algitex

AI Cost Tracking

🤖 LLM usage: $11.6877 (67 commits)
👤 Human dev: ~$1345 (13.4h @ $100/h, 30min dedup)

Generated on 2026-04-20 using openrouter/qwen/qwen3-coder-next

Progressive algorithmization toolchain — from LLM to deterministic code, from proxy to tickets.

The only framework that automates the path from "LLM handles everything" to "most traffic runs deterministically, LLM only for edge cases."

pip install algitex
algitex init ./my-app
algitex go

Why "algitex"?

The name reflects the core cycle: analyze → plan → execute → validate → repeat. Each iteration makes your codebase healthier and your LLM usage cheaper. The progressive algorithmization loop gradually replaces LLM calls with deterministic rules.

Algitex = Algorithmic + Intelligence + Execution + Engine

Semantically:

Alg- → algorithms, logic, determinism
-i- → intelligence layer
-tex → texture / system / framework / execution layer

Algitex is the intelligence compilation engine that transforms LLM-driven behavior into deterministic, cost-efficient algorithmic systems. It enables progressive algorithmization from probabilistic AI reasoning to structured, deterministic logic.

Progressive Algorithmization

The 5-stage transition from LLM to deterministic:

Stage 1: Discovery    → LLM performs tasks, collect traces
Stage 2: Extraction   → Identify recurring patterns
Stage 3: Rules        → Generate deterministic replacements
Stage 4: Hybrid       → Route by confidence: rules vs LLM
Stage 5: Optimization → Minimize LLM dependency, reduce costs

Result: Systems that start with LLM flexibility but evolve into efficient, deterministic engines—maintaining AI reasoning benefits with traditional software performance.

Name alternatives considered

Name	Why it works	Why we picked algitex
algitex	Core concept: the continuous improvement loop	Clear, memorable, tech-neutral
prollama	"progressive" + llama vibes	Ties too much to one model family
codefact	Code + factory/fact	Sounds like a trivia app
algopact	Algorithm + Propact	Hard to pronounce
loopcode	Loop + code	Reverse reads awkward
prodev	Progressive + dev	Too generic, SEO nightmare

Layer 1: Code Quality Loop

from algitex import Project

p = Project("./my-app")
p.analyze()    # code2llm + vallm + redup → health report
p.plan()       # auto-generate tickets from analysis
p.execute()    # LLM handles tasks via proxym
p.status()     # health + tickets + budget + cost ledger

Layer 2: Progressive Algorithmization

from algitex import Loop

loop = Loop("./my-app")
loop.discover()        # Stage 1: collect all LLM traces
loop.extract()         # Stage 2: find repeating patterns
loop.generate_rules()  # Stage 3: AI writes its own replacement
loop.route()           # Stage 4: rules vs LLM by confidence
loop.optimize()        # Stage 5: monitor, minimize LLM usage
print(loop.report())   # "42% deterministic, $12.50 saved"

Layer 3: Propact Workflows

from algitex import Workflow

wf = Workflow("./refactor-v1.md")
wf.execute()   # runs propact:shell, propact:rest, propact:llm blocks

Core loop

algitex init ./my-app # initialize project algitex analyze # health check algitex plan --sprints 3 # generate sprint strategy + tickets algitex go # full pipeline algitex status # dashboard

Progressive algorithmization

algitex algo discover # start trace collection algitex algo extract # find patterns in traces algitex algo rules # generate deterministic replacements algitex algo report # show % deterministic vs LLM

Propact workflows

algitex workflow run fix.md # execute Markdown workflow algitex workflow validate f.md

Tickets

algitex ticket add "Fix auth" --priority high algitex ticket list algitex ticket board algitex sync # push to GitHub/Jira

Quick queries

algitex ask "Explain this race condition" --tier premium algitex tools # show installed tools


