Convex Agents 上下文管理技能：自定义AI助手对话上下文、跨线程搜索与记忆注入

🇺🇸English

Purpose

By default, the Agent includes recent messages as context. This skill covers customizing that behavior for advanced patterns like cross-thread search, memory injection, summarization, and filtering.

When to Use This Skill

• Limiting context window to prevent token overflow
• Searching across multiple threads for relevant context
• Injecting memories or user profiles into every prompt
• Summarizing long conversations before continuing
• Filtering out sensitive or irrelevant messages
• Adding few-shot examples to guide LLM

Configure Default Context Options

const myAgent = new Agent(components.agent, {
  name: "My Agent",
  languageModel: openai.chat("gpt-4o-mini"),
  contextOptions: {
    recentMessages: 50,
    excludeToolMessages: true,
    searchOptions: {
      limit: 10,
      textSearch: true,
      vectorSearch: false,
    },
  },
});

Override Context Per Call

export const generateWithCustomContext = action({
  args: { threadId: v.string(), prompt: v.string() },
  handler: async (ctx, { threadId, prompt }) => {
    const result = await myAgent.generateText(
      ctx,
      { threadId },
      { prompt },
      {
        contextOptions: {
          recentMessages: 20,
          searchOptions: {
            limit: 5,
            textSearch: true,
            vectorSearch: true,
          },
        },
      }
    );

    return result.text;
  },
});

Search Across Threads

export const generateWithCrossThreadContext = action({
  args: { threadId: v.string(), userId: v.string(), prompt: v.string() },
  handler: async (ctx, { threadId, userId, prompt }) => {
    const result = await myAgent.generateText(
      ctx,
      { threadId, userId },
      { prompt },
      {
        contextOptions: {
          searchOtherThreads: true,
          searchOptions: {
            limit: 15,
            textSearch: true,
            vectorSearch: true,
          },
        },
      }
    );

    return result.text;
  },
});

Custom Context Handler

Completely customize context:

const myAgent = new Agent(components.agent, {
  name: "My Agent",
  languageModel: openai.chat("gpt-4o-mini"),
  contextHandler: async (ctx, args) => {
    const userMemories = await getUserMemories(ctx, args.userId);
    const examples = getExamples();

    return [
      ...userMemories,
      ...examples,
      ...args.search,
      ...args.recent,
      ...args.inputMessages,
    ];
  },
});

Fetch Context Manually

Get context without calling LLM:

import { fetchContextWithPrompt } from "@convex-dev/agent";

export const getContextForPrompt = action({
  args: { threadId: v.string(), prompt: v.string() },
  handler: async (ctx, { threadId, prompt }) => {
    const { messages } = await fetchContextWithPrompt(ctx, components.agent, {
      threadId,
      prompt,
      contextOptions: {
        recentMessages: 20,
        searchOptions: { limit: 10, textSearch: true },
      },
    });

    return messages;
  },
});

Key Principles

• Default context is sufficient : Most use cases work with defaults
• Search improves relevance : Enable for long conversations
• userId required for cross-thread : Provide when searching multiple threads
• Context handlers are powerful : Use for memories, examples, special formatting
• Recent messages take precedence : Used after search in context order

Next Steps

• See rag for knowledge base context injection
• See fundamentals for agent setup
• See rate-limiting for token management

Weekly Installs

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Repository

sstobo/convex-skills

GitHub Stars

23

First Seen

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Security Audits

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