diff --git a/.cursor/rules/convex_rules.mdc b/.cursor/rules/convex_rules.mdc
index 1d984804..58f1e3a5 100644
--- a/.cursor/rules/convex_rules.mdc
+++ b/.cursor/rules/convex_rules.mdc
@@ -180,7 +180,7 @@ Note: `paginationOpts` is an object with the following properties:
## Schema guidelines
- Always define your schema in `convex/schema.ts`.
-- Always import the schema definition functions from `convex/server`:
+- Always import the schema definition functions from `convex/server`.
- System fields are automatically added to all documents and are prefixed with an underscore. The two system fields that are automatically added to all documents are `_creationTime` which has the validator `v.number()` and `_id` which has the validator `v.id(tableName)`.
- Always include all index fields in the index name. For example, if an index is defined as `["field1", "field2"]`, the index name should be "by_field1_and_field2".
- Index fields must be queried in the same order they are defined. If you want to be able to query by "field1" then "field2" and by "field2" then "field1", you must create separate indexes.
@@ -480,8 +480,8 @@ import OpenAI from "openai";
import { internal } from "./_generated/api";
/**
- * Create a user with a given name.
- */
+ * Create a user with a given name.
+ */
export const createUser = mutation({
args: {
name: v.string(),
@@ -493,8 +493,8 @@ export const createUser = mutation({
});
/**
- * Create a channel with a given name.
- */
+ * Create a channel with a given name.
+ */
export const createChannel = mutation({
args: {
name: v.string(),
@@ -506,8 +506,8 @@ export const createChannel = mutation({
});
/**
- * List the 10 most recent messages from a channel in descending creation order.
- */
+ * List the 10 most recent messages from a channel in descending creation order.
+ */
export const listMessages = query({
args: {
channelId: v.id("channels"),
@@ -532,8 +532,8 @@ export const listMessages = query({
});
/**
- * Send a message to a channel and schedule a response from the AI.
- */
+ * Send a message to a channel and schedule a response from the AI.
+ */
export const sendMessage = mutation({
args: {
channelId: v.id("channels"),
@@ -672,5 +672,4 @@ export default defineSchema({
export default function App() {
return Hello World
;
}
-```
-
+```
\ No newline at end of file
diff --git a/example/convex/README.md b/example/convex/README.md
new file mode 100644
index 00000000..7fda0c3d
--- /dev/null
+++ b/example/convex/README.md
@@ -0,0 +1,90 @@
+# Welcome to your Convex functions directory!
+
+Write your Convex functions here.
+See https://docs.convex.dev/functions for more.
+
+A query function that takes two arguments looks like:
+
+```ts
+// convex/myFunctions.ts
+import { query } from "./_generated/server";
+import { v } from "convex/values";
+
+export const myQueryFunction = query({
+ // Validators for arguments.
+ args: {
+ first: v.number(),
+ second: v.string(),
+ },
+
+ // Function implementation.
+ handler: async (ctx, args) => {
+ // Read the database as many times as you need here.
+ // See https://docs.convex.dev/database/reading-data.
+ const documents = await ctx.db.query("tablename").collect();
+
+ // Arguments passed from the client are properties of the args object.
+ console.log(args.first, args.second);
+
+ // Write arbitrary JavaScript here: filter, aggregate, build derived data,
+ // remove non-public properties, or create new objects.
+ return documents;
+ },
+});
+```
+
+Using this query function in a React component looks like:
+
+```ts
+const data = useQuery(api.myFunctions.myQueryFunction, {
+ first: 10,
+ second: "hello",
+});
+```
+
+A mutation function looks like:
+
+```ts
+// convex/myFunctions.ts
+import { mutation } from "./_generated/server";
+import { v } from "convex/values";
+
+export const myMutationFunction = mutation({
+ // Validators for arguments.
+ args: {
+ first: v.string(),
+ second: v.string(),
+ },
+
+ // Function implementation.
+ handler: async (ctx, args) => {
+ // Insert or modify documents in the database here.
+ // Mutations can also read from the database like queries.
+ // See https://docs.convex.dev/database/writing-data.
+ const message = { body: args.first, author: args.second };
+ const id = await ctx.db.insert("messages", message);
+
+ // Optionally, return a value from your mutation.
+ return await ctx.db.get(id);
+ },
+});
+```
+
+Using this mutation function in a React component looks like:
+
+```ts
+const mutation = useMutation(api.myFunctions.myMutationFunction);
+function handleButtonPress() {
+ // fire and forget, the most common way to use mutations
+ mutation({ first: "Hello!", second: "me" });
+ // OR
+ // use the result once the mutation has completed
+ mutation({ first: "Hello!", second: "me" }).then((result) =>
+ console.log(result),
+ );
+}
+```
+
+Use the Convex CLI to push your functions to a deployment. See everything
+the Convex CLI can do by running `npx convex -h` in your project root
+directory. To learn more, launch the docs with `npx convex docs`.
diff --git a/example/convex/_generated/api.d.ts b/example/convex/_generated/api.d.ts
index 24150fcf..a1d0bcfb 100644
--- a/example/convex/_generated/api.d.ts
+++ b/example/convex/_generated/api.d.ts
@@ -8,10 +8,16 @@
* @module
*/
+import type * as agents_analyst from "../agents/analyst.js";
import type * as agents_config from "../agents/config.js";
+import type * as agents_creative from "../agents/creative.js";
import type * as agents_fashion from "../agents/fashion.js";
+import type * as agents_judge from "../agents/judge.js";
+import type * as agents_pragmatist from "../agents/pragmatist.js";
+import type * as agents_researcher from "../agents/researcher.js";
import type * as agents_simple from "../agents/simple.js";
import type * as agents_story from "../agents/story.js";
+import type * as agents_synthesizer from "../agents/synthesizer.js";
import type * as agents_weather from "../agents/weather.js";
import type * as chat_basic from "../chat/basic.js";
import type * as chat_human from "../chat/human.js";
@@ -46,7 +52,11 @@ import type * as usage_tracking_invoicing from "../usage_tracking/invoicing.js";
import type * as usage_tracking_tables from "../usage_tracking/tables.js";
import type * as usage_tracking_usageHandler from "../usage_tracking/usageHandler.js";
import type * as utils from "../utils.js";
+import type * as workflows_actor_critique from "../workflows/actor_critique.js";
