Engram v1.1.2

AI Intelligence Platform -- knowledge graph + semantic search + reasoning + learning in a single binary.

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What is Engram?

Engram is a self-hosted AI knowledge engine that combines graph storage, semantic search, logical reasoning, and continuous learning into a single Rust binary with a single .brain file. No cloud, no external dependencies.

• Single binary -- no runtime dependencies, no Docker, no cloud
• Single file -- one .brain file is your entire knowledge base. Copy = backup, move = migrate
• No external database -- custom mmap storage, everything built in
• Hybrid search -- BM25 full-text + HNSW vector similarity + bitmap filtering
• Confidence lifecycle -- knowledge strengthens with confirmation, weakens with time, corrects on contradiction
• Inference engine -- forward/backward chaining, rule evaluation, transitive reasoning
• Ingest pipeline -- NER (GLiNER2 ONNX, GPU-accelerated), entity resolution, conflict detection, PDF/HTML/table extraction
• Multi-agent debate -- 7 analysis modes with War Room live dashboard and 3-layer synthesis
• Chat system -- 47 tools across 8 clusters (analysis, investigation, reporting, temporal, assessment)
• Assessment engine -- Bayesian confidence with living assessments and evidence boards
• Temporal facts -- valid_from / valid_to on edges with automatic extraction
• Contradiction detection -- automatic conflict detection with resolution workflows
• Knowledge mesh -- peer-to-peer sync with ed25519 identity and trust scoring
• Built-in web UI -- Leptos WASM frontend with 3D graph visualization, onboarding wizard, and SSE live updates
• Multiple APIs -- HTTP REST (230+ endpoints), MCP, gRPC, A2A, LLM tool-calling

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Who is Engram for?

As a backend memory layer -- integrate Engram into your AI pipeline via REST, MCP, or gRPC. Use the onboarding wizard once, then run headless.

As an intelligence workbench -- ingest documents, build knowledge graphs, run multi-agent debate, assess with Bayesian confidence. Full web UI with interactive 3D graph, chat, and War Room.

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Quick Start

1. Download

Download the latest release from Releases.

| Platform | Download | |----------|----------| | Windows x86_64 | engram-windows-x86_64.zip | | Linux x86_64 | engram-linux-x86_64.zip | | Linux aarch64 | engram-linux-aarch64.zip | | macOS aarch64 | engram-macos-aarch64.zip |

Unzip and run. The web UI frontend is bundled inside the zip.

2. Start

engram serve my.brain
# HTTP API + Web UI: http://localhost:3030

3. Configure

Open http://localhost:3030 -- the onboarding wizard guides you through setup.

We recommend Gemma 4 as the LLM. Run it locally with Ollama:

ollama pull gemma4:e4b

Any OpenAI-compatible LLM endpoint works (Ollama, vLLM, OpenAI, Azure, etc.).

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Web UI

Four sections accessible after login:

• Knowledge -- interactive 3D graph explorer, entity search, Knowledge Chat with 47 tools
• Insights -- knowledge stats, contradictions, documents, intelligence gaps
• Debate -- 7 AI analysis modes: Analyze, Red Team, Outcome Engineering, Scenario Forecast, Stakeholder Simulation, Pre-mortem, Decision Matrix
• System -- hardware, embeddings, NER, LLM config, web search providers, ingestion sources, domain taxonomy

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CLI Reference

| Command | Description | |---------|-------------| | engram create [path] | Create a new .brain file | | engram store <label> [path] | Store a node | | engram relate <from> <rel> <to> [path] | Create a relationship | | engram query <label> [depth] [path] | Query and traverse edges | | engram search <query> [path] | Search (BM25, filters, boolean) | | engram serve [path] [addr] | Start HTTP + gRPC server | | engram mcp [path] | Start MCP server (stdio) | | engram reindex [path] | Re-embed all nodes after model change | | engram stats [path] | Show node and edge counts | | engram delete <label> [path] | Soft-delete a node |

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Documentation

| Page | Description | |------|-------------| | Getting Started | Download, install, first brain, quick start | | Configuration | Onboarding wizard, LLM setup, embeddings, SearXNG | | HTTP API | Full REST API reference (230+ endpoints) | | MCP Server | MCP tools for Claude, Cursor, Windsurf (24 tools) | | Python Integration | EngramClient, bulk import, LangChain, auth, debate, chat | | SearxNG Setup | Self-hosted web search: installation, engines, rate limits | | Architecture | System design, layers, storage engine, compute | | Use Cases | 13 end-to-end walkthroughs with Python demos |

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Use Cases

| # | Use Case | Description | |---|----------|-------------| | 1 | Wikipedia Import | Build a knowledge graph from Wikipedia summaries | | 2 | Document Import | Ingest markdown/text with metadata and entity extraction | | 3 | Inference & Reasoning | Vulnerability propagation and SLA mismatch detection | | 4 | Support Knowledge Base | IT support error/cause/solution graphs | | 5 | Threat Intelligence | Threat actor, malware, CVE, and TTP graphs | | 6 | Learning Lifecycle | Full lifecycle: store, reinforce, correct, decay, archive | | 7 | OSINT | Open Source Intelligence with multi-source correlation | | 8 | Fact Checker | Multi-source claim verification | | 9 | Web Search Import | Progressive knowledge building from web search | | 10 | NER Entity Extraction | spaCy NER pipeline for entity extraction | | 11 | Semantic Web | JSON-LD import/export for linked data | | 12 | Codebase Understanding | AST analysis for codebase knowledge graphs | | 13 | Intel Analyst | OSINT intelligence dashboard with real-time ingest and gap detection |

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Built with Engram

| Project | Description | |---------|-------------| | Intel Analyst | OSINT intelligence dashboard powered by engram's knowledge graph, ingest pipeline, and gap detection engine |

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License

Engram is free for personal use, research, education, and non-profit organizations.

Commercial use requires a paid license. Contact sven.andreas@gmail.com for commercial licensing.

See LICENSE for full terms.