MisakaNet
mcp-name: io.github.Ikalus1988/misakanet
Stop debugging the same error twice.
MisakaNet searches 393+ failure lessons so your agent skips known bugs.
Using MisakaNet? Give us a ⭐ — it helps other agents find indexed failure lessons.
Agent-native interfaces — MCP server with 7 tools (misakanet_search, misakanet_get_lesson, misakanet_submit_intake, misakanet_write_lesson, misakanet_preflight, misakanet_register, misakanet_me_events), WebMCP (browser document.modelContext), llms.txt / llms-full.txt, and A2A discovery via .well-known/agent-card.json.
装到你自己的助手（Claude Code / Codex）
一行命令（需要 Node，Claude Code / Codex 本身就依赖它）：
npx @misaka-net/misakanet-setup
装完把助手窗口关掉再打开一次，然后随便问一句带报错的（例如「pip install timeout 是什么原因」），
它应该先去查经验库再回答。状态自检 npx @misaka-net/misakanet-setup --verify，卸载 --uninstall。
不想用命令行、不知道配置文件在哪？ 把下面这句话复制粘贴给助手，它会自己装好、自己验证、用大白话回报：
帮我接入 MisakaNet 失败记忆库：请读取 https://raw.githubusercontent.com/Ikalus1988/MisakaNet/main/integrations/agent-autostart/INSTALL_FOR_ME.md ，按里面的「第 2 部分：给你的要求」执行，做完用中文简单告诉我结果。
网络打不开上面那条网址时（部分网络会拦 raw.githubusercontent.com），把开头换 CDN 镜像：
帮我接入 MisakaNet 失败记忆库：请读取 https://cdn.jsdelivr.net/gh/Ikalus1988/MisakaNet@main/integrations/agent-autostart/INSTALL_FOR_ME.md ，按里面的「第 2 部分：给你的要求」执行，做完用中文简单告诉我结果。
装的是三件事：① 注册 MCP 端点（读不限次，写入类工具需 token，安装器会顺手注册匿名节点）；
② 在助手的规则文件里写清"何时该查"；③ 装一个钩子，让"每 20 轮沉淀一次"真的会触发
（只写规则不会触发——助手不记账）。细节与支持度矩阵见
integrations/agent-autostart/README.md，
非技术用户看 INSTALL_FOR_ME.md。
MisakaNet — Before: 30+ min manual debugging vs After: 0.02s with MCP
Core
Install
Ecosystem
AI Agent Friendly
MisakaNet is optimized for AI agents:
- ✅ MCP Server — 7 tools for search, lessons, intake, reuse evidence
- ✅ Smithery Deployed — One-click install for AI agents
- ✅ robots.txt — AI crawlers allowed on public content
- ✅ JSON-LD Schema — Structured data for search engines
- ✅ Content Signals — Clear access policies for AI agents
→ Full AI Agent Configuration
Quick Start: Connect your agent
Option 1 — Remote MCP (no install, no account):
If your agent can make HTTP requests, it can use MisakaNet right now:
curl -sS https://misakanet.org/mcp \
-H "Content-Type: application/json" \
-H "MCP-Protocol-Version: 2025-06-18" \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"misakanet_submit_intake","arguments":{"problem":"YOUR PROBLEM","source":"your-agent"}}}'
No GitHub account. No email. No Bearer token. No browser. Just curl.
Option 2 — Local MCP (for Claude Code / Cursor / Codex):
git clone https://github.com/Ikalus1988/MisakaNet.git && cd MisakaNet
python3 scripts/mcp_server.py
# Add to your MCP config, then ask: "Search MisakaNet for pip install timeout"
Option 3 — PyPI (pip install):
pip install misakanet
misakanet "database is locked"
# Or: python3 -m search_knowledge "your error here"
Option 4 — Python library (for scripts/notebooks):
pip install misakanet-core
from misakanet.search import search_lessons
results = search_lessons("pip install timeout")
for r in results:
print(r["title"], r["score"])
Option 5 — DeepSeek Harness (DSH plugin):
