,
|
||||
}
|
||||
commands[args.command](args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -1,11 +1,9 @@
|
|||
Top level plan:
|
||||
|
||||
1. Research 1000 projects on github that are importnat and popular and in Python, javascript/typescript and java. They are either used by important companies or are used by many people.
|
||||
2. For each of the above project, start a new virtual machine on Azure with Ubuntu and 4 CPUs and 16GB RAM (choose a similar machine). On this machine download claude and setup the API key to access claude (to fill in later).
|
||||
1. Research 1000 projects on github that are important and popular and in Python, javascript/typescript and java. They are either used by important companies or are used by many people.
|
||||
2. For each of the above project, start a new virtual machine on Azure with Ubuntu and 4 CPUs and 16GB RAM (choose a similar machine). On this machine download claude and setup the API key to access claude (to fill in later). When you start claude, run it in --dangerously-skip-permissions mode and with --plugin-dir=.path/to/codeflash-agent/dist. Codeflash agent should be copied to this machine from /Users/saurabh/Library/CloudStorage/Dropbox/hacks/codeflash-agent/dist. Instructions on how to use this codeflash-agent will follow
|
||||
3. Fork the chosen repo on to the codeflash-ai github org and clone the fork on the new virtual machine.
|
||||
4. Understand the source code of the project and try to understand what it does and how.
|
||||
5. Setup the project to run on the new virtual machine. Install the dependencies and try to get the unit tests to pass.
|
||||
6. Now we want to optimize the project. To do so, we will first use the understanding of the project to come with a few end to end use cases as performance benchmarks. Run these and ensure that they run and seem reasonable.
|
||||
7. Use the codeflash-agent to optimize the found benchmarks. Codeflash agent will profile the code and work in an incremental fashion to optimize the benchmark piece by piece. This will open individual PRs to the github repo.
|
||||
8. Try to create a branch with all the optimizations merged in for a the given performance benchmark and try to measure the end to end performance optimization results wrt the original benchmark. Try to fix any bugs that might arise from merging all the branches together and try to gain confidence on the benchmarking scheme here.
|
||||
9. Write an email at the end with the purpose of it being selling the results of the optimization to an engineering leader at the copmany of the project or the main developer of the project.
|
||||
5. From there we will initiate optimization process start using `/codeflash-optimize start` command. Do read what the plugin does to know what to expect. This will setup the project on the VM, and setup benchmarks and find optimizations.
|
||||
6. Codeflash agents will find optimizations and create a stacked branch with a lot of the optimizations. Collect the results of the optimizations for the next step.
|
||||
9. Write an email at the end with the purpose of it being selling the results of the optimization to an engineering leader at the company of the project or the main developer of the project.
|
||||
|
|
|
|||
720
experiments/universe-optimize/program.md
Normal file
720
experiments/universe-optimize/program.md
Normal file
|
|
@ -0,0 +1,720 @@
|
|||
# Universe Optimize: Automated Optimization Outbound Program
|
||||
|
||||
## Overview
|
||||
|
||||
This program automates the full pipeline: discover high-value open-source projects, fork them, spin up Azure VMs, run Codeflash optimizations via Claude Code, collect results, and draft personalized outreach emails with real optimization proof points. The orchestrator runs locally and manages everything end-to-end.
|
||||
|
||||
---
|
||||
|
||||
## Architecture
|
||||
|
||||
```
|
||||
Local Machine (Orchestrator)
|
||||
|
|
||||
|-- orchestrator.py (Python script that manages the full pipeline)
|
||||
| |
|
||||
| |-- Azure SDK: provisions/destroys VMs
|
||||
| |-- GitHub API: forks repos to codeflash-ai org
|
||||
| |-- SSH (paramiko): executes commands on VMs
|
||||
| |-- Results DB: SQLite tracking all projects/VMs/results
|
||||
| |-- Email drafter: generates personalized emails from results
|
||||
|
|
||||
+-- VM 1 (Azure Ubuntu, 4 CPU / 16GB RAM)
|
||||
| |-- Claude Code (--dangerously-skip-permissions, --plugin-dir=codeflash-agent)
|
||||
| |-- Forked repo cloned
|
||||
| |-- optimization.md injected as CLAUDE.md instructions
|
||||
| |-- Results written to .codeflash/results.tsv + summary.json
|
||||
|
|
||||
+-- VM 2 ...
|
||||
+-- VM N ...
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Phase 0: Project Discovery (Manual for POC, Automated Later)
|
||||
|
||||
### POC: Hand-pick 2 projects
|
||||
|
||||
For the proof of concept, manually select 2 projects that are:
|
||||
- Popular (>1k stars), actively maintained
|
||||
- Python, JavaScript/TypeScript, or Java
|
||||
- Have a test suite (pytest, jest, JUnit) so optimizations can be verified
|
||||
- Backed by a company or used by companies where we can identify an engineering leader to email
|
||||
- Performance-sensitive domain (data processing, web frameworks, databases, ML infra, etc.)
|
||||
|
||||
Store project metadata in `projects.json`:
|
||||
|
||||
```json
|
||||
[
|
||||
{
|
||||
"id": "project-001",
|
||||
"repo": "org/repo-name",
|
||||
"language": "python",
|
||||
"stars": 5200,
|
||||
"description": "...",
|
||||
"company": "Company Name",
|
||||
"domain": "data processing",
|
||||
"target_contact": {
|
||||
"name": "First Last",
|
||||
"title": "CTO",
|
||||
"email": "...",
|
||||
"linkedin": "..."
