Первый коммит

.dockerignore

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+.venv
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+*.swp
+.git
+.gitignore

.env

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+# PostgreSQL
+POSTGRES_USER=agent
+POSTGRES_PASSWORD=agent
+POSTGRES_DB=agent
+POSTGRES_PORT=5432
+
+# Application DB DSN (used by backend)
+DATABASE_URL=postgresql+psycopg://agent:agent@db:5432/agent
+
+# GigaChat
+GIGACHAT_TOKEN=MGMyOGExMzctZDY1YS00OGNkLTk3NGYtYzFkZWVjOTEzM2RkOjFjOTc0YjFlLWNlMDUtNDM4Zi04ZDA2LWZkODA5MjRhZTY3NA==
+GIGACHAT_AUTH_URL=https://ngw.devices.sberbank.ru:9443/api/v2/oauth
+GIGACHAT_API_URL=https://gigachat.devices.sberbank.ru/api/v1
+GIGACHAT_SCOPE=GIGACHAT_API_PERS
+GIGACHAT_SSL_VERIFY=true
+GIGACHAT_MODEL=GigaChat
+GIGACHAT_EMBEDDING_MODEL=Embeddings
+GIGACHAT_SSL_VERIFY=false
+
+# Optional
+AGENT_PROMPTS_DIR=
+
+
+RAG_EMBED_BATCH_SIZE=16

.env.example

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+# PostgreSQL
+POSTGRES_USER=agent
+POSTGRES_PASSWORD=agent
+POSTGRES_DB=agent
+POSTGRES_PORT=5432
+
+# Application DB DSN (used by backend)
+DATABASE_URL=postgresql+psycopg://agent:agent@db:5432/agent
+
+# GigaChat
+GIGACHAT_TOKEN=
+GIGACHAT_AUTH_URL=https://ngw.devices.sberbank.ru:9443/api/v2/oauth
+GIGACHAT_API_URL=https://gigachat.devices.sberbank.ru/api/v1
+GIGACHAT_SCOPE=GIGACHAT_API_PERS
+# If your corporate environment uses MITM/self-signed cert chain, set false.
+GIGACHAT_SSL_VERIFY=true
+GIGACHAT_MODEL=GigaChat
+GIGACHAT_EMBEDDING_MODEL=Embeddings
+
+# Optional
+BACKEND_PORT=15000
+AGENT_PROMPTS_DIR=
+
+RAG_EMBED_BATCH_SIZE=16

Dockerfile

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+FROM python:3.12-slim
+
+WORKDIR /app
+
+ENV PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1
+
+COPY requirements.txt ./
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY app ./app
+
+EXPOSE 15000
+
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "15000"]

README.md

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+# Agent Backend MVP
+
+## Run (Local)
+
+```bash
+python3 -m venv .venv
+source .venv/bin/activate
+pip install -r requirements.txt
+uvicorn app.main:app --reload --port 15000
+```
+
+## Run (Docker Compose)
+
+1. Fill env values:
+
+```bash
+cp .env.example .env
+```
+
+Set `GIGACHAT_TOKEN` in `.env`.
+
+2. Start:
+
+```bash
+docker compose up --build
+```
+
+- Public API: `http://localhost:15000/api/*`
+- PostgreSQL + pgvector runs in `db` service on `localhost:5432`
+
+Stop:
+
+```bash
+docker compose down
+```
+
+## Public API
+
+- `POST /api/rag/sessions`
+- `POST /api/rag/sessions/{rag_session_id}/changes`
+- `GET /api/rag/sessions/{rag_session_id}/jobs/{index_job_id}`
+- `GET /api/rag/sessions/{rag_session_id}/jobs/{index_job_id}/events` (SSE progress)
+- `POST /api/chat/dialogs`
+- `POST /api/chat/messages`
+- `GET /api/tasks/{task_id}`
+- `GET /api/events?task_id=...`
+- `POST /api/index/snapshot`
+- `POST /api/index/changes`
+- `GET /api/index/jobs/{index_job_id}`
+- `GET /api/index/jobs/{index_job_id}/events` (legacy SSE progress)
+
+RAG indexing SSE events (`/api/rag/sessions/{rag_session_id}/jobs/{index_job_id}/events`):
+- `index_status`: `{"index_job_id","status","total_files",...}`
+- `index_progress`: `{"index_job_id","current_file_index","total_files","processed_files","current_file_path","current_file_name"}`
+- `terminal`: `{"index_job_id","status":"done|error",...}` (final event; stream closes after this event)
+
+## Session Model
+
+- `rag_session_id`: identifies indexed project chunks in RAG.
+- `dialog_session_id`: identifies one independent dialog context within a `rag_session_id`.
+- Multiple dialogs can share one `rag_session_id`.
+
+Recommended flow:
+1. call `POST /api/rag/sessions` after project selection;
+2. wait for indexing job completion;
+3. call `POST /api/chat/dialogs` with `rag_session_id`;
+4. call `POST /api/chat/messages` with `dialog_session_id` + `rag_session_id`.
+
+`/api/chat/messages` supports explicit routing hint:
+- `mode: "auto" | "project_qa" | "project_edits" | "docs_generation" | "qa"`
+- Legacy aliases are still accepted: `code_change`, `analytics_review`.
+
+## Persistence
+
+Persisted in PostgreSQL:
+- `rag_sessions`, `rag_chunks`, `rag_index_jobs`
+- `dialog_sessions`, `chat_messages`
+- `router_context` (agent routing context per dialog session)
+- LangGraph checkpoints via `PostgresSaver` (thread id = `dialog_session_id`)
+
+Notes:
+- RAG vectors are stored in `pgvector` column (`rag_chunks.embedding`).
+- Agent context/history is restored after restart for same `dialog_session_id`.
+
+## Agent Runtime
+
+- Router + context store (`app/modules/agent/engine/router/*`)
+- LangGraph flow execution (`app/modules/agent/engine/graphs/*`)
+- Route selection:
+  - `default/general` -> answer flow
+  - `project/qa` -> answer flow
+  - `project/edits` -> conservative changeset flow for non-code file updates
+  - `docs/generation` -> answer and/or changeset flow
+- LLM provider: GigaChat (`chat/completions`)
+- Prompts for graph LLM nodes: `app/modules/agent/prompts/*.txt`
+  - `general_answer.txt`
+  - `project_answer.txt`
+  - `project_edits_plan.txt`
+  - `project_edits_apply.txt`
+  - `project_edits_self_check.txt`
+  - `docs_generation.txt`
+  - `docs_execution_summary.txt`
+
+## Modules Structure
+
+- `app/modules/chat/*`: chat API, tasks, session-scoped orchestration, event streaming.
+- `app/modules/agent/*`: intent router, LangGraph flows, confluence integration, changeset validation.
+- `app/modules/rag/*`: indexing API/jobs and retrieval service.
+- `app/modules/shared/*`: cross-module primitives (event bus, retry, idempotency).
+- `app/modules/contracts.py`: fixed inter-module contracts (`AgentRunner`, `RagRetriever`, `RagIndexer`).
+
+## Module Boundaries
+
+- `chat` depends on contract `AgentRunner`, but not on concrete `agent` internals.
+- `agent` depends on contract `RagRetriever`, but not on `rag` indexing internals.
+- `rag` exposes public/internal API and service implementation.
+- wiring is centralized in `app/modules/application.py`.
+
+## Internal API (for integration)
+
+- `POST /internal/rag/index/snapshot`
+- `POST /internal/rag/index/changes`
+- `GET /internal/rag/index/jobs/{index_job_id}`
+- `POST /internal/rag/retrieve`
+- `POST /internal/tools/confluence/fetch`
+
+## Environment
+
+- `.env.example` contains full list of parameters.
+- `.env` is used by Docker Compose for `db` and `backend`.
+- `GIGACHAT_TOKEN`: Basic credentials for OAuth exchange (required for LLM/embeddings).
+- `DATABASE_URL`: PostgreSQL DSN, default `postgresql+psycopg://agent:agent@db:5432/agent`.
+- `GIGACHAT_AUTH_URL`: default `https://ngw.devices.sberbank.ru:9443/api/v2/oauth`.
+- `GIGACHAT_API_URL`: default `https://gigachat.devices.sberbank.ru/api/v1`.
+- `GIGACHAT_SCOPE`: default `GIGACHAT_API_PERS`.
+- `GIGACHAT_SSL_VERIFY`: `true|false`, default `true`.
+- `GIGACHAT_MODEL`: chat model name, default `GigaChat`.
+- `GIGACHAT_EMBEDDING_MODEL`: embedding model name, default `Embeddings`.
+- `AGENT_PROMPTS_DIR`: optional override path for prompt files.
+
+Troubleshooting:
+- If indexing shows `failed_files > 0` and `indexed_files = 0`, check backend logs for TLS/auth errors to GigaChat.
+- In corporate environments with custom TLS chain, set `GIGACHAT_SSL_VERIFY=false` (or install proper CA certs in container).

app/core/constants.py

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+from datetime import timedelta
+
+IDEMPOTENCY_TTL = timedelta(minutes=10)
+MAX_RETRIES = 5
+SUPPORTED_SCHEMA_VERSION = "1.0"

app/core/error_handlers.py

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+from fastapi import FastAPI, Request
+from fastapi.responses import JSONResponse
+from pydantic import ValidationError
+
+from app.core.exceptions import AppError
+from app.schemas.common import ModuleName
+
+
+def register_error_handlers(app: FastAPI) -> None:
+    @app.exception_handler(AppError)
+    async def app_error_handler(_: Request, exc: AppError) -> JSONResponse:
+        return JSONResponse(
+            status_code=400,
+            content={"code": exc.code, "desc": exc.desc, "module": exc.module.value},
+        )
+
+    @app.exception_handler(ValidationError)
+    async def validation_error_handler(_: Request, exc: ValidationError) -> JSONResponse:
+        return JSONResponse(
+            status_code=422,
+            content={
+                "code": "validation_error",
+                "desc": str(exc),
+                "module": ModuleName.BACKEND.value,
+            },
+        )
+
+    @app.exception_handler(Exception)
+    async def generic_error_handler(_: Request, exc: Exception) -> JSONResponse:
+        return JSONResponse(
+            status_code=500,
+            content={
+                "code": "internal_error",
+                "desc": str(exc),
+                "module": ModuleName.BACKEND.value,
+            },
+        )

app/core/exceptions.py

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+from app.schemas.common import ModuleName
+
+
+class AppError(Exception):
+    def __init__(self, code: str, desc: str, module: ModuleName) -> None:
+        super().__init__(desc)
+        self.code = code
+        self.desc = desc
+        self.module = module

app/main.py

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+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+
+from app.core.error_handlers import register_error_handlers
+from app.modules.application import ModularApplication
+
+
+def create_app() -> FastAPI:
+    app = FastAPI(title="Agent Backend MVP", version="0.1.0")
+    modules = ModularApplication()
+    app.state.modules = modules
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=["*"],
+        allow_credentials=False,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
+
+    app.include_router(modules.chat.public_router())
+    app.include_router(modules.rag.public_router())
+    app.include_router(modules.rag.internal_router())
+    app.include_router(modules.agent.internal_router())
+
+    register_error_handlers(app)
+
+    @app.on_event("startup")
+    async def startup() -> None:
+        modules.startup()
+
+    @app.get("/health")
+    async def health() -> dict:
+        return {"status": "ok"}
+
+    return app
+
+
+app = create_app()

app/modules/agent/changeset_validator.py

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+from app.core.constants import SUPPORTED_SCHEMA_VERSION
+from app.core.exceptions import AppError
+from app.schemas.changeset import ChangeItem, ChangeSetPayload
+from app.schemas.common import ModuleName
+
+
+class ChangeSetValidator:
+    def validate(self, task_id: str, changeset: list[ChangeItem]) -> list[ChangeItem]:
+        payload = ChangeSetPayload(
+            schema_version=SUPPORTED_SCHEMA_VERSION,
+            task_id=task_id,
+            changeset=changeset,
+        )
+        if payload.schema_version != SUPPORTED_SCHEMA_VERSION:
+            raise AppError(
+                "unsupported_schema",
+                f"Unsupported schema version: {payload.schema_version}",
+                ModuleName.AGENT,
+            )
+        return payload.changeset

app/modules/agent/confluence_service.py

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+from datetime import datetime, timezone
+from urllib.parse import urlparse
+from uuid import uuid4
+
+from app.core.exceptions import AppError
+from app.schemas.common import ModuleName
+
+
+class ConfluenceService:
+    async def fetch_page(self, url: str) -> dict:
+        parsed = urlparse(url)
+        if not parsed.scheme.startswith("http"):
+            raise AppError("invalid_url", "Invalid Confluence URL", ModuleName.CONFLUENCE)
+        return {
+            "page_id": str(uuid4()),
+            "title": "Confluence page",
+            "content_markdown": f"Fetched content from {url}",
+            "version": 1,
+            "fetched_at": datetime.now(timezone.utc).isoformat(),
+        }

app/modules/agent/engine/graphs/__init__.py

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+from app.modules.agent.engine.graphs.base_graph import BaseGraphFactory
+from app.modules.agent.engine.graphs.docs_graph import DocsGraphFactory
+from app.modules.agent.engine.graphs.project_edits_graph import ProjectEditsGraphFactory
+from app.modules.agent.engine.graphs.project_qa_graph import ProjectQaGraphFactory
+
+__all__ = [
+    "BaseGraphFactory",
+    "DocsGraphFactory",
+    "ProjectEditsGraphFactory",
+    "ProjectQaGraphFactory",
+]

