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ContextLattice

Write-optimized control plane that agents inhabit as a runtime contract — durable handoff over amnesia

What's Peculiar

The Write-Path Problem

Most agent memory systems optimize the read path: Mem0 for knowledge graphs, Zep for temporal reasoning, Hindsight for multi-strategy retrieval. ContextLattice uniquely optimizes the write path.

Context freshness under load: In long-horizon agents, write latency cascades → queue congestion → stale retrieval indexes → poor context → unreliable decisions. ContextLattice explicitly models the efficacy chain:

Write speed ↑ → queue pressure ↓ → sink freshness ↑ → retrieval freshness ↑ → recall quality ↑ → task completion reliability ↑

Architecture: Staged Dual-Path Retrieval

Write Path

┌─────────────────────────────────────────────┐
│ Single Orchestrator Entry Point             │
│ - Durable raw write (Mongo ledger)         │
│ - Async fanout to specialized sinks         │
│ - Admission control + queue monitoring      │
└──────┬──────────────────────────────────────┘
       │
       ├─→ Fast Sinks: Topic rollups, Qdrant, pgvector
       ├─→ Deep Sinks: MindsDB, Letta, memory-bank
       └─→ Durable Outbox: Coalescing, retries, backpressure

Read Path (Two-Lane Retrieval)

Fast Lane (p50: 450ms-2s)
├─ Topic rollups (high-signal summaries)
├─ Qdrant (semantic vectors)
└─ PostgreSQL-pgvector (dense embeddings)
   ↓
Fusion + Learning Reranking
├─ Multi-source result merging
├─ Feedback-driven ranking improvement
└─ Code-context enrichment (symbol overlap, file-path proximity)
   ↓
Deep Continuation (async, non-blocking)
├─ MindsDB (ML/analytics queries)
├─ Mongo raw (full ledger search)
├─ Letta (agent memory integration)
└─ memory-bank (specialized retrieval)

Fail-open degradation: Returns fast-lane results even if deep lanes timeout. Retrieval never blocks on slow backends.

Agent Runtime Contract (v3.4.0)

v3.4.0 (2026-06-05) crosses CL from a callable memory service to an inhabitable agent runtime. The website headline shifted from write-optimization / freshness framing to amnesia prevention: "Stop giving your agents amnesia and calling it workflow."

Technical Stack

Gateway: Go service (41 files, 212KB) — HTTP ingress, write admission, retrieval coordination
Core Engine: Rust crates — context_engine (memory graph primitives), context_retrieval (ranking/indexing), context_codec (serialization)
Vector Stores: Qdrant, Weaviate, LanceDB backends
Relational: PostgreSQL-pgvector, MongoDB, MindsDB
LLM Routing: Ollama (local models), Letta integration
Protocol: MCP-native (Model Context Protocol, 100 msgs/sec writes)

Comparison With Read-Optimized Systems

System Primary Focus Write Path Read Path
ContextLattice Context freshness + efficacy Durable orchestrator + fanout Staged multi-lane + learning rerank
Letta Stateful agent memory blocks Agent tools (editable) In-context injection
Mem0 Structured knowledge graphs Vector + entity extraction Graph + vector search
Zep Temporal knowledge graphs Entity extraction Temporal graph traversal
Hindsight Multi-strategy retrieval Standard RAG 4 parallel strategies (semantic, BM25, graph, temporal)

ContextLattice's niche: Long-horizon agent reliability where write latency matters. Less useful if you need temporal reasoning (Zep) or knowledge graphs (Mem0/Cognee).

Isolation & Multi-Tenancy

Isolation mechanism: None / Prompt-level on the spectrum. Local-first, API-key auth on protected endpoints, HTTP+MCP transport only. No container, micro-VM, or network-level isolation.

Multi-tenancy trade-offs: Single-node local-first by design — there is no cross-tenant firewall because there is no multi-node tenancy model. The boundary is the box it runs in.

Recommended architecture: Single-tenant on-premises for air-gapped deployments; any multi-tenant SaaS use must layer RBAC and process isolation on top.

Production Evidence

Active deployments: Payment processing features (Stripe webhooks, PayPal verification) indicate real paying customers.
Release velocity: Very high — 30 GitHub releases, v3.3.x through v3.4.23 in roughly Feb–June 2026; repo pushed 2026-06-14, not archived.
Community: 113 stars, 7 forks (small but focused), registered on Glama MCP server platform.
License: Business Source License 1.1 (non-open, commons clause).
Version boundary: v3.4.0 is the stable public agent-runtime-contract baseline; v4 remains a private tuning/experiment lane gated on benchmark/recall/soak.

Performance baseline concerns: Public perf-baseline.md shows degradation on complex workloads (multi-agent: p50=21s with 33% timeout rate). No external benchmarks comparing retrieval efficacy against competitors.

When to Use ContextLattice

Choose ContextLattice if:

Choose alternatives if:

Links

GitHub Repository · Official Website · Glama MCP Registry

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