|
||||
},
|
||||
"why_selected": "High star count, performance-critical data pipeline, active maintenance, company has 50-200 employees"
|
||||
}
|
||||
]
|
||||
```
|
||||
|
||||
### At Scale (Post-POC)
|
||||
|
||||
Use GitHub API to search for repos matching criteria:
|
||||
- `stars:>1000 language:python` (repeat for JS/TS, Java)
|
||||
- Filter by: has CI, recent commits, has test suite, identifiable company/maintainer
|
||||
- Cross-reference with Sumble/Apollo APIs (as in existing outbound program) to find the right engineering contact
|
||||
- Target 1000 projects total across Python, JS/TS, Java
|
||||
|
||||
---
|
||||
|
||||
## Phase 1: Infrastructure Provisioning
|
||||
|
||||
### What the Orchestrator Does
|
||||
|
||||
The orchestrator (`orchestrator.py`) is a Python script that manages the full lifecycle. It uses:
|
||||
- **Azure SDK (`azure-mgmt-compute`, `azure-identity`)** to provision and destroy VMs
|
||||
- **Paramiko** for SSH command execution on VMs
|
||||
- **GitHub API (`PyGithub`)** for forking repos
|
||||
- **SQLite** for tracking state across runs
|
||||
|
||||
### VM Provisioning
|
||||
|
||||
For each project in `projects.json`:
|
||||
|
||||
1. **Fork the repo** to the `codeflash-ai` GitHub org:
|
||||
```python
|
||||
github.get_repo("org/repo").create_fork(organization="codeflash-ai")
|
||||
```
|
||||
|
||||
2. **Provision an Azure VM**:
|
||||
- Image: Ubuntu 24.04 LTS
|
||||
- Size: `Standard_D4s_v5` (4 vCPU, 16 GB RAM)
|
||||
- Region: East US (or nearest to minimize latency)
|
||||
- Disk: 128 GB SSD
|
||||
- NSG: SSH only (port 22), locked to orchestrator's IP
|
||||
- Tags: `{"project": "universe-optimize", "repo": "org/repo-name", "id": "project-001"}`
|
||||
|
||||
3. **Bootstrap the VM** via SSH (single setup script):
|
||||
```bash
|
||||
#!/bin/bash
|
||||
set -euo pipefail
|
||||
|
||||
# System deps
|
||||
sudo apt-get update && sudo apt-get install -y git curl build-essential python3-dev
|
||||
|
||||
# Install language runtimes as needed
|
||||
# Python: install uv
|
||||
curl -LsSf https://astral.sh/uv/install.sh | sh
|
||||
source $HOME/.local/bin/env
|
||||
|
||||
# Node (for JS/TS projects)
|
||||
curl -fsSL https://deb.nodesource.com/setup_22.x | sudo -E bash -
|
||||
sudo apt-get install -y nodejs
|
||||
|
||||
# Java (for Java projects)
|
||||
sudo apt-get install -y openjdk-21-jdk maven gradle
|
||||
|
||||
# Install Claude Code CLI
|
||||
curl -fsSL https://claude.ai/install.sh | bash
|
||||
|
||||
# Set up these API keys in .bashrc
|
||||
CODEFLASH_API_KEY=cf-kNZFz7nM3Tl3wY4t5Kh1E58A-UhxKvLhokyTfFIz5YJ_xJJLcBBFrdp8kJF6G8ld
|
||||
CLAUDE_CODE_USE_BEDROCK=1
|
||||
AWS_REGION=us-east-1
|
||||
AWS_BEARER_TOKEN_BEDROCK=ABSKc2FydGhha0Bjb2RlZmxhc2guYWkrMS1hdC05OTIzODI0NjM5MDc6bS9tdGx4SW0wQ08yazMwU3QxZFdlbWRWeTM0NnJWZElBZmFNNmVobC9UU2tRVTBPQm4wUXVPS3ZFQWs9
|
||||
LC_ALL=en_US.UTF-8
|
||||
|
||||
source ~/.bashrc
|
||||
|
||||
# Copy codeflash-agent plugin
|
||||
mkdir -p ~/codeflash-agent
|
||||
# (orchestrator SCPs the dist/ directory here)
|
||||
|
||||
# Clone the forked repo
|
||||
git clone https://github.com/codeflash-ai/<repo-name>.git ~/project
|
||||
cd ~/project
|
||||
|
||||
# Inject optimization.md as the project's CLAUDE.md
|
||||
cp ~/optimization.md ~/project/CLAUDE.md
|
||||
```
|
||||
|
||||
### State Tracking
|
||||
|
||||
The orchestrator maintains a SQLite DB (`universe_optimize.db`):
|
||||
|
||||
```sql
|
||||
CREATE TABLE projects (
|
||||
id TEXT PRIMARY KEY,
|
||||
repo TEXT NOT NULL,
|
||||
language TEXT NOT NULL,
|
||||
fork_url TEXT,
|
||||
company TEXT,
|
||||
contact_name TEXT,
|
||||
contact_email TEXT,
|
||||
contact_title TEXT,
|
||||
status TEXT DEFAULT 'pending', -- pending, provisioning, running, completed, failed, destroyed
|
||||
vm_id TEXT,
|
||||
vm_ip TEXT,
|
||||
created_at TIMESTAMP,
|
||||
started_at TIMESTAMP,
|
||||
completed_at TIMESTAMP,
|
||||
optimization_branch TEXT,
|
||||
num_optimizations INTEGER DEFAULT 0,
|
||||
best_speedup TEXT,
|
||||
summary_json TEXT,
|
||||
email_draft_path TEXT
|
||||
);
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Phase 2: Running the Optimization
|
||||
|
||||
### optimization.md (Injected as CLAUDE.md on Each VM)
|
||||
|
||||
This file tells Claude Code exactly what to do when it starts. It is placed as `CLAUDE.md` in the project root before Claude is launched.
