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MisakaNet
Ikalus1988/MisakaNet
Failure-recovery memory: search and record failure-recovery lessons from real engineering sessions, with BM25 + semantic RAG retrieval and a lessons knowledge base.
Install
Add MisakaNet to DeepSeek Harness
via GitHub · GitHub source
Installing from GitHub runs the project's build script, which pnpm blocks until you allowlist it — run the command once and pnpm prints the exact key to add under `allowBuilds` in ~/.dsh/profiles/web/pnpm-workspace.yaml.
Due diligence
Before you install MisakaNet
- Source of record: Ikalus1988/MisakaNet — present in the community registry that DSH's own plugin market installs from.
- Licensed under Apache-2.0.
- Detected: terminal surface. Read the source before granting these.
- A listing here is not a security review. Plugins run with your agent's permissions.
What MisakaNet does
MisakaNet is a DeepSeek Harness plugin that provides a Git-backed knowledge layer for recovering from previously documented engineering failures. It is intended for agents that need to look up a known fix during task execution rather than maintain general-purpose memory.
The local implementation stores lessons in the repository and searches them with BM25 keyword matching; the README describes 289 lessons covering failures such as pip installation errors, GitHub authentication, DCO checks, MCP and encoding issues. A failure can be searched with python3 search_knowledge.py "your error here" or checked with python3 scripts/misakanet_cli.py smoke. The MCP entry point is python3 scripts/mcp_server.py; the DeepSeek Harness adapter is python3 scripts/mcp_deepseek_adapter.py and exposes deepseek.recovery.* tools. When no lesson matches, an agent may opt in to submit a redacted failure report. The project states that prompts are not leaked and raw logs are not stored.
MisakaNet suits coding agents that repeatedly encounter operational or build failures and can use a shared, version-controlled lesson set. It is the wrong choice for general memory, an agent runtime, or a vector database, and the README does not present it as a replacement for those systems. The plugin reaches the terminal because its server, adapter and search utilities are run as Python commands; those processes inherit the permissions of the agent environment. Local use is described as requiring no server, database or external dependency, while the project also documents a remote MCP endpoint.
MisakaNet documentation
How it behaves
MisakaNet models recovery as a lookup-and-apply flow. An agent searches for a failure, reads a matching lesson, and applies the documented fix. If no lesson matches, it can opt in to send a redacted failure report. Accepted reports are reviewed and converted into draft lessons. The README states that prompts are not leaked and raw logs are not stored.
The lesson set is Git-backed and contains 289 lessons in the documented release. The repository examples cover DCO sign-off, pip install timeout or SSL errors, secret-scan findings, GitHub API 401 errors, MCP, encoding and CI failures.
Commands and interfaces
| Purpose | Command or endpoint |
|---|---|
| Run the smoke check | python3 scripts/misakanet_cli.py smoke |
| Search from the repository | python3 search_knowledge.py "your error here" |
| Start the local MCP server | python3 scripts/mcp_server.py |
| Start the DeepSeek Harness adapter | python3 scripts/mcp_deepseek_adapter.py |
| Remote MCP | https://misakanet.org/mcp |
The adapter is MCP-compatible and exposes deepseek.recovery.* tools. The local server is added to an agent's MCP configuration; the README does not specify a universal configuration schema because that depends on the MCP client.
Requirements
The repository advertises Python 3.10+. Its local quickstart describes cloning the repository and running the Python scripts directly. The local mode is described as requiring zero dependencies, zero server and zero database. The README also documents Docker and a remote MCP option, but does not provide their full setup details in the supplied material.
Known limits
MisakaNet is purpose-built for failure-recovery knowledge. It is explicitly not a general-purpose memory system, agent runtime, vector database or skill marketplace. Local operation uses the terminal to launch Python scripts, so the server and adapter run with the permissions available to the agent environment. The documented local search mechanism is BM25 keyword search; the supplied README does not specify configuration keys or their defaults.
Written from the project's own documentation and kept in sync with it. Where the two disagree, the source is authoritative — read the README on GitHub
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