+import type * as workflows_batching from "../workflows/batching.js";
import type * as workflows_chaining from "../workflows/chaining.js";
+import type * as workflows_council_of_agents from "../workflows/council_of_agents.js";
+import type * as workflows_deep_agent from "../workflows/deep_agent.js";
import type * as workflows_human_in_the_loop from "../workflows/human_in_the_loop.js";
import type * as workflows_orchestrator from "../workflows/orchestrator.js";
import type * as workflows_parallel from "../workflows/parallel.js";
@@ -62,10 +72,16 @@ import type {
} from "convex/server";
declare const fullApi: ApiFromModules<{
+ "agents/analyst": typeof agents_analyst;
"agents/config": typeof agents_config;
+ "agents/creative": typeof agents_creative;
"agents/fashion": typeof agents_fashion;
+ "agents/judge": typeof agents_judge;
+ "agents/pragmatist": typeof agents_pragmatist;
+ "agents/researcher": typeof agents_researcher;
"agents/simple": typeof agents_simple;
"agents/story": typeof agents_story;
+ "agents/synthesizer": typeof agents_synthesizer;
"agents/weather": typeof agents_weather;
"chat/basic": typeof chat_basic;
"chat/human": typeof chat_human;
@@ -100,7 +116,11 @@ declare const fullApi: ApiFromModules<{
"usage_tracking/tables": typeof usage_tracking_tables;
"usage_tracking/usageHandler": typeof usage_tracking_usageHandler;
utils: typeof utils;
+ "workflows/actor_critique": typeof workflows_actor_critique;
+ "workflows/batching": typeof workflows_batching;
"workflows/chaining": typeof workflows_chaining;
+ "workflows/council_of_agents": typeof workflows_council_of_agents;
+ "workflows/deep_agent": typeof workflows_deep_agent;
"workflows/human_in_the_loop": typeof workflows_human_in_the_loop;
"workflows/orchestrator": typeof workflows_orchestrator;
"workflows/parallel": typeof workflows_parallel;
diff --git a/example/convex/workflows/actor_critique.ts b/example/convex/workflows/actor_critique.ts
new file mode 100644
index 00000000..eaec1612
--- /dev/null
+++ b/example/convex/workflows/actor_critique.ts
@@ -0,0 +1,216 @@
+// See the docs at https://docs.convex.dev/agents/workflows
+import { WorkflowManager } from "@convex-dev/workflow";
+import { components, internal } from "../_generated/api";
+import { internalMutation, mutation } from "../_generated/server";
+import { v } from "convex/values";
+import { Agent, createThread, saveMessage, stepCountIs } from "@convex-dev/agent";
+import { getAuthUserId } from "../utils";
+import { defaultConfig } from "convex/agents/config";
+import { z } from "zod/v4";
+
+/**
+ * Actor-Critique Pattern: LLM as a judge / self-improvement loop
+ *
+ * This workflow demonstrates iterative refinement where:
+ * 1. An "actor" agent produces output
+ * 2. A "critic" agent evaluates and scores the output
+ * 3. If score < threshold (and iterations < max), feed critique back to actor
+ * 4. Repeat until quality threshold is met or max iterations reached
+ */
+
+const workflow = new WorkflowManager(components.workflow);
+
+export const actorCritiqueWorkflow = workflow.define({
+ args: {
+ task: v.string(),
+ threadId: v.string(),
+ targetScore: v.optional(v.number()), // 0-10, defaults to 7
+ maxIterations: v.optional(v.number()), // defaults to 3
+ },
+ returns: v.object({
+ iterations: v.number(),
+ finalScore: v.number(),
+ output: v.string(),
+ critiqueSummary: v.string(),
+ }),
+ handler: async (
+ ctx,
+ args,
+ ): Promise<{
+ iterations: number;
+ finalScore: number;
+ output: string;
+ critiqueSummary: string;
+ }> => {
+ const targetScore = args.targetScore ?? 7;
+ const maxIterations = args.maxIterations ?? 3;
+
+ console.log(
+ `Starting actor-critique loop: target=${targetScore}, maxIter=${maxIterations}`,
+ );
+
+ let iteration = 0;
+ let currentOutput = "";
+ let currentScore = 0;
+ let critiqueSummary = "";
+
+ // Create a separate thread for critiques
+ const critiqueThreadId = await ctx.runMutation(
+ internal.workflows.actor_critique.createCritiqueThread,
+ { parentThreadId: args.threadId, task: args.task },
+ );
+ console.log(`Created critique thread: ${critiqueThreadId}`);
+
+ // Initial prompt to actor (main thread)
+ let actorPromptMsg = await saveMessage(ctx, components.agent, {
+ threadId: args.threadId,
+ prompt: `Task: ${args.task}\n\nPlease complete this task to the best of your ability.`,
+ });
+
+ while (iteration < maxIterations) {
+ iteration++;
+ console.log(`Iteration ${iteration}/${maxIterations}`);
+
+ // Step 1: Actor generates output (main thread)
+ const actorResult = await ctx.runAction(
+ internal.workflows.actor_critique.actorGenerate,
+ {
+ promptMessageId: actorPromptMsg.messageId,
+ threadId: args.threadId,
+ },
+ { retry: true },
+ );
+ currentOutput = actorResult.text;
+ console.log(`Actor output (${currentOutput.length} chars)`);
+
+ // Step 2: Critic evaluates the output (critique thread)
+ const critiquePromptMsg = await saveMessage(ctx, components.agent, {
+ threadId: critiqueThreadId,
+ prompt: `Iteration ${iteration}: Evaluate this output for the task "${args.task}":\n\n${currentOutput}`,
+ });
+
+ const { object: critique } = await ctx.runAction(
+ internal.workflows.actor_critique.criticEvaluate,
+ {
+ promptMessageId: critiquePromptMsg.messageId,
+ threadId: critiqueThreadId,
+ },
+ { retry: true },
+ );
+
+ currentScore = critique.score;
+ critiqueSummary = critique.feedback;
+ console.log(`Critic score: ${currentScore}/10`);
+
+ // Step 3: Check if we've met the threshold
+ if (currentScore >= targetScore) {
+ console.log(`Target score ${targetScore} achieved!`);
+ break;
+ }
+
+ // Step 4: If not at max iterations, feed critique back to actor (main thread)
+ if (iteration < maxIterations) {
+ console.log("Feeding critique back to actor for improvement");
+ actorPromptMsg = await saveMessage(ctx, components.agent, {
+ threadId: args.threadId,
+ prompt: `Your previous attempt scored ${currentScore}/10.
+
+Feedback: ${critique.feedback}
+
+Specific improvements needed:
+${critique.improvements.map((imp: string, i: number) => `${i + 1}. ${imp}`).join("\n")}
+
+Please revise your response addressing this feedback.`,
+ });
+ }
+ }
+
+ return {
+ iterations: iteration,
+ finalScore: currentScore,
+ output: currentOutput,
+ critiqueSummary,
+ };
+ },
+});
+
+// Actor agent - produces the output
+const actorAgent = new Agent(components.agent, {
+ name: "Actor Agent",
+ ...defaultConfig,
+ instructions: `You are a skilled writer/producer. Complete tasks thoroughly and thoughtfully.