|
||||
|
||||
```markdown
|
||||
# Codeflash Optimization Run
|
||||
|
||||
You are running an automated optimization session on this open-source project.
|
||||
Your goal is to find as many provably-faster code implementations as possible
|
||||
and stack them as commits on a single branch.
|
||||
|
||||
## Your Mission
|
||||
|
||||
1. Understand the project: read the README, project structure, and key source files.
|
||||
2. Set up the project: install dependencies, verify tests pass.
|
||||
3. Run `/codeflash-optimize start` and when asked for context, respond with "go".
|
||||
4. Let the optimization agent work. It will:
|
||||
- Profile the codebase (CPU, memory, GC)
|
||||
- Identify bottleneck functions
|
||||
- Implement optimizations one at a time
|
||||
- Verify each with tests
|
||||
- Commit each successful optimization
|
||||
5. When the optimization agent completes (or plateaus), collect results.
|
||||
|
||||
## After Optimization Completes
|
||||
|
||||
Write a file `~/results/summary.json` with the following structure:
|
||||
|
||||
```json
|
||||
{
|
||||
"repo": "<org/repo>",
|
||||
"language": "<python|javascript|java>",
|
||||
"branch": "<optimization branch name>",
|
||||
"total_experiments": <N>,
|
||||
"total_keeps": <N>,
|
||||
"total_discards": <N>,
|
||||
"optimizations": [
|
||||
{
|
||||
"commit": "<sha>",
|
||||
"function": "<function_name>",
|
||||
"file": "<file_path>",
|
||||
"description": "<what was optimized>",
|
||||
"cpu_speedup": "<e.g. 2.3x faster>",
|
||||
"memory_reduction": "<e.g. -50 MiB>",
|
||||
"technique": "<e.g. replaced list with set, eliminated deepcopy>"
|
||||
}
|
||||
],
|
||||
"headline_stats": {
|
||||
"best_single_speedup": "<e.g. 5x faster>",
|
||||
"best_function": "<function_name>",
|
||||
"total_cpu_improvement_pct": <number>,
|
||||
"total_memory_saved_mb": <number>
|
||||
},
|
||||
"pr_ready_commits": <N>,
|
||||
"status": "completed|plateaued|failed",
|
||||
"error": "<if failed, why>"
|
||||
}
|
||||
```
|
||||
|
||||
Also copy `.codeflash/results.tsv` and `.codeflash/HANDOFF.md` to `~/results/`.
|
||||
|
||||
## Important
|
||||
|
||||
- Work fully autonomously. Do not ask questions -- make reasonable decisions.
|
||||
- If tests fail during setup, note the pre-existing failures and work around them.
|
||||
- If the project cannot be set up (missing deps, private packages), write summary.json with status "failed" and an error message, then stop.
|
||||
- After optimization completes, push the optimization branch to the remote fork: `git push origin codeflash/optimize`.
|
||||
- Time limit: aim to complete within 8 hours. If still running after 8 hours, wrap up, write summary.json with whatever results you have, and stop.
|
||||
```
|
||||
|
||||
### Launching Claude Code
|
||||
|
||||
The orchestrator SSHs into each VM and runs:
|
||||
|
||||
```bash
|
||||
cd ~/project && claude \
|
||||
--dangerously-skip-permissions \
|
||||
--plugin-dir ~/codeflash-agent/dist \
|
||||
--model opus \
|
||||
--max-turns 400 \
|
||||
--print \
|
||||
"Read the CLAUDE.md file and follow its instructions exactly." \
|
||||
2>&1 | tee ~/results/claude_output.log
|
||||
```
|
||||
|
||||
Key flags:
|
||||
- `--dangerously-skip-permissions`: no human approval needed
|
||||
- `--plugin-dir`: loads the codeflash-agent plugin with `/codeflash-optimize` skill
|
||||
- `--print`: non-interactive mode, outputs to stdout
|
||||
- `--max-turns 400`: generous turn limit for thorough optimization
|
||||
- Output captured to log file for debugging
|
||||
|
||||
### Monitoring
|
||||
|
||||
The orchestrator polls each VM periodically (every 10 minutes):
|
||||
|
||||
```python
|
||||
def check_vm_status(vm_ip):
|
||||
"""Check if Claude is still running and if results are ready."""