## Parallel TODO Task Processing

Execute TODO tasks from prefact analysis in parallel with automatic categorization and fix strategies:

```bash
# Verify which TODO tasks are still valid vs already fixed
algitex todo verify-prefact

# Remove outdated tasks from TODO.md
algitex todo verify-prefact --prune

# BatchFix: grupowanie i optymalizacja podobnych zadań
algitex todo batch --dry-run              # Symulacja
algitex todo batch --execute              # Wykonaj fixy
algitex todo batch --limit 10 --parallel 2  # Limit i równoległość
algitex todo batch --execute --prune      # Wykonaj + wyczyść nieaktualne
algitex todo batch --execute --no-log    # Wyłącz logowanie markdown
algitex todo batch --model qwen2.5-coder:7b  # Wybór modelu Ollama

# Auto-fix mechanical issues in parallel (dry-run)
algitex todo fix-auto --workers 8

# Actually apply fixes
algitex todo fix-auto --execute

MicroTask — Atomic Tasks for Small LLMs

Pipeline for breaking down and executing atomic micro-tasks optimized for small LLMs:

# Classify tasks by complexity
algitex microtask classify

# Generate execution plan
algitex microtask plan

# Execute micro-tasks
algitex microtask run --workers 4

NLP — Deterministic Refactor Helpers

Deterministic NLP-based refactoring without LLM calls:

# Fix docstrings
algitex nlp docstrings --dry-run
algitex nlp docstrings --execute

# Optimize imports
algitex nlp imports --execute

# Remove dead code
algitex nlp dead-code --execute

# Find and refactor duplicates
algitex nlp duplicates --execute

Benchmark — Performance Testing

Measure and compare performance across cache, tiers, and memory usage:

# Quick benchmark (30 seconds)
algitex benchmark quick

# Test cache performance
algitex benchmark cache --entries 100 --lookups 500

# Compare tier throughput
algitex benchmark tiers

# Memory profiling for large files
algitex benchmark memory --lines 1000

# Full benchmark suite with export
algitex benchmark full --export results.json

Dashboard — Real-time Monitoring

Live TUI dashboard for monitoring algitex operations:

# Live dashboard with auto-refresh
algitex dashboard live

# Dashboard for 60 seconds
algitex dashboard live --duration 60

# Monitor existing cache/metrics
algitex dashboard monitor --cache .algitex/cache --metrics .algitex/metrics.json

# Export metrics to JSON
algitex dashboard export --format json --output metrics.json --duration 60

# Export to Prometheus format
algitex dashboard export --format prometheus --output metrics.prom

Live dashboard during 3-tier fix

algitex todo fix --all --dashboard

Dashboard for hybrid autofix

algitex todo hybrid --execute --dashboard

Dashboard for batch operations

algitex todo batch --execute --dashboard


### Python API

```python
from algitex.todo import verify_todos, fix_todos, benchmark_fix, compare_modes

# Verify task validity
result = verify_todos("TODO.md")
print(f"Still open: {result.still_open}, Fixed: {result.already_fixed}")

# Parallel auto-fix (mechanical tasks only)
stats = fix_todos("TODO.md", workers=8, dry_run=False)
print(f"Fixed: {stats['fixed']}, Skipped: {stats['skipped']}")

# Benchmark performance
result = benchmark_fix("TODO.md", limit=100, workers=8, mode="parallel")
result.print_report()

# Compare modes
comparison = compare_modes("TODO.md", limit=50, workers=8)
### Auto-fix Categories

| Category | Auto-fixable | Description |
|----------|--------------|-------------|
| `unused_import` | ✅ Yes | Remove unused imports (import X, from Y import X) |
| `return_type` | ✅ Yes | Add missing return type annotations |
| `fstring` | ⚠️ Partial | Convert concatenations to f-strings |
| `magic` | ✅ Yes | Suggest names for magic numbers |
| `docstring` | ✅ Yes | Rewrite verbose docstrings |
| `rename` | ✅ Yes | Improve variable names |
| `split_function` | ✅ Yes | Extract methods from large functions |
| `dependency_cycle` | ✅ Yes | Break import cycles |
| `architecture` | ✅ Yes | Reorganize module structure |
| `other` | ⚠️ Varies | Complex issues requiring reasoning |

