handlebar-langchain 0.2.0

AI control layer for Langchain agents

Handlebar, Agent Control Layer (for Langchain Agents)

Handlebar is a runtime control layer for your AI agents.

Enforce deterministic rules on your agents as they act, so you can guarantee they don't violate your team's policies.

Without Handlebar	With Handlebar
"Whoops the agent deleted prod DB"	Deterministically block dangerous tool actions. Full auditability into what your agent tried to do.
"Our costs are ballooning with no way to control them"	Track token usage and USD spend, and set hard limits on your agents. When the limit is reached, Handlebar can block the agent from taking further actions.
"Someone convinced the agent to leak another user's emails"	Limit tool permissions to the user.
"The agent is going off-the-rails and spamming heavy APIs"	Set rate limits on tool use and prevent runaway actions
"We can't be sure the agent isn't leaking sensitive data"	Enforce hard data boundaries between tools and your output. Filter PII before it leaks through agent context

Features

• Collects auditable event logs of your agent's actions
• Block dangerous tools use (e.g. send_email(internalAddress) -> PASS | send_email([e-mail]) -> BLOCK)
• Block dangerous tool chaining (e.g. get_pii -> send_slack_message -> BLOCK: risk of data exfil)
• Require human reviews on dangerous actions
• Enforce hard cost budgets and token usage limits for your agents
• Track usage from each enduser and enforce per-user budgets
• Rate limit agent actions

How it works

1. Wrap a Handlebar client (this codebase) around your agent
2. The client sends event logs of your agent's actions to the Handlebar platform, where you can analyse them
3. As your agent receives an action from the LLM, Handlebar intercepts and evaluates the proposed action against your configured policies
4. If there are violations, Handlebar either permits the action, blocks it, or exits the run

Get started

You will need:

• an agent...
• Wrap your agent with a Handlebar client
• Connect to the Handlebar platform
• Configure policies to enforce on your agent

Wrap your agent with Handlebar

This repository is a monorepo containing installable packages for different Python agent building frameworks. We provide some pre-built wrappers for agent frameworks, with more on the way soon. If your agent is not directly supported, you can still easily plug Handlebar into your agent.

Framework	Install command	Where to read more
Langchain (Python)	uv add handlebar-langchain
Langchain (Javascript)	bun i @handlebar/langchain
google-adk	uv add handlebar-google-adk	Integration guide
Other frameworks + custom agents	uv add handlebar-core	Custom integration guide coming soon
Javascript agents (Langchain, Vercel ai etc.)	bun i @handlebar/core	Checkout the Handlebar JS codebase

Connect your agent to the Handlebar platform

The client SDKs interact with the Handlebar API to emit agent telemetry and event data it collects, and to evaluate your configured policies.

Sign up at https://app.gethandlebar.com.
If you are waitlisted, get in touch with us to get access.

Once on the platform, create an API key and activate your agent by setting the HANDLEBAR_API_KEY environment variable in your agent codebase.

Configure policies to enforce on your agent

On the platform you can create policies from simple templates: usage limits, dangerous tool use, GDPR, finance agents, and more.

Alternatively, run the Handlebar claude code skill to generate rules custom to your agent, by running:

npx skills add gethandlebar/agent-skills

Go to the skill repository for full instructions.

Get in touch

Please open an issue if you have any feedback, suggestions, or requests for framework support. Alternatively, book a call to talk to us about how Handlebar could help to protect your team's agents.

License

Apache 2.0. See LICENSE.