helen-lang 1.25.2

Helen — A Prompt-first Agent Programming Language for AI-native applications. Build multi-agent systems with built-in LLM primitives, bilingual support (English/Chinese), and automatic context management. Alternative to LangChain, CrewAI, AutoGen for agent orchestration.

Helen — A Prompt-First Programming Language for AI Agents

Helen is an AI-native DSL (Domain-Specific Language) designed specifically for AI Agent development. It fuses deterministic constructs (variables, functions, control flow) with first-class LLM primitives (llm act, llm if) into a single language.

✨ Why Helen?

• Prompt-first: agent is a first-class citizen — agents are language constructs, not library patterns
• 287 built-in stdlib functions: 287 bilingual (Chinese/English) functions covering the full AI application development pipeline
• 5-layer graduated compression + working memory: Long-conversation agents automatically manage context, no manual tuning required
• Transcript SSOT: Conversation records persisted as SQLite/JSONL, supporting audit and replay
• Multi-agent concurrency: spawn + Channel message queues, with built-in mailbox_select multi-select
• Python bidirectional integration: Helen → Python FFI + Python → Helen Bridge
• 89 bilingual keywords: 44.5 English + 44.5 Chinese, native Chinese programming support

🎯 When to Use Helen?

✅ Choose Helen if you need:

• Agents as language constructs: Not library patterns — agents are first-class citizens
• Bilingual support: Native Chinese and English programming, lowering the learning curve for teams
• Automatic context management: Long-conversation agents with automatic compression, no manual tuning
• Complete DSL: Variables, functions, control flow + LLM primitives fused into one language
• Multi-agent concurrency: spawn + Channel message queues for fine-grained concurrency control
• Session persistence: Built-in TranscriptStore with audit and replay support
• Excellent debugging experience: REPL + Transcript + Observability

🔄 Helen vs Other Frameworks

Scenario	Recommended	Reason
Rapid prototyping	Helen	Concise syntax, automatic context management
Complex RAG pipelines	LangChain	Large number of pre-built components
Multi-agent team collaboration	CrewAI / Helen	Helen provides finer-grained concurrency control
Chinese-English bilingual apps	Helen	Native bilingual support
Long-conversation agents	Helen	5-layer graduated compression + working memory
Session audit & replay	Helen	Built-in TranscriptStore SSOT

📖 Detailed comparison: Helen vs LangChain vs CrewAI vs AutoGen

🚀 Quick Start

Installation

pip install helen-lang

Hello Helen

Create hello.helen:

agent Greeter(name: str) {
    description "A friendly greeter"
    prompt "Greet {{name}} warmly in one sentence"

    main {
        return llm act "Greet {{name}} warmly"
    }
}

main {
    let g = Greeter("World")
    print(g)
}

Run:

helen hello.helen
# Hello, World! It's wonderful to meet you!

REPL Interaction

helen repl
> let x = 1 + 2
> print(x)
3
> :help

Python Bridge Usage

Helen Agents can be used directly in Python via the Python Bridge, just like ordinary Python classes:

1. Create a Helen Agent file translator.helen:

agent TranslatorAgent(text: str, target: str) {
    description "Translate text to the target language"
    prompt "Translate '{{text}}' to {{target}}"

    main {
        return llm act "Translate '{{text}}' to {{target}}"
    }
}

2. Import and call in Python:

from translator import TranslatorAgent

agent = TranslatorAgent()
result = agent("Hello", "French")
print(result)  # "Bonjour"

Python Integration Features

• Direct .helen file import: from my_agents import TranslatorAgent
• Type hint support: IDE auto-completion for Helen Agents
• Async calls: await agent.async_call(...)
• Decorator pattern: @helen_agent decorates Python functions
• Parameter validation: Helen automatically validates agent parameter types

from helen.python_bridge import helen_agent

@helen_agent("translator.helen", "TranslatorAgent")
def translate(text: str, target: str) -> str:
    pass

result = translate("Hello", "French")

🎯 Use Cases

AI Agent Development

from agents import ResearchAgent, AnalysisAgent

# Research phase
researcher = ResearchAgent()
findings = researcher("quantum computing", depth="deep")

# Analysis phase
analyzer = AnalysisAgent()
insights = analyzer(findings)

Multi-Agent Collaboration

from workflow import PlannerAgent, ExecutorAgent, ReviewerAgent

planner = PlannerAgent()
plan = planner("Build a web app")

executor = ExecutorAgent()
result = executor(plan)

reviewer = ReviewerAgent()
feedback = reviewer(result)

LLM Applications

from llm_agents import ChatBot, Summarizer, Translator

chatbot = ChatBot()
response = chatbot("What is AI?")

summarizer = Summarizer()
summary = summarizer(long_text)

translator = Translator()
translated = translator(summary, target="Chinese")