+When receiving feedback, carefully address each point while maintaining the strengths of your work.`,
+});
+
+export const actorGenerate = actorAgent.asTextAction({
+ stopWhen: stepCountIs(3),
+});
+
+// Critic agent - evaluates and provides feedback
+const criticAgent = new Agent(components.agent, {
+ name: "Critic Agent",
+ ...defaultConfig,
+ instructions: `You are a constructive critic. Evaluate outputs fairly and provide:
+1. A score from 0-10 (be honest but fair)
+2. Specific, actionable feedback
+3. Concrete improvements the author should make
+
+Be constructive, not harsh. Focus on how to improve, not just what's wrong.`,
+});
+
+export const criticEvaluate = criticAgent.asObjectAction({
+ schema: z.object({
+ score: z.number().min(0).max(10).describe("Quality score from 0-10"),
+ feedback: z.string().describe("Overall feedback summary"),
+ improvements: z
+ .array(z.string())
+ .describe("List of specific improvements to make"),
+ strengths: z.array(z.string()).describe("What was done well"),
+ }),
+});
+
+// Internal mutation to create a separate thread for critiques
+export const createCritiqueThread = internalMutation({
+ args: {
+ parentThreadId: v.string(),
+ task: v.string(),
+ },
+ returns: v.string(),
+ handler: async (ctx, args) => {
+ const threadId = await createThread(ctx, components.agent, {
+ title: `Critique Thread: ${args.task.slice(0, 40)}`,
+ });
+ return threadId;
+ },
+});
+
+// Mutation to start the actor-critique workflow
+export const startActorCritique = mutation({
+ args: {
+ task: v.string(),
+ targetScore: v.optional(v.number()),
+ maxIterations: v.optional(v.number()),
+ },
+ handler: async (
+ ctx,
+ args,
+ ): Promise<{ threadId: string; workflowId: string }> => {
+ const userId = await getAuthUserId(ctx);
+ const threadId = await createThread(ctx, components.agent, {
+ userId,
+ title: `Actor-Critique: ${args.task.slice(0, 50)}`,
+ });
+ const workflowId = await workflow.start(
+ ctx,
+ internal.workflows.actor_critique.actorCritiqueWorkflow,
+ {
+ task: args.task,
+ threadId,
+ targetScore: args.targetScore,
+ maxIterations: args.maxIterations,
+ },
+ );
+ return { threadId, workflowId };
+ },
+});
+
diff --git a/example/convex/workflows/batching.ts b/example/convex/workflows/batching.ts
new file mode 100644
index 00000000..115f6f66
--- /dev/null
+++ b/example/convex/workflows/batching.ts
@@ -0,0 +1,481 @@
+// See the docs at https://docs.convex.dev/agents/workflows
+import { WorkflowManager } from "@convex-dev/workflow";
+import { components, internal } from "../_generated/api";
+import { mutation } from "../_generated/server";
+import { v } from "convex/values";
+import { createThread, saveMessage, stepCountIs, Agent } from "@convex-dev/agent";
+import { getAuthUserId } from "../utils";
+import { defaultConfig } from "../agents/config";
+
+/**
+ * Advanced Batching Patterns for Deep Research Workflows
+ *
+ * USE CASE: Deep Research / Multi-Topic Analysis
+ * ================================================
+ * Imagine you're building a research assistant that needs to:
+ * - Analyze 50+ competitors for a market research report
+ * - Research 100+ scientific papers for a literature review
+ * - Investigate multiple angles of a complex topic (legal, technical, financial)
+ * - Generate comprehensive reports across many data points
+ *
+ * Without batching, you'd either:
+ * - Hit rate limits from LLM providers (most allow 50-100 RPM)
+ * - Overwhelm system resources trying to run 100+ concurrent requests
+ * - Lose all progress if one request fails
+ *
+ * WHY BATCHING MATTERS:
+ * =====================
+ * 1. Rate Limit Management: LLM providers (OpenAI, Anthropic, etc.) enforce
+ * rate limits. Batching lets you control throughput (e.g., 5 at a time).
+ *
+ * 2. Resource Efficiency: Running 100 concurrent LLM calls wastes resources
+ * waiting on I/O. A pool of 5-10 workers is typically optimal.
+ *
+ * 3. Fault Tolerance: If one research task fails, you don't lose the others.
+ * Sub-workflows provide isolation for independent retries.
+ *
+ * 4. Progress Tracking: Batching gives natural checkpoints to track progress
+ * and resume from failures.
+ *
+ * This file demonstrates three patterns:
+ * - Batch Pattern: Process in fixed-size chunks (simple, predictable rate)
+ * - Pool Pattern: Worker pool pulls from queue (efficient, consistent throughput)
+ * - Nested Batch: Sub-workflows for isolation (fault tolerant, resumable)
+ */
+
+const workflow = new WorkflowManager(components.workflow);
+
+// Define the researcher agent inline to keep the demo self-contained
+const researcherAgent = new Agent(components.agent, {
+ name: "Researcher",
+ instructions: `You are a thorough researcher. When given a topic:
+- Provide key facts and insights
+- Summarize the most important information
+- Be concise but comprehensive
+- Focus on actionable knowledge`,
+ stopWhen: stepCountIs(3),
+ ...defaultConfig,
+});
+
+// ============================================================================
+// PATTERN 1: Batch Processing
+// ============================================================================
+// Process items in fixed-size batches. Each batch runs in parallel,
+// but we wait for the entire batch to complete before starting the next.
+//
+// EXAMPLE USE CASE:
+// Research 50 companies for a market analysis. Process 5 at a time to stay
+// under your LLM provider's rate limit of 60 requests per minute.
+//
+// Pros:
+// - Simple to understand and implement
+// - Predictable rate of API calls (batchSize calls, then pause)
+// - Easy to add delays between batches for rate limiting
+// - Natural checkpoints for progress tracking
+//
+// Cons:
+// - Inefficient if items have varying processing times
+// - Slowest item in batch holds up the entire batch
+//
+// RATE LIMIT TIP: If you need 60 RPM max, use batchSize=5 and you'll make
+// 5 requests per batch. Each batch takes ~10-30 seconds, keeping you safe.
+// ============================================================================
+
+export const batchResearchWorkflow = workflow.define({
+ args: {
+ topics: v.array(v.string()),
+ threadId: v.string(),
+ batchSize: v.optional(v.number()), // Default: 5
+ },
+ returns: v.array(
+ v.object({
+ topic: v.string(),
+ research: v.string(),
+ }),
+ ),
+ handler: async (
+ ctx,
+ args,
+ ): Promise> => {
+ const batchSize = args.batchSize ?? 5;
+ const results: Array<{ topic: string; research: string }> = [];
+
+ console.log(
+ `Starting batch research: ${args.topics.length} topics, batch size ${batchSize}`,
+ );
+
+ // Process topics in batches
+ for (let i = 0; i < args.topics.length; i += batchSize) {
+ const batch = args.topics.slice(i, i + batchSize);
+ const batchNumber = Math.floor(i / batchSize) + 1;
+ const totalBatches = Math.ceil(args.topics.length / batchSize);
+
+ console.log(`Processing batch ${batchNumber}/${totalBatches}: ${batch.join(", ")}`);
+
+ // Run all items in this batch in parallel
+ const batchPromises = batch.map(async (topic) => {
+ const promptMsg = await saveMessage(ctx, components.agent, {
+ threadId: args.threadId,
+ prompt: `Research the following topic and provide key insights: ${topic}`,
+ });
+
+ const result = await ctx.runAction(
+ internal.workflows.batching.researchTopic,
+ {
+ promptMessageId: promptMsg.messageId,
+ threadId: args.threadId,
+ },
+ { retry: true },
+ );
+
+ return { topic, research: result.text };
+ });
+
+ // Wait for entire batch to complete before starting next
+ const batchResults = await Promise.all(batchPromises);
+ results.push(...batchResults);
+
+ console.log(`Batch ${batchNumber} complete`);
+
+ // Optional: Add delay between batches to respect rate limits
+ // The workflow system handles this gracefully - you could also
+ // use ctx.runMutation to schedule a delay if needed
+ }
+
+ console.log(`Batch research complete: ${results.length} topics researched`);
+ return results;
+ },
+});
+
+// ============================================================================
+// PATTERN 2: Worker Pool
+// ============================================================================
+// Maintain a fixed pool of concurrent "workers". As soon as one worker
+// finishes, it immediately picks up the next item from the queue.
+//
+// EXAMPLE USE CASE:
+// Research 100 scientific papers where some are short abstracts (fast) and
+// some are full papers (slow). A pool keeps all workers busy - when one
+// finishes a quick abstract, it immediately grabs the next paper.
+//
+// Pros:
+// - More efficient utilization (no waiting for slowest item in batch)
+// - Consistent concurrency level (always N requests in flight)
+// - Better throughput for items with varying processing times
+// - Maximizes throughput within rate limits
+//
+// Cons:
+// - More complex implementation
+// - Harder to reason about ordering (results come back out of order)
+// - Less predictable checkpoints
+//
+// RATE LIMIT TIP: Set poolSize to match your desired concurrent requests.
+// If your rate limit is 100 RPM and each request takes ~30s, poolSize=50
+// keeps you at ~100 requests per minute.
