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An event-sourced knowledge runtime for coding agents.

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The agent-first, event-sourced knowledge runtime for AI coding sessions.

---

## What is OpenEmpiric?

**OpenEmpiric (OEM)** is a local-first, agent-first learning runtime. It acts as long-term memory for your AI coding agents, capturing crucial architectural decisions, experiments, tradeoffs, failures, and outcomes directly from your developer-agent conversations. It stores this knowledge in an event-sourced ledger and structures it as a local knowledge graph, making it immediately available to guide future coding sessions.

Instead of writing and updating guidelines manually, you simply work with your agent. OEM automatically builds your project's memory map, preventing your agent from making the same mistake twice.

---

## What's New in v1.1.0

v1.1.0 is a **ground-up architectural rebuild** of OpenEmpiric's core memory module. The external API (MCP tools, CLI commands) is fully preserved — no action needed for existing users.

**Highlights:**

- **Three-layer architecture** — Storage, Computation, and API layers with strict unidirectional dependencies and protocol contracts
- **Event-sourced snapshots** — `events.jsonl` is the sole source of truth; the concept registry is a derived materialized view rebuilt on every `session_end`
- **Export/Import** — Transfer memory between machines via `.tar.gz` archives with automatic dedup
- **Bug fixes** — `knowledge_add_memory` schema mismatch fixed; `knowledge_reflect` now correctly processes structured events
- **Engine.py shrunk** from ~1,640 lines to ~200 lines of thin orchestration

See [RELEASE_NOTES.md](RELEASE_NOTES.md) for the full changelog.

---

## How it Works

OpenEmpiric shifts the model of AI coding sessions from a simple wrapped process to an **active knowledge infrastructure**. It is best explained through two layers: the user mental model (why it exists) and the internal runtime flow (how it works).

### 1. User Mental Model (Why OEM Exists)

Rather than just receiving static prompts, a highly productive AI coding agent relies on three balanced pillars:

* **Developer Intent**: The immediate task, constraints, and goals you provide.
* **Project Workflows** (e.g., `AGENTS.md` / `CLAUDE.md`): Guidelines detailing *how* the project is structured, run, and styled.
* **Project Memory** (e.g., `.oem/`): The persistent knowledge layer managed by **OpenEmpiric**, recording *what has been learned* (prior decisions, resolved failures, validated designs).

```mermaid
graph TD
 Intent["Developer Intent (What to do)"] --> Agent("Coding Agent")
 Workflows["Project Workflows (How to work - e.g., AGENTS.md)"] --> Agent
 Memory["Project Memory (.oem/) (What we've learned)"] --> Agent
```

---

### 2. Internal Runtime & Tooling Flow (How OEM Works)

The `.oem/` directory at the project root acts as the center of gravity. During execution, the coding agent actively queries this memory infrastructure using MCP tools rather than just reading static startup files.

```mermaid
graph TD
 Developer(["Developer"]) -->|"Runs oem run agent"| OEM["OpenEmpiric Runtime"]
 OEM -->|"1. Restore State"| Folder[(".oem/ Project Memory")]
 Folder -.->|"Injected Context"| Agent("Coding Agent")
 
 Developer ---|"2. Normal Work Session"| Agent
 
 Agent ---|"3. Active MCP Queries (knowledge_search, explain_concept, etc.)"| Folder
 
 Agent -->|"4. Exits"| OEM
 OEM -->|"5. Reflects Transcripts & Diffs"| Ledger[(".oem/ Event Ledger")]
 Ledger -->|"Updates"| Registry["concept_registry.json"]
 Ledger -->|"Generates"| Wiki[".oem/wiki/ Concept Wiki"]
 Registry -->|"Sync"| Folder
 Wiki -->|"Sync"| Folder
```

---

## Layered Architecture (v1.1.0+)

OpenEmpiric is built as a three-layer architecture with strict unidirectional dependencies:

```
Storage Layer (file I/O only)
    |
    v
Computation Layer (pure logic over Storage)
    |
    v
API Layer (MCP/CLI glue)
```

### Storage Layer (`oem_knowledge/storage/`)

File I/O only. No business logic. No compute.

