Coder preparations

CHANGELOG.md

@@ -1,3 +1,9 @@
+### 2026-09-22
+1. Add verification step - ensure the issue can be solved by coding
+2. Add repo download step
+3. Add repo analyzer: find entrypoint, build dependency graph
+4. Add IssueComment class instead of dic
+
 ### 2026-09-21
 1. Now comments are directed to team members with care of the language they can read
 2. Comments for context are filtered from TriageAgent to avoid hallucination

agents/base_agent.py

@@ -1,6 +1,45 @@
 from abc import ABC, abstractmethod
+from datetime import timezone, datetime
+import json, os, tempfile
+
 
 class BaseAgent(ABC):
     @abstractmethod
     def run(self, issue_id: str):
-        pass  # To be implemented by subclasses
+        pass  # To be implemented by subclasses
+
+REQUIRED_KEYS = {
+    "schema_version", "agent_id", "issue_id", "state",
+    "state_since", "run_id", "attempt", "counters", "max",
+    "branch_name", "events",
+}
+
+def write_state(agent_dir: str, issue_id: str, state: dict) -> None:
+    path = os.path.join(agent_dir, "state", f"{issue_id}.json")
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+    state["updated_at"] = datetime.now(timezone.utc).isoformat()
+    fd, tmp = tempfile.mkstemp(dir=os.path.dirname(path), suffix=".tmp")
+    try:
+        with os.fdopen(fd, "w") as f:
+            json.dump(state, f, indent=2, sort_keys=False)
+            f.flush()
+            os.fsync(f.fileno())
+        os.replace(tmp, path)               # atomic on POSIX and Windows
+    except Exception:
+        os.unlink(tmp)
+        raise
+
+def read_state(agent_dir: str, issue_id: str) -> dict | None:
+    path = os.path.join(agent_dir, "state", f"{issue_id}.json")
+    if not os.path.exists(path):
+        return None
+    try:
+        with open(path) as f:
+            data = json.load(f)
+    except (json.JSONDecodeError, OSError):
+        return None                          # caller treats as corrupt → re-run
+    if data.get("schema_version") != 1:
+        return None                          # schema mismatch → re-run
+    if not REQUIRED_KEYS.issubset(data):
+        return None
+    return data

agents/codebase_analyst/entrypoint.py

@@ -0,0 +1,409 @@
+"""
+Idempotent repository entrypoint analyzer (LLMClient edition).
+
+Flow:
+  1. List repo files (deterministic walk).
+  2. Ask LLM for primary language.
+  3. Loop:
+       a. Ask LLM to pick ONE candidate file.
+       b. Read an excerpt.
+       c. Ask LLM: is it the entrypoint / does it forward to another file / wrong?
+       d. On "entrypoint" -> stop. On "forward" -> probe next_file. On "wrong" -> pick again.
+
+All LLM calls go through the project's `LLMClient.chat_with_schema` with a
+strict JSON schema, validated against a Pydantic model. Validation failures are
+retried at most 2 times (per project convention). Successful responses are
+cached keyed on (schema_name, system, user) so re-running against the same
+repository is idempotent and makes zero additional LLM calls.
+"""
+from __future__ import annotations
+
+import hashlib
+import json
+import logging
+import os
+from pathlib import Path
+from typing import List, Literal, Optional, Type, TypeVar
+
+from pydantic import BaseModel, Field, ValidationError, TypeAdapter
+
+from agents.coders.repo import RepoContext
+from common.llm_client import LLMClient
+from contracts.RepoContext import Language, LanguageLiteral
+from contracts.coders.VerifyOutput import sanitize_for_openai
+
+log = logging.getLogger("entrypoint_analyzer")
+
+MAX_LLM_RETRIES = 2          # retries AFTER the initial attempt (so <= 3 total)
+EXCERPT_CHARS = 4_000
+MAX_ITERATIONS = 40
+MAX_LISTING_ENTRIES = 2_000
+
+IGNORED_DIRS = {
+    ".git", ".hg", ".svn", "node_modules", "__pycache__", ".venv", "venv",
+    "env", "dist", "build", ".idea", ".vscode", ".mypy_cache", ".pytest_cache",
+    ".tox", "target", "out", "obj", "bin", "coverage",
+}
+IGNORED_EXTS = {
+    ".png", ".jpg", ".jpeg", ".gif", ".pdf", ".zip", ".tar", ".gz", ".lock",
+    ".pyc", ".so", ".dll", ".dylib", ".exe", ".woff", ".woff2", ".ico",
+    ".svg", ".mp4", ".mp3", ".class", ".jar",
+}
+
+
+# --------------------------------------------------------------------------- #
+# Pydantic schemas                                                             #
+# --------------------------------------------------------------------------- #
+
+class LanguageGuess(BaseModel):
+    """Primary language of the repository."""
+    language: LanguageLiteral = Field(description="Primary programming language, lowercase (e.g. 'python').")
+    confidence: float = Field(ge=0.0, le=1.0)
+    reasoning: str
+
+
+class FilePick(BaseModel):
+    """A single candidate file to inspect, chosen from the listing."""
+    file_path: str = Field(description="Relative path exactly as it appears in the listing.")
+    reason: str
+
+
+class ProbeVerdict(BaseModel):
+    """Verdict for the currently inspected file."""
+    verdict: Literal["entrypoint", "forward", "wrong"]
+    next_file: Optional[str] = Field(
+        default=None,
+        description="If verdict='forward': the next file to inspect (must be in listing).",
+    )
+    reason: str
+
+
+class Attempt(BaseModel):
+    file_path: str
+    excerpt: str
+    verdict: Optional[ProbeVerdict] = None
+
+
+class AnalysisResult(BaseModel):
+    language: Language
+    entrypoint: str
+    attempts: List[Attempt]
+    reasoning: str
+
+
+# --------------------------------------------------------------------------- #
+# LLM plumbing: LLMClient wrapper with strict schema + cache + retries         #
+# --------------------------------------------------------------------------- #
+
+T = TypeVar("T", bound=BaseModel)
+
+
+class ResponseCache:
+    """File-backed cache: prompt-hash -> validated-model JSON. Makes runs idempotent."""
+
+    def __init__(self, path: Optional[Path] = None) -> None:
+        self.path = path
+        self._mem: dict[str, str] = {}
+        if path and path.exists():
+            try:
+                self._mem = json.loads(path.read_text("utf-8"))
+            except Exception:  # noqa: BLE001
+                log.warning("Corrupt cache at %s; starting fresh", path)
+
+    @staticmethod
+    def key(system: str, user: str, schema: Type[BaseModel]) -> str:
+        h = hashlib.sha256()
+        h.update(schema.__name__.encode())
+        h.update(b"\x00")
+        h.update(system.encode())
+        h.update(b"\x00")
+        h.update(user.encode())
+        return h.hexdigest()
+
+    def get(self, k: str) -> Optional[str]:
+        return self._mem.get(k)
+
+    def put(self, k: str, v: str) -> None:
+        self._mem[k] = v
+        if self.path:
+            self.path.parent.mkdir(parents=True, exist_ok=True)
+            tmp = self.path.with_suffix(self.path.suffix + ".tmp")
+            tmp.write_text(json.dumps(self._mem, indent=2, sort_keys=True), "utf-8")
+            tmp.replace(self.path)
+
+
+class StructuredLLM:
+    """
+    Wrap the project's `LLMClient` with:
+
+      * strict JSON-schema enforcement (same shape as the reference usage),
+      * Pydantic validation of the response,
+      * at most MAX_LLM_RETRIES retries on validation failure,
+      * a deterministic prompt-keyed cache so reruns are idempotent.
+
+    Usage mirrors the reference snippet but is generic over the output model.
+    """
+
+    def __init__(
+        self,
+        client: LLMClient,
+        cache: Optional[ResponseCache] = None,
+        max_retries: int = MAX_LLM_RETRIES,
+    ) -> None:
+        self.client = client
+        self.cache = cache
+        self.max_retries = max_retries
+
+    def call(self, system: str, user: str, schema: Type[T]) -> T:
+        schema_adapter = TypeAdapter(schema)
+        # --- idempotency: serve from cache when prompt+system+schema match ---
+        cache_key = ResponseCache.key(system, user, schema) if self.cache else None
+        if cache_key and (hit := self.cache.get(cache_key)) is not None:
+            try:
+                return schema.model_validate_json(hit)
+            except ValidationError:
+                log.warning("Stale cache entry for %s; ignoring", schema.__name__)
+
+        # --- build the strict response_format exactly as in the reference ---
+        response_format = {
+            "type": "json_schema",
+            "json_schema": {
+                "name": schema.__name__,
+                "schema": sanitize_for_openai(schema_adapter.json_schema()),
+                "strict": True,
+            },
+        }
+
+        last_err: Optional[BaseException] = None
+
+        for attempt in range(self.max_retries + 1):
+            augmented_system = system
+            if attempt > 0 and last_err is not None:
+                augmented_system = (
+                    f"{system}\n\n"
+                    f"Your previous response failed schema validation with: {last_err}. "
+                    f"Return ONLY a single JSON object conforming exactly to the "
+                    f"provided schema — no prose, no code fences."
+                )
+            try:
+                raw = self.client.chat_with_schema(
+                    user,
+                    response_format,
+                    system=augmented_system,
+                )
+                if isinstance(raw, str):
+                    raw = json.loads(raw)
+                parsed = schema_adapter.validate_python(raw)  # pydantic v2
+            except (ValidationError, json.JSONDecodeError, ValueError) as exc:
+                last_err = exc
+                log.warning(
+                    "Schema %s failed validation (attempt %d/%d): %s",
+                    schema.__name__, attempt + 1, self.max_retries + 1, exc,
+                )
+                continue
+            except Exception as exc:  # noqa: BLE001  (transport / API errors)
+                last_err = exc
+                log.warning(
+                    "LLM transport error for %s (attempt %d/%d): %s",
+                    schema.__name__, attempt + 1, self.max_retries + 1, exc,
+                )
+                continue
+
+            # --- memoize under the *original* prompt key ---------------------
+            if cache_key and self.cache:
+                self.cache.put(cache_key, parsed.model_dump_json())
+            return parsed
+
+        # Exhausted retries — hard failure, per project convention.
+        raise RuntimeError(
+            f"LLM failed to produce a valid {schema.__name__} "
+            f"after {self.max_retries + 1} attempt(s)"
+        ) from last_err
+
+
+# --------------------------------------------------------------------------- #
+# Repository listing                                                           #
+# --------------------------------------------------------------------------- #
+
+def list_repo(root: Path) -> List[str]:
+    """Deterministic, sorted listing of analyzable files relative to `root`."""
+    root = root.resolve()
+    out: List[str] = []
+    for dirpath, dirnames, filenames in os.walk(root):
+        dirnames[:] = sorted(d for d in dirnames if d not in IGNORED_DIRS)
+        for fname in sorted(filenames):
+            if Path(fname).suffix.lower() in IGNORED_EXTS:
+                continue
+            rel = (Path(dirpath) / fname).relative_to(root)
+            out.append(rel.as_posix())
+    return out
+
+
+def _listing_repr(listing: List[str], cap: int = MAX_LISTING_ENTRIES) -> str:
+    if len(listing) <= cap:
+        return "\n".join(listing)
+    return "\n".join(listing[:cap]) + f"\n...<{len(listing) - cap} more entries truncated>..."
+
+
+# --------------------------------------------------------------------------- #
+# Analyzer                                                                     #
+# --------------------------------------------------------------------------- #
+
+class EntrypointAnalyzer:
+    def __init__(
+        self,
+        llm: StructuredLLM,
+        max_iterations: int = MAX_ITERATIONS,
+        excerpt_chars: int = EXCERPT_CHARS,
+    ) -> None:
+        self.llm = llm
+        self.max_iterations = max_iterations
+        self.excerpt_chars = excerpt_chars
+
+    # -- public ------------------------------------------------------------ #
+
+    def analyze(self, repo_root: Path) -> AnalysisResult:
+        repo_root = Path(repo_root).resolve()
+        if not repo_root.is_dir():
+            raise ValueError(f"Not a directory: {repo_root}")
+
+        listing = list_repo(repo_root)
+        if not listing:
+            raise ValueError(f"No analyzable files under {repo_root}")
+
+        guess = self._guess_language(listing)
+        language: Language = guess.language
+        log.info("Detected language=%s (confidence=%.2f)", language.value, guess.confidence)
+
+        attempts: List[Attempt] = []
+        carried: Optional[str] = None  # "forward" hint from a previous verdict
+
+        for iteration in range(1, self.max_iterations + 1):
+            # Resolve to a definitely-non-None local for this iteration.
+            current_file: str = (
+                carried if carried is not None
+                else self._pick_file(listing, language, attempts)
+            )
+
+            if current_file not in listing:
+                log.warning(
+                    "LLM suggested %r which is not in the listing; discarding.",
+                    current_file,
+                )
+                carried = None
+                continue
+
+            excerpt = self._read_excerpt(repo_root / current_file)
+            verdict = self._probe(language, current_file, excerpt, attempts)
+            attempts.append(
+                Attempt(file_path=current_file, excerpt=excerpt, verdict=verdict)
+            )
+
+            log.info(
+                "iter=%d file=%s verdict=%s reason=%s",
+                iteration, current_file, verdict.verdict, verdict.reason,
+            )
+
+            if verdict.verdict == "entrypoint":
+                return AnalysisResult(
+                    language=language,
+                    entrypoint=current_file,
+                    attempts=attempts,
+                    reasoning=verdict.reason,
+                )
+
+            if verdict.verdict == "forward" and verdict.next_file:
+                carried = verdict.next_file
+            else:
+                if verdict.verdict == "forward":
+                    log.warning("Verdict 'forward' without next_file; falling back to fresh pick.")
+                carried = None
+
+        raise RuntimeError(
+            f"Entrypoint not found after {self.max_iterations} iterations. "
+            f"Tried: {[a.file_path for a in attempts]}"
+        )
+
+    # -- LLM steps --------------------------------------------------------- #
+
+    def _guess_language(self, listing: List[str]) -> LanguageGuess:
+        system = (
+            "You are a senior code analyst. Given a repository's file listing "
+            "(relative paths), determine the primary programming language of "
+            "the project. Weigh file extensions, conventional filenames "
+            "(setup.py, package.json, Cargo.toml, go.mod, pom.xml, ...), and "
+            "directory structure."
+        )
+        user = "Repository file listing:\n" + _listing_repr(listing)
+        return self.llm.call(system, user, LanguageGuess)
+
+    def _pick_file(
+        self, listing: List[str], language: Language, attempts: List[Attempt]
+    ) -> str:
+        system = (
+            "You are a senior code analyst. Pick exactly ONE file from the "
+            "provided listing that is the most promising candidate to be, or "
+            "to lead toward, the program's entrypoint. Prefer conventional "
+            "entrypoint names for the detected language "
+            "(main.py / __main__.py / app.py / cli.py / manage.py for Python; "
+            "index.js / server.js / main.ts for JS/TS; cmd/main.go for Go; "
+            "Program.cs / Startup.cs for C#; main.rs / lib.rs for Rust). "
+            "You MUST NOT pick a file that has already been tried. Return the "
+            "path exactly as it appears in the listing."
+        )
+        tried = [a.file_path for a in attempts]
+        user = (
+            f"Language: {language.value}\n\n"
+            f"Listing:\n{_listing_repr(listing)}\n\n"
+            f"Already tried (DO NOT pick these): {tried or '[]'}"
+        )
+        return self.llm.call(system, user, FilePick).file_path
+
+    def _probe(
+        self,
+        language: Language,
+        path: str,
+        excerpt: str,
+        attempts: List[Attempt],
+    ) -> ProbeVerdict:
+        system = (
+            f"You are a senior code analyst locating the entrypoint of a "
+            f"{language.value} project. You are shown one file at a time and must "
+            f"decide exactly one of:\n"
+            f"  - 'entrypoint': this file IS the program's entrypoint — e.g. it "
+            f"contains 'if __name__ == \"__main__\"', calls main() at module "
+            f"scope, does top-level CLI dispatch, is a declared binary target, "
+            f"or is the bootstrapping module itself.\n"
+            f"  - 'forward': this file is not the entrypoint itself but clearly "
+            f"imports/dispatches into another file that is closer — set "
+            f"'next_file' to that file's path (it MUST appear in the listing).\n"
+            f"  - 'wrong': this file is unrelated or a dead end — a different "
+            f"file must be picked.\n"
+            f"Be strict: only return 'entrypoint' when the file itself "
+            f"bootstraps execution."
+        )
+        tried = [a.file_path for a in attempts]
+        user = (
+            f"File under inspection: {path}\n\n"
+            f"--- content excerpt ---\n{excerpt}\n--- end excerpt ---\n\n"
+            f"Previously tried files: {tried or '[]'}"
+        )
+        return self.llm.call(system, user, ProbeVerdict)
+
+    # -- helpers ----------------------------------------------------------- #
+
+    def _read_excerpt(self, path: Path) -> str:
+        try:
+            text = path.read_text(encoding="utf-8", errors="replace")
+        except Exception as exc:  # noqa: BLE001
+            return f"<unreadable: {exc}>"
+        if len(text) > self.excerpt_chars:
+            return text[: self.excerpt_chars] + "\n...<truncated>..."
+        return text
+
+
+
+def gather_repo_entrypoint(llm: LLMClient, repo: RepoContext, cache_path: Optional[str] = None) -> AnalysisResult:
+    return EntrypointAnalyzer(
+        StructuredLLM(llm, cache=ResponseCache(Path(cache_path)) if cache_path else None)
+    ).analyze(Path(repo.repo_dir))

