Metadata-Version: 2.4
Name: dakera
Version: 0.9.14
Summary: Python SDK for Dakera - AI memory platform
Author: Dakera Team
License: MIT
Project-URL: Homepage, https://dakera.ai
Project-URL: Documentation, https://dakera.ai/docs/python
Project-URL: Repository, https://github.com/dakera-ai/dakera-py
Project-URL: Issues, https://github.com/dakera-ai/dakera-py/issues
Keywords: vector,database,embedding,similarity,search,ai,ml,memory,dakera
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Database
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Typing :: Typed
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: requests>=2.33.0
Requires-Dist: urllib3>=2.6.3
Provides-Extra: dev
Requires-Dist: pytest>=7.0.0; extra == "dev"
Requires-Dist: pytest-asyncio>=0.21.0; extra == "dev"
Requires-Dist: responses>=0.23.0; extra == "dev"
Requires-Dist: mypy>=1.0.0; extra == "dev"
Requires-Dist: ruff>=0.1.0; extra == "dev"
Requires-Dist: types-requests>=2.28.0; extra == "dev"
Provides-Extra: async
Requires-Dist: httpx>=0.24.0; extra == "async"
Dynamic: license-file

# ⚡ dakera-py

Python SDK for Dakera AI — store, recall, and search agent memories against a Dakera instance.

Part of [Dakera AI](https://dakera.ai) — the memory engine for AI agents.

---

## Install

```bash
pip install dakera
```

## Quick Start

```python
from dakera import DakeraClient

client = DakeraClient(base_url="http://localhost:3300", api_key="your-key")

# Store a vector
client.vectors.upsert(
    id="vec-001",
    values=[0.1, 0.2, 0.3],
    metadata={"text": "agent completed task", "agent_id": "my-agent"}
)

# Full-text search
results = client.fulltext.search(query="completed task", top_k=5)
for r in results:
    print(r.id, r.score)

# Store an agent memory
client.memories.store(
    agent_id="my-agent",
    content="User prefers concise responses",
    importance=0.8,
    tags=["preference", "ux"]
)
```

## Connect to Dakera

```python
from dakera import DakeraClient

# Self-hosted
client = DakeraClient(base_url="http://your-server:3300", api_key="your-key")

# Cloud (early access)
client = DakeraClient(base_url="https://api.dakera.ai", api_key="your-key")
```

## Documentation

→ [Full docs](https://dakera.ai/docs)  
→ [API reference](https://dakera.ai/docs/api)  
→ [Python SDK reference](https://dakera.ai/docs/sdk/python)

## Related

| Repo | What it is |
|---|---|
| [dakera-js](https://github.com/dakera-ai/dakera-js) | TypeScript SDK |
| [dakera-go](https://github.com/dakera-ai/dakera-go) | Go SDK |
| [dakera-rs](https://github.com/dakera-ai/dakera-rs) | Rust client |
| [dakera-cli](https://github.com/dakera-ai/dakera-cli) | CLI |
| [dakera-mcp](https://github.com/dakera-ai/dakera-mcp) | MCP server · 83 tools |
| [dakera-deploy](https://github.com/dakera-ai/dakera-deploy) | Self-host Dakera |

---

*Part of the Dakera AI open core. The engine is proprietary. The tools are yours.*