app/modules/agent/engine/graphs/base_graph.py

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+from langgraph.graph import END, START, StateGraph
+
+from app.modules.agent.engine.graphs.progress import emit_progress_sync
+from app.modules.agent.llm import AgentLlmService
+from app.modules.agent.engine.graphs.state import AgentGraphState
+
+
+class BaseGraphFactory:
+    def __init__(self, llm: AgentLlmService) -> None:
+        self._llm = llm
+
+    def build(self, checkpointer=None):
+        graph = StateGraph(AgentGraphState)
+        graph.add_node("context", self._context_node)
+        graph.add_node("answer", self._answer_node)
+        graph.add_edge(START, "context")
+        graph.add_edge("context", "answer")
+        graph.add_edge("answer", END)
+        return graph.compile(checkpointer=checkpointer)
+
+    def _context_node(self, state: AgentGraphState) -> dict:
+        emit_progress_sync(
+            state,
+            stage="graph.default.context",
+            message="Готовлю контекст ответа по данным запроса.",
+        )
+        rag = state.get("rag_context", "")
+        conf = state.get("confluence_context", "")
+        emit_progress_sync(
+            state,
+            stage="graph.default.context.done",
+            message="Контекст собран, перехожу к формированию ответа.",
+        )
+        return {"rag_context": rag, "confluence_context": conf}
+
+    def _answer_node(self, state: AgentGraphState) -> dict:
+        emit_progress_sync(
+            state,
+            stage="graph.default.answer",
+            message="Формирую текст ответа для пользователя.",
+        )
+        msg = state.get("message", "")
+        rag = state.get("rag_context", "")
+        conf = state.get("confluence_context", "")
+        user_input = "\n\n".join(
+            [
+                f"User request:\n{msg}",
+                f"RAG context:\n{rag}",
+                f"Confluence context:\n{conf}",
+            ]
+        )
+        answer = self._llm.generate("general_answer", user_input)
+        emit_progress_sync(
+            state,
+            stage="graph.default.answer.done",
+            message="Черновик ответа подготовлен.",
+        )
+        return {"answer": answer}

app/modules/agent/engine/graphs/docs_examples_loader.py

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+from pathlib import Path
+import os
+
+
+class DocsExamplesLoader:
+    def __init__(self, prompts_dir: Path | None = None) -> None:
+        base = prompts_dir or Path(__file__).resolve().parents[2] / "prompts"
+        env_override = os.getenv("AGENT_PROMPTS_DIR", "").strip()
+        root = Path(env_override) if env_override else base
+        self._examples_dir = root / "docs_examples"
+
+    def load_bundle(self, *, max_files: int = 6, max_chars_per_file: int = 1800) -> str:
+        if not self._examples_dir.is_dir():
+            return ""
+        files = sorted(
+            [p for p in self._examples_dir.iterdir() if p.is_file() and p.suffix.lower() in {".md", ".txt"}],
+            key=lambda p: p.name.lower(),
+        )[:max_files]
+        chunks: list[str] = []
+        for path in files:
+            content = path.read_text(encoding="utf-8", errors="ignore").strip()
+            if not content:
+                continue
+            excerpt = content[:max_chars_per_file].strip()
+            chunks.append(f"### Example: {path.name}\n{excerpt}")
+        return "\n\n".join(chunks).strip()

app/modules/agent/engine/graphs/docs_graph.py

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+from langgraph.graph import END, START, StateGraph
+import logging
+
+from app.modules.agent.engine.graphs.file_targeting import FileTargeting
+from app.modules.agent.engine.graphs.docs_graph_logic import DocsContentComposer, DocsContextAnalyzer
+from app.modules.agent.engine.graphs.progress import emit_progress_sync
+from app.modules.agent.engine.graphs.state import AgentGraphState
+from app.modules.agent.llm import AgentLlmService
+
+LOGGER = logging.getLogger(__name__)
+
+
+class DocsGraphFactory:
+    _max_validation_attempts = 2
+
+    def __init__(self, llm: AgentLlmService) -> None:
+        self._targeting = FileTargeting()
+        self._analyzer = DocsContextAnalyzer(llm, self._targeting)
+        self._composer = DocsContentComposer(llm, self._targeting)
+
+    def build(self, checkpointer=None):
+        graph = StateGraph(AgentGraphState)
+        graph.add_node("collect_code_context", self._collect_code_context)
+        graph.add_node("detect_existing_docs", self._detect_existing_docs)
+        graph.add_node("decide_strategy", self._decide_strategy)
+        graph.add_node("load_rules_and_examples", self._load_rules_and_examples)
+        graph.add_node("plan_incremental_changes", self._plan_incremental_changes)
+        graph.add_node("plan_new_document", self._plan_new_document)
+        graph.add_node("generate_doc_content", self._generate_doc_content)
+        graph.add_node("self_check", self._self_check)
+        graph.add_node("build_changeset", self._build_changeset)
+        graph.add_node("summarize_result", self._summarize_result)
+
+        graph.add_edge(START, "collect_code_context")
+        graph.add_edge("collect_code_context", "detect_existing_docs")
+        graph.add_edge("detect_existing_docs", "decide_strategy")
+        graph.add_edge("decide_strategy", "load_rules_and_examples")
+        graph.add_conditional_edges(
+            "load_rules_and_examples",
+            self._route_after_rules_loading,
+            {
+                "incremental": "plan_incremental_changes",
+                "from_scratch": "plan_new_document",
+            },
+        )
+        graph.add_edge("plan_incremental_changes", "generate_doc_content")
+        graph.add_edge("plan_new_document", "generate_doc_content")
+        graph.add_edge("generate_doc_content", "self_check")
+        graph.add_conditional_edges(
+            "self_check",
+            self._route_after_self_check,
+            {"retry": "generate_doc_content", "ready": "build_changeset"},
+        )
+        graph.add_edge("build_changeset", "summarize_result")
+        graph.add_edge("summarize_result", END)
+        return graph.compile(checkpointer=checkpointer)
+
+    def _collect_code_context(self, state: AgentGraphState) -> dict:
+        return self._run_node(state, "collect_code_context", "Собираю контекст кода и файлов.", self._analyzer.collect_code_context)
+
+    def _detect_existing_docs(self, state: AgentGraphState) -> dict:
+        return self._run_node(
+            state,
+            "detect_existing_docs",
+            "Определяю, есть ли существующая документация проекта.",
+            self._analyzer.detect_existing_docs,
+        )
+
+    def _decide_strategy(self, state: AgentGraphState) -> dict:
+        return self._run_node(state, "decide_strategy", "Выбираю стратегию: инкремент или генерация с нуля.", self._analyzer.decide_strategy)
+
+    def _load_rules_and_examples(self, state: AgentGraphState) -> dict:
+        return self._run_node(
+            state,
+            "load_rules_and_examples",
+            "Загружаю правила и примеры формата документации.",
+            self._composer.load_rules_and_examples,
+        )
+
+    def _plan_incremental_changes(self, state: AgentGraphState) -> dict:
+        return self._run_node(
+            state,
+            "plan_incremental_changes",
+            "Планирую точечные изменения в существующей документации.",
+            lambda st: self._composer.plan_incremental_changes(st, self._analyzer),
+        )
+
+    def _plan_new_document(self, state: AgentGraphState) -> dict:
+        return self._run_node(state, "plan_new_document", "Проектирую структуру новой документации.", self._composer.plan_new_document)
+
+    def _generate_doc_content(self, state: AgentGraphState) -> dict:
+        return self._run_node(state, "generate_doc_content", "Генерирую содержимое документации.", self._composer.generate_doc_content)
+
+    def _self_check(self, state: AgentGraphState) -> dict:
+        return self._run_node(state, "self_check", "Проверяю соответствие результата правилам.", self._composer.self_check)
+
+    def _build_changeset(self, state: AgentGraphState) -> dict:
+        return self._run_node(state, "build_changeset", "Формирую итоговый набор изменений файлов.", self._composer.build_changeset)
+
+    def _summarize_result(self, state: AgentGraphState) -> dict:
+        return self._run_node(
+            state,
+            "summarize_result",
+            "Формирую краткий обзор выполненных действий и измененных файлов.",
+            self._composer.build_execution_summary,
+        )
+
+    def _route_after_rules_loading(self, state: AgentGraphState) -> str:
+        if state.get("docs_strategy") == "incremental_update":
+            return "incremental"
+        return "from_scratch"
+
+    def _route_after_self_check(self, state: AgentGraphState) -> str:
+        if state.get("validation_passed"):
+            return "ready"
+        attempts = int(state.get("validation_attempts", 0) or 0)
+        return "ready" if attempts >= self._max_validation_attempts else "retry"
+
+    def _run_node(self, state: AgentGraphState, node_name: str, message: str, fn):
+        emit_progress_sync(state, stage=f"graph.docs.{node_name}", message=message)
+        try:
+            result = fn(state)
+            emit_progress_sync(state, stage=f"graph.docs.{node_name}.done", message=f"Шаг '{node_name}' завершен.")
+            LOGGER.warning("docs graph node completed: node=%s keys=%s", node_name, sorted(result.keys()))
+            return result
+        except Exception:
+            LOGGER.exception("docs graph node failed: node=%s", node_name)
+            raise

