Agentbyte

Agentbyte is an observability-first agentic AI framework for building and studying multiagent systems with a learning-first, implementation-oriented workflow.

Current release: 0.23.0

Current Capabilities

Agent execution loop with run() and run_stream() APIs.
Tooling system (function tools + core tools + memory tool).
Middleware chain for request/response/error handling.
Built-in middleware: logging, security, rate limiting, approval, telemetry.
Console stream printing presets for readable agent, orchestrator, and workflow debugging.
Memory abstractions: list memory, file memory, context injection.
OpenAI and Azure OpenAI model client support.
OpenTelemetry-first tracing with model-call and task-level usage telemetry.
Multi-agent orchestration: RoundRobinOrchestrator, AIOrchestrator, HandoffOrchestrator, PlanBasedOrchestrator with composable termination conditions.
Workflow runtime: typed step graphs (FunctionStep, EchoStep, HttpStep, TransformStep, AgentStep, SubWorkflowStep) with conditional routing, parallel execution, checkpoint/resume, human-in-the-loop suspend/resume, staged state semantics, structured streaming events, and declarative JSON/YAML schema serialisation.
Dataset module: file-based, engine-agnostic datasets with single-table (list[dict]) and multi-table (dict[str, list[dict]]) modes; SQLite multi-table loads all tables into one shared connection enabling SQL JOINs; JSON multi-table supported; S3 load and publish for both modes; pluggable engine registry.

Observability-First Telemetry

Agentbyte exposes two complementary telemetry layers:

Per-call middleware spans (chat ..., tool ...) for model/tool-level diagnostics.
Task-level root span attributes (agent ...) for final aggregated usage and outcome.

Enable telemetry:

export AGENTBYTE_ENABLE_OTEL=true

Per-call span attributes emitted by OTelMiddleware:

gen_ai.usage.input_tokens, gen_ai.usage.output_tokens, gen_ai.usage.total_tokens
gen_ai.usage.cost_estimate_usd
gen_ai.response.finish_reason
gen_ai.request.model
gen_ai.tool.name, gen_ai.tool.success

Debugging Traces without UI

Practical interpretation:

chat gpt-4.1-mini spans show per-call usage/cost/finish reason.
agent <name> span shows final accumulated usage and final task outcome.

Installation

Python requirement: 3.11+

uv sync --all-groups

Optional extras:

uv sync --extra openai
uv sync --extra azureopenai
uv sync --extra otel
uv sync --extra webui

Install in another project (pip / uv add)

Use extras to enable provider + telemetry support:

pip install "agentbyte[azureopenai,otel]"

uv add "agentbyte[azureopenai,otel]"

For the browser WebUI:

pip install "agentbyte[webui]"
# or
uv add "agentbyte[webui]"

Install all optional features:

pip install "agentbyte[all]"
# or
uv add "agentbyte[all]"

Note: the Azure extra is azureopenai.

Quick Start

from agentbyte.agents import Agent
from agentbyte.middleware import LoggingMiddleware

# model_client = OpenAIChatCompletionClient(...) or AzureOpenAIChatCompletionClient(...)

def quick_faq_lookup(topic: str) -> str:
    faq = {
        "middleware": "Middleware handles cross-cutting runtime concerns.",
        "memory": "Memory helps agents keep useful context across interactions.",
    }
    return faq.get(topic.lower(), "No FAQ found.")

agent = Agent(
    name="helpful-assistant",
    description="Helpful assistant with middleware",
    instructions="Answer clearly and use tools when needed.",
    model_client=model_client,
    tools=[quick_faq_lookup],
    middlewares=[LoggingMiddleware()],
)

Run The WebUI

Option 1: Run the preset-backed app

This is the easiest way to see the WebUI working end to end with real preset entities:

preset agents
preset orchestrators
preset workflow

Step 1. Install the WebUI extra:

uv sync --extra webui

Step 2. Start the preset-backed app:

uv run python examples/webui/presets_webui.py

Step 3. Open the browser:

http://127.0.0.1:8080

If auto-open is enabled in your environment, the browser may open automatically.

Option 2: Run the WebUI against your current project directory

Use this when you want Agentbyte to scan a directory for exported agent, workflow, or orchestrator objects.

Important: discovery is convention-based. The scanned directory must contain Python modules that expose top-level variables literally named agent, workflow, or orchestrator. If you point --dir at a folder that does not export those names, the UI will load but show No entities found.

Step 1. Install the WebUI extra:

uv sync --extra webui

Step 2. Launch the WebUI and scan the current directory:

uv run agentbyte webui --dir .

Step 3. Open the browser:

http://127.0.0.1:8080

Useful variants:

uv run agentbyte webui --dir . --port 8080 --host 127.0.0.1 --no-open
uv run agentbyte webui --dir examples --port 8090

For this repository, the most reliable first-run path is the preset-backed launcher:

uv run python examples/webui/presets_webui.py

Use agentbyte webui --dir ... when you have a directory of exportable demo modules, for example:

# my_entities.py
agent = ...
workflow = ...
orchestrator = ...

Option 3: Run it programmatically

Use this when you want to serve in-memory entities directly from Python.

from agentbyte.webui import serve

serve(entities=[agent], port=8080, auto_open=True)

Quick Troubleshooting

If the app does not start:

uv sync --extra webui

If port 8080 is already in use:

uv run agentbyte webui --dir . --port 8090

If you do not want the browser to open automatically:

uv run agentbyte webui --dir . --no-open

Project Layout

src/agentbyte/
  agents/
  llm/
  memory/
  middleware/
  tools/
  messages.py
  context.py
  types.py

Development

uv run ruff check src tests
uv run pytest tests -v

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

agentbyte-0.23.0.tar.gz (705.4 kB view details)

Uploaded Aug 27, 2026 Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

agentbyte-0.23.0-py3-none-any.whl (674.5 kB view details)

Uploaded Aug 27, 2026 Python 3

File details

Details for the file agentbyte-0.23.0.tar.gz.

File metadata

Download URL: agentbyte-0.23.0.tar.gz
Upload date: Aug 27, 2026
Size: 705.4 kB
Tags: Source
Uploaded using Trusted Publishing? Yes
Uploaded via: uv/0.12.6 {"installer":{"name":"uv","version":"0.12.6","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Debian GNU/Linux","version":"13","id":"trixie","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}

File hashes

Hashes for agentbyte-0.23.0.tar.gz
Algorithm	Hash digest
SHA256	`155a2411cd0755dfd157e2023359546a381d1674c90f7a25375eee69aacfc84c`
MD5	`4dee78eb438bac1190eb07ca099097cf`
BLAKE2b-256	`c21a8b4b498c2bf75c7efce5b85ced7c72d9fad893aa9c4f562762fa337e1169`

See more details on using hashes here.

File details

Details for the file agentbyte-0.23.0-py3-none-any.whl.

File metadata

Download URL: agentbyte-0.23.0-py3-none-any.whl
Upload date: Aug 27, 2026
Size: 674.5 kB
Tags: Python 3
Uploaded using Trusted Publishing? Yes
Uploaded via: uv/0.12.6 {"installer":{"name":"uv","version":"0.12.6","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Debian GNU/Linux","version":"13","id":"trixie","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}

File hashes

Hashes for agentbyte-0.23.0-py3-none-any.whl
Algorithm	Hash digest
SHA256	`38c349452b090b6372f473995fc3e772e1322cae43e4a078d41b8ae9da38e009`
MD5	`eb63cd66244f21f9114cf6273511b11f`
BLAKE2b-256	`c34fb35139f9fd951f06697ba0162190914284c4235848c080eeaf26ad7aa6fa`