+// ============================================================================
+
+export const poolResearchWorkflow = workflow.define({
+ args: {
+ topics: v.array(v.string()),
+ threadId: v.string(),
+ poolSize: v.optional(v.number()), // Default: 5
+ },
+ returns: v.array(
+ v.object({
+ topic: v.string(),
+ research: v.string(),
+ }),
+ ),
+ handler: async (
+ ctx,
+ args,
+ ): Promise> => {
+ const poolSize = args.poolSize ?? 5;
+ const results: Array<{ topic: string; research: string }> = [];
+
+ // In a production app, you would define a 'jobs' table in your schema.
+ // This workflow would then fetch from that table to manage state across
+ // potential restarts or large volume research tasks.
+ //
+ // Example Table Schema:
+ // research_jobs: { batchId: string, topic: string, status: "pending" | "done" }
+
+ // For this demo, we'll use an in-memory queue to simulate the pooling.
+ const queue = [...args.topics];
+ let nextIndex = 0;
+
+ console.log(
+ `Starting pool research: ${args.topics.length} topics, pool size ${poolSize}`,
+ );
+
+ /**
+ * PRODUCTION TIP: Instead of an in-memory queue, you'd use a function like:
+ * const getNextJob = async () => {
+ * // Atomically claim the next 'pending' job for this research session
+ * return await ctx.runMutation(internal.research.claimNextJob, { batchId });
+ * };
+ *
+ * The loop would then be:
+ * while (await hasPendingJobs(batchId)) { ... }
+ */
+ const getNextJob = () => {
+ if (nextIndex >= queue.length) return null;
+ return queue[nextIndex++];
+ };
+
+ // Helper to process a single topic
+ const processTopic = async (topic: string): Promise<{ topic: string; research: string }> => {
+ const promptMsg = await saveMessage(ctx, components.agent, {
+ threadId: args.threadId,
+ prompt: `Research the following topic and provide key insights: ${topic}`,
+ });
+
+ const result = await ctx.runAction(
+ internal.workflows.batching.researchTopic,
+ {
+ promptMessageId: promptMsg.messageId,
+ threadId: args.threadId,
+ },
+ { retry: true },
+ );
+
+ return { topic, research: result.text };
+ };
+
+ // Worker function: pulls from the "pending requests" until none remain.
+ const worker = async (): Promise => {
+ // "While pending requests for this session exist..."
+ while (true) {
+ const topic = getNextJob();
+ if (!topic) {
+ break; // No more pending requests
+ }
+
+ console.log(`Worker processing topic: ${topic}`);
+ const result = await processTopic(topic);
+ results.push(result);
+ }
+ };
+
+ // Start pool of workers
+ const workers: Array> = [];
+ const actualPoolSize = Math.min(poolSize, queue.length);
+
+ for (let i = 0; i < actualPoolSize; i++) {
+ workers.push(worker());
+ }
+
+ // Wait for all workers in the pool to complete their queues
+ await Promise.all(workers);
+
+ console.log(`Pool research complete: ${results.length} topics researched`);
+
+ // Note: Results may be out of order since workers complete at different times
+ // Sort by original order if needed
+ const topicOrder = new Map(args.topics.map((t, i) => [t, i]));
+ results.sort((a, b) => (topicOrder.get(a.topic) ?? 0) - (topicOrder.get(b.topic) ?? 0));
+
+ return results;
+ },
+});
+
+// ============================================================================
+// Sub-workflow for individual topic research
+// ============================================================================
+// Breaking out individual research into a sub-workflow allows:
+// - Better error isolation (one failure doesn't affect others)
+// - Cleaner retry logic (Convex handles retries per workflow)
+// - Easier testing (can test single topic research independently)
+// - Resumability (failed workflows can be retried without re-running successes)
+//
+// EXAMPLE: If researching 100 topics and topic #47 fails due to a transient
+// error, only that sub-workflow needs to retry - the other 99 are unaffected.
+// ============================================================================
+
+export const topicResearchSubWorkflow = workflow.define({
+ args: {
+ topic: v.string(),
+ threadId: v.string(),
+ },
+ returns: v.string(),
+ handler: async (ctx, args): Promise => {
+ const promptMsg = await saveMessage(ctx, components.agent, {
+ threadId: args.threadId,
+ prompt: `Research the following topic thoroughly: ${args.topic}
+
+Provide:
+1. Key facts and background
+2. Current state/trends
+3. Important considerations
+4. Actionable insights`,
+ });
+
+ const result = await ctx.runAction(
+ internal.workflows.batching.researchTopic,
+ {
+ promptMessageId: promptMsg.messageId,
+ threadId: args.threadId,
+ },
+ { retry: true },
+ );
+
+ return result.text;
+ },
+});
+
+// ============================================================================
+// PATTERN 3: Nested Batch with Sub-workflows (Recommended for Production)
+// ============================================================================
+// For very large workloads, use nested workflows for better isolation.
+// Each item is its own workflow, allowing independent retries and state.
+//
+// EXAMPLE USE CASE:
+// Deep research on 200 topics for a comprehensive report. Each topic is a
+// separate sub-workflow, so if 3 fail due to rate limits or timeouts, you
+// can retry just those 3 without re-running the successful 197.
+//
+// This is the RECOMMENDED pattern for production deep research because:
+// - Each research task has independent state and retry logic
+// - Partial failures don't lose completed work
+// - You can monitor progress per-topic in the Convex dashboard
+// - Easy to add more sophisticated per-topic logic (multi-step research, etc.)
+// ============================================================================
+
+export const nestedBatchResearchWorkflow = workflow.define({
+ args: {
+ topics: v.array(v.string()),
+ threadId: v.string(),
+ batchSize: v.optional(v.number()),
+ },
+ returns: v.array(
+ v.object({
+ topic: v.string(),
+ research: v.string(),
+ }),
+ ),
+ handler: async (
+ ctx,
+ args,
+ ): Promise> => {
+ const batchSize = args.batchSize ?? 5;
+ const results: Array<{ topic: string; research: string }> = [];
+
+ console.log(
+ `Starting nested batch research: ${args.topics.length} topics, batch size ${batchSize}`,
+ );
+
+ for (let i = 0; i < args.topics.length; i += batchSize) {
+ const batch = args.topics.slice(i, i + batchSize);
+ const batchNumber = Math.floor(i / batchSize) + 1;
+ const totalBatches = Math.ceil(args.topics.length / batchSize);
+
+ console.log(`Processing batch ${batchNumber}/${totalBatches}`);
+
+ // Each topic gets its own sub-workflow for better isolation
+ const batchPromises = batch.map(async (topic) => {
+ const research = await ctx.runWorkflow(
+ internal.workflows.batching.topicResearchSubWorkflow,
+ { topic, threadId: args.threadId },
+ );
+ return { topic, research };
+ });
+
+ const batchResults = await Promise.all(batchPromises);
+ results.push(...batchResults);
+
+ console.log(`Batch ${batchNumber} complete`);
+ }
+
+ return results;
+ },
+});
+
+// Agent action
+export const researchTopic = researcherAgent.asTextAction({
+ stopWhen: stepCountIs(3),
+});
+
+// ============================================================================
+// Entry point mutations
+// ============================================================================
+// Choose your pattern based on your needs:
+//
+// startBatchResearch → Simple, predictable. Good for small-medium workloads
+// where you want easy-to-understand rate limiting.
+//
+// startPoolResearch → Efficient, fast. Good when items have varying
+// processing times and you want max throughput.
+//
+// startNestedBatchResearch → Robust, production-ready. Good for large workloads
+// where fault tolerance and resumability matter.