### How Parallel Processing Works

┌─────────────────────────────────────────────────────────┐ │ Parallel TODO Processing │ ├─────────────────────────────────────────────────────────┤ │ │ │ 1. Parse TODO.md → filter worktree duplicates │ │ 2. Categorize tasks (unused_import, return_type...) │ │ 3. Group by file (1 worker per file, zero conflicts) │ │ 4. Sort tasks bottom-up (line DESC) → preserve numbers │ │ 5. Execute in ThreadPoolExecutor (8 workers default) │ │ 6. Collect results: fixed, skipped, errors │ │ │ │ Safety: Each worker touches different file. │ │ Within file: bottom-up prevents line number shifts. │ │ │ └─────────────────────────────────────────────────────────┘


### Three Execution Paths

| Path | LLM? | Parallel? | Throughput | Use Case |
|------|------|-----------|------------|----------|
| `todo fix-auto` | ❌ No | ✅ Yes (8 workers) | ~1500 tickets/sec | Mechanical fixes: unused imports, return types |
| `todo run --tool ollama-mcp` | ✅ Yes | ❌ Sequential (queue) | ~1-10 tickets/sec | Complex fixes requiring reasoning |
| `autofix via proxy` | ✅ Yes | ⚠️ Batch | ~5-50 tickets/sec | Intelligent fixes via litellm-proxy |

**When to use which:**

**Path 1: Mechanical Fixes (`todo fix-auto`)**
- No LLM calls — pure regex/text manipulation
- 8 parallel workers, thread-safe per-file isolation
- Handles: `unused_import`, `return_type`, `fstring` (via flynt)
- Best for: bulk cleanup of 100+ simple issues

```python
from algitex.todo import fix_todos
stats = fix_todos("TODO.md", workers=8, dry_run=False)
# 2679 tasks → ~1.8 seconds total

Path 2: LLM-based Fixes (todo run)

Uses Ollama/aider via Docker MCP
Sequential execution (respects LLM rate limits)
Handles: complex refactoring, architectural changes
Best for: issues requiring code understanding

algitex todo run --tool ollama-mcp --limit 10

Path 3: Hybrid via Proxy (autofix)

Routes through litellm-proxy with cost tracking
Batch processing with retry logic
Handles: smart fixes with context awareness
Best for: production workflows with budget constraints

from algitex.tools.autofix import AutoFix
autofix = AutoFix(backend="litellm-proxy", proxy_url="http://localhost:4000")
autofix.fix_all(limit=5)  # $0.12 per batch avg

Path 4: Hybrid CLI (todo hybrid) — Fast + Parallel + LLM

Phase 1: Parallel mechanical fixes (no LLM)
Phase 2: Rate-limited parallel LLM fixes
Handles: complete TODO workflow in one command

# Dry run (preview)
algitex todo hybrid --workers 4 --rate-limit 10

# Execute with rate limiting
algitex todo hybrid --execute --backend litellm-proxy --workers 4 --rate-limit 10

# Local Ollama (100% offline)
algitex todo hybrid --execute --backend ollama --workers 2 --rate-limit 5

The Missing Piece: Fast + Parallel + LLM

To achieve szybkie + równoległe + LLM, you need to combine ThreadPoolExecutor with ProxyBackend:

from algitex.todo import HybridAutofix

# Combines parallel task distribution with LLM backend
fixer = HybridAutofix(
    backend="litellm-proxy",
    workers=4,              # Parallel workers
    rate_limit=10,          # Requests per second
    retry_attempts=3,
    timeout=30
)

# Mechanical fixes: parallel, no LLM
fixer.fix_mechanical("TODO.md")  # 1000+ tickets/sec

# Complex fixes: parallel LLM with rate limiting
fixer.fix_complex("TODO.md")     # 10-50 tickets/sec, cost-tracked

Requirements for parallel LLM:

Rate limiting (prevent 429 errors)
Retry logic with exponential backoff
Cost tracking per batch
Circuit breaker for failed requests

The 5-Stage Progressive Algorithmization

Stage 1: Discovery     → LLM handles 100%, collect traces
Stage 2: Extraction    → identify hot paths + repeating patterns
Stage 3: Rules         → AI generates deterministic replacements
Stage 4: Hybrid        → confidence-based: known patterns → rules, unknown → LLM
Stage 5: Optimization  → most traffic deterministic, LLM for edge cases only

No existing framework automates this path. DSPy goes LLM→smaller LLM. algitex goes LLM→algorithm.

Fix Authentication Module

Analyze current state:

code2llm ./src/auth -f toon --json

Ask LLM for a fix plan:

POST http://localhost:4000/v1/chat/completions
{"model": "balanced", "messages": [{"role": "user", "content": "Fix auth"}]}

Validate the result:

vallm batch ./src/auth --recursive


## Planfile-Aware Proxy Headers

Every LLM request through algitex carries context:

X-Planfile-Ref: my-project/current/DLP-0042 X-Workflow-Ref: refactor-v1.md X-Task-Tier: complex X-Inject-Context: true


Proxym logs cost/model/latency **per ticket**. The cost ledger shows exactly what each task costs.

## Installation