- **EventStore** — Append-only event log (`events.jsonl`) with schema migration
- **RegistryStore** — Concept registry JSON read/write under `FileLock`
- **ConceptFiles** — `wiki/*.md` write with collision handling
- **SessionFiles** — Session state serialization
- **UserStore** — User-scoped event log at `~/.config/openempiric/`

### Computation Layer (`oem_knowledge/computation/`)

Pure computation over Storage data. No MCP awareness. No direct file I/O.

- **SnapshotComputation** — Replay events -> registry + concepts (`rebuild_registry` is the primary snapshot path)
- **ReflectionComputation** — Event extraction (structured, marker, dense LLM)
- **IndexingComputation** — Chunk, embed, index into vector store
- **SearchComputation** — Hybrid BM25 + dense search with relevance ranking
- **FitnessComputation** — Concept fitness scoring
- **EvolutionComputation** — Decay, merge, promotion
- **MaterializationComputation** — Concept file generation from events
- **PreflightComputation** — Preflight routing against memory, concepts, skills
- **SkillsComputation** — Skill candidate management + promotion

### API Layer

Thin glue between MCP/CLI and Computation layer:

- **KnowledgeEngine** — Orchestrator (session lifecycle, scope resolution)
- **MCP Tools** — `knowledge_session_start`, `knowledge_read`, `knowledge_search`, `knowledge_reflect`, `knowledge_session_end`, `knowledge_add_memory`, `knowledge_export`, `knowledge_import`, and more
- **CLI Commands** — `oem run`, `oem search`, `oem reflect`, `oem index`, etc.

### Event-Sourced Model

`events.jsonl` is the **sole source of truth**. The concept registry (`concept_registry.json`) and wiki files (`wiki/*.md`) are **derived materialized views** computed by replaying the event log. Every `session_end` rebuilds the registry from scratch via `rebuild_registry()`.

### Scope Model

Memory is isolated by scope:

- **Project** — `.oem/events.jsonl` in the project root
- **User** — `~/.config/openempiric/user_events.jsonl` (cross-project)
- **Session** — Transient, scoped to a single session

### Export/Import

Transfer memory between machines without a remote:

- `knowledge_export` — Exports `.oem/` + user events to a `.tar.gz` archive
- `knowledge_import` — Merges archive into existing memory with event_id dedup and alias-merge for concept conflicts

---

## Key Features

- **🧠 Zero-Config Agent Memory**: No manual updates or prompt-engineering required. OEM learns continuously in the background.
- **🔄 Automatic Reflection**: Analyzes chat transcripts and file diffs to automatically extract new concepts, decisions, experiments, and root causes of failures.
- **🛡️ Secure File System Guards**: SFS wrapper protects the host workspace with strict path-traversal limits and truncation protection (prevents agents from accidentally erasing large files).
- **🔎 Local RAG Retrieval**: Perform hybrid vector-BM25 search queries across your project's memory repository locally.
- **📚 Materialized Wiki**: Auto-generates clean, human-readable markdown documentation in `.oem/wiki/` as the knowledge graph evolves.
- **📦 Three-Layer Architecture**: Clean separation of Storage, Computation, and API with protocol contracts between layers.
- **📤 Export/Import**: Transfer memory between machines via `.tar.gz` archives with automatic dedup.

---

## Quick Start

### 1. Install Globally

Install the unified `oem` CLI runtime globally using `uv` with semantic retrieval support:

```bash
uv tool install "git+https://github.com/xpajonx/openempiric.git#subdirectory=packages/oem-knowledge[semantic]"
```

For a lighter BM25-only install, you can omit `[semantic]`, but the default user path assumes semantic retrieval is available when possible.

### 2. Setup Agent Integration

For terminal-based workstation environments (like OpenCode):

```bash
oem setup opencode
```

For desktop-based non-terminal environments (like Codex App):

```bash
oem setup codex-app
```

### 3. Launch a Session

From any project directory, launch a managed session:

```bash
mkdir demo-project
cd demo-project
oem run opencode
```

`oem run opencode` bootstraps the project-local `.oem/` memory folder automatically and starts a managed session.

