auzek 0.1.0

Auzek — an autonomous coding agent that plans, executes, self-verifies and self-heals across multiple LLM providers.

Auzek

An autonomous coding agent by Azaan (Auzek).

Auzek is an autonomous coding agent that understands the repo, plans before it codes, executes one step at a time, verifies its own work, and self-heals on failure before moving on. It runs on any major LLM provider — bring your own API key (Anthropic, OpenAI, Groq, Google, Mistral, DeepSeek, or local Ollama).

It is built on LangGraph (orchestration) and LiteLLM (provider gateway).

pip install auzek
auzek run "add input validation to the /signup endpoint" --provider groq

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Why it's different from a "blind" coding bot

Naive agent	This agent
Starts editing immediately	Onboards to the repo first (stack, tests, layout, git history)
Holds the plan in context	Writes the plan to disk (.agent/plan.md) — survives crashes
"Looks done" after writing	Marks a step done only after running its verification
Retries forever	Hard stop after N recovery attempts, then escalates
One giant change	Atomic steps, optionally micro-committed
"Done" = code written	"Done" = full test/lint/typecheck pass + diff reviewed vs. the task

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The lifecycle (a LangGraph state machine)

context → planning → [human approval] → execution ⇄ recovery → verification → report

1. Context – lists/reads files, searches code, reads git history → a briefing.
2. Planning – emits a structured, ordered, atomic plan (submit_plan tool).
3. Approval – optional human gate (pause/approve the plan).
4. Execution – implements one step, then runs its verification.
5. Recovery – on failure, widens investigation and retries (capped).
6. Verification – runs the full suite, reviews the whole diff vs. the task.
7. Report – writes an honest .agent/report.md.

State and plan live in .agent/ so a run is inspectable and resumable.

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Install

# from PyPI (once published)
pip install auzek

# or with pipx so the `auzek` command is globally available, isolated
pipx install auzek

From source (for development):

cd Autonomous_Agent
python -m venv .venv && . .venv/Scripts/activate   # Windows
# or:  source .venv/bin/activate                    # macOS/Linux
pip install -e .

Configure keys

cp .env.example .env
# fill in the provider(s) you use, e.g. GROQ_API_KEY=...

Check what's wired up:

auzek providers

Run

# operate on the current repo
auzek run "Add input validation to the /signup endpoint and a test for it"

# pick a provider/model explicitly (Groq example)
auzek run "Refactor utils.py to remove the duplicated date parsing" \
    --provider groq --model llama-3.3-70b-versatile

# point at another repo, auto-approve the plan, micro-commit each step
auzek run "Fix the failing login test" \
    --workspace ../my-project --yes --auto-commit

Useful flags: --provider, --model, --api-key, --workspace, --yes (auto-approve), --no-approval, --max-steps, --auto-commit, --temperature.

Inspect the plan any time:

auzek plan-show --workspace ../my-project

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Configuration (config.yaml)

Verification commands auto-detect when blank; set them to be explicit:

provider: anthropic
model: claude-sonnet-4-6
max_recovery_attempts: 3
max_steps: 40
auto_commit: false
require_plan_approval: true
test_command: "pytest -q"
lint_command: "ruff check ."
typecheck_command: "mypy ."

Resolution order: CLI flags > env vars (AGENT_*) > config.yaml > defaults.

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Project layout

src/auzek/
  cli.py            # Typer CLI, approval gate, output
  config.py         # layered config
  llm.py            # multi-provider gateway (LiteLLM) + key handling
  runtime.py        # shared deps + the core tool-calling loop
  state.py          # LangGraph state schema
  graph.py          # the state machine (nodes + conditional edges)
  prompts.py        # per-phase system prompts
  memory/plan_store.py   # the durable plan (json + markdown)
  tools/            # read/write/edit, list, search, shell, git
  nodes/            # context, planning, approval, execution, recovery,
                    # verification, report

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Adding a provider

Add one line to PROVIDERS in llm.py:

"xai": ProviderSpec("xai", "XAI_API_KEY", "grok-2-latest"),

LiteLLM handles the wire format; nothing else changes.

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Safety

• All file access is sandboxed to the workspace; deny_globs blocks .env, .git, node_modules, etc.
• The shell tool has a destructive-command guardrail and output/time limits — but it is not a security boundary. For untrusted tasks, run in a container or VM.

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A note on SWE-bench / "beating" other models

This is a strong, production-shaped harness. On agentic benchmarks the score is dominated by (a) the underlying model and (b) harness discipline — plan/verify/self-heal loops, tight diffs, real test execution — all of which this implements. To actually measure it, wire auzek run to the SWE-bench task format (clone repo at the given commit, feed the issue as the task, export the resulting git diff as the prediction patch) and run the official evaluation. Treat any ranking as something you measure, not assume.

License

MIT