|
||||
# Check if claude process is still running
|
||||
is_running = ssh_exec(vm_ip, "pgrep -f 'claude' > /dev/null && echo running || echo done")
|
||||
|
||||
# Check if summary.json exists (optimization complete)
|
||||
has_results = ssh_exec(vm_ip, "test -f ~/results/summary.json && echo yes || echo no")
|
||||
|
||||
# Check elapsed time
|
||||
elapsed = ssh_exec(vm_ip, "stat -c %Y ~/project/CLAUDE.md | xargs -I{} expr $(date +%s) - {}")
|
||||
|
||||
return {
|
||||
"is_running": is_running.strip() == "running",
|
||||
"has_results": has_results.strip() == "yes",
|
||||
"elapsed_seconds": int(elapsed.strip())
|
||||
}
|
||||
```
|
||||
|
||||
If a VM has been running >5 hours with no results, the orchestrator:
|
||||
1. SSHs in and sends SIGTERM to claude
|
||||
2. Waits 60s for graceful shutdown
|
||||
3. Checks if partial results exist in `.codeflash/results.tsv`
|
||||
4. Marks project as `failed` or `completed` (with partial results)
|
||||
|
||||
---
|
||||
|
||||
## Phase 3: Results Collection
|
||||
|
||||
Once Claude completes on a VM:
|
||||
|
||||
1. **SCP the results** from the VM to local:
|
||||
```python
|
||||
scp_download(vm_ip, "~/results/summary.json", f"results/{project_id}/summary.json")
|
||||
scp_download(vm_ip, "~/results/results.tsv", f"results/{project_id}/results.tsv")
|
||||
scp_download(vm_ip, "~/results/HANDOFF.md", f"results/{project_id}/HANDOFF.md")
|
||||
scp_download(vm_ip, "~/results/claude_output.log", f"results/{project_id}/claude_output.log")
|
||||
```
|
||||
|
||||
2. **Verify the branch was pushed**. The VM Claude pushes the branch as part of its workflow. Verify:
|
||||
```bash
|
||||
ssh vm "cd ~/project && git log --oneline origin/codeflash/optimize -5"
|
||||
```
|
||||
If not pushed (e.g. Claude failed before that step), push from the VM before destroying it:
|
||||
```bash
|
||||
ssh vm "cd ~/project && git push origin codeflash/optimize"
|
||||
```
|
||||
|
||||
3. **Update the DB** with results from summary.json.
|
||||
|
||||
4. **Destroy the VM** to stop burning money:
|
||||
```python
|
||||
azure_client.virtual_machines.begin_delete(resource_group, vm_name)
|
||||
```
|
||||
|
||||
### Results Directory Structure
|
||||
|
||||
```
|
||||
results/
|
||||
project-001/
|
||||
summary.json # Full optimization results (from VM)
|
||||
results.tsv # Per-experiment log (from VM)
|
||||
HANDOFF.md # Session state (from VM)
|
||||
claude_output.log # Full Claude transcript (for debugging)
|
||||
context.json # Template variables (built by orchestrator from summary + project data)
|
||||
emails/
|
||||
email_1_proof.md # Rendered email (regenerated on template edit)
|
||||
email_2_followup.md
|
||||
email_3_risk.md
|
||||
project-002/
|
||||
...
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Phase 4: Email Drafting
|
||||
|
||||
The email system separates **data** from **templates**. All optimization results and contact info are stored locally as structured data (`context.json`). Templates are standalone files with `{placeholder}` variables. You can edit a template and re-render all emails with one command.
|
||||
|
||||
### Data Layer: context.json
|
||||
|
||||
After results are collected, the orchestrator builds `context.json` for each project by merging `summary.json` (optimization results from the VM) with `projects.json` (contact/company info). This is the single source of truth for all email variables.
|
||||
|
||||
```json
|
||||
{
|
||||
"first_name": "Gil",
|
||||
"full_name": "Gil Tene",
|
||||
"title": "CTO",
|
||||
"company_name": "Azul",
|
||||
"repo": "azul/zulu-openjdk",
|
||||
"repo_name": "zulu-openjdk",
|
||||
"fork_url": "https://github.com/codeflash-ai/zulu-openjdk",
|
||||
"branch_url": "https://github.com/codeflash-ai/zulu-openjdk/tree/codeflash/optimize",
|
||||
"num_optimizations": 12,
|
||||
"best_function": "parseClassFile",
|
||||
"best_speedup": "5x faster",
|
||||
"best_description": "replaced linear scan with hash lookup in class file parser",
|
||||
"second_best_function": "resolveMethod",
|
||||
"second_best_technique": "eliminated redundant deepcopy in method resolution",
|
||||
"total_cpu_improvement_pct": 34,
|
||||
"total_memory_saved_mb": 120,
|
||||
"optimizations_summary": "12 merge-ready commits including 5x faster class file parsing, 3x faster method resolution, and 40% memory reduction in bytecode verification",
|
||||
"calendly_link": "https://calendly.com/codeflash-saurabh/30min"
|
||||
}
|
||||
```
|
||||
|
||||
### Template Layer: Editable Email Templates
|
||||
|
||||
Templates live in `email_templates/` and use `{variable_name}` placeholders. Edit these any time, then re-render.
|
||||
|
||||
**`email_templates/email_1_proof.md`**
|
||||
|
||||
```
|
||||
Subject: I created {num_optimizations} PRs that speed up {repo_name}
|
||||
|
||||
Hi {first_name},
|
||||
|
||||
I'm Saurabh, ex-CMU and Meta, and CEO and Founder of Codeflash.
|
||||
|
||||
We work with companies like Unstructured.io and HuggingFace who all face a
|
||||
growing challenge: as AI coding tools generate more of the codebase, performance
|
||||
regressions slip in faster than teams can catch them.
|
||||
|
||||
Codeflash is the performance layer that sits on top of your AI coding workflow.
|
||||
It finds provably faster implementations for your existing code and ensures
|
||||
every new PR ships optimized.
|
||||
|
||||
I ran Codeflash on a fork of {repo} and created {num_optimizations} merge-ready
|
||||
commits that significantly speed up several crucial components -- {best_function}
|
||||
now runs {best_speedup}! {optimizations_summary}
|
||||
|
||||
Unstructured.io used Codeflash across their entire infrastructure and cut
|
||||
compute costs by 50%.