app/modules/agent/engine/graphs/docs_graph_logic.py

@@ -0,0 +1,519 @@
+import json
+from difflib import SequenceMatcher
+
+from app.modules.agent.engine.graphs.docs_examples_loader import DocsExamplesLoader
+from app.modules.agent.engine.graphs.file_targeting import FileTargeting
+from app.modules.agent.engine.graphs.state import AgentGraphState
+from app.modules.agent.llm import AgentLlmService
+from app.schemas.changeset import ChangeItem
+import logging
+
+LOGGER = logging.getLogger(__name__)
+
+
+class DocsContextAnalyzer:
+    def __init__(self, llm: AgentLlmService, targeting: FileTargeting) -> None:
+        self._llm = llm
+        self._targeting = targeting
+
+    def collect_code_context(self, state: AgentGraphState) -> dict:
+        message = state.get("message", "")
+        files_map = state.get("files_map", {}) or {}
+        requested_path = self._targeting.extract_target_path(message)
+        target_file = self._targeting.lookup_file(files_map, requested_path) if requested_path else None
+        docs_candidates = self._collect_doc_candidates(files_map)
+        target_path = str((target_file or {}).get("path") or (requested_path or "")).strip() or ""
+        return {
+            "docs_candidates": docs_candidates,
+            "target_path": target_path,
+            "target_file_content": str((target_file or {}).get("content", "")),
+            "target_file_hash": str((target_file or {}).get("content_hash", "")),
+            "validation_attempts": 0,
+        }
+
+    def detect_existing_docs(self, state: AgentGraphState) -> dict:
+        docs_candidates = state.get("docs_candidates", []) or []
+        if not docs_candidates:
+            return {
+                "existing_docs_detected": False,
+                "existing_docs_summary": "No documentation files detected in current project context.",
+            }
+
+        snippets = "\n\n".join(
+            [
+                f"Path: {item.get('path', '')}\nSnippet:\n{self._shorten(item.get('content', ''), 500)}"
+                for item in docs_candidates[:8]
+            ]
+        )
+        user_input = "\n\n".join(
+            [
+                f"User request:\n{state.get('message', '')}",
+                f"Requested target path:\n{state.get('target_path', '') or '(not specified)'}",
+                f"Detected documentation candidates:\n{snippets}",
+            ]
+        )
+        raw = self._llm.generate("docs_detect", user_input)
+        exists = self.parse_bool_marker(raw, "exists", default=True)
+        summary = self.parse_text_marker(raw, "summary", default="Documentation files detected.")
+        return {"existing_docs_detected": exists, "existing_docs_summary": summary}
+
+    def decide_strategy(self, state: AgentGraphState) -> dict:
+        message = (state.get("message", "") or "").lower()
+        if any(token in message for token in ("с нуля", "from scratch", "new documentation", "создай документацию")):
+            return {"docs_strategy": "from_scratch"}
+        if any(token in message for token in ("дополни", "обнови документацию", "extend docs", "update docs")):
+            return {"docs_strategy": "incremental_update"}
+
+        user_input = "\n\n".join(
+            [
+                f"User request:\n{state.get('message', '')}",
+                f"Existing docs detected:\n{state.get('existing_docs_detected', False)}",
+                f"Existing docs summary:\n{state.get('existing_docs_summary', '')}",
+            ]
+        )
+        raw = self._llm.generate("docs_strategy", user_input)
+        strategy = self.parse_text_marker(raw, "strategy", default="").lower()
+        if strategy not in {"incremental_update", "from_scratch"}:
+            strategy = "incremental_update" if state.get("existing_docs_detected", False) else "from_scratch"
+        return {"docs_strategy": strategy}
+
+    def resolve_target_for_incremental(self, state: AgentGraphState) -> tuple[str, dict | None]:
+        files_map = state.get("files_map", {}) or {}
+        preferred_path = state.get("target_path", "")
+        preferred = self._targeting.lookup_file(files_map, preferred_path)
+        if preferred:
+            return str(preferred.get("path") or preferred_path), preferred
+        candidates = state.get("docs_candidates", []) or []
+        if candidates:
+            first_path = str(candidates[0].get("path", ""))
+            resolved = self._targeting.lookup_file(files_map, first_path) or candidates[0]
+            return first_path, resolved
+        fallback = preferred_path.strip() or "docs/AGENT_DRAFT.md"
+        return fallback, None
+
+    def _collect_doc_candidates(self, files_map: dict[str, dict]) -> list[dict]:
+        candidates: list[dict] = []
+        for raw_path, payload in files_map.items():
+            path = str(raw_path or "").replace("\\", "/").strip()
+            if not path:
+                continue
+            low = path.lower()
+            is_doc = low.startswith("docs/") or low.endswith(".md") or low.endswith(".rst") or "/readme" in low or low.startswith("readme")
+            if not is_doc:
+                continue
+            candidates.append(
+                {
+                    "path": str(payload.get("path") or path),
+                    "content": str(payload.get("content", "")),
+                    "content_hash": str(payload.get("content_hash", "")),
+                }
+            )
+        candidates.sort(key=lambda item: (0 if str(item.get("path", "")).lower().startswith("docs/") else 1, str(item.get("path", "")).lower()))
+        return candidates
+
+    def _shorten(self, text: str, max_chars: int) -> str:
+        value = (text or "").strip()
+        if len(value) <= max_chars:
+            return value
+        return value[:max_chars].rstrip() + "\n...[truncated]"
+
+    @staticmethod
+    def parse_bool_marker(text: str, marker: str, *, default: bool) -> bool:
+        value = DocsContextAnalyzer.parse_text_marker(text, marker, default="")
+        if not value:
+            return default
+        token = value.split()[0].strip().lower()
+        if token in {"yes", "true", "1", "да"}:
+            return True
+        if token in {"no", "false", "0", "нет"}:
+            return False
+        return default
+
+    @staticmethod
+    def parse_text_marker(text: str, marker: str, *, default: str) -> str:
+        low_marker = f"{marker.lower()}:"
+        for line in (text or "").splitlines():
+            raw = line.strip()
+            if raw.lower().startswith(low_marker):
+                return raw.split(":", 1)[1].strip()
+        return default
+
+
+class DocsBundleFormatter:
+    def shorten(self, text: str, max_chars: int) -> str:
+        value = (text or "").strip()
+        if len(value) <= max_chars:
+            return value
+        return value[:max_chars].rstrip() + "\n...[truncated]"
+
+    def normalize_file_output(self, text: str) -> str:
+        value = (text or "").strip()
+        if value.startswith("```") and value.endswith("```"):
+            lines = value.splitlines()
+            if len(lines) >= 3:
+                return "\n".join(lines[1:-1]).strip()
+        return value
+
+    def parse_docs_bundle(self, raw_text: str) -> list[dict]:
+        text = (raw_text or "").strip()
+        if not text:
+            return []
+
+        candidate = self.normalize_file_output(text)
+        parsed = self._parse_json_candidate(candidate)
+        if parsed is None:
+            start = candidate.find("{")
+            end = candidate.rfind("}")
+            if start != -1 and end > start:
+                parsed = self._parse_json_candidate(candidate[start : end + 1])
+        if parsed is None:
+            return []
+
+        files: list[dict]
+        if isinstance(parsed, dict):
+            raw_files = parsed.get("files")
+            files = raw_files if isinstance(raw_files, list) else []
+        elif isinstance(parsed, list):
+            files = parsed
+        else:
+            files = []
+
+        out: list[dict] = []
+        seen: set[str] = set()
+        for item in files:
+            if not isinstance(item, dict):
+                continue
+            path = str(item.get("path", "")).replace("\\", "/").strip()
+            content = str(item.get("content", ""))
+            if not path or not content.strip():
+                continue
+            if path in seen:
+                continue
+            seen.add(path)
+            out.append(
+                {
+                    "path": path,
+                    "content": content,
+                    "reason": str(item.get("reason", "")).strip(),
+                }
+            )
+        return out
+
+    def bundle_has_required_structure(self, bundle: list[dict]) -> bool:
+        if not bundle:
+            return False
+        has_api = any(str(item.get("path", "")).replace("\\", "/").startswith("docs/api/") for item in bundle)
+        has_logic = any(str(item.get("path", "")).replace("\\", "/").startswith("docs/logic/") for item in bundle)
+        return has_api and has_logic
+
+    def similarity(self, original: str, updated: str) -> float:
+        return SequenceMatcher(None, original or "", updated or "").ratio()
+
+    def line_change_ratio(self, original: str, updated: str) -> float:
+        orig_lines = (original or "").splitlines()
+        new_lines = (updated or "").splitlines()
+        if not orig_lines and not new_lines:
+            return 0.0
+        matcher = SequenceMatcher(None, orig_lines, new_lines)
+        changed = 0
+        for tag, i1, i2, j1, j2 in matcher.get_opcodes():
+            if tag == "equal":
+                continue
+            changed += max(i2 - i1, j2 - j1)
+        total = max(len(orig_lines), len(new_lines), 1)
+        return changed / total
+
+    def added_headings(self, original: str, updated: str) -> int:
+        old_heads = {line.strip() for line in (original or "").splitlines() if line.strip().startswith("#")}
+        new_heads = {line.strip() for line in (updated or "").splitlines() if line.strip().startswith("#")}
+        return len(new_heads - old_heads)
+
+    def collapse_whitespace(self, text: str) -> str:
+        return " ".join((text or "").split())
+
+    def _parse_json_candidate(self, text: str):
+        try:
+            return json.loads(text)
+        except Exception:
+            return None
+
+
+class DocsContentComposer:
+    def __init__(self, llm: AgentLlmService, targeting: FileTargeting) -> None:
+        self._llm = llm
+        self._targeting = targeting
+        self._examples = DocsExamplesLoader()
+        self._bundle = DocsBundleFormatter()
+
+    def load_rules_and_examples(self, _state: AgentGraphState) -> dict:
+        return {"rules_bundle": self._examples.load_bundle()}
+
+    def plan_incremental_changes(self, state: AgentGraphState, analyzer: DocsContextAnalyzer) -> dict:
+        target_path, target = analyzer.resolve_target_for_incremental(state)
+        user_input = "\n\n".join(
+            [
+                "Strategy: incremental_update",
+                f"User request:\n{state.get('message', '')}",
+                f"Target path:\n{target_path}",
+                f"Current target content:\n{self._bundle.shorten((target or {}).get('content', ''), 3000)}",
+                f"RAG context:\n{self._bundle.shorten(state.get('rag_context', ''), 6000)}",
+                f"Examples bundle:\n{state.get('rules_bundle', '')}",
+            ]
+        )
+        plan = self._llm.generate("docs_plan_sections", user_input)
+        return {
+            "doc_plan": plan,
+            "target_path": target_path,
+            "target_file_content": str((target or {}).get("content", "")),
+            "target_file_hash": str((target or {}).get("content_hash", "")),
+        }
+
+    def plan_new_document(self, state: AgentGraphState) -> dict:
+        target_path = state.get("target_path", "").strip() or "docs/AGENT_DRAFT.md"
+        user_input = "\n\n".join(
+            [
+                "Strategy: from_scratch",
+                f"User request:\n{state.get('message', '')}",
+                f"Target path:\n{target_path}",
+                f"RAG context:\n{self._bundle.shorten(state.get('rag_context', ''), 6000)}",
+                f"Examples bundle:\n{state.get('rules_bundle', '')}",
+            ]
+        )
+        plan = self._llm.generate("docs_plan_sections", user_input)
+        return {"doc_plan": plan, "target_path": target_path, "target_file_content": "", "target_file_hash": ""}
+
+    def generate_doc_content(self, state: AgentGraphState) -> dict:
+        user_input = "\n\n".join(
+            [
+                f"Strategy:\n{state.get('docs_strategy', 'from_scratch')}",
+                f"User request:\n{state.get('message', '')}",
+                f"Target path:\n{state.get('target_path', '')}",
+                f"Document plan:\n{state.get('doc_plan', '')}",
+                f"Current target content:\n{self._bundle.shorten(state.get('target_file_content', ''), 3500)}",
+                f"RAG context:\n{self._bundle.shorten(state.get('rag_context', ''), 7000)}",
+                f"Examples bundle:\n{state.get('rules_bundle', '')}",
+            ]
+        )
+        raw = self._llm.generate("docs_generation", user_input)
+        bundle = self._bundle.parse_docs_bundle(raw)
+        if bundle:
+            first_content = str(bundle[0].get("content", "")).strip()
+            return {"generated_docs_bundle": bundle, "generated_doc": first_content}
+        content = self._bundle.normalize_file_output(raw)
+        return {"generated_docs_bundle": [], "generated_doc": content}
+
+    def self_check(self, state: AgentGraphState) -> dict:
+        attempts = int(state.get("validation_attempts", 0) or 0) + 1
+        bundle = state.get("generated_docs_bundle", []) or []
+        generated = state.get("generated_doc", "")
+        if not generated.strip() and not bundle:
+            return {
+                "validation_attempts": attempts,
+                "validation_passed": False,
+                "validation_feedback": "Generated document is empty.",
+            }
+        strategy = state.get("docs_strategy", "from_scratch")
+        if strategy == "from_scratch" and not self._bundle.bundle_has_required_structure(bundle):
+            return {
+                "validation_attempts": attempts,
+                "validation_passed": False,
+                "validation_feedback": "Bundle must include both docs/api and docs/logic for from_scratch strategy.",
+            }
+        if strategy == "incremental_update":
+            if bundle and len(bundle) > 1 and not self._is_broad_rewrite_request(str(state.get("message", ""))):
+                return {
+                    "validation_attempts": attempts,
+                    "validation_passed": False,
+                    "validation_feedback": "Incremental update should not touch multiple files without explicit broad rewrite request.",
+                }
+            original = str(state.get("target_file_content", ""))
+            broad = self._is_broad_rewrite_request(str(state.get("message", "")))
+            if original and generated:
+                if self._bundle.collapse_whitespace(original) == self._bundle.collapse_whitespace(generated):
+                    return {
+                        "validation_attempts": attempts,
+                        "validation_passed": False,
+                        "validation_feedback": "Only formatting/whitespace changes detected.",
+                    }
+                similarity = self._bundle.similarity(original, generated)
+                change_ratio = self._bundle.line_change_ratio(original, generated)
+                added_headings = self._bundle.added_headings(original, generated)
+                min_similarity = 0.75 if broad else 0.9
+                max_change_ratio = 0.7 if broad else 0.35
+                if similarity < min_similarity:
+                    return {
+                        "validation_attempts": attempts,
+                        "validation_passed": False,
+                        "validation_feedback": f"Incremental update is too broad (similarity={similarity:.2f}).",
+                    }
+                if change_ratio > max_change_ratio:
+                    return {
+                        "validation_attempts": attempts,
+                        "validation_passed": False,
+                        "validation_feedback": f"Incremental update changes too many lines (change_ratio={change_ratio:.2f}).",
+                    }
+                if not broad and added_headings > 0:
+                    return {
+                        "validation_attempts": attempts,
+                        "validation_passed": False,
+                        "validation_feedback": "New section headings were added outside requested scope.",
+                    }
+
+        bundle_text = "\n".join([f"- {item.get('path', '')}" for item in bundle[:30]])
+        user_input = "\n\n".join(
+            [
+                f"Strategy:\n{strategy}",
+                f"User request:\n{state.get('message', '')}",
+                f"Document plan:\n{state.get('doc_plan', '')}",
+                f"Generated file paths:\n{bundle_text or '(single-file mode)'}",
+                f"Generated document:\n{generated}",
+            ]
+        )
+        raw = self._llm.generate("docs_self_check", user_input)
+        passed = DocsContextAnalyzer.parse_bool_marker(raw, "pass", default=False)
+        feedback = DocsContextAnalyzer.parse_text_marker(raw, "feedback", default="No validation feedback provided.")
+        return {"validation_attempts": attempts, "validation_passed": passed, "validation_feedback": feedback}
+
+    def build_changeset(self, state: AgentGraphState) -> dict:
+        files_map = state.get("files_map", {}) or {}
+        bundle = state.get("generated_docs_bundle", []) or []
+        strategy = state.get("docs_strategy", "from_scratch")
+        if strategy == "from_scratch" and not self._bundle.bundle_has_required_structure(bundle):
+            LOGGER.warning(
+                "build_changeset fallback bundle used: strategy=%s bundle_items=%s",
+                strategy,
+                len(bundle),
+            )
+            bundle = self._build_fallback_bundle_from_text(state.get("generated_doc", ""))
+        if bundle:
+            changes: list[ChangeItem] = []
+            for item in bundle:
+                path = str(item.get("path", "")).replace("\\", "/").strip()
+                content = str(item.get("content", ""))
+                if not path or not content.strip():
+                    continue
+                target = self._targeting.lookup_file(files_map, path)
+                reason = str(item.get("reason", "")).strip() or f"Documentation {strategy}: generated file from structured bundle."
+                if target and target.get("content_hash"):
+                    changes.append(
+                        ChangeItem(
+                            op="update",
+                            path=str(target.get("path") or path),
+                            base_hash=str(target.get("content_hash", "")),
+                            proposed_content=content,
+                            reason=reason,
+                        )
+                    )
+                else:
+                    changes.append(
+                        ChangeItem(
+                            op="create",
+                            path=path,
+                            proposed_content=content,
+                            reason=reason,
+                        )
+                    )
+            if changes:
+                return {"changeset": changes}
+
+        target_path = (state.get("target_path", "") or "").strip() or "docs/AGENT_DRAFT.md"
+        target = self._targeting.lookup_file(files_map, target_path)
+        content = state.get("generated_doc", "")
+        if target and target.get("content_hash"):
+            change = ChangeItem(
+                op="update",
+                path=str(target.get("path") or target_path),
+                base_hash=str(target.get("content_hash", "")),
+                proposed_content=content,
+                reason=f"Documentation {strategy}: update existing document increment.",
+            )
+        else:
+            change = ChangeItem(
+                op="create",
+                path=target_path,
+                proposed_content=content,
+                reason=f"Documentation {strategy}: create document from current project context.",
+            )
+        return {"changeset": [change]}
+
+    def build_execution_summary(self, state: AgentGraphState) -> dict:
+        changeset = state.get("changeset", []) or []
+        if not changeset:
+            return {"answer": "Документация не была изменена: итоговый changeset пуст."}
+
+        file_lines = self._format_changed_files(changeset)
+        user_input = "\n\n".join(
+            [
+                f"User request:\n{state.get('message', '')}",
+                f"Documentation strategy:\n{state.get('docs_strategy', 'from_scratch')}",
+                f"Document plan:\n{state.get('doc_plan', '')}",
+                f"Validation feedback:\n{state.get('validation_feedback', '')}",
+                f"Changed files:\n{file_lines}",
+            ]
+        )
+        try:
+            summary = self._llm.generate("docs_execution_summary", user_input).strip()
+        except Exception:
+            summary = ""
+        if not summary:
+            summary = self._build_fallback_summary(state, file_lines)
+        return {"answer": summary}
+
+    def _build_fallback_bundle_from_text(self, text: str) -> list[dict]:
+        content = (text or "").strip()
+        if not content:
+            content = (
+                "# Project Documentation Draft\n\n"
+                "## Overview\n"
+                "Documentation draft was generated, but structured sections require уточнение.\n"
+            )
+        return [
+            {
+                "path": "docs/logic/project_overview.md",
+                "content": content,
+                "reason": "Fallback: generated structured logic document from non-JSON model output.",
+            },
+            {
+                "path": "docs/api/README.md",
+                "content": (
+                    "# API Methods\n\n"
+                    "This file is a fallback placeholder for API method documentation.\n\n"
+                    "## Next Step\n"
+                    "- Add one file per API method under `docs/api/`.\n"
+                ),
+                "reason": "Fallback: ensure required docs/api structure exists.",
+            },
+        ]
+
+    def _format_changed_files(self, changeset: list[ChangeItem]) -> str:
+        lines: list[str] = []
+        for item in changeset[:30]:
+            lines.append(f"- {item.op.value} {item.path}: {item.reason}")
+        return "\n".join(lines)
+
+    def _build_fallback_summary(self, state: AgentGraphState, file_lines: str) -> str:
+        request = (state.get("message", "") or "").strip()
+        return "\n".join(
+            [
+                "Выполненные действия:",
+                f"- Обработан запрос: {request or '(пустой запрос)'}",
+                f"- Применена стратегия документации: {state.get('docs_strategy', 'from_scratch')}",
+                "- Сформирован и проверен changeset для документации.",
+                "",
+                "Измененные файлы:",
+                file_lines or "- (нет изменений)",
+            ]
+        )
+
+    def _is_broad_rewrite_request(self, message: str) -> bool:
+        low = (message or "").lower()
+        markers = (
+            "перепиши",
+            "полностью",
+            "целиком",
+            "с нуля",
+            "full rewrite",
+            "rewrite all",
+            "реорганизуй",
+        )
+        return any(marker in low for marker in markers)