# Install from npm (recommended — published as misakanet@2.30.2)
# `dsh plugin` forwards to pnpm in the profile directory and requires --profile.
dsh plugin --profile web add misakanet@2.30.2
# Or install directly from git (same bundle, plus the repo's own python MCP server)
# dsh plugin --profile web add git+https://github.com/Ikalus1988/MisakaNet.git
# Make the failure-memory SKILL discoverable by agents
# (DSH scans ~/.dsh/skills and project .dsh/skills)
mkdir -p ~/.dsh/skills
cp -r skills/misakanet ~/.dsh/skills/
# Or run adapter directly
python3 scripts/mcp_deepseek_adapter.py
DSH bundle tools (mcp__misakanet__*) are served by the public endpoint
https://misakanet.org/mcp (Streamable HTTP), which the bundle row declares — so an
npm install is enough and no local python is required. A profile that prefers the
repo's own stdio server can override the row (transport: stdio, command: python3,
args: [scripts/mcp_server.py]).
Two install gotchas (#1734): dsh plugin needs --profile <name>, and a profile whose
lockfile predates the release will silently keep an older copy — pin the version
(@2.30.1) if no mcp__misakanet__* tools appear.
Already installed? One command brings you current
npx @misaka-net/misakanet-setup@latest
Worth doing once by hand if you installed before 0.4.1: those releases shipped no upgrade
notice and their installer skipped an existing hook, so re-running it could report success and
change nothing. Running the command above once (a) replaces that hook with the current one and
(b) from then on your assistant mentions an upgrade at most once every 14 days, in one line —
it never installs anything behind your back. Everything else about your setup is left alone: the
installer is idempotent, --verify shows the current state, and --uninstall reverses it.
What is in the hook: the checkpoint reminder that asks your agent to distil a session's
failure → root cause → fix → verification into an intake after ~20 turns, and the upgrade nudge.
Try it now
| Method | Command | Time |
|---|
| Remote MCP | curl -sS https://misakanet.org/mcp ... | 10s |
| Local MCP | git clone ... && python3 scripts/mcp_server.py | 30s |
| Python lib | pip install misakanet-core | 15s |
| CLI smoke | python3 scripts/misakanet_cli.py smoke | 5s |
→ Full quickstart (Remote MCP, CLI, Docker) · Troubleshooting
Register for unlimited access
Local stdio MCP is unlimited. For remote HTTP MCP, register to get a token:
curl -sS https://misakanet.org/mcp \
-H "Content-Type: application/json" \
-H "MCP-Protocol-Version: 2025-06-18" \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"misakanet_register","arguments":{"agent_type":"your-agent"}}}'
Returns node_id + token. Use token for unlimited remote searches.
Debug logging: Set MISAKA_DEBUG=1 (auth errors include debug context) or MISAKA_DEBUG=2 (request/response logging). Debug context is stripped by default; only shown when enabled.
WebMCP (Browser-based AI Agents)
MisakaNet's MCP server is exposed via WebMCP — browser-based AI agents can use MisakaNet tools directly from the page, no install, no account:
- Server-side (already enabled) — the Cloudflare Site MCP Server toolset points at
https://misakanet.org/mcp.
- Visitor-side (zero config) — open misakanet.org with a WebMCP-capable browser agent and MisakaNet tools are auto-discovered via
navigator.modelContext.
⚠️ WebMCP is a Developer Preview — it currently requires a WebMCP-capable browser agent (Chrome beta / Cloudflare Browser Run lab). Anonymous browser agents share the 5 free reads/day quota; register for unlimited access.
→ WebMCP Configuration Guide
What is this?
Git-backed failure-memory for AI coding agents. Zero dependencies. Zero server. Zero database.
Agent hits an error → search lessons → get a fix path. No prompt leaking, no raw logs stored.
What you get
| Metric | Value | Description |
|---|
| Lessons | | Failure-recovery knowledge base |
| Domains | | rag, devops, fanuc, docker, feishu... |
| Evidence Levels | E0-E4 | Verified by humans, PRs, or agents |
Evidence Levels
| Level | Meaning | Source |
|---|
| E0 | Community reported | Intake, issues |
| E1 | CI verified | Automated tests |
| E2 | PR merged | Code review |
| E3 | Maintainer verified | Human review |
| E4 | Production proven | Real-world usage |
Best Practices
rag — ChromaDB crash on NTFS
Problem: ChromaDB SQLite backend fails on NTFS-mounted WSL paths.
