langdeep 2.0.1

LangChain + LangGraph Agent Workflow Framework

LangDeep

An annotation-driven multi-agent workflow framework designed for enterprise scenarios

Language: English | 简体中文

Project Status: Beta - v2.0.1

The core API has entered its second major version and is suitable for controlled internal enterprise pilots. Critical production deployments should still complete provider, secret, audit, sandbox, persistence, and operations reviews before release.

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✨ Why LangDeep?

LangDeep is built on LangChain and LangGraph. It provides a registry-based and decorator-first framework for multi-agent workflows. You can register components through declarative APIs such as @model, @regist_tool, and @agent, then let FlowOrchestrator coordinate Supervisor routing, Planner generation, Executor execution, and Aggregator merging.

The focus of v2.0.0 is robustness: runtime diagnostics, stronger health checks, public model configuration snapshots, and stricter test and coverage gates. These capabilities make LangDeep easier to validate before service startup and safer to operate in CI pipelines.

• 🎨 Annotation-driven registration: register components with @model, @provider, @regist_tool, @agent, @memory, @cache, @im_channel, and @sandbox.
• 🧠 Supervisor routing: use keyword routing first, then LLM tool-call routing for more complex requests.
• 📋 Planning and execution: support LLM-generated plans, explicit workflow_plan, dependency ordering, concurrent execution, and retries.
• 🔌 Provider extensibility: built-in OpenAI, Anthropic, Azure OpenAI, Ollama, Vertex AI, Google GenAI, DeepSeek, and mock providers, with custom provider support.
• 🧩 Enterprise extension points: replace RoutingStrategy, PlanGenerator, TaskRunner, and ResultMerger.
• 🗄️ Storage and cache abstractions: register pluggable backends with @memory and @cache; LLM response cache is disabled by default and must be enabled explicitly.
• 💬 IM integration layer: provide @im_channel, WebhookReceiver, IMMessage, and PlatformType for messaging platform adapters.
• 🔒 Sandbox execution: built-in SubprocessSandbox and @sandbox; suitable for trusted or semi-trusted local tasks, not a complete boundary for untrusted code.
• 🔐 Secret management: use SecretsManager and EnvSecretsProvider to avoid hard-coded secrets in application code.
• 🔄 Process lifecycle: use ProcessManager and ProcessState as infrastructure for long-running workflows.
• 📊 Observability: HealthChecker, MetricsCollector, and FlowOrchestrator.health() expose runtime status and basic metrics.
• ✅ Startup diagnostics: validate_runtime() checks models, providers, agent/tool references, and optional agent instantiation to catch configuration drift early.

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📦 Installation

Install from PyPI

pip install langdeep

Install from source

git clone https://github.com/ZChunzi/LangDeep.git
cd LangDeep
pip install -e .

Optional dependencies

# Optional model provider dependencies
pip install -e ".[all]"

# Individual provider dependency groups are also available:
# .[anthropic], .[google-genai], .[vertexai], .[ollama]

# Test, coverage, lint, and build tooling
pip install -e ".[dev]"

---

🚀 Quick Start: No External API Key Required

The example below uses the built-in mock provider and can run locally without external credentials. Note: functions wrapped by @regist_tool must have a docstring because LangChain validates tool descriptions when creating tools.

from langchain_core.messages import AIMessage, HumanMessage

from langdeep import FlowOrchestrator, agent, model, regist_tool, validate_runtime


@model(name="mock_chat", provider="mock", model_name="mock-chat")
def mock_chat():
    pass


@regist_tool(name="get_weather", description="Return mock weather.")
def get_weather(city: str) -> str:
    """Return mock weather."""
    return f"{city}: sunny, 25C"


@agent(
    name="weather_agent",
    description="Answer simple weather questions.",
    routing_keywords=["weather", "天气"],
    model="mock_chat",
    tools=["get_weather"],
)
def weather_agent():
    class WeatherAgent:
        def invoke(self, state):
            question = ""
            for message in reversed(state.get("messages", [])):
                if isinstance(message, HumanMessage):
                    question = str(message.content)
                    break
            answer = f"Question: {question}\n{get_weather('Beijing')}"
            return {"messages": [AIMessage(content=answer)]}

        async def ainvoke(self, state):
            return self.invoke(state)

    return WeatherAgent()


