opencode-export
Automatic redaction of GitHub secrets and tokens in exported session archives.
Automatic redaction of GitLab secrets and tokens in exported session archives.
Automatic redaction of Google API keys and secrets in exported session archives.
Automatic redaction of OpenAI API keys and secrets in exported session archives.
Automatic redaction of SendGrid API keys and secrets in exported session archives.
Automatic redaction of Slack tokens and secrets in exported session archives.
Automatic redaction of Stripe API keys and secrets in exported session archives.
Click on "Install 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., "@opencode-exportList my recent opencode sessions"
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.
opencode-export
Export your opencode AI coding sessions to beautiful, self-contained HTML + JSON archives — with secret redaction, subagent inlining, token backfill, and an MCP server for agents.

Features
Beautiful HTML output — Sticky headers with session metadata, collapsible tool-call and reasoning blocks, a slide-in TOC of user prompts, lazy syntax highlighting, KaTeX math rendering, and Mermaid diagram support.
Secret redaction built-in — 18 patterns (OpenRouter, OpenAI, Anthropic, GitHub, AWS, Slack, GitLab, Google, Stripe, SendGrid, and more) are auto-masked. Add your own exact strings or regex patterns via config.
Subagent inlining — When your session dispatched sub-agents (explore, general, code-reviewer), their full conversation is rendered inline at the dispatch point inside the parent session.
5-field token statistics — Input, output, reasoning, cache-read, and cache-write tokens are tracked per session. Older sessions with zero token data are backfilled by summing per-step usage from
step-finishparts.Fully offline — Vendor assets (highlight.js, Mermaid, KaTeX, Marked) are downloaded once and bundled into the export. No CDN, no internet required to view.
MCP server included — AI agents can call
list_sessions,show_session, andexport_sessionsas native tools via the Model Context Protocol.Structured JSON archive — Alongside HTML, a
data/sessions.jsonfile preserves full, untruncated content (tool inputs/outputs, reasoning text) for programmatic analysis.
Related MCP server: Memex
Quick Start
Prerequisites: Python 3.11+ and an existing opencode installation (the session database is auto-detected at
%LOCALAPPDATA%\opencode\opencode.dbon Windows,~/Library/Application Support/opencode/opencode.dbon macOS, or$XDG_DATA_HOME/opencode/opencode.dbon Linux).
# Install
pip install git+https://github.com/ZelinZhou-THU/opencode-export.git
# See what projects and sessions exist in your opencode database
opencode-export list
# Export a project's sessions to a browsable HTML + JSON archive
opencode-export export --project my-project --out ./export
# Preview a single session's metadata
opencode-export show ses_xxxxOpen ./export/index.html in your browser. That's it — no server, no internet needed.
How it differs
Several tools exist for exporting opencode or Claude Code sessions, typically producing Markdown transcripts or basic HTML pages. opencode-export was built to address a few gaps encountered when archiving a full semester of AI-assisted development:
Visual fidelity — Tool calls, reasoning blocks, code patches, and sub-agent threads are preserved in their original structure, not flattened to plain text.
Safety by default — Secrets are masked before any HTML or JSON is written, using both built-in patterns and user-configured rules. The redaction engine is recursive (handles nested tool state) and idempotent (safe to run twice).
Token reconstruction — opencode's database schema evolved over time; many older sessions have zero token data.
opencode-exportreconstructs usage by summing per-step token counts fromstep-finishmessage parts, and independently backfills cache fields.Agent-callable — Rather than only a human-facing CLI, the MCP server lets your AI agent discover, inspect, and export sessions on its own.
CLI Reference
Command | Purpose | Key flags |
| List projects or sessions in the database |
|
| Preview a session's metadata (title, model, token usage, etc.) | — |
| Generate HTML + JSON archive |
|
Both HTML and JSON are generated by default. Use --no-html or --no-json to skip a format.
Configuration
All options can be set via YAML/JSON config file or CLI flags. See examples/config.example.yaml for the full schema:
db: auto # auto-detect, or explicit path
project:
directory: "my-project" # substring match on session.directory
select:
min_messages: 30 # skip tiny sessions
min_text_bytes: 10240 # skip near-empty sessions
exclude: [] # session IDs to skip
output:
dir: ./opencode_export
formats: [html, json]
vendor: download # download | skip | copy-from
timezone: Asia/Shanghai
redact:
builtin: true # 18 built-in secret patterns
exact: [] # your own [secret, replacement] pairs
patterns: [] # your own [regex, replacement] pairsMCP Server
Install with the [mcp] extra to let AI agents call the export tools directly:
git clone https://github.com/ZelinZhou-THU/opencode-export.git
cd opencode-export
pip install -e ".[mcp]"Add to your opencode.json (or ~/.config/opencode/opencode.json):
{
"mcp": {
"opencode-export": {
"type": "local",
"command": ["opencode-export-mcp"]
}
}
}Three tools become available to the agent:
Tool | Purpose | Key params |
| List root sessions (by project) |
|
| Preview metadata + recent messages |
|
| Generate HTML + JSON archive to disk |
|
See skill/SKILL.md for the agent-facing usage guide, or examples/opencode.json.example for a ready-to-use config.
Output Structure
opencode_export/
├── index.html # Session index (stats grid + sortable table)
├── sessions/
│ ├── 01_2026-04-02_session-title.html
│ └── ...
├── data/
│ └── sessions.json # Full archive (untruncated, structured)
└── assets/
├── style.css
├── renderer.js # KaTeX / Mermaid / lazy highlight.js
└── vendor/ # Offline JS librariesToken Statistics
Each session and the index page display five token fields:
Field | Meaning |
| Input tokens (new prompt consumption) |
| Output tokens (model generation) |
| Reasoning tokens (o1 / DeepSeek-R1 style thinking) |
| Prompt cache hit reads |
| Prompt cache writes |
Total = sum of all five fields. This matches the billing granularity of OpenRouter, Anthropic, Google AI, and other providers where cache reads are billed as input tokens.
For older sessions where the database has zero token values, usage is reconstructed by summing per-step d["tokens"] across all step-finish parts.
Development
# Install in dev mode (CLI only)
pip install -e .
# Install with MCP server support
pip install -e ".[mcp]"
# Run tests
pytest tests/ -vLicense
This server cannot be installed
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Latest Blog Posts
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/ZelinZhou-THU/opencode-export'
If you have feedback or need assistance with the MCP directory API, please join our Discord server