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A wrapper for LangGraph agents that handles observability, memory, and guardrails

Project description

graph-abstract

A local-first Python library that provides zero-config PostgreSQL checkpointing and telemetry logging for LangGraph agents simply by replacing your standard StateGraph import.


Installation

Core Observability and Checkpointing

To install the core package with database checkpointing and telemetry logging:

pip install graph-abstract

With Optional UI Console

To install the package along with the Streamlit-based visualization console:

pip install "graph-abstract[ui]"

Quick Start

Replace your standard LangGraph StateGraph import with graph_wrap:

from typing import Dict, Any
from typing_extensions import TypedDict
from graph_wrap import StateGraph

class AgentState(TypedDict):
    messages: list[str]

def my_node(state: AgentState) -> Dict[str, Any]:
    return {"messages": ["hello"]}

db_uri = "postgresql://postgres:postgres@localhost:5432/my_database"
graph = StateGraph(AgentState, db_uri=db_uri)
graph.add_node("my_node", my_node)
graph.set_entry_point("my_node")
graph.set_finish_point("my_node")

compiled = graph.compile()

When you call compiled.ainvoke, the library dynamically setups Postgres checkpoint tables and logs execution traces (chains, tools, and LLMs) into the database under the configured thread_id.


Observability UI Console

If you installed graph-abstract with the ui extra, you can launch the console to inspect threads, visualize trace events, and view state checkpoints.

Run the console via python:

python -m graph_wrap --db-uri postgresql://postgres:postgres@localhost:5432/my_database

Or run the command line executable:

graph-abstract-ui --db-uri postgresql://postgres:postgres@localhost:5432/my_database

Custom Guardrails

graph-abstract provides custom inbound and outbound safety guardrails. Evaluations for prompt injection, content safety, and hallucination are performed using LangChain's native structured output bindings for Ollama and OpenAI. Local regex-based PII redaction runs locally with zero LLM costs.

Quick Example

from typing_extensions import TypedDict
from graph_wrap import StateGraph, GuardrailConfig, GuardrailProvider

class AgentState(TypedDict):
    messages: list[str]
    context: str

safety_config = GuardrailConfig(
    inbound_provider=GuardrailProvider.OLLAMA,
    safety_model="llama3.1:8b",
    check_prompt_injection=True,
    
    outbound_provider=GuardrailProvider.OLLAMA,
    eval_model="llama3.1:8b",
    check_hallucination=True,
    
    redact_pii=True,
    fallback_message="I cannot answer that, please try to rephrase your question."
)

workflow = StateGraph(
    AgentState, 
    db_uri="postgresql://postgres:postgres@localhost:5432/my_database",
    guardrails=safety_config
)

Node-Specific Guardrails

To apply guardrails to specific nodes only, pass a dictionary mapping node names to GuardrailConfig objects:

workflow = StateGraph(
    AgentState,
    db_uri="postgresql://postgres:postgres@localhost:5432/my_database",
    guardrails={
        "Trader": GuardrailConfig(
            outbound_provider=GuardrailProvider.OLLAMA,
            eval_model="llama3.1:8b",
            check_hallucination=True
        )
    }
)

Config Options

Parameter Type Default Description
inbound_provider GuardrailProvider None Provider for inbound validation (OLLAMA or OPENAI).
safety_model str None Model name to use for inbound safety/prompt injection checks.
check_prompt_injection bool False Enable inbound prompt injection and jailbreak detection.
outbound_provider GuardrailProvider None Provider for outbound validation (OLLAMA or OPENAI).
eval_model str None Model name to use for outbound hallucination or safety checks.
check_hallucination bool False Enable outbound hallucination check comparing node outputs to graph context.
redact_pii bool False Enable local regex-based redaction of emails, credit cards, SSNs, and phone numbers.
fallback_message Optional[str] "I cannot answer..." The message returned when a guardrail is tripped. Set to None to raise a GuardrailValidationError instead.

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