zeph-core 0.12.5

Core agent loop, configuration, context builder, metrics, and vault for Zeph
Documentation

zeph-core

Crates.io docs.rs License: MIT MSRV

Core agent loop, configuration, context builder, metrics, vault, and sub-agent orchestration for Zeph.

Overview

Core orchestration crate for the Zeph agent. Manages the main agent loop, bootstraps the application from TOML configuration with environment variable overrides, and assembles the LLM context from conversation history, skills, and memory. Includes sub-agent orchestration with zero-trust permission grants, background execution, filtered tool/skill access, A2A-based in-process communication channels, and /agent CLI commands for runtime management. All other workspace crates are coordinated through zeph-core.

Key modules

Module Description
agent Agent<C> — main loop driving inference and tool execution; ToolExecutor erased via Box<dyn ErasedToolExecutor>; supports external cancellation via with_cancel_signal(); EnvironmentContext cached at bootstrap and partially refreshed (git branch, model name) on skill reload only
agent::context_manager ContextManager — owns token budget, compaction threshold, and safety margin; should_compact() is O(1) — reads cached_prompt_tokens set by the LLM response rather than scanning the message list
agent::tool_orchestrator ToolOrchestrator — owns max iteration limit, doom-loop detection (rolling hash window with in-place hashing, no intermediate String allocation), summarization flag, and overflow config
agent::learning_engine LearningEngine — owns LearningConfig, tracks per-turn reflection state; delegates self-learning decisions to is_enabled() / mark_reflection_used()
agent::tool_execution Tool call handling, redaction, result processing; both the fenced-block path (handle_tool_result) and the structured tool-call path unconditionally emit LoopbackEvent::ToolStart (UUID generated per call) before execution and LoopbackEvent::ToolOutput (matching UUID, is_error flag) after; call_llm_with_retry() / call_chat_with_tools_retry() — auto-detect ContextLengthExceeded, compact context, and retry (max 2 attempts); prune_stale_tool_outputs invokes count_tokens once per ToolResult part
agent::message_queue Message queue management
agent::builder Agent builder API
agent::commands Chat command dispatch (skills, feedback, skill management via /skill install, /skill remove, /skill reject <name> <reason>, sub-agent management via /agent, etc.)
agent::utils Shared agent utilities
bootstrap AppBuilder — fluent builder for application startup; split into submodules: config (config resolution, vault arg parsing), health (health check, provider warmup), mcp (MCP manager and registry), provider (provider factory functions), skills (skill matcher, embedding model helpers)
channel Channel trait defining I/O adapters; LoopbackChannel / LoopbackHandle for headless daemon I/O (LoopbackHandle exposes cancel_signal: Arc<Notify> for session cancellation); LoopbackEvent::ToolStart / LoopbackEvent::ToolOutput carry per-tool UUIDs and is_error flag for ACP lifecycle notifications; Attachment / AttachmentKind for multimodal inputs
config TOML config with ZEPH_* env overrides; typed ConfigError (Io, Parse, Validation, Vault)
context LLM context assembly from history, skills, memory; resilient compaction with reactive context-overflow retry (max 2 attempts), middle-out progressive tool response removal (10/20/50/100% tiers), 9-section structured compaction prompt, LLM-free metadata fallback via build_metadata_summary() with safe UTF-8 truncation; parallel chunked summarization; tool-pair summarization via maybe_summarize_tool_pair() — when visible pairs exceed tool_call_cutoff, oldest pair is LLM-summarized with XML-delimited prompt and originals hidden via agent_visible=false; visibility-aware history loading (agent-only vs user-visible messages); durable compaction via replace_conversation(); uses shared Arc<TokenCounter> for accurate tiktoken-based budget tracking
cost Token cost tracking and budgeting
daemon Background daemon mode with PID file lifecycle (optional feature)
metrics Runtime metrics collection
project Project-level context detection
redact Regex-based secret redaction (AWS, OpenAI, Anthropic, Google, GitLab, HuggingFace, npm, Docker)
vault Secret storage and resolution via vault providers (age-encrypted read/write); secrets stored as BTreeMap for deterministic JSON serialization on every vault.save() call; scans ZEPH_SECRET_* keys to build the custom-secrets map used by skill env injection; all secret values are held as Zeroizing<String> (zeroize-on-drop) and are not Clone
instructions load_instructions() — auto-detects and loads provider-specific instruction files (CLAUDE.md, AGENTS.md, GEMINI.md, zeph.md) from the working directory; injects content into the volatile system prompt section with symlink boundary check, null byte guard, and 256 KiB per-file size cap
skill_loader SkillLoaderExecutor — ToolExecutor that exposes the load_skill tool to the LLM; accepts a skill name, looks it up in the shared Arc<RwLock<SkillRegistry>>, and returns the full SKILL.md body (truncated to MAX_TOOL_OUTPUT_CHARS); skill name is capped at 128 characters; unknown names return a human-readable error message rather than a hard error
hash content_hash — BLAKE3 hex digest utility
pipeline Composable, type-safe step chains for multi-stage workflows
subagent Sub-agent orchestration: SubAgentManager lifecycle with background execution, SubAgentDef TOML definitions, PermissionGrants zero-trust delegation, FilteredToolExecutor scoped tool access, A2A in-process channels, SubAgentState lifecycle enum (Submitted, Working, Completed, Failed, Canceled), real-time status tracking