*(Note: For Codex App, standard MCP tools are configured automatically. The agent uses the `knowledge_session_end` conversational tool to commit and persist learnings at the end of a session).*

### 4. Diagnose Problems

Verify your workspace integration and bridge health:

```bash
oem doctor
```

---

## Supported Agents & Custom Adapters

OpenEmpiric officially supports the following agent runtimes out-of-the-box:

- **OpenCode**: Workstation-level integration, native plugins, and session supervisor for terminal environments.
- **Grok**: xAI Grok Build TUI/CLI support via `oem run grok` and `oem setup grok` (MCP + rules).
- **Antigravity**: Terminal co-pilot and command-line companion integration.
- **Codex App**: First-class MCP-based non-terminal desktop runtime, configured automatically via the WSL bridge architecture (`oem setup codex-app`).

### Extensibility: Write Your Own Adapter!

You can easily extend OpenEmpiric to support other environments (such as Claude Code, Cursor, or your own proprietary CLI agent). All you need to do is subclass the base adapter:

```python
from oem_knowledge.adapters.base import BaseAdapter
from oem_knowledge.adapters.registry import register_adapter

@register_adapter("my-custom-agent")
class MyCustomAgentAdapter(BaseAdapter):
    def verify_mcp(self) -> bool:
        # Check if agent is installed and configured
        return True

def parse_transcript(self, transcript_path) -> str:
        # Extract dialogue text from agent logs
        return transcript_path.read_text()
```

Refer to the [Adapter Architecture Guide](docs/adapter-architecture.md) and [Adapter Specification](docs/adapter-spec.md) for details.

## The Agent Lifecycle Model

The core mental model of OpenEmpiric revolves around the 5-step agent lifecycle contract. Agents must understand and transition through these steps:

```text
knowledge_session_start
 └─► knowledge_read (Orientation & Learning)
      └─► knowledge_search (Specific Retrieval)
           └─► knowledge_reflect (Capturing Decisions & Failures)
                └─► knowledge_session_end (Finalize & Commit)
```

- **`knowledge_session_start`** — Begin or restore a managed workspace session.
- **`knowledge_read`** — Learn and orient from broad project context, background, recent sessions, and conventions (use at start or when orientation is needed).
- **`knowledge_search`** — Query the vector/keyword database for a specific project memory or concept.
- **`knowledge_reflect`** — Record structured events (hypotheses, experiments, validations, decisions, failures) during development.
- **`knowledge_session_end`** — Finalize, generate session report, update derived indexes, and close/commit the session.

---

## Recommended `.gitignore` Default Policy

To keep project repositories clean while preserving valuable knowledge, follow this default `.gitignore` configuration:

```gitignore
# Commit-friendly project memory
# (Do NOT ignore these)
! .oem/wiki/**
! .oem/concept_registry.json
! .oem/skills/**

# Local runtime state and caches
# (Usually ignore these)
.oem/state/**
.oem/reports/**
.oem/backups/**
.oem/**/*.sqlite
.oem/**/*.db
.oem/.cache/**
.oem/.runtime/**

# Project-policy dependent (may contain private agent transcript traces)
.oem/runtime_events.jsonl
.oem/outcomes.jsonl
```

---

## CLI Command Reference

### Expected Public CLI Surface (v1.0 Frozen)

| Command | Category | Description |
| ------- | -------- | ----------- |
| `oem init` | **User** | Initialize the `.oem/` memory repository in the current workspace. |
| `oem setup opencode` | **User** | Configure and register OpenCode workstation-level integration. |
| `oem setup grok` | **User** | Configure and register Grok (MCP + .grok integration). |
| `oem setup codex-app` | **User** | Configure and register Codex App bridge integration. |
| `oem run opencode` | **User** | Launch a managed OpenCode agent session with dynamic config injection. |
| `oem run grok` | **User** | Launch a managed Grok agent session with dynamic context + transcript capture. |
| `oem doctor` | **User** | Verify workspace health, plugin links, and agent integration state. |
| `oem read` | **User** | Read the project memory baseline based on scope (`project`, `recent`, `skills`, `health`). |
| `oem search` | **User** | Search the project knowledge base with keyword or hybrid search. |
| `oem reflect` | **User** | Dry-run reflection and concept extraction from conversation transcript. |
| `oem session-start` | **Internal** | Restore pre-injection context and prepare workspace before agent run. |
| `oem session-end` | **Internal** | Finalize context, run extraction, and commit learnings. |
| `oem index` | **Advanced** | Rebuild derived search index for the project. |
| `oem clean` | **Advanced** | Analyze or apply safe `.oem` cleanup actions (dry-run supported). |
| `oem recover` | **Advanced** | Recover, commit, or abort crashed or unfinished agent sessions (dry-run supported). |
| `oem mcp` | **Advanced** | Start the background FastMCP server. |
| `oem skills` | **Advanced** | Review, list, approve, or reject skill candidates. |