app/modules/agent/engine/graphs/file_targeting.py

@@ -0,0 +1,28 @@
+import re
+
+
+class FileTargeting:
+    _path_pattern = re.compile(r"([A-Za-z0-9_.\-/]+?\.[A-Za-z0-9_]+)")
+
+    def extract_target_path(self, message: str) -> str | None:
+        text = (message or "").replace("\\", "/")
+        candidates = self._path_pattern.findall(text)
+        if not candidates:
+            return None
+        for candidate in candidates:
+            cleaned = candidate.strip("`'\".,:;()[]{}")
+            if "/" in cleaned or cleaned.startswith("."):
+                return cleaned
+        return candidates[0].strip("`'\".,:;()[]{}")
+
+    def lookup_file(self, files_map: dict[str, dict], path: str | None) -> dict | None:
+        if not path:
+            return None
+        normalized = path.replace("\\", "/")
+        if normalized in files_map:
+            return files_map[normalized]
+        low = normalized.lower()
+        for key, value in files_map.items():
+            if key.lower() == low:
+                return value
+        return None

app/modules/agent/engine/graphs/progress.py

@@ -0,0 +1,44 @@
+from collections.abc import Awaitable, Callable
+import inspect
+import asyncio
+
+from app.modules.agent.engine.graphs.progress_registry import progress_registry
+from app.modules.agent.engine.graphs.state import AgentGraphState
+
+ProgressCallback = Callable[[str, str, str, dict | None], Awaitable[None] | None]
+
+
+async def emit_progress(
+    state: AgentGraphState,
+    *,
+    stage: str,
+    message: str,
+    kind: str = "task_progress",
+    meta: dict | None = None,
+) -> None:
+    callback = progress_registry.get(state.get("progress_key"))
+    if callback is None:
+        return
+    result = callback(stage, message, kind, meta or {})
+    if inspect.isawaitable(result):
+        await result
+
+
+def emit_progress_sync(
+    state: AgentGraphState,
+    *,
+    stage: str,
+    message: str,
+    kind: str = "task_progress",
+    meta: dict | None = None,
+) -> None:
+    callback = progress_registry.get(state.get("progress_key"))
+    if callback is None:
+        return
+    result = callback(stage, message, kind, meta or {})
+    if inspect.isawaitable(result):
+        try:
+            loop = asyncio.get_running_loop()
+            loop.create_task(result)
+        except RuntimeError:
+            pass

app/modules/agent/engine/graphs/progress_registry.py

@@ -0,0 +1,27 @@
+from collections.abc import Awaitable, Callable
+from threading import Lock
+
+ProgressCallback = Callable[[str, str, str, dict | None], Awaitable[None] | None]
+
+
+class ProgressRegistry:
+    def __init__(self) -> None:
+        self._items: dict[str, ProgressCallback] = {}
+        self._lock = Lock()
+
+    def register(self, key: str, callback: ProgressCallback) -> None:
+        with self._lock:
+            self._items[key] = callback
+
+    def get(self, key: str | None) -> ProgressCallback | None:
+        if not key:
+            return None
+        with self._lock:
+            return self._items.get(key)
+
+    def unregister(self, key: str) -> None:
+        with self._lock:
+            self._items.pop(key, None)
+
+
+progress_registry = ProgressRegistry()

app/modules/agent/engine/graphs/project_edits_graph.py

@@ -0,0 +1,79 @@
+from langgraph.graph import END, START, StateGraph
+
+from app.modules.agent.engine.graphs.progress import emit_progress_sync
+from app.modules.agent.engine.graphs.project_edits_logic import ProjectEditsLogic
+from app.modules.agent.engine.graphs.state import AgentGraphState
+from app.modules.agent.llm import AgentLlmService
+
+
+class ProjectEditsGraphFactory:
+    _max_validation_attempts = 2
+
+    def __init__(self, llm: AgentLlmService) -> None:
+        self._logic = ProjectEditsLogic(llm)
+
+    def build(self, checkpointer=None):
+        graph = StateGraph(AgentGraphState)
+        graph.add_node("collect_context", self._collect_context)
+        graph.add_node("plan_changes", self._plan_changes)
+        graph.add_node("generate_changeset", self._generate_changeset)
+        graph.add_node("self_check", self._self_check)
+        graph.add_node("build_result", self._build_result)
+
+        graph.add_edge(START, "collect_context")
+        graph.add_edge("collect_context", "plan_changes")
+        graph.add_edge("plan_changes", "generate_changeset")
+        graph.add_edge("generate_changeset", "self_check")
+        graph.add_conditional_edges(
+            "self_check",
+            self._route_after_self_check,
+            {"retry": "generate_changeset", "ready": "build_result"},
+        )
+        graph.add_edge("build_result", END)
+        return graph.compile(checkpointer=checkpointer)
+
+    def _collect_context(self, state: AgentGraphState) -> dict:
+        emit_progress_sync(
+            state,
+            stage="graph.project_edits.collect_context",
+            message="Собираю контекст и релевантные файлы для правок.",
+        )
+        return self._logic.collect_context(state)
+
+    def _plan_changes(self, state: AgentGraphState) -> dict:
+        emit_progress_sync(
+            state,
+            stage="graph.project_edits.plan_changes",
+            message="Определяю, что именно нужно изменить и в каких файлах.",
+        )
+        return self._logic.plan_changes(state)
+
+    def _generate_changeset(self, state: AgentGraphState) -> dict:
+        emit_progress_sync(
+            state,
+            stage="graph.project_edits.generate_changeset",
+            message="Формирую предлагаемые правки по выбранным файлам.",
+        )
+        return self._logic.generate_changeset(state)
+
+    def _self_check(self, state: AgentGraphState) -> dict:
+        emit_progress_sync(
+            state,
+            stage="graph.project_edits.self_check",
+            message="Проверяю, что правки соответствуют запросу и не трогают лишнее.",
+        )
+        return self._logic.self_check(state)
+
+    def _build_result(self, state: AgentGraphState) -> dict:
+        emit_progress_sync(
+            state,
+            stage="graph.project_edits.build_result",
+            message="Формирую итоговый changeset и краткий обзор.",
+        )
+        return self._logic.build_result(state)
+
+    def _route_after_self_check(self, state: AgentGraphState) -> str:
+        if state.get("validation_passed"):
+            return "ready"
+        attempts = int(state.get("validation_attempts", 0) or 0)
+        return "ready" if attempts >= self._max_validation_attempts else "retry"

