langgraph-genai-bridge 0.1.0

Native Google GenAI SDK integration for LangGraph with context caching, tool auto-conversion, and bidirectional message translation.

langgraph-genai-bridge

Native Google GenAI SDK integration for LangGraph with context caching, tool auto-conversion, and bidirectional message translation.

By BrainSurfing Technologies

Why?

LangGraph is the best orchestration framework for AI agents. Google's native GenAI SDK has features (context caching, native structured output) that LangChain's wrapper doesn't expose. This bridge gives you both.

Feature	LangChain Wrapper	This Bridge
Context Caching	Not supported	Built-in (5x cost reduction)
Structured Output	Via wrapper (buggy)	Native response_schema
Tool Calling	Wrapped	Native FunctionDeclaration
Latency	Higher (abstraction layer)	Lower (direct SDK)
LangGraph Compatible	Yes	Yes

Install

pip install langgraph-genai-bridge

Quick Start

from langgraph_genai_bridge import GenAIBridge

# Initialize
bridge = GenAIBridge(api_key="your-google-api-key", model="gemini-2.5-flash")

# Register your LangChain tools
bridge.set_tools(my_langchain_tools)

# Enable context caching (saves ~80% on input tokens)
bridge.enable_caching(ttl_seconds=3600)

# Use inside a LangGraph node — returns LangChain AIMessage
def orchestrator_node(state):
    response = bridge.invoke(
        state["messages"],
        system_prompt="You are a helpful trading agent."
    )
    return {"messages": [response]}

Features

Context Caching

Google's context caching lets you pay for your system prompt once per hour instead of every API call. For an agent running 12 cycles/hour with a 2000-token system prompt, that's 24,000 tokens/hour saved.

bridge.enable_caching(ttl_seconds=3600)  # Cache for 1 hour

# First call: creates cache (normal cost)
# Subsequent calls: uses cache (near-free input tokens)
response = bridge.invoke(messages, system_prompt=my_long_prompt)

Tool Auto-Conversion

Automatically converts LangChain @tool decorated functions to Google GenAI FunctionDeclaration format. No manual schema writing needed.

from langchain_core.tools import tool

@tool
def get_stock_price(ticker: str) -> str:
    """Get the current price for a stock ticker."""
    return f"{ticker}: $150.00"

bridge.set_tools([get_stock_price])  # Auto-converts

Bidirectional Message Translation

Seamlessly converts between LangChain message types and Google GenAI Content objects:

LangChain	Direction	Google GenAI
SystemMessage	->	Context Cache / system_instruction
HumanMessage	->	Content(role="user")
AIMessage (with tool_calls)	->	Content(role="model") with FunctionCall
ToolMessage	->	Content with FunctionResponse
	<-	AIMessage(content=..., tool_calls=[...])

Graceful Fallback

If the native SDK fails, automatically falls back to your LangChain wrapper:

from langchain_google_genai import ChatGoogleGenerativeAI

# Set up fallback
langchain_llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash")
langchain_with_tools = langchain_llm.bind_tools(my_tools)
bridge.set_langchain_fallback(langchain_with_tools)

# If native SDK fails -> seamlessly falls back to LangChain
response = bridge.invoke(messages)

Full LangGraph Example

from langgraph.graph import StateGraph, START, END
from langgraph.prebuilt import tools_condition
from langchain_core.tools import tool
from langgraph_genai_bridge import GenAIBridge

# Define tools
@tool
def search_web(query: str) -> str:
    """Search the web for information."""
    return f"Results for: {query}"

# Initialize bridge
bridge = GenAIBridge(api_key="...", model="gemini-2.5-flash")
bridge.set_tools([search_web])
bridge.enable_caching(ttl_seconds=3600)

# LangGraph nodes
def agent(state):
    return {"messages": [bridge.invoke(state["messages"], system_prompt="You are helpful.")]}

def tool_node(state):
    # Your existing tool execution logic
    ...

# Build graph (standard LangGraph pattern)
workflow = StateGraph(...)
workflow.add_node("agent", agent)
workflow.add_node("tools", tool_node)
workflow.add_edge(START, "agent")
workflow.add_conditional_edges("agent", tools_condition)
workflow.add_edge("tools", "agent")
app = workflow.compile()

Cost Savings Benchmark

Measured on a trading agent with 35+ tools, 2000-token system prompt, 12 cycles/hour:

Metric	LangChain Wrapper	GenAI Bridge
Input tokens/hour	~120,000	~25,000
Cost/day (Gemini Flash)	~5 EUR	~1 EUR
Latency per call	~800ms	~500ms

API Reference

GenAIBridge(api_key, model, temperature, max_output_tokens)

Main bridge class.

bridge.set_tools(langchain_tools)

Register LangChain @tool functions for native function calling.

bridge.enable_caching(ttl_seconds=3600)

Enable context caching for system prompts.

bridge.invoke(messages, system_prompt=None, max_tool_output=3000)

Call Gemini and return a LangChain AIMessage. Compatible with tools_condition.

bridge.set_langchain_fallback(langchain_llm)

Set a LangChain ChatModel as fallback.

bridge.invalidate_cache()

Force cache invalidation.

License

MIT License. By BrainSurfing Technologies.