Agentbyte

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

Current release: 0.44.1

Building an Agent

Every example below builds on the same domain: a customer support agent. Start with a plain agent and a model client — no tools, no middleware:

pip install "agentbyte[openai]"   # the model client lives in an extra; see Install below

from agentbyte import Agent
from agentbyte.llm import OpenAIChatCompletionClient

model_client = OpenAIChatCompletionClient.from_api_key(model="gpt-4.1-mini")

support_agent = Agent(
    name="support_agent",
    description="Answers customer support questions about orders and shipping.",
    instructions="You are a helpful support agent. Be concise and accurate.",
    model_client=model_client,
)

response = await support_agent.run("Where is my order #1234?")

print(response.final_message.content)
print(response.usage)          # tokens, cost, cache hits
print(response.finish_reason)  # "stop" | "max_iterations" | ...

run() executes to completion and returns one AgentResponse. For live progress — token-by-token streaming, tool calls as they happen — use run_stream() instead, which yields events and finishes with that same AgentResponse:

async for item in support_agent.run_stream("Where is my order #1234?", verbose=True):
    print(item)

Adding Tools

A support agent is only useful once it can look things up. Turn any function into a tool with @tool; gate risky ones with approval_mode:

from agentbyte import Agent
from agentbyte.tools import ApprovalMode, tool

@tool
def get_order_status(order_id: str) -> str:
    """Look up the status of an order."""
    return f"Order {order_id} is in transit."

@tool(approval_mode=ApprovalMode.ALWAYS)
def issue_refund(order_id: str, amount: float) -> str:
    """Issue a refund for an order (requires human approval)."""
    return f"Refunded ${amount} for order {order_id}"

support_agent = Agent(
    name="support_agent",
    description="Answers order/shipping questions and can issue refunds.",
    instructions="Look up orders before answering. Refunds always need approval.",
    model_client=model_client,
    tools=[get_order_status, issue_refund],
)

Agentbyte also ships ready-made tools you can drop in without writing any code — pass them straight into tools=[...]:

Core: ThinkTool, TaskStatusTool, CalculatorTool, DateTimeTool, JSONParserTool, RegexTool (all at once via create_core_tools())
Coding: ReadFileTool, WriteFileTool, ListDirectoryTool, GrepSearchTool, BashExecuteTool, PythonREPLTool
Memory: MemoryTool — lets the agent read/write a memory backend (ListMemory or FileMemory) as a tool call, on top of the automatic context injection every agent already gets

Agentic Workflows

When support handling is a fixed multi-step pipeline rather than a single agent call — triage, then route — model it as a Workflow instead:

import asyncio
from pydantic import BaseModel
from agentbyte.workflow import FunctionStep, StepMetadata, Workflow, WorkflowConfig, WorkflowRunner

class TicketInput(BaseModel):
    text: str

class TriagedTicket(BaseModel):
    text: str
    priority: str

async def triage(input_data: TicketInput, context) -> TriagedTicket:
    priority = "high" if "urgent" in input_data.text.lower() else "normal"
    return TriagedTicket(text=input_data.text, priority=priority)

async def route(input_data: TriagedTicket, context) -> TriagedTicket:
    return input_data  # e.g. assign to a queue here

workflow = Workflow(WorkflowConfig(name="support_ticket_pipeline"))
workflow.chain(
    FunctionStep("triage", StepMetadata(name="triage"), TicketInput, TriagedTicket, triage),
    FunctionStep("route", StepMetadata(name="route"), TriagedTicket, TriagedTicket, route),
)

execution = asyncio.run(
    WorkflowRunner().run(workflow, {"text": "urgent: order not received"})
)
print(execution.state["route_output"])

Steps can also wrap an agent (AgentStep), call HTTP endpoints (HttpStep), transform data (TransformStep), or nest another workflow (SubWorkflowStep) — with conditional routing, parallel branches, checkpoint/resume, and human-in-the-loop suspend/resume.

Choosing Agents or Graph-Based Orchestration

Graph-based orchestration makes routing deterministic: after one defined step completes, the next defined step runs. It does not make an LLM's interpretation, tool selection, or output deterministic. Use a graph or Workflow when the business process is known, stable, and requires explicit control over every transition.

Start with an Agent when the work requires the system to decide how to solve an open-ended request: researching, interpreting documents, selecting tools, synthesizing results, or handling long-tail exceptions. A single agent can complete many such multi-step tasks without encoding every anticipated reasoning path as graph infrastructure.

Keep deterministic controls at operational boundaries. Validate structured output, apply policy checks and approvals, use idempotent writes, and emit audit records before an agent can produce external side effects. This separates deterministic process control from the model's inherently probabilistic reasoning, while allowing a workflow to be introduced where a stable business process truly needs one.

