Sekha Roadmap¶

Vision¶

Build the world's most capable AI memory system - from personal assistants to self-improving autonomous agents.

Timeframe: 2026-2029

✅ Current (Production Ready)¶

Core Storage & Retrieval¶

✅ SQLite database with full ACID guarantees
✅ ChromaDB vector storage for semantic search
✅ Full-text search via SQLite FTS5
✅ Label and folder hierarchical organization
✅ Importance scoring (1-10 scale)
✅ Status tracking (active/archived)
✅ Conversation metadata (word count, timestamps, sessions)

APIs¶

✅ 17 REST endpoints (create, query, update, delete, search, stats)
✅ 7 MCP protocol tools for Claude Desktop integration
✅ OpenAPI/Swagger documentation
✅ Rate limiting and CORS
✅ Bearer token authentication

Orchestration¶

✅ Context assembly with semantic + recency + importance ranking
✅ Hierarchical summarization (daily → weekly → monthly)
✅ AI-powered label suggestions
✅ Pruning recommendations
✅ Deduplication and token budget optimization

LLM Integration¶

✅ Ollama support (nomic-embed-text for embeddings)
✅ Llama 3.1 for summarization
✅ Async embedding pipeline with retry logic

Production Features¶

✅ Docker multi-arch builds (amd64/arm64)
✅ Comprehensive CI/CD with 80%+ coverage
✅ Security audits (cargo-deny, cargo-audit)
✅ Health checks and Prometheus metrics
✅ Hot config reload
✅ Structured logging (JSON + pretty)

🎯 Q1 2026 - Multi-LLM Support¶

Goal: Make Sekha work with any LLM provider.

OpenAI Integration¶

GPT-4 API integration
OpenAI embeddings (text-embedding-3-small, text-embedding-3-large)
Streaming responses
Function calling for summarization
Cost tracking per conversation

Anthropic Claude Integration¶

Claude 3 Opus/Sonnet/Haiku support
Anthropic embeddings (Voyage AI)
Extended context windows (200K+)
Claude-specific prompt optimization

Google Gemini Support¶

Gemini Pro/Ultra API integration
Google embeddings
Multi-modal support (text + images)

LLM Provider Abstraction¶

Plug-and-play provider configuration
Unified interface for all LLM operations
Automatic failover between providers
Provider-agnostic conversation storage
Switch LLMs mid-conversation without losing context

Deliverables:

[llm]
provider = "openai" # or "anthropic" | "google" | "ollama" | "custom"
api_key = "sk-..."
embedding_model = "text-embedding-3-small"
summarization_model = "gpt-4-turbo"

🚀 Q2 2026 - Scale & Performance¶

Goal: Handle enterprise scale and high-concurrency workloads.

PostgreSQL Backend¶

PostgreSQL as alternative to SQLite
Multi-user support with user isolation
Team/organization data partitioning
Horizontal read replicas
Connection pooling optimization

Redis Caching Layer¶

Cache frequently accessed conversations
Query result caching
Session management
Rate limiting via Redis
Distributed locks for concurrent writes

Horizontal Scaling¶

Kubernetes & Helm¶

Performance Benchmarks¶

Academic paper submission
Public benchmark repository
Million+ conversation tests
Multi-user concurrent access patterns
Real-world production workload simulation

Targets:

10M+ conversations per instance
1,000+ concurrent users
Sub-50ms query latency (P95)
99.9% uptime SLA

🧠 Q3 2026 - Advanced Features¶

Goal: Transform conversations into knowledge.

Knowledge Graph Extraction¶

Entity extraction from conversations (people, places, concepts)
Relationship mapping ("X works with Y", "A caused B")
Temporal reasoning ("Before/After", timelines)
Graph queries ("Find all conversations about X that mention Y")
Neo4j integration for graph storage

Enhanced Relationship Mapping¶

Conversation clustering (similar topics)
Thread detection (related conversations)
Cross-conversation references
"Similar to this" recommendations
Automatic conversation chains

Temporal Reasoning¶

Time-aware context assembly
"What was I thinking about X last month?" queries
Trend detection over time
Periodic summary generation
Historical diff ("How did my thinking evolve?")

Federated Sync¶

Use Cases:

"Show me all conversations where Alice and Bob discussed the new feature"
"What was my opinion on AI safety 6 months ago vs today?"
"Find all project decisions related to authentication"

🏢 Q4 2026 - Enterprise & Collaboration¶

Goal: Enable team and organizational use.

Multi-Tenant Architecture¶

Organization/workspace isolation
User authentication (OAuth, SAML)
Tenant-specific configuration
Usage quotas and billing integration
Admin dashboard for tenant management

Team Collaboration¶

Role-Based Access Control (RBAC)¶

User roles (admin, member, viewer)
Permission system (read, write, delete, share)
Folder-level permissions
Label-based access control
Audit logs for compliance

Compliance & Security¶

WebSocket Real-Time Updates¶

Pricing Model:

Free: Personal use (<50 employees)
Pro: $50/user/month (teams)
Enterprise: Custom (compliance, SLA, support)

🤖 2027 - AI Agent Ecosystem¶

Goal: Power the next generation of autonomous agents.

Shared memory pools between agents
Agent discovery protocol
Trust and permission models
Memory handoff (agent A → agent B)
Collective knowledge accumulation

Autonomous Agent Memory Management¶

Agents manage their own memory
Automatic labeling and organization
Self-pruning low-value memories
Priority-based context assembly
Long-term vs. short-term memory distinction

Self-Improving Agent Frameworks¶

Agents learn from every interaction
Mistake tracking and avoidance
Strategy evolution over time
Automatic skill acquisition
Performance benchmarking

Cross-Agent Knowledge Transfer¶

Research Collaborations:

OpenAI (agent frameworks)
Anthropic (Constitutional AI)
DeepMind (multi-agent systems)
Stanford (agent safety)

🔬 2028-2029 - Advanced Intelligence¶

Goal: Contribute to AGI research.

CRDT-Based Conflict Resolution¶

Distributed memory architecture
Conflict-free replicated data types
Eventual consistency guarantees
Offline-first operation
Multi-datacenter synchronization

GPU-Accelerated Vector Operations¶

CUDA/ROCm support for embeddings
10x faster semantic search
Batch processing optimization
Real-time embedding generation
Hardware acceleration API

Plugin System¶

Custom LLM backend plugins
Storage backend plugins (Milvus, Pinecone)
Orchestration strategy plugins
Custom embedding models
Community plugin marketplace

Zero-Knowledge Encryption¶

End-to-end encryption for all conversations
Client-side encryption keys
Secure multi-party computation
Homomorphic encryption for search
Privacy-preserving analytics

Blockchain Provenance Tracking (Optional)¶

Immutable conversation history
Cryptographic proof of memory
Timestamping via blockchain
Decentralized storage option
Smart contract integration

AGI Research Contributions¶

Memory consolidation algorithms
Hierarchical abstraction learning
Metacognitive reasoning
Episodic vs. semantic memory models
Continual learning without catastrophic forgetting

Research Publications:

NeurIPS (memory architectures)
ICML (agent learning)
ACL (natural language memory)
AAAI (knowledge representation)

🌍 Community & Ecosystem¶

Developer Tools¶

Q1 2026:

Python SDK v1.0
JavaScript/TypeScript SDK v1.0
Ruby SDK
Go SDK

Q2 2026:

Rust SDK (native)
Java/Kotlin SDK
.NET SDK (C#)

Integrations¶