```bash
pip install algitex                # core
pip install algitex[all]           # + all tools
pip install algitex[proxy]         # + proxym
pip install algitex[analysis]      # + code2llm, vallm, redup
pip install algitex[tickets]       # + planfile
pip install algitex[routing]       # + llx

Quickstart — three main objects (Project, Loop, Workflow)

cd examples/01-quickstart make run

Progressive Algorithmization — 5-stage loop

cd examples/02-algo-loop make run

Composable Pipeline — fluent API

cd examples/03-pipeline make run

IDE Integration — generate configs for Roo Code, Cline, etc.

cd examples/04-ide-integration make setup && make run

Cost Tracking — per-ticket cost ledger

cd examples/05-cost-tracking make run

Local LLM with Ollama — 100% offline, zero API costs

cd examples/18-ollama-local make setup && make run

Local MCP Tools — self-hosted code analysis & validation

cd examples/19-local-mcp-tools make up && make run

Self-Hosted Pipeline — complete local CI/CD stack

cd examples/20-self-hosted-pipeline make build && make up && make run

Aider CLI + Ollama — local refactoring with prefact TODO workflow

cd examples/21-aider-cli-ollama make setup && make run

Claude Code + Ollama — AI assistant with local LLM

cd examples/22-claude-code-ollama make setup && make run

Continue.dev + Ollama — VS Code extension setup

cd examples/23-continue-dev-ollama make setup

Ollama Batch Processing — parallel code analysis

cd examples/24-ollama-batch python batch_analyze.py --dir ./src

Local Model Comparison — benchmark Ollama models

cd examples/25-local-model-comparison make benchmark

LiteLLM Proxy + Ollama — native algitex integration (better than aider)

cd examples/26-litellm-proxy-ollama make setup && make proxy # Terminal 1 make fix # Terminal 2

Hybrid AutoFix — fast parallel + LLM with rate limiting

cd examples/33-hybrid-autofix make dry-run # Preview make hybrid # Execute with LiteLLM proxy make ollama # Execute with Ollama (100% offline)

BatchFix — grupowanie i optymalizacja podobnych zadań

cd examples/34-batch-fix algitex todo batch --dry-run # Symulacja algitex todo batch --execute # Wykonaj fixy


