antcrew-engine 0.3.5

Capability-driven autonomous project execution engine (antcrew Layer 2)

antcrew-engine

Capability-driven autonomous project execution engine — Layer 2 of the antcrew stack.

The engine iterates an EngineLoop over a natural-language goal, dispatching modular Capability executors until every condition in the desired project state is satisfied. No LangGraph dependency — designed to be embedded directly or wrapped by antcrew (Layer 1).

When to use antcrew-engine vs antcrew

Use antcrew-engine (Layer 2) when you want a fully autonomous loop that builds or modifies code without a fixed pipeline of named roles. The EngineLoop is goal-directed — it reads the current artifact state, picks the cheapest capability that closes the gap toward the desired conditions, and repeats. The set of steps is not known in advance; it emerges from what's already been done. This is the right model for brownfield work (--from-dir), resume runs, and any task where the pipeline structure shouldn't be hardcoded.

Use antcrew (Layer 1) when you want a structured pipeline of named agents (Business Analyst → PM → Backend Dev → QA → Reviewer) orchestrated with LangGraph, with explicit human-in-the-loop between roles, project sessions across multiple runs, and semantic memory. Layer 1 depends on Layer 2 — all its capabilities are re-exported from antcrew_engine.

Architecture

Goal + Constraints
       │
       ▼
  EngineLoop ─── CapabilityRegistry
       │              │
       │    ┌─────────┴──────────────────────────────┐
       │    │  Architect   TaskPlanner   CodeGenerator │
       │    │  TestRunner  BugFixer      HitlReviewer  │
       │    │  CodeReviewer  DocGenerator  ...         │
       │    └─────────────────────────────────────────┘
       │
       ▼
  ArtifactStore  (MemoryStore | FilesystemStore | MultiRepoStore)

Each capability reads from and writes to the store. The EngineLoop picks the cheapest applicable capability until the DesiredProjectState is reached.

Install

pip install antcrew-engine
# with Anthropic support:
pip install "antcrew-engine[anthropic]"
# all model providers:
pip install "antcrew-engine[all]"

Quick start — CLI

# Build a REST API, write output to ./my-api
antcrew-engine "Build a FastAPI REST API with user authentication" \
  --tech Python --output ./my-api

# Load an existing project (skips planning, jumps to coding/testing)
antcrew-engine "Add docstrings to all public functions" \
  --from-dir ./my-project --output ./my-project

# Multi-repo routing (write to different repos by file prefix)
antcrew-engine "Build a full-stack app" \
  --repo backend:/repos/api --repo frontend:/repos/ui \
  --route src/api/:backend --route src/ui/:frontend

# Resume a prior run
antcrew-engine --resume --output ./my-api

# Inspect an existing output directory
antcrew-engine status ./my-api

Quick start — Python API

from antcrew_engine import (
    Operator, MemoryStore, Goal, DesiredProjectState, Constraints,
    Condition, ConditionId, CapabilityRegistry, EventLog, build_llm,
    Architect, TaskPlanner, CodeGenerator, TestGenerator, TestRunner,
    BugFixer, CodeReviewer, DocGenerator, DependencyInstaller,
)

llm = build_llm("claude")   # or "gpt-4o", "groq:llama3-70b", "simulated"

registry = CapabilityRegistry()
registry.register(Architect(llm=llm))
registry.register(TaskPlanner(llm=llm))
registry.register(CodeGenerator(llm=llm))
registry.register(TestGenerator(llm=llm))
registry.register(TestRunner())
registry.register(BugFixer(llm=llm))
registry.register(CodeReviewer(llm=llm))
registry.register(DocGenerator(llm=llm))
registry.register(DependencyInstaller(llm=llm))

store = MemoryStore()

goal = Goal(
    description="Build a CLI tool that converts Markdown to HTML",
    desired_state=DesiredProjectState(frozenset([
        Condition(ConditionId("requirements_exists"), "requirements written"),
        Condition(ConditionId("architecture_exists"), "architecture designed"),
        Condition(ConditionId("task_graph_exists"),   "tasks planned"),
        Condition(ConditionId("implementation_exists"), "code written"),
        Condition(ConditionId("tests_pass"),           "tests passing"),
        Condition(ConditionId("documentation_exists"), "README written"),
    ])),
    constraints=Constraints(tech_stack=("Python",)),
)

event_log = EventLog()
operator  = Operator(registry, [], event_log, max_iterations=40)
state     = operator.run(store, goal)

print("Success:", state.is_complete)
for artifact in store.list():
    print(f"  {artifact.id}: {artifact.kind.value}")

Multi-repo store

Route artifact writes to different filesystem roots by file-path prefix:

from antcrew_engine import MultiRepoStore

store = MultiRepoStore(
    repos={
        "backend":  "/repos/api",
        "frontend": "/repos/ui",
        "shared":   "/repos/shared",
    },
    routes={
        "src/api/": "backend",
        "src/ui/":  "frontend",
    },
    default="shared",
)

Capabilities

Capability	Description
Architect	Writes requirements + architecture documents
TaskPlanner	Breaks architecture into a task graph
CodeGenerator	Implements tasks in parallel
CodeRegenerator	Regenerates code after a bad review
TestGenerator	Writes a pytest test suite
TestRunner	Runs pytest, produces a test report
BugFixer	Reads the traceback, patches failing files
CodeReviewer	Reviews code, produces an approval or rejection
ReviewFixer	Applies reviewer feedback to source files
DependencyInstaller	Infers requirements.txt and installs a venv
DocGenerator	Writes README.md
HitlReviewer	Pauses for human approval before proceeding
SpecExtractor	Extracts a structured spec from a free-form description

Model support

Pass a model string to build_llm():

String	Provider
"claude"	Anthropic claude-sonnet (default)
"claude-haiku-4-5-20251001"	Anthropic Haiku (cheap/fast)
"gpt-4o"	OpenAI
"groq:llama3-70b-8192"	Groq
"ollama:llama3"	Ollama (local)
"gemini"	Google Gemini
"simulated"	Deterministic stub (tests / CI)

License

MIT