+// ============================================================================
+
+export const startBatchResearch = mutation({
+ args: {
+ topics: v.array(v.string()),
+ batchSize: v.optional(v.number()),
+ },
+ handler: async (
+ ctx,
+ args,
+ ): Promise<{ threadId: string; workflowId: string }> => {
+ const userId = await getAuthUserId(ctx);
+ const threadId = await createThread(ctx, components.agent, {
+ userId,
+ title: `Batch Research: ${args.topics.length} topics`,
+ });
+ const workflowId = await workflow.start(
+ ctx,
+ internal.workflows.batching.batchResearchWorkflow,
+ { topics: args.topics, threadId, batchSize: args.batchSize },
+ );
+ return { threadId, workflowId };
+ },
+});
+
+export const startPoolResearch = mutation({
+ args: {
+ topics: v.array(v.string()),
+ poolSize: v.optional(v.number()),
+ },
+ handler: async (
+ ctx,
+ args,
+ ): Promise<{ threadId: string; workflowId: string }> => {
+ const userId = await getAuthUserId(ctx);
+ const threadId = await createThread(ctx, components.agent, {
+ userId,
+ title: `Pool Research: ${args.topics.length} topics`,
+ });
+ const workflowId = await workflow.start(
+ ctx,
+ internal.workflows.batching.poolResearchWorkflow,
+ { topics: args.topics, threadId, poolSize: args.poolSize },
+ );
+ return { threadId, workflowId };
+ },
+});
+
+export const startNestedBatchResearch = mutation({
+ args: {
+ topics: v.array(v.string()),
+ batchSize: v.optional(v.number()),
+ },
+ handler: async (
+ ctx,
+ args,
+ ): Promise<{ threadId: string; workflowId: string }> => {
+ const userId = await getAuthUserId(ctx);
+ const threadId = await createThread(ctx, components.agent, {
+ userId,
+ title: `Nested Batch Research: ${args.topics.length} topics`,
+ });
+ const workflowId = await workflow.start(
+ ctx,
+ internal.workflows.batching.nestedBatchResearchWorkflow,
+ { topics: args.topics, threadId, batchSize: args.batchSize },
+ );
+ return { threadId, workflowId };
+ },
+});
+
diff --git a/example/convex/workflows/council_of_agents.ts b/example/convex/workflows/council_of_agents.ts
new file mode 100644
index 00000000..5c93b310
--- /dev/null
+++ b/example/convex/workflows/council_of_agents.ts
@@ -0,0 +1,253 @@
+// See the docs at https://docs.convex.dev/agents/workflows
+import { WorkflowManager } from "@convex-dev/workflow";
+import { components, internal } from "../_generated/api";
+import { mutation } from "../_generated/server";
+import { v } from "convex/values";
+import { createThread, saveMessage, stepCountIs, Agent } from "@convex-dev/agent";
+import { getAuthUserId } from "../utils";
+import { z } from "zod/v4";
+import { defaultConfig } from "../agents/config";
+
+/**
+ * Council of Agents / Ensemble Pattern: Fan out → Fan in → Select best
+ *
+ * This workflow demonstrates:
+ * 1. Fan out: Multiple agents work on the same problem in parallel
+ * 2. Fan in: Collect all responses
+ * 3. Select: Either pick the best by score, or synthesize a final answer
+ */
+
+const workflow = new WorkflowManager(components.workflow);
+
+// Define council agents inline to keep the demo self-contained
+const analystAgent = new Agent(components.agent, {
+ name: "Analyst",
+ instructions: `You are the Analyst on a council. You focus on:
+- Data and evidence-based reasoning
+- Logical analysis and structured thinking
+- Identifying patterns and root causes
+Be thorough and precise in your analysis.`,
+ stopWhen: stepCountIs(3),
+ ...defaultConfig,
+});
+
+const creativeAgent = new Agent(components.agent, {
+ name: "Creative",
+ instructions: `You are the Creative on a council. You focus on:
+- Novel and unconventional solutions
+- Thinking outside the box
+- Connecting disparate ideas in new ways
+Be bold and imaginative in your suggestions.`,
+ stopWhen: stepCountIs(3),
+ ...defaultConfig,
+});
+
+const pragmatistAgent = new Agent(components.agent, {
+ name: "Pragmatist",
+ instructions: `You are the Pragmatist on a council. You focus on:
+- Practical, implementable solutions
+- Real-world constraints and trade-offs
+- Quick wins and iterative approaches
+Be grounded and action-oriented.`,
+ stopWhen: stepCountIs(3),
+ ...defaultConfig,
+});
+
+const judgeAgent = new Agent(components.agent, {
+ name: "Judge",
+ instructions: `You are an impartial judge. Score responses based on:
+- Relevance to the problem
+- Quality of reasoning
+- Practicality and feasibility
+- Completeness of the solution`,
+ stopWhen: stepCountIs(3),
+ ...defaultConfig,
+});
+
+const synthesisAgent = new Agent(components.agent, {
+ name: "Synthesizer",
+ instructions: `You synthesize multiple perspectives into one cohesive answer.
+Extract the best ideas from each response while maintaining consistency.`,
+ stopWhen: stepCountIs(3),
+ ...defaultConfig,
+});
+
+export const councilWorkflow = workflow.define({
+ args: {
+ problem: v.string(),
+ threadId: v.string(),
+ mode: v.optional(v.union(v.literal("best"), v.literal("synthesize"))),
+ },
+ returns: v.object({
+ responses: v.array(
+ v.object({
+ agent: v.string(),
+ response: v.string(),
+ score: v.number(),
+ }),
+ ),
+ finalAnswer: v.string(),
+ selectedAgent: v.optional(v.string()),
+ }),
+ handler: async (
+ ctx,
+ args,
+ ): Promise<{
+ responses: Array<{ agent: string; response: string; score: number }>;
+ finalAnswer: string;
+ selectedAgent?: string;
+ }> => {
+ const mode = args.mode ?? "best";
+ console.log(`Starting council deliberation in "${mode}" mode`);
+
+ // Step 1: Fan out - Have each council member respond in parallel
+ const councilMembers = [
+ { name: "Analyst", action: internal.workflows.council_of_agents.analystRespond },
+ { name: "Creative", action: internal.workflows.council_of_agents.creativeRespond },
+ { name: "Pragmatist", action: internal.workflows.council_of_agents.pragmatistRespond },
+ ];
+
+ // Create prompts and run all agents in parallel
+ const memberPromises = councilMembers.map(async (member) => {
+ const promptMsg = await saveMessage(ctx, components.agent, {
+ threadId: args.threadId,
+ prompt: `Problem: ${args.problem}\n\nProvide your perspective and solution.`,
+ });
+
+ const result = await ctx.runAction(
+ member.action,
+ {
+ promptMessageId: promptMsg.messageId,
+ threadId: args.threadId,
+ },
+ { retry: true },
+ );
+
+ return {
+ agent: member.name,
+ response: result.text,
+ };
+ });
+
+ const memberResponses = await Promise.all(memberPromises);
+ console.log(`Received ${memberResponses.length} council responses`);
+
+ // Step 2: Score each response in parallel
+ const scorePromises = memberResponses.map(async (response) => {
+ const scorePrompt = await saveMessage(ctx, components.agent, {
+ threadId: args.threadId,
+ prompt: `Rate this solution to "${args.problem}" from 1-10:
+
+Response from ${response.agent}:
+${response.response}`,
+ });
+
+ const { object: scoring } = await ctx.runAction(
+ internal.workflows.council_of_agents.scoreResponse,
+ {
+ promptMessageId: scorePrompt.messageId,