agents/codebase_analyst/graph_tool.py

@@ -0,0 +1,850 @@
+import json
+import shutil
+import subprocess
+from abc import ABC, abstractmethod
+from functools import partial
+from pathlib import Path
+from typing import Optional, Callable
+
+import networkx as nx
+import pandas as pd
+from networkx.algorithms.community import louvain_communities
+
+from pydantic import BaseModel, Field, ValidationError
+
+from common.llm_client import LLMClient
+from contracts.RepoContext import Language, ToolRequirement, DependencyGraph, DependencyNode, DependencyEdge, \
+    AnalysisResult, AnalysisInput, AnalysisError
+
+
+def find_tool(command: str) -> Optional[str]:
+    """Return the absolute path to `command` if it is on PATH, else None."""
+    return shutil.which(command)
+
+TOOL_REGISTRY: dict[Language, ToolRequirement] = {
+    Language.PYTHON: ToolRequirement(
+        tool_name="pydeps",
+        command="pydeps",
+        install_guide="""\
+## Install pydeps
+
+```bash
+pip install pydeps
+You also need Graphviz for rendering:
+Ubuntu/Debian: sudo apt install graphviz
+macOS: brew install graphviz
+Windows: Download from https://graphviz.org/download/
+Ensure the dot command is on your PATH.""",
+        docs_url="https://github.com/thebjorn/pydeps",
+    ),
+        Language.JAVASCRIPT: ToolRequirement(
+        tool_name="madge",
+        command="madge",
+        install_guide="""\
+Install madge
+bash
+npm -g install madge
+Graphviz (optional, for image output):
+Ubuntu/Debian: sudo apt install graphviz
+macOS: brew install graphviz""",
+        docs_url="https://github.com/pahen/madge",
+    ),
+    Language.TYPESCRIPT: ToolRequirement(
+        tool_name="madge",
+        command="madge",
+        install_guide="""\
+
+Install madge (TypeScript support)
+bash
+npm -g install madge
+For TypeScript projects, ensure typescript is also installed:
+
+bash
+npm install -g typescript
+```""",
+        docs_url="https://github.com/pahen/madge",
+    ),
+    Language.JAVA: ToolRequirement(
+        tool_name="mvn",
+        command="mvn",
+        install_guide="""\
+## Java dependency analysis
+
+**Maven:**
+```bash
+mvn dependency:tree -DoutputFile=deps.txt
+Gradle:
+
+bash
+gradle dependencies > deps.txt
+Install Maven: https://maven.apache.org/install.html
+Install Gradle: https://gradle.org/install/[reference:9]""",
+        docs_url="https://maven.apache.org/plugins/maven-dependency-plugin/",
+    ),
+    Language.GO: ToolRequirement(
+        tool_name="go",
+        command="go",
+        install_guide="""\
+
+Install Go
+The Go toolchain provides built-in dependency listing:
+
+bash
+# Full module graph
+go mod graph
+
+# All modules (direct + indirect)
+go list -m all
+
+# Package import graph
+go list -f '{{.ImportPath}} {{.Imports}}' ./...
+Install Go: https://go.dev/doc/install[reference:10]""",
+        docs_url="https://go.dev/ref/mod#go-list-m",
+    ),
+    Language.RUST: ToolRequirement(
+        tool_name="cargo",
+        command="cargo",
+        install_guide="""\
+Install Rust / Cargo
+cargo tree is built-in since Rust 1.44:
+
+bash
+cargo tree
+cargo tree --depth 1      # direct deps only
+cargo tree -i <crate>     # reverse deps
+Install Rust: https://www.rust-lang.org/tools/install[reference:11]""",
+        docs_url="https://doc.rust-lang.org/cargo/commands/cargo-tree.html",
+    ),
+    Language.CPP: ToolRequirement(
+        tool_name="include-what-you-use",
+        command="include-what-you-use",
+        install_guide="""\
+
+Install include-what-you-use (IWYU)
+Ubuntu/Debian: sudo apt-get install iwyu
+macOS: brew install include-what-you-use
+Arch: sudo pacman -S include-what-you-use
+Ensure include-what-you-use is on your PATH.""",
+        docs_url="https://include-what-you-use.org/",
+    ),
+    Language.C: ToolRequirement(
+        tool_name="include-what-you-use",
+        command="include-what-you-use",
+        install_guide="""\
+Install include-what-you-use (IWYU)
+Ubuntu/Debian: sudo apt-get install iwyu
+macOS: brew install include-what-you-use
+Arch: sudo pacman -S include-what-you-use""",
+        docs_url="https://include-what-you-use.org/",
+    ),
+}
+
+class LanguageAdapter(ABC):
+    """Contract for every language-specific analyzer."""
+
+    @property
+    @abstractmethod
+    def language(self) -> Language: ...
+
+    @property
+    @abstractmethod
+    def required_tools(self) -> list[ToolRequirement]: ...
+
+    @abstractmethod
+    def build_graph(
+        self, repo_path: Path, entrypoint: str
+    ) -> DependencyGraph: ...
+
+class PythonAdapter(LanguageAdapter):
+    @property
+    def language(self) -> Language:
+        return Language.PYTHON
+
+    @property
+    def required_tools(self) -> list[ToolRequirement]:
+        return [TOOL_REGISTRY[Language.PYTHON]]
+
+    def build_graph(self, repo_path: Path, entrypoint: str) -> DependencyGraph:
+        entry_abs = repo_path / entrypoint
+
+        # pydeps emits JSON when --show-deps is used
+        result = subprocess.run(
+            [
+                "pydeps",
+                str(entry_abs),
+                "--show-deps",
+                "--no-output",
+                "--no-config",
+                "-T", "json",
+            ],
+            capture_output=True,
+            text=True,
+            cwd=str(repo_path),
+            timeout=120,
+        )
+        if result.returncode != 0:
+            raise RuntimeError(
+                f"pydeps failed (rc={result.returncode}): {result.stderr.strip()}"
+            )
+
+        raw = json.loads(result.stdout)
+        nodes: list[DependencyNode] = []
+        edges: list[DependencyEdge] = []
+        seen: set[str] = set()
+
+        def _add_node(name: str, external: bool = False) -> None:
+            if name not in seen:
+                seen.add(name)
+                nodes.append(
+                    DependencyNode(
+                        id=name,
+                        label=name.split(".")[-1],
+                        language=Language.PYTHON,
+                        is_external=external,
+                    )
+                )
+
+        _add_node(entrypoint)
+        for mod_name, info in raw.items():
+            _add_node(mod_name, external=info.get("external", False))
+            for imported in info.get("imports", []):
+                _add_node(imported)
+                edges.append(
+                    DependencyEdge(source=mod_name, target=imported, kind="import")
+                )
+
+        return DependencyGraph(
+            root=entrypoint,
+            nodes=nodes,
+            edges=edges,
+            metadata={"tool": "pydeps", "entrypoint": entrypoint},
+        )
+
+class JsTsAdapter(LanguageAdapter):
+    def __init__(self, language: Language) -> None:
+        self._language = language
+
+    @property
+    def language(self) -> Language:
+        return self._language
+
+    @property
+    def required_tools(self) -> list[ToolRequirement]:
+        return [TOOL_REGISTRY[self._language]]
+
+    def build_graph(self, repo_path: Path, entrypoint: str) -> DependencyGraph:
+        entry_abs = repo_path / entrypoint
+
+        cmd = ["madge", "--json", str(entry_abs), "--basedir", str(repo_path)]
+        if self._language == Language.TYPESCRIPT:
+            cmd.append("--ts-config")
+            cmd.append(str(repo_path / "tsconfig.json"))
+
+        result = subprocess.run(
+            cmd,
+            capture_output=True,
+            text=True,
+            cwd=str(repo_path),
+            timeout=180,
+        )
+        if result.returncode != 0:
+            raise RuntimeError(
+                f"madge failed (rc={result.returncode}): {result.stderr.strip()}"
+            )
+
+        raw: dict[str, list[str]] = json.loads(result.stdout)
+        nodes: list[DependencyNode] = []
+        edges: list[DependencyEdge] = []
+        seen: set[str] = set()
+
+        def _add_node(name: str) -> None:
+            if name not in seen:
+                seen.add(name)
+                nodes.append(
+                    DependencyNode(
+                        id=name,
+                        label=Path(name).name,
+                        language=self._language,
+                        is_external=not name.startswith("."),
+                    )
+                )
+
+        _add_node(entrypoint)
+        for src, deps in raw.items():
+            _add_node(src)
+            for dep in deps:
+                _add_node(dep)
+                edges.append(
+                    DependencyEdge(source=src, target=dep, kind="import")
+                )
+
+        return DependencyGraph(
+            root=entrypoint,
+            nodes=nodes,
+            edges=edges,
+            metadata={"tool": "madge", "entrypoint": entrypoint},
+        )
+
+class GoAdapter(LanguageAdapter):
+    @property
+    def language(self) -> Language:
+        return Language.GO
+
+    @property
+    def required_tools(self) -> list[ToolRequirement]:
+        return [TOOL_REGISTRY[Language.GO]]
+
+    def build_graph(self, repo_path: Path, entrypoint: str) -> DependencyGraph:
+        result = subprocess.run(
+            [
+                "go", "list",
+                "-f", "{{.ImportPath}} {{join .Imports \" \"}}",
+                "./...",
+            ],
+            capture_output=True,
+            text=True,
+            cwd=str(repo_path),
+            timeout=180,
+        )
+        if result.returncode != 0:
+            raise RuntimeError(
+                f"go list failed (rc={result.returncode}): {result.stderr.strip()}"
+            )
+
+        nodes: list[DependencyNode] = []
+        edges: list[DependencyEdge] = []
+        seen: set[str] = set()
+
+        def _add_node(name: str) -> None:
+            if name not in seen:
+                seen.add(name)
+                nodes.append(
+                    DependencyNode(
+                        id=name,
+                        label=name.split("/")[-1],
+                        language=Language.GO,
+                        is_external="/" in name and not name.startswith("."),
+                    )
+                )
+
+        for line in result.stdout.strip().splitlines():
+            parts = line.split()
+            if not parts:
+                continue
+            pkg = parts[0]
+            _add_node(pkg)
+            for imp in parts[1:]:
+                _add_node(imp)
+                edges.append(
+                    DependencyEdge(source=pkg, target=imp, kind="import")
+                )
+
+        return DependencyGraph(
+            root=entrypoint,
+            nodes=nodes,
+            edges=edges,
+            metadata={"tool": "go list", "entrypoint": entrypoint},
+        )
+
+
+class RustAdapter(LanguageAdapter):
+    @property
+    def language(self) -> Language:
+        return Language.RUST
+
+    @property
+    def required_tools(self) -> list[ToolRequirement]:
+        return [TOOL_REGISTRY[Language.RUST]]
+
+    def build_graph(self, repo_path: Path, entrypoint: str) -> DependencyGraph:
+        result = subprocess.run(
+            ["cargo", "tree", "--prefix", "none", "--format", "{p} {f}"],
+            capture_output=True,
+            text=True,
+            cwd=str(repo_path),
+            timeout=180,
+        )
+        if result.returncode != 0:
+            raise RuntimeError(
+                f"cargo tree failed (rc={result.returncode}): {result.stderr.strip()}"
+            )
+
+        nodes: list[DependencyNode] = []
+        edges: list[DependencyEdge] = []
+        seen: set[str] = set()
+
+        def _add_node(name: str) -> None:
+            if name not in seen:
+                seen.add(name)
+                nodes.append(
+                    DependencyNode(
+                        id=name,
+                        label=name,
+                        language=Language.RUST,
+                        is_external=False,  # refined below
+                    )
+                )
+
+        # cargo tree --prefix none outputs one package per line,
+        # with indentation indicating depth. We parse the flat form
+        # and reconstruct edges from indentation.
+        lines = result.stdout.strip().splitlines()
+        stack: list[str] = []
+        for line in lines:
+            if not line.strip():
+                continue
+            indent = len(line) - len(line.lstrip("│ ├─└ "))
+            name = line.strip().split(" ")[0]
+            _add_node(name)
+            while len(stack) > indent:
+                stack.pop()
+            if stack:
+                edges.append(
+                    DependencyEdge(source=stack[-1], target=name, kind="depends_on")
+                )
+            stack.append(name)
+
+        return DependencyGraph(
+            root=entrypoint,
+            nodes=nodes,
+            edges=edges,
+            metadata={"tool": "cargo tree", "entrypoint": entrypoint},
+        )
+
+
+class JavaAdapter(LanguageAdapter):
+    @property
+    def language(self) -> Language:
+        return Language.JAVA
+
+    @property
+    def required_tools(self) -> list[ToolRequirement]:
+        return [TOOL_REGISTRY[Language.JAVA]]
+
+    def build_graph(self, repo_path: Path, entrypoint: str) -> DependencyGraph:
+        if (repo_path / "pom.xml").exists():
+            return self._maven_graph(repo_path, entrypoint)
+        if (repo_path / "build.gradle").exists() or (repo_path / "build.gradle.kts").exists():
+            return self._gradle_graph(repo_path, entrypoint)
+        raise RuntimeError("No pom.xml or build.gradle found in repository root")
+
+    def _maven_graph(self, repo_path: Path, entrypoint: str) -> DependencyGraph:
+        result = subprocess.run(
+            ["mvn", "dependency:tree", "-DoutputType=text"],
+            capture_output=True,
+            text=True,
+            cwd=str(repo_path),
+            timeout=300,
+        )
+        if result.returncode != 0:
+            raise RuntimeError(f"mvn dependency:tree failed: {result.stderr.strip()}")