|
||||
|
||||
I'd love to walk you through the results and show you how much more free
|
||||
performance is hiding across your full codebase. Would sometime this week work?
|
||||
|
||||
{calendly_link}
|
||||
|
||||
Thanks,
|
||||
Saurabh
|
||||
Founder, Codeflash.ai
|
||||
```
|
||||
|
||||
**`email_templates/email_2_followup.md`**
|
||||
|
||||
```
|
||||
Subject: Re: I created {num_optimizations} PRs that speed up {repo_name}
|
||||
|
||||
Hi {first_name},
|
||||
|
||||
Wanted to follow up -- did your team get a chance to look at the optimizations?
|
||||
|
||||
You can see all {num_optimizations} commits here:
|
||||
{branch_url}
|
||||
|
||||
If those didn't hit the right area of your codebase, I have an open offer:
|
||||
share any performance benchmark your team cares about, and I'll run Codeflash
|
||||
against it and send you the results. No commitment, just proof.
|
||||
|
||||
{calendly_link}
|
||||
|
||||
Saurabh
|
||||
Founder, codeflash.ai
|
||||
```
|
||||
|
||||
**`email_templates/email_3_risk.md`**
|
||||
|
||||
```
|
||||
Subject: Re: I created {num_optimizations} PRs that speed up {repo_name}
|
||||
|
||||
Hi {first_name},
|
||||
|
||||
With AI coding tools writing more of the code, performance regressions are
|
||||
showing up faster and quieter than before. By the time they surface, it's
|
||||
production issues and fire drills.
|
||||
|
||||
Unstructured.io plugged Codeflash into their workflow and cut compute costs
|
||||
by 50% -- and now every PR is automatically checked before it merges.
|
||||
|
||||
Happy to show you what that looks like for {company_name}.
|
||||
|
||||
{calendly_link}
|
||||
|
||||
Saurabh
|
||||
Founder, codeflash.ai
|
||||
```
|
||||
|
||||
### Rendering: Template + Data = Emails
|
||||
|
||||
```python
|
||||
def build_context(project_id):
|
||||
"""Build context.json from summary.json + projects.json. Idempotent."""
|
||||
project = load_project(project_id)
|
||||
summary = json.load(open(f"results/{project_id}/summary.json"))
|
||||
|
||||
if summary["status"] == "failed" or summary["total_keeps"] == 0:
|
||||
return None
|
||||
|
||||
best = summary["headline_stats"]
|
||||
opts = summary["optimizations"]
|
||||
|
||||
context = {
|
||||
"first_name": project["target_contact"]["name"].split()[0],
|
||||
"full_name": project["target_contact"]["name"],
|
||||
"title": project["target_contact"]["title"],
|
||||
"company_name": project["company"],
|
||||
"repo": project["repo"],
|
||||
"repo_name": project["repo"].split("/")[1],
|
||||
"fork_url": f"https://github.com/codeflash-ai/{project['repo'].split('/')[1]}",
|
||||
"branch_url": f"https://github.com/codeflash-ai/{project['repo'].split('/')[1]}/tree/codeflash/optimize",
|
||||
"num_optimizations": summary["total_keeps"],
|
||||
"best_function": best["best_function"],
|
||||
"best_speedup": best["best_single_speedup"],
|
||||
"best_description": opts[0]["description"] if opts else "",
|
||||
"second_best_function": opts[1]["function"] if len(opts) > 1 else "",
|
||||
"second_best_technique": opts[1]["technique"] if len(opts) > 1 else "",
|
||||
"total_cpu_improvement_pct": best.get("total_cpu_improvement_pct", 0),
|
||||
"total_memory_saved_mb": best.get("total_memory_saved_mb", 0),
|
||||
"optimizations_summary": build_summary_sentence(opts),
|
||||
"calendly_link": "https://calendly.com/codeflash-saurabh/30min",
|
||||
}
|
||||
|
||||
write_json(f"results/{project_id}/context.json", context)
|
||||
return context
|
||||
|
||||
|
||||
def render_emails(project_id):
|
||||
"""Render all email templates for a project. Re-run after template edits."""
|
||||
context = json.load(open(f"results/{project_id}/context.json"))
|
||||
os.makedirs(f"results/{project_id}/emails", exist_ok=True)
|
||||
|
||||
for template_file in sorted(glob("email_templates/email_*.md")):
|
||||
template = open(template_file).read()
|
||||
rendered = template.format(**context)
|
||||
out_name = os.path.basename(template_file)
|
||||
write_file(f"results/{project_id}/emails/{out_name}", rendered)
|
||||
|
||||
|
||||
def render_all_emails():
|
||||
"""Re-render emails for ALL completed projects. Use after editing a template."""