+ threadId: args.threadId,
+ },
+ { retry: true },
+ );
+
+ return {
+ ...response,
+ score: scoring.score,
+ };
+ });
+
+ const scoredResponses = await Promise.all(scorePromises);
+
+ // Sort by score descending
+ scoredResponses.sort((a, b) => b.score - a.score);
+ console.log("Scores:", scoredResponses.map((r) => `${r.agent}: ${r.score}`));
+
+ // Step 3: Select best or synthesize
+ let finalAnswer: string;
+ let selectedAgent: string | undefined;
+
+ if (mode === "best") {
+ // Simply pick the highest scored response
+ finalAnswer = scoredResponses[0].response;
+ selectedAgent = scoredResponses[0].agent;
+ console.log(`Selected best response from: ${selectedAgent}`);
+ } else {
+ // Synthesize a final answer combining insights from all
+ const synthesisPrompt = await saveMessage(ctx, components.agent, {
+ threadId: args.threadId,
+ prompt: `Synthesize the best parts of these council responses into one comprehensive answer:
+
+${scoredResponses.map((r) => `## ${r.agent} (Score: ${r.score}/10)\n${r.response}`).join("\n\n")}
+
+Create a unified response that combines the strongest elements of each perspective.`,
+ });
+
+ const synthesis = await ctx.runAction(
+ internal.workflows.council_of_agents.synthesizeResponses,
+ {
+ promptMessageId: synthesisPrompt.messageId,
+ threadId: args.threadId,
+ },
+ { retry: true },
+ );
+
+ finalAnswer = synthesis.text;
+ console.log("Synthesized final answer from all perspectives");
+ }
+
+ return {
+ responses: scoredResponses,
+ finalAnswer,
+ selectedAgent,
+ };
+ },
+});
+
+// Agent actions
+export const analystRespond = analystAgent.asTextAction({
+ stopWhen: stepCountIs(3),
+});
+
+export const creativeRespond = creativeAgent.asTextAction({
+ stopWhen: stepCountIs(3),
+});
+
+export const pragmatistRespond = pragmatistAgent.asTextAction({
+ stopWhen: stepCountIs(3),
+});
+
+export const scoreResponse = judgeAgent.asObjectAction({
+ schema: z.object({
+ score: z.number().min(1).max(10),
+ reasoning: z.string(),
+ }),
+});
+
+export const synthesizeResponses = synthesisAgent.asTextAction({
+ stopWhen: stepCountIs(3),
+});
+
+// Mutation to start the council workflow
+export const startCouncil = mutation({
+ args: {
+ problem: v.string(),
+ mode: v.optional(v.union(v.literal("best"), v.literal("synthesize"))),
+ },
+ handler: async (
+ ctx,
+ args,
+ ): Promise<{ threadId: string; workflowId: string }> => {
+ const userId = await getAuthUserId(ctx);
+ const threadId = await createThread(ctx, components.agent, {
+ userId,
+ title: `Council: ${args.problem.slice(0, 50)}`,
+ });
+ const workflowId = await workflow.start(
+ ctx,
+ internal.workflows.council_of_agents.councilWorkflow,
+ { problem: args.problem, threadId, mode: args.mode },
+ );
+ return { threadId, workflowId };
+ },
+});
diff --git a/example/convex/workflows/deep_agent.ts b/example/convex/workflows/deep_agent.ts
new file mode 100644
index 00000000..ec8ee57a
--- /dev/null
+++ b/example/convex/workflows/deep_agent.ts
@@ -0,0 +1,337 @@
+// See the docs at https://docs.convex.dev/agents/workflows
+import { WorkflowId, WorkflowManager } from "@convex-dev/workflow";
+import { components, internal } from "../_generated/api";
+import { internalAction, internalMutation, internalQuery, mutation } from "../_generated/server";
+import { v } from "convex/values";
+import {
+ Agent,
+ createThread,
+ createTool,
+ saveMessage,
+ stepCountIs,
+} from "@convex-dev/agent";
+import { getAuthUserId } from "../utils";
+import { defaultConfig } from "convex/agents/config";
+import { z } from "zod/v4";
+import { TypedToolCall } from "ai";
+
+/**
+ * Deep Agent Pattern: One-Turn-Per-Action Workflow with Completion Injection.
+ *
+ * This pattern demonstrates:
+ * 1. Strict one-turn turns to stay within 10m limit.
+ * 2. Parallel sub-agent delegation orchestrated by the workflow.
+ * 3. Completion injection (Assistant tool-call -> Tool result) for structured feedback.
+ */
+
+const workflow = new WorkflowManager(components.workflow);
+
+// =============================================================================
+// TOOLS
+// =============================================================================
+
+const internetSearch = createTool({
+ description: "Run a web search to find information on the internet",
+ args: z.object({
+ query: z.string().describe("The search query"),
+ }),
+ handler: async (_ctx, args) => {
+ console.log(`Searching for: ${args.query}`);
+ return `Search results for "${args.query}":\n1. [Mock Result] Relevant information about ${args.query}...`;
+ },
+});
+
+const updateScratchpad = createTool({
+ description: "Update the scratchpad with your current plan, findings, or final report.",
+ args: z.object({
+ content: z.string().describe("The new content for the scratchpad"),
+ }),
+ handler: async (ctx, args) => {
+ await ctx.runMutation(internal.workflows.deep_agent.setScratchpad, {
+ threadId: (ctx as any).threadId,
+ content: args.content,
+ });
+ return "Scratchpad updated.";
+ },
+});
+
+const readScratchpad = createTool({
+ description: "Read the current contents of the scratchpad.",
+ args: z.object({}),
+ handler: async (ctx): Promise => {
+ const content: string | null = await ctx.runQuery(internal.workflows.deep_agent.getScratchpad, {
+ threadId: (ctx as any).threadId,
+ });
+ return content ?? "Scratchpad is empty.";
+ },
+});
+
+const delegateToSubagent = createTool({
+ description: "Request help from a sub-agent for a specific task. Results will be injected later.",
+ args: z.object({
+ taskTitle: z.string().describe("Short title for the task"),
+ taskPrompt: z.string().describe("Detailed instructions for the sub-agent"),
+ }),
+ handler: async (_ctx, args) => {
+ return JSON.stringify({
+ status: "DELEGATION_STARTED",
+ title: args.taskTitle,
+ prompt: args.taskPrompt,
+ });
+ },
+});
+
+const subagentCompletion = createTool({
+ description: "Internal tool used to inject sub-agent results. Do not call this yourself.",
+ args: z.object({
+ results: z.array(z.object({
+ title: z.string(),
+ output: z.string(),
+ })),
+ }),
+ handler: async (_ctx, args) => {
+ return `Received results for ${args.results.length} sub-agent(s).`;
+ },
+});
+
+const toolset = {
+ internetSearch,
+ updateScratchpad,
+ readScratchpad,
+ delegateToSubagent,
+ subagentCompletion,
+}
+
+
+
+// =============================================================================
+// AGENT DEFINITIONS
+// =============================================================================
+
+const deepAgent = new Agent(components.agent, {
+ name: "Deep Agent",
+ ...defaultConfig,
+ instructions: `You are a research agent.
+1. Use the scratchpad to track your progress and findings.
+2. Conduct research using internetSearch.
+3. Delegate complex or long sub-tasks using delegateToSubagent (can use multiple in parallel).
+4. Results from sub-agents will be provided to you via the subagentCompletion tool.
