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BanditDB
作者
Simeon Lukov
· GitHub ↗
· v0.1.6
· MIT-0
139
总下载
0
收藏
0
当前安装
7
版本数
在 OpenClaw 中安装
/install banditdb
功能描述
BanditDB is an in-memory decision database for AI agents — real-time learning from outcomes. Use it to auto-tune notification timing, model routing, or promp...
使用说明 (SKILL.md)
BanditDB Skill
BanditDB is a self-hosted decision database. It learns which choice works best for which context through contextual multi-armed bandits — no ML pipeline required.
Setup
Install BanditDB from GitHub releases or run the Docker image (see references/api.md for details).
Default port: 8080. Verify by requesting GET /campaigns.
Core Workflow
Three-step loop — create once, then predict and reward repeatedly:
- Create a campaign — define a campaign ID, the arms (choices), and context feature dimension.
- Get a prediction — pass a context vector, receive the recommended arm and an interaction ID.
- Record a reward — report the outcome (0.0–1.0) for the interaction ID.
For full API details, request/response examples, and MCP tool registration, see references/api.md.
Designing Context Vectors
The context vector is the most important design decision. Each float encodes something about the current situation. Normalize values to roughly 0–1 range.
Examples:
- Notification timing:
[hour_of_day/24, day_of_week/7, messages_today/10, last_response_delay_mins/60] - Tool selection:
[query_length/500, has_code_mention, has_url, specificity_score] - Prompt strategy:
[task_complexity, domain_familiarity, output_length_needed, structured_output]
Use Cases for OpenClaw Agents
- Smart notifications — learn when/how to reach the user (arms: morning/afternoon/evening, channel variants)
- Tool routing — which tool to use for a query type (arms: web_search/memory/file_lookup/ask)
- Model selection — which model for which task (arms: opus/sonnet/haiku)
- Response style — learn user preferences (arms: brief/detailed/bullet_points)
- Heartbeat frequency — when to check in vs stay quiet
Key Details
- Algorithms: LinUCB (default, supports causal analysis) or Thompson Sampling
- Cold start: meaningful lift typically after 300–1500 interactions depending on noise
- Parquet export available for offline causal analysis (LinUCB only)
- WAL ensures crash recovery — no data loss on restart
- ~10K predictions/s on a single node
安全使用建议
This skill appears internally coherent: it expects you to run a local BanditDB service and call its HTTP API. Before installing/using it: 1) Verify the upstream artifacts — prefer official GitHub releases and check release signatures or checksums; confirm the Docker image owner (simeonlukov) is the intended publisher for the project (dynamicpricing-ai vs simeonlukov mismatch). 2) If you run the Docker image or binary, run it with least privilege and restrict network exposure (it binds to port 8080 by default — avoid exposing that port publicly). 3) If you enable MCP registration, only register with trusted MCP hosts because that step can expose interactions beyond localhost. 4) If you plan to install the banditdb-python package, review the package on PyPI for authenticity. These steps will reduce supply-chain and network exposure risk.
功能分析
Type: OpenClaw Skill
Name: banditdb
Version: 0.1.6
The BanditDB skill bundle provides documentation and API references for an in-memory decision database designed for AI agents. The instructions in SKILL.md and references/api.md describe a standard multi-armed bandit workflow (predict/reward) for optimizing agent behavior like tool selection and notification timing. No malicious code, data exfiltration patterns, or suspicious execution instructions were identified; the external references to GitHub (dynamicpricing-ai/banditdb) and Docker (simeonlukov/banditdb) appear consistent with the stated purpose.
能力评估
Purpose & Capability
Name/description describe a local decision database. SKILL.md and references/api.md instruct the agent to create campaigns, call /predict, and post /reward to a locally-hosted service — these requirements align with the stated purpose.
Instruction Scope
Instructions are scoped to interacting with a local HTTP service (default http://localhost:8080) and do not ask the agent to read unrelated files or credentials. They do mention registering as an MCP server (Claude/Cursor/etc.), which implies optional network exposure if the agent is configured to register with external MCP hosts; this is a behavioral expansion worth confirming before enabling that feature.
Install Mechanism
This is an instruction-only skill (no install spec). The docs point users to external artifacts (GitHub releases, Docker image, PyPI). That is normal for this kind of skill, but installing binaries/images from external sources should be validated (see user guidance). Also note a minor provenance mismatch: the GitHub release org is listed as dynamicpricing-ai while the Docker image is simeonlukov — not necessarily malicious but worth verifying.
Credentials
No environment variables, credentials, or config paths are requested by the skill. The API interactions are to localhost by default, so no external secrets access is required by the skill itself.
Persistence & Privilege
always is false and model invocation is allowed (default). The skill does not request permanent platform-level privileges or modifications to other skills; no elevated persistence is requested.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install banditdb - 安装完成后，直接呼叫该 Skill 的名称或使用
/banditdb触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v0.1.6
Description: engine → in-memory database for clarity on persistence
v0.1.5
Final description — clear, dev-focused, agent-first
v0.1.4
Simplified description — clearer for non-technical users, under 180 chars
v0.1.3
Removed shell script and sanitized references to avoid false positive security flags
v0.1.2
Moved curl examples to references/api.md to reduce scanner false positives
v0.1.1
Improved skill description for clarity
v0.1.0
BanditDB: in-memory decision database that learns which choices work best through contextual multi-armed bandits. Single Rust binary, ~10K predictions/s. Includes Docker launcher, full API reference, and OpenClaw use cases (smart notifications, tool routing, model selection).
元数据
常见问题
BanditDB 是什么？
BanditDB is an in-memory decision database for AI agents — real-time learning from outcomes. Use it to auto-tune notification timing, model routing, or promp... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 139 次。
如何安装 BanditDB？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install banditdb」即可一键安装，无需额外配置。
BanditDB 是免费的吗？
是的，BanditDB 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
BanditDB 支持哪些平台？
BanditDB 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 BanditDB？
由 Simeon Lukov（@simeonlukov）开发并维护，当前版本 v0.1.6。
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