Metadata-Version: 2.4
Name: contexara
Version: 2.0.0
Summary: CLI-first memory engine for LLM applications.
Author: Contexara Contributors
Project-URL: Homepage, https://github.com/Prajwal-Narayan/Contexara
Project-URL: Repository, https://github.com/Prajwal-Narayan/Contexara
Keywords: llm,memory,agentic-ai,retrieval,context
Requires-Python: >=3.10
Description-Content-Type: text/markdown
Requires-Dist: boto3>=1.34.0
Requires-Dist: python-dotenv>=1.0.0
Requires-Dist: numpy>=1.26.0
Requires-Dist: rich>=13.0.0
Requires-Dist: mcp>=1.0.0
Requires-Dist: fastapi>=0.100.0
Requires-Dist: uvicorn>=0.24.0
Requires-Dist: httpx>=0.24.0
Provides-Extra: dev
Requires-Dist: pytest>=8.0.0; extra == "dev"
Requires-Dist: build>=1.2.0; extra == "dev"

# Contexara

Persistent memory engine for LLM applications. Drop it into any agent or chatbot — it remembers what matters across sessions, with no vector database, no infra.

```bash
pip install contexara
contexara setup        # configure AWS Bedrock credentials
contexara ask "what was I working on?"
```

---

## Memory model

| Tier | Stores | When |
|------|--------|------|
| Raw turns | Every message in the session | Live |
| Episodes | LLM-crystallized session summaries | On session close |
| Semantic facts | Extracted atomic memories | Permanent |
| Cold archive | Turns older than 30 days | FTS5, auto-swept |

Sessions idle for 60 minutes crystallize automatically in the background.

---

## Interfaces

**CLI** — `ask`, `store`, `search`, `list`, `chat`, `ingest`, `namespace`, `mcp`, `eval`

**MCP server** — 13 tools over stdio or SSE. Plug into Claude Desktop or any agent SDK.

**Python SDK**
```python
from contexara import ContexaraClient
client = ContexaraClient(namespace="my_agent")
client.memory.store("user prefers Python", kind="preference")
memories = client.memory.search("current goals")
client.chat.ingest(user_msg, assistant_msg)
```

**Dashboard**
```bash
contexara dashboard --port 8000
```
Latency charts, trace explorer, namespace management, LLM-as-judge eval scores — all in one UI.

---

**Stack:** SQLite · AWS Bedrock · Amazon Titan Embeddings · RRF hybrid retrieval · FastAPI · React

Built by [Prajwal Narayan](https://github.com/Prajwal-Narayan)
