Conversation with Gemini

This is a world-class engineering challenge. Transitioning a multi-agent swarm from a "fat-boot" architecture (74,000 tokens overhead) to a dynamic, Just-In-Time (JIT) "thin-boot" architecture (<700 tokens) requires the MCP server to act as a highly intelligent, low-latency central nervous system.

Here is the comprehensive architectural blueprint for the Genesis-Bloodstream MCP Server, built around FastMCP, Python asyncio, and Recursive Language Models (RLMs).

🧠 1. Recursive Language Models (RLMs) & System Architecture

What are RLMs? In advanced multi-agent memory systems, Recursive Language Models (RLMs) solve "context rot" and infinite-context token bloat. Instead of dumping massive retrieved logs directly into an Orchestrator’s context window, an RLM architecture treats context as an external programmable environment. A system recursively spawns cheaper, smaller models to continuously map-reduce, filter, and synthesize raw data into dense knowledge.

Importance & Optimal Cost-Efficient Architecture for Genesis: Without an RLM subsystem, your episodic memory (PostgreSQL/Supermemory) will grow linearly until a search_memory call returns 20,000 tokens of noise, destroying your <700 token budget.

• The Orchestrator: Claude Opus 4.6 (Expensive, high-reasoning) makes the high-level queries.

• The RLM Janitors: Gemini 1.5 Flash / Claude 3.5 Haiku (Ultra-cheap, fast).

• The Workflow: Bloodstream runs background asynchronous tasks. Every 10 minutes, an RLM Janitor fetches raw, verbose episodic logs from PostgreSQL, synthesizes them into immutable semantic relationships, writes the vectors to Qdrant, and builds edges in FalkorDB.

• The Result: When Opus queries memory, it retrieves RLM-compressed graph axioms instead of raw logs. You achieve infinite memory retrieval while maintaining sub-700 token JIT contexts, reducing orchestration token costs by >90%.

System Component Diagram

[Claude Opus / Agents] <--(FastMCP Protocol)--> [Bloodstream MCP Server]
                                                      |
    +-------------------------------------------------+-----------------------+
    |                        |                        |                       |
[Redis L1 Cache]       [FalkorDB Local]        [PostgreSQL / Qdrant]     [Supermemory API]
(Working Mem)          (Axioms/Graph)          (Episodic / Semantic)     (War Room Logs)
 5-20ms latency         1-10ms latency          50-300ms latency         200-500ms latency

🚦 2. Query Routing Strategy (Deep Think 1)

Do not default to a blind fan-out. A scatter-gather across all 5 systems will guarantee you bottleneck on your 500ms budget due to Supermemory and Qdrant.

Optimal Strategy: Intent-Based Tiered Routing When an agent calls search_memory(query, systems=["all"]):

1. L1 Fast-Path (5ms): Hash the query. Check Redis. Return instantly on an exact semantic cache hit.

2. Heuristic Intent Classification (0ms): Use a fast regex/keyword heuristic on the query string:

   • Relational Intent ("who depends on", "architecture", "blocks"): Route strictly to FalkorDB + Redis.

   • Factual/Audit Intent ("health logs", "what time did", "error code"): Route to PostgreSQL.

   • Semantic/Broad Intent ("similar issues", "learnings"): Route to Qdrant + Supermemory.

3. Scatter-Gather Execution: Dispatch the targeted queries concurrently.

⚡ 3. Multi-Layer Caching Strategy (Deep Think 2)

Redis (Elestio) acts as your L1 Read-Through / Write-Through cache.

• TTL Definitions by Volatility:

  • mcp:res:project-rules: 24 hours (Static; manually invalidated on update).

  • mcp:res:axiom-store: 1 hour (Graph topology evolves moderately as the swarm learns).

  • mcp:search:query_hash: 10 minutes (Prevents redundant search executions during a swarm burst).

  • mcp:res:current-mission: 5 minutes (Highly dynamic war-room state).

• Cache Invalidation: When commit_learning is called, Bloodstream updates the primary DBs and instantly executes a Write-Through update to the specific Redis key. It then publishes a genesis:events:invalidated payload to a Redis Pub/Sub channel so all worker nodes sync immediately.

🛡️ 4. Concurrent Access & Swarm Safety (Deep Think 3)

With 10 agents writing and reading simultaneously, state management is critical.

• Read-After-Write Consistency: Handled by the Write-Through cache. Because the commit_learning tool synchronously updates the L1 cache before returning a success message to the agent, the next agent querying memory 5ms later is guaranteed to hit the warmed, accurate L1 cache.

• Write Conflicts: Implement Optimistic Concurrency Control (OCC). Add a version integer to Postgres records. For FalkorDB, use Cypher MERGE (Upsert) statements instead of CREATE. If a conflict occurs, the MCP server returns a tool error directly to the agent LLM: "Conflict detected. Latest state is X. Please re-evaluate."