Each example has:
- [01-quickstart/README.md](examples/01-quickstart/README.md) — Project, Loop, Workflow basics
- [02-algo-loop/README.md](examples/02-algo-loop/README.md) — Progressive algorithmization
- [03-pipeline/README.md](examples/03-pipeline/README.md) — Composable fluent API
- [04-ide-integration/README.md](examples/04-ide-integration/README.md) — IDE configs
- [05-cost-tracking/README.md](examples/05-cost-tracking/README.md) — Cost tracking
- [06-telemetry/README.md](examples/06-telemetry/README.md) — Telemetry & observability
- [07-context/README.md](examples/07-context/README.md) — Context building
- [08-feedback/README.md](examples/08-feedback/README.md) — Feedback loops
- [09-workspace/README.md](examples/09-workspace/README.md) — Workspace management
- [10-cicd/README.md](examples/10-cicd/README.md) — CI/CD pipelines
- [11-aider-mcp/README.md](examples/11-aider-mcp/README.md) — Aider MCP code refactoring
- [12-filesystem-mcp/README.md](examples/12-filesystem-mcp/README.md) — Filesystem operations
- [13-vallm/README.md](examples/13-vallm/README.md) — Vallm validation
- [14-docker-mcp/README.md](examples/14-docker-mcp/README.md) — Docker container management
- [15-github-mcp/README.md](examples/15-github-mcp/README.md) — GitHub repository operations
- [16-test-workflow/README.md](examples/16-test-workflow/README.md) — Comprehensive test pipeline
- [17-docker-workflow/README.md](examples/17-docker-workflow/README.md) — Refactoring workflow
- [18-ollama-local/README.md](examples/18-ollama-local/README.md) — Local LLM with Ollama (100% offline)
- [19-local-mcp-tools/README.md](examples/19-local-mcp-tools/README.md) — Self-hosted MCP tools (Docker)
- [20-self-hosted-pipeline/README.md](examples/20-self-hosted-pipeline/README.md) — Complete local CI/CD pipeline
- [21-aider-cli-ollama/README.md](examples/21-aider-cli-ollama/README.md) — Aider CLI + Ollama local refactoring
- [22-claude-code-ollama/README.md](examples/22-claude-code-ollama/README.md) — Claude Code + Ollama AI assistant
- [23-continue-dev-ollama/README.md](examples/23-continue-dev-ollama/README.md) — Continue.dev VS Code extension + Ollama
- [24-ollama-batch/README.md](examples/24-ollama-batch/README.md) — Parallel batch processing with Ollama
- [25-local-model-comparison/README.md](examples/25-local-model-comparison/README.md) — Benchmark Ollama models
- [26-litellm-proxy-ollama/README.md](examples/26-litellm-proxy-ollama/README.md) — LiteLLM Proxy + Ollama (native algitex)
- [28-mcp-orchestration/README.md](examples/28-mcp-orchestration/README.md) — MCP Service Orchestration
- [30-parallel-execution/README.md](examples/30-parallel-execution/README.md) — Parallel Execution with Region-Based Coordination
- [31-abpr-workflow/README.md](examples/31-abpr-workflow/README.md) — ABPR Workflow
- [32-workspace-coordination/README.md](examples/32-workspace-coordination/README.md) — Multi-Repo Workspace Coordination
- [33-hybrid-autofix/README.md](examples/33-hybrid-autofix/README.md) — Fast parallel + LLM with rate limiting
- [34-batch-fix/README.md](examples/34-batch-fix/README.md) — BatchFix: grupowanie i optymalizacja fixów
- [35-sprint3-patterns/README.md](examples/35-sprint3-patterns/README.md) — Sprint 3 CC Reduction Patterns
- [36-dashboard/README.md](examples/36-dashboard/README.md) — Live Dashboard TUI
- [37-benchmarks/README.md](examples/37-benchmarks/README.md) — Performance Benchmarks
- [38-new-modules/README.md](examples/38-new-modules/README.md) — New Module Usage
- [39-microtask-pipeline/README.md](examples/39-microtask-pipeline/README.md) — MicroTask Pipeline
- [40-three-tier-autofix/README.md](examples/40-three-tier-autofix/README.md) — Three-Tier AutoFix
- [41-god-module-splitting/README.md](examples/41-god-module-splitting/README.md) — God Module Splitting
- [42-duplicate-removal/README.md](examples/42-duplicate-removal/README.md) — Duplicate Code Removal
- [43-code-health/README.md](examples/43-code-health/README.md) — Code Health Monitoring
- [44-plugin-system/README.md](examples/44-plugin-system/README.md) — Plugin System Architecture
- `run.sh` — executable script
- `Makefile` — `make run`, `make setup`, `make clean`
- `.env.example` — configuration template (where applicable)

> ✅ **44 examples available** (2026-04-25). Examples 01-34 tested and verified working.

