plyra-trace 2.0.1

Universal observability for AI agents — tracing, flamegraph, live graph, and cost tracking for any framework or LLM provider.

plyra-trace

Behavioral tracing for agentic AI. OTel-compatible. Framework-agnostic.

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The Problem

LLM observability tools trace tokens and latency. But agents don't just call models—they reason, delegate, coordinate, and enforce policy. Without agent-native tracing, you're blind to:

• Multi-agent handoffs — which agent handled what, and why
• Policy decisions — what guards fired, what actions were blocked
• Cross-process coordination — how agents communicate via HTTP, queues, or MCP
• Behavioral patterns — ReAct loops, chain-of-thought, tree search

plyra-trace is the instrumentation layer that understands agent behavior as a first-class primitive. Not a vendor lock-in. Not another dashboard. Just OpenTelemetry-native tracing with agent semantics baked in.

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Install

pip install plyra-trace

Requires Python 3.11+. Works with any OTLP backend: Jaeger, Grafana Tempo, Phoenix, Langfuse, Datadog, or the future plyra collector.

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Quickstart

import plyra_trace

# Initialize (exports to console by default — no collector needed for dev)
pt = plyra_trace.init(project="my-agent")

@plyra_trace.agent(name="researcher")
def research_agent(query: str) -> dict:
    docs = web_search(query)
    return {"docs": docs}

@plyra_trace.tool(name="web-search")
def web_search(query: str) -> list:
    return [{"title": "Result 1", "snippet": "..."}]

# Run with session + user context
with plyra_trace.session("session-abc"):
    with plyra_trace.user("user-123"):
        result = research_agent("What are the top EV companies?")

pt.shutdown()

That's it. Traces export to console (dev mode). To send to an OTLP backend:

pt = plyra_trace.init(
    project="my-agent",
    endpoint="http://localhost:4318",  # Jaeger, Tempo, Phoenix, etc.
)

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Core Concepts

Span Kinds

plyra-trace understands agent-specific span types. Every span has a plyra.span.kind attribute:

Span Kind	Use Case	Decorator
AGENT	Autonomous reasoning block	@agent()
TOOL	External function or API call	@tool()
GUARD	Policy/safety check (plyra-native)	@guard()
LLM	Language model call	@llm()
CHAIN	Glue code linking pipeline steps	@trace()
RETRIEVER	Vector/document retrieval	@trace(kind=...)
ROUTER	Agent routing/delegation (plyra-native)	@trace(kind=...)

All spans are OpenInference-compatible. Both plyra.span.kind and openinference.span.kind are set automatically.

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Decorators

@plyra_trace.trace()                    # Root span (CHAIN)
@plyra_trace.agent(name="planner")      # AGENT span
@plyra_trace.tool(name="search")        # TOOL span
@plyra_trace.guard(policy="safety")     # GUARD span
@plyra_trace.llm(model="gpt-4")         # LLM span

Works with both sync and async functions. Input/output auto-captured as JSON. Exceptions set span status to ERROR.

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Context Managers

Propagate session, user, metadata, and tags to all child spans:

with plyra_trace.session("session-id"):
    with plyra_trace.user("user-id"):
        with plyra_trace.metadata({"tier": "enterprise"}):
            with plyra_trace.tags(["production"]):
                result = run_agent(query)

Context is thread-safe and doesn't leak outside the with block.

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Multi-Agent Propagation

In-Process Handoffs

Nested decorators automatically create parent-child spans:

@plyra_trace.agent(name="orchestrator")
def orchestrator(query: str):
    research = research_agent(query)  # Child span
    return analysis_agent(research)   # Child span

Cross-Process Handoffs (HTTP, gRPC, Queues)

Inject context when calling another agent:

headers = {}
plyra_trace.inject_context(
    headers,
    agent_name="orchestrator",
    handoff_reason="needs research"
)
response = httpx.post("http://agent-2/task", headers=headers, json=payload)

Extract and continue the trace on the receiving side:

ctx = plyra_trace.extract_context(request.headers)
with plyra_trace.continue_trace(ctx):
    result = process_task(request.body)

This creates a distributed trace across agents. Works with HTTP, gRPC, message queues, or any carrier that supports headers.

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Guard Integration

Guards are first-class citizens in plyra-trace. Not just logged—traced as GUARD spans:

from plyra_trace import GuardResult

@plyra_trace.guard(name="input-safety", policy="content-safety")
def check_input(text: str) -> GuardResult:
    triggered = "dangerous" in text.lower()
    return GuardResult(
        policy="content-safety",
        action="block" if triggered else "allow",
        triggered=triggered,
        confidence=0.95,
        details={"reason": "unsafe keyword detected"}
    )

@plyra_trace.agent(name="executor")
def executor(command: str):
    result = check_input(command)
    if result.triggered:
        return f"Blocked: {result.policy}"
    return execute(command)

Guard results automatically populate guard.policy, guard.action, guard.triggered, guard.confidence, and guard.details span attributes.

