Your agent
needs a
database.
Here's one.

import { createClawDBAgentTools, withClawDBMemory }
  from '@clawdb/openai-agents'

// Add tools to any OpenAI agent
const tools = createClawDBAgentTools(db, {
  enableBranching: true
})

// Or wrap the whole agent — auto memory in/out
const agent = withClawDBMemory(myAgent, db)
// Injects top memories before each run.
// Stores user + assistant turns after.

import { clawdbTools, clawdbMiddleware } from '@clawdb/vercel-ai'

// Vercel AI tools — generateText / streamText
const result = await generateText({
  model, tools: clawdbTools(db), prompt
})

// Or automatic middleware — zero agent code changes
const model = wrapLanguageModel({
  model: openai('gpt-4o'),
  middleware: clawdbMiddleware(db)
})
// ✓ Memory injected before · stored after every call

import { clawdbTools, handleClawDBToolCall,
         withClawDBMemory } from '@clawdb/anthropic'

// Native Anthropic tool format for Claude
const response = await anthropic.messages.create({
  model: 'claude-sonnet-4-20250514',
  tools: clawdbTools(db),
  messages
})

// Handle tool use blocks in one line
const result = await handleClawDBToolCall(db, response)
// ✓ clawdb_remember · clawdb_search · clawdb_recall

# One-command install for every AI editor:
npx @clawdb/mcp-adapter --install-claude    # Claude Desktop
npx @clawdb/mcp-adapter --install-cursor    # Cursor
npx @clawdb/mcp-adapter --install-vscode    # VS Code + Copilot
npx @clawdb/mcp-adapter --install-continue  # Continue.dev
npx @clawdb/mcp-adapter --install-zed       # Zed

# Tools exposed to all hosts:
# clawdb_remember · clawdb_search · clawdb_recall
# clawdb_branch_fork · clawdb_branch_merge · clawdb_status

import { ClawDBPlugin, withClawDB } from '@clawdb/openclaw'

// Plugin API — one line gives the agent a full database
const agent = new OpenClawAgent({
  plugins: [ClawDBPlugin({
    autoStore: true,     // store every exchange
    autoSearch: true,    // inject memory context
    topK: 3,             // memories per turn
    syncOnShutdown: true // persist to cloud
  })]
})

// Or HOC API:
const agent = withClawDB(new OpenClawAgent({...}))
// ✓ Zero boilerplate. Fully automatic.

import { clawdbTools, handleClawDBFunctionCall,
         withClawDBMemory } from '@clawdb/google-genai'

// Google GenAI FunctionDeclarations
const model = genAI.getGenerativeModel({
  model: 'gemini-2.0-flash',
  tools: [{ functionDeclarations: clawdbTools(db) }]
})

// Handle function calls
const result = await handleClawDBFunctionCall(db, call)

// Or auto-memory wrapper
const model = withClawDBMemory(generativeModel, db)
// ✓ Works with Gemini 1.5, 2.0, and future models

// Works in any MCP-compatible editor — auto-detects config path

// Cursor (~/.cursor/mcp.json):
npx @clawdb/mcp-adapter --install-cursor

// Zed (~/.config/zed/settings.json):
npx @clawdb/mcp-adapter --install-zed

// Print config for any host to paste manually:
npx @clawdb/mcp-adapter --print-config --host cursor
npx @clawdb/mcp-adapter --print-config --host vscode

// ✓ Your coding agent now has persistent project memory

// go get github.com/Claw-DB/claw-sdk/sdks/go
import clawdb "github.com/Claw-DB/claw-sdk/sdks/go"

db, _ := clawdb.New(clawdb.Options{
  AgentID: "my-agent",
  // Endpoint: auto-detected from CLAWDB_URL env var
})
defer db.Close()

id, _ := db.Memory.Remember(ctx, "deploy scheduled", nil)
hits, _ := db.Memory.Search(ctx, "deployment",
  &clawdb.SearchOptions{TopK: 5})
// ✓ Full parity with TypeScript and Python SDKs

// Cargo.toml: clawdb-client = "0.1"
use clawdb_client::ClawDBClient;

let db = ClawDBClient::auto_provision().await?;
// or:
let db = ClawDBClient::builder()
  .endpoint("http://localhost:50050")
  .agent_id("my-agent")
  .build().await?;

let id = db.memory().remember("deploy scheduled").await?;
let hits = db.memory().search("deployment").top_k(5).call().await?;
// ✓ Builder pattern · tokio async · full type safety