+5. When done, write your final report to the scratchpad and stop.`,
+ tools: toolset,
+});
+
+export const runAgentStep = internalAction({
+ args: {
+ threadId: v.string(),
+ promptMessageId: v.string(),
+ },
+ handler: async (ctx, args): Promise<{ text: string, toolCalls: TypedToolCall[], finishReason: string }> => {
+ const { text, toolCalls, finishReason } = await deepAgent.generateText(ctx, { threadId: args.threadId }, {
+ promptMessageId: args.promptMessageId,
+ stopWhen: [stepCountIs(1)], // Only run one step so we can handle the tool calls as their own actions
+ });
+
+ return {
+ text,
+ toolCalls,
+ finishReason,
+ };
+ },
+});
+
+/**
+ * Injects sub-agent results as structured tool calls into the thread.
+ */
+export const injectSubagentResults = internalAction({
+ args: {
+ threadId: v.string(),
+ results: v.array(v.object({
+ title: v.string(),
+ output: v.string(),
+ })),
+ },
+ handler: async (ctx, args) => {
+ const toolCallId = `subagent-results-${Date.now()}`;
+
+ // 1. Assistant message with tool-call
+ await deepAgent.saveMessage(ctx, {
+ threadId: args.threadId,
+ message: {
+ role: "assistant",
+ content: [
+ {
+ type: "tool-call",
+ toolCallId,
+ toolName: "subagentCompletion",
+ args: { results: args.results },
+ },
+ ],
+ },
+ skipEmbeddings: true,
+ });
+
+ // 2. Tool result message
+ const { messageId } = await deepAgent.saveMessage(ctx, {
+ threadId: args.threadId,
+ message: {
+ role: "tool",
+ content: [
+ {
+ type: "tool-result",
+ toolCallId,
+ toolName: "subagentCompletion",
+ result: JSON.stringify({
+ success: true,
+ message: `Here are the results from your delegated tasks.`,
+ results: args.results,
+ }),
+ },
+ ],
+ },
+ skipEmbeddings: true,
+ });
+
+ return messageId;
+ },
+});
+
+// =============================================================================
+// WORKFLOW
+// =============================================================================
+
+export const deepAgentWorkflow = workflow.define({
+ args: {
+ task: v.string(),
+ threadId: v.string(),
+ maxIterations: v.optional(v.number()),
+ },
+ returns: v.object({
+ iterations: v.number(),
+ finalReport: v.string(),
+ }),
+ handler: async (ctx, args): Promise<{ iterations: number; finalReport: string }> => {
+ const maxIterations = args.maxIterations ?? 20;
+ let iterations = 0;
+ let isComplete = false;
+ let promptMessageId: string | undefined;
+
+ const initialMsg = await saveMessage(ctx, components.agent, {
+ threadId: args.threadId,
+ prompt: `Task: ${args.task}\n\nPlan your research in the scratchpad.`,
+ });
+ promptMessageId = initialMsg.messageId;
+
+ while (!isComplete && iterations < maxIterations) {
+ iterations++;
+ console.log(`Deep agent turn ${iterations}`);
+
+ const stepResult = await ctx.runAction(
+ internal.workflows.deep_agent.runAgentStep,
+ {
+ promptMessageId: promptMessageId!,
+ threadId: args.threadId,
+ }
+ );
+
+ // 1. Handle Parallel Delegations
+ const delegationCalls = (stepResult.toolCalls || []).filter((tc: any) => tc.toolName === "delegateToSubagent");
+ if (delegationCalls.length > 0) {
+ console.log(`[Workflow] Orchestrating ${delegationCalls.length} parallel delegation(s)`);
+
+ const results = await Promise.all(
+ delegationCalls.map(async (tc: any) => {
+ const signal = JSON.parse(tc.result as string);
+ const output: string = await ctx.runWorkflow(internal.workflows.deep_agent.subagentWorkflow, {
+ parentThreadId: args.threadId,
+ taskTitle: signal.title,
+ taskPrompt: signal.prompt,
+ });
+ return { title: signal.title, output };
+ })
+ );
+
+ // Structured injection of all results at once
+ const injectionMessageId: string = await ctx.runAction(internal.workflows.deep_agent.injectSubagentResults, {
+ threadId: args.threadId,
+ results,
+ });
+ promptMessageId = injectionMessageId;
+ continue;
+ }
+
+ // 2. Detect Completion
+ if (stepResult.finishReason === "stop" && stepResult.toolCalls.length === 0) {
+ isComplete = true;
+ break;
+ }
+
+ // 3. Prompt Continuation
+ if (stepResult.finishReason === "stop" || stepResult.finishReason === "tool-calls") {
+ const continueMsg = await saveMessage(ctx, components.agent, {
+ threadId: args.threadId,
+ prompt: "Continue with your research until the report in the scratchpad is finished.",
+ });
+ promptMessageId = continueMsg.messageId;
+ }
+ }
+
+ const finalReport: string | null = await ctx.runQuery(internal.workflows.deep_agent.getScratchpad, {
+ threadId: args.threadId,
+ });
+
+ return {
+ iterations,
+ finalReport: finalReport ?? "No report generated.",
+ };
+ },
+});
+
+// =============================================================================
+// SUB-AGENT WORKFLOW
+// =============================================================================
+
+export const subagentWorkflow = workflow.define({
+ args: { parentThreadId: v.string(), taskTitle: v.string(), taskPrompt: v.string() },
+ returns: v.string(),
+ handler: async (_ctx, args) => {
+ return `Detailed research results for: ${args.taskTitle}`;
+ },
+});
+
+// =============================================================================
+// DATA STORE
+// =============================================================================
+
+export const getScratchpad = internalQuery({
+ args: { threadId: v.string() },
+ returns: v.union(v.string(), v.null()),
+ handler: async (_ctx, _args) => {
+ return "## Research Report\nResearch in progress...";
+ },
+});
+
+export const setScratchpad = internalMutation({
+ args: { threadId: v.string(), content: v.string() },
+ returns: v.null(),
+ handler: async (_ctx, _args) => {
+ return null;
+ },
+});
+
+// =============================================================================
+// ENTRY POINT
+// =============================================================================
+
+export const invoke = mutation({
+ args: {
+ messages: v.array(v.object({ role: v.union(v.literal("user"), v.literal("assistant")), content: v.string() })),
+ maxIterations: v.optional(v.number()),
+ },
+ handler: async (ctx, args) => {
+ const task = args.messages[args.messages.length - 1].content;
+ const userId = await getAuthUserId(ctx);
+ const threadId = await createThread(ctx, components.agent, { userId, title: `Deep Research: ${task.slice(0, 50)}` });
+ const workflowId: WorkflowId = await workflow.start(ctx, internal.workflows.deep_agent.deepAgentWorkflow, { task, threadId, maxIterations: args.maxIterations });
+ return { threadId, workflowId };
+ },
+});
