neruva-record 0.2.0

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
)

Claude Code hook recorder (0.2.0+)

Capture everything in a Claude Code session — built-in tool calls (Bash, Read, Write, Edit, Grep, Glob, WebFetch, WebSearch), every MCP tool call, every user prompt, every assistant turn end, every subagent invocation, every task — into one Neruva namespace. No code change in your agent; one settings snippet.

Install

pip install neruva-record

This installs the neruva-record-hook console script.

Wire into ~/.claude/settings.json (or per-project .claude/settings.json)

{
  "env": {
    "NERUVA_API_KEY": "nv_...",
    "NERUVA_AUTO_RECORD": "brain/main:30"
  },
  "hooks": {
    "UserPromptSubmit": [
      { "hooks": [{ "type": "command", "command": "neruva-record-hook", "async": true }] }
    ],
    "PostToolUse": [
      { "matcher": "*", "hooks": [{ "type": "command", "command": "neruva-record-hook", "async": true }] }
    ],
    "PostToolUseFailure": [
      { "matcher": "*", "hooks": [{ "type": "command", "command": "neruva-record-hook", "async": true }] }
    ],
    "Stop": [
      { "hooks": [{ "type": "command", "command": "neruva-record-hook", "async": true }] }
    ],
    "SessionStart": [
      { "hooks": [{ "type": "command", "command": "neruva-record-hook", "async": true }] }
    ],
    "SessionEnd": [
      { "hooks": [{ "type": "command", "command": "neruva-record-hook", "async": true }] }
    ],
    "SubagentStart": [
      { "hooks": [{ "type": "command", "command": "neruva-record-hook", "async": true }] }
    ],
    "SubagentStop": [
      { "hooks": [{ "type": "command", "command": "neruva-record-hook", "async": true }] }
    ],
    "TaskCreated": [
      { "hooks": [{ "type": "command", "command": "neruva-record-hook", "async": true }] }
    ],
    "TaskCompleted": [
      { "hooks": [{ "type": "command", "command": "neruva-record-hook", "async": true }] }
    ]
  }
}

async: true is the magic — Claude Code fires the hook fire-and-forget without waiting for it to return, so your tool calls never slow down. Recording happens in parallel.

What gets recorded

Per Claude Code event, one record. Each carries metadata.kind and metadata.event so you can filter at query time.

Event	metadata.kind	What's captured
UserPromptSubmit	user_prompt	The user's prompt text
PostToolUse	tool_call	tool name + input summary + result
PostToolUseFailure	tool_failure	tool name + input + error
Stop	assistant_stop	session boundary marker
SessionStart	session_start	source + model + cwd
SessionEnd	session_end	end reason
SubagentStart	subagent_start	agent type + prompt
SubagentStop	subagent_stop	agent type
TaskCreated	task_created	task title + description
TaskCompleted	task_completed	task title

Events to/from Neruva's own MCP (mcp__neruva__memory_*) are auto-skipped to prevent recording the recording.

Query later

curl -X POST https://api.neruva.io/v1/indexes/brain/text/query \
  -H "Api-Key: $NERUVA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "namespace": "main",
    "text": "what bash commands did I run?",
    "topK": 10,
    "filter": { "kind": { "$eq": "tool_call" }, "tool": { "$eq": "Bash" } }
  }'

Or via the Neruva MCP installed in another agent: memory_query_text.

Stacks with auto_record(Anthropic())

You can run both at once. The hook recorder captures Claude Code's tool calls + prompts + assistant turns; auto_record(Anthropic()) captures the LLM turns of any Python script you write that uses Anthropic SDK directly. They share the same namespace if you want.

Roadmap

• v0.1: Anthropic Python (sync + async, non-streaming) ✅
• v0.2: Claude Code hook recorder (neruva-record-hook) ✅
• v0.3: Anthropic streaming + tool-use turn capture
• v0.4: OpenAI Python
• v0.5: Google Gemini Python
• v0.6: 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.