🛠️ API Reference

HelenAgentWrapper

class HelenAgentWrapper:
    def __init__(self, agent_name: str, helen_file: str, interpreter=None)

    def __call__(self, *args, **kwargs) -> Any
        """Call agent"""

    async def async_call(self, *args, **kwargs) -> Any
        """Async call agent"""

Decorators

@helen_agent(helen_file: str, agent_name: str = None)
def my_function(...):
    """Wrap function as a Helen agent call"""

@helen_module(helen_file: str)
class MyModule:
    """Wrap class as a collection of Helen agents"""

Import Hook

from helen.python_bridge import install_import_hook

# Auto-install (default)
install_import_hook()

# Manual uninstall
from helen.python_bridge import uninstall_import_hook
uninstall_import_hook()

📖 More Examples

Batch Processing

from agents import TranslatorAgent

agent = TranslatorAgent()
texts = ["Hello", "World", "AI"]

results = [agent(text, target="French") for text in texts]
print(results)  # ["Bonjour", "Monde", "IA"]

Error Handling

from agents import TranslatorAgent

agent = TranslatorAgent()

try:
    result = agent("Hello", target="French")
except TypeError as e:
    print(f"Parameter error: {e}")
except Exception as e:
    print(f"Execution error: {e}")

Shared Interpreter

from helen.interpreter import Interpreter
from helen.python_bridge import HelenAgentWrapper

# Create a shared interpreter
interpreter = Interpreter()

# Multiple agents share the same interpreter
agent1 = HelenAgentWrapper("Agent1", "agents.helen", interpreter)
agent2 = HelenAgentWrapper("Agent2", "agents.helen", interpreter)

🤝 Contributing

Contributions welcome! See CONTRIBUTING.md for details.

📄 License

MIT License

🔗 Links

• Documentation: https://helen.readthedocs.io
• GitHub: https://github.com/hahalee000000/helen
• PyPI: https://pypi.org/project/helen-lang

📚 Documentation

• Wiki Documentation - Complete technical documentation
• Tutorial - Learn Helen from scratch
• Python Bridge Tutorial - Python integration guide
• Context Management - Intelligent context handling (v1.20)
• Skill System - Skill loading and usage

🆕 Version History

v1.20 - Transcript Session Scope

• Transcripts are isolated per application in .helen/sessions/ by default (REPL scenario opts in to global)
• session_scope configuration: auto | global | project
• HELEN_SESSION_DIR environment variable to force a specific path
• New get_session_dir() / set_session_dir() stdlib functions

v1.19 - Context Management API Completion

• Complete 6-dimension API (Inspection / Working Memory / Fine-grained Mutation / Runtime Config / Query / Multi-agent Transfer)
• 24 new stdlib functions: context_stats / context_usage / pin_message / working_memory_* / export_context, etc.
• Message.pinned: bool field — pinned messages are immune to all 5 compression layers
• Internalized classify_message

v1.18 - spawn Concurrency Primitives

• spawn Agent(...) returns a Channel, replacing async/await/detach
• Channel message queue: send/receive/try_receive/cancel/close
• mailbox_select() multi-select primitive
• Streaming interrupt: on_chunk callback returns false to stop streaming; Ctrl+C interrupt

v1.16 - TranscriptStore SSOT

• Conversation history SSOT with SQLite/JSONL dual backends
• LRU cache (10K messages ~10MB)
• UUID addressing, O(1) lookups
• Non-destructive compression (BoundaryMarker audit trail)

v1.15 - Context Management Enhancement

• Working Memory
• Graduated Compression
• Cache-Aware Compression
• Three-Channel Context
• Agent context configuration

v1.14 - LLM Streaming Support

• llm act supports streaming output (on_chunk/on_complete callbacks)
• llm stream removed (functionality merged into llm act)

v1.13 - Python Bridge

• Direct Python import and usage of Helen Agents
• Bidirectional FFI (Helen ↔ Python)

v1.12 - Agent Isolation Enhancement

• Agent isolation levels (@open, @strict, @sandbox)
• Shared store and channel
• ReadOnlyView
• Closure value capture

v1.10 - Core Features

• Agent scope isolation
• Short-circuit evaluation
• Subscript/field assignment
• Alias statements

🤝 Community & Contributing

• GitHub: https://github.com/hahalee000000/helen — Report issues, submit PRs, join discussions
• License: MIT — Business-friendly, open-source-friendly
• Python: 3.12+ required
• Platforms: Linux / macOS / Windows

Contributions welcome! See CLAUDE.md for the development workflow, or wiki/index.md for complete documentation.

📊 Project Stats

• Code size: ~40,000 lines of Python (96 source files)
• Test coverage: 2917 tests, 137 test files
• Built-in stdlib: 287 functions, 287 Chinese aliases
• Built-in skills: 17 (helen-syntax, helen-stdlib, code-quality, github, etc.)
• Bilingual keywords: 89 (44.5 English + 44.5 Chinese)