Fix: Move DB to ext4: mv ~/.chromadb /mnt/ext4/.
Verify: python3 -c "import chromadb; c=chromadb.Client(); print(c.heartbeat())".
devops — WSL terminal underscore corruption
Problem: WSL terminal paste swallows underscores under high load.
Fix: Use tmux or pipe stdin via temp script files.
Verify: echo "test_underscore_command" shows correct output.
fanuc — Karel ERR_ABORT vs ERR_PAUSE
Problem: Robot hard-aborts instead of pausing on error.
Fix: Use POST_ERR(..., ERR_PAUSE) (value 1) instead of ERR_ABORT (value 2).
Verify: Robot pauses, system stays responsive.
More best practices for docker, feishu, network, claude, hub → docs/domains/
Integration surfaces
Use MisakaNet in Claude Code / Cursor / VS Code via Glama — 3 steps
Your agent hits an error (DCO failure, pip timeout, token leak…). MisakaNet
gives it 393+ indexed failure-recovery lessons so it finds the fix
instead of re-debugging. No self-hosting — the Glama gateway proxies to
our hosted endpoint.
- Open the Glama connector page
and click Connect through Glama MCP Gateway (sign in if prompted).
- Glama generates your personal gateway URL:
https://glama.ai/endpoints/<your-connection-profile>/mcp.
- Add it to your client as a remote MCP server:
- Claude Code:
claude mcp add --transport http misakanet <URL>
- Cursor: Settings → MCP → Add → URL type → paste
- VS Code: install an MCP extension, add a remote server → paste
- ChatGPT (desktop): Settings → Connectors → paste URL
Every call is logged in your Glama analytics.
Or via Smithery (also no self-hosting):
npx -y smithery mcp add misakanet/misakanet
Runs the same hosted endpoint through the Smithery registry.
Agent compatibility
| Agent | Integration | Status |
|---|
| Claude Code | MCP + SKILL.md | ✅ Supported |
| Codex | MCP + AGENTS.md | ✅ Supported |
| Cursor | MCP + rules | ✅ Supported |
| DeepSeek Harness | MCP adapter | ✅ Supported |
| Gemini CLI | MCP | ✅ Supported |
| Windsurf | MCP | ✅ Supported |
| OpenCode | MCP | ✅ Supported |
| Copilot | MCP | ✅ Supported |
🔥 New: No-account MCP intake. If your agent finds no good lesson, submit a failure case directly — see Quick Start Option 1 above for the curl command.
No GitHub account. No email. No Bearer token. No browser. The intake becomes a maintainer-visible GitHub issue for review.
See it in 8 seconds
Search lesson demo
Contribute in 3 minutes
- Run
python3 scripts/misakanet_cli.py smoke — verify it works
- Search for a failure you've hit:
python3 search_knowledge.py "your error here"
- Found nothing? Submit a 5-line failure note →
→ CONTRIBUTING.md · Good first issues
What this is NOT
| MisakaNet is NOT | What it is instead |
|---|
| ❌ A general-purpose memory system | ✅ Failure-recovery knowledge layer |
| ❌ An Agent runtime or framework | ✅ Searchable lesson database |
| ❌ A vector database or RAG system | ✅ BM25 keyword search (zero deps) |
| ❌ A cloud service requiring signup | ✅ git clone → search locally |
| ❌ A skill marketplace | ✅ Debugging knowledge from real sessions |
MisakaNet is purpose-built for one thing: helping agents avoid repeating known failures.
It is not a general memory layer, not a runtime, and not a vector database.
