A11

A streaming action runtime for AI agents, model serving, and multimodal APIs.

A11 gives ordinary asynchronous code a stable boundary for tools, model calls, pipelines, and services. Its Python API runs on a native C++20 runtime, with TypeScript, Kotlin, C++, and Flow interfaces for the boundaries that need them.

• Named streams instead of mixed provider events. Read model text, reasoning, tool activity, progress, and durable interaction state from separate ports.
• The same action contract locally and remotely. Move a handler to a GPU host, browser, or service without changing its inputs and outputs.
• Deterministic runtime composition with Flow. Check and load compositions at runtime with defined concurrency, draining, deadlines, cancellation, and error propagation.

Install in five minutes · Try the live research agent · Read the documentation · Star A11

Try A11 live

The hosted parallel research agent plans a topic, runs several investigations concurrently, and streams one report to the browser. Its default Ollama backend needs no account or API key.

Five-minute quickstart

Install the core runtime on Python 3.11 or later:

python -m venv .venv
source .venv/bin/activate
pip install a11-kit

Save this as quickstart.py. The function signature becomes an action schema, and its asynchronous iterator becomes a named streaming output:

import asyncio
from collections.abc import AsyncIterator

import a11


REGISTRY = a11.ActionRegistry()


@REGISTRY.action(name="split-words", output="words")
async def split_words(text: str) -> AsyncIterator[str]:
    """Emit the words in a line as they become available."""
    for word in text.split():
        yield word


async def main() -> None:
    action = REGISTRY.make_action("split-words")
    await action["text"].finalize("named streams arrive early")
    action.run()

    async for word in action["words"]:
        print(word)
    await action.wait()


asyncio.run(main())

Run it:

python quickstart.py

The output port streams four values and then closes successfully. The same REGISTRY can be served over WebSocket, HTTP SSE, or WebRTC; callers still use the text and words ports.

Add model providers when the application needs them. With Ollama already running locally:

pip install "a11-kit[llm]"
a11 chat --provider ollama --no-voice --no-shell-tools

Continue from a working example

• Build a parallel research agent in Python.
• Stream one model interface across Claude, Gemini, and Ollama.
• Turn an application action into an LLM tool.
• Move a local action behind a remote service.
• Compose allowed actions safely at runtime.

Questions and rough edges are useful project signals: start a discussion, report friction, or tell us about an integration.

Building the C++ runtime

A11's runtime is a standalone C++20 library you can build and link without Python. The steps below are self-contained; for the editable Python build, wheel matrix, testing workflow, and architecture, see BUILDING.md.

1. Install the tools, then build the C++ libraries. A11 links a pinned set of statically-built libraries (Boost, OpenSSL, libcurl, nghttp2, hiredis, nlohmann-json, uvw) rather than system copies; scripts/bootstrap_wheel_deps.sh builds them into a per-architecture prefix. From Homebrew you install only the tools (a C++20 compiler, CMake ≥ 3.28, Ninja; Linux tool package names vary):

brew install cmake googletest ninja pkg-config

export A11_DEPS_PREFIX="$HOME/.cache/a11-deps/$(uname -m)"
export CMAKE_PREFIX_PATH="$A11_DEPS_PREFIX"
export OPENSSL_ROOT_DIR="$A11_DEPS_PREFIX"
export PKG_CONFIG_PATH="$A11_DEPS_PREFIX/lib/pkgconfig"
export MACOSX_DEPLOYMENT_TARGET=14.4   # macOS only
scripts/bootstrap_wheel_deps.sh

CMake still fetches the pinned Abseil (and libdatachannel, for WebRTC) automatically. See BUILDING.md for the full rundown.

2. Configure, build, and install to a prefix (the exports above point CMake at the dependency prefix):

cmake -S . -B build -G Ninja \
  -DCMAKE_BUILD_TYPE=Release \
  -DA11_BUILD_PYTHON=OFF \
  -DCMAKE_OSX_DEPLOYMENT_TARGET=14.4 \
  -DCMAKE_INSTALL_PREFIX="$PWD/install"

cmake --build build -j
cmake --install build

On macOS, pass -DCMAKE_OSX_DEPLOYMENT_TARGET=14.4 as shown — it must match the value the prefix was bootstrapped with. Setting it as a cache variable here (not only via the MACOSX_DEPLOYMENT_TARGET environment export, which CMake may not pick up) is what enables the Boost.Fiber futex spinlock; a lower target compiles Boost.Fiber without futex support and fails with "futex not supported on this platform". The flag is ignored on Linux.

3. Use it from your own CMake project. The install exports a CMake package named a11 with per-component targets (a11::service links the whole runtime). Point your consumer's CMAKE_PREFIX_PATH at both the install prefix and the dependency prefix from step 1, so the transitive static Boost/OpenSSL/... resolve:

find_package(a11 CONFIG REQUIRED)

add_executable(my_agent main.cc)
target_link_libraries(my_agent PRIVATE a11::service)
target_compile_features(my_agent PRIVATE cxx_std_20)

#include "a11/nodes/node_map.h"

int main() {
  auto node_map = a11::nodes::NodeMap::Create();
  return node_map.ok() ? 0 : 1;
}

Configure your project with -DCMAKE_PREFIX_PATH=/path/to/install so find_package locates it. The generated C++ API reference is published alongside the docs.