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timowhite88/farnsworth-syntek

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Farnsworth SYNTEK — 7-layer recursive agent memory with context branching, holographic recall, and on-chain persistence.

T

MCP

timowhite88/farnsworth-syntek

Added 1 June 2026

Overview

Farnsworth SYNTEK implements a 7-layer recursive memory system for AI agents with context branching and holographic recall. It stores agent state on-chain for persistence and auditability. The project is at an early experimental stage with a single star.

Best for

Best for
Developers experimenting with advanced agent memory architectures or blockchain-anchored agent state

Use cases

  • Maintaining branching conversational context in agent interactions
  • Persisting agent memory on a blockchain for transparency
  • Exploring recursive memory layers for complex reasoning

Notes

Farnsworth SYNTEK implements a 7-layer recursive memory system for AI agents with context branching and holographic recall. It stores agent state on-chain for persistence and auditability. The project is at an early experimental stage with a single star.

1 stars on GitHub. Last updated 2026-02-24.

Use cases

  • Maintaining branching conversational context in agent interactions
  • Persisting agent memory on a blockchain for transparency
  • Exploring recursive memory layers for complex reasoning

Pros

  • Open source and available for inspection
  • Multi-layer memory architecture for deep context
  • On-chain persistence provides immutable state records

Cons

  • Very early stage with minimal adoption and testing
  • On-chain storage may incur latency and cost
  • Novel concepts like holographic recall lack proven benchmarks

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Open source and available for inspection
  • Multi-layer memory architecture for deep context
  • On-chain persistence provides immutable state records

Cons

  • Very early stage with minimal adoption and testing
  • On-chain storage may incur latency and cost
  • Novel concepts like holographic recall lack proven benchmarks
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