# Recommended before enterprise service startup
validate_runtime(instantiate_agents=True).raise_for_errors()

orchestrator = FlowOrchestrator(
    supervisor_model="mock_chat",
    enable_checkpoint=False,
)

result = orchestrator.invoke("How is the weather in Beijing?")

for message in reversed(result["messages"]):
    if isinstance(message, AIMessage) and message.content:
        print(message.content)
        break

---

🧠 Core Concepts

Registries and decorators

LangDeep runtime is centered around a set of in-process singleton registries. Decorators write metadata into registries at module import time. FlowOrchestrator reads the current registry state during initialization and builds the LangGraph workflow from it.

Decorator / API	Registered content	Primary purpose
@model	ModelConfig	Register model configuration; model instances are lazy-loaded
@provider / register_provider	Provider factory	Add or override model providers
@regist_tool	LangChain Tool	Register tools available to agents
@agent	Agent factory and metadata	Register routable and executable agents
@memory	Memory backend factory	Register session memory backends
@cache	Cache backend factory	Register cache backends
@im_channel	IM handler	Register messaging platform handlers
@sandbox	Sandbox backend	Register code execution backends

FlowOrchestrator

FlowOrchestrator is the main entry point. The public methods in the current version are:

• invoke(user_input, context=None, workflow_plan=None, template_name=None)
• ainvoke(user_input, context=None, workflow_plan=None, template_name=None)
• astream(user_input, context=None, **kwargs)
• health()
• get_metrics()
• graph

v2.0.0 does not expose a public run() method. Use invoke() instead.

Agent runtime contract

The factory registered by @agent should return a runnable object with at least:

def invoke(self, state): ...

For async execution compatibility, it is recommended to also provide:

async def ainvoke(self, state): ...

If auto_build=True is set and the factory returns None, LangDeep will try to build the agent through the registered Agent Builder.

---

🏗️ Architecture

Runtime flow

flowchart LR
    User["User Request"] --> Orchestrator["FlowOrchestrator"]
    Orchestrator --> Supervisor["Supervisor Routing Node"]

    Supervisor -->|"Keyword match"| AgentNode["Target Agent Node"]
    Supervisor -->|"LLM selects agent"| AgentNode
    Supervisor -->|"Complex task"| Planner["Planner creates workflow_plan"]
    Supervisor -->|"Custom route"| CustomNode["Custom Node"]

    Planner --> Executor["Executor runs plan"]
    Executor -->|"Dependencies ready / concurrency policy"| AgentA["Agent A"]
    Executor --> AgentB["Agent B"]
    Executor --> AgentN["Agent N"]

    AgentNode --> Aggregator["Aggregator merges results"]
    CustomNode --> Aggregator
    AgentA --> Aggregator
    AgentB --> Aggregator
    AgentN --> Aggregator

    Aggregator --> FinalState["Final State"]
    FinalState --> User

Registries and infrastructure

flowchart TB
    subgraph App["Application Code"]
        Models["@model"]
        Providers["@provider / register_provider"]
        Tools["@regist_tool"]
        Agents["@agent"]
        Memory["@memory"]
        Cache["@cache"]
        IM["@im_channel"]
        Sandbox["@sandbox"]
    end

    subgraph Registries["LangDeep Registries"]
        ModelRegistry["model_registry"]
        ProviderRegistry["provider_registry"]
        ToolRegistry["tool_registry"]
        AgentRegistry["agent_registry"]
        MemoryRegistry["memory_registry"]
        CacheRegistry["cache_registry"]
        IMRegistry["im_channel_registry"]
        SandboxRegistry["sandbox_registry"]
    end

    subgraph Runtime["Runtime Core"]
        Orchestrator["FlowOrchestrator"]
        PromptLoader["MarkdownPromptLoader"]
        ExecutionPolicy["ExecutionPolicy"]
        Diagnostics["validate_runtime"]
        Health["HealthChecker"]
        Metrics["MetricsCollector"]
        Secrets["SecretsManager"]
        Process["ProcessManager"]
    end

    Models --> ModelRegistry
    Providers --> ProviderRegistry
    Tools --> ToolRegistry
    Agents --> AgentRegistry
    Memory --> MemoryRegistry
    Cache --> CacheRegistry
    IM --> IMRegistry
    Sandbox --> SandboxRegistry

