aurra 0.3.0

Memory infrastructure for AI agents — citations, audit trails, bi-temporal versioning, BYO-LLM

Aurra Python SDK

Memory infrastructure for AI agents. Stores conversation memories with source citations, full audit trails, and Bring-Your-Own-LLM extraction.

pip install aurra

Quick start

from aurra import Aurra

client = Aurra(api_key="aurra_...")

# Add memories from a dialog (Aurra extracts atomic facts via LLM)
result = client.memories.add(
    messages=[
        {"role": "user", "content": "Hi, I'm Alice. I play pickleball every Tuesday."},
        {"role": "assistant", "content": "Nice to meet you Alice!"},
    ],
    session_id="user_alice_session_1",
)
print(f"Saved {result.saved_count} memories: {result.memory_ids}")

# Read memories — every record includes a source_citation object
for m in client.memories.list(limit=5):
    print(m["topic"], "—", m["summary"])
    print("  source:", m["source_citation"]["type"], "/", m["source_citation"]["channel"])

# Ask a question — Aurra runs semantic search + LLM answer
result = client.memories.query("What does Alice do on Tuesdays?")
print(result.answer)

Bring Your Own LLM

By default, Aurra extracts memories using its hosted Anthropic key. Pass an llm config to use your own provider + key per request:

# Extract with your own OpenAI key
client.memories.add(
    messages=[...],
    session_id="...",
    llm={
        "provider": "openai",
        "api_key": "sk-proj-...",
        "model": "gpt-4o",
    },
)

# Or your own Anthropic key
client.memories.add(
    messages=[...],
    session_id="...",
    llm={
        "provider": "anthropic",
        "api_key": "sk-ant-...",
        "model": "claude-opus-4-5",
    },
)

Supported providers today: anthropic, openai. User-supplied keys are never logged or stored.

Source citations

Every memory comes back with a source_citation object so you can trace where it came from:

mem = client.memories.list(limit=1)[0]
print(mem["source_citation"])
# {
#   "type": "agent_session",
#   "channel": "user_alice_session_1",
#   "captured_at": "2026-04-30T22:31:09.123456+00:00",
#   "session_id": "user_alice_session_1",
#   "original_message": "User: Hi, I'm Alice...\nAssistant: Nice to meet you!"
# }

Audit trails

Get the full provenance + extraction metadata + history for any memory:

audit = client.memories.get_audit(memory_id="aa5edf37-0475-4e40-bcd5-17c2557f4d6a")
print(audit["provenance"]["original_input"])
print(audit["extraction"]["model"])           # "claude-opus-4-5"
print(audit["history"])                        # [{"event": "created", "at": "...", "by": "aurra-extractor"}]

Errors

Typed exceptions for predictable handling:

from aurra import (
    AurraAuthError,           # 401: bad Aurra key
    AurraValidationError,     # 400: bad request shape
    AurraNotFoundError,       # 404: memory not found
    AurraRateLimitError,      # 429: Aurra rate limit
    AurraLLMProviderError,    # BYO-LLM: provider rejected (bad key, model not found, etc.)
    AurraServerError,         # 5xx: Aurra backend error
    AurraConnectionError,     # network: couldn't reach Aurra
    AurraError,               # base class for all of the above
)

try:
    client.memories.add(
        messages=[{"role": "user", "content": "test"}],
        session_id="s",
        llm={"provider": "openai", "api_key": "sk-bad", "model": "gpt-4o"},
    )
except AurraLLMProviderError as e:
    print(f"{e.provider}: {e.message}")  # "openai: Invalid OpenAI API key"

Use with Claude

import anthropic
from aurra import Aurra

memory = Aurra(api_key="aurra_...")
claude = anthropic.Anthropic()

def ask_with_memory(question: str) -> str:
    context = memory.memories.query(question)
    response = claude.messages.create(
        model="claude-opus-4-5",
        max_tokens=1024,
        messages=[{
            "role": "user",
            "content": f"Context:\n{context.answer}\n\nQuestion: {question}",
        }],
    )
    return response.content[0].text

Use with OpenAI

from openai import OpenAI
from aurra import Aurra

memory = Aurra(api_key="aurra_...")
openai_client = OpenAI()

def ask_with_memory(question: str) -> str:
    context = memory.memories.query(question)
    response = openai_client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": f"Context: {context.answer}"},
            {"role": "user", "content": question},
        ],
    )
    return response.choices[0].message.content

API reference

Method	Description
Aurra(api_key, base_url=None, timeout=60.0)	Create a client.
client.memories.add(messages, session_id, tenant_id=None, llm=None)	Extract memories from a dialog. Returns AddResult.
client.memories.add(content, topic, importance, source)	Write a single memory verbatim (legacy shape).
client.memories.list(limit=20)	List recent memories with citations.
client.memories.query(question, limit=10)	Ask a question, get AI-answered response. Returns QueryResult.
client.memories.get_audit(memory_id)	Full provenance + extraction + history for one memory.

Links

• Homepage: https://aurra.us
• Benchmarks: https://github.com/aurra-memory/benchmarks
• Issues: [e-mail]

License

MIT