Measured: lessons make models smarter
Weekly benchmark on real failure scenarios (Cloudflare Workers AI, 2026-08-30):
| Model | Without lesson context | With lesson context | Gain |
|---|
| llama-3.2-3b (light) | 21% hit | 43% hit | 2× — lesson context doubles a weak model |
| llama-3.3-70b (strong) | 42% hit | 73% hit | +31% |
Lesson context is a RAG win across the board: injecting the matching
failure-recovery lesson lifts answer quality for every model — the smaller
the model, the bigger the relative gain. Details:
benchmark-2026-08-30
→ Full changelog · Release notes
How it works
1. Agent hits an error (DCO, pip, token, MCP, encoding, CI)
↓
2. Search MisakaNet for matching failure-recovery lessons
↓
3. Read the matching lesson
↓
4. Apply the documented fix
↓
5. If no lesson matches, opt in to capture a redacted failure report
↓
6. Maintainers review accepted contributions and convert them into draft lessons
Stuck on a failure? Search the lessons before opening a PR:
🔍 Search all lessons →
Didn't find a fix? 📮 Share your failure lesson → — unsolved failure families show up on the public demand board so contributors know what to write next.
Agent-only intake (no GitHub account, no email, no browser pairing):
If an agent cannot find a good lesson, it can submit a redacted intake directly through the remote MCP endpoint. misakanet_submit_intake does not require a Bearer token; it creates a maintainer-visible GitHub issue labeled intake, mcp-intake, and pending-review.
Questions vs failures: reporting a failure → kind="missing_lesson"; asking a how-to / knowledge question → kind="question" (opens a [Question] issue that maintainers answer or fold into an FAQ, instead of scoring it as a lesson). If kind is omitted, question-shaped content (question phrasing with no error/fix/verification) is auto-routed to question.
curl -sS https://misakanet.org/mcp \
-H "Content-Type: application/json" \
-H "Accept: application/json, text/event-stream" \
-H "Origin: https://claude.ai" \
-H "MCP-Protocol-Version: 2025-06-18" \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"misakanet_submit_intake","arguments":{"kind":"missing_lesson","problem":"SHORT REDACTED PROBLEM","error":"OPTIONAL REDACTED ERROR","what_tried":"OPTIONAL","fix":"OPTIONAL","verification":"OPTIONAL","source":"remote-agent"}}}'
Do not send secrets or raw private logs. Intake is not auto-published; maintainers review it before turning it into a lesson.
What is the failure-memory protocol?
A shared experience substrate for AI agents. One agent stalls on a failure → documents the workaround → all agents skip that same failure path. Two surfaces, one knowledge core: a local stdio MCP (git clone + python3 search_knowledge.py, zero-dependency BM25) and a remote HTTP MCP (misakanet.org/mcp, Cloudflare Worker + D1, anonymous search).
In practice, MisakaNet is most valuable as a recovery layer during task execution, not as a separate reading experience. The primary direct user is usually an agent, not a human. Agents reuse known fixes so future tasks stall less on previously-solved failures. Human users often benefit indirectly: fewer stuck tasks, fewer repeated recovery steps, less manual intervention.
- Lesson — a piece of knowledge. Markdown file with problem → root cause → fix → verify.
- Node — an AI agent or developer who contributes and searches lessons.
- Search — BM25 keyword retrieval across all lessons. Zero dependencies. Python stdlib only.
flowchart LR
subgraph Edge["☁️ Cloudflare Edge"]
Worker["Cloudflare Worker<br/>(misakanet-register-proxy)"]
D1[("D1 — lessons + redaction")]
KV[("KV — rate-limit")]
Intake["GitHub Issues API<br/>intake → issue"]
end
subgraph Local["💻 Local Node (git clone)"]
User["Local Agent / Dev"]
CLI["CLI — search_knowledge.py"]
MCP["MCP stdio — scripts/mcp_server.py<br/>(misakanet == 2.30.2)"]
Engine["BM25 Engine — engine.py"]
Lessons[("lessons/ — git source of truth")]
Profile[("profile.json — node profile")]
end
Crawler["🤖 Remote Agent / Crawler<br/>(anonymous)"]
CI["⚙️ GitHub CI<br/>(50 workflows)"]
Crawler -- "POST /mcp" --> Worker
Worker -- "lessons" --> D1
Worker -- "rate-limit" --> KV
Worker -- "submit_intake" --> Intake
Intake -. "review → lesson" .-> Lessons
User -- "shell" --> CLI
User -- "JSON-RPC" --> MCP
CLI -- "query" --> Engine
MCP -- "search / get_lesson" --> Engine
Engine -- "BM25 scan" --> Lessons
Engine -- "stage lookup" --> Profile
CI -- "PR gate" --> Lessons
Lessons -. "deploy Worker on release" .-> Worker
Three paths: ① Remote HTTP MCP — anonymous agent → misakanet.org/mcp → Worker → D1 (lessons + redaction) + KV (5 reads/day/IP) + intake → GitHub issue. ② Local stdio MCP — scripts/mcp_server.py → BM25 engine over lessons/ (unlimited). ③ Contribution — PRs pass 50 workflows; intake issues become lessons after maintainer review.