|
||||
for project_id in get_completed_project_ids():
|
||||
render_emails(project_id)
|
||||
```
|
||||
|
||||
### Orchestrator Email Commands
|
||||
|
||||
```bash
|
||||
# Build context + render emails for one project (after results collected)
|
||||
python orchestrator.py email <project-id>
|
||||
|
||||
# Re-render ALL project emails after you edit a template
|
||||
python orchestrator.py email --rerender-all
|
||||
|
||||
# Preview rendered emails in terminal
|
||||
python orchestrator.py email <project-id> --show
|
||||
|
||||
# Show raw context data (for debugging or manual override)
|
||||
python orchestrator.py email <project-id> --show-context
|
||||
```
|
||||
|
||||
### Email Editing Workflow
|
||||
|
||||
1. **Edit the template** (affects all future projects):
|
||||
- Edit `email_templates/email_1_proof.md` (or email_2, email_3)
|
||||
- Run `python orchestrator.py email --rerender-all`
|
||||
- All project emails are regenerated
|
||||
|
||||
2. **Override data for one project** (e.g. fix a name):
|
||||
- Edit `results/<id>/context.json` directly
|
||||
- Run `python orchestrator.py email <project-id>`
|
||||
- Only that project's emails are regenerated
|
||||
|
||||
3. **One-off edit for a single email** (e.g. add a personal note):
|
||||
- Edit `results/<id>/emails/email_1_proof.md` directly
|
||||
- This is the final rendered copy -- it won't be overwritten unless you explicitly re-render
|
||||
|
||||
---
|
||||
|
||||
## Phase 5: Review, Present Results & Send
|
||||
|
||||
This phase is intentionally manual. The orchestrator gives you all the data and rendered emails; you decide what ships.
|
||||
|
||||
### Results Presentation
|
||||
|
||||
```bash
|
||||
# Dashboard: all projects at a glance
|
||||
python orchestrator.py status
|
||||
|
||||
ID Repo Status Opts Best Email
|
||||
project-001 pallets/flask completed 12 5x faster DRAFT READY
|
||||
project-002 encode/httpx completed 8 3x faster DRAFT READY
|
||||
project-003 tiangolo/fastapi failed 0 -- --
|
||||
|
||||
# Deep dive into one project
|
||||
python orchestrator.py results <project-id>
|
||||
|
||||
Project: pallets/flask
|
||||
Status: completed
|
||||
Branch: https://github.com/codeflash-ai/flask/tree/codeflash/optimize
|
||||
Experiments: 18 total (12 kept, 6 discarded)
|
||||
|
||||
Top optimizations:
|
||||
1. parse_rule() 5.0x faster replaced regex with string split
|
||||
2. match_request() 3.2x faster eliminated redundant dict copy
|
||||
3. send_file() 2.1x faster switched to sendfile() syscall
|
||||
4. url_for() 1.8x faster cached reverse route lookup
|
||||
...
|
||||
|
||||
Headline: 34% total CPU improvement, 120 MiB memory saved
|
||||
|
||||
Contact: Armin Ronacher (Creator) -- armin@palletsprojects.com
|
||||
Emails: results/project-001/emails/
|
||||
```
|
||||
|
||||
### Review Checklist
|
||||
|
||||
For each project:
|
||||
|
||||
1. **Review the optimizations**: Open the fork on GitHub (link from `orchestrator.py results <id>`). Scan the commits. Are they legit?
|
||||
|
||||
2. **Review the emails**: `orchestrator.py email <id> --show`. Check numbers, names, tone.
|
||||
|
||||
3. **Edit if needed** (see Email Editing Workflow above).
|
||||
|
||||
4. **Send**: Copy from `results/<id>/emails/` into your email client or Apollo. Mark as sent:
|
||||
```bash
|
||||
python orchestrator.py mark-sent <project-id>
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## POC Plan: 2 Projects
|
||||
|
||||
### Step 1: Select Projects
|
||||
|
||||
Pick 2 projects. Suggested criteria for POC:
|
||||
- One Python project, one Java or JS/TS project (to prove multi-language)
|
||||
- Both should have >2k stars and active test suites
|
||||
- Companies behind them should be in the 20-500 employee range
|
||||
- The engineering contact should be identifiable via LinkedIn/Apollo
|
||||
|
||||
### Step 2: Build the Orchestrator (MVP)
|
||||
|
||||
For the POC, the orchestrator can be simplified:
|
||||
|
||||
```
|
||||
experiments/universe-optimize/
|
||||
orchestrator.py # Main script
|
||||
optimization.md # Template injected as CLAUDE.md on VMs
|
||||
projects.json # The 2 POC projects
|
||||
bootstrap.sh # VM setup script
|
||||
email_templates/
|
||||
email_1_proof.md
|
||||
email_2_followup.md
|
||||
email_3_risk.md
|
||||
results/ # Collected results (gitignored)
|
||||
```
|
||||
|
||||
MVP orchestrator commands:
|
||||
- `python orchestrator.py provision <project-id>` -- Create VM, fork repo, bootstrap
|
||||
- `python orchestrator.py run <project-id>` -- SSH in and launch Claude Code
|
||||
- `python orchestrator.py status [project-id]` -- Check status of all or one project
|
||||
- `python orchestrator.py collect <project-id>` -- SCP results, push branch, destroy VM
|
||||
- `python orchestrator.py email <project-id>` -- Generate email draft from results
|
||||
- `python orchestrator.py status-all` -- Dashboard view
|
||||
|
||||
### Step 3: Run & Iterate
|
||||
|
||||
1. Provision both VMs in parallel
|
||||
2. Launch Claude Code on both
|
||||
3. Monitor (check every 10 min)
|
||||
4. Collect results when done
|
||||
5. Review optimizations on GitHub
|
||||
6. Review and edit email drafts
|
||||
7. Send emails manually
|
||||
8. Destroy VMs
|
||||
|
||||
### Step 4: Evaluate POC
|
||||
|
||||
After the 2 projects complete, assess:
|
||||
- **Quality**: Were the optimizations real and meaningful? Would they impress an engineering leader?
|
||||
- **Reliability**: Did Claude complete successfully, or did it get stuck/fail?
