const agent = new Agent({
model: "claude-opus-4-7",
tools: [search, scrape, email],
memory: new VectorStore(),
})
async function extractLeads(url: string) {
const page = await firecrawl.scrape(url)
const contacts = parseContacts(page.markdown)
return contacts.filter(c => c.email)
}
type Lead = {
name: string
email: string
company: string
score: number
}
const stream = await streamText({
model: claude,
messages,
tools: { web_search, calculator },
maxSteps: 10,
})
export async function embed(
chunks: string[],
opts: { model: string }
) {
return openai.embeddings.create({
input: chunks, model: opts.model,
})
}
const pipeline = [
scrape,
chunk,
embed,
store,
retrieve,
rerank,
generate,
]
if (lead.score > threshold) {
await notify(lead, "slack")
await crm.upsert(lead)
await sequence.enroll(lead.email)
}
export const config = {
runtime: "edge",
regions: ["cdg1", "iad1"],
maxDuration: 300,
}
for await (const delta of stream) {
process.stdout.write(delta)
tokens += delta.length
}
const results = await Promise.all(
urls.map(url =>
extractLeads(url).catch(() => [])
)
)
function classify(
text: string,
labels: string[]
): Promise<{ label: string; score: number }> {
return model.classify({ input: text, labels })
}
await db.upsert("leads", rows, {
onConflict: "email",
returning: ["id", "created_at"],
})
const { text, usage } = await generateText({
model: claude,
system: SYSTEM_PROMPT,
prompt: userMessage,
})
type Project = {
name: string
status: "active" | "archived"
url?: string
stack: string[]
}
const embeddings = await embed(chunks, {
model: "text-embedding-3-large",
dimensions: 1536,
})
const agent = new Agent({
model: "claude-opus-4-7",
tools: [search, scrape, email],
memory: new VectorStore(),
})
async function extractLeads(url: string) {
const page = await firecrawl.scrape(url)
const contacts = parseContacts(page.markdown)
return contacts.filter(c => c.email)
}
type Lead = {
name: string
email: string
company: string
score: number
}
const stream = await streamText({
model: claude,
messages,const stream = await streamText({
model: claude,
messages,
tools: { web_search, calculator },
maxSteps: 10,
})
export async function embed(
chunks: string[],
opts: { model: string }
) {
return openai.embeddings.create({
input: chunks, model: opts.model,
})
}
const pipeline = [
scrape,
chunk,
embed,
store,
retrieve,
rerank,
generate,
]
if (lead.score > threshold) {
await notify(lead, "slack")
await crm.upsert(lead)
await sequence.enroll(lead.email)
}
export const config = {
runtime: "edge",
regions: ["cdg1", "iad1"],
maxDuration: 300,
}
for await (const delta of stream) {
process.stdout.write(delta)
tokens += delta.length
}
const results = await Promise.all(
urls.map(url =>
extractLeads(url).catch(() => [])
)
)
function classify(
text: string,
labels: string[]
): Promise<{ label: string; score: number }> {
return model.classify({ input: text, labels })
}
await db.upsert("leads", rows, {
onConflict: "email",
returning: ["id", "created_at"],
})
const { text, usage } = await generateText({
model: claude,
system: SYSTEM_PROMPT,
prompt: userMessage,
})
type Project = {
name: string
status: "active" | "archived"
url?: string
stack: string[]
}
const embeddings = await embed(chunks, {
model: "text-embedding-3-large",
dimensions: 1536,
})
const agent = new Agent({
model: "claude-opus-4-7",
tools: [search, scrape, email],
memory: new VectorStore(),
})
async function extractLeads(url: string) {
const page = await firecrawl.scrape(url)
const contacts = parseContacts(page.markdown)
return contacts.filter(c => c.email)
}
type Lead = {
name: string
email: string
company: string
score: number
}
const stream = await streamText({
model: claude,
messages,
tools: { web_search, calculator },
maxSteps: 10,
})
export async function embed(
chunks: string[],
opts: { model: string }
) {
return openai.embeddings.create({
input: chunks, model: opts.model,
})
}
const pipeline = [
scrape,
chunk,
embed,
store,
retrieve,export const config = {