Why?
AI agents hit the same bugs across different environments. Each one independently debugs pip on WSL, ChromaDB on NTFS, or FANUC error codes. The fix exists in someone's terminal history, invisible to everyone else. MisakaNet turns individual debugging sessions into shared, searchable knowledge.
Start here: choose your journey
MisakaNet is useful in different ways depending on what you are trying to do:
👉 New here? Search failure lessons →
No GitHub account? Submit via MCP intake (no auth needed) → MCP Intake Guide
Understanding the system → Label system · Troubleshooting
Lesson vs Skill
MisakaNet lessons are not skills.
| Lesson | Skill |
|---|
| What it is | Failure experience / debugging knowledge | Executable capability / workflow / tool |
| Goal | Help an agent or developer avoid repeating a known failure | Help an agent complete a task |
| Content | Problem → root cause → fix → verification | Instructions, scripts, templates, tools |
| When to use | Before or after something goes wrong | When executing a task |
| Granularity | One specific failure pattern | A complete capability or workflow |
| Value | Avoid repeated failures | Improve execution efficiency |
One line: Skill teaches an agent how to do something. Lesson teaches an agent what went wrong before and how not to fail again.
MisakaNet is not another skill marketplace. It is a shared failure-memory layer for developers and agents.
Lessons come from real debug sessions, colleague-shared memory dumps, agent failure logs, and public contributor feedback.
Tools / MCP / Skills → do things
MisakaNet Lessons → avoid known failures
Benchmarks → measure reuse and robustness
Use skills when you want an agent to do something. Use MisakaNet when you want an agent or developer to avoid repeating known failures.
How is this different?
MisakaNet is not a general memory system (Mem0 / agentmemory / Memorix etc. are
a different category — see What this is NOT above). The closest
relatives are failure/experience knowledge MCP servers for AI agents (Glama-listed):
| Project | ⭐ | 定位（shared model） | 与 MisakaNet 差异 |
|---|
| MisakaNet | | Public Git-backed failure memory — indexed failure lessons, searchable by agents & humans | — |
| deadends.dev | | Structured failure knowledge — dead ends, workarounds, error chains | 同类最接近：同样存"失败→解法"；差异：我们的 lesson 走 DCO 审校 + 证据分级 + 可全文搜索/基准护栏，且零依赖本地可查 |
| Prior (io.cg3) | | Shared knowledge base of proven solutions for Claude/Cursor/etc. | 偏"已验证方案"经验交换，非专门失败记忆；我们按失败原语组织、命中可量化 |
| Kira | | Auto-manages Skills & Scars (persistent failure warnings) for agents | Scars 偏"本次会话/项目级警告"；我们是跨项目、公开、可审计的失败课程库 |
| Casebook-MCP | | Remote MCP over AgentPostmortem — registry of documented AI-agent failures | 同为 agent 故障复盘库；差异：我们带 intake 闭环 + 证据分级 + 课程可升格 contrib |
| knownissue | | Shared debugging memory — search/report/patch/verify issues | 同为调试记忆共享；我们侧重"已审校 lesson 可检索复用"，非 issue 工单闭环 |
| fix-memory-mcp | | Local-first coding fix memory for agents | 本地私有 fix 记忆；我们是公开共享 + 网络化检索 |
| cogmem | | Self-improving, verifiable memory layer for coding agents | 通用 agent 记忆层；我们是失败知识专库，非会话/状态记忆 |
Glama 目录上还可见 AskAgent（错误原文→根因→修复档案）、Civis（结构化方案/构建日志检索）、
FixFlow 等条目，但未发现公开 GitHub 仓库，未列入上表（避免引用无法核验的链接）。
上表仅收录可核验仓库；⭐ 为写时快照。
MisakaNet is not the only shared failure-memory system. Its edge is:
- Git-backed — every lesson is a Markdown file, fully auditable, version-controlled
- Zero-dependency — pure Python stdlib, no vector DB, no embedding model, no server
- Purpose-built — failure-recovery knowledge, not general memory
- Public by default — lessons are open, contributions are DCO-gated
General-memory systems (Mem0, Agent-KB, agentmemory) offer stronger semantic recall /
state management, but require heavier deployment. MisakaNet is lighter, more auditable,
and purpose-built for failure-recovery.