app/modules/agent/engine/graphs/project_edits_logic.py

@@ -0,0 +1,271 @@
+import json
+from difflib import SequenceMatcher
+import re
+
+from app.modules.agent.engine.graphs.file_targeting import FileTargeting
+from app.modules.agent.engine.graphs.state import AgentGraphState
+from app.modules.agent.llm import AgentLlmService
+from app.schemas.changeset import ChangeItem
+
+
+class ProjectEditsSupport:
+    def __init__(self, max_context_files: int = 12, max_preview_chars: int = 2500) -> None:
+        self._max_context_files = max_context_files
+        self._max_preview_chars = max_preview_chars
+
+    def pick_relevant_files(self, message: str, files_map: dict[str, dict]) -> list[dict]:
+        tokens = {x for x in (message or "").lower().replace("/", " ").split() if len(x) >= 4}
+        scored: list[tuple[int, dict]] = []
+        for path, payload in files_map.items():
+            content = str(payload.get("content", ""))
+            score = 0
+            low_path = path.lower()
+            low_content = content.lower()
+            for token in tokens:
+                if token in low_path:
+                    score += 3
+                if token in low_content:
+                    score += 1
+            scored.append((score, self.as_candidate(payload)))
+        scored.sort(key=lambda x: (-x[0], x[1]["path"]))
+        return [item for _, item in scored[: self._max_context_files]]
+
+    def as_candidate(self, payload: dict) -> dict:
+        return {
+            "path": str(payload.get("path", "")).replace("\\", "/"),
+            "content": str(payload.get("content", "")),
+            "content_hash": str(payload.get("content_hash", "")),
+        }
+
+    def build_summary(self, state: AgentGraphState, changeset: list[ChangeItem]) -> str:
+        if not changeset:
+            return "Правки не сформированы: changeset пуст."
+        lines = [
+            "Выполненные действия:",
+            f"- Проанализирован запрос: {state.get('message', '')}",
+            "- Собран контекст проекта и выбран набор файлов для правок.",
+            f"- Проведен self-check: {state.get('validation_feedback', 'без замечаний')}",
+            "",
+            "Измененные файлы:",
+        ]
+        for item in changeset[:30]:
+            lines.append(f"- {item.op.value} {item.path}: {item.reason}")
+        return "\n".join(lines)
+
+    def normalize_file_output(self, text: str) -> str:
+        value = (text or "").strip()
+        if value.startswith("```") and value.endswith("```"):
+            lines = value.splitlines()
+            if len(lines) >= 3:
+                return "\n".join(lines[1:-1]).strip()
+        return value
+
+    def parse_json(self, raw: str):
+        text = self.normalize_file_output(raw)
+        try:
+            return json.loads(text)
+        except Exception:
+            return {}
+
+    def similarity(self, original: str, updated: str) -> float:
+        return SequenceMatcher(None, original or "", updated or "").ratio()
+
+    def shorten(self, text: str, max_chars: int | None = None) -> str:
+        limit = max_chars or self._max_preview_chars
+        value = (text or "").strip()
+        if len(value) <= limit:
+            return value
+        return value[:limit].rstrip() + "\n...[truncated]"
+
+    def collapse_whitespace(self, text: str) -> str:
+        return re.sub(r"\s+", " ", (text or "").strip())
+
+    def line_change_ratio(self, original: str, updated: str) -> float:
+        orig_lines = (original or "").splitlines()
+        new_lines = (updated or "").splitlines()
+        if not orig_lines and not new_lines:
+            return 0.0
+        matcher = SequenceMatcher(None, orig_lines, new_lines)
+        changed = 0
+        for tag, i1, i2, j1, j2 in matcher.get_opcodes():
+            if tag == "equal":
+                continue
+            changed += max(i2 - i1, j2 - j1)
+        total = max(len(orig_lines), len(new_lines), 1)
+        return changed / total
+
+    def added_headings(self, original: str, updated: str) -> int:
+        old_heads = {line.strip() for line in (original or "").splitlines() if line.strip().startswith("#")}
+        new_heads = {line.strip() for line in (updated or "").splitlines() if line.strip().startswith("#")}
+        return len(new_heads - old_heads)
+
+
+class ProjectEditsLogic:
+    def __init__(self, llm: AgentLlmService) -> None:
+        self._llm = llm
+        self._targeting = FileTargeting()
+        self._support = ProjectEditsSupport()
+
+    def collect_context(self, state: AgentGraphState) -> dict:
+        message = state.get("message", "")
+        files_map = state.get("files_map", {}) or {}
+        requested_path = self._targeting.extract_target_path(message)
+        preferred = self._targeting.lookup_file(files_map, requested_path) if requested_path else None
+        candidates = self._support.pick_relevant_files(message, files_map)
+        if preferred and not any(x["path"] == preferred.get("path") for x in candidates):
+            candidates.insert(0, self._support.as_candidate(preferred))
+        return {
+            "edits_requested_path": str((preferred or {}).get("path") or (requested_path or "")).strip(),
+            "edits_context_files": candidates[:12],
+            "validation_attempts": 0,
+        }
+
+    def plan_changes(self, state: AgentGraphState) -> dict:
+        context_files = state.get("edits_context_files", []) or []
+        user_input = json.dumps(
+            {
+                "request": state.get("message", ""),
+                "requested_path": state.get("edits_requested_path", ""),
+                "context_files": [
+                    {
+                        "path": item.get("path", ""),
+                        "content_preview": self._support.shorten(str(item.get("content", ""))),
+                    }
+                    for item in context_files
+                ],
+            },
+            ensure_ascii=False,
+        )
+        parsed = self._support.parse_json(self._llm.generate("project_edits_plan", user_input))
+        files = parsed.get("files", []) if isinstance(parsed, dict) else []
+        planned: list[dict] = []
+        for item in files[:8] if isinstance(files, list) else []:
+            if not isinstance(item, dict):
+                continue
+            path = str(item.get("path", "")).replace("\\", "/").strip()
+            if not path:
+                continue
+            planned.append(
+                {
+                    "path": path,
+                    "reason": str(item.get("reason", "")).strip() or "Requested user adjustment.",
+                }
+            )
+        if not planned:
+            fallback_path = state.get("edits_requested_path", "").strip() or "docs/REQUESTED_UPDATES.md"
+            planned = [{"path": fallback_path, "reason": "Fallback path from user request."}]
+        return {"edits_plan": planned}
+
+    def generate_changeset(self, state: AgentGraphState) -> dict:
+        files_map = state.get("files_map", {}) or {}
+        planned = state.get("edits_plan", []) or []
+        changeset: list[ChangeItem] = []
+        for item in planned:
+            path = str(item.get("path", "")).replace("\\", "/").strip()
+            if not path:
+                continue
+            current = self._targeting.lookup_file(files_map, path)
+            current_content = str((current or {}).get("content", ""))
+            user_input = json.dumps(
+                {
+                    "request": state.get("message", ""),
+                    "path": path,
+                    "reason": item.get("reason", ""),
+                    "current_content": current_content,
+                    "previous_validation_feedback": state.get("validation_feedback", ""),
+                    "rag_context": self._support.shorten(state.get("rag_context", ""), 5000),
+                    "confluence_context": self._support.shorten(state.get("confluence_context", ""), 5000),
+                    "instruction": "Modify only required parts and preserve unrelated content unchanged.",
+                },
+                ensure_ascii=False,
+            )
+            raw = self._llm.generate("project_edits_apply", user_input).strip()
+            normalized = self._support.normalize_file_output(raw)
+            if not normalized:
+                continue
+            if current:
+                if normalized == current_content:
+                    continue
+                if self._support.collapse_whitespace(normalized) == self._support.collapse_whitespace(current_content):
+                    continue
+            reason = str(item.get("reason", "")).strip() or "User-requested update."
+            if current and current.get("content_hash"):
+                changeset.append(
+                    ChangeItem(
+                        op="update",
+                        path=str(current.get("path") or path),
+                        base_hash=str(current.get("content_hash", "")),
+                        proposed_content=normalized,
+                        reason=reason,
+                    )
+                )
+            else:
+                changeset.append(ChangeItem(op="create", path=path, proposed_content=normalized, reason=reason))
+        return {"changeset": changeset}
+
+    def self_check(self, state: AgentGraphState) -> dict:
+        attempts = int(state.get("validation_attempts", 0) or 0) + 1
+        changeset = state.get("changeset", []) or []
+        files_map = state.get("files_map", {}) or {}
+        is_broad_rewrite = self._is_broad_rewrite_request(str(state.get("message", "")))
+        if not changeset:
+            return {"validation_attempts": attempts, "validation_passed": False, "validation_feedback": "Generated changeset is empty."}
+
+        for item in changeset:
+            if item.op.value != "update":
+                continue
+            source = self._targeting.lookup_file(files_map, item.path)
+            if not source:
+                continue
+            original = str(source.get("content", ""))
+            proposed = item.proposed_content or ""
+            similarity = self._support.similarity(original, proposed)
+            change_ratio = self._support.line_change_ratio(original, proposed)
+            headings_added = self._support.added_headings(original, proposed)
+            min_similarity = 0.75 if is_broad_rewrite else 0.9
+            max_change_ratio = 0.7 if is_broad_rewrite else 0.35
+            if similarity < min_similarity:
+                return {
+                    "validation_attempts": attempts,
+                    "validation_passed": False,
+                    "validation_feedback": f"File {item.path} changed too aggressively (similarity={similarity:.2f}).",
+                }
+            if change_ratio > max_change_ratio:
+                return {
+                    "validation_attempts": attempts,
+                    "validation_passed": False,
+                    "validation_feedback": f"File {item.path} changed too broadly (change_ratio={change_ratio:.2f}).",
+                }
+            if not is_broad_rewrite and headings_added > 0:
+                return {
+                    "validation_attempts": attempts,
+                    "validation_passed": False,
+                    "validation_feedback": f"File {item.path} adds new sections outside requested scope.",
+                }
+
+        payload = {
+            "request": state.get("message", ""),
+            "changeset": [{"op": x.op.value, "path": x.path, "reason": x.reason} for x in changeset[:20]],
+            "rule": "Changes must match request and avoid unrelated modifications.",
+        }
+        parsed = self._support.parse_json(self._llm.generate("project_edits_self_check", json.dumps(payload, ensure_ascii=False)))
+        passed = bool(parsed.get("pass")) if isinstance(parsed, dict) else False
+        feedback = str(parsed.get("feedback", "")).strip() if isinstance(parsed, dict) else ""
+        return {"validation_attempts": attempts, "validation_passed": passed, "validation_feedback": feedback or "No feedback provided."}
+
+    def build_result(self, state: AgentGraphState) -> dict:
+        changeset = state.get("changeset", []) or []
+        return {"changeset": changeset, "answer": self._support.build_summary(state, changeset)}
+
+    def _is_broad_rewrite_request(self, message: str) -> bool:
+        low = (message or "").lower()
+        markers = (
+            "перепиши",
+            "полностью",
+            "целиком",
+            "с нуля",
+            "full rewrite",
+            "rewrite all",
+            "реорганизуй документ",
+        )
+        return any(marker in low for marker in markers)

app/modules/agent/engine/graphs/project_qa_graph.py

@@ -0,0 +1,38 @@
+from langgraph.graph import END, START, StateGraph
+
+from app.modules.agent.engine.graphs.progress import emit_progress_sync
+from app.modules.agent.engine.graphs.state import AgentGraphState
+from app.modules.agent.llm import AgentLlmService
+
+
+class ProjectQaGraphFactory:
+    def __init__(self, llm: AgentLlmService) -> None:
+        self._llm = llm
+
+    def build(self, checkpointer=None):
+        graph = StateGraph(AgentGraphState)
+        graph.add_node("answer", self._answer_node)
+        graph.add_edge(START, "answer")
+        graph.add_edge("answer", END)
+        return graph.compile(checkpointer=checkpointer)
+
+    def _answer_node(self, state: AgentGraphState) -> dict:
+        emit_progress_sync(
+            state,
+            stage="graph.project_qa.answer",
+            message="Готовлю ответ по контексту текущего проекта.",
+        )
+        user_input = "\n\n".join(
+            [
+                f"User request:\n{state.get('message', '')}",
+                f"RAG context:\n{state.get('rag_context', '')}",
+                f"Confluence context:\n{state.get('confluence_context', '')}",
+            ]
+        )
+        answer = self._llm.generate("project_answer", user_input)
+        emit_progress_sync(
+            state,
+            stage="graph.project_qa.answer.done",
+            message="Ответ по проекту сформирован.",
+        )
+        return {"answer": answer}

app/modules/agent/engine/graphs/state.py

@@ -0,0 +1,32 @@
+from typing import TypedDict
+
+from app.schemas.changeset import ChangeItem
+
+
+class AgentGraphState(TypedDict, total=False):
+    task_id: str
+    project_id: str
+    message: str
+    progress_key: str
+    rag_context: str
+    confluence_context: str
+    files_map: dict[str, dict]
+    docs_candidates: list[dict]
+    target_path: str
+    target_file_content: str
+    target_file_hash: str
+    existing_docs_detected: bool
+    existing_docs_summary: str
+    docs_strategy: str
+    rules_bundle: str
+    doc_plan: str
+    generated_doc: str
+    generated_docs_bundle: list[dict]
+    validation_passed: bool
+    validation_feedback: str
+    validation_attempts: int
+    answer: str
+    changeset: list[ChangeItem]
+    edits_requested_path: str
+    edits_context_files: list[dict]
+    edits_plan: list[dict]

app/modules/agent/engine/router/__init__.py

@@ -0,0 +1,34 @@
+from pathlib import Path
+
+from app.modules.agent.engine.graphs import (
+    BaseGraphFactory,
+    DocsGraphFactory,
+    ProjectEditsGraphFactory,
+    ProjectQaGraphFactory,
+)
+from app.modules.agent.repository import AgentRepository
+from app.modules.agent.llm import AgentLlmService
+from app.modules.agent.engine.router.context_store import RouterContextStore
+from app.modules.agent.engine.router.intent_classifier import IntentClassifier
+from app.modules.agent.engine.router.registry import IntentRegistry
+from app.modules.agent.engine.router.router_service import RouterService
+
+
+def build_router_service(llm: AgentLlmService, agent_repository: AgentRepository) -> RouterService:
+    registry_path = Path(__file__).resolve().parent / "intents_registry.yaml"
+    registry = IntentRegistry(registry_path=registry_path)
+    registry.register("default", "general", BaseGraphFactory(llm).build)
+    registry.register("project", "qa", ProjectQaGraphFactory(llm).build)
+    registry.register("project", "edits", ProjectEditsGraphFactory(llm).build)
+    registry.register("docs", "generation", DocsGraphFactory(llm).build)
+
+    classifier = IntentClassifier(llm)
+    context_store = RouterContextStore(agent_repository)
+    return RouterService(
+        registry=registry,
+        classifier=classifier,
+        context_store=context_store,
+    )
+
+
+__all__ = ["build_router_service", "IntentRegistry", "RouterService"]

app/modules/agent/engine/router/context_store.py

@@ -0,0 +1,29 @@
+from app.modules.agent.repository import AgentRepository
+from app.modules.agent.engine.router.schemas import RouterContext
+
+
+class RouterContextStore:
+    def __init__(self, repository: AgentRepository) -> None:
+        self._repo = repository
+
+    def get(self, conversation_key: str) -> RouterContext:
+        return self._repo.get_router_context(conversation_key)
+
+    def update(
+        self,
+        conversation_key: str,
+        *,
+        domain_id: str,
+        process_id: str,
+        user_message: str,
+        assistant_message: str,
+        max_history: int = 10,
+    ) -> None:
+        self._repo.update_router_context(
+            conversation_key,
+            domain_id=domain_id,
+            process_id=process_id,
+            user_message=user_message,
+            assistant_message=assistant_message,
+            max_history=max_history,
+        )