runtime: "edge",
regions: ["cdg1", "iad1"],
maxDuration: 300,
}
for await (const delta of stream) {
process.stdout.write(delta)
tokens += delta.length
}
const results = await Promise.all(
urls.map(url =>
extractLeads(url).catch(() => [])
)
)
function classify(
text: string,
labels: string[]
): Promise<{ label: string; score: number }> {
return model.classify({ input: text, labels })
}
await db.upsert("leads", rows, {
onConflict: "email",
returning: ["id", "created_at"],
})
const { text, usage } = await generateText({
model: claude,
system: SYSTEM_PROMPT,
prompt: userMessage,
})
type Project = {
name: string
status: "active" | "archived"
url?: string
stack: string[]
}
const embeddings = await embed(chunks, {
model: "text-embedding-3-large",
dimensions: 1536,
})
const agent = new Agent({
model: "claude-opus-4-7",
tools: [search, scrape, email],
memory: new VectorStore(),
})
async function extractLeads(url: string) {
const page = await firecrawl.scrape(url)
const contacts = parseContacts(page.markdown)
return contacts.filter(c => c.email)
}
type Lead = {
name: string
email: string
company: string
score: number
}
const stream = await streamText({
model: claude,
messages,
tools: { web_search, calculator },
maxSteps: 10,
})
export async function embed(
chunks: string[],
opts: { model: string }
) {
return openai.embeddings.create({
input: chunks, model: opts.model,
})
}
const pipeline = [
scrape,
chunk,
embed,
store,
retrieve,
rerank,
generate,
]
if (lead.score > threshold) {
await notify(lead, "slack")
await crm.upsert(lead)
await sequence.enroll(lead.email)
}
export const config = {
runtime: "edge",
regions: ["cdg1", "iad1"],
maxDuration: 300,
}
for await (const delta of stream) {
process.stdout.write(delta)
tokens += delta.length
}
const results = await Promise.all(
urls.map(url =>
extractLeads(url).catch(() => [])
)
)
function classify(
text: string,
labels: string[]
): Promise<{ label: string; score: number }> {
return model.classify({ input: text, labels })await db.upsert("leads", rows, {
onConflict: "email",
returning: ["id", "created_at"],
})
const { text, usage } = await generateText({
model: claude,
system: SYSTEM_PROMPT,
prompt: userMessage,
})
type Project = {
name: string
status: "active" | "archived"
url?: string
stack: string[]
}
const embeddings = await embed(chunks, {
model: "text-embedding-3-large",
dimensions: 1536,
})
const agent = new Agent({
model: "claude-opus-4-7",
tools: [search, scrape, email],
memory: new VectorStore(),
})
async function extractLeads(url: string) {
const page = await firecrawl.scrape(url)
const contacts = parseContacts(page.markdown)
return contacts.filter(c => c.email)
}
type Lead = {
name: string
email: string
company: string
score: number
}
const stream = await streamText({
model: claude,
messages,
tools: { web_search, calculator },
maxSteps: 10,
})
export async function embed(
chunks: string[],
opts: { model: string }
) {
return openai.embeddings.create({
input: chunks, model: opts.model,
})
}
const pipeline = [
scrape,
chunk,
embed,
store,
retrieve,
rerank,
generate,
]
if (lead.score > threshold) {
await notify(lead, "slack")
await crm.upsert(lead)
await sequence.enroll(lead.email)
}
export const config = {
runtime: "edge",
regions: ["cdg1", "iad1"],
maxDuration: 300,
}
for await (const delta of stream) {
process.stdout.write(delta)
tokens += delta.length
}
const results = await Promise.all(
urls.map(url =>
extractLeads(url).catch(() => [])
)
)
function classify(
text: string,
labels: string[]
): Promise<{ label: string; score: number }> {
return model.classify({ input: text, labels })
}
await db.upsert("leads", rows, {
onConflict: "email",
returning: ["id", "created_at"],
})
const { text, usage } = await generateText({
model: claude,
system: SYSTEM_PROMPT,
prompt: userMessage,
})
type Project = {
name: string
status: "active" | "archived"
url?: string
stack: string[]
}
const embeddings = await embed(chunks, {
model: "text-embedding-3-large",
dimensions: 1536,
})