|
||||
- **Cost**: What was the Azure + Anthropic API cost per project?
|
||||
- **Time**: How long did each optimization run take?
|
||||
- **Email quality**: Did the auto-generated emails need heavy editing?
|
||||
|
||||
Use these findings to decide whether to scale to 10, then 100, then 1000 projects.
|
||||
|
||||
---
|
||||
|
||||
## Scaling Considerations (Post-POC)
|
||||
|
||||
### Parallelism
|
||||
- POC: 2 VMs at a time
|
||||
- Scale: batch in groups of 10-20 VMs to manage cost and API rate limits
|
||||
- Use Azure VMSS (Virtual Machine Scale Sets) for easier provisioning at scale
|
||||
|
||||
### Cost Control
|
||||
- Auto-destroy VMs after 24 hours regardless of status
|
||||
- Estimated per-project cost: ~$2-5 Azure compute + $5-20 Anthropic API = $7-25/project
|
||||
- At 1000 projects: $7k-25k total
|
||||
|
||||
### Project Discovery Automation
|
||||
- GitHub API search + filtering pipeline
|
||||
- Sumble API for company enrichment (as in existing outbound program)
|
||||
- Apollo API for contact discovery (as in existing outbound program)
|
||||
- Auto-populate projects.json with enriched data
|
||||
|
||||
### Results Quality Gate
|
||||
- Auto-skip email generation if <3 optimizations found
|
||||
- Auto-skip if no optimization exceeds 1.5x speedup (not impressive enough)
|
||||
- Flag projects where Claude failed for manual investigation
|
||||
|
||||
### Email Pipeline Integration
|
||||
- Instead of manual send, integrate with Apollo sequences
|
||||
- Auto-create drafts in Apollo, linked to the contact list
|
||||
- Still require manual approval before sending
|
||||
|
||||
---
|
||||
|
||||
## File Inventory
|
||||
|
||||
| File | Purpose |
|
||||
|------|---------|
|
||||
| `program.md` | This document -- the full program specification |
|
||||
| `plan.md` | Original high-level plan (kept for reference) |
|
||||
| `orchestrator.py` | Main orchestrator script |
|
||||
| `optimization.md` | Template injected as CLAUDE.md on each VM |
|
||||
| `bootstrap.sh` | VM setup script (copied to VM and executed) |
|
||||
| `projects.json` | Project list with metadata and contacts |
|
||||
| `universe_optimize.db` | SQLite state database |
|
||||
| `email_templates/*.md` | Email templates with placeholders |
|
||||
| `results/<id>/` | Per-project results directory |
|
||||
|
||||
---
|
||||
|
||||
## Open Questions
|
||||
|
||||
1. **API key management**: How to securely inject the Anthropic API key onto VMs? Options: Azure Key Vault, environment variable via SSH, or baked into VM image. -> Env vars via SSH
|
||||
2. **GitHub auth on VMs**: Need a GitHub PAT or deploy key on each VM for cloning and pushing. Use a dedicated bot account? -> PAT
|
||||
3. **Claude Code model**: Use Opus for maximum quality, or Sonnet for cost savings? POC should use Opus; scale run could use Sonnet with Opus fallback for stuck sessions.
|
||||
4. **Java/JS optimization**: The codeflash-agent plugin is Python-focused today. For Java/JS projects, Claude will need to optimize without the plugin -- just using its own profiling/optimization knowledge. This may produce lower quality results. Should we restrict POC to Python only? Lets restrict POC to python
|
||||
5. **Rate limits**: Anthropic API rate limits at scale. May need multiple API keys or request a rate limit increase.
|
||||
34
experiments/universe-optimize/projects.json
Normal file
34
experiments/universe-optimize/projects.json
Normal file
|
|
@ -0,0 +1,34 @@
|
|||
[
|
||||
{
|
||||
"id": "unstructured",
|
||||
"repo": "Unstructured-IO/unstructured",
|
||||
"language": "python",
|
||||
"stars": 14400,
|
||||
"description": "Open-source ETL for transforming complex documents (PDFs, HTML, Word) into structured data for LLMs",
|
||||
"company": "Unstructured.io",
|
||||
"domain": "data processing / ML infrastructure",
|
||||
"target_contact": {
|
||||
"name": "Matt Robinson",
|
||||
"title": "Co-founder",
|
||||
"email": "",
|
||||
"linkedin": "https://linkedin.com/in/maborobinson"
|
||||
},
|
||||
"why_selected": "Existing customer relationship, 14k+ stars, performance-critical document processing pipelines, Python-heavy codebase with good test coverage"
|
||||
},
|
||||
{
|
||||
"id": "pillow",
|
||||
"repo": "python-pillow/Pillow",
|
||||
"language": "python",
|
||||
"stars": 13500,
|
||||
"description": "The friendly PIL fork -- Python Imaging Library with extensive format support and image manipulation",
|
||||
"company": "python-pillow (community)",
|
||||
"domain": "image processing",
|
||||
"target_contact": {
|
||||
"name": "Andrew Murray",
|
||||
"title": "Lead Maintainer",
|
||||
"email": "",
|
||||
"linkedin": ""
|
||||
},
|
||||
"why_selected": "13k+ stars, performance-critical image processing library used everywhere, Python with C extensions, active test suite, widely depended upon"
