A11

A concurrent action and streaming runtime for building AI agents.

A11 lets you write agents as ordinary async def code: values stream between producers and consumers, work is packaged as composable actions, and the same code runs in one process or across a network with a transport swap. The API is Python; the runtime underneath is a native C++20 implementation, so the streaming and concurrency stay fast and off the event loop's critical path.

📖 Documentation →

Install

pip install "a11-kit[llm]"

The [llm] extra pulls in the Anthropic and Google model SDKs. Drop it for the core runtime only.

See it in 30 seconds

Chat with a model right from the terminal (streaming its reply, and its thoughts with -v):

export GEMINI_API_KEY=...        # or ANTHROPIC_API_KEY
a11 chat -v

The ideas

A11 is small at its core — a few ideas compose into everything from a one-file helper to a fleet of networked agents. (The Principles page goes deeper.)

  • Everything is asynchronous. Every operation that can wait is a coroutine you await; the runtime schedules thousands cooperatively. Completion is an event (await action.done.wait()) and lifecycles are context managers that finalise — or abort with the right status — for you.
  • Everything is a stream. The unit of state is a node: a single ordered sequence of chunks with a writer end and a reader end. An agent rarely has its whole answer at once — it has the next token, frame, or tool call — so nodes make incremental production and consumption the natural shape, with backpressure built in.
  • Actions are wired streams. An action's typed input/output ports are nodes, so calling one is wiring streams together. A handler can emit output before it has finished reading input — exactly what streaming an LLM response through a pipeline looks like.
  • Two extension points: storage and transport. A ChunkStore is the log behind a node (swap the in-memory default for disk, a database, or fault injection); a WireStream moves bytes between peers (in-process, WebSocket, HTTP SSE, WebRTC). Everything above them is unchanged, so making an agent distributed is a transport swap, not a rewrite.
  • Sessions tie it together. A Session multiplexes wire streams, dispatches incoming action calls against a registry, and drains and closes the connection cleanly.

A taste

Produce into a node and read it back — backpressure and finalisation included:

import asyncio
import a11


async def main() -> None:
    async with a11.AsyncNode.create("tokens") as node:   # seals on exit
        for word in ["A11", "streams", "everything"]:
            await node.put(word)                          # await = backpressure

    async for token in node:
        print(token)


asyncio.run(main())

Stream a model's reply through that same node abstraction — interact_with_llm is just an action whose text_output port carries tokens as they arrive:

import a11
from a11.sdk.interact_with_llm import INTERACT_WITH_LLM_SCHEMA, interact_with_llm
from a11.sdk.llm import LlmHeaders

interact = (
    a11.Action(INTERACT_WITH_LLM_SCHEMA)
    .bind_handler(interact_with_llm)
    .set_header(LlmHeaders.PROVIDER.value, "gemini")
    .set_header(LlmHeaders.MODEL.value, "gemini-3.5-flash")
    .run()
)

# ...then, inside your async code, feed the conversation in and stream the reply:
async for chunk in interact["text_output"]:   # tokens as the model emits them
    print(chunk, end="", flush=True)

The guides build these up step by step — from a node, to a WebSocket echo session, to calling an action on a remote server, to a tool-using agent.

Learn more

  • Documentation — principles, guides, and the full Python API reference.
  • Guides — hands-on walkthroughs from a single stream to a networked, tool-using agent.
  • Examples — runnable programs under examples/.

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.

Key dates

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  • Released:
Newer release available (0.5.6)

1 maintainer

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Avatar for helenapankov from gravatar.com helenapankov

Requires

Python >=3.11

Provides Extra

claude gemini ollama llm

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Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

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0.5.6

17 files

0.5.5

17 files

0.5.4

17 files

0.5.3

17 files

0.5.2

17 files

0.5.1

17 files

0.5.0

17 files

0.4.6

17 files

0.4.4

17 files

0.4.2

17 files

0.4.1

17 files

This release

0.3.3 This release

17 files

0.3.2

17 files

0.3.1

17 files

0.3.0

17 files

0.2.5

17 files

0.2.3

17 files

0.2.2

17 files

0.2.1

17 files

0.2.0

17 files

0.1.8

17 files

0.1.7

17 files

0.1.6

17 files

0.1.5

16 files

0.1.4

16 files

Yanked

0.1.3

8 files

Yanked reason: broken

0.1.2

8 files

0.1.1

17 files

0.1.0

3 files

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