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How to Build an Agent Memory Wiki with Search

Build a diffable markdown wiki for agent memory with search grounding for freshness. Architecture pattern for persistent agents.

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An open-source 'super memory' for AI agents was posted — multi-agent RAG + hybrid wiki search + transparency score. This tutorial builds the search-grounded memory layer.

Prerequisites

  • Scavio API key
  • Git repository for the wiki
  • LLM API key

Walkthrough

Step 1: Design the wiki structure

Markdown files organized by topic with metadata frontmatter.

# wiki/topics/search-api-pricing.md
# ---
# topic: Search API Pricing
# last_verified: 2026-05-03
# confidence: high
# sources: [scavio.dev, serpapi.com, tavily.com]
# ---
# Scavio: $0.005/query, $30/mo for 7K credits
# SerpAPI: $75/mo for 5K searches
# ...

Step 2: Build the refresh function

Search API verifies and updates stale entries.

Python
import requests, os, datetime
H = {'x-api-key': os.environ['SCAVIO_API_KEY']}

def refresh_entry(topic, current_content):
    results = requests.post('https://api.scavio.dev/api/v1/search', headers=H,
        json={'platform': 'google', 'query': f'{topic} 2026 current'}).json()
    # LLM compares current wiki content vs fresh search results
    # Updates only if facts changed
    # Logs the diff for transparency

Step 3: Add transparency scoring

Each entry gets a confidence score based on recency and source count.

Python
def transparency_score(entry):
    days_since_verified = (datetime.date.today() - entry['last_verified']).days
    if days_since_verified < 7: return 'high'
    if days_since_verified < 30: return 'medium'
    return 'stale'

Step 4: Schedule refresh cron

Daily cron refreshes stale entries.

Bash
# crontab: 0 3 * * * python refresh_wiki.py
# Refreshes entries with confidence < 'high'
# Budget: ~50 queries/day = $0.25/day

Step 5: Git-track changes

Every refresh is a git commit for full diff history.

Bash
# After refresh:
git add wiki/
git commit -m "refresh: $(date +%Y-%m-%d) - N entries updated"
# Full diff history in git log

Python Example

Python
# Agent memory wiki: markdown files + search grounding + git history
# The wiki IS the memory. Git IS the audit trail.
# Search API keeps it fresh. LLM decides what changed.

JavaScript Example

JavaScript
// Same pattern in JS/TS with file system operations.

Expected Output

JSON
Git-tracked markdown wiki with daily search-grounded freshness verification, transparency scores, and full diff history.

Related Tutorials

  • How to Build a RAG Agent with LangChain and Scavio

Frequently Asked Questions

Most developers complete this tutorial in 15 to 30 minutes. You will need a Scavio API key (free tier works) and a working Python or JavaScript environment.

Scavio API key. Git repository for the wiki. LLM API key. A Scavio API key gives you 50 free credits on signup.

Yes. The free tier includes 50 credits on signup, which is more than enough to complete this tutorial and prototype a working solution.

Scavio has a native LangChain package (langchain-scavio), an MCP server, and a plain REST API that works with any HTTP client. This tutorial uses the raw REST API, but you can adapt to your framework of choice.

Related Resources

Solution

Agent Memory with Search Grounding

Read more
Best Of

Best APIs for Agent Memory Search Grounding (2026)

Read more
Best Of

Best Agent Search Fallback Architectures (2026)

Read more
Glossary

Agent Memory Wiki

Read more
Solution

Verify Search Results Before Agent Takes Action

Read more
Use Case

Combined Memory and Search Agent Grounding

Read more

Start Building

Build a diffable markdown wiki for agent memory with search grounding for freshness. Architecture pattern for persistent agents.

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