Orchestrator Patterns

Two different ways to combine multiple agents — pick based on who's in control:

AgentAsTool — one agent decides if and when to delegate. The support agent stays the single decision-maker and calls the billing agent like any other tool.
An orchestrator (e.g. RoundRobinOrchestrator) — a separate controller drives the conversation between agents in turns, until a termination condition fires. Neither agent decides when the other speaks.

Agent as a tool — the support agent delegates billing questions:

from agentbyte import Agent
from agentbyte.agents import AgentAsTool

billing_agent = Agent(
    name="billing_agent",
    description="Handles billing and refund questions.",
    instructions="Answer billing questions and process refund requests.",
    model_client=model_client,
)

support_agent = Agent(
    name="support_agent",
    description="Front-line support agent that can delegate billing issues.",
    instructions="Handle general support; delegate billing questions to the billing tool.",
    model_client=model_client,
    tools=[AgentAsTool(agent=billing_agent)],
)

Orchestrated turns — support and escalation agents collaborate until the ticket is resolved:

from agentbyte import (
    MaxMessageTermination,
    RoundRobinOrchestrator,
    TextMentionTermination,
    UserMessage,
)

orchestrator = RoundRobinOrchestrator(
    agents=[support_agent, escalation_agent],
    termination=TextMentionTermination("RESOLVED") | MaxMessageTermination(6),
)

task = UserMessage(content="Customer says their package never arrived.", source="user")
async for item in orchestrator.run_stream(task, verbose=True):
    print(item)

Other orchestrators follow the same run()/run_stream() shape: AIOrchestrator (a model picks the next speaker), HandoffOrchestrator (agents explicitly hand off control), PlanBasedOrchestrator (a plan is drafted, then executed step by step).

Middleware

Built in: LoggingMiddleware, PIIRedactionMiddleware, GuardrailMiddleware, MetricsMiddleware, RateLimitMiddleware, ApprovalMiddleware, ContextCompactionMiddleware, RetryMiddleware, OTelMiddleware. Attach any combination to the same support agent via middlewares=[...]:

from agentbyte import Agent
from agentbyte.middleware import ApprovalMiddleware, LoggingMiddleware, RateLimitMiddleware

support_agent = Agent(
    name="support_agent",
    description="Answers order/shipping questions and can issue refunds.",
    instructions="Look up orders before answering. Refunds always need approval.",
    model_client=model_client,
    tools=[get_order_status, issue_refund],
    middlewares=[
        LoggingMiddleware(),
        RateLimitMiddleware(max_requests=10, window_seconds=60),
        ApprovalMiddleware(tool_names=["issue_refund"]),
    ],
)

Observability-First Telemetry

Agentbyte exposes two complementary telemetry layers via OTelMiddleware:

Per-call spans (chat <model>, tool <name>, embedding <model>) for model/tool/embedding-level diagnostics.
Task-level root span (agent <name>) wrapping every per-call span in one run, carrying the final aggregated usage and outcome.

Enable telemetry:

export AGENTBYTE_ENABLE_OTEL=true

Per-call span attributes emitted by OTelMiddleware:

gen_ai.system, gen_ai.operation.name, gen_ai.agent.name, gen_ai.session.id
gen_ai.request.model, gen_ai.response.model, gen_ai.response.finish_reason
gen_ai.usage.input_tokens, gen_ai.usage.output_tokens, gen_ai.usage.total_tokens, gen_ai.usage.cost_estimate_usd
gen_ai.tool.name, gen_ai.tool.success
gen_ai.embedding.input_count, gen_ai.embedding.output_count
gen_ai.input.messages, gen_ai.output.messages, gen_ai.tool.parameters, gen_ai.tool.result (opt-in content capture — only when AGENTBYTE_OTEL_CAPTURE_CONTENT=true, since these can carry PII)

Reading a trace: chat gpt-4.1-mini spans show per-call usage/cost/finish reason; the wrapping agent <name> span shows the final accumulated usage and task outcome for the whole run()/run_stream() call.