*Note on other commands: Commands like `health`, `config`, `merge`, `rebuild`, `events`, `explain`, `identity`, `concept`, `contradictions`, `outcome`, `todo` are classified as `internal`, `advanced`, `hidden`, `deprecated`, or `post-v1.0` and are suppressed in help outputs.*

---

## MCP Tool Reference

The OpenEmpiric MCP server exposes the following tools categorized by target role:

### 1. Core Lifecycle Tools (Must Remain Stable)
- **`knowledge_session_start`** — Start or restore an OpenEmpiric session lifecycle.
- **`knowledge_read`** — Read the project memory baseline for orientation.
- **`knowledge_search`** — Fast lookup and search across concepts.
- **`knowledge_reflect`** — Extract structured events and write a session report.
- **`knowledge_session_end`** — End session, commit learnings, and update indexes.

### 2. Core Tools
- **`knowledge_init`** — Bootstrap the `.oem/` memory structure.
- **`knowledge_index`** — Re-index markdown files in the project's concept tree.
- **`knowledge_explain_concept`** — Explain concept details and recent evidence.
- **`knowledge_stats`** — Show project memory stats (concepts count, DB size).

### 3. Advanced Tools
- **`knowledge_materialize`** — Promote candidates to canonical status and write wiki markdown.
- **`knowledge_health_check`** — Scan for duplicate concepts, staleness, or contradictions.
- **`knowledge_get_events`** / **`knowledge_get_event`** — Retrieve events from the ledger.
- **`knowledge_merge_concepts`** — Merge duplicate concepts.
- **`knowledge_skill_candidates`** / **`knowledge_skill_candidate_show`** / **`knowledge_skill_candidate_approve`** / **`knowledge_skill_candidate_reject`** / **`knowledge_skill_candidate_defer`** — Review and promote skill candidates.

### 4. Optional / Tasks / Telemetry Tools
- **`oem_todo_read`** / **`oem_todo_write`** / **`oem_todo_advance`** — Tasks/todos management.
- **`knowledge_usage_report`** — Experimental telemetry report.

### 5. Deprecated Tools
- **`knowledge_session_commit`** — Deprecated alias; forwards to `session_end` and returns warning.

---

## How It Learns: High-Value Cues

OEM works automatically, but it extracts the highest-quality knowledge when developers state reasoning and results explicitly during a conversation.

| Concept Signal  | Better (OEM Captures Rationale)                                                                      | Worse (Vague/Context-Free)      |
| ----------------- | ------------------------------------------------------------------------------------------------------ | --------------------------------- |
| **Decisions**   | *"We decided to use TypeScript because Python startup latency caused MCP timeouts."*                 | *"Use TypeScript."*             |
| **Failures**    | *"The pagination job failed because the pagination cursor was not reset between retries."*           | *"Pagination is broken."*       |
| **Tradeoffs**   | *"We chose client-side caching to avoid Redis dependency, accepting up to 5 minutes of stale data."* | *"Use client-side cache."*      |
| **Experiments** | *"We tested BM25 vs hybrid search. Hybrid scored 23% higher on recall@5, so we made it default."*    | *"Hybrid search works better."* |
| **Outcomes**    | *"Restructuring the DB index reduced retrieval latency from 5.5s to 450ms."*                         | *"Looks faster now."*           |

Check out the [Best Practices Guide](docs/best-practices.md) for more details.

---

## Repository Anatomy

- [packages/oem-knowledge](packages/oem-knowledge) — Core RAG logic, SQLite event database, extraction services, and CLI.
- [plugins](plugins) — Native TypeScript plugins for IDE/workstation agent integrations.
- [docs](docs) — Complete specifications, architecture details, and lifecycle logs.

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