agentbyte 0.3.0

A toolkit for designing multiagent systems

Agentbyte

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

Current release: 0.3.0

Repository: gitlab.com/pyninja/aiengineering/agentbyte

What's New in 0.3.0

• Breaking: BaseAgent no longer stores context. self.context, reset_context(), clear_messages(), window_messages(), and reset() are removed. Agent.__init__ no longer accepts a context= kwarg.
• Agent.run(task=None, context=None, ...) and Agent.run_stream(task=None, context=None, ...) accept a per-call AgentContext. A fresh context is created automatically when None.
• AgentResponse.context returns the fully-populated working context to the caller — enables clean multi-turn patterns without agent-side state.
• AgentAsTool.execute() and execute_stream() thread the caller-supplied context through to the underlying agent.
• Notebooks, study docs (topic_agents.md, topic_approval.md), and OTel span guide (topic_otel_spans.md) updated to the caller-owned context pattern.

See CHANGELOG.md for the complete release history.

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.
• 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.

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

Degugging Traces without UI

details can be found in the OTel spans guide.

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

uv sync --all-groups

Optional extras:

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

Install in another project (pip / uv add)

Use extras to enable provider + telemetry support:

pip install "agentbyte[azureopenai,otel]"

uv add "agentbyte[azureopenai,otel]"

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()],
)

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

License

MIT — see LICENSE.

References

• Designing Multi-Agent Systems
• Microsoft Agent Framework