Metadata-Version: 2.4
Name: anusara
Version: 1.1.5
Summary: Deterministic execution engine with traceable, immutable history
Author: Nataraj Narayana
License: THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
Project-URL: Homepage, https://github.com/Nataraj-Narayana/anusara
Project-URL: Repository, https://github.com/Nataraj-Narayana/anusara
Project-URL: Issues, https://github.com/Nataraj-Narayana/anusara/issues
Keywords: deterministic-execution,event-sourcing,workflow-engine,orchestration,traceability
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Operating System :: OS Independent
Requires-Python: <3.13,>=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
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Dynamic: license-file

# Anusara

**Deterministic execution runtime for agent workflows.**

![CI](https://github.com/Nataraj-Narayana/anusara/actions/workflows/ci.yml/badge.svg)
![PyPI](https://img.shields.io/pypi/v/anusara)
![Python](https://img.shields.io/pypi/pyversions/anusara)
![License](https://img.shields.io/github/license/Nataraj-Narayana/anusara)
![Architecture](https://img.shields.io/badge/architecture-event--sourced--runtime-blue)

---

## What is Anusara?

**Anusara is a deterministic runtime for executing agent workflows.**

It executes graphs of agents using:

- deterministic scheduling  
- event-sourced execution history  
- replay-driven state reconstruction  

Every execution becomes:

- replayable  
- inspectable  
- debuggable  
- reproducible  

Anusara treats workflow execution as a **runtime system problem**, not a
collection of loosely connected function calls.

---

## 🚀 Quickstart (2 minutes)

### Install

```bash
pip install anusara[api]
```

### Run the server

```bash
PYTHONPATH=src python -m uvicorn anusara.adapters.http.app:create_app --factory --reload
```

### Register agent

```bash
curl -X POST http://127.0.0.1:8000/agents/register \
  -H "Content-Type: application/json" \
  -d '{"name": "hello"}'
```

### Execute

```bash
curl -X POST http://127.0.0.1:8000/executions \
  -H "Content-Type: application/json" \
  -d '{"entry_nodes": ["hello"]}'
```

👉 You just ran your first deterministic workflow.

➡️ Full guide: `docs/guides/http-api-quickstart.md`

---

## The Mental Model

```text
Agent Graph
     ↓
Deterministic Execution Engine
     ↓
Event Log (source of truth)
     ↓
Replay • Debug • Observability
```

Execution is not hidden state.

It is a **recorded, replayable system**.

---

## Why Anusara?

Most agent orchestration systems rely on implicit runtime behavior.

This leads to:

- nondeterministic execution  
- difficult debugging  
- hidden state transitions  
- unreliable retries  
- poor reproducibility  

Anusara solves this by enforcing:

- deterministic execution  
- event log authority  
- replay-based debugging  
- explicit lifecycle guarantees  

---

## Architecture Overview

```mermaid
flowchart LR

Client[Client / API / CLI]
Client --> Runtime

Runtime[Execution Runtime]

Runtime --> Graph[Graph Compiler]
Runtime --> Registry[Agent Registry]
Runtime --> Router[Router]
Runtime --> Mutation[Mutation Engine]

Runtime --> EventLog[(Event Log)]

EventLog --> Replay[Replay Engine]
EventLog --> Debugger[Debugger / Analysis]

Runtime --> Observability[Observability]
```

---

## Core Responsibilities

| Layer | Responsibility |
|------|------|
| Runtime | deterministic execution of workflows |
| Event Log | authoritative execution history |
| Registry | agent lifecycle & resolution |
| Router | execution decision logic |
| Mutation Engine | controlled graph evolution |
| Observability | debugging, metrics, visualization |

---

## Local Python Example

```python
import asyncio
from anusara import ExecutionEngine, ExecutionRequest, AgentRegistry

engine = ExecutionEngine()

registry = AgentRegistry()
registry.register("hello", HelloAgent())

request = ExecutionRequest(
    request_id="demo",
    entry_nodes=["hello"],
    payload={},
    metadata={},
)

asyncio.run(engine.execute(request, registry))
```

---

## Key Features

- deterministic execution  
- event-sourced execution history  
- replayable workflows  
- HTTP + CLI + Python runtime  
- agent lifecycle management  
- built-in observability  
- controlled workflow mutation  

---

## Documentation

### 🚀 Getting Started

- `docs/guides/http-api-quickstart.md`
- `docs/guides/running-anusara-locally.md`
- `docs/guides/creating-your-first-agent.md`

### 📚 Concepts

- agents  
- deterministic execution  
- event sourcing  
- replay  

### 🏗 Architecture

- execution model  
- runtime architecture  
- event log  
- mutation model  

### 📖 Reference

- HTTP API  
- agent registry  
- execution engine  

---

## Project Status

Anusara is under active development.

Current focus:

- developer experience (HTTP API & usability)  
- onboarding and documentation  
- production readiness  

---

## Contributing

Contributions and discussions are welcome.

- CONTRIBUTING.md  
- CODE_OF_CONDUCT.md  
- SECURITY.md  

---

## Security

Anusara Docker images are built on `python:3.12-slim-bookworm`
and regularly updated to minimize vulnerabilities.

We prioritize:
- eliminating high/critical vulnerabilities
- maintaining minimal runtime footprint
- reproducible builds

Low-severity base image CVEs are tracked but not always actionable.

---

## License

MIT License
