sologate-langchain

Human-in-the-loop approval gates for LangChain agents.

When your agent is about to do something risky — delete files, send bulk email, make API calls — Sologate pauses it and routes the action to a human for approval. One approve/reject click. Full audit trail. Agent resumes automatically.

pip install sologate-langchain

3-line integration

from sologate_langchain import SologateCallbackHandler

handler = SologateCallbackHandler(
    sologate_url="https://www.sologate.app",
    api_key="at_your_key_here",
)

agent_executor = AgentExecutor(agent=agent, tools=tools, callbacks=[handler])

That's it. Every tool call is scored automatically. Low-risk actions pass through silently. High-risk actions pause your agent and open an approval request in the Sologate Decision Center.

How it works

Agent calls tool
      ↓
SologateCallbackHandler.on_tool_start()
      ↓
Risk score 0–100 calculated locally (no API call for low-risk)
      ↓
score < threshold (60)?  →  runs silently ✓
score ≥ threshold?       →  POST /api/agent/request-approval
                              ↓
                         Agent PAUSES (WebSocket wait)
                              ↓
                    Human approves/rejects in dashboard
                              ↓
                    Approved → agent continues ✓
                    Rejected → GateRejectedError raised ✗

Full example

import os
from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_react_agent
from langchain_community.tools import ShellTool, WriteFileTool
from langchain import hub
from sologate_langchain import SologateCallbackHandler

llm = ChatOpenAI(model="gpt-4o")
tools = [ShellTool(), WriteFileTool()]
prompt = hub.pull("hwchase17/react")

agent = create_react_agent(llm, tools, prompt)

handler = SologateCallbackHandler(
    sologate_url=os.environ["SOLOGATE_URL"],
    api_key=os.environ["SOLOGATE_KEY"],
    threshold=60,  # gate anything scored ≥ 60/100
)

agent_executor = AgentExecutor(agent=agent, tools=tools, callbacks=[handler])
result = agent_executor.invoke({"input": "Clean up the project folder"})

When the agent tries rm -rf ./backups/ it scores 97/100 — HIGH and your terminal shows:

[sologate] 🔴 Gating: terminal (score 97/100 — HIGH)
[sologate] Reason: Shell command contains rm -rf (irreversible bulk deletion)
[sologate] Flags:  • rm -rf detected
[sologate] Waiting for human decision at https://www.sologate.app/decisions...

Approve in the dashboard → agent continues. Reject → GateRejectedError raised.

Environment variables

export SOLOGATE_URL=https://www.sologate.app
export SOLOGATE_KEY=at_your_key_here

Or pass directly to SologateCallbackHandler(sologate_url=..., api_key=...).

Risk scoring

Scores are calculated locally — no network round-trip for safe actions.

Customize the threshold:

handler = SologateCallbackHandler(..., threshold=80)  # only gate HIGH risk

Low-level gate function

For custom agent frameworks (not LangChain):

from sologate_langchain import gate, GateRejectedError

try:
    gate(
        "delete_customer_records",
        sologate_url="https://www.sologate.app",
        api_key="at_...",
        context="Agent is about to delete 312 inactive customer records",
        payload={"count": 312, "table": "customers"},
    )
    # human approved — proceed
    db.execute("DELETE FROM customers WHERE status='inactive'")

except GateRejectedError:
    print("Rejected — no records deleted")

Links

• Sologate dashboard
• Get an API key
• Full docs
• Node.js / OpenClaw integration