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Documentation Index

Fetch the complete documentation index at: https://docs.enconvo.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Overview

Agents in EnConvo are AI assistants with specialized capabilities, custom instructions, and access to tools. While standard chat provides general-purpose AI conversation, agents are purpose-built for specific tasks — they have defined personalities, access to particular tools, and follow structured workflows to deliver consistent, high-quality results. EnConvo comes with powerful built-in agents and lets you create your own custom agents (bots) tailored to your exact needs.

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Agent Mode vs Chat Mode

Understanding the difference between these two modes is key to getting the most out of EnConvo:
FeatureChat ModeAgent Mode
Tool UseNo tools — pure conversationFull access to configured tools
PlanningDirect responsePlans steps, executes tools, synthesizes results
ContextConversation history onlyConversation + tool outputs + external data
Use CaseQuick questions, brainstormingComplex tasks, research, multi-step work
SpeedFaster responsesSlower but more capable
You can switch between Chat Mode and Agent Mode at any time using