Installation

Python requirement: 3.11+

The base package (pip install agentbyte) has no model provider or storage driver. Add the extras for the features you use. Using a feature without its extra raises MissingExtraError (an ImportError) that names the extra to install.

pip install "agentbyte[openai,sqlite]"
# or
uv add "agentbyte[openai,sqlite]"

Extra	Installs	Needed for
`openai`	`openai`	`OpenAIChatCompletionClient`, `OpenAIEmbeddingClient`
`azureopenai`	`openai`, `azure-identity`	Azure OpenAI clients and Entra ID credentials
`aws`	`aioboto3`	S3 dataset sources, writers and publishers
`otel`	OpenTelemetry API, SDK, OTLP exporter	OpenTelemetry tracing and metrics
`webui`	`fastapi`, `uvicorn`	`agentbyte webui`
`gunicorn`	`webui` + `gunicorn`	Production single-worker serving (`gunicorn -k uvicorn.workers.UvicornWorker -w 1`)
`sqlite`	`aiosqlite`	SQLite datasets, workflow checkpoints, evaluation history
`postgres`	`sqlalchemy[asyncio]`, `sqlmodel`, `asyncpg`	`SqlUsageBackend`, `SqlEvalRunStore`
`sql`	`sqlite` + `postgres`	Both SQL backends
`tiktoken`	`tiktoken`	`TiktokenCounter` for exact compaction token counts
`optim`	`gepa`	`optimize_with_gepa()`
`viz`	`graphviz`	`WorkflowVisualizer.to_dot()`
`research`	`beautifulsoup4`, `html2text`, `arxiv`, `youtube-transcript-api`	`agentbyte.tools.research_tools`
`all`	every extra above	Everything

Contributors working in this repository install every extra plus the local tooling groups (test, dev, experiment):

uv sync --all-extras --all-groups

Run The WebUI

The WebUI has two sections: the Playground (chat with agents, orchestrators and workflows) and Evaluations (run registered evaluation suites and inspect the results). Install the extra first:

uv sync --extra webui            # add --extra sqlite for Evaluations

Option 1: Sample apps

No API key needed: three demo agents, a team and a workflow on a fake model client.

uv run python examples/webui/in_memory.py                          # http://127.0.0.1:8070
uv run python examples/webui/in_memory.py --eval-history eval.sqlite   # plus Evaluations

With your OpenAI or Azure OpenAI credentials, the preset agents, teams and workflow:

uv sync --extra webui --extra openai        # or --extra azureopenai
uv run python examples/webui/presets_webui.py                      # http://127.0.0.1:8080
uv run python examples/webui/presets_webui.py --eval-history eval.sqlite

Both launchers accept --port, --no-open, --eval-history PATH and --eval-catalog CATALOG (default: the example suites in examples/eval/workbench_catalog.py).

Option 2: Evaluations for your suites

agentbyte eval ui opens the Evaluations pages on their own (no Playground). Point it at the Python file that defines your EvalSuiteRegistry:

uv sync --extra webui --extra sqlite
uv run agentbyte eval ui --catalog examples/eval/workbench_catalog.py      # http://127.0.0.1:8090

--catalog takes a .py file or a module path (myapp.evaluations); add :name only when the file defines more than one registry. Without --catalog it serves the built-in smoke suite.
Runs are stored in ./eval.sqlite unless you pass --history PATH. Only one process can write a history file: stop the UI before agentbyte eval run on the same file, or use another file.
The same catalog works in the terminal: uv run agentbyte eval run capitals --catalog examples/eval/workbench_catalog.py.
To serve agents and Evaluations together: agentbyte webui --dir my_entities --eval-catalog myapp/evaluations.py.

Start with the end-to-end guide, docs/guides/evaluation-end-to-end.md (dataset → suite → history → UI), and the notebook notebooks/usecases/eval/06-eval-end-to-end.ipynb (real OpenAI/Azure model). Details: docs/design-guide/05-eval_topics/.

Option 3: Scan a project directory

agentbyte webui --dir PATH loads entities from Python files. Discovery is convention-based: each module (or package) must define a top-level variable named agent, orchestrator or workflow holding an Agentbyte object. Folders without those names show "No agents, orchestrators, or workflows found"; this is the case for examples/, which uses the launchers above instead.

# my_entities/support.py
agent = Agent(name="support", ...)

uv run agentbyte webui --dir my_entities
uv run agentbyte webui --dir my_entities --eval-catalog myapp/evaluations.py   # both sections

Option 4: From Python

from agentbyte.webui import serve

serve(entities=[agent], port=8080)
serve(entities=[agent], eval_registry=registry, eval_history="eval.sqlite")   # with Evaluations

Quick Troubleshooting

Port in use: add --port 8090. The UI works on any port.
No browser wanted: add --no-open.
"No agents, orchestrators, or workflows found": the --dir folder has no module with a top-level agent, orchestrator or workflow; use a launcher from Option 1 to see the samples.
No Evaluations switch in the header: start agentbyte webui with --eval-catalog, or use agentbyte eval ui.
After upgrading, hard-reload the page (Cmd/Ctrl-Shift-R) to load the new UI.

Development

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

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