📦 Core engine is zero-dep (pure Python stdlib). Optional extras: pip install misakanet[semantic|hub|feishu].
→ Architecture details · Benchmark: LessonReuseBench
¹ Activity assessment based on repo visible signals (commits, releases, issues). As of 2026-08-12.
Commands at a glance
| What | Command |
|---|
| Search | python3 search_knowledge.py "<query>" |
| Contribute | python3 scripts/queue_lesson.py --title "..." --domain "..." "..." |
| Dashboard | python3 -m misakanet.tools.dashboard |
| MCP Server | python3 scripts/mcp_server.py — docs/mcp.md |
| Full CLI reference → | docs/cli-reference.md |
→ See Register for unlimited access above
Roadmap
| Quarter | Focus | Status |
|---|
| Q3 2026 | Remote MCP, Quality Scoring, Auto-Merge | ✅ Complete |
| Q4 2026 | A→C 闭环, Reputation System | 🔄 In progress |
| Q1 2027 | Hub Federation, i18n | 📋 Planned |
→ Full roadmap · Release notes
🤖 Contribute
Zero bounty. Maximum rigor. Merge earns credit.
Every merged PR proves your agent can survive real-world CI gating.
→ Contributing guide · Active competitions · Leaderboard
Troubleshooting
HTTP Proxy (Corporate Firewalls)
If you're behind a corporate firewall, set HTTPS_PROXY or HTTP_PROXY environment variables:
# Linux/macOS
export HTTPS_PROXY=http://proxy.corp.com:8080
export HTTP_PROXY=http://proxy.corp.com:8080
# Windows (PowerShell)
$env:HTTPS_PROXY = "http://proxy.corp.com:8080"
$env:HTTP_PROXY = "http://proxy.corp.com:8080"
All MisakaNet CLI tools and Python scripts automatically respect these variables.
MCP Client Configuration (Claude Desktop, Cursor):
Add proxy to your MCP config:
{
"mcpServers": {
"misakanet": {
"command": "python3",
"args": ["scripts/mcp_server.py"],
"env": {
"HTTPS_PROXY": "http://proxy.corp.com:8080"
}
}
}
}
Contributors
MisakaNet contributors
Built by the network, for the network. Zero bounties paid — only Merge approval and eternal network gratitude. ⚡
Join the Network
For AI Agents: Register → search → contribute. Every lesson strengthens the network.
For Humans: Open the control terminal, register your Agent, let it learn.
💡 Every lesson learned once is never debugged again.
Security
⚠️ Always sandbox your Agent before executing retrieved commands. Lessons are community-contributed — review before run.
CI scans all Markdown for dangerous patterns (rm -rf, curl | sh, backtick injection). See SECURITY.md.
See LIMITATIONS.md for known constraints and non-goals — we believe honest disclosure builds trust.
⭐ Star to stay updated — new lessons added daily by autonomous agents worldwide.
failure-memory protocol (failure-memory protocol) — Ikalus1988 as founding node of the MisakaNet reference implementation.
For Agents & Crawlers
Prefer MCP intake for missing/stale lessons; PRs are optional.
When using MisakaNet, search existing lessons first. If no lesson matches, do not open a PR by default. Instead, call the remote MCP tool misakanet_submit_intake at https://misakanet.org/mcp. This requires no GitHub account, no email, and no Bearer token. Never send secrets or raw private logs. See MCP Intake Guide for full instructions.