+        return self._parse_tree_text(result.stdout, entrypoint)
+
+    def _gradle_graph(self, repo_path: Path, entrypoint: str) -> DependencyGraph:
+        result = subprocess.run(
+            ["gradle", "dependencies", "--configuration", "runtimeClasspath"],
+            capture_output=True,
+            text=True,
+            cwd=str(repo_path),
+            timeout=300,
+        )
+        if result.returncode != 0:
+            raise RuntimeError(f"gradle dependencies failed: {result.stderr.strip()}")
+        return self._parse_tree_text(result.stdout, entrypoint)
+
+    def _parse_tree_text(self, text: str, entrypoint: str) -> DependencyGraph:
+        # Simplified parser for the textual tree format.
+        # In production, use the JSON output of the Maven/Gradle plugin.
+        nodes: list[DependencyNode] = []
+        edges: list[DependencyEdge] = []
+        seen: set[str] = set()
+
+        def _add_node(name: str) -> None:
+            if name not in seen:
+                seen.add(name)
+                nodes.append(
+                    DependencyNode(
+                        id=name,
+                        label=name.split(":")[-1] if ":" in name else name,
+                        language=Language.JAVA,
+                        is_external=True,
+                    )
+                )
+
+        _add_node(entrypoint)
+        for line in text.splitlines():
+            stripped = line.strip()
+            if not stripped or stripped.startswith("+-") or stripped.startswith("\\-"):
+                continue
+            # crude: treat every artifact line as a dependency of the root
+            name = stripped.split(":")[0]
+            if name and name != entrypoint:
+                _add_node(name)
+                edges.append(
+                    DependencyEdge(source=entrypoint, target=name, kind="depends_on")
+                )
+
+        return DependencyGraph(
+            root=entrypoint,
+            nodes=nodes,
+            edges=edges,
+            metadata={"tool": "maven/gradle", "entrypoint": entrypoint},
+        )
+
+
+class CppAdapter(LanguageAdapter):
+    @property
+    def language(self) -> Language:
+        return Language.CPP
+
+    @property
+    def required_tools(self) -> list[ToolRequirement]:
+        return [TOOL_REGISTRY[Language.CPP]]
+
+    def build_graph(self, repo_path: Path, entrypoint: str) -> DependencyGraph:
+        entry_abs = repo_path / entrypoint
+
+        result = subprocess.run(
+            [
+                "include-what-you-use",
+                "-Xiwyu", "--no_fwd_decls",
+                str(entry_abs),
+            ],
+            capture_output=True,
+            text=True,
+            cwd=str(repo_path),
+            timeout=180,
+        )
+        # IWYU returns non-zero when it recommends changes; parse stdout anyway
+        output = result.stdout + result.stderr
+
+        nodes: list[DependencyNode] = []
+        edges: list[DependencyEdge] = []
+        seen: set[str] = set()
+
+        def _add_node(name: str) -> None:
+            if name not in seen:
+                seen.add(name)
+                nodes.append(
+                    DependencyNode(
+                        id=name,
+                        label=Path(name).name,
+                        language=Language.CPP,
+                        is_external=not name.startswith("."),
+                    )
+                )
+
+        _add_node(entrypoint)
+        # IWYU output contains lines like:
+        #   #include <vector>  // for ...
+        #   #include "foo.h"   // for ...
+        import re
+        pattern = re.compile(r'#include\s+[<"]([^>"]+)[>"]')
+        for match in pattern.finditer(output):
+            header = match.group(1)
+            _add_node(header)
+            edges.append(
+                DependencyEdge(source=entrypoint, target=header, kind="include")
+            )
+
+        return DependencyGraph(
+            root=entrypoint,
+            nodes=nodes,
+            edges=edges,
+            metadata={"tool": "include-what-you-use", "entrypoint": entrypoint},
+        )
+
+class LLMDependencyInference(BaseModel):
+    """Schema the LLM must fill to infer dependencies."""
+    nodes: list[dict[str, str]]
+    edges: list[dict[str, str]]
+    confidence: float = Field(ge=0.0, le=1.0)
+
+
+SYSTEM_PROMPT = (
+    "You are a static-analysis assistant. Given the source code of a file, "
+    "extract all direct dependencies (imports, includes, requires). "
+    "Return ONLY valid JSON matching the provided schema."
+)
+
+
+def infer_dependencies_with_llm(
+    llm: LLMClient,
+    file_content: str,
+    language: str,
+) -> LLMDependencyInference:
+    prompt = (
+        f"Language: {language}\n"
+        f"Source code:\n```\n{file_content}\n```\n"
+        "Extract all dependencies as a list of nodes and edges."
+    )
+
+    schema = LLMDependencyInference.model_json_schema()
+    raw = llm.chat_with_schema(
+        prompt,
+        {
+            "type": "json_schema",
+            "json_schema": {
+                "name": "LLMDependencyInference",
+                "schema": schema,
+                "strict": True,
+            },
+        },
+        system=SYSTEM_PROMPT,
+    )
+
+    try:
+        if isinstance(raw, str):
+            raw = json.loads(raw)
+        return LLMDependencyInference.model_validate(raw)
+    except ValidationError as exc:
+        # Hard failure — do not build downstream steps on malformed data.
+        raise RuntimeError(f"LLM returned invalid dependency schema: {exc}") from exc
+
+
+def emit_youtrack_comment(result: AnalysisResult, issue_id: str) -> None:
+    """Post a single comment to Youtrack describing the analysis outcome."""
+    if result.success:
+        # Optionally post a summary; by convention, avoid duplicating comments.
+        return
+
+    err = result.error
+    body = f"""
+    **Dependency analysis failed** (`{err.error_code}`)
+
+    {err.message}
+
+    """
+    if err.missing_tools:
+        for tool in err.missing_tools:
+            body += f"\n\n### {tool.tool_name}\n\n{tool.install_guide}"
+
+    print(body)
+
+
+AdapterFactory = Callable[[], LanguageAdapter]
+
+ADAPTER_REGISTRY: dict[Language, AdapterFactory] = {
+    Language.PYTHON:     PythonAdapter,                              # type[LanguageAdapter] is Callable[[], LanguageAdapter]
+    Language.JAVASCRIPT: partial(JsTsAdapter, Language.JAVASCRIPT),  # -> JsTsAdapter
+    Language.TYPESCRIPT: partial(JsTsAdapter, Language.TYPESCRIPT),  # -> JsTsAdapter
+    Language.JAVA:       JavaAdapter,
+    Language.GO:         GoAdapter,
+    Language.RUST:       RustAdapter,
+    Language.CPP:        partial(CppAdapter, Language.CPP),          # if CppAdapter takes a language
+    Language.C:          partial(CppAdapter, Language.C),
+}
+
+
+def analyze_repository(inp: AnalysisInput) -> AnalysisResult:
+    """
+    Idempotent entry point.
+
+    Given the same AnalysisInput, always returns the same AnalysisResult
+    (either a graph or a structured error). No files are written to the
+    repository under analysis.
+    """
+    adapter_factory = ADAPTER_REGISTRY.get(inp.language)
+    if adapter_factory is None:
+        return AnalysisResult(
+            success=False,
+            error=AnalysisError(
+                error_code="UNSUPPORTED_LANGUAGE",
+                message=f"No adapter registered for language '{inp.language}'",
+                language=inp.language,
+                entrypoint=inp.entrypoint,
+            ),
+        )
+
+    adapter: LanguageAdapter = adapter_factory()
+
+    # --- Tool detection ---
+    missing: list[ToolRequirement] = []
+    for req in adapter.required_tools:
+        if find_tool(req.command) is None:
+            missing.append(req)
+
+    if missing:
+        return AnalysisResult(
+            success=False,
+            error=AnalysisError(
+                error_code="MISSING_TOOL",
+                message=(
+                    f"Cannot build dependency graph for '{inp.language.value}': "
+                    f"required tool(s) not found on PATH: "
+                    f"{', '.join(r.tool_name for r in missing)}"
+                ),
+                missing_tools=missing,
+                language=inp.language,
+                entrypoint=inp.entrypoint,
+            ),
+        )
+
+    # --- Build graph ---
+    try:
+        graph = adapter.build_graph(inp.repo_path, inp.entrypoint)
+    except subprocess.TimeoutExpired:
+        return AnalysisResult(
+            success=False,
+            error=AnalysisError(
+                error_code="TOOL_TIMEOUT",
+                message=f"Tool timed out while analyzing {inp.entrypoint}",
+                language=inp.language,
+                entrypoint=inp.entrypoint,
+            ),
+        )
+    except Exception as exc:
+        return AnalysisResult(
+            success=False,
+            error=AnalysisError(
+                error_code="ANALYSIS_FAILED",
+                message=str(exc),
+                language=inp.language,
+                entrypoint=inp.entrypoint,
+            ),
+        )
+
+    G_all = to_networkx(graph, include_external=True)
+    G = to_networkx(graph, include_external=False)  # internal-only analysis
+    res_internal = analyze(G, graph, label="_internal")
+    res_all = analyze(G_all, graph, label="_all")
+    leaf_clusters = cluster_leaves(G, res_internal["leaf_no_deps"])
+
+    return AnalysisResult(success=True, graph=graph, res_all=res_all, leaf_clusters=leaf_clusters)
+
+
+def to_networkx(dep_graph: DependencyGraph, include_external=False):
+    G = nx.DiGraph()
+
+    # Nodes
+    for n in dep_graph.nodes:
+        if not include_external and n.is_external:
+            continue
+        G.add_node(n.id, label=n.label, language=n.language.value, is_external=n.is_external)
+
+    # Edges (source depends on target)
+    for e in dep_graph.edges:
+        if e.source in G and e.target in G:
+            G.add_edge(e.source, e.target, kind=e.kind)
+
+    # Add isolated internal files that never appear in edges
+    if not include_external:
+        for n in dep_graph.nodes:
+            if not n.is_external and n.id not in G:
+                G.add_node(n.id, label=n.label, language=n.language.value, is_external=False)
+
+    return G
+
+def package_of(path: str) -> str:
+    parts = path.split("/")
+    return "/".join(parts[:-1]) or "."
+
+
+COLS = [
+    "file", "language", "in_degree", "out_degree",
+    "isolated", "leaf_no_deps", "entry_no_dependents",
+    "used_by_files", "used_by_packages", "community",
+]
+
+
+def analyze(G, graph, label=""):
+    in_deg  = dict(G.in_degree())
+    out_deg = dict(G.out_degree())
+
+    # 1. Truly isolated: no in and no out edges
+    isolated = [n for n in G if G.degree(n) == 0]
+
+    # 2. Leaf: depends on nothing
+    leaf_no_deps = [n for n in G if G.out_degree(n) == 0]
+
+    # 3. Entrypoint: nothing depends on it
+    entry_no_dependents = [n for n in G if G.in_degree(n) == 0]
+
+    # 4. Widely used: how many distinct files depend on it
+    widely_used = sorted(in_deg.items(), key=lambda kv: kv[1], reverse=True)
+
+    # 5. How many distinct packages depend on it
+    pkg = {n: package_of(n) for n in G}
+    used_by_packages = {
+        n: len({pkg[p] for p in G.predecessors(n)}) for n in G
+    }
+
+    # 6. Weakly connected components = isolated groups
+    wccs = sorted(nx.weakly_connected_components(G), key=len, reverse=True)
+
+    # 7. Strongly connected components = cycles / tightly coupled groups
+    sccs = sorted(
+        (c for c in nx.strongly_connected_components(G) if len(c) > 1),
+        key=len, reverse=True,
+    )
+
+    # 8. Communities via Louvain
+    U = G.to_undirected()
+    comms = louvain_communities(U, seed=42) if U.number_of_edges() else []
+    node2comm = {n: i for i, c in enumerate(comms) for n in c}
+
+    # Per-file table
+    rows = []
+    for n, data in G.nodes(data=True):
+        rows.append({
+            "file": n,
+            "language": data.get("language"),
+            "in_degree": in_deg[n],
+            "out_degree": out_deg[n],
+            "isolated": G.degree(n) == 0,
+            "leaf_no_deps": out_deg[n] == 0,
+            "entry_no_dependents": in_deg[n] == 0,
+            "used_by_files": in_deg[n],
+            "used_by_packages": used_by_packages[n],
+            "community": node2comm.get(n, -1),
+        })
+
+    df = pd.DataFrame(rows, columns=COLS)
+    if not df.empty:
+        df = df.sort_values(
+            ["used_by_packages", "used_by_files"],
+            ascending=False,
+        )
+    df.to_csv(f"file_metrics{label}.csv", index=False)
+
+    pd.DataFrame([
+        {"group_id": i, "size": len(c), "members": ";".join(sorted(c))}
+        for i, c in enumerate(wccs)
+    ]).to_csv(f"isolated_groups{label}.csv", index=False)
+
+    pd.DataFrame([
+        {"scc_id": i, "size": len(c), "members": ";".join(sorted(c))}
+        for i, c in enumerate(sccs)
+    ]).to_csv(f"cycles{label}.csv", index=False)
+
+    return {
+        "df": df,
+        "isolated": isolated,
+        "leaf_no_deps": leaf_no_deps,
+        "entry_no_dependents": entry_no_dependents,
+        "widely_used": widely_used,
+        "used_by_packages": used_by_packages,
+        "wccs": wccs,
+        "sccs": sccs,
+        "node2comm": node2comm,
+        "comms": comms,
+    }
+
+
+def cluster_leaves(G, leaves):
+    L = nx.Graph()
+    L.add_nodes_from(leaves)
+
+    parent_to_leaves = {}
+    for leaf in leaves:
+        for parent in G.predecessors(leaf):
+            parent_to_leaves.setdefault(parent, []).append(leaf)
+
+    # Leaves used by the same parent get connected
+    for parent, ls in parent_to_leaves.items():
+        for i in range(len(ls)):
+            for j in range(i + 1, len(ls)):
+                L.add_edge(ls[i], ls[j])
+
+    clusters = sorted(nx.connected_components(L), key=len, reverse=True)
+    return clusters

