agent-observe 0.1.2

Framework-agnostic observability, audit, and eval for AI agent applications

agent-observe

Framework-agnostic observability, audit, and eval for AI agent applications.

Overview

agent-observe is a lightweight runtime layer that provides:

• Observability - Track agent runs, tool calls, and model invocations
• Audit/Compliance - Policy engine with deny/allow patterns for tools
• Label-free Eval - Automatic risk scoring based on behavioral signals
• Tool Replay - Cache tool results for deterministic testing
• Local Viewer - FastAPI UI for browsing and debugging runs

Designed to be enterprise-safe by default - stores only metadata (hashes, sizes, timings), not raw content.

Installation

# Core package
pip install agent-observe

# With viewer UI
pip install agent-observe[viewer]

# With PostgreSQL support
pip install agent-observe[postgres]

# All extras
pip install agent-observe[all]

Quick Start

from agent_observe import observe, tool, model_call

# Initialize (zero-config, auto-detects environment)
observe.install()

# Define tools
@tool(name="search", kind="http")
def search_web(query: str) -> list[dict]:
    # Your implementation
    return [{"title": "Result", "url": "https://..."}]

@model_call(provider="openai", model="gpt-4")
def call_llm(prompt: str) -> str:
    # Your LLM call
    return "Response..."

# Run your agent
with observe.run("my-agent", task={"goal": "Research topic"}):
    results = search_web("AI agents")
    analysis = call_llm(f"Analyze: {results}")
    observe.emit_artifact("analysis", analysis)

View the results:

agent-observe view

Async Support

Full async/await support for modern agent frameworks:

@tool(name="fetch_data", kind="http")
async def fetch_data(url: str) -> dict:
    async with httpx.AsyncClient() as client:
        return await client.get(url)

@model_call(provider="anthropic", model="claude-3")
async def call_claude(prompt: str) -> str:
    return await anthropic.messages.create(...)

# Use async context manager
async with observe.arun("async-agent"):
    data = await fetch_data("https://api.example.com")
    response = await call_claude(f"Analyze: {data}")

Framework Integration

See AGENTS.md for detailed integration examples with:

• OpenAI Function Calling
• Anthropic Claude
• Google Vertex AI / Gemini
• LangChain
• Custom ReAct agents

Features

Zero-Config Defaults

Just call observe.install() - it automatically:

• Selects the right sink based on environment
• Uses SQLite for local dev, Postgres if DATABASE_URL is set
• Captures metadata only (enterprise-safe)

Automatic Sink Selection

Condition	Sink
DATABASE_URL set	PostgreSQL
OTEL_EXPORTER_OTLP_ENDPOINT set	OTLP (OpenTelemetry)
AGENT_OBSERVE_ENV=dev	SQLite
Default	JSONL

Policy Engine

Create .riff/observe.policy.yml:

tools:
  allow:
    - "db.*"
    - "http.*"
  deny:
    - "shell.*"
    - "*.destructive"

limits:
  max_tool_calls: 100
  max_retries: 10
  max_model_calls: 50

Coming Soon: SQL query validation and network domain restrictions are planned for a future release.

Risk Scoring

Automatic risk scoring (0-100) based on:

Signal	Weight	Tag
Policy violations	+40	POLICY_VIOLATION
Tool success rate < 90%	+25	TOOL_FAILURE
Repeated tool calls (loops)	+15	LOOP_SUSPECTED
5+ retries	+10	RETRY_STORM
Latency exceeds budget	+10	LATENCY_BREACH

Capture Modes

Mode	Description
off	Disable observability
metadata_only	Store hashes, sizes, timings only (default)
evidence_only	Store small blobs with redaction
full	Store all content (with caps)

Environment Variables

# Core
AGENT_OBSERVE_MODE=metadata_only    # off|metadata_only|evidence_only|full
AGENT_OBSERVE_ENV=prod              # dev|staging|prod
AGENT_OBSERVE_PROJECT=my-app        # Project name
AGENT_OBSERVE_AGENT_VERSION=1.0.0   # Agent version

# Sink selection
AGENT_OBSERVE_SINK=auto             # auto|sqlite|jsonl|postgres|otlp
DATABASE_URL=postgresql://...     # Enables Postgres sink

# Policy
AGENT_OBSERVE_POLICY_FILE=.riff/observe.policy.yml
AGENT_OBSERVE_FAIL_ON_VIOLATION=0   # 1 to raise on violations

# Replay
AGENT_OBSERVE_REPLAY=off            # off|write|read

# Performance
AGENT_OBSERVE_LATENCY_BUDGET_MS=20000

CLI

# Start the viewer
agent-observe view
agent-observe view --port 8080

# Export to JSONL
agent-observe export-jsonl -o ./export

# With specific database
agent-observe view --db .riff/observe.db
agent-observe view --database-url postgresql://...

API Reference

Core

from agent_observe import observe

# Initialize (reads from environment variables automatically)
observe.install()

# Or with mode override
observe.install(mode="metadata_only")

# Create a run context
with observe.run("agent-name", task={"goal": "..."}) as run:
    pass

# Emit events
observe.emit_event("custom.event", {"key": "value"})

# Emit artifacts
observe.emit_artifact("report", {"data": "..."}, provenance=["tool1", "tool2"])

Explicit Configuration

When you need full control, pass a Config object. Important: You must include all connection strings explicitly - they are NOT read from environment variables when using a custom Config:

import os
from agent_observe import observe
from agent_observe.config import Config, CaptureMode, Environment, SinkType

# Option 1: Let the library auto-detect from env vars (recommended)
observe.install()

# Option 2: Explicit config - must include ALL required fields
database_url = os.environ.get("DATABASE_URL")

config = Config(
    mode=CaptureMode.METADATA_ONLY,
    env=Environment.PROD,
    sink_type=SinkType.POSTGRES,
    project="my-agent",
    database_url=database_url,  # Required for Postgres!
)
observe.install(config=config)

Decorators

from agent_observe import tool, model_call

@tool(name="my_tool", kind="db", version="1")
def my_tool(arg: str) -> dict:
    pass

@model_call(provider="openai", model="gpt-4")
def call_model(prompt: str) -> str:
    pass

Architecture

agent_observe/
├── observe.py      # Core runtime (install, run, emit_*)
├── decorators.py   # @tool, @model_call (sync and async)
├── policy.py       # YAML policy engine
├── metrics.py      # Risk scoring and eval
├── replay.py       # Tool result caching
├── sinks/
│   ├── sqlite_sink.py   # Local dev
│   ├── jsonl_sink.py    # Fallback
│   ├── postgres_sink.py # Production (Neon, Supabase compatible)
│   └── otel_sink.py     # OTLP export (Jaeger, Honeycomb, Datadog, etc.)
└── viewer/
    └── app.py      # FastAPI viewer

PostgreSQL Sink Design

The Postgres sink follows production best practices:

• Parameterized queries - All queries use %s placeholders (SQL injection safe)
• Batch inserts - Uses executemany for efficient bulk writes
• Retry with backoff - Transient connection errors retry with exponential backoff
• Connection timeout - 10-second timeout prevents hanging connections
• Graceful degradation - Works with pre-created tables (no CREATE permission needed)
• Efficient schema checks - Single query to verify all tables exist

Roadmap

• Auto-instrumentation for OpenAI SDK
• Auto-instrumentation for Anthropic SDK
• SQL query validation policies
• Network domain restriction policies
• Streaming support for LLM responses
• Sampling for high-volume production

Development

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run linting
ruff check .

# Type checking
mypy agent_observe

License

MIT License - see LICENSE file for details.