# Supported Agent Harnesses in ai-memory: Complete Integration Guide > Explore supported agent harnesses in ai-memory including Claude Code, OpenAI Codex, Devin and Gemini CLI. Seamlessly integrate 14+ agents with our unified MCP server architecture. - Repository: [Fabio Akita/ai-memory](https://github.com/akitaonrails/ai-memory) - Tags: how-to-guide - Published: 2026-09-12 --- **ai-memory supports 14+ agent harnesses—including Claude Code, OpenAI Codex, Cursor, Devin, and Gemini CLI—through a unified MCP server architecture that enables seamless cross-harness memory continuity.** The ai-memory project by akitaonrails functions as a harness-agnostic memory layer for AI coding agents. By implementing lifecycle hooks and a shared MCP (Model Context Protocol) server, ai-memory allows developers to terminate a session in one supported agent and resume the exact same context in another without losing conversational history or context. ## Native AI Coding Agent Harnesses ### Claude Code and OpenAI Codex The **Claude Code** harness integrates via MCP configuration paired with native lifecycle hooks (`SessionStart`, `SessionEnd`, …) as documented in `docs/managed-workstreams.md#L4`. These native hooks enforce capture exclusions automatically, ensuring sensitive data remains outside the memory store. **OpenAI Codex** follows an identical integration pattern through MCP config and hooks, with **auto-hand-off** functionality available starting from Codex CLI version 0.145.0+. When a Codex session ends, the harness automatically triggers the hand-off protocol to make the session context available to other agents. ### OpenCode and Pi **OpenCode** connects through MCP configuration and lifecycle hooks, supporting both the first-generation engine and the newer **OpenCode 2 beta**. The **Pi** harness implements the same hook-based architecture, registering itself with the managed workstreams framework upon initialization. ## Commercial and Enterprise Agent Support ### Cursor, Devin, and Gemini CLI According to `docs/support-matrix.md#L12`, ai-memory maintains a "supported profiles" matrix for commercial IDE agents including **Devin**, **Cursor**, and **Gemini CLI**. These harnesses utilize MCP configuration combined with custom hooks to persist session state into the shared memory store, enabling enterprise teams to switch between AI assistants without interrupting development workflows. ### Specialized CLI Agents Ai-memory provides first-class support for specialized and regional coding agents: - **Crush** – Hooks added via the standard managed workstreams interface - **Kimi Code** – Integrated through the same lifecycle hook system - **Command Code** – Supports full session persistence - **Kiro CLI** – Offers dual-engine support within a single harness wrapper - **OMP** – Implements standard SessionStart/SessionEnd hooks ## Emerging and Beta Harness Integrations The **managed-workstreams** framework (described in [`docs/managed-workstreams.md`](https://github.com/akitaonrails/ai-memory/blob/main/docs/managed-workstreams.md)) extends support to emerging CLI tools including **Grok Build CLI** and **Antigravity CLI**. These integrations leverage the same underlying hooks infrastructure, though some require manual session finalization when native `SessionEnd` hooks are absent. ## Technical Integration Architecture ### The Managed Workstreams Framework All supported harnesses communicate through the **ai-memory MCP server** using the managed-workstreams abstraction. When you execute `ai-memory run `, the CLI entry point in [`src/ai-memory-cli/main.rs`](https://github.com/akitaonrails/ai-memory/blob/main/src/ai-memory-cli/main.rs) inspects the host `PATH` for the native executable, initializes the appropriate session, and links the native session ID back to the shared memory store. Per-harness lifecycle scripts reside in the `hooks/` directory (e.g., `hooks/claude-code/*.sh`). The acceptance-test script in [`scripts/managed-workstream-acceptance.sh`](https://github.com/akitaonrails/ai-memory/blob/main/scripts/managed-workstream-acceptance.sh) validates integration across the full harness matrix, ensuring cross-compatibility. ### Cross-Harness Session Continuity The core innovation lies in **session hand-offs**. Because each harness persists to the