agents/coders/BaseCoder.py

@@ -0,0 +1,84 @@
+"""
+Can read repo, branching, commit, push, create MR, review changes
+"""
+import logging
+from pathlib import Path
+
+from agents.coders.steps.gather import GatherStep
+from agents.coders.steps.plan import handle_plan, PlanInput
+from agents.coders.steps.verify import handle_verify
+from agents.issue_triage.context_builder import YouTrackContextBuilder
+from agents.registry import agent, AgentRegistry
+from common.gitea_mcp_client import GiteaMCPClient
+from common.llm_client import LLMClient
+from common.youtrack_mcp_client import YouTrackMCPClient
+from contracts.IssueContext import IssueContext
+from contracts.coders.RunContext import RunContext, new_run_id, AgentConfig
+from contracts.coders.VerifyOutput import VerifyPossibilityInput
+
+logger = logging.getLogger("base_coder")
+logger.setLevel(logging.DEBUG)
+
+
+@agent(
+    name="BaseCoderAgent",
+    description="Base Coder Agent",
+    version="1.0.0",
+    capabilities={"read_issue", "comment_issue", "marker_issue", "comment_classify"},
+    tags={"development"},
+    input_schema={"type": "object", "properties": {"text": {"type": "string"}}},
+    priority=10,
+)
+class CoderAgent:
+    def __init__(self, id: str, context_builder: YouTrackContextBuilder, llm: LLMClient, agent_registry: AgentRegistry,
+                 youtrack_mcp: YouTrackMCPClient, gitea_mcp: GiteaMCPClient):
+        self.name = "BaseCoderAgent"
+        self.id = id
+        self.ctx_builder = context_builder
+        self.llm = llm
+        self.agent_registry = agent_registry
+        self.youtrack_mcp = youtrack_mcp
+        self.gitea_mcp = gitea_mcp
+
+    async def fix_an_issue(self, issue_ctx: IssueContext):
+        gather_result = await GatherStep(
+            Path(__file__).resolve().parents[2] / "projects" / "agents",
+            self.llm,
+            self.agent_registry,
+            self.youtrack_mcp,
+            self.id
+        ).run(issue_ctx)
+
+        if gather_result.state.state == "INIT":
+            run_ctx = RunContext(
+                agent_id=self.id,
+                issue_id=issue_ctx.issue_id,
+                run_id=new_run_id(),
+                attempt=gather_result.attempt,
+                llm=self.llm,
+                youtrack=self.youtrack_mcp,
+                gitea=self.gitea_mcp,
+                state=gather_result.state,
+                config=AgentConfig(
+                    youtrack_project=issue_ctx.issue_id.split(":")[0],
+                    gitea_repo=""
+                ),
+            )
+            step = await handle_verify(
+                VerifyPossibilityInput(gather_result),
+                run_ctx
+            )
+            if step.kind != "continue":
+                return "Verified step await"
+
+            step = await handle_plan(
+                PlanInput(gather_result, step.output),
+                llm=self.llm,
+            )
+            print(step)
+
+        # Init
+        return "Other loop"
+
+
+

agents/coders/prompts/follow_up.py

@@ -0,0 +1,62 @@
+FOLLOW_UP_SYSTEM_PROMPT = """
+You are an autonomous coding agent that communicates with humans through
+issue-tracker comments.
+
+CONTEXT
+You previously posted a QUESTION on an issue. A human replied with RESPONSE,
+but the reply is INCOMPLETE: it partially addresses the question or leaves
+out information you need before you can continue. You must now post a short,
+polite follow-up that asks ONLY for the missing information.
+
+INPUTS
+  - QUESTION:  the exact text you posted earlier (may contain an HTML
+               marker comment `<!-- coding-agent:... -->` and boilerplate).
+  - RESPONSE:  the human's reply.
+
+GOAL
+Produce a single comment that:
+  1. Acknowledges what the human already answered (briefly, one clause).
+  2. States precisely what is still missing or ambiguous.
+  3. Asks a focused question (or a short numbered list of questions)
+     that a human can answer without re-reading the whole thread.
+  4. Stays under 120 words.
+
+HARD RULES
+  - Do NOT repeat the full original question; reference it in a clause.
+  - Do NOT invent requirements, constraints, or options the original
+    QUESTION did not contain.
+  - Do NOT include the HTML marker `<!-- coding-agent:... -->`; the
+    caller appends it.
+  - Do NOT wrap the output in code fences, quotes, or a preamble like
+    "Here is the follow-up:".
+  - Do NOT emit a JSON object. Output plain comment text only.
+  - Match the language of the QUESTION. If the QUESTION was in English,
+    answer in English; if the RESPONSE was in a different language,
+    still answer in the QUESTION's language.
+  - Preserve any usernames, file paths, identifiers, or code snippets
+    the human used, without paraphrasing them.
+  - Use Markdown formatting only if the original QUESTION used it.
+  - Do NOT mention that you are an AI, an LLM, or that a classifier
+    deemed the reply incomplete.
+
+TONE
+  - Concise, courteous, direct.
+  - No filler ("Thanks for getting back to me!" is acceptable once,
+    but keep it to a single short clause).
+  - No apologies for asking a follow-up.
+  - No emojis unless the human used them first.
+"""
+
+FOLLOW_UP_USER_PROMPT = """
+QUESTION:
+\"\"\"
+{question_text}
+\"\"\"
+
+RESPONSE (incomplete):
+\"\"\"
+{response_text}
+\"\"\"
+
+Write the follow-up comment. Plain text only, no marker, no fences.
+"""

agents/coders/prompts/relevance.py

@@ -0,0 +1,81 @@
+from pydantic import BaseModel
+
+CHECK_RELEVANCE_SYSTEM_PROMPT = """
+You are a relevance classifier for an autonomous coding agent that
+communicates with humans through issue-tracker comments.
+
+CONTEXT
+The agent previously posted a QUESTION as an issue comment and is now
+waiting for a HUMAN to answer it. A new human comment has appeared. Your
+job is to decide whether that comment is a valid answer to the QUESTION,
+and if so, whether the answer is sufficient for the agent to proceed.
+
+You will receive:
+  - QUESTION:  the exact text the agent posted (may contain an
+               HTML marker comment `<!-- coding-agent:... -->` and
+               possibly other boilerplate).
+  - RESPONSE:  the latest human comment on the issue.
+
+You do NOT know the agent's internal state or the wider issue history.
+Judge ONLY the semantic relationship between QUESTION and RESPONSE.
+
+CLASSIFICATION RULES
+Classify the RESPONSE into exactly one of three labels:
+
+1. "unrelated"
+   The response does not address the question at all.
+   Examples:
+     - Off-topic chatter, greetings, emojis, "any update?".
+     - A response to a DIFFERENT agent comment, not to the QUESTION.
+     - A comment that merely quotes the question back without answering.
+     - A comment that says "I'll look into it" or "ping me later" — this
+       is an acknowledgement, not an answer.
+   When in doubt between "unrelated" and "incomplete", prefer "unrelated"
+   only if the response clearly does not attempt to answer.
+
+2. "incomplete"
+   The response attempts to answer but is missing required information,
+   is ambiguous, contradicts itself, or only partially covers the
+   question's asks. The agent cannot proceed without more detail.
+   Examples:
+     - The question asks for two things and only one is provided.
+     - The answer is vague ("just do the obvious thing", "make it work").
+     - The answer references information the agent cannot see ("see the
+       doc I mentioned earlier", "same as before").
+     - The answer contains a question back to the agent that must be
+       answered before the original question can be resolved.
+
+3. "relevant"
+   The response fully and unambiguously answers the QUESTION with enough
+   information for the agent to continue the pipeline.
+   Examples:
+     - A yes/no decision where either is acceptable.
+     - A concrete value, list, path, name, or configuration the agent
+       requested.
+     - A clear choice among the options the agent presented.
+
+IGNORE THESE WHEN CLASSIFYING
+  - HTML marker comments (`<!-- coding-agent:... -->`).
+  - Quoted text: if the human replies quoting the QUESTION, strip the
+    quoted lines and judge only the NEW prose the human wrote.
+  - Signature blocks, email footers, and issue-tracker metadata.
+  - Language: the response may be in a different language than the
+    question. Judge semantics, not language.
+
+OUTPUT
+Return ONLY a single JSON object, no prose, no code fences:
+
+{
+  "label": "unrelated" | "incomplete" | "relevant",
+  "confidence": <float between 0.0 and 1.0>,
+  "reason": "<one short sentence, <= 200 chars>"
+}
+
+Do not add any other keys.
+"""
+
+class RelevanceResponse(BaseModel):
+    label: str
+    confidence: float
+    reason: str
+

agents/coders/repo.py

@@ -0,0 +1,205 @@
+import os
+import re
+import subprocess
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Optional
+
+
+@dataclass
+class RepoContext:
+    """Result object carrying all info a coding agent needs."""
+    repo_dir: Path
+    remote_url: str
+    branch: str
+    base_branch: str
+    head_commit: str
+    is_new_clone: bool
+    is_new_branch: bool
+    original_branch: Optional[str] = None
+    uncommitted_changes: Optional[str] = None  # git diff (tracked)
+    untracked_files: list[str] = field(default_factory=list)
+    stash_ref: Optional[str] = None
+    env: dict = field(default_factory=dict)
+
+
+def _run(cmd: list[str], cwd: Path | None = None, check: bool = True) -> subprocess.CompletedProcess:
+    """Small helper around subprocess.run that captures output cleanly."""
+    result = subprocess.run(
+        cmd,
+        cwd=str(cwd) if cwd else None,
+        capture_output=True,
+        text=True,
+        check=False,
+    )
+    if check and result.returncode != 0:
+        raise RuntimeError(
+            f"Command failed ({result.returncode}): {' '.join(cmd)}\n"
+            f"stdout: {result.stdout}\nstderr: {result.stderr}"
+        )
+    return result
+
+
+def _branch_exists(repo_dir: Path, branch: str) -> bool:
+    """Check local OR remote-tracking branch existence."""
+    local = _run(["git", "rev-parse", "--verify", "--quiet", f"refs/heads/{branch}"], cwd=repo_dir, check=False)
+    if local.returncode == 0:
+        return True
+    remote = _run(["git", "ls-remote", "--heads", "origin", branch], cwd=repo_dir, check=False)
+    return bool(remote.stdout.strip())
+
+
+def _repo_name_from_url(remote_url: str) -> str:
+    """
+    Извлекает имя репозитория из URL.
+    Поддерживает:
+      - https://github.com/org/repo.git
+      - git@github.com:org/repo.git
+      - ssh://git@host/org/repo.git
+      - /local/path/to/repo.git
+      - /local/path/to/repo
+    """
+    # Нормализуем scp-подобный синтаксис: git@host:org/repo.git -> git@host/org/repo.git
+    url = remote_url.rstrip("/")
+    if "://" not in url and ":" in url:
+        # scp-like
+        url = url.replace(":", "/", 1)
+
+    # Берём последний сегмент пути
+    name = url.rsplit("/", 1)[-1]
+    if name.endswith(".git"):
+        name = name[:-4]
+    if not name:
+        raise ValueError(f"Cannot derive repo name from URL: {remote_url!r}")
+
+    # Санитайзим (на случай странных имён)
+    name = re.sub(r"[^A-Za-z0-9._-]+", "-", name).strip("-")
+    if not name:
+        raise ValueError(f"Cannot derive repo name from URL: {remote_url!r}")
+    return name
+
+
+def ensure_repo_ready(
+    remote_url: str,
+    repo_dir: Path,
+    issue_branch: str,
+    base_branch: str = "main",
+    repo_name: Optional[str] = None,
+    username: Optional[str] = None,
+    token: Optional[str] = None,
+    author_name: Optional[str] = None,
+    author_email: Optional[str] = None,
+) -> RepoContext:
+    """
+    Idempotently prepare a working copy of a repo for a coding agent.
+
+    Steps (each is idempotent):
+      1. Ensure repo_dir exists.
+      2. Clone if empty; otherwise fetch and reuse.
+      3. Snapshot current changes (diff + untracked + stash) into context.
+      4. Checkout/create issue_branch (based on base_branch when new).
+
+    Safe to call repeatedly: existing clone is fetched (not re-cloned),
+    existing branch is reused, and any pending changes are captured
+    before switching branches so nothing is lost.
+    """
+    parent_dir = Path(repo_dir).expanduser().resolve()
+
+    if repo_name is None:
+        repo_name: str = _repo_name_from_url(remote_url)
+
+    repo_dir = (repo_dir / repo_name).resolve()
+    repo_dir.mkdir(parents=True, exist_ok=True)
+
+    # Защита от path traversal (repo_name не должен вылезти из parent_dir)
+    if parent_dir != repo_dir and parent_dir not in repo_dir.parents:
+        raise ValueError(f"Resolved repo_dir {repo_dir} escapes parent_dir {parent_dir}")
+
+    is_new_clone = not (repo_dir / ".git").exists()
+
+    # --- 1. Clone or fetch ---
+    if is_new_clone:
+        _run(["git", "clone", remote_url, str(repo_dir)])
+    else:
+        # Make sure origin points where we expect.
+        current_remote = _run(
+            ["git", "config", "--get", "remote.origin.url"],
+            cwd=repo_dir, check=False,
+        ).stdout.strip()
+        if current_remote != remote_url:
+            _run(["git", "remote", "set-url", "origin", remote_url], cwd=repo_dir)
+        _run(["git", "fetch", "origin", "--prune"], cwd=repo_dir)
+
+    # --- 2. Optional committer identity for the agent ---
+    if author_name:
+        _run(["git", "config", "user.name", author_name], cwd=repo_dir)
+    if author_email:
+        _run(["git", "config", "user.email", author_email], cwd=repo_dir)
+
+    # --- 3. Snapshot current state BEFORE switching ---
+    original_branch = _run(
+        ["git", "rev-parse", "--abbrev-ref", "HEAD"],
+        cwd=repo_dir, check=False,
+    ).stdout.strip() or None
+
+    uncommitted_changes = _run(
+        ["git", "diff", "HEAD"], cwd=repo_dir, check=False,
+    ).stdout or None
+
+    untracked = _run(
+        ["git", "ls-files", "--others", "--exclude-standard"],
+        cwd=repo_dir, check=False,
+    ).stdout.strip()
+    untracked_files = untracked.splitlines() if untracked else []
+
+    stash_ref: Optional[str] = None
+    if uncommitted_changes or untracked_files:
+        # Safe stash: keep index, include untracked; verify a stash was made.
+        before = _run(["git", "rev-parse", "--verify", "refs/stash"], cwd=repo_dir, check=False).stdout.strip()
+        _run(["git", "stash", "push", "-u", "-m", f"agent-snapshot-{original_branch or 'detached'}"], cwd=repo_dir)
+        after = _run(["git", "rev-parse", "--verify", "refs/stash"], cwd=repo_dir, check=False).stdout.strip()
+        if after and after != before:
+            stash_ref = "stash@{0}"
+
+    # --- 4. Ensure issue branch ---
+    existing_branch = _branch_exists(repo_dir, issue_branch)
+    is_new_branch = not existing_branch
+
+    if existing_branch:
+        _run(["git", "checkout", issue_branch], cwd=repo_dir)
+        _run(["git", "pull", "--ff-only", "origin", issue_branch], cwd=repo_dir, check=False)
+    else:
+        # Prefer starting from the freshest origin/<base_branch>.
+        base_ref = f"origin/{base_branch}"
+        have_base = _run(
+            ["git", "rev-parse", "--verify", "--quiet", base_ref],
+            cwd=repo_dir, check=False,
+        ).returncode == 0
+        if have_base:
+            _run(["git", "checkout", "-B", issue_branch, base_ref], cwd=repo_dir)
+        else:
+            _run(["git", "checkout", "-b", issue_branch], cwd=repo_dir)
+
+    head_commit = _run(["git", "rev-parse", "HEAD"], cwd=repo_dir).stdout.strip()
+
+    # --- Auth env (for later push operations) ---
+    env: dict = {}
+    if token and username:
+        env["GIT_ASKPASS"] = "echo"
+        env["GIT_USERNAME"] = username
+        env["GIT_PASSWORD"] = token
+
+    return RepoContext(
+        repo_dir=repo_dir,
+        remote_url=remote_url,
+        branch=issue_branch,
+        base_branch=base_branch,
+        head_commit=head_commit,
+        is_new_clone=is_new_clone,
+        is_new_branch=is_new_branch,
+        original_branch=original_branch,
+        uncommitted_changes=uncommitted_changes,
+        untracked_files=untracked_files,
+        stash_ref=stash_ref,
+        env=env,
+    )

