agentlens/README.md

<p align="center">
  <h1 align="center">AgentLens</h1>
  <p align="center">Agent observability that traces <strong>decisions</strong>, not just API calls.</p>
  <p align="center">See <em>why</em> your AI agents chose what they chose.</p>
</p>

<p align="center">
  <a href="https://pypi.org/project/vectry-agentlens/"><img src="https://img.shields.io/pypi/v/vectry-agentlens?color=blue" alt="PyPI"></a>
  <a href="https://gitea.repi.fun/repi/agentlens/src/branch/main/LICENSE"><img src="https://img.shields.io/badge/license-MIT-green" alt="License"></a>
  <a href="https://agentlens.vectry.tech"><img src="https://img.shields.io/badge/demo-live-brightgreen" alt="Demo"></a>
</p>

---

## The Problem

Existing observability tools show you _what_ LLM calls were made. AgentLens shows you _why_ your agent made each decision along the way -- which tool it picked, what alternatives it rejected, and the reasoning behind every choice.

## Quick Start

```bash
pip install vectry-agentlens
```

```python
import agentlens

agentlens.init(api_key="your-key", endpoint="https://agentlens.vectry.tech")

with agentlens.trace("my-agent-task", tags=["production"]):
    # Your agent logic here...
    agentlens.log_decision(
        type="TOOL_SELECTION",
        chosen={"name": "search_web", "confidence": 0.92},
        alternatives=[{"name": "search_docs", "reason_rejected": "query too broad"}],
        reasoning="User query requires real-time data not in local docs"
    )

agentlens.shutdown()
```

Open `https://agentlens.vectry.tech/dashboard` to see your traces.

## Features

- **Decision Tracing** -- Log every decision point with reasoning, alternatives, and confidence scores
- **OpenAI Integration** -- Auto-instrument OpenAI calls with one line: `wrap_openai(client)`
- **LangChain Integration** -- Drop-in callback handler for LangChain agents
- **Nested Traces** -- Multi-agent workflows with parent-child span relationships
- **Real-time Dashboard** -- SSE-powered live trace streaming with filtering and search
- **Decision Tree Viz** -- Interactive React Flow visualization of agent decision paths
- **Analytics** -- Token usage, cost tracking, duration timelines per trace
- **Self-Hostable** -- Docker Compose deployment, bring your own Postgres + Redis

## Architecture

```
SDK (Python)                 API (Next.js)              Dashboard (React)
 agentlens.trace() ------>  POST /api/traces  ------>  Real-time SSE stream
 agentlens.log_decision()   Prisma + Postgres          Decision tree viz
 wrap_openai(client)        Redis pub/sub              Analytics & filters
```

## Integrations

### OpenAI

```python
import openai
from agentlens.integrations.openai import wrap_openai

client = openai.OpenAI()
wrap_openai(client)  # Auto-traces all completions

with agentlens.trace("openai-task"):
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "Hello!"}]
    )
```

### LangChain

```python
from agentlens.integrations.langchain import AgentLensCallbackHandler

handler = AgentLensCallbackHandler()
agent.run("Do something", callbacks=[handler])
```

### Custom Agents

```python
with agentlens.trace("planner"):
    agentlens.log_decision(
        type="ROUTING",
        chosen={"name": "research_agent"},
        alternatives=[{"name": "writer_agent"}],
        reasoning="Task requires data gathering first"
    )
    with agentlens.trace("researcher"):
        # Nested trace creates child span automatically
        agentlens.log_decision(
            type="TOOL_SELECTION",
            chosen={"name": "web_search"},
            alternatives=[{"name": "database_query"}],
            reasoning="Need real-time information"
        )
```

## Decision Types

| Type | Use Case |
|------|----------|
| `TOOL_SELECTION` | Agent chose which tool/function to call |
| `ROUTING` | Agent decided which sub-agent or path to take |
| `PLANNING` | Agent formulated a multi-step plan |
| `RETRY` | Agent decided to retry a failed operation |
| `ESCALATION` | Agent escalated to human or higher-level agent |
| `MEMORY_RETRIEVAL` | Agent chose what context to retrieve |
| `CUSTOM` | Any other decision type |

## Self-Hosting

```bash
git clone https://gitea.repi.fun/repi/agentlens.git
cd agentlens
docker compose up -d
```

The dashboard will be available at `http://localhost:4200`.

### Environment Variables

| Variable | Default | Description |
|----------|---------|-------------|
| `DATABASE_URL` | `postgresql://agentlens:agentlens@postgres:5432/agentlens` | PostgreSQL connection string |
| `REDIS_URL` | `redis://redis:6379` | Redis connection string |
| `NODE_ENV` | `production` | Node environment |

## Project Structure

```
agentlens/
  apps/web/           # Next.js 15 dashboard + API
  packages/database/  # Prisma schema + client
  packages/sdk-python/  # Python SDK (PyPI: vectry-agentlens)
  examples/           # Example agent scripts
  docker-compose.yml  # Production deployment
```

## SDK Reference

See the full [Python SDK documentation](packages/sdk-python/README.md).

## Examples

See the [examples directory](examples/) for runnable agent scripts:

- `basic_agent.py` -- Minimal AgentLens usage with decision logging
- `openai_agent.py` -- OpenAI wrapper auto-instrumentation
- `multi_agent.py` -- Nested multi-agent workflows
- `customer_support_agent.py` -- Realistic support bot with routing and escalation

## Contributing

AgentLens is open source under the MIT license. Contributions welcome.

```bash
# Development setup
npm install
npx turbo dev          # Start web app in dev mode
cd packages/sdk-python
pip install -e ".[dev]" # Install SDK in dev mode
pytest                  # Run SDK tests
```

## License

MIT