|
||||
}
|
||||
]
|
||||
9
experiments/universe-optimize/pyproject.toml
Normal file
9
experiments/universe-optimize/pyproject.toml
Normal file
|
|
@ -0,0 +1,9 @@
|
|||
[project]
|
||||
name = "universe-optimize"
|
||||
version = "0.1.0"
|
||||
description = "Add your description here"
|
||||
requires-python = ">=3.13"
|
||||
dependencies = [
|
||||
"paramiko>=4.0.0",
|
||||
"pygithub>=2.9.0",
|
||||
]
|
||||
388
experiments/universe-optimize/uv.lock
Normal file
388
experiments/universe-optimize/uv.lock
Normal file
|
|
@ -0,0 +1,388 @@
|
|||
version = 1
|
||||
revision = 2
|
||||
requires-python = ">=3.13"
|
||||
|
||||
[[package]]
|
||||
name = "bcrypt"
|
||||
version = "5.0.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/d4/36/3329e2518d70ad8e2e5817d5a4cac6bba05a47767ec416c7d020a965f408/bcrypt-5.0.0.tar.gz", hash = "sha256:f748f7c2d6fd375cc93d3fba7ef4a9e3a092421b8dbf34d8d4dc06be9492dfdd", size = 25386, upload-time = "2025-09-25T19:50:47.829Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/13/85/3e65e01985fddf25b64ca67275bb5bdb4040bd1a53b66d355c6c37c8a680/bcrypt-5.0.0-cp313-cp313t-macosx_10_12_universal2.whl", hash = "sha256:f3c08197f3039bec79cee59a606d62b96b16669cff3949f21e74796b6e3cd2be", size = 481806, upload-time = "2025-09-25T19:49:05.102Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/44/dc/01eb79f12b177017a726cbf78330eb0eb442fae0e7b3dfd84ea2849552f3/bcrypt-5.0.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:200af71bc25f22006f4069060c88ed36f8aa4ff7f53e67ff04d2ab3f1e79a5b2", size = 268626, upload-time = "2025-09-25T19:49:06.723Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/8c/cf/e82388ad5959c40d6afd94fb4743cc077129d45b952d46bdc3180310e2df/bcrypt-5.0.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:baade0a5657654c2984468efb7d6c110db87ea63ef5a4b54732e7e337253e44f", size = 271853, upload-time = "2025-09-25T19:49:08.028Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/ec/86/7134b9dae7cf0efa85671651341f6afa695857fae172615e960fb6a466fa/bcrypt-5.0.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:c58b56cdfb03202b3bcc9fd8daee8e8e9b6d7e3163aa97c631dfcfcc24d36c86", size = 269793, upload-time = "2025-09-25T19:49:09.727Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/cc/82/6296688ac1b9e503d034e7d0614d56e80c5d1a08402ff856a4549cb59207/bcrypt-5.0.0-cp313-cp313t-manylinux_2_28_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:4bfd2a34de661f34d0bda43c3e4e79df586e4716ef401fe31ea39d69d581ef23", size = 289930, upload-time = "2025-09-25T19:49:11.204Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/d1/18/884a44aa47f2a3b88dd09bc05a1e40b57878ecd111d17e5bba6f09f8bb77/bcrypt-5.0.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:ed2e1365e31fc73f1825fa830f1c8f8917ca1b3ca6185773b349c20fd606cec2", size = 272194, upload-time = "2025-09-25T19:49:12.524Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/0e/8f/371a3ab33c6982070b674f1788e05b656cfbf5685894acbfef0c65483a59/bcrypt-5.0.0-cp313-cp313t-manylinux_2_34_aarch64.whl", hash = "sha256:83e787d7a84dbbfba6f250dd7a5efd689e935f03dd83b0f919d39349e1f23f83", size = 269381, upload-time = "2025-09-25T19:49:14.308Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/b1/34/7e4e6abb7a8778db6422e88b1f06eb07c47682313997ee8a8f9352e5a6f1/bcrypt-5.0.0-cp313-cp313t-manylinux_2_34_x86_64.whl", hash = "sha256:137c5156524328a24b9fac1cb5db0ba618bc97d11970b39184c1d87dc4bf1746", size = 271750, upload-time = "2025-09-25T19:49:15.584Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/c0/1b/54f416be2499bd72123c70d98d36c6cd61a4e33d9b89562c22481c81bb30/bcrypt-5.0.0-cp313-cp313t-musllinux_1_1_aarch64.whl", hash = "sha256:38cac74101777a6a7d3b3e3cfefa57089b5ada650dce2baf0cbdd9d65db22a9e", size = 303757, upload-time = "2025-09-25T19:49:17.244Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/13/62/062c24c7bcf9d2826a1a843d0d605c65a755bc98002923d01fd61270705a/bcrypt-5.0.0-cp313-cp313t-musllinux_1_1_x86_64.whl", hash = "sha256:d8d65b564ec849643d9f7ea05c6d9f0cd7ca23bdd4ac0c2dbef1104ab504543d", size = 306740, upload-time = "2025-09-25T19:49:18.693Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/d5/c8/1fdbfc8c0f20875b6b4020f3c7dc447b8de60aa0be5faaf009d24242aec9/bcrypt-5.0.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:741449132f64b3524e95cd30e5cd3343006ce146088f074f31ab26b94e6c75ba", size = 334197, upload-time = "2025-09-25T19:49:20.523Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/a6/c1/8b84545382d75bef226fbc6588af0f7b7d095f7cd6a670b42a86243183cd/bcrypt-5.0.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:212139484ab3207b1f0c00633d3be92fef3c5f0af17cad155679d03ff2ee1e41", size = 352974, upload-time = "2025-09-25T19:49:22.254Z" },
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Loading…
Reference in a new issue