app/modules/agent/engine/router/intent_classifier.py

@@ -0,0 +1,191 @@
+import json
+import re
+
+from app.modules.agent.engine.router.schemas import RouteDecision, RouterContext
+from app.modules.agent.llm import AgentLlmService
+
+
+class IntentClassifier:
+    _short_confirmations = {"да", "ок", "делай", "поехали", "запускай"}
+    _route_mapping = {
+        "default/general": ("default", "general"),
+        "project/qa": ("project", "qa"),
+        "project/edits": ("project", "edits"),
+        "docs/generation": ("docs", "generation"),
+    }
+
+    def __init__(self, llm: AgentLlmService) -> None:
+        self._llm = llm
+
+    def classify(self, user_message: str, context: RouterContext, mode: str = "auto") -> RouteDecision:
+        forced = self._from_mode(mode)
+        if forced:
+            return forced
+
+        text = (user_message or "").strip().lower()
+        if text in self._short_confirmations and context.last_routing:
+            return RouteDecision(
+                domain_id=context.last_routing["domain_id"],
+                process_id=context.last_routing["process_id"],
+                confidence=1.0,
+                reason="short_confirmation",
+                use_previous=True,
+            )
+
+        deterministic = self._deterministic_route(text)
+        if deterministic:
+            return deterministic
+
+        llm_decision = self._classify_with_llm(user_message, context)
+        if llm_decision:
+            return llm_decision
+
+        return RouteDecision(
+            domain_id="default",
+            process_id="general",
+            confidence=0.8,
+            reason="default",
+        )
+
+    def _from_mode(self, mode: str) -> RouteDecision | None:
+        mapping = {
+            "project_qa": ("project", "qa"),
+            "project_edits": ("project", "edits"),
+            "docs_generation": ("docs", "generation"),
+            # Legacy aliases kept for API compatibility.
+            "analytics_review": ("project", "qa"),
+            "code_change": ("project", "edits"),
+            "qa": ("default", "general"),
+        }
+        route = mapping.get((mode or "auto").strip().lower())
+        if not route:
+            return None
+        return RouteDecision(
+            domain_id=route[0],
+            process_id=route[1],
+            confidence=1.0,
+            reason=f"mode_override:{mode}",
+        )
+
+    def _classify_with_llm(self, user_message: str, context: RouterContext) -> RouteDecision | None:
+        history = context.message_history[-8:]
+        user_input = json.dumps(
+            {
+                "message": user_message,
+                "history": history,
+                "allowed_routes": list(self._route_mapping.keys()),
+            },
+            ensure_ascii=False,
+        )
+        try:
+            raw = self._llm.generate("router_intent", user_input).strip()
+        except Exception:
+            return None
+
+        payload = self._parse_llm_payload(raw)
+        if not payload:
+            return None
+
+        route = self._route_mapping.get(payload["route"])
+        if not route:
+            return None
+
+        confidence = self._normalize_confidence(payload.get("confidence"))
+        return RouteDecision(
+            domain_id=route[0],
+            process_id=route[1],
+            confidence=confidence,
+            reason=f"llm_router:{payload.get('reason', 'ok')}",
+        )
+
+    def _parse_llm_payload(self, raw: str) -> dict[str, str | float] | None:
+        candidate = self._strip_code_fence(raw.strip())
+        if not candidate:
+            return None
+        try:
+            parsed = json.loads(candidate)
+        except json.JSONDecodeError:
+            return None
+        if not isinstance(parsed, dict):
+            return None
+        route = str(parsed.get("route", "")).strip().lower()
+        if not route:
+            return None
+        return {
+            "route": route,
+            "confidence": parsed.get("confidence"),
+            "reason": str(parsed.get("reason", "ok")).strip().lower(),
+        }
+
+    def _normalize_confidence(self, value: object) -> float:
+        if isinstance(value, (float, int)):
+            return max(0.0, min(1.0, float(value)))
+        return 0.75
+
+    def _strip_code_fence(self, text: str) -> str:
+        if not text.startswith("```"):
+            return text
+        lines = text.splitlines()
+        if len(lines) < 3:
+            return text
+        if lines[-1].strip() != "```":
+            return text
+        return "\n".join(lines[1:-1]).strip()
+
+    def _deterministic_route(self, text: str) -> RouteDecision | None:
+        if self._is_targeted_file_edit_request(text):
+            return RouteDecision(
+                domain_id="project",
+                process_id="edits",
+                confidence=0.97,
+                reason="deterministic_targeted_file_edit",
+            )
+        if self._is_broad_docs_request(text):
+            return RouteDecision(
+                domain_id="docs",
+                process_id="generation",
+                confidence=0.95,
+                reason="deterministic_docs_generation",
+            )
+        return None
+
+    def _is_targeted_file_edit_request(self, text: str) -> bool:
+        if not text:
+            return False
+        edit_markers = (
+            "добавь",
+            "добавить",
+            "измени",
+            "исправь",
+            "обнови",
+            "удали",
+            "замени",
+            "вставь",
+            "в конец",
+            "в начале",
+            "append",
+            "update",
+            "edit",
+            "remove",
+            "replace",
+        )
+        has_edit_marker = any(marker in text for marker in edit_markers)
+        has_file_marker = (
+            "readme" in text
+            or bool(re.search(r"\b[\w.\-/]+\.(md|txt|rst|yaml|yml|json|toml|ini|cfg)\b", text))
+        )
+        return has_edit_marker and has_file_marker
+
+    def _is_broad_docs_request(self, text: str) -> bool:
+        if not text:
+            return False
+        docs_markers = (
+            "подготовь документац",
+            "сгенерируй документац",
+            "создай документац",
+            "опиши документац",
+            "generate documentation",
+            "write documentation",
+            "docs/",
+        )
+        return any(marker in text for marker in docs_markers)

app/modules/agent/engine/router/intents_registry.yaml

@@ -0,0 +1,17 @@
+intents:
+  - domain_id: "default"
+    process_id: "general"
+    description: "General Q&A"
+    priority: 1
+  - domain_id: "project"
+    process_id: "qa"
+    description: "Project-specific Q&A with RAG and confluence context"
+    priority: 2
+  - domain_id: "project"
+    process_id: "edits"
+    description: "Project file edits from user request with conservative changeset generation"
+    priority: 3
+  - domain_id: "docs"
+    process_id: "generation"
+    description: "Documentation generation as changeset"
+    priority: 2

app/modules/agent/engine/router/registry.py

@@ -0,0 +1,46 @@
+from collections.abc import Callable
+from pathlib import Path
+from typing import Any
+
+import yaml
+
+
+class IntentRegistry:
+    def __init__(self, registry_path: Path) -> None:
+        self._registry_path = registry_path
+        self._factories: dict[tuple[str, str], Callable[..., Any]] = {}
+
+    def register(self, domain_id: str, process_id: str, factory: Callable[..., Any]) -> None:
+        self._factories[(domain_id, process_id)] = factory
+
+    def get_factory(self, domain_id: str, process_id: str) -> Callable[..., Any] | None:
+        return self._factories.get((domain_id, process_id))
+
+    def is_valid(self, domain_id: str, process_id: str) -> bool:
+        return self.get_factory(domain_id, process_id) is not None
+
+    def load_intents(self) -> list[dict[str, Any]]:
+        if not self._registry_path.is_file():
+            return []
+        with self._registry_path.open("r", encoding="utf-8") as fh:
+            payload = yaml.safe_load(fh) or {}
+        intents = payload.get("intents")
+        if not isinstance(intents, list):
+            return []
+        output: list[dict[str, Any]] = []
+        for item in intents:
+            if not isinstance(item, dict):
+                continue
+            domain_id = item.get("domain_id")
+            process_id = item.get("process_id")
+            if not isinstance(domain_id, str) or not isinstance(process_id, str):
+                continue
+            output.append(
+                {
+                    "domain_id": domain_id,
+                    "process_id": process_id,
+                    "description": str(item.get("description") or ""),
+                    "priority": int(item.get("priority") or 0),
+                }
+            )
+        return output

app/modules/agent/engine/router/router_service.py

@@ -0,0 +1,62 @@
+from app.modules.agent.engine.router.context_store import RouterContextStore
+from app.modules.agent.engine.router.intent_classifier import IntentClassifier
+from app.modules.agent.engine.router.registry import IntentRegistry
+from app.modules.agent.engine.router.schemas import RouteResolution
+
+
+class RouterService:
+    def __init__(
+        self,
+        registry: IntentRegistry,
+        classifier: IntentClassifier,
+        context_store: RouterContextStore,
+        min_confidence: float = 0.7,
+    ) -> None:
+        self._registry = registry
+        self._classifier = classifier
+        self._ctx = context_store
+        self._min_confidence = min_confidence
+
+    def resolve(self, user_message: str, conversation_key: str, mode: str = "auto") -> RouteResolution:
+        context = self._ctx.get(conversation_key)
+        decision = self._classifier.classify(user_message, context, mode=mode)
+        if decision.confidence < self._min_confidence:
+            return self._fallback("low_confidence")
+        if not self._registry.is_valid(decision.domain_id, decision.process_id):
+            return self._fallback("invalid_route")
+        return RouteResolution(
+            domain_id=decision.domain_id,
+            process_id=decision.process_id,
+            confidence=decision.confidence,
+            reason=decision.reason,
+            fallback_used=False,
+        )
+
+    def persist_context(
+        self,
+        conversation_key: str,
+        *,
+        domain_id: str,
+        process_id: str,
+        user_message: str,
+        assistant_message: str,
+    ) -> None:
+        self._ctx.update(
+            conversation_key,
+            domain_id=domain_id,
+            process_id=process_id,
+            user_message=user_message,
+            assistant_message=assistant_message,
+        )
+
+    def graph_factory(self, domain_id: str, process_id: str):
+        return self._registry.get_factory(domain_id, process_id)
+
+    def _fallback(self, reason: str) -> RouteResolution:
+        return RouteResolution(
+            domain_id="default",
+            process_id="general",
+            confidence=0.0,
+            reason=reason,
+            fallback_used=True,
+        )

app/modules/agent/engine/router/schemas.py

@@ -0,0 +1,27 @@
+from pydantic import BaseModel, Field, field_validator
+
+
+class RouteDecision(BaseModel):
+    domain_id: str = "default"
+    process_id: str = "general"
+    confidence: float = 0.0
+    reason: str = ""
+    use_previous: bool = False
+
+    @field_validator("confidence")
+    @classmethod
+    def clamp_confidence(cls, value: float) -> float:
+        return max(0.0, min(1.0, float(value)))
+
+
+class RouteResolution(BaseModel):
+    domain_id: str
+    process_id: str
+    confidence: float
+    reason: str
+    fallback_used: bool = False
+
+
+class RouterContext(BaseModel):
+    last_routing: dict[str, str] | None = None
+    message_history: list[dict[str, str]] = Field(default_factory=list)

app/modules/agent/llm/__init__.py

@@ -0,0 +1,3 @@
+from app.modules.agent.llm.service import AgentLlmService
+
+__all__ = ["AgentLlmService"]

app/modules/agent/llm/service.py

@@ -0,0 +1,14 @@
+from app.modules.agent.prompt_loader import PromptLoader
+from app.modules.shared.gigachat.client import GigaChatClient
+
+
+class AgentLlmService:
+    def __init__(self, client: GigaChatClient, prompts: PromptLoader) -> None:
+        self._client = client
+        self._prompts = prompts
+
+    def generate(self, prompt_name: str, user_input: str) -> str:
+        system_prompt = self._prompts.load(prompt_name)
+        if not system_prompt:
+            system_prompt = "You are a helpful assistant."
+        return self._client.complete(system_prompt=system_prompt, user_prompt=user_input)

app/modules/agent/module.py

@@ -0,0 +1,44 @@
+from fastapi import APIRouter
+from pydantic import BaseModel, HttpUrl
+
+from app.modules.agent.changeset_validator import ChangeSetValidator
+from app.modules.agent.confluence_service import ConfluenceService
+from app.modules.agent.llm import AgentLlmService
+from app.modules.agent.prompt_loader import PromptLoader
+from app.modules.agent.service import GraphAgentRuntime
+from app.modules.agent.repository import AgentRepository
+from app.modules.contracts import RagRetriever
+from app.modules.shared.gigachat.client import GigaChatClient
+from app.modules.shared.gigachat.settings import GigaChatSettings
+from app.modules.shared.gigachat.token_provider import GigaChatTokenProvider
+
+
+class ConfluenceFetchRequest(BaseModel):
+    url: HttpUrl
+
+
+class AgentModule:
+    def __init__(self, rag_retriever: RagRetriever, agent_repository: AgentRepository) -> None:
+        self.confluence = ConfluenceService()
+        self.changeset_validator = ChangeSetValidator()
+        settings = GigaChatSettings.from_env()
+        token_provider = GigaChatTokenProvider(settings)
+        client = GigaChatClient(settings, token_provider)
+        prompt_loader = PromptLoader()
+        llm = AgentLlmService(client=client, prompts=prompt_loader)
+        self.runtime = GraphAgentRuntime(
+            rag=rag_retriever,
+            confluence=self.confluence,
+            changeset_validator=self.changeset_validator,
+            llm=llm,
+            agent_repository=agent_repository,
+        )
+
+    def internal_router(self) -> APIRouter:
+        router = APIRouter(prefix="/internal/tools/confluence", tags=["internal-confluence"])
+
+        @router.post("/fetch")
+        async def fetch_page(request: ConfluenceFetchRequest) -> dict:
+            return await self.confluence.fetch_page(str(request.url))
+
+        return router

app/modules/agent/prompt_loader.py

@@ -0,0 +1,15 @@
+from pathlib import Path
+import os
+
+
+class PromptLoader:
+    def __init__(self, prompts_dir: Path | None = None) -> None:
+        base = prompts_dir or Path(__file__).resolve().parent / "prompts"
+        env_override = os.getenv("AGENT_PROMPTS_DIR", "").strip()
+        self._dir = Path(env_override) if env_override else base
+
+    def load(self, name: str) -> str:
+        path = self._dir / f"{name}.txt"
+        if not path.is_file():
+            return ""
+        return path.read_text(encoding="utf-8").strip()

app/modules/agent/prompts/docs_detect.txt

@@ -0,0 +1,18 @@
+Ты анализируешь, есть ли в проекте существующая документация, в которую нужно встраиваться.
+
+Оцени входные данные:
+- User request
+- Requested target path
+- Detected documentation candidates (пути и сниппеты)
+
+Критерии EXISTS=yes:
+- Есть хотя бы один релевантный doc-файл, и
+- Он по смыслу подходит под запрос пользователя.
+
+Критерии EXISTS=no:
+- Нет релевантных doc-файлов, или
+- Есть только нерелевантные/пустые заготовки.
+
+Верни строго две строки:
+EXISTS: yes|no
+SUMMARY: <короткое объяснение на 1-2 предложения>

app/modules/agent/prompts/docs_examples/from_scratch_example.md

@@ -0,0 +1,27 @@
+# Feature X Documentation
+
+## Goal
+Describe how Feature X works and how to integrate it safely.
+
+## Architecture Overview
+- Input enters through HTTP endpoint.
+- Request is validated and transformed.
+- Worker executes business logic and persists result.
+
+## Data Flow
+1. Client sends request payload.
+2. Service validates payload.
+3. Domain layer computes output.
+4. Repository stores entities.
+
+## Configuration
+- Required environment variables.
+- Optional tuning parameters.
+
+## Deployment Notes
+- Migration prerequisites.
+- Rollback strategy.
+
+## Risks and Constraints
+- Throughput is bounded by downstream API limits.
+- Partial failures require retry-safe handlers.

