Shodh Memory - Persistent Context for AI Agents

Shodh Memory gives you persistent memory across conversations. Unlike your context window which resets each session, memories stored here persist indefinitely and can be recalled semantically.

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"

Best Practices

1. Call proactive_context First

Every user message → call proactive_context with the message

This automatically:

• Retrieves relevant memories
• Stores the conversation context
• Builds association graph over time

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_by_tags(["project-x", "api"])

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.

5. Leverage Different Recall Modes

Mode	When to Use
semantic	Pure meaning-based search ("database optimization")
associative	Follow learned connections ("what else relates to X?")
hybrid	Best of both (default, recommended)

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 recall_by_tags if you know relevant tags
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

Memory Lifecycle

New Memory → Working Memory (hot, fast access)
            ↓ (consolidation)
         Session Memory (warm, recent context)
            ↓ (importance threshold)
         Long-term Memory (persistent, searchable)

The system automatically:

• Strengthens frequently-accessed memories
• Decays unused memories (but never fully forgets)
• Forms associations between co-retrieved memories
• Replays important memories during maintenance

API Quick Reference

Core Tools

Tool	Purpose
proactive_context	Call every message. Surfaces relevant memories, stores context
remember	Store a new memory
recall	Search memories by meaning
recall_by_tags	Filter memories by tags
recall_by_date	Filter memories by time range
forget	Delete a specific memory
forget_by_tags	Delete memories matching tags

Diagnostic Tools

Tool	Purpose
memory_stats	Get counts and health status
context_summary	Quick overview of recent learnings/decisions
consolidation_report	See what the memory system is learning
verify_index	Check index health
repair_index	Fix orphaned memories

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 context_summary to see what's accumulated
5. Trust the system - It strengthens useful memories automatically

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Shodh Memory: Because context shouldn't reset with every conversation.