## Additional Documentation

- [README2.md](./README2.md) — Detailed conceptual overview of Algitex as intelligence compilation engine
- [docs/todo.md](./docs/todo.md) — TODO task processing and BatchFix
- [docs/BATCHFIX.md](./docs/BATCHFIX.md) — BatchFix: grupowanie i optymalizacja fixów
- [docs/MICROTASK.md](./docs/MICROTASK.md) — Atomic micro-tasks for small LLMs
- [docs/NLP.md](./docs/NLP.md) — Deterministic NLP refactor helpers
- [docs/NEW_FEATURES.md](./docs/NEW_FEATURES.md) — Overview of new modules and features
- [docs/autofix.md](./docs/autofix.md) — AutoFix module documentation
- [docs/REFACTORING_SUMMARY.md](./docs/REFACTORING_SUMMARY.md) — Codebase refactoring summary

## Architecture

src/algitex/ ├── init.py # Project, Loop, Workflow, Config, Pipeline ├── config.py # Unified config (env + YAML) ├── project.py # Main Project class (expanded) ├── cli.py # Typer CLI backward compatibility shim ├── cli/ # Modular CLI commands │ ├── init.py # Main app with all subcommands │ ├── core.py # init, analyze, plan, go, status │ ├── ticket.py # Ticket management │ ├── algo.py # Progressive algorithmization │ ├── workflow.py # Propact workflows │ ├── docker.py # Docker MCP tools │ ├── todo.py # TODO processing │ ├── microtask.py # Atomic micro-task pipeline │ ├── nlp.py # Deterministic NLP helpers │ ├── metrics.py # Metrics and observability │ ├── benchmark.py # Performance benchmarks │ └── dashboard.py # Real-time monitoring ├── algo/ # Progressive algorithmization │ ├── init.py # Loop, TraceEntry, Pattern, Rule, LoopState │ └── loop.py # Re-export ├── propact/ # Markdown workflow engine │ ├── init.py # Workflow, WorkflowStep, WorkflowResult │ └── workflow.py # Re-export ├── todo/ # TODO fixing system (Sprint 3: CC reduced) │ ├── init.py # Public API exports │ ├── fixer.py # Orchestrator (was 724L, now ~450L) │ ├── classify.py # Task classification (CC: 50→4) │ ├── repair.py # Repair strategies (CC: 30→6) │ ├── verify.py # Verification pipeline (CC: 29→5) │ ├── micro.py # Small LLM fixes │ ├── hybrid.py # Big LLM fixes │ ├── tiering.py # Task classification helpers │ └── benchmark.py # Performance benchmarking ├── microtask/ # Atomic tasks for small LLMs │ ├── init.py # MicroTask, TaskType, MicroTaskBatch │ ├── classifier.py # Task classification │ ├── executor.py # Three-phase execution │ └── slicer.py # Context extraction ├── nlp/ # Deterministic NLP refactors │ └── init.py # DocstringShortener, DeadCodeDetector, etc. ├── tools/ │ ├── init.py # Tool discovery │ ├── proxy.py # proxym wrapper + planfile headers │ ├── analysis.py # code2llm + vallm + redup │ └── tickets.py # planfile wrapper + cost ledger └── workflows/ ├── init.py # Pipeline (composable steps) └── pipeline.py # Re-export


## How it connects to the ecosystem

┌─────────────────────────────────────────────────────┐ │ algitex │ │ (orchestration layer) │ ├─────────────────────────────────────────────────────┤ │ │ │ analyze() plan() execute() algo.discover() │ │ │ │ │ │ │ │ code2llm planfile proxym trace → │ │ vallm tickets llx patterns → │ │ redup strategy models rules → │ │ hybrid routing │ │ │ │ MicroTask Pipeline: │ │ microtask classify → atomic task decomposition │ │ microtask plan → execution strategy │ │ microtask run → three-phase execution │ │ │ │ NLP Helpers (deterministic): │ │ nlp docstrings → shorten verbose docs │ │ nlp imports → sort and organize │ │ nlp dead-code → detect unused functions │ │ nlp duplicates → find repeated blocks │ │ │ │ run_workflow("fix.md") │ │ │ │ │ propact:shell → subprocess │ │ propact:rest → httpx │ │ propact:llm → proxym │ │ propact:mcp → MCP tool call │ │ │ └─────────────────────────────────────────────────────┘