diff --git a/pnpm-lock.yaml b/pnpm-lock.yaml
new file mode 100644
index 00000000..ea1a22a4
--- /dev/null
+++ b/pnpm-lock.yaml
@@ -0,0 +1,7349 @@
+lockfileVersion: '9.0'
+
+settings:
+ autoInstallPeers: true
+ excludeLinksFromLockfile: false
+
+importers:
+
+ .:
+ devDependencies:
+ '@ai-sdk/anthropic':
+ specifier: 2.0.39
+ version: 2.0.39(zod@3.25.76)
+ '@ai-sdk/groq':
+ specifier: 2.0.26
+ version: 2.0.26(zod@3.25.76)
+ '@ai-sdk/openai':
+ specifier: 2.0.57
+ version: 2.0.57(zod@3.25.76)
+ '@ai-sdk/provider':
+ specifier: 2.0.0
+ version: 2.0.0
+ '@ai-sdk/provider-utils':
+ specifier: 3.0.14
+ version: 3.0.14(zod@3.25.76)
+ '@convex-dev/rag':
+ specifier: 0.6.0
+ version: 0.6.0(@convex-dev/workpool@0.3.0(convex-helpers@0.1.104(@standard-schema/spec@1.1.0)(convex@1.29.3(react@19.2.0))(react@19.2.0)(typescript@5.9.3)(zod@3.25.76))(convex@1.29.3(react@19.2.0)))(convex-helpers@0.1.104(@standard-schema/spec@1.1.0)(convex@1.29.3(react@19.2.0))(react@19.2.0)(typescript@5.9.3)(zod@3.25.76))(convex@1.29.3(react@19.2.0))(zod@3.25.76)
+ '@convex-dev/rate-limiter':
+ specifier: 0.3.0
+ version: 0.3.0(convex@1.29.3(react@19.2.0))(react@19.2.0)
+ '@convex-dev/workflow':
+ specifier: 0.3.2
+ version: 0.3.2(@convex-dev/workpool@0.3.0(convex-helpers@0.1.104(@standard-schema/spec@1.1.0)(convex@1.29.3(react@19.2.0))(react@19.2.0)(typescript@5.9.3)(zod@3.25.76))(convex@1.29.3(react@19.2.0)))(convex-helpers@0.1.104(@standard-schema/spec@1.1.0)(convex@1.29.3(react@19.2.0))(react@19.2.0)(typescript@5.9.3)(zod@3.25.76))(convex@1.29.3(react@19.2.0))
+ '@edge-runtime/vm':
+ specifier: 5.0.0
+ version: 5.0.0
+ '@eslint/js':
+ specifier: 9.38.0
+ version: 9.38.0
+ '@hookform/resolvers':
+ specifier: 5.2.2
+ version: 5.2.2(react-hook-form@7.65.0(react@19.2.0))
+ '@langchain/textsplitters':
+ specifier: 0.1.0
+ version: 0.1.0(@langchain/core@0.3.80(@opentelemetry/api@1.9.0)(openai@5.23.2(zod@3.25.76)))
+ '@openrouter/ai-sdk-provider':
+ specifier: 1.2.0
+ version: 1.2.0(ai@5.0.82(zod@3.25.76))(zod@3.25.76)
+ '@radix-ui/react-accordion':
+ specifier: 1.2.12
+ version: 1.2.12(@types/react-dom@19.2.2(@types/react@19.2.2))(@types/react@19.2.2)(react-dom@19.2.0(react@19.2.0))(react@19.2.0)
+ '@radix-ui/react-checkbox':
+ specifier: 1.3.3
+ version: 1.3.3(@types/react-dom@19.2.2(@types/react@19.2.2))(@types/react@19.2.2)(react-dom@19.2.0(react@19.2.0))(react@19.2.0)
+ '@radix-ui/react-label':
+ specifier: 2.1.7
+ version: 2.1.7(@types/react-dom@19.2.2(@types/react@19.2.2))(@types/react@19.2.2)(react-dom@19.2.0(react@19.2.0))(react@19.2.0)
+ '@radix-ui/react-select':
+ specifier: 2.2.6
+ version: 2.2.6(@types/react-dom@19.2.2(@types/react@19.2.2))(@types/react@19.2.2)(react-dom@19.2.0(react@19.2.0))(react@19.2.0)
+ '@radix-ui/react-slot':
+ specifier: 1.2.3
+ version: 1.2.3(@types/react@19.2.2)(react@19.2.0)
+ '@radix-ui/react-toast':
+ specifier: 1.2.15
+ version: 1.2.15(@types/react-dom@19.2.2(@types/react@19.2.2))(@types/react@19.2.2)(react-dom@19.2.0(react@19.2.0))(react@19.2.0)
+ '@tailwindcss/typography':
+ specifier: 0.5.19
+ version: 0.5.19(tailwindcss@3.4.18)
+ '@types/node':
+ specifier: 20.19.24
+ version: 20.19.24
+ '@types/react':
+ specifier: 19.2.2
+ version: 19.2.2
+ '@types/react-dom':
+ specifier: 19.2.2
+ version: 19.2.2(@types/react@19.2.2)
+ '@vitejs/plugin-react':
+ specifier: 4.7.0
+ version: 4.7.0(vite@6.4.1(@types/node@20.19.24)(jiti@1.21.7))
+ ai:
+ specifier: 5.0.82
+ version: 5.0.82(zod@3.25.76)
+ autoprefixer:
+ specifier: 10.4.21
+ version: 10.4.21(postcss@8.5.6)
+ chokidar-cli:
+ specifier: 3.0.0
+ version: 3.0.0
+ class-variance-authority:
+ specifier: 0.7.1
+ version: 0.7.1
+ clsx:
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+ version: 2.1.1
+ convex:
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+ version: 1.29.3(react@19.2.0)
+ convex-helpers:
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+ version: 0.1.104(@standard-schema/spec@1.1.0)(convex@1.29.3(react@19.2.0))(react@19.2.0)(typescript@5.9.3)(zod@3.25.76)
+ convex-test:
+ specifier: 0.0.38
+ version: 0.0.38(convex@1.29.3(react@19.2.0))
+ cpy-cli:
+ specifier: 5.0.0
+ version: 5.0.0
+ dayjs:
+ specifier: 1.11.18
+ version: 1.11.18
+ dotenv:
+ specifier: 16.6.1
+ version: 16.6.1
+ eslint:
+ specifier: 9.38.0
+ version: 9.38.0(jiti@1.21.7)
+ eslint-plugin-jsx-a11y:
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+ version: 6.10.2(eslint@9.38.0(jiti@1.21.7))
+ eslint-plugin-react:
+ specifier: 7.37.5
+ version: 7.37.5(eslint@9.38.0(jiti@1.21.7))
+ eslint-plugin-react-hooks:
+ specifier: 5.2.0
+ version: 5.2.0(eslint@9.38.0(jiti@1.21.7))
+ eslint-plugin-react-refresh:
+ specifier: 0.4.24
+ version: 0.4.24(eslint@9.38.0(jiti@1.21.7))
+ globals:
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+ version: 16.4.0
+ lucide-react:
+ specifier: 0.548.0
+ version: 0.548.0(react@19.2.0)
+ npm-run-all2:
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+ version: 8.0.4
+ ollama-ai-provider:
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+ version: 1.2.0(zod@3.25.76)
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+ version: 5.23.2(zod@3.25.76)
+ pkg-pr-new:
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+ version: 8.5.6
+ prettier:
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+ version: 3.6.2
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+ version: 19.2.0
+ react-dom:
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+ version: 19.2.0(react@19.2.0)
+ react-hook-form:
+ specifier: 7.65.0
+ version: 7.65.0(react@19.2.0)
+ react-markdown:
+ specifier: 10.1.0
+ version: 10.1.0(@types/react@19.2.2)(react@19.2.0)
+ react-router-dom:
+ specifier: 7.9.5
+ version: 7.9.5(react-dom@19.2.0(react@19.2.0))(react@19.2.0)
+ readline:
+ specifier: 1.3.0
+ version: 1.3.0
+ tailwind-merge:
+ specifier: 3.3.1
+ version: 3.3.1
+ tailwindcss:
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+ typescript:
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+ typescript-eslint:
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+ version: 8.46.2(eslint@9.38.0(jiti@1.21.7))(typescript@5.9.3)
+ vite:
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+ version: 6.4.1(@types/node@20.19.24)(jiti@1.21.7)
+ vitest:
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+ version: 3.25.76
+ optionalDependencies:
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+ zod-to-json-schema@3.25.0(zod@3.25.76):
+ dependencies:
+ zod: 3.25.76
+
+ zod@3.25.76: {}
+
+ zwitch@2.0.4: {}