agentguard47 0.5.0

Zero-dependency observability and runtime guards for AI agents

AgentGuard SDK (Python)

Zero-dependency observability and runtime guards for AI agents. Pure Python stdlib — nothing to audit, nothing that can break. Trace reasoning steps, catch loops, enforce budgets, and replay runs deterministically.

Install

pip install agentguard47

With LangChain support:

pip install agentguard47[langchain]

Quickstart (2 minutes)

pip install agentguard47

from agentguard import Tracer, LoopGuard
from agentguard.tracing import JsonlFileSink

tracer = Tracer(sink=JsonlFileSink("traces.jsonl"), service="my-agent")
guard = LoopGuard(max_repeats=3)

with tracer.trace("agent.run") as span:
    span.event("reasoning.step", data={"thought": "search docs"})
    guard.check(tool_name="search", tool_args={"query": "agent loops"})
    with span.span("tool.search"):
        pass  # your tool here

agentguard report traces.jsonl   # summary table
agentguard view traces.jsonl     # Gantt timeline in browser

No config, no dependencies, no account needed.

Tracing

from agentguard.tracing import Tracer

tracer = Tracer()

with tracer.trace("agent.run", data={"user_id": "u123"}) as span:
    span.event("reasoning.step", data={"step": 1, "thought": "search docs"})
    with span.span("tool.call", data={"tool": "search", "query": "agent loops"}):
        pass

Guards

from agentguard.guards import LoopGuard, BudgetGuard, TimeoutGuard

# Detect repeated tool calls
guard = LoopGuard(max_repeats=3)
guard.check(tool_name="search", tool_args={"query": "agent loops"})

# Track token and call budgets
budget = BudgetGuard(max_tokens=50000, max_calls=100)
budget.record_tokens(150)
budget.record_call()

# Enforce wall-clock time limits
timeout = TimeoutGuard(max_seconds=30)
timeout.start()
timeout.check()  # raises TimeoutExceeded if over limit

Replay

from agentguard.recording import Recorder, Replayer

recorder = Recorder("runs.jsonl")
recorder.record_call("llm", {"prompt": "hi"}, {"text": "hello"})

replayer = Replayer("runs.jsonl")
resp = replayer.replay_call("llm", {"prompt": "hi"})

Evaluation as Code

from agentguard import EvalSuite

result = (
    EvalSuite("traces.jsonl")
    .assert_no_loops()
    .assert_tool_called("search", min_times=1)
    .assert_budget_under(tokens=50000)
    .assert_completes_within(30.0)
    .assert_no_errors()
    .run()
)
print(result.summary)

Auto-Instrumentation

from agentguard import Tracer
from agentguard.instrument import trace_agent, trace_tool

tracer = Tracer()

@trace_agent(tracer)
def my_agent(query):
    return search(query)

@trace_tool(tracer)
def search(q):
    return f"results for {q}"

# Monkey-patch OpenAI/Anthropic (safe if not installed)
from agentguard.instrument import patch_openai, patch_anthropic
patch_openai(tracer)
patch_anthropic(tracer)

CLI

# Summarize trace events
agentguard summarize traces.jsonl

# Human-readable report
agentguard report traces.jsonl

# Open Gantt trace viewer in browser
agentguard view traces.jsonl

# Run evaluation assertions
agentguard eval traces.jsonl

Trace Viewer

agentguard view traces.jsonl --port 8080

Gantt-style timeline with color-coded spans (reasoning, tool, LLM, guard, error), click-to-expand detail panel, and aggregate stats.

Integrations

• LangChain: agentguard.integrations.langchain

Cloud (Hosted Dashboard)

Send traces to the hosted dashboard instead of local JSONL files:

from agentguard import Tracer
from agentguard.sinks.http import HttpSink

sink = HttpSink(url="https://app.agentguard47.com/api/ingest", api_key="ag_...")
tracer = Tracer(sink=sink, service="my-agent")

with tracer.trace("agent.run") as span:
    span.event("reasoning.step", data={"thought": "search docs"})

Get your API key at app.agentguard47.com. Free tier: 10K events/month.

Links

• GitHub
• Dashboard
• Trace Schema
• Examples