• Cache Stampede (Thundering Herd): If Redis resets, 10 agents querying current-mission will trigger 10 simultaneous Supermemory API calls. Implement the Singleflight (Promise Caching) pattern using asyncio.Event locks. Agent 1 initiates the database fetch and locks the key; Agents 2-10 safely await Agent 1's future. 1 DB call is made, 10 agents get the data.

⏱️ 5. Query Optimization & Merging (Deep Think 4)

To unify 5 disparate latencies and scoring mechanisms within a <500ms target:

• Asynchronous Timeboxing: Wrap the parallel execution in asyncio.wait(tasks, timeout=0.45). If Redis (5ms) and FalkorDB (10ms) return instantly, they are locked in. If Supermemory hasn't returned by 450ms, the task is explicitly cancelled. Fast results are guaranteed; slow results are best-effort.

• Deduplication: Postgres and Supermemory often store overlapping logs. Run a rapid Jaccard Similarity (or MinHash) on the returned strings in Python. If a log is >85% similar across two DBs, merge them and concatenate their source tags.

• Heterogeneous Ranking: You cannot directly compare Qdrant's Cosine Distance (0-1) with Postgres's BM25. Use Reciprocal Rank Fusion (RRF): RRF_Score = 1 / (60 + rank_in_system). Rank results independently within their own system, then combine them mathematically.

📡 6. Resource Design: Push vs. Pull (Deep Think 5)

• Optimal Strategy: Server-Sent Push (Event-Driven).

• Do not make agents waste tokens polling for rule or mission updates. The MCP protocol supports notifications/resources/updated.

• Flow:

  1. Agents subscribe to genesis://current-mission at boot.

  2. Bloodstream listens to PostgreSQL pg_notify and Redis Pub/Sub.

  3. When an external script or agent updates the mission, Bloodstream triggers ctx.session.send_resource_updated("genesis://current-mission").

  4. Claude Opus automatically pulls the update directly into its context window, ensuring the swarm never operates on stale data.

🔌 7. Failure Modes & Resilience (Deep Think 6)

• Qdrant Down: Wrap the call in a Circuit Breaker (pybreaker). If Qdrant fails, the breaker trips, and semantic queries seamlessly degrade to PostgreSQL using pg_trgm (trigram text similarity).

• Redis Cold Start: The Singleflight pattern safely absorbs the initial shock. A background task on MCP boot pre-warms active rules and axioms.

• FalkorDB / Redis Port 6379 Isolation: FalkorDB is a Redis module. If they run on the same Redis engine, they natively isolate via keyspace. However, best practice is to use Logical Database 0 (SELECT 0) strictly for Bloodstream caching/rate-limits, and Database 1 (SELECT 1) strictly for FalkorDB GRAPH.QUERY commands to prevent any flush collisions.

• Supermemory API Limits: If the API returns HTTP 429, instantly trip the circuit breaker for 60 seconds. Subsequent tool calls will return degraded_systems: ["supermemory"] locally rather than wasting 500ms waiting for timeouts.

🗄️ 8. Schema Design & Data Contracts (Deep Think 7)

A. Redis Cache Schema (DB 0)

bloodstream:cache:query:<md5>      -> String JSON Unified Results [TTL: 10m]
bloodstream:state:mission          -> String JSON War Room State  [TTL: 5m]
bloodstream:lock:<resource_id>     -> Mutex for Singleflight      [TTL: 5s, NX]
bloodstream:events:updates         -> Pub/Sub Invalidation Channel

B. PostgreSQL MCP Metadata

CREATE TABLE bloodstream_telemetry (
    id UUID PRIMARY KEY,
    agent_id VARCHAR(50),
    query_intent VARCHAR(50),
    systems_queried TEXT[],
    latency_ms INT,
    degraded_systems TEXT[],
    tokens_returned INT,
    created_at TIMESTAMP DEFAULT NOW()
);

C. Unified Response Format (Returned to the Agent Tool)

Must be strictly typed and concise to save tokens.

{
  "query": "deployment 502 error",
  "latency_ms": 310,
  "degraded_systems": ["supermemory"],
  "results": [
    {
      "source": "falkordb", 
      "type": "axiom", 
      "content": "(Nginx)-[BLOCKS]->(FastMCP)", 
      "rrf_score": 0.016
    },
    {
      "source": "postgres", 
      "type": "episodic", 
      "content": "Agent Kinan restarted Nginx at 10:00 PST", 
      "rrf_score": 0.015
    }
  ]
}

Implementation Stack Recommendation

Build this natively in Python 3.12+ using the official mcp SDK via FastMCP. Use asyncpg (PostgreSQL), redis.asyncio (Redis/Cache), qdrant-client (async), and the falkordb Python client.

Host the Python server via Docker Compose directly adjacent to your Elestio infrastructure or on localhost alongside FalkorDB. If hosted locally, the 1-10ms Falkor connection remains pristine, while background asyncio tasks effortlessly handle the 50-150ms cloud hops.