Re-exports: Agent, content_hash, DiffData

Configuration

Key AgentConfig fields (TOML section [agent]):

Field Type Default Env override Description
name string "zeph" — Agent display name
max_tool_iterations usize 10 — Max tool calls per turn
summary_model string? null — Model used for context summarization
auto_update_check bool true ZEPH_AUTO_UPDATE_CHECK Check GitHub releases for a newer version on startup / via scheduler

Key InstructionConfig fields (TOML section [agent.instructions]):

Field Type Default Description
auto_detect bool true Auto-detect provider-specific files (CLAUDE.md, AGENTS.md, GEMINI.md)
extra_files Vec<PathBuf> [] Additional instruction files (absolute or relative to cwd)
max_size_bytes u64 262144 Per-file size cap (256 KiB); files exceeding this are skipped

[!NOTE] zeph.md and .zeph/zeph.md are always loaded regardless of auto_detect. Use --instruction-file <path> at the CLI to supply extra files at startup without modifying the config file.

Key DocumentConfig fields (TOML section [memory.documents]):

Field Type Default Description
collection string "zeph_documents" Qdrant collection for document chunks
chunk_size usize 512 Target tokens per chunk
chunk_overlap usize 64 Overlap between chunks
top_k usize 3 Max chunks injected per context-build turn
rag_enabled bool false Enable automatic RAG context injection from zeph_documents

Key MemoryConfig fields (TOML section [memory]):

Field Type Default Description
vector_backend "qdrant" / "sqlite" "qdrant" Vector search backend
token_safety_margin f32 1.0 Safety multiplier for tiktoken-based token budget (validated: must be >= 1.0)
redact_credentials bool true Scrub secrets and paths before LLM context injection
autosave_assistant bool false Persist assistant responses to semantic memory automatically
autosave_min_length usize 20 Minimum response length (chars) to trigger autosave
tool_call_cutoff usize 6 Max visible tool call/response pairs before oldest is summarized via LLM
sqlite_pool_size u32 5 SQLite connection pool size for memory storage
response_cache_cleanup_interval_secs u64 3600 Interval for expiring stale response cache entries 
[agent]
auto_update_check = true   # set to false to disable update notifications

Set ZEPH_AUTO_UPDATE_CHECK=false to disable without changing the config file.

Skill commands

Command Description
/skill list List loaded skills with trust level and match count
/skill install <url> Install a skill from a remote URL
/skill remove <name> Remove an installed skill
/skill reject <name> <reason> Record a typed rejection and trigger immediate skill improvement

[!TIP] /skill reject provides the strongest feedback signal. The rejection is persisted with a FailureKind discriminant to the outcome_detail column and immediately updates the Wilson score posterior for Bayesian re-ranking.

Self-learning configuration

Key AgentConfig.learning fields (TOML section [agent.learning]):

Field Type Default Description
correction_detection bool true Enable FeedbackDetector implicit correction capture
correction_confidence_threshold f64 0.7 Minimum detector confidence to persist a UserCorrection
correction_recall_limit usize 5 Max corrections retrieved per context-build turn
correction_min_similarity f64 0.75 Minimum embedding similarity for correction recall

Key LlmConfig fields for EMA routing (TOML section [llm]):

Field Type Default Description
router_ema_enabled bool false Enable per-provider EMA latency tracking and reordering
router_ema_alpha f64 0.1 EMA smoothing factor (lower = slower adaptation)
router_reorder_interval u64 60 Seconds between provider list reordering

Sub-agent Commands

In-session commands for managing sub-agents:

Command Description
/agent list List available sub-agent definitions
/agent spawn <name> <prompt> Spawn a sub-agent with a task prompt
/agent bg <name> <prompt> Spawn a background sub-agent
/agent status Show active sub-agents with state, turns, and elapsed time
/agent cancel <id> Cancel a running sub-agent by ID prefix
/agent approve <id> Approve a pending secret request
/agent deny <id> Deny a pending secret request
@agent_name <prompt> Mention shorthand for /agent spawn (disambiguated from file references)

Sub-agents run as independent tokio tasks with their own LLM provider and filtered tool executor. Each sub-agent receives only explicitly granted tools, skills, and secrets via PermissionGrants.

Installation

cargo add zeph-core

License

MIT