agents/coders/state.py

@@ -0,0 +1,174 @@
+import json
+import os
+import re
+import tempfile
+from datetime import datetime
+from enum import Enum
+from pathlib import Path
+from typing import Optional, Dict, Any
+
+from pydantic import BaseModel, Field
+
+
+def atomic_write_json(path, data):
+    tmp = path.with_suffix(path.suffix + ".tmp")
+    with tmp.open("w") as f:
+        json.dump(data, f)
+        f.flush()
+        os.fsync(f.fileno())
+    os.replace(tmp, path)
+
+STATE_PATH_MASK = "<agent_dir>/state/<agent_id>/<issue_id>.json"
+STATE_SCHEMA_CURRENT_VERSION = 1
+STATE_SCHEMA_EXAMPLE = {
+  "schema_version": 1,
+  "agent_id": "agent-123",
+  "issue_id": "YT-456",
+  "state": "AWAITING_CLARIFICATION",
+  "run_id": "run-...",
+  "attempt": 1,
+  "counters": {
+    "attempt_act": 0,
+    "attempt_plan": 0,
+    "ci_self_resolve_attempts": 0,
+    "consecutive_questions": 1
+  },
+  "shas": {
+    "last_pushed_sha": None,
+    "remote_sha": None,
+    "base_sha": None
+  },
+  "mr_id": None,
+  "signatures": [],
+  "pending_question": {
+    "comment_id": "c-1",
+    "kind": "clarification",
+    "asked_at": "2026-01-01T00:00:00Z",
+    "last_ping_at": None
+  },
+  "event_log": [],
+  "updated_at": "2026-01-01T00:00:00Z"
+}
+
+class CoarseState(str, Enum):
+    AWAITING_CLARIFICATION = "AWAITING_CLARIFICATION"
+    AWAITING_FEASIBILITY = "AWAITING_FEASIBILITY"
+    AWAITING_CONFIRMATION = "AWAITING_CONFIRMATION"
+    AWAITING_CI = "AWAITING_CI"
+    AWAITING_CI_CODE_DECISION = "AWAITING_CI_CODE_DECISION"
+    AWAITING_REVIEW = "AWAITING_REVIEW"
+    AWAITING_MERGE = "AWAITING_MERGE"
+    AWAITING_REWORK_DECISION = "AWAITING_REWORK_DECISION"
+    ABANDONED = "ABANDONED"
+    COMPLETED = "COMPLETED"
+
+class Routing(str, Enum):
+    CONTINUE = "CONTINUE"
+    RETRY_ACT = "RETRY_ACT"
+    RETRY_PLAN = "RETRY_PLAN"
+    ESCALATE = "ESCALATE"
+
+class CommentKind(str, Enum):
+    CLARIFICATION = "clarification"
+    FEASIBILITY = "feasibility"
+    CONFIRMATION = "confirmation"
+    CI_CODE_FAIL = "ci_code_fail"
+    REVIEW_BRIEF = "review_brief"
+    REWORK = "rework"
+
+MARKER_FORMAT = "<!-- coding-agent:v1 agent=<agent_id> kind=<kind> run=<run_id> attempt=<attempt> ts=<ts_iso8601> -->"
+MARKER_RE = re.compile(r"<!--\s*coding-agent:v1\s+(?P<body>.*?)-->", re.S)
+FIELD_RE = re.compile(r"(\w+)=([^\s]+)")
+
+class AgentState(BaseModel):
+    state_file: Path
+    schema_version: int = STATE_SCHEMA_CURRENT_VERSION
+    issue_id: str = ""
+    agent_id: str = ""
+    state: str = "INIT"
+    updated_at: str = ""
+    last_processed_comment_id: Optional[str] = None
+    pending_question_comment_id: Optional[str] = None
+    run_id: str = ""
+    attempt: int = 0
+    extra: Dict[str, Any] = Field(default_factory=dict)
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "schema_version": self.schema_version,
+            "issue_id": self.issue_id,
+            "agent_id": self.agent_id,
+            "state": self.state,
+            "updated_at": self.updated_at,
+            "last_processed_comment_id": self.last_processed_comment_id,
+            "pending_question_comment_id": self.pending_question_comment_id,
+            "run_id": self.run_id,
+            "attempt": self.attempt,
+            **self.extra,
+        }
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any], state_file: Path) -> AgentState:
+        known = {
+            "schema_version", "issue_id", "agent_id", "state", "updated_at",
+            "last_processed_comment_id", "pending_question_comment_id",
+            "run_id", "attempt",
+        }
+        extra = {k: v for k, v in data.items() if k not in known}
+        return cls(
+            schema_version=data.get("schema_version", STATE_SCHEMA_CURRENT_VERSION),
+            issue_id=data.get("issue_id", ""),
+            agent_id=data.get("agent_id", ""),
+            state=data.get("state", "INIT"),
+            updated_at=data.get("updated_at", ""),
+            last_processed_comment_id=data.get("last_processed_comment_id"),
+            pending_question_comment_id=data.get("pending_question_comment_id"),
+            run_id=data.get("run_id", ""),
+            attempt=data.get("attempt", 0),
+            extra=extra,
+            state_file=state_file,
+        )
+
+    def with_values(self, data: Dict[str, Any]):
+        as_dict = self.to_dict()
+        for key, value in data.items():
+            as_dict.__setattr__(key, value)
+        return self.from_dict(as_dict, self.state_file)
+
+    def store(self):
+        save_state(self.state_file, self)
+
+def load_state(state_dir: Path, agent_id: str, issue_id: str) -> AgentState:
+    path = state_dir / agent_id / f"{issue_id}.json"
+    if not path.exists():
+        return AgentState(issue_id=issue_id, agent_id=agent_id, state="INIT", state_file=path)
+    try:
+        with open(path, "r", encoding="utf-8") as f:
+            data = json.load(f)
+        if data.get("schema_version") != STATE_SCHEMA_CURRENT_VERSION:
+            # Schema mismatch -> re-run
+            return AgentState(issue_id=issue_id, agent_id=agent_id, state="INIT", state_file=path)
+        return AgentState.from_dict(data, path)
+    except (json.JSONDecodeError, KeyError, OSError):
+        # Corrupt or unreadable -> re-run
+        return AgentState(issue_id=issue_id, agent_id=agent_id, state="INIT", state_file=path)
+
+
+def save_state(state_dir: Path, state: AgentState) -> None:
+    path = state_dir / state.agent_id / f"{state.issue_id}.json"
+    path.parent.mkdir(parents=True, exist_ok=True)
+    state.updated_at = datetime.utcnow().isoformat() + "Z"
+    data = state.to_dict()
+
+    fd, tmp_path = tempfile.mkstemp(dir=path.parent)
+    try:
+        with os.fdopen(fd, "w", encoding="utf-8") as f:
+            json.dump(data, f, indent=2)
+            f.flush()
+            os.fsync(f.fileno())
+        os.replace(tmp_path, path)
+    except Exception:
+        if os.path.exists(tmp_path):
+            os.unlink(tmp_path)
+        raise
+

agents/coders/steps/gather.py

@@ -0,0 +1,281 @@
+# gather.py
+
+import re
+from datetime import datetime
+from pathlib import Path
+from typing import List, Optional, Dict
+
+from agents.coders.prompts.follow_up import FOLLOW_UP_USER_PROMPT, FOLLOW_UP_SYSTEM_PROMPT
+from agents.coders.prompts.relevance import RelevanceResponse, CHECK_RELEVANCE_SYSTEM_PROMPT
+from agents.coders.state import MARKER_FORMAT, AgentState, load_state, save_state
+from agents.registry import AgentRegistry
+from common.llm_client import LLMClient
+from common.youtrack_mcp_client import YouTrackMCPClient
+from contracts.IssueContext import IssueContext, IssueComment
+from contracts.coders.GatherOutput import GatherOutput
+
+# ---------------------------------------------------------------------------
+# Marker parsing
+# ---------------------------------------------------------------------------
+
+MARKER_REGEX = re.compile(
+    r'<!--\s*coding-agent:v1\s+'
+    r'agent=(\S+)\s+'
+    r'kind=(\S+)\s+'
+    r'run=(\S+)\s+'
+    r'attempt=(\d+)\s+'
+    r'ts=(\S+)\s*-->'
+)
+
+
+def parse_marker(text: str) -> Optional[Dict[str, str]]:
+    """Scan text for a coding-agent marker. Tolerates quoted markers."""
+    match = MARKER_REGEX.search(text)
+    if not match:
+        return None
+    return {
+        "agent": str(match.group(1)),
+        "kind": str(match.group(2)),
+        "run": str(match.group(3)),
+        "attempt": str(int(match.group(4))),
+        "ts": str(match.group(5)),
+    }
+
+
+def format_marker(agent_id: str, kind: str, run_id: str, attempt: int) -> str:
+    ts = datetime.utcnow().isoformat() + "Z"
+    return (
+        MARKER_FORMAT
+            .replace('<agent_id>', agent_id)
+            .replace('<kind>', kind)
+            .replace('<run_id>', run_id)
+            .replace('<attempt>', str(attempt))
+            .replace('<ts_iso8601>', ts)
+    )
+
+
+# ---------------------------------------------------------------------------
+# Gather step
+# ---------------------------------------------------------------------------
+
+class GatherStep:
+    """
+    Reads current state, decides whether the agent should run, and gathers
+    context from a YouTrack issue.
+
+    If the state is awaiting a human response, it checks for new human comments
+    and uses the LLM to verify relevance. If no relevant response is found it
+    returns AWAITING.
+    """
+
+    AWAITING_HUMAN_STATES = {
+        "AWAITING_CLARIFICATION",
+        "AWAITING_FEASIBILITY",
+        "AWAITING_CONFIRMATION",
+        "AWAITING_REWORK_DECISION",
+    }
+
+    def __init__(
+        self,
+        state_dir: Path,
+        llm: LLMClient,
+        agent_registry: AgentRegistry,
+        youtrack_mcp: YouTrackMCPClient,
+        agent_id: str,
+    ):
+        self.state_dir = state_dir
+        self.llm = llm
+        self.agent_registry = agent_registry
+        self.youtrack_mcp = youtrack_mcp
+        self.agent_id = agent_id
+
+    async def run(self, issue_context: IssueContext) -> GatherOutput:
+        # 1. Load state
+        state = load_state(self.state_dir, self.agent_id, issue_context.issue_id)
+
+        # 2. Terminal states -> return immediately with empty context
+        if state.state in ("COMPLETED", "ABANDONED"):
+            return GatherOutput(
+                state=state,
+                issue_context=issue_context,
+                pending_questions=[],
+                run_id=state.run_id,
+                attempt=state.attempt,
+            )
+
+        # 6. Handle awaiting-human states
+        if state.state in self.AWAITING_HUMAN_STATES:
+            return await self._handle_awaiting_human(state, issue_context)
+
+        # 7. Otherwise just return the gathered context for the orchestrator.
+        return GatherOutput(
+            state=state,
+            issue_context=issue_context,
+            pending_questions=[],
+            run_id=state.run_id,
+            attempt=state.attempt,
+        )
+
+    # -----------------------------------------------------------------------
+    # Awaiting human response handling
+    # -----------------------------------------------------------------------
+
+    async def _handle_awaiting_human(
+        self,
+        state: AgentState,
+        issue_context: IssueContext,
+    ) -> GatherOutput:
+        pending_ids = list(state.pending_questions)
+        if not pending_ids:
+            # No pending question recorded – reset to INIT
+            state.state = "INIT"
+            save_state(self.state_dir, state)
+            return GatherOutput(
+                state=state,
+                issue_context=issue_context,
+                pending_questions=[],
+                run_id=state.run_id,
+                attempt=state.attempt,
+            )
+
+        # Resolve pending question comments
+        comments_by_id = {c.id: c for c in issue_context.comments}
+        pending_comments: List[IssueComment] = [
+            comments_by_id[pid] for pid in pending_ids if pid in comments_by_id
+        ]
+        if not pending_comments:
+            # All pending questions disappeared – reset
+            state.state = "INIT"
+            state.pending_questions = []
+            save_state(self.state_dir, state)
+            return GatherOutput(
+                state=state,
+                issue_context=issue_context,
+                pending_questions=[],
+                run_id=state.run_id,
+                attempt=state.attempt,
+            )
+
+        # Anchor on the earliest pending question (chronological ordering)
+        pending_comments.sort(key=lambda c: c.created_at)
+        anchor_question = pending_comments[0]
+
+        # Find new human comments that appear after the anchor question
+        found_anchor = False
+        new_human_comments: List[IssueComment] = []
+        for c in issue_context.comments:
+            if c.id == anchor_question.id:
+                found_anchor = True
+                continue
+            if found_anchor and not c.is_agent:
+                new_human_comments.append(c)
+
+        if not new_human_comments:
+            # No new human comment -> stay in AWAITING
+            return GatherOutput(
+                state=state,
+                issue_context=issue_context,
+                pending_questions=list(state.pending_questions),
+                run_id=state.run_id,
+                attempt=state.attempt,
+            )
+
+        # Use the latest human comment as the response
+        latest_human = new_human_comments[-1]
+
+        # LLM-based relevance check anchored on the pending question's comment
+        system = CHECK_RELEVANCE_SYSTEM_PROMPT
+        user = ("""
+        QUESTION:
+        \"\"\"
+        {question_text}
+        \"\"\"
+
+        RESPONSE:
+        \"\"\"
+        {response_text}
+        \"\"\"
+
+        Classify the RESPONSE according to the rules.
+        """
+                .replace("{question_text}", anchor_question.body)
+                .replace("{response_text}", latest_human.body)
+        )
+        relevance = self.llm.chat_with_schema(
+            user, {
+                "type": "json_schema",
+                "json_schema": {
+                    "name": "RelevanceResponse",
+                    "schema": RelevanceResponse.model_json_schema(),
+                    "strict": True,
+                },
+            }, system
+        )
+
+        if relevance == "unrelated":
+            # Stay in AWAITING, do not consume the response
+            return GatherOutput(
+                state=state,
+                issue_context=issue_context,
+                pending_questions=list(state.pending_questions),
+                run_id=state.run_id,
+                attempt=state.attempt,
+            )
+
+        if relevance == "incomplete":
+            # Post a follow-up question, keep state AWAITING
+            follow_up_text = self.llm.chat(
+                FOLLOW_UP_SYSTEM_PROMPT,
+                (FOLLOW_UP_USER_PROMPT
+                 .replace("{question_text}", anchor_question.body)
+                 .replace("{response_text}", latest_human.body)
+                 ),
+            ) or ""
+
+            marker = format_marker(
+                agent_id=self.agent_id,
+                kind=self._kind_for_state(state.state),
+                run_id=state.run_id,
+                attempt=state.attempt,
+            )
+
+            new_comment_id = await self.youtrack_mcp.call_tool(
+                "add_issue_comment",
+                {
+                    "issueId": issue_context.issue_id,
+                    "text": marker + follow_up_text,
+                },
+            )
+
+            state.pending_questions = list(state.pending_questions) + [new_comment_id]
+            state.last_processed_comment_id = latest_human.id
+            save_state(self.state_dir, state)
+            return GatherOutput(
+                state=state,
+                issue_context=issue_context,
+                pending_questions=list(state.pending_questions),
+                run_id=state.run_id,
+                attempt=state.attempt,
+            )
+
+        # Relevant response -> clear awaiting state and proceed
+        state.last_processed_comment_id = latest_human.id
+        state.pending_questions = []
+        state.state = "INIT"  # ready for next pipeline step
+        save_state(self.state_dir, state)
+        return GatherOutput(
+            state=state,
+            issue_context=issue_context,
+            pending_questions=[],
+            run_id=state.run_id,
+            attempt=state.attempt,
+        )
+
+    @staticmethod
+    def _kind_for_state(state: str) -> str:
+        return {
+            "AWAITING_CLARIFICATION": "clarification",
+            "AWAITING_FEASIBILITY": "feasibility",
+            "AWAITING_CONFIRMATION": "confirmation",
+            "AWAITING_REWORK_DECISION": "rework",
+        }.get(state, "clarification")