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OTLP Backend Compatibility

plyra-trace exports to any OTLP-compatible backend:

Backend	Protocol	Endpoint Example
Jaeger	HTTP/gRPC	http://localhost:4318
Grafana Tempo	HTTP/gRPC	http://tempo:4318
Arize Phoenix	HTTP	http://localhost:6006/v1/traces
Langfuse	HTTP	https://cloud.langfuse.com/api/public
Datadog	HTTP	https://trace.agent.datadoghq.com
SigNoz	HTTP/gRPC	http://signoz:4318

Example with Jaeger:

pt = plyra_trace.init(
    project="my-agent",
    endpoint="http://localhost:4318",
    protocol="http/protobuf"  # or "grpc"
)

Example with Grafana Cloud:

pt = plyra_trace.init(
    project="my-agent",
    endpoint="https://otlp-gateway-prod-us-central-0.grafana.net/otlp",
    headers={"Authorization": f"Basic {api_key}"}
)

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Programmatic Span Creation

When decorators aren't enough, use SpanBuilder:

from plyra_trace import SpanBuilder, SpanKind

with SpanBuilder("orchestrator-step", kind=SpanKind.AGENT) as span:
    span.set_input({"query": "analyze competitors"})
    span.set_agent(name="orchestrator", framework="custom")
    
    result = do_complex_work()
    
    span.set_output(result)
    span.add_event("checkpoint", attributes={"step": 1})

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Semantic Conventions

plyra-trace extends OpenInference semantic conventions with agent-native attributes:

Agent Attributes (plyra-native)

• agent.name — Agent identifier
• agent.framework — "langgraph", "crewai", "autogen", etc.
• agent.pattern — "ReAct", "Chain-of-Thought", "Tree-of-Thoughts"
• agent.handoff.from — Source agent in a handoff
• agent.handoff.to — Target agent in a handoff
• agent.handoff.reason — Why the handoff occurred
• agent.handoff.protocol — "http", "grpc", "mcp", "queue"

Guard Attributes (plyra-native)

• guard.policy — Policy name (e.g., "content-safety")
• guard.action — "allow", "block", "redact", "flag", "modify"
• guard.triggered — Boolean: was the guard triggered?
• guard.confidence — Float [0.0, 1.0]
• guard.details — JSON string with structured details
• guard.provider — "plyra-guard", "guardrails-ai", "nemo-guardrails"

Standard OpenInference Attributes

All standard OpenInference attributes are fully supported:

• input.value, output.value, input.mime_type, output.mime_type
• llm.model_name, llm.provider, llm.token_count.*
• tool.name, tool.description, tool.parameters
• session.id, user.id, metadata, tag.tags

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Compatibility with Existing Instrumentation

OpenInference

plyra-trace uses the same semantic conventions as OpenInference. Spans created by OpenInference auto-instrumentation (e.g., LlamaIndex, LangChain via Phoenix) work seamlessly with plyra-trace.

OpenLLMetry (Traceloop)

Use the compatibility processor to normalize OpenLLMetry spans:

from plyra_trace.compat import OpenLLMetrySpanProcessor

pt = plyra_trace.init(
    project="my-agent",
    endpoint="http://localhost:4318",
    span_processors=[OpenLLMetrySpanProcessor()]
)

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Examples

• basic_agent.py — Single agent with console output
• multi_agent.py — Multi-agent orchestration
• with_guard.py — Guard integration

Run examples:

python examples/basic_agent.py

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Development

# Install uv if not already installed
pip install uv

# Sync dependencies
uv sync --all-extras

# Run tests
uv run pytest

# Lint
uv run ruff check .
uv run ruff format .

# Build
rm -rf dist/ && uv build

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Roadmap

v0.1 (current):

• ✅ Core SDK with decorators, context managers, span builders
• ✅ OTLP export (HTTP + gRPC)
• ✅ OpenInference semantic conventions
• ✅ Agent-native attributes (guards, handoffs, routing)

v0.2 (planned):

• Auto-instrumentation for LangGraph, CrewAI, AutoGen
• Async context propagation improvements
• Streaming span export
• MCP (Model Context Protocol) propagation

v1.0 (future):

• plyra collector with agent-aware sampling
• GuardRails integration
• Policy event correlation

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License

Apache 2.0. See LICENSE.

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Links

• Documentation: plyraai.github.io/plyra-trace
• PyPI: pypi.org/project/plyra-trace
• GitHub: github.com/plyraAI/plyra-trace
• Issues: github.com/plyraAI/plyra-trace/issues
• Plyra Stack:
  • plyra-guard — Action middleware for policy enforcement
  • plyra-memory — Persistent structured memory

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Built with ❤️ by the Plyra team. Questions? [e-mail]