Zeph

Lightweight AI agent with hybrid inference (Ollama / Claude / OpenAI / HuggingFace via candle), skills-first architecture, semantic memory with Qdrant, MCP client, A2A protocol support, multi-model orchestration, self-learning skill evolution, and multi-channel I/O.

Only relevant skills and MCP tools are injected into each prompt via vector similarity — keeping token usage minimal regardless of how many are installed.

Cross-platform: Linux, macOS, Windows (x86_64 + ARM64).

Key Features

• Hybrid inference — Ollama (local), Claude (Anthropic), OpenAI (GPT + compatible APIs), Candle (HuggingFace GGUF)
• Skills-first architecture — embedding-based skill matching selects only top-K relevant skills per query, not all
• Semantic memory — SQLite for structured data + Qdrant for vector similarity search
• MCP client — connect external tool servers via Model Context Protocol (stdio + HTTP transport)
• A2A protocol — agent-to-agent communication via JSON-RPC 2.0 with SSE streaming
• Model orchestrator — route tasks to different providers with automatic fallback chains
• Self-learning — skills evolve through failure detection, self-reflection, and LLM-generated improvements
• Code indexing — AST-based code RAG with tree-sitter, hybrid retrieval (semantic + grep routing), repo map
• Context engineering — proportional budget allocation, semantic recall injection, runtime compaction, smart tool output summarization, ZEPH.md project config
• Multi-channel I/O — CLI, Telegram, and TUI with streaming support
• Token-efficient — prompt size is O(K) not O(N), where K is max active skills and N is total installed

Quick Start

git clone https://github.com/bug-ops/zeph
cd zeph
cargo build --release
./target/release/zeph

See Installation for pre-built binaries and Docker options.

Requirements

• Rust 1.88+ (Edition 2024)
• Ollama (for local inference and embeddings) or cloud API key (Claude / OpenAI)
• Docker (optional, for Qdrant semantic memory and containerized deployment)