agents/coders/steps/plan.py

@@ -0,0 +1,170 @@
+# steps/plan.py
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass
+
+from pydantic import TypeAdapter, ValidationError
+
+from agents.codebase_analyst.entrypoint import gather_repo_entrypoint
+from agents.codebase_analyst.graph_tool import analyze_repository
+from agents.coders.repo import ensure_repo_ready, RepoContext
+from common.llm_client import LLMClient
+from contracts.RepoContext import AnalysisInput
+from contracts.coders.GatherOutput import GatherOutput
+from contracts.coders.PlanOutput import PlanOutput
+from contracts.coders.VerifyOutput import FeasibleVerdict
+
+PlanAdapter = TypeAdapter(PlanOutput)
+
+
+SYSTEM_PROMPT = """\
+You are a senior engineer writing an implementation plan for a coding agent.
+
+You will receive an issue (title, body, acceptance criteria, comments), a \
+repository context, the feasibility verdict from a prior triage, and a list \
+of files linked from the issue.
+
+Produce a plan with these properties:
+
+1. **One step per file.** Each step describes a single file change. If two \
+   files must change together, that's two steps.
+
+2. **Commit to a language.** Infer the primary language from the repository \
+   context and the files you plan to touch. Choose the test runner, formatter, \
+   and linter idiomatic to that language. Populate `language` accordingly.
+
+3. **Target files, not file contents.** Describe *what* changes in each file, \
+   not the code itself. The Act step writes the code; you decide the shape.
+
+4. **Be honest about breaking changes.** Set `breaks_existing=true` only if \
+   the change modifies behavior that callers or users rely on: public API \
+   changes, schema changes, config default changes, removal of features. \
+   Pure additions, bug fixes, and internal refactors are NOT breaking.
+
+5. **Risk notes.** If `breaks_existing=true`, list the specific risks. If not, \
+   you may still add notes for reviewer attention (migrations, security, \
+   performance), but this is optional.
+
+Rules:
+- Do not plan to modify files outside the repository.
+- Do not invent files that clearly don't exist unless the plan is to create them.
+- Prefer the smallest plan that satisfies the acceptance criteria.
+- Return ONLY valid JSON matching the schema.
+"""
+
+
+class PlanError(ValueError):
+    """LLM output violated the PlanOutput contract."""
+
+
+@dataclass(frozen=True)
+class PlanInput:
+    gather: GatherOutput
+    feasibility: FeasibleVerdict
+
+
+async def handle_plan(inp: PlanInput, *, llm: LLMClient) -> PlanOutput:
+    if not inp.gather.issue_context.project or len(inp.gather.issue_context.project.repos) == 0:
+        raise "No project repo"
+
+    repo = ensure_repo_ready(
+        inp.gather.issue_context.project.repos[0].remote_url,
+        inp.gather.state.state_file.parent / "repos",
+        "zarch_" + inp.gather.issue_context.issue_id,
+        inp.gather.issue_context.project.repos[0].base_branch,
+        author_name="zarch",
+        author_email="zarch@zaek.eu"
+    )
+    entrypoint_analyze = gather_repo_entrypoint(llm, repo)
+    repo_analyze = analyze_repository(AnalysisInput(
+        repo_path=repo.repo_dir,
+        language=entrypoint_analyze.language,
+        entrypoint=entrypoint_analyze.entrypoint,
+    ))
+
+    if repo_analyze.error:
+        raise repo_analyze.error
+    print(repo_analyze)
+
+    prompt = _build_user_prompt(inp, repo)
+    schema = PlanAdapter.json_schema()
+
+    raw = llm.chat_with_schema(
+        prompt,
+        {
+            "type": "json_schema",
+            "json_schema": {
+                "name": "PlanOutput",
+                "schema": schema,
+                "strict": True,
+            },
+        },
+        system=SYSTEM_PROMPT,
+    )
+
+    raw = _ensure_dict(raw)
+    try:
+        return PlanAdapter.validate_python(raw)
+    except ValidationError as e:
+        raise PlanError(str(e)) from e
+
+
+# ── helpers ────────────────────────────────────────────────────
+
+def _ensure_dict(raw):
+    """Guard against LLM clients that return raw JSON strings."""
+    if isinstance(raw, str):
+        try:
+            return json.loads(raw)
+        except json.JSONDecodeError as e:
+            raise PlanError(f"LLM returned non-JSON string: {e}") from e
+    return raw
+
+
+def _build_user_prompt(inp: PlanInput, repo: RepoContext) -> str:
+    g = inp.gather
+    issue = g.issue_context
+
+    parts: list[str] = []
+    parts.append(f"# Issue {issue.issue_id}: {issue.title}")
+    parts.append("")
+    parts.append("## Description")
+    parts.append(issue.body.strip() or "(empty)")
+    parts.append("")
+
+    parts.append("## Acceptance criteria")
+    if issue.acceptance_criteria:
+        for i, ac in enumerate(issue.acceptance_criteria, 1):
+            parts.append(f"{i}. {ac}")
+    else:
+        parts.append("(none stated)")
+    parts.append("")
+
+    if issue.comments:
+        parts.append("## Prior comments")
+        for c in issue.comments:
+            parts.append(f"- [{c.created_at}] {c.author}: {c.body.strip()}")
+        parts.append("")
+
+    parts.append("## Feasibility verdict (already confirmed)")
+    parts.append(f"Verdict: feasible")
+    parts.append(f"Reasoning: {inp.feasibility.reasoning}")
+    parts.append(f"Confidence: {inp.feasibility.confidence}")
+    parts.append("")
+
+    parts.append("## Repository context")
+    parts.append(f"- url: {repo.remote_url}")
+    parts.append(f"- default branch: {repo.base_branch}")
+    parts.append("")
+
+    parts.append("## Linked files")
+    if g.linked_files:
+        for f in g.linked_files:
+            parts.append(f"- {f}")
+    else:
+        parts.append("(none)")
+    parts.append("")
+
+    parts.append("Produce the plan. Return ONLY the JSON object.")
+    return "\n".join(parts)

agents/coders/steps/verify.py

@@ -0,0 +1,195 @@
+# steps/verify_possibility.py
+from __future__ import annotations
+
+import json
+from datetime import datetime
+
+from pydantic import TypeAdapter, ValidationError
+
+from agents.coders.steps.gather import format_marker
+from common.llm_client import LLMClient
+from common.time import now_iso_plus, now_iso
+from contracts.coders.GatherOutput import GatherOutput
+from contracts.coders.RunContext import RunContext
+from contracts.coders.StepResult import StepResult
+from contracts.coders.VerifyOutput import VerifyPossibilityInput, VerifyPossibilityOutput, Option, FeasibleVerdict, \
+    AmbiguousVerdict, InfeasibleVerdict, sanitize_for_openai
+
+VerifyPossibilityAdapter = TypeAdapter(VerifyPossibilityOutput)
+
+
+SYSTEM_PROMPT = """\
+You are a senior engineer performing a feasibility triage on a software task \
+before any code is written.
+
+You will receive an issue (title, body, acceptance criteria, comments), the \
+repository context, and a list of files linked from the issue. If there is \
+a comment about missing capabilities - ignore it.
+
+Your job is to classify the task into exactly one of three verdicts:
+
+- "feasible": The task is clear enough to plan, and it can be done within the \
+  repository as it exists. You do NOT need to know *how* — only that it is \
+  possible and well-specified.
+
+- "ambiguous": The task cannot be planned because a required decision, scope \
+  boundary, or piece of information is missing. You must ask exactly one \
+  clarifying question. Do not ask multiple questions. Do not ask about things \
+  you could reasonably infer from the issue or the codebase.
+
+- "infeasible": The task is clear but cannot be accomplished as stated. \
+  Examples: it contradicts an architectural invariant, requires permissions \
+  the agent does not have, depends on an external system that is unavailable, \
+  or requires a decision that belongs to a human. Provide 1-3 concrete \
+  alternative options.
+
+Rules:
+- Prefer "feasible" if the task is clear and the only unknowns are HOW to do \
+  it. HOW is Plan's job, not yours.
+- Prefer "ambiguous" only when a missing decision blocks planning entirely.
+- Prefer "infeasible" only when no reasonable plan could exist as stated.
+- Confidence is your honest probability that the verdict is correct.
+
+Return ONLY valid JSON matching the schema. No prose, no markdown fences.
+"""
+
+
+class InvalidVerdictError(ValueError):
+    """The provider returned output that violates the contract."""
+
+
+def verify_possibility(
+    inp: VerifyPossibilityInput,
+    *,
+    llm: LLMClient,
+) -> VerifyPossibilityOutput:
+    """
+    Classify a task as feasible / ambiguous / infeasible.
+
+    Pure with respect to the world: no comments posted, no state written.
+    The orchestrator consumes the output and performs side effects.
+    """
+    prompt = _build_user_prompt(inp.gather)
+    schema = sanitize_for_openai(VerifyPossibilityAdapter.json_schema())
+    raw = llm.chat_with_schema(
+        prompt,
+        {
+            "type": "json_schema",
+            "json_schema": {
+                "name": "VerifyPossibilityOutput",
+                "schema": schema,
+                "strict": True,
+            },
+        },
+        system=SYSTEM_PROMPT,
+    )
+
+    try:
+        if isinstance(raw, str):
+            raw = json.loads(raw)
+        return VerifyPossibilityAdapter.validate_python(raw)
+    except ValidationError as e:
+        # Should be rare under strict mode; treat as a hard failure so the
+        # caller doesn't build downstream steps on a malformed verdict.
+        raise InvalidVerdictError(str(e)) from e
+
+
+# ── Prompt construction ────────────────────────────────────────
+
+def _build_user_prompt(g: GatherOutput) -> str:
+    issue = g.issue_context
+
+    parts: list[str] = [f"# Issue {issue.issue_id}: {issue.title}", "", "## Description",
+                        issue.body.strip() or "(empty)", ""]
+
+    if issue.comments:
+        parts.append("## Prior comments")
+        for c in issue.comments:
+            parts.append(f"- [{datetime.fromtimestamp(c.created_at / 1000).isoformat()}] {c.author}: {c.body.strip()}")
+        parts.append("")
+
+    parts.append(
+        "Classify this task. Return ONLY the JSON object described in the "
+        "schema hint."
+    )
+    return "\n".join(parts)
+
+
+async def handle_verify(inp: VerifyPossibilityInput, ctx: RunContext) -> StepResult:
+    out = verify_possibility(inp, llm=ctx.llm).result
+
+    if isinstance(out, FeasibleVerdict):
+        return StepResult.continue_to("PLANNING", output=out)
+
+    if isinstance(out, AmbiguousVerdict):
+        # out.question is str, not str | None — no check needed
+        comment_id = ctx.youtrack.extract_comment_id(await ctx.youtrack.call_tool(
+            "add_issue_comment",
+            {
+                "issueId": ctx.issue_id,
+                "text": format_marker(
+                agent_id=ctx.agent_id, kind="clarification",
+                run_id=ctx.run_id, attempt=ctx.attempt,
+            ) + _render_ambiguous(out),
+            },
+        ))
+
+        ctx.state.with_values({
+            "comment_id": comment_id,
+            "state": "AWAITING_CLARIFICATION",
+            "updated_at": now_iso(),
+            "abandon_at": now_iso_plus(days=5)
+        }).store()
+
+        return StepResult.awaiting("AWAITING_CLARIFICATION",
+                                   output=out.model_dump())
+
+    if isinstance(out, InfeasibleVerdict):
+        comment_id = ctx.youtrack.extract_comment_id(await ctx.youtrack.call_tool(
+            "add_issue_comment",
+            {
+                "issueId": ctx.issue_id,
+                "text": format_marker(
+                    agent_id=ctx.agent_id, kind="feasibility",
+                    run_id=ctx.run_id, attempt=ctx.attempt,
+                ) + _render_infeasible(out),
+            },
+        ))
+
+        ctx.state.with_values({
+            "pending_question_comment_id": comment_id,
+            "state": "AWAITING_FEASIBILITY",
+            "updated_at": now_iso(),
+            "abandon_at": now_iso_plus(days=5)
+        }).store()
+
+        return StepResult.awaiting("AWAITING_FEASIBILITY", out)
+
+    print(json.dumps(out, indent=2))
+
+    raise AssertionError("unreachable")
+
+
+def _render_ambiguous(out: VerifyPossibilityOutput) -> str:
+    return (
+        f"I can't plan this yet — one thing is unclear.\n\n"
+        f"**Question:** {out.question}\n\n" if isinstance(out, AmbiguousVerdict) else ""
+        f"**Why this blocks planning:** {out.reasoning}\n"
+    )
+
+
+def _render_infeasible(out: VerifyPossibilityOutput) -> str:
+    lines = [
+        "I can't do this as stated. Here are some ways forward:",
+        "",
+        f"**Why:** {out.reasoning}",
+        "",
+    ]
+    for i, o in enumerate(out.options if isinstance(out, InfeasibleVerdict) else [], 1):
+        lines.append(f"**{i}. {o.label}**")
+        lines.append(o.description)
+        if o.tradeoff:
+            lines.append(f"_Trade-off:_ {o.tradeoff}")
+        lines.append("")
+    lines.append("Let me know which direction you'd like, or adjust the issue.")
+    return "\n".join(lines)

