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Agent Assembly Examples

Runnable examples showing how to integrate Agent Assembly with real AI agent frameworks, enforce policies, gate approvals, capture audit traces, and control runtime budgets.

What is Agent Assembly?

Agent Assembly is a multi-layer AI agent governance platform that intercepts, inspects, and enforces policies on tool calls made by AI agents — without requiring you to rewrite your agent code. It works via three independently deployable layers: SDK wrappers (Python, Node.js, Go), a sidecar MitM proxy, and kernel-level eBPF probes. A central gateway holds the agent registry, evaluates policies, tracks budgets, and exposes gRPC and HTTP APIs for observability and control.

Who is this repo for?

  • Application developers who are adding Agent Assembly to an existing Python, Node.js, or Go agent application.
  • Platform engineers who are deploying Agent Assembly as a runtime governance layer.
  • Evaluators who want to understand policy enforcement, approval gates, audit tracing, and budget controls through working code before committing to adoption.

Quick navigation

You want to… Start here
Use Python + LangChain python/
Use Node.js / TypeScript + LangChain node/
Use Go go/
Explore policy enforcement scenarios/
Understand approval gates scenarios/
See audit trace and budget controls scenarios/
Read core concepts first docs/concepts.md
Choose the right example docs/choosing-an-example.md

Prerequisites

Install the prerequisites for the ecosystem you want to run.

Python examples

python3 --version   # requires Python >= 3.12
pip install uv      # optional but recommended

Node.js / TypeScript examples

node --version    # requires Node.js >= 20 LTS
pnpm --version    # install via: npm install -g pnpm

Go examples

go version   # requires Go >= 1.22

Agent Assembly runtime (all examples)

Each example sub-project documents which Agent Assembly component it requires (SDK version, gateway address, sidecar config). See the sub-project README.md for the exact setup.

Security

No secrets are ever committed to this repository.

  • Configuration files that require API keys or connection strings provide a .env.example template only.
  • Copy .env.example to .env, fill in your values, and keep that file local — .env is listed in .gitignore.
  • Never paste real credentials into a file tracked by git.

Compatibility

Each example sub-project pins or documents the Agent Assembly SDK and gateway version it was written and tested against. Check the sub-project README.md for the requires: block before running.

Contributing an example

  1. Pick a directory: python/, node/, go/, or scenarios/.
  2. Create a sub-directory named after the framework or scenario (e.g. python/langchain-basic-agent/).
  3. Add a README.md inside the sub-directory with:
    • What it demonstrates (one paragraph)
    • Prerequisites (language version, Agent Assembly version, any API keys needed)
    • Setup (git clone, install deps)
    • Run (exact command and expected output)
    • Troubleshooting (common errors and fixes)
  4. Add a .env.example if the example needs any configuration.
  5. Open a PR with title [AAASM-XXXX] <GitEmoji> (<scope>): <summary>.

See docs/concepts.md for background on how Agent Assembly works before building an example.

Repository layout

agent-assembly-examples/
  README.md                     ← you are here
  LICENSE                       ← Apache-2.0
  .gitignore
  docs/
    concepts.md                 ← Agent Assembly core concepts
    choosing-an-example.md      ← decision guide: which example to run first
  python/
    README.md                   ← Python examples index
  node/
    README.md                   ← Node.js / TypeScript examples index
  go/
    README.md                   ← Go examples index
  scenarios/
    README.md                   ← Cross-language scenario examples index
  .github/
    workflows/
      README.md                 ← CI workflow documentation

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Runnable examples for integrating Agent Assembly with real AI agent frameworks, SDKs, policy enforcement, approvals, audit, trace, and runtime workflows.

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