## Tool Roles

| Tool | What | Install |
|------|------|---------|
| **proxym** | LLM gateway, 10 providers, routing, budget | `pip install proxym` |
| **planfile** | Sprint planning, tickets, GitHub/Jira sync | `pip install planfile` |
| **llx** | Metric-driven model selection, MCP server | `pip install llx` |
| **code2llm** | Static analysis → .toon diagnostics | `pip install code2llm` |
| **vallm** | 4-tier code validation | `pip install vallm` |
| **redup** | Duplication detection | `pip install redup` |

## License

Licensed under Apache-2.0.
## Status

_Last updated by [taskill](https://github.com/oqlos/taskill) at 2026-04-25 09:28 UTC_

| Metric | Value |
|---|---|
| HEAD | `6e56e90` |
| Coverage | — |
| Failing tests | — |
| Commits in last cycle | 50 |

> The project focused on extensive refactoring of documentation and examples, along with fixes and features enhancing the CLI interface and deep code analysis engine.

<!-- taskill:status:end -->

Name		Name	Last commit message	Last commit date
Latest commit History 72 Commits
.idea		.idea
.pfix		.pfix
articles		articles
docker		docker
docs		docs
examples		examples
project		project
scripts		scripts
src/algitex		src/algitex
testql-scenarios		testql-scenarios
tests		tests
.aider.chat.history.md		.aider.chat.history.md
.aider.input.history		.aider.input.history
.code2llm_ignore		.code2llm_ignore
.code2llmignore		.code2llmignore
.gitignore		.gitignore
.prefactignore		.prefactignore
CHANGELOG.md		CHANGELOG.md
Dockerfile		Dockerfile
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
SUMD.md		SUMD.md
SUMR.md		SUMR.md
TODO.md		TODO.md
Taskfile.yml		Taskfile.yml
VERSION		VERSION
app.doql.less		app.doql.less
docker-compose.mcp.yml		docker-compose.mcp.yml
docker-compose.yml		docker-compose.yml
docker-tools.yaml		docker-tools.yaml
goal.yaml		goal.yaml
litellm_config.yaml		litellm_config.yaml
planfile.yaml		planfile.yaml
prefact.yaml		prefact.yaml
project.sh		project.sh
pyproject.toml		pyproject.toml
pyqual.yaml		pyqual.yaml

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algitex

AI Cost Tracking

Why "algitex"?

Progressive Algorithmization

Name alternatives considered

Layer 1: Code Quality Loop

Layer 2: Progressive Algorithmization

Layer 3: Propact Workflows

Core loop

Progressive algorithmization

Propact workflows

Tickets

Quick queries

MicroTask — Atomic Tasks for Small LLMs

NLP — Deterministic Refactor Helpers

Benchmark — Performance Testing

Dashboard — Real-time Monitoring

Live dashboard during 3-tier fix

Dashboard for hybrid autofix

Dashboard for batch operations

The Missing Piece: Fast + Parallel + LLM

The 5-Stage Progressive Algorithmization

Fix Authentication Module

Quickstart — three main objects (Project, Loop, Workflow)

Progressive Algorithmization — 5-stage loop

Composable Pipeline — fluent API

IDE Integration — generate configs for Roo Code, Cline, etc.

Cost Tracking — per-ticket cost ledger

Local LLM with Ollama — 100% offline, zero API costs

Local MCP Tools — self-hosted code analysis & validation

Self-Hosted Pipeline — complete local CI/CD stack

Aider CLI + Ollama — local refactoring with prefact TODO workflow

Claude Code + Ollama — AI assistant with local LLM

Continue.dev + Ollama — VS Code extension setup

Ollama Batch Processing — parallel code analysis

Local Model Comparison — benchmark Ollama models

LiteLLM Proxy + Ollama — native algitex integration (better than aider)

Hybrid AutoFix — fast parallel + LLM with rate limiting

BatchFix — grupowanie i optymalizacja podobnych zadań

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