agents/issue_triage/IssueTriageAgent.py

@@ -13,9 +13,8 @@ from agents.issue_triage.triage import render_validation_failure, render_plan_co
     render_questions_comment, render_blocker_comment
 from agents.registry import agent, AgentRegistry, Capability
 from common.llm_client import LLMClient
-from common.youtrack_mcp_client import IssueNotFound
-from contracts.IssueContext import IssueContext
-from contracts.TaskPlan import TaskPlan
+from common.youtrack_mcp_client import IssueNotFound, YouTrackMCPClient
+from contracts.IssueContext import IssueContext, IssueComment
 
 logger = logging.getLogger("triage_agent")
 logger.setLevel(logging.DEBUG)
@@ -551,7 +550,7 @@ def precheck(issue: IssueContext, registry: AgentRegistry) -> tuple[Action, Tria
     priority=10,
 )
 class IssueTriageAgent:
-    def __init__(self, context_builder: YouTrackContextBuilder, llm: LLMClient, agent_registry: AgentRegistry, youtrack_mcp):
+    def __init__(self, context_builder: YouTrackContextBuilder, llm: LLMClient, agent_registry: AgentRegistry, youtrack_mcp: YouTrackMCPClient):
         self.name = "IssueTriageAgent"
         self.ctx_builder = context_builder
         self.decomposer = IssueDecomposer(llm)
@@ -576,7 +575,7 @@ class IssueTriageAgent:
         self, issue: IssueContext
     ) -> CapabilityResult:
         cloned_issue = issue
-        cloned_issue.comments = [c for c in cloned_issue.comments if c.get("author") != os.getenv("YOUTRACK_TRIAGE_AUTHOR")]
+        cloned_issue.comments = [c for c in cloned_issue.comments if c.author != os.getenv("YOUTRACK_TRIAGE_AUTHOR")]
         prompt = (CAPABILITY_PROMPT
              .replace("{{AGENTS}}", self.agent_registry.to_prompt_text())
              .replace("{{COMMENT_LANGUAGE}}", issue.project.project_language if issue.project else "english")
@@ -602,7 +601,7 @@ class IssueTriageAgent:
     ) -> AmbiguityResult:
         cloned_issue = issue
         cloned_issue.comments = [c for c in cloned_issue.comments if
-                                 c.get("author") != os.getenv("YOUTRACK_TRIAGE_AUTHOR")]
+                                 c.author != os.getenv("YOUTRACK_TRIAGE_AUTHOR")]
         prompt = (
             AMBIGUITY_PROMPT
             .replace("{{ISSUE}}", cloned_issue.to_prompt_text())

agents/issue_triage/context_builder.py

@@ -9,7 +9,7 @@ import httpx
 from agents.issue_triage.project_loader import load_project
 from common.as_type import _as_dict, _as_list, _first, _extract_base64
 from common.youtrack_mcp_client import IssueNotFound
-from contracts.IssueContext import IssueContext
+from contracts.IssueContext import IssueContext, IssueComment
 
 logger = logging.getLogger("context_builder")
 logger.setLevel(logging.DEBUG)
@@ -64,12 +64,12 @@ class YouTrackContextBuilder:
             title=_first(issue, "summary", "title", "name", default=""),
             body=_first(issue, "description", "body", default=""),
             comments=[
-                {
-                    "id": c.get("url"),
-                    "author": c.get("author") or "unknown",
-                    "body": c.get("text") or c.get("body") or "",
-                    "created_at": str(c.get("created") or c.get("createdAt") or ""),
-                }
+                IssueComment(
+                    id=c.get("url") or "undefined",
+                    author=c.get("author") or "unknown",
+                    body=c.get("text") or c.get("body") or "",
+                    created_at=c.get("created") or c.get("createdAt") or 0,
+                )
                 for c in comments
             ],
             labels=[

agents/issue_triage/marker.py

@@ -61,9 +61,9 @@ def find_prior_marker(issue: IssueContext) -> TriageMarker | None:
 def has_reporter_reply_since(issue: IssueContext, marker_comment_id: str) -> bool:
     found = False
     for c in issue.comments:
-        if c['id'] == marker_comment_id:
+        if c.id == marker_comment_id:
             found = True
             continue
-        if found and c['author'] != os.getenv('YOUTRACK_TRIAGE_AUTHOR'):
+        if found and c.author != os.getenv('YOUTRACK_TRIAGE_AUTHOR'):
             return True
     return False

common/gitea_mcp_client.py

@@ -0,0 +1 @@
+class GiteaMCPClient: ...

common/llm_client.py

@@ -6,11 +6,14 @@ class LLMClient:
         self.model = model
         self.client = OpenAI(base_url=base_url, api_key=api_key or "ollama")
 
-    def chat_with_schema(self, prompt: str, schema: dict):
+    def chat_with_schema(self, prompt: str, schema: dict, system: str | None = None) -> str | None:
         assert_strict_mode_clean(schema)
         kwargs = {
             "model": self.model,
             "messages": [
+                {"role": "system", "content": system},
+                {"role": "user", "content": prompt},
+            ] if system else [
                 {"role": "user", "content": prompt},
             ],
             "response_format": schema,

common/time.py

@@ -0,0 +1,31 @@
+# orchestrator/time.py
+from __future__ import annotations
+
+from datetime import datetime, timedelta, timezone
+
+# Single canonical format for all timestamps the agent writes.
+# ISO 8601, UTC, second precision, trailing "Z".
+# Examples: "2026-09-21T11:45:00Z"
+ISO_FORMAT = "%Y-%m-%dT%H:%M:%SZ"
+
+
+def now_iso() -> str:
+    """Current UTC time as an ISO 8601 string."""
+    return datetime.now(timezone.utc).strftime(ISO_FORMAT)
+
+
+def now_iso_plus(
+    *,
+    days: int = 0,
+    hours: int = 0,
+    minutes: int = 0,
+    seconds: int = 0,
+) -> str:
+    """Current UTC time plus a delta, as an ISO 8601 string."""
+    delta = timedelta(days=days, hours=hours, minutes=minutes, seconds=seconds)
+    return (datetime.now(timezone.utc) + delta).strftime(ISO_FORMAT)
+
+
+def parse_iso(s: str) -> datetime:
+    """Parse an ISO 8601 string produced by now_iso / now_iso_plus."""
+    return datetime.strptime(s, ISO_FORMAT).replace(tzinfo=timezone.utc)

common/youtrack_mcp_client.py

@@ -1,5 +1,7 @@
 import asyncio
+import json
 import logging
+from typing import Optional, Any
 
 import httpx
 from contextlib import AsyncExitStack
@@ -109,4 +111,50 @@ class YouTrackMCPClient:
 
     async def list_tools(self):
         """List all available tools from the MCP server."""
-        return await self._client.list_tools()
+        return await self._client.list_tools()
+
+    @classmethod
+    def extract_comment_id(cls, result: Any) -> Optional[str]:
+        """
+        Best-effort extraction of the created comment's ID from a
+        CallToolResult (or any MCP-ish response).
+
+        Returns None if the ID can't be determined. Callers MUST tolerate
+        None by falling back to marker-based lookup on the next Gather.
+        """
+        # 1. Unwrap the MCP envelope.
+        content = getattr(result, "content", None)
+        if content is None and isinstance(result, dict):
+            content = result.get("content")
+
+        if not content:
+            return None
+
+        # 2. Take the first text block.
+        first = content[0]
+        text = getattr(first, "text", None)
+        if text is None and isinstance(first, dict):
+            text = first.get("text")
+
+        if not text:
+            return None
+
+        # 3. Try JSON first.
+        try:
+            data = json.loads(text or "{}")
+        except json.JSONDecodeError:
+            # 4. Not JSON — assume the server returned a bare ID.
+            return text.strip() or None
+
+        # 5. Common shapes: {"id": ...} or {"comment": {"id": ...}}.
+        if isinstance(data, dict):
+            if "id" in data:
+                return str(data["id"])
+            if "comment" in data and isinstance(data["comment"], dict):
+                return str(data["comment"].get("id")) or None
+            # YouTrack often uses "idReadable" for the human-facing ID.
+            for key in ("idReadable", "commentId", "comment_id"):
+                if key in data:
+                    return str(data[key])
+
+        return None

contracts/IssueContext.py

@@ -2,6 +2,8 @@ import hashlib
 from dataclasses import dataclass, field
 from typing import Optional
 
+from pydantic import BaseModel
+
 from contracts.ProjectContext import ProjectContext
 
 
@@ -10,8 +12,8 @@ class IssueContext:
     issue_id: str
     title: str
     body: str
-    acceptance_criteria: str | None
-    comments: list[dict]
+    acceptance_criteria: str | None = None
+    comments: list[IssueComment] = field(default_factory=list)
     labels: list[str] = field(default_factory=list)
     repo: Optional[str] = None
     metadata: dict = field(default_factory=dict)
@@ -44,6 +46,14 @@ class IssueContext:
         if self.comments:
             parts.append("\n## Discussion / Comments")
             for c in self.comments:
-                parts.append(f"- @{c['author']} ({c['created_at']}): {c['body']}")
+                parts.append(f"- @{c.author} ({c.created_at}): {c.body}")
 
         return "\n".join(parts)
+
+class IssueComment(BaseModel):
+    id: str
+    author: str
+    body: str
+    created_at: int
+    is_agent: bool = False
+    marker: dict[str, str] = field(default_factory=dict)

contracts/ProjectContext.py

@@ -7,6 +7,11 @@ class TeamMemberModel(BaseModel):
     role: str
     nick: str
 
+class Repo(BaseModel):
+    id: str
+    remote_url: str
+    base_branch: str = "main"
+
 class ProjectContext(BaseModel):
     """Static, per-project knowledge injected into every decomposition."""
     project_key: str                      # e.g. "ARCH"
@@ -22,6 +27,7 @@ class ProjectContext(BaseModel):
     deployment: str = ""                  # "Docker Compose on Hetzner"
     project_language: str = "english"
     default_responder: str | None = None
+    repos: list[Repo] = field(default_factory=list)
     team: list[TeamMemberModel] = field(default_factory=list)
     conventions: list[str] = field(default_factory=list)  # free-form notes
     extra: dict[str, str] = field(default_factory=dict)   # anything else

contracts/RepoContext.py

@@ -0,0 +1,113 @@
+from dataclasses import dataclass
+from enum import Enum
+from pathlib import Path
+from typing import Optional, Literal, Any
+
+from pydantic import field_validator, BaseModel, Field
+
+
+@dataclass(frozen=True)
+class RepoContext:
+    url: str
+    default_branch: str
+    language_hint: str | None      # "python" | "typescript" | "go" | None
+
+class Language(str, Enum):
+    PYTHON = "python"
+    JAVASCRIPT = "javascript"
+    TYPESCRIPT = "typescript"
+    JAVA = "java"
+    GO = "go"
+    RUST = "rust"
+    CPP = "cpp"
+    C = "c"
+
+LanguageLiteral = Literal[
+    Language.PYTHON,
+    Language.JAVASCRIPT,
+    Language.TYPESCRIPT,
+    Language.JAVA,
+    Language.GO,
+    Language.RUST,
+    Language.CPP,
+    Language.C,
+]
+
+class DependencyNode(BaseModel):
+    """A single node in the dependency graph."""
+    id: str = Field(..., description="Unique identifier (file path or module name)")
+    label: str = Field(..., description="Human-readable label")
+    language: Language
+    is_external: bool = Field(
+        default=False,
+        description="True if the dependency is outside the repository",
+    )
+
+
+class DependencyEdge(BaseModel):
+    """A directed edge from source → target (source depends on target)."""
+    source: str = Field(..., description="Node id of the dependant")
+    target: str = Field(..., description="Node id of the dependency")
+    kind: str = Field(
+        default="import",
+        description="Edge kind: import, include, inherit, call, etc.",
+    )
+
+
+class DependencyGraph(BaseModel):
+    """Complete dependency graph result."""
+    root: str = Field(..., description="Entrypoint node id")
+    nodes: list[DependencyNode]
+    edges: list[DependencyEdge]
+    metadata: dict[str, str] = Field(default_factory=dict)
+
+
+class ToolRequirement(BaseModel):
+    """Describes a required external tool and how to install it."""
+    tool_name: str
+    command: str = Field(..., description="CLI command that must be on PATH")
+    install_guide: str = Field(..., description="Markdown installation instructions")
+    docs_url: Optional[str] = None
+
+
+class AnalysisError(BaseModel):
+    """Structured error returned when analysis cannot proceed."""
+    error_code: str
+    message: str
+    missing_tools: list[ToolRequirement] = Field(default_factory=list)
+    language: Optional[Language] = None
+    entrypoint: Optional[str] = None
+
+
+class AnalysisInput(BaseModel):
+    """Input schema for the analyzer."""
+    repo_path: Path = Field(..., description="Absolute path to the cloned repository")
+    language: Language
+    entrypoint: str = Field(
+        ...,
+        description="Relative path from repo_path to the entrypoint file",
+    )
+
+    @field_validator("repo_path")
+    @classmethod
+    def repo_must_exist(cls, v: Path) -> Path:
+        if not v.is_dir():
+            raise ValueError(f"Repository path does not exist or is not a directory: {v}")
+        return v.resolve()
+
+    @field_validator("entrypoint")
+    @classmethod
+    def entrypoint_must_exist(cls, v: str, info) -> str:
+        repo = info.data.get("repo_path")
+        if repo and not (repo / v).is_file():
+            raise ValueError(f"Entrypoint not found: {repo / v}")
+        return v
+
+
+class AnalysisResult(BaseModel):
+    """Top-level result — either a graph or an error."""
+    success: bool
+    graph: Optional[DependencyGraph] = None
+    error: Optional[AnalysisError] = None
+    leaf_clusters: Any
+    res_all: Any

contracts/base.py

@@ -1,6 +1,13 @@
 from abc import abstractmethod, ABC
 
-from pydantic import BaseModel
+from pydantic import BaseModel, ConfigDict
+
+
+class _StrictModel(BaseModel):
+    # Required for OpenAI strict JSON-schema mode: forbids extra keys,
+    # no defaults, every field required.
+    model_config = ConfigDict(extra="forbid")
+
 
