Shodh Cloudflare - Persistent Context for AI Agents

Shodh Cloudflare gives you persistent memory across conversations, powered by Cloudflare's globally distributed edge network. Unlike your context window which resets each session, memories stored here persist indefinitely and can be recalled semantically from anywhere in the world.

When to Use Memory

ALWAYS call proactive_context at the start of every conversation with the user's first message. This surfaces relevant memories automatically.

Store memories (remember) when:

• User makes a decision ("Let's use PostgreSQL for this project")
• You learn something new ("The codebase uses a monorepo structure")
• An error occurs and you find the fix
• You discover a pattern in the user's preferences
• Important context that will be useful later

Recall memories (recall) when:

• User asks about past decisions or context
• You need to remember project-specific information
• Looking for patterns in how problems were solved before

Memory Types

Choose the right type for better retrieval:

Type	When to Use	Example
Decision	User choices, architectural decisions	"User chose React over Vue for the frontend"
Learning	New knowledge gained	"This API requires OAuth2 with PKCE flow"
Error	Bugs found and fixes	"TypeError in auth.js fixed by null check"
Discovery	Insights, aha moments	"The performance issue was caused by N+1 queries"
Pattern	Recurring behaviors	"User prefers functional components over classes"
Context	Background information	"Working on e-commerce platform for client X"
Task	Work in progress	"Currently refactoring the payment module"
Observation	General notes	"User typically works in the morning"
Conversation	Auto-ingested conversations	Automatically stored conversation context

Best Practices

1. Call proactive_context First

Every user message → call proactive_context with the message

This automatically:

• Retrieves relevant memories based on semantic similarity
• Surfaces context from previous sessions
• Helps maintain continuity across conversations

2. Write Rich, Searchable Memories

Good:

"Decision: Use PostgreSQL with pgvector extension for the RAG application.
Reasoning: Need vector similarity search, user already has Postgres expertise,
avoids adding new infrastructure. Alternative considered: Pinecone (rejected
due to cost)."

Bad:

"Use postgres"

3. Use Tags for Organization

Tags enable fast filtering without semantic search:

{
  "content": "API rate limit is 100 requests/minute",
  "tags": ["api", "rate-limit", "backend", "project-x"]
}

Later recall with: recall using tag filters or forget_by_tags for cleanup

4. Memory Types Affect Importance

The system automatically weights memory types:

• Decision and Error → Higher importance, slower decay
• Context and Observation → Lower importance, faster decay

Choose types accurately for better long-term retention.

Common Patterns

Starting a Session

1. User sends first message
2. Call proactive_context(context: user_message)
3. Review surfaced memories
4. Respond with relevant context

After Making Progress

1. Complete a significant task
2. Call remember() with:
   - What was done
   - Why it was done
   - Key decisions made
   - Any gotchas discovered

When User Asks "Do you remember..."

1. Call recall(query: "what user is asking about")
2. Also try list_memories to browse recent entries
3. Synthesize memories into response

Debugging a Recurring Issue

1. recall(query: "error in [component]")
2. Check if similar errors were solved before
3. Apply previous fix or note new solution
4. remember() the resolution

API Quick Reference

Core Tools

Tool	Purpose
proactive_context	Call every message. Surfaces relevant memories automatically
remember	Store a new memory with type and tags
recall	Semantic search across all memories
list_memories	Browse all stored memories
forget	Delete a specific memory by ID
forget_by_tags	Delete memories matching specific tags

Diagnostic Tools

Tool	Purpose
memory_stats	Get counts, storage usage, and health status
context_summary	Quick overview of recent learnings and decisions

Example Workflow

User: "Let's start building the user authentication system"

You:
1. proactive_context("Let's start building the user authentication system")
   → Surfaces: Previous auth decisions, security preferences, tech stack

2. Response incorporates remembered context:
   "Based on our earlier decision to use PostgreSQL and your preference
   for JWT tokens, I'll set up auth with..."

3. After implementation:
   remember(
     content: "Implemented JWT authentication with refresh token rotation.
               Used bcrypt for password hashing (cost factor 12).
               Tokens expire in 15 minutes, refresh tokens in 7 days.",
     type: "Learning",
     tags: ["auth", "jwt", "security", "user-system"]
   )

Tips for Effective Memory

1. Be specific - "React 18 with TypeScript" not "frontend framework"
2. Include reasoning - Why decisions were made, not just what
3. Tag consistently - Use a tagging convention across the project
4. Review periodically - Use memory_stats to see what's accumulated
5. Trust the system - Semantic search finds relevant memories automatically

Cloudflare Edge Benefits

This implementation runs on Cloudflare's edge network, providing:

• Global low-latency access - Memories accessible from anywhere with minimal delay
• High availability - Cloudflare's distributed infrastructure ensures uptime
• Automatic scaling - Handles varying workloads seamlessly
• D1 + Vectorize - SQLite for metadata, vector database for semantic search
• Workers AI - BGE-small-en-v1.5 embeddings (384 dimensions) for semantic matching

---

Shodh Cloudflare: Because context shouldn't reset with every conversation - and it should be fast, globally.