app/modules/agent/prompts/docs_examples/incremental_update_example.md

@@ -0,0 +1,21 @@
+# API Client Module
+
+## Purpose
+This document explains how the API client authenticates and retries requests.
+
+## Current Behavior
+- Access token is fetched before outbound request.
+- Retry policy uses exponential backoff for transient failures.
+
+## Recent Increment (v2)
+### Added cache for tokens
+- Token is cached in memory for a short TTL.
+- Cache invalidates on 401 responses.
+
+### Operational impact
+- Reduced auth latency for repetitive calls.
+- Fewer token endpoint requests.
+
+## Limitations
+- Single-process cache only.
+- No distributed cache synchronization.

app/modules/agent/prompts/docs_execution_summary.txt

@@ -0,0 +1,12 @@
+Ты технический писатель и готовишь краткий итог по выполненной задаче документации.
+
+Верни только markdown-текст без JSON и без лишних вступлений.
+Структура ответа:
+1) "Что сделано" — 3-6 коротких пунктов по основным частям пользовательского запроса.
+2) "Измененные файлы" — список файлов с кратким описанием изменения по каждому файлу.
+3) "Ограничения" — добавляй только если в данных есть явные пробелы или ограничения.
+
+Правила:
+- Используй только входные данные.
+- Не выдумывай изменения, которых нет в списке changed files.
+- Пиши коротко и по делу.

app/modules/agent/prompts/docs_generation.txt

@@ -0,0 +1,53 @@
+Ты senior technical writer и пишешь только проектную документацию в markdown.
+
+Твоя задача:
+1) Если strategy=incremental_update, встроиться в существующую документацию и добавить только недостающий инкремент.
+2) Если strategy=from_scratch, создать целостный документ с нуля.
+
+Правила:
+- Опирайся только на входной контекст (request, plan, rag context, current file content, examples bundle).
+- Не выдумывай факты о коде, которых нет во входных данных.
+- Сохраняй стиль существующего документа при incremental_update.
+- Если контекст неполный, отмечай ограничения явно и коротко в отдельном разделе "Ограничения".
+- Структура должна быть логичной и пригодной для реального репозитория.
+- Агент должен спроектировать структуру папок и файлов документации под правила ниже.
+- Документация должна быть разделена минимум на 2 направления:
+  - отдельная папка для описания методов API;
+  - отдельная папка для описания логики/требований.
+- В одном markdown-файле допускается описание только:
+  - одного метода API, или
+  - одного атомарного куска логики/требования.
+- Для описания одного метода API используй структуру:
+  - название метода;
+  - параметры запроса;
+  - параметры ответа;
+  - use case (сценарий последовательности вызова метода);
+  - функциональные требования (если нужны технические детали).
+- Для описания логики используй аналогичный подход:
+  - сценарий;
+  - ссылки из шагов сценария на функциональные требования;
+  - отдельные функциональные требования с техническими деталями.
+- Правила для сценариев:
+  - без объемных шагов;
+  - каждый шаг краткий, не более 2 предложений;
+  - если нужны технические детали, вынеси их из шага в отдельное функциональное требование и дай ссылку на него из шага.
+
+Формат ответа:
+- Верни только JSON-объект без пояснений и без markdown-оберток.
+- Строгий формат:
+{
+  "files": [
+    {
+      "path": "docs/api/<file>.md",
+      "content": "<полное содержимое markdown-файла>",
+      "reason": "<кратко зачем создан/обновлен файл>"
+    },
+    {
+      "path": "docs/logic/<file>.md",
+      "content": "<полное содержимое markdown-файла>",
+      "reason": "<кратко зачем создан/обновлен файл>"
+    }
+  ]
+}
+- Для from_scratch сформируй несколько файлов и обязательно покрой обе папки: `docs/api` и `docs/logic`.
+- Для incremental_update также соблюдай правило атомарности: один файл = один метод API или один атомарный кусок логики/требования.

app/modules/agent/prompts/docs_plan_sections.txt

@@ -0,0 +1,25 @@
+Ты составляешь план изменений документации перед генерацией текста.
+
+Вход:
+- Strategy
+- User request
+- Target path
+- Current target content (для incremental_update)
+- RAG context по коду
+- Examples bundle
+
+Требования к плану:
+- Сначала спроектируй структуру папок и файлов документации под формат:
+  - отдельная папка для API-методов;
+  - отдельная папка для логики/требований;
+  - один файл = один метод API или один атомарный кусок логики/требования.
+- Для API-файлов закладывай структуру: название метода, параметры запроса, параметры ответа, use case, функциональные требования.
+- Для логики закладывай структуру: сценарий, ссылки из шагов на функциональные требования, отдельные функциональные требования.
+- Для сценариев закладывай короткие шаги (не более 2 предложений на шаг), а технические детали выноси в функциональные требования.
+- Дай нумерованный список разделов будущего документа.
+- Для incremental_update отмечай, какие разделы добавить/обновить, не переписывая все целиком.
+- Для from_scratch давай полный каркас документа.
+- Каждый пункт должен включать краткую цель раздела.
+- Если контекст частичный, включи пункт "Ограничения и допущения".
+
+Формат ответа: только план в markdown, без вступлений и без JSON.

app/modules/agent/prompts/docs_self_check.txt

@@ -0,0 +1,22 @@
+Ты валидатор качества документации.
+
+Проверь:
+- Соответствие strategy и user request.
+- Соответствие generated document плану секций.
+- Отсутствие очевидных выдуманных фактов.
+- Практическую применимость текста к проекту.
+- Для incremental_update: минимально необходимый инкремент без лишнего переписывания.
+- Проверку структуры документации:
+  - есть разбиение по папкам `docs/api` и `docs/logic`;
+  - один файл описывает только один API-метод или один атомарный кусок логики;
+  - сценарии состоят из коротких шагов, а технические детали вынесены в функциональные требования.
+
+Если документ приемлем:
+PASS: yes
+FEEDBACK: <коротко, что ок>
+
+Если документ неприемлем:
+PASS: no
+FEEDBACK: <коротко, что исправить в следующей попытке>
+
+Верни ровно две строки в этом формате.

app/modules/agent/prompts/docs_strategy.txt

@@ -0,0 +1,14 @@
+Ты выбираешь стратегию генерации документации.
+
+Доступные стратегии:
+- incremental_update: дописать недостающий инкремент в существующий документ.
+- from_scratch: создать новый документ с нуля.
+
+Правила выбора:
+- Если Existing docs detected=true и это не противоречит user request, выбирай incremental_update.
+- Если Existing docs detected=false, выбирай from_scratch.
+- Если пользователь явно просит "с нуля", приоритет у from_scratch.
+- Если пользователь явно просит "дописать/обновить", приоритет у incremental_update.
+
+Верни строго одну строку:
+STRATEGY: incremental_update|from_scratch

app/modules/agent/prompts/general_answer.txt

@@ -0,0 +1,3 @@
+Ты инженерный AI-ассистент. Ответь по проекту коротко и по делу.
+Если в контексте недостаточно данных, явно укажи пробелы.
+Не выдумывай факты, используй только входные данные.

app/modules/agent/prompts/project_answer.txt

@@ -0,0 +1,9 @@
+Ты инженерный AI-ассистент по текущему проекту.
+
+Сформируй точный ответ на вопрос пользователя, используя только входной контекст.
+Приоритет источников: сначала RAG context, затем Confluence context.
+
+Правила:
+- Не выдумывай факты и явно помечай пробелы в данных.
+- Отвечай структурировано и коротко.
+- Если пользователь просит шаги, дай практичный пошаговый план.

app/modules/agent/prompts/project_edits_apply.txt

@@ -0,0 +1,10 @@
+Ты вносишь правку в один файл по запросу пользователя.
+На вход приходит JSON с request, path, reason, current_content, previous_validation_feedback, rag_context, confluence_context.
+
+Верни только полное итоговое содержимое файла (без JSON).
+
+Критичные правила:
+- Измени только те части, которые нужны по запросу.
+- Не переписывай файл целиком без необходимости.
+- Сохрани структуру, стиль и все нерелевантные разделы без изменений.
+- Если данных недостаточно, внеси минимально безопасную правку и явно отрази ограничение в тексте файла.

app/modules/agent/prompts/project_edits_plan.txt

@@ -0,0 +1,15 @@
+Ты анализируешь запрос на правки файлов проекта (не про написание нового кода).
+На вход приходит JSON с request, requested_path, context_files.
+
+Верни только JSON:
+{
+  "files": [
+    {"path": "<path>", "reason": "<why this file should be edited>"}
+  ]
+}
+
+Правила:
+- Выбирай только файлы, реально нужные для выполнения запроса.
+- Не добавляй лишние файлы.
+- Обычно 1-3 файла, максимум 8.
+- Если в request указан конкретный файл, включи его в первую очередь.

app/modules/agent/prompts/project_edits_self_check.txt

@@ -0,0 +1,12 @@
+Ты валидируешь changeset правок файла.
+На вход приходит JSON с request и changeset (op, path, reason).
+
+Проверь:
+1) изменения соответствуют запросу,
+2) нет лишних нерелевантных правок,
+3) изменены только действительно нужные файлы,
+4) нет косметических правок (пробелы/форматирование без смысла),
+5) нет добавления новых секций/заголовков, если это не запрошено явно.
+
+Верни только JSON:
+{"pass": true|false, "feedback": "<short reason>"}

app/modules/agent/prompts/router_intent.txt

@@ -0,0 +1,23 @@
+Ты классификатор маршрутов агента.
+На вход ты получаешь JSON с полями:
+- message: текущий запрос пользователя
+- history: последние сообщения диалога
+- allowed_routes: допустимые маршруты
+
+Выбери ровно один маршрут из allowed_routes.
+Верни только JSON без markdown и пояснений.
+
+Строгий формат ответа:
+{"route":"<one_of_allowed_routes>","confidence":<number_0_to_1>,"reason":"<short_reason>"}
+
+Правила маршрутизации:
+- project/qa: пользователь задает вопросы про текущий проект, его код, архитектуру, модули, поведение, ограничения.
+- project/edits: пользователь просит внести правки в существующие файлы проекта (контент, конфиги, тексты, шаблоны), без реализации новой кодовой логики.
+- docs/generation: пользователь просит подготовить/обновить документацию, инструкции, markdown-материалы.
+- default/general: остальные случаи, включая общие вопросы и консультации.
+
+Приоритет:
+- Если в запросе есть явная команда правки конкретного файла (например `README.md`, путь к файлу, "добавь в конец файла"), выбирай project/edits.
+- docs/generation выбирай для задач подготовки документации в целом, а не для точечной правки одного файла.
+
+Если есть сомнения, выбирай default/general и confidence <= 0.6.

app/modules/agent/repository.py

@@ -0,0 +1,106 @@
+from __future__ import annotations
+
+import json
+
+from sqlalchemy import text
+
+from app.modules.agent.engine.router.schemas import RouterContext
+from app.modules.shared.db import get_engine
+
+
+class AgentRepository:
+    def ensure_tables(self) -> None:
+        with get_engine().connect() as conn:
+            conn.execute(
+                text(
+                    """
+                    CREATE TABLE IF NOT EXISTS router_context (
+                        conversation_key VARCHAR(64) PRIMARY KEY,
+                        last_domain_id VARCHAR(64) NULL,
+                        last_process_id VARCHAR(64) NULL,
+                        message_history_json TEXT NOT NULL DEFAULT '[]',
+                        updated_at TIMESTAMPTZ DEFAULT CURRENT_TIMESTAMP
+                    )
+                    """
+                )
+            )
+            conn.commit()
+
+    def get_router_context(self, conversation_key: str) -> RouterContext:
+        with get_engine().connect() as conn:
+            row = conn.execute(
+                text(
+                    """
+                    SELECT last_domain_id, last_process_id, message_history_json
+                    FROM router_context
+                    WHERE conversation_key = :key
+                    """
+                ),
+                {"key": conversation_key},
+            ).fetchone()
+
+        if not row:
+            return RouterContext()
+
+        history_raw = row[2] or "[]"
+        try:
+            history = json.loads(history_raw)
+        except json.JSONDecodeError:
+            history = []
+
+        last = None
+        if row[0] and row[1]:
+            last = {"domain_id": str(row[0]), "process_id": str(row[1])}
+
+        clean_history = []
+        for item in history if isinstance(history, list) else []:
+            if not isinstance(item, dict):
+                continue
+            role = str(item.get("role") or "")
+            content = str(item.get("content") or "")
+            if role in {"user", "assistant"} and content:
+                clean_history.append({"role": role, "content": content})
+
+        return RouterContext(last_routing=last, message_history=clean_history)
+
+    def update_router_context(
+        self,
+        conversation_key: str,
+        *,
+        domain_id: str,
+        process_id: str,
+        user_message: str,
+        assistant_message: str,
+        max_history: int,
+    ) -> None:
+        current = self.get_router_context(conversation_key)
+        history = list(current.message_history)
+        if user_message:
+            history.append({"role": "user", "content": user_message})
+        if assistant_message:
+            history.append({"role": "assistant", "content": assistant_message})
+        if max_history > 0:
+            history = history[-max_history:]
+
+        with get_engine().connect() as conn:
+            conn.execute(
+                text(
+                    """
+                    INSERT INTO router_context (
+                        conversation_key, last_domain_id, last_process_id, message_history_json
+                    ) VALUES (:key, :domain, :process, :history)
+                    ON CONFLICT (conversation_key) DO UPDATE SET
+                        last_domain_id = EXCLUDED.last_domain_id,
+                        last_process_id = EXCLUDED.last_process_id,
+                        message_history_json = EXCLUDED.message_history_json,
+                        updated_at = CURRENT_TIMESTAMP
+                    """
+                ),
+                {
+                    "key": conversation_key,
+                    "domain": domain_id,
+                    "process": process_id,
+                    "history": json.dumps(history, ensure_ascii=False),
+                },
+            )
+            conn.commit()