    ModelRegistry --> Orchestrator
    ProviderRegistry --> Orchestrator
    ToolRegistry --> Orchestrator
    AgentRegistry --> Orchestrator
    PromptLoader --> Orchestrator
    ExecutionPolicy --> Orchestrator

    ModelRegistry --> Diagnostics
    ToolRegistry --> Diagnostics
    AgentRegistry --> Diagnostics

    ModelRegistry --> Health
    MemoryRegistry --> Health
    CacheRegistry --> Health
    ToolRegistry --> Health
    AgentRegistry --> Health

    Secrets -.-> Orchestrator
    Process -.-> Orchestrator
    Metrics -.-> Orchestrator

Planning and execution sequence

sequenceDiagram
    participant U as User
    participant O as FlowOrchestrator
    participant S as Supervisor
    participant P as Planner
    participant E as Executor
    participant A as Agent(s)
    participant G as Aggregator

    U->>O: invoke(user_input)
    O->>S: route(messages, available_agents)
    alt direct agent route
        S->>A: run selected agent
        A-->>G: agent result
    else planner route
        S->>P: create or reuse workflow_plan
        P-->>E: task list
        E->>A: execute tasks by dependency and policy
        A-->>E: task results
        E-->>G: agent_results
    end
    G-->>O: final messages/state
    O-->>U: Dict[str, Any]

---

🔌 Models and Providers

Use a built-in provider

import os
from langdeep import model


@model(
    name="gpt4o",
    provider="openai",
    model_name="gpt-4o",
    api_key=os.getenv("OPENAI_API_KEY"),
    temperature=0.2,
)
def gpt4o():
    pass

Built-in provider names:

• openai
• anthropic
• azure_openai
• ollama
• vertexai
• google_genai
• deepseek
• mock

Register a custom provider

A provider factory receives ModelConfig and returns a LangChain BaseChatModel.

from langchain_core.language_models import BaseChatModel
from langdeep import ModelConfig, provider, register_provider


@provider(name="custom_provider")
def create_custom_provider(config: ModelConfig) -> BaseChatModel:
    ...


def create_other_provider(config: ModelConfig) -> BaseChatModel:
    ...


register_provider("other_provider", create_other_provider)

Note: the current signature of register_provider() is register_provider(name, factory). It is not a configuration-style function. Provider settings such as base_url and api_key should be passed through @model(..., base_url=..., api_key=...) or ModelConfig.

---

🧩 Agents, Tools, and Auto-Building

Register a tool

from langdeep import regist_tool


@regist_tool(
    name="search_docs",
    description="Search internal documentation.",
    category="knowledge",
    tags=["internal", "docs"],
)
def search_docs(query: str) -> str:
    """Search internal documentation."""
    return f"results for {query}"

Register an agent

from langchain_core.messages import AIMessage
from langdeep import agent


@agent(
    name="support_agent",
    description="Handle customer support questions.",
    capabilities=["support"],
    routing_keywords=["support", "help", "支持"],
    model="gpt4o",
    tools=["search_docs"],
)
def support_agent():
    class SupportAgent:
        def invoke(self, state):
            return {"messages": [AIMessage(content="Support response")]}

        async def ainvoke(self, state):
            return self.invoke(state)

    return SupportAgent()

ReAct auto-building

@agent(
    name="react_support",
    description="Auto-built ReAct support agent.",
    model="gpt4o",
    tools=["search_docs"],
    auto_build=True,
    agent_type="react",
)
def react_support():
    pass

---

📋 Workflow Plans and Execution Policy

Explicit workflow_plan

plan = [
    {"id": "collect", "agent": "research_agent", "depends_on": [], "status": "pending"},
    {"id": "write", "agent": "writer_agent", "depends_on": ["collect"], "status": "pending"},
]

result = orchestrator.invoke("Create a market briefing", workflow_plan=plan)

Plan validation

from langdeep import validate_workflow_plan


validate_workflow_plan(
    plan,
    available_agents=["research_agent", "writer_agent"],
    available_tools=[],
)

Execution policy

from langdeep import ExecutionPolicy


policy = ExecutionPolicy(
    strategy="priority_queue",
    max_concurrency=3,
    max_retries=3,
    retry_backoff="exponential",
    timeout_seconds=30,
    fail_fast=False,
)

Supported strategies:

• gather: run ready tasks concurrently, limited by max_concurrency.
• sequential: run tasks sequentially according to dependencies.
• priority_queue: run higher-priority tasks first.

