baton-mcp
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@baton-mcpconvert my latest claude code session to opencode"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Pass the baton between coding agents.
Convert any coding-agent session to any other. One command. Keep going where you left off.
A passed transcript kept 14/17 concrete facts. A hand-written handoff summary kept 3/17. Benchmark ↓
The 4 p.m. problem
It's 4 p.m. Claude Code says "usage limit reached — resets at 10 p.m." You're three hours into a session: architecture decided, edge cases mapped, half the diff written.
❌ Without baton — open another agent and start from zero. Re-explain the plan. Re-read the files. Re-litigate every decision you already made.
✅ With baton — pass the session and keep going:
baton convert --from claude-code --to opencode --latest --import
# using latest claude-code session: 2026-07-09 15:58 Refactor the auth middleware to…
# passed baton: claude-code → opencode (1388 messages) → handoff.json
# Imported session: ses_8c4c973a521549e2
opencode -s ses_8c4c973a521549e2 # same conversation, different runnerWorks in every direction: switch agents mid-task, try a second opinion on a hard bug, move a session from your editor agent to a terminal agent, or archive everything in one format.
Related MCP server: AI Switch MCP Server
Quick start
# zero-install run (downloads prebuilt binary)
npx @kasabeh/baton-mcp --help
# convert your most recent session + auto-import into the target agent
baton convert --from claude-code --to opencode --latest --import
# or omit the path to pick interactively — newest first,
# each session previewed by its first user message
baton convert --from claude-code --to opencode --import
# or pass an explicit session file
baton convert --from claude-code --to opencode <session.jsonl> --import
# see every session on your machine, across all agents
baton listWhere do session files live?
You never have to hunt these down (--latest and the interactive picker find them for you), but for reference — each agent stores its transcripts on disk:
Agent | Location |
Claude Code |
|
Codex CLI |
|
baton list prints the path of every session it can find, across all agents.
Supported formats
Agent | Read | Write | Auto-import |
Claude Code | ✅ | ✅ | — |
OpenCode | ✅ | ✅ | ✅ |
Codex CLI | ✅ | ✅ | — |
Gemini CLI | ✅ | ✅ | — |
Zed | ✅ | ✅ | — |
Aider | ✅ | ✅ | — |
Cursor | ✅¹ | —² | — |
Continue | ✅ | — | — |
Cline / Roo | ✅ | —² | — |
¹ Cursor reads from exported JSON (sqlite3 state.vscdb "SELECT value FROM ItemTable WHERE key='aiService:chats'")
² Not planned: Cursor and Cline keep session state inside editor databases (SQLite / VS Code globalState) with no file-level import path.
Benchmark
Does carrying the full transcript beat writing a handoff summary for the next agent? We measured both on a real 3.4 MB Claude Code session (same model both arms, only the context differs):
session size | baton transcript | handoff summary |
sm (93 KB) | 3/3 details recalled | 1/3 |
md (198 KB) | 6/6 | 1/6 |
lg (599 KB) | 5/8 | 1/8 |
total | 14/17 | 3/17 |
The summary lost concrete facts (versions, line counts, MSRV) even on the smallest slice — the receiving agent had to re-read files and re-run commands to rediscover them. Mechanical fidelity: all 896 messages are written to every target; round-trip loss reflects each target format's expressiveness (claude-code 896/896, codex 736, gemini-cli 723, aider 111 — it stores chat text only).
We also measured task continuation: cut the session at three mid-task points, ask a fresh agent to state the task, state, and next steps. Result: parity (baton 10/12, handoff 10/12) — a good summary is enough for what to do next; the transcript is what answers the specific factual questions the summary's author didn't anticipate. And the summary only exists if an agent spends a full transcript read writing it — baton makes the transfer free.
Full methodology, caveats, and reproduction steps: benchmark/RESULTS.md.
MCP server
baton is also an MCP server — your agent can pass the baton itself, mid-conversation:
Tool | Description |
| Scan all agents, return a unified list |
| Convert a session from one format to another |
| Convert + run the target agent's import command |
| Sniff a file/dir and report which agent produced it |
baton install # registers baton in every detected agent's MCP config
baton doctor # verify
baton uninstall # remove from all agentsHow it works
Claude Code session (.jsonl)
│
▼
baton read ──► canonical Session { messages: [Text, Reasoning, ToolCall, ToolResult] }
│
▼
baton write ──► OpenCode import JSON (SessionV1 schema)Every agent format is read into a canonical intermediate representation, then written out in the target format. Adding a new format is O(1), not O(N×M) per-pair converters.
Install
# npm (prebuilt binary, no Rust needed)
npm install -g @kasabeh/baton-mcp
# Homebrew
brew install kaseban/tap/baton-mcp
# Cargo binstall (prebuilt binary)
cargo binstall baton-mcp
# Cargo (from source)
cargo install baton-mcp
# Shell installer (prebuilt binary)
curl --proto '=https' --tlsv1.2 -LsSf https://github.com/Kaseban/baton/releases/latest/download/baton-mcp-installer.sh | shOr grab a binary from GitHub Releases.
Building
git clone https://github.com/Kaseban/baton.git
cd baton
cargo build --release
./target/release/baton --helpContributing
Each format lives in src/formats/<name>.rs and implements the Format trait (read + write). See src/formats/claude_code.rs for a complete reference implementation.
All nine formats have readers; Claude Code, OpenCode, Codex, Zed, Aider, and Gemini CLI also have writers. The most impactful contribution now is a writer for Continue.
Regenerating the demo
The README GIF is scripted with VHS: vhs assets/demo.tape. It records against a sandboxed $HOME (/tmp/demo) populated with fabricated sessions, so no real session data ends up in the GIF.
Don't drop the baton.
License
Dual-licensed under MIT OR Apache-2.0.
This server cannot be deployed
Maintenance
Related MCP Connectors
Your coding agent tells a coworker's agent what you found or changed. Invite-only.
Coding agents build full-stack apps in persistent workspaces and share them by link.
Shared memory across AI dev tools: each session hands its context to the next, not starting cold.
Persistent cross-session memory shared by Codex, Claude Code, ChatGPT, and other AI agents.
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