same memory store via standardized hooks, you can quit Claude Code mid-task and immediately continue the identical workstream in Codex or Cursor. The wrapper detects the most recent native session through the managed workstreams UI and automatically resumes the appropriate context. ## Configuring and Running Agent Harnesses ### Listing and Launching Harnesses Use the CLI to discover installed agents and initiate managed sessions: ```bash # List all harnesses currently installed on the host ai-memory run --list # Output: Claude Code Codex OpenCode OpenCode2 Pi Crush Kimi Code Command Code Kiro OMP Grok Build Antigravity Devin Cursor Gemini # Start with the auto-selected default harness ai-memory run # Detects the most recent native session and launches the appropriate agent. # Explicitly select a specific harness ai-memory run codex # Starts Codex, records the session ID, and makes memory visible to other harnesses. ``` ### Manual Session Finalization For harnesses lacking native `SessionEnd` hooks (such as Antigravity CLI), manually trigger the hand-off: ```bash ai-memory finalize-session --agent antigravity # Forces hand-off creation so the next harness can retrieve the context. ``` ## Summary - ai-memory supports **14+ agent harnesses** ranging from Claude Code and Codex to Cursor and Gemini CLI through a unified MCP server architecture. - Integration occurs via **lifecycle hooks** (`SessionStart`, `SessionEnd`) documented in [`docs/managed-workstreams.md`](https://github.com/akitaonrails/ai-memory/blob/main/docs/managed-workstreams.md), enabling automatic session persistence. - The **managed-workstreams** framework in [`src/ai-memory-cli/main.rs`](https://github.com/akitaonrails/ai-memory/blob/main/src/ai-memory-cli/main.rs) automatically detects installed harnesses on `PATH` and manages cross-hander continuity. - **Cross-harness hand-offs** allow switching between agents (e.g., Claude Code to Codex) without losing conversational context or memory state. - Acceptance testing in [`scripts/managed-workstream-acceptance.sh`](https://github.com/akitaonrails/ai-memory/blob/main/scripts/managed-workstream-acceptance.sh) validates the entire supported matrix. ## Frequently Asked Questions ### Which agent harnesses are officially supported by ai-memory? Ai-memory officially supports Claude Code, OpenAI Codex, OpenCode (including v2 beta), Pi, Crush, Kimi Code, Command Code, Kiro CLI, OMP, Grok Build CLI, Antigravity CLI, Devin, Cursor, and Gemini CLI. The definitive list appears in `docs/managed-workstreams.md#L4` and the compatibility matrix in `docs/support-matrix.md#L12`. ### How does ai-memory enable switching between different agents? Ai-memory utilizes a shared MCP server with standardized lifecycle hooks. When a session ends in one harness, the `SessionEnd` hook persists the context to the common memory store. When you launch a different harness via `ai-memory run`, the wrapper retrieves the last active session ID and restores the context, enabling seamless transitions between agents like Claude Code and Codex. ### What configuration files manage agent harness integration? The primary documentation resides in [`docs/managed-workstreams.md`](https://github.com/akitaonrails/ai-memory/blob/main/docs/managed-workstreams.md), which defines the cross-harness workflow and hook specifications. The support matrix in [`docs/support-matrix.md`](https://github.com/akitaonrails/ai-memory/blob/main/docs/support-matrix.md) outlines specific compatibility profiles for commercial agents like Devin and Cursor. Actual hook implementations live in the `hooks/` directory, organized by harness name. ### How do I add support for a new agent harness to ai-memory? To add a new harness, create lifecycle hook scripts (implementing `SessionStart` and `SessionEnd`) in a new subdirectory under `hooks/`. Register the harness in the managed-workstreams configuration, then update [`scripts/managed-workstream-acceptance.sh`](https://github.com/akitaonrails/ai-memory/blob/main/scripts/managed-workstream-acceptance.sh) to include the new agent in acceptance testing. The CLI dispatcher in [`src/ai-memory-cli/main.rs`](https://github.com/akitaonrails/ai-memory/blob/main/src/ai-memory-cli/main.rs) automatically detects the harness if it exists on the system `PATH`.