neruva-record 0.1.0

Drop-in auto-record wrapper for the Anthropic Python SDK — every LLM turn captured to Neruva Memory. Recall later via the Neruva MCP / API. Cross-session, cross-vendor, portable.

neruva-record

Drop-in auto-record wrapper for the Anthropic Python SDK. Every messages.create() call captures both directions to a Neruva Memory namespace as a side-effect. Stop manually saving things; query the namespace later for "what did this agent do?"

Pairs with @neruva/mcp (captures MCP tool calls). Together: full agent-loop capture.

Install

pip install neruva-record anthropic
export NERUVA_API_KEY=...        # https://app.neruva.io

Use

import anthropic
from neruva_record import auto_record

client = auto_record(
    anthropic.Anthropic(),
    index="brain",                # one per user/account
    namespace="main",             # one per agent (free-form)
    ttl_days=30,                  # optional: auto-expire records after N days
)

# Drop-in: client behaves identically to bare Anthropic.
response = client.messages.create(
    model="claude-opus-4-7",
    max_tokens=200,
    messages=[{"role": "user", "content": "Hi!"}],
)

# Recording happened as a side-effect. Query it later via the MCP
# or the REST API.

Async

from anthropic import AsyncAnthropic
from neruva_record import auto_record

client = auto_record(
    AsyncAnthropic(),
    index="brain", namespace="main",
)

response = await client.messages.create(
    model="claude-opus-4-7", max_tokens=200,
    messages=[{"role": "user", "content": "Hi!"}],
)

Naming convention

Field	What	Example
index	One per user/account. Agent-type-neutral.	brain
namespace	One per agent the user runs. Free-form.	main, support-bot, research

# multi-agent setup -- same brain, one namespace per agent
support  = auto_record(Anthropic(), index="brain", namespace="support-bot")
research = auto_record(Anthropic(), index="brain", namespace="research")
ops      = auto_record(Anthropic(), index="brain", namespace="orchestrator")

What gets recorded

Per LLM turn, one record:

{
  "id": "llm-<unix-ms>-<rand>",
  "text": "USER: <user-message>\n\nASSISTANT: <response>",
  "metadata": {
    "kind": "llm_turn",
    "vendor": "anthropic",
    "model": "claude-opus-4-7",
    "stop_reason": "end_turn",
    "input_tokens": 12,
    "output_tokens": 87,
    "latency_ms": 1240,
    "ts": <unix-ms>,
    "_auto_expire_at": <unix-ms-or-omitted>
  }
}

User text is truncated at 600 chars, assistant at 1800 chars (for embedding quality and storage cost). The full conversation history isn't stored — only the most recent user message and the response. For full trace capture, also use @neruva/mcp which records every tool call alongside this.

Querying back

Use the Neruva MCP or the REST API to query the recorded turns:

curl -X POST https://api.neruva.io/v1/indexes/brain/text/query \
  -H "Api-Key: $NERUVA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "namespace": "main",
    "text": "what did I ask about deployment?",
    "topK": 5,
    "includeMetadata": true,
    "filter": { "vendor": { "$eq": "anthropic" } }
  }'

Failure mode

Recording is fire-and-forget. If the recording POST fails for any reason, your messages.create() call returns normally — the recorder swallows its own errors. You will never get fewer responses than you asked for; you may get fewer records than you'd see in a perfect world.

TTL / decay

Set ttl_days=N and each record carries _auto_expire_at = ts + N days. The Neruva server-side decay sweep automatically removes records past their expiry on the next query/upsert touch (max once per 15 minutes per namespace). No background job to run.

client = auto_record(
    anthropic.Anthropic(),
    index="brain", namespace="main",
    ttl_days=7,    # only the last week is retained
)

Roadmap

• v0.1: Anthropic Python (sync + async, non-streaming)
• v0.2: Anthropic streaming + tool-use turn capture
• v0.3: OpenAI Python
• v0.4: Google Gemini Python
• v0.5: TypeScript versions of all the above
• v1.0: Stable API, security audit

Related

• @neruva/mcp — MCP tool-call auto-record
• neruva-mcp — Python MCP server
• neruva.io/docs — full documentation

License

MIT — Clouthier Simulation Labs.