---

✅ Enterprise Diagnostics and Health Checks

Startup diagnostics

from langdeep import validate_runtime


diagnostics = validate_runtime(instantiate_agents=True)
diagnostics.raise_for_errors()

validate_runtime() checks:

• Whether model registry names, model names, and providers are valid.
• Whether temperature and max_tokens are reasonable.
• Whether agent metadata names match registry keys.
• Whether models and tools referenced by agents are registered.
• Whether agents can be instantiated when instantiate_agents=True.
• Whether tool metadata is consistent and descriptions are present.

The result is serializable:

{
    "ok": True,
    "error_count": 0,
    "warning_count": 0,
    "issues": [],
}

Health checks and metrics

health = orchestrator.health()
metrics = orchestrator.get_metrics()

HealthChecker probes model, memory, and cache backends, and reports Agent/Tool registry consistency. MetricsCollector provides counters, gauges, histograms, and snapshot reads.

---

🗄️ Memory, Cache, and Prompt

Memory

from langdeep import memory


@memory(name="session_memory", description="In-process session memory")
def session_memory():
    pass

If the factory returns None, the built-in InMemoryBackend is used.

Cache

from langdeep import cache


@cache(name="llm_cache", ttl=300, max_entries=1024)
def llm_cache():
    pass

If the factory returns None, the built-in MemoryCache is used. LLM response cache is disabled by default and must be enabled explicitly:

from langdeep.core.registry.model_registry import model_registry


model_registry.enable_response_cache(ttl=300, max_entries=1024)

Prompt

MarkdownPromptLoader supports built-in prompts and external prompt_dir overrides. Prompts are cached, and the cache can be cleared through the loader's reload() method.

orchestrator = FlowOrchestrator(
    supervisor_model="gpt4o",
    prompt_dir="./prompts",
)

---

🔒 Security and Production Boundaries

LangDeep provides enterprise-oriented infrastructure, but it does not replace production governance.

• SubprocessSandbox is not a complete security boundary for untrusted code. Production systems should use container, permission, network, and resource isolation.
• API keys should be injected through SecretsManager, environment variables, or platform secret systems. Do not hard-code them in source code.
• When integrating IM platforms, verify signatures, retry policies, permission boundaries, and audit requirements in the application layer.
• When accepting external workflow_plan input, run validate_workflow_plan() first.
• At service startup, run validate_runtime(instantiate_agents=True).raise_for_errors().
• For critical paths, record user request, routing target, workflow plan, tool usage, failure reason, and trace id.

---

🧪 Tests and Quality Gates

# Static checks
python -m ruff check src tests

# Standard pytest with coverage
python -m pytest --cov --cov-report=term-missing --cov-report=xml

# Project custom test runner
python tests/run_all.py

# Compile check
python -m compileall -q src tests

# Build package
python -m build --no-isolation

The current coverage threshold is configured as 90% in pyproject.toml.

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📚 More Documentation

Full developer guide:

• docs/developer-guide.md

Other languages:

• 简体中文

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📌 v2.0.1 Release Notes

v2.0.1 is a maintenance release focused on new tool policy infrastructure and improved packaging.

• Added PolicyAwareTool, ToolAuditLog, ToolExecutionPolicy, and ToolExecutionRecord for fine-grained tool governance.
• Added packaging tests (test_packaging.py) to verify build and import integrity.
• Updated documentation with tool policy usage guides and packaging verification steps.
• Updated project status to Beta - v2.0.1.

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📌 v2.0.0 Release Notes

v2.0.0 highlights:

• Added validate_runtime(), RuntimeValidator, and RuntimeDiagnostics.
• Enhanced HealthChecker with memory/cache backend probes and Agent/Tool registry consistency reports.
• Added public model configuration snapshot APIs: ModelRegistry.get_config() and ModelRegistry.list_model_configs().
• Updated package version to 2.0.0 and project status to Beta.
• Added diagnostics tests while keeping the standard pytest suite and custom test runner passing.

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📄 License

LangDeep is released under the MIT License.