 '''
 A contract between agents

contracts/coders/GatherOutput.py

@@ -0,0 +1,16 @@
+from dataclasses import dataclass, field
+from typing import Optional, List
+
+from pydantic import BaseModel, Field
+
+from agents.coders.state import AgentState
+from contracts.IssueContext import IssueContext
+
+
+class GatherOutput(BaseModel):
+    state: AgentState
+    issue_context: IssueContext
+    pending_questions: List[str] = Field(default_factory=list)
+    run_id: str = ""
+    attempt: int = 0
+

contracts/coders/PlanOutput.py

@@ -0,0 +1,89 @@
+from typing import Literal
+
+from pydantic import Field, model_validator
+
+from contracts.base import _StrictModel
+
+
+class LanguageSnapshot(_StrictModel):
+    """
+    The language the plan commits to, plus the toolchain it will use.
+    Chosen by Plan from the target files; consumed by Act and Verify.
+    """
+    name: str = Field(
+        min_length=1,
+        description="Language name, e.g. 'python', 'typescript', 'go'.",
+    )
+    version: str | None = Field(
+        description="Language version if known, else null.",
+    )
+    test_runner: str = Field(
+        min_length=1,
+        description="How tests are run, e.g. 'pytest', 'vitest', 'go test'.",
+    )
+    formatter: str | None = Field(
+        description="Formatter command, or null if none.",
+    )
+    linter: str | None = Field(
+        description="Linter command, or null if none.",
+    )
+    detected_from: list[str] = Field(
+        description="Marker files used to detect, e.g. ['pyproject.toml'].",
+    )
+
+
+class PlanStep(_StrictModel):
+    order: int = Field(ge=1, description="1-based ordering of this step.")
+    file: str = Field(
+        min_length=1,
+        description="Repo-relative path (no leading '/', no '..').",
+    )
+    change_kind: Literal["create", "modify", "delete"]
+    description: str = Field(
+        min_length=1,
+        description="What changes in this file and why, 1–3 sentences.",
+    )
+
+
+class PlanOutput(_StrictModel):
+    summary: str = Field(
+        min_length=1, description="1–3 sentence summary of the whole change."
+    )
+    steps: list[PlanStep] = Field(
+        min_length=1, description="Ordered steps. Each step is one file."
+    )
+    target_files: list[str] = Field(
+        min_length=1, description="Unique repo-relative files this plan touches."
+    )
+    target_tests: list[str] = Field(
+        description="Tests that must pass. May be empty for non-code changes."
+    )
+    language: LanguageSnapshot
+    breaks_existing: bool = Field(
+        description="True if this changes existing behavior a user or caller "
+                    "could rely on.",
+    )
+    risk_notes: list[str] = Field(
+        description="Reasons this might be risky. Non-empty if breaks_existing.",
+    )
+
+    @model_validator(mode="after")
+    def _consistency(self) -> "PlanOutput":
+        # 1. target_files must equal the set of files in steps.
+        step_files = {s.file for s in self.steps}
+        if set(self.target_files) != step_files:
+            raise ValueError(
+                f"target_files {sorted(self.target_files)} must equal "
+                f"the set of step files {sorted(step_files)}"
+            )
+
+        # 2. breaks_existing implies risk_notes non-empty.
+        if self.breaks_existing and not self.risk_notes:
+            raise ValueError("breaks_existing=True requires at least one risk note")
+
+        # 3. No absolute paths, no traversal.
+        for f in self.target_files:
+            if f.startswith("/") or ".." in f.split("/"):
+                raise ValueError(f"unsafe path: {f!r}")
+
+        return self

contracts/coders/RunContext.py

@@ -0,0 +1,91 @@
+# orchestrator/run_context.py
+from __future__ import annotations
+
+import secrets
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from typing import Any
+
+from agents.coders.state import AgentState
+from common.gitea_mcp_client import GiteaMCPClient
+from common.llm_client import LLMClient
+from common.time import now_iso, now_iso_plus
+from common.youtrack_mcp_client import YouTrackMCPClient
+
+
+@dataclass(frozen=True)
+class AgentConfig:
+    # ── project-level ──────────────────────────────────────────
+    youtrack_project: str
+    gitea_repo: str
+    gitea_target_branch: str = "main"
+
+    # ── limits (snapshotted into state at run start) ───────────
+    max_plan_attempts: int = 3
+    max_act_attempts: int = 5
+    max_ci_self_resolve_attempts: int = 2
+    max_clarification_rounds: int = 3
+    max_rework_rounds: int = 2
+    wall_clock_minutes: int = 60
+    token_budget: int = 500_000
+
+    # ── human-await timers ─────────────────────────────────────
+    re_ping_after_days: int = 3
+    abandon_after_days: int = 5
+
+    # ── reviewer policy ────────────────────────────────────────
+    reviewer_fallback: str = "codeowners"   # codeowners | blame | on_call
+
+    # ── language hints (for Plan) ──────────────────────────────
+    language_markers: dict[str, list[str]] = None   # filled from project
+
+
+@dataclass
+class RunContext:
+    """
+    Everything a handler needs to do its job: the ports it talks to, the
+    persisted state for this issue, and the identifiers it must stamp into
+    comments and state.
+    """
+    # ── identifiers ────────────────────────────────────────────
+    agent_id: str
+    issue_id: str
+    run_id: str                    # one logical attempt; stable across calls
+    attempt: int                   # retry counter within the run
+
+    # ── ports (outbound systems) ───────────────────────────────
+    llm: LLMClient
+    youtrack: YouTrackMCPClient
+    gitea: GiteaMCPClient
+
+    # ── persisted pipeline state for this issue ────────────────
+    state: AgentState
+
+    # ── static config for this run ─────────────────────────────
+    config: AgentConfig
+
+    # ── convenience (thin, no hidden behavior) ─────────────────
+
+    def now(self) -> str:
+        return now_iso()
+
+    def now_plus(self, **delta) -> str:
+        return now_iso_plus(**delta)
+
+
+def new_run_id() -> str:
+    """
+    Generate a run id: one logical attempt at a task.
+
+    Format: run-YYYYMMDD-HHMMSS-<6 hex chars>
+    Example: run-20260921-114500-a3f1c2
+
+    - Timestamp prefix makes ids sortable and human-readable in logs.
+    - Random suffix prevents collisions when two runs start in the same
+      second (e.g., parallel issues, fast retries, tests).
+    - The "run-" prefix makes it obvious what kind of id this is when it
+      appears next to a comment marker or in a state file.
+    """
+    ts = datetime.now(timezone.utc).strftime("%Y%m%d-%H%M%S")
+    suffix = secrets.token_hex(3)   # 3 bytes -> 6 hex chars
+    return f"run-{ts}-{suffix}"

contracts/coders/StepResult.py

@@ -0,0 +1,56 @@
+# orchestrator/step_result.py
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, Literal
+
+# Terminal reasons are strings, not an enum, so you can add domain-specific
+# reasons (e.g. "escalated:ci_infra") without touching this module.
+TerminalReason = str
+AwaitState = str   # must be one of the AWAITING_* names, or "ABANDONED"
+
+
+@dataclass(frozen=True)
+class StepResult:
+    """
+    The outcome of a step handler.
+
+    This is control flow, not data. The step's contract *output* lives in
+    `output`; the fields here tell the runner what to do next. The runner
+    is the only thing that reads these fields; handlers only construct them.
+    """
+    kind: Literal["continue", "await", "terminal"]
+    next_step: str | None = None        # set iff kind == "continue"
+    await_state: AwaitState | None = None   # set iff kind == "await"
+    terminal_reason: TerminalReason | None = None  # set iff kind == "terminal"
+    output: Any = None                  # the step's contract output
+
+    # ── constructors ─────────────────────────────────────────────
+
+    @classmethod
+    def continue_to(cls, step: str, output: Any = None) -> "StepResult":
+        """Advance to another step in the pipeline."""
+        return cls(kind="continue", next_step=step, output=output)
+
+    @classmethod
+    def awaiting(cls, state: AwaitState, output: Any = None) -> "StepResult":
+        """Stop this call; resume when the await trigger fires."""
+        if not (state.startswith("AWAITING_") or state == "ABANDONED"):
+            raise ValueError(f"not an await state: {state!r}")
+        return cls(kind="await", await_state=state, output=output)
+
+    @classmethod
+    def complete(cls, output: Any = None) -> "StepResult":
+        """Terminal: MR merged and task closed."""
+        return cls(kind="terminal", terminal_reason="completed", output=output)
+
+    @classmethod
+    def abandoned(cls, output: Any = None) -> "StepResult":
+        """Terminal: no response within the timeout window."""
+        return cls(kind="terminal", terminal_reason="abandoned", output=output)
+
+    @classmethod
+    def escalated(cls, reason: str, output: Any = None) -> "StepResult":
+        """Terminal: stopping because a human must intervene."""
+        return cls(kind="terminal", terminal_reason=f"escalated:{reason}",
+                   output=output)

contracts/coders/VerifyOutput.py

@@ -0,0 +1,121 @@
+# contracts/verify_possibility.py
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Literal, Protocol, Annotated, Union, Any
+
+from pydantic import BaseModel, ConfigDict, Field
+
+from contracts.base import _StrictModel
+from contracts.coders.GatherOutput import GatherOutput
+
+@dataclass(frozen=True)
+class VerifyPossibilityInput:
+    gather: GatherOutput
+
+
+def sanitize_for_openai(schema: dict[str, Any]) -> dict[str, Any]:
+    """
+    Recursively rewrite a Pydantic-generated JSON schema into the subset
+    OpenAI strict mode accepts.
+
+    - oneOf          -> anyOf   (OpenAI rejects oneOf)
+    - discriminator  -> removed (OpenAI rejects it; the const fields
+                                 already make branches disjoint)
+    - adds additionalProperties: false to every object missing it
+    - strips None defaults
+    """
+    return _walk(schema)
+
+
+def _walk(node: Any) -> Any:
+    if isinstance(node, list):
+        return [_walk(x) for x in node]
+    if not isinstance(node, dict):
+        return node
+
+    # Recurse into every nested schema container first.
+    for key in ("properties", "$defs", "definitions"):
+        if key in node and isinstance(node[key], dict):
+            node[key] = {k: _walk(v) for k, v in node[key].items()}
+
+    for key in ("items", "additionalProperties"):
+        if key in node and isinstance(node[key], (dict, list)):
+            node[key] = _walk(node[key])
+
+    for key in ("anyOf", "allOf"):
+        if key in node and isinstance(node[key], list):
+            node[key] = [_walk(v) for v in node[key]]
+
+    # ── the two rewrites that matter ──────────────────────────
+    if "oneOf" in node and isinstance(node["oneOf"], list):
+        existing = node.get("anyOf", [])
+        if not isinstance(existing, list):
+            existing = []
+        node["anyOf"] = existing + [_walk(v) for v in node["oneOf"]]
+        node.pop("oneOf")
+
+    node.pop("discriminator", None)   # OpenAI doesn't accept it
+
+    # ── strict-mode hygiene ───────────────────────────────────
+    if node.get("type") == "object":
+        node.setdefault("additionalProperties", False)
+        props = node.get("properties")
+        if isinstance(props, dict):
+            # Strict mode: every property must be in required.
+            node["required"] = list(props.keys())
+
+    if node.get("default", object()) is None:
+        node.pop("default", None)
+
+    return node
+
+class Option(_StrictModel):
+    label: str = Field(min_length=1, max_length=80)
+    description: str = Field(min_length=1)
+    tradeoff: str | None = Field(
+        description="Optional trade-off; null if none.",
+    )
+
+class _BaseVerdict(_StrictModel):
+    reasoning: str = Field(
+        min_length=1,
+        description="2–5 sentences explaining the verdict.",
+    )
+    confidence: float = Field(
+        ge=0.0, le=1.0,
+        description="Your honest probability that the verdict is correct.",
+    )
+
+
+class FeasibleVerdict(_BaseVerdict):
+    verdict: Literal["feasible"]
+
+
+class AmbiguousVerdict(_BaseVerdict):
+    verdict: Literal["ambiguous"]
+    question: str = Field(
+        min_length=1,
+        description="Exactly one clarifying question that unblocks planning.",
+    )
+
+
+class InfeasibleVerdict(_BaseVerdict):
+    verdict: Literal["infeasible"]
+    options: list[Option] = Field(
+        min_length=1, max_length=3,
+        description="1–3 concrete alternatives.",
+    )
+
+
+Verdict = Annotated[
+    Union[FeasibleVerdict, AmbiguousVerdict, InfeasibleVerdict],
+    Field(discriminator="verdict"),
+]
+
+
+class VerifyPossibilityOutput(_StrictModel):
+    """Top-level object required by OpenAI strict mode."""
+    result: Verdict = Field(
+        description="The feasibility verdict.",
+    )

main.py

@@ -5,9 +5,12 @@ from contextlib import asynccontextmanager
 import httpx
 from dotenv import load_dotenv
 from fastapi import FastAPI, HTTPException
+
+from agents.coders.BaseCoder import CoderAgent
 from agents.issue_triage.IssueTriageAgent import IssueTriageAgent
 from agents.issue_triage.context_builder import YouTrackContextBuilder
 from agents.registry import AgentRegistry
+from common.gitea_mcp_client import GiteaMCPClient
 from common.llm_client import LLMClient
 from common.youtrack_mcp_client import YouTrackMCPClient, IssueNotFound
 
@@ -23,7 +26,7 @@ def env(key: str) -> str:
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
-    mcp = await YouTrackMCPClient(
+    app.state.youtrack_mcp = await YouTrackMCPClient(
         str(os.getenv('YOUTRACK_MCP_SERVER')),
         str(os.getenv('YOUTRACK_MCP_TOKEN')),
     ).connect()
@@ -43,20 +46,20 @@ async def lifespan(app: FastAPI):
     )
 
     app.state.issue_reader_agent = IssueTriageAgent(
-        YouTrackContextBuilder(mcp, http_for_attachments),
+        YouTrackContextBuilder(app.state.youtrack_mcp, http_for_attachments),
         LLMClient(
             base_url=env("LLM_ADDRESS"),
             api_key=env("LLM_API_KEY"),
             model=env("LLM_MODEL"),
         ),
         AgentRegistry(),
-        mcp
+        app.state.youtrack_mcp
     )
 
     yield
 
     # ---- shutdown ----
-    await mcp.close()
+    await app.state.youtrack_mcp.close()
 app = FastAPI(lifespan=lifespan)
 
 @app.get("/")
@@ -84,3 +87,32 @@ async def decomposing_issue(issue: str):
     except Exception as e:
         logger.exception("An unexpected error occurred")
         return {"error": str(e)}
+
+@app.get("/code_issue")
+async def code_issue(issue: str):
+    issue_agent: IssueTriageAgent = app.state.issue_reader_agent
+    gitea_mcp_client = GiteaMCPClient()
+    coder_agent: CoderAgent = CoderAgent(
+        str(os.getenv("CODER_ID")),
+        issue_agent.ctx_builder,
+        issue_agent.llm,
+        issue_agent.agent_registry,
+        issue_agent.youtrack_mcp,
+        gitea_mcp_client
+    )
+
+    try:
+        logger.info(f"Resolve an issue: {issue}")
+        issue_ctx = await issue_agent.ctx_builder.build(issue)
+        if issue_ctx:
+            response = await coder_agent.fix_an_issue(issue_ctx)
+            logger.info("Task planning successful.")
+            return {"status": "ok", "response": response}
+    except IssueNotFound:
+        logger.error(f"Issue {issue} not found.")
+        raise HTTPException(status_code=404, detail=f"Issue {issue} not found")
+    except Exception as e:
+        logger.exception("An unexpected error occurred")
+        return {"error": str(e)}
+
+    return {"error": "No context was created"}