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dreamgraph
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dreamgraph

Active·★ 88·NOASSERTION·Updated 2026-05-29
★ Trending★ Memory & Context★ LLM Infra

A semantic cognitive engine for software systems. By combining MCP context with iterative "dream cycles," DreamGraph builds an abstract representation of system intent—acting as a universal architect capable of autonomous reasoning, system abstraction, and cross-ecosystem transcompilation.

DreamGraph is a cognitive layer for software systems that continuously discovers, verifies, and resolves problems using structured reasoning loops. It augments software development with autonomous reasoning loops, structured knowledge graphs, evidence-based validation, and controlled speculative exploration, ultimately building an abstract representation of system intent.

#agent-memory#ai-agents#autonomous-agents#causal-discovery#causal-inference#claude#cognitive-architecture#cursor
$ Install
$ git clone https://github.com/mikajussila/dreamgraph.git && cd dreamgraph && npm install && npm run build
↗ Visit site★ GitHub
01

Features

01Autonomous reasoning loops and knowledge graphs
02Evidence-based verification with Truth Filter
03Controlled speculative exploration ('dreaming')
04Self-cleaning memory via decay and resolution
05Adversarial security scanning (NIGHTMARE state)
02

Compatibility

Claude Desktop
Claude Desktop
Verified via docs
VS Code
VS Code
Verified via docs
Cursor
Cursor
Verified via docs
Windsurf
Windsurf
Verified via docs
OpenClaw
OpenClaw
Verified via docs
03

Quick start

1
$ git clone https://github.com/mikajussila/dreamgraph.git
2
$ cd dreamgraph
3
$ npm install
4
$ npm run build
04

Use cases

↳Automated system architecture discovery and documentation
↳Continuous security vulnerability detection and remediation
↳Cross-platform application transcompilation from intent abstraction
05

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Related searches

dreamgraph AlternativesBest Memory & Context Tools 2026Open Source Memory & Contextdreamgraph Tutorialdreamgraph Vs Competitorsagent-memoryai-agentsautonomous-agents

Comments

Log in to leave a comment
  • S
    Sage RiveraMay 17, 2026

    The MCP context integration means the graph is queryable from any AI workflow

  • O
    Oaklyn ZhangApr 30, 2026

    Used for architecture documentation automation, the relationship extraction is solid

  • J
    Jesse RiveraMar 23, 2026

    Software system understanding across large codebases is genuinely improved with this

  • H
    Harper JohnsonMar 10, 2026

    Semantic cognitive engine framing is accurate, this is more than a knowledge graph

On this page
01Features02Compatibility03Quick start04Use cases05Alternatives
Stats
GitHub Stars★ 88
Last commit1d ago
StatusActive
LicenseNOASSERTION
CategoryMemory & Context
Trend (30d)
+3.5↑ 0.7%
Links
Documentation↗Discussion↗Issues↗Releases↗

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