app/modules/agent/service.py

@@ -0,0 +1,296 @@
+from dataclasses import dataclass, field
+from collections.abc import Awaitable, Callable
+import inspect
+import asyncio
+import logging
+import re
+
+from app.modules.agent.engine.router import build_router_service
+from app.modules.agent.engine.graphs.progress_registry import progress_registry
+from app.modules.agent.llm import AgentLlmService
+from app.modules.agent.changeset_validator import ChangeSetValidator
+from app.modules.agent.confluence_service import ConfluenceService
+from app.modules.agent.repository import AgentRepository
+from app.modules.contracts import RagRetriever
+from app.modules.shared.checkpointer import get_checkpointer
+from app.schemas.changeset import ChangeItem
+from app.schemas.chat import TaskResultType
+from app.core.exceptions import AppError
+from app.schemas.common import ModuleName
+
+LOGGER = logging.getLogger(__name__)
+
+
+@dataclass
+class AgentResult:
+    result_type: TaskResultType
+    answer: str | None = None
+    changeset: list[ChangeItem] = field(default_factory=list)
+    meta: dict = field(default_factory=dict)
+
+
+class GraphAgentRuntime:
+    def __init__(
+        self,
+        rag: RagRetriever,
+        confluence: ConfluenceService,
+        changeset_validator: ChangeSetValidator,
+        llm: AgentLlmService,
+        agent_repository: AgentRepository,
+    ) -> None:
+        self._rag = rag
+        self._confluence = confluence
+        self._changeset_validator = changeset_validator
+        self._router = build_router_service(llm, agent_repository)
+        self._checkpointer = None
+
+    async def run(
+        self,
+        *,
+        task_id: str,
+        dialog_session_id: str,
+        rag_session_id: str,
+        mode: str,
+        message: str,
+        attachments: list[dict],
+        files: list[dict],
+        progress_cb: Callable[[str, str, str, dict | None], Awaitable[None] | None] | None = None,
+    ) -> AgentResult:
+        LOGGER.warning(
+            "GraphAgentRuntime.run started: task_id=%s dialog_session_id=%s mode=%s",
+            task_id,
+            dialog_session_id,
+            mode,
+        )
+        await self._emit_progress(progress_cb, "agent.route", "Определяю тип запроса и подбираю граф.", meta={"mode": mode})
+        route = self._router.resolve(message, dialog_session_id, mode=mode)
+        await self._emit_progress(
+            progress_cb,
+            "agent.route.resolved",
+            "Маршрут выбран, готовлю контекст для выполнения.",
+            meta={"domain_id": route.domain_id, "process_id": route.process_id},
+        )
+        graph = self._resolve_graph(route.domain_id, route.process_id)
+        files_map = self._build_files_map(files)
+
+        await self._emit_progress(progress_cb, "agent.rag", "Собираю релевантный контекст из RAG.")
+        rag_ctx = await self._rag.retrieve(rag_session_id, message)
+        await self._emit_progress(progress_cb, "agent.attachments", "Обрабатываю дополнительные вложения.")
+        conf_pages = await self._fetch_confluence_pages(attachments)
+        state = {
+            "task_id": task_id,
+            "project_id": rag_session_id,
+            "message": message,
+            "progress_key": task_id,
+            "rag_context": self._format_rag(rag_ctx),
+            "confluence_context": self._format_confluence(conf_pages),
+            "files_map": files_map,
+        }
+
+        await self._emit_progress(progress_cb, "agent.graph", "Запускаю выполнение графа.")
+        if progress_cb is not None:
+            progress_registry.register(task_id, progress_cb)
+        try:
+            result = await asyncio.to_thread(
+                self._invoke_graph,
+                graph,
+                state,
+                dialog_session_id,
+            )
+        finally:
+            if progress_cb is not None:
+                progress_registry.unregister(task_id)
+        await self._emit_progress(progress_cb, "agent.graph.done", "Граф завершил обработку результата.")
+        answer = result.get("answer")
+        changeset = result.get("changeset") or []
+        if changeset:
+            await self._emit_progress(progress_cb, "agent.changeset", "Проверяю и валидирую предложенные изменения.")
+            changeset = self._enrich_changeset_hashes(changeset, files_map)
+            changeset = self._sanitize_changeset(changeset, files_map)
+            if not changeset:
+                final_answer = (answer or "").strip() or "Предложенные правки были отброшены как нерелевантные или косметические."
+                await self._emit_progress(progress_cb, "agent.answer", "После фильтрации правок формирую ответ без changeset.")
+                self._router.persist_context(
+                    dialog_session_id,
+                    domain_id=route.domain_id,
+                    process_id=route.process_id,
+                    user_message=message,
+                    assistant_message=final_answer,
+                )
+                return AgentResult(
+                    result_type=TaskResultType.ANSWER,
+                    answer=final_answer,
+                    meta={
+                        "route": route.model_dump(),
+                        "used_rag": True,
+                        "used_confluence": bool(conf_pages),
+                        "changeset_filtered_out": True,
+                    },
+                )
+            validated = self._changeset_validator.validate(task_id, changeset)
+            final_answer = (answer or "").strip() or None
+            self._router.persist_context(
+                dialog_session_id,
+                domain_id=route.domain_id,
+                process_id=route.process_id,
+                user_message=message,
+                assistant_message=final_answer or f"changeset:{len(validated)}",
+            )
+            final = AgentResult(
+                result_type=TaskResultType.CHANGESET,
+                answer=final_answer,
+                changeset=validated,
+                meta={"route": route.model_dump(), "used_rag": True, "used_confluence": bool(conf_pages)},
+            )
+            LOGGER.warning(
+                "GraphAgentRuntime.run completed: task_id=%s route=%s/%s result_type=%s changeset_items=%s",
+                task_id,
+                route.domain_id,
+                route.process_id,
+                final.result_type.value,
+                len(final.changeset),
+            )
+            return final
+
+        final_answer = answer or ""
+        await self._emit_progress(progress_cb, "agent.answer", "Формирую финальный ответ.")
+        self._router.persist_context(
+            dialog_session_id,
+            domain_id=route.domain_id,
+            process_id=route.process_id,
+            user_message=message,
+            assistant_message=final_answer,
+        )
+        final = AgentResult(
+            result_type=TaskResultType.ANSWER,
+            answer=final_answer,
+            meta={"route": route.model_dump(), "used_rag": True, "used_confluence": bool(conf_pages)},
+        )
+        LOGGER.warning(
+            "GraphAgentRuntime.run completed: task_id=%s route=%s/%s result_type=%s answer_len=%s",
+            task_id,
+            route.domain_id,
+            route.process_id,
+            final.result_type.value,
+            len(final.answer or ""),
+        )
+        return final
+
+    async def _emit_progress(
+        self,
+        progress_cb: Callable[[str, str, str, dict | None], Awaitable[None] | None] | None,
+        stage: str,
+        message: str,
+        *,
+        kind: str = "task_progress",
+        meta: dict | None = None,
+    ) -> None:
+        if progress_cb is None:
+            return
+        result = progress_cb(stage, message, kind, meta or {})
+        if inspect.isawaitable(result):
+            await result
+
+    def _resolve_graph(self, domain_id: str, process_id: str):
+        if self._checkpointer is None:
+            self._checkpointer = get_checkpointer()
+        factory = self._router.graph_factory(domain_id, process_id)
+        if factory is None:
+            factory = self._router.graph_factory("default", "general")
+        if factory is None:
+            raise RuntimeError("No graph factory configured")
+        LOGGER.warning("_resolve_graph resolved: domain_id=%s process_id=%s", domain_id, process_id)
+        return factory(self._checkpointer)
+
+    def _invoke_graph(self, graph, state: dict, dialog_session_id: str):
+        return graph.invoke(
+            state,
+            config={"configurable": {"thread_id": dialog_session_id}},
+        )
+
+    async def _fetch_confluence_pages(self, attachments: list[dict]) -> list[dict]:
+        pages: list[dict] = []
+        for item in attachments:
+            if item.get("type") == "confluence_url":
+                pages.append(await self._confluence.fetch_page(item["url"]))
+        LOGGER.warning("_fetch_confluence_pages completed: pages=%s", len(pages))
+        return pages
+
+    def _format_rag(self, items: list[dict]) -> str:
+        return "\n".join(str(x.get("content", "")) for x in items)
+
+    def _format_confluence(self, pages: list[dict]) -> str:
+        return "\n".join(str(x.get("content_markdown", "")) for x in pages)
+
+    def _build_files_map(self, files: list[dict]) -> dict[str, dict]:
+        output: dict[str, dict] = {}
+        for item in files:
+            path = str(item.get("path", "")).replace("\\", "/").strip()
+            if not path:
+                continue
+            output[path] = {
+                "path": path,
+                "content": str(item.get("content", "")),
+                "content_hash": str(item.get("content_hash", "")),
+            }
+        LOGGER.warning("_build_files_map completed: files=%s", len(output))
+        return output
+
+    def _lookup_file(self, files_map: dict[str, dict], path: str) -> dict | None:
+        normalized = (path or "").replace("\\", "/")
+        if normalized in files_map:
+            return files_map[normalized]
+        low = normalized.lower()
+        for key, value in files_map.items():
+            if key.lower() == low:
+                return value
+        return None
+
+    def _enrich_changeset_hashes(self, items: list[ChangeItem], files_map: dict[str, dict]) -> list[ChangeItem]:
+        enriched: list[ChangeItem] = []
+        for item in items:
+            if item.op.value == "update":
+                source = self._lookup_file(files_map, item.path)
+                if not source or not source.get("content_hash"):
+                    raise AppError(
+                        "missing_base_hash",
+                        f"Cannot build update for {item.path}: no file hash in request context",
+                        ModuleName.AGENT,
+                    )
+                item.base_hash = str(source["content_hash"])
+            enriched.append(item)
+        LOGGER.warning("_enrich_changeset_hashes completed: items=%s", len(enriched))
+        return enriched
+
+    def _sanitize_changeset(self, items: list[ChangeItem], files_map: dict[str, dict]) -> list[ChangeItem]:
+        sanitized: list[ChangeItem] = []
+        dropped_noop = 0
+        dropped_ws = 0
+        for item in items:
+            if item.op.value != "update":
+                sanitized.append(item)
+                continue
+            source = self._lookup_file(files_map, item.path)
+            if not source:
+                sanitized.append(item)
+                continue
+            original = str(source.get("content", ""))
+            proposed = item.proposed_content or ""
+            if proposed == original:
+                dropped_noop += 1
+                continue
+            if self._collapse_whitespace(proposed) == self._collapse_whitespace(original):
+                dropped_ws += 1
+                continue
+            sanitized.append(item)
+        if dropped_noop or dropped_ws:
+            LOGGER.warning(
+                "_sanitize_changeset dropped items: noop=%s whitespace_only=%s kept=%s",
+                dropped_noop,
+                dropped_ws,
+                len(sanitized),
+            )
+        return sanitized
+
+    def _collapse_whitespace(self, text: str) -> str:
+        return re.sub(r"\s+", " ", (text or "").strip())

app/modules/application.py

@@ -0,0 +1,31 @@
+from app.modules.agent.module import AgentModule
+from app.modules.agent.repository import AgentRepository
+from app.modules.chat.repository import ChatRepository
+from app.modules.chat.module import ChatModule
+from app.modules.rag.repository import RagRepository
+from app.modules.rag.module import RagModule
+from app.modules.shared.bootstrap import bootstrap_database
+from app.modules.shared.event_bus import EventBus
+from app.modules.shared.retry_executor import RetryExecutor
+
+
+class ModularApplication:
+    def __init__(self) -> None:
+        self.events = EventBus()
+        self.retry = RetryExecutor()
+        self.rag_repository = RagRepository()
+        self.chat_repository = ChatRepository()
+        self.agent_repository = AgentRepository()
+
+        self.rag = RagModule(event_bus=self.events, retry=self.retry, repository=self.rag_repository)
+        self.agent = AgentModule(rag_retriever=self.rag.rag, agent_repository=self.agent_repository)
+        self.chat = ChatModule(
+            agent_runner=self.agent.runtime,
+            event_bus=self.events,
+            retry=self.retry,
+            rag_sessions=self.rag.sessions,
+            repository=self.chat_repository,
+        )
+
+    def startup(self) -> None:
+        bootstrap_database(self.rag_repository, self.chat_repository, self.agent_repository)