agentbouncr 0.1.2

Python SDK for AgentBouncr — Governance for AI Agents

agentbouncr

Governance for autonomous AI agents. Every tool call is checked against your policies before execution.

Works with LangChain, LangGraph, CrewAI, AutoGen — or any framework. Drop-in callback for LangChain users, plain HTTP client for everyone else.

Installation

pip install agentbouncr

For LangChain integration:

pip install "agentbouncr[langchain]"

30-Second Quickstart

from agentbouncr import AgentBouncr

client = AgentBouncr(api_key="sk_live_your_key_here")

result = client.evaluate(
    agent_id="my-research-agent",
    tool="send_email",
    params={"to": "[e-mail]"},
)

if result.allowed:
    send_email(to="[e-mail]")
else:
    print(f"Blocked by policy: {result.reason}")

LangChain in 3 Lines

from agentbouncr import AgentBouncr
from agentbouncr.langchain import AgentBouncrCallbackHandler

client = AgentBouncr(api_key="sk_live_your_key_here")
handler = AgentBouncrCallbackHandler(client, agent_id="my-research-agent")

executor = AgentExecutor(agent=agent, tools=tools)
executor.invoke({"input": "Find the latest AI news"}, config={"callbacks": [handler]})

Every tool call now passes through AgentBouncr first. Denied calls raise GovernanceDeniedError.

Important — pass callbacks at invoke time, not in the constructor. In LangChain 0.3.x, passing callbacks=[handler] to AgentExecutor(...) attaches the handler to the executor but does not reliably propagate it to the inner tool runs. The on_tool_start hook then never fires and governance is silently bypassed. Always pass callbacks via the per-call config={"callbacks": [handler]} argument to .invoke() / .stream() / .ainvoke() — this is the supported path and the one we test against.

LangGraph works the same way:

app = create_react_agent(llm, tools)
app.invoke({"messages": [...]}, config={"callbacks": [handler]})

Coverage

The handler intercepts standard LangChain/LangGraph tool runs via on_tool_start. That covers the patterns used by create_react_agent, AgentExecutor with Tool / StructuredTool / BaseTool subclasses, and native function-calling tool wrappers.

It does not intercept code paths that bypass the tool abstraction — for example a raw Runnable chain that calls an external API directly, or a custom node in a LangGraph that invokes an HTTP client without wrapping it as a tool. For those, call client.evaluate(...) yourself before the side effect.

Authentication

Get an API key from your AgentBouncr Dashboard under Settings → API Keys.

client = AgentBouncr(api_key="sk_live_...")

For multi-workspace setups:

client = AgentBouncr(
    api_key="sk_live_...",
    tenant_id="your-workspace-id",
)

Configuration

client = AgentBouncr(
    api_key="sk_live_...",
    base_url="https://agentbouncr.com",  # default
    timeout=5.0,                              # request timeout in seconds
    max_retries=3,                            # exponential backoff for 5xx/429
    tenant_id=None,                           # optional X-Tenant-Id header
)

Error Handling

from agentbouncr import (
    AgentBouncrError,        # base class
    AuthenticationError,     # 401
    ValidationError,         # 400
    RateLimitError,          # 429 after retries exhausted
    ServerError,             # 5xx after retries exhausted
    TimeoutError,            # request exceeded timeout
    GovernanceDeniedError,   # raised by LangChain handler when policy denies
)

Links

• API Documentation: https://agentbouncr.com/api/docs
• Dashboard: https://agentbouncr.com/dashboard
• Quickstart Guide: docs/quickstart.md
• TypeScript SDK: agentbouncr on npm

License

MIT — see LICENSE.

The AgentBouncr backend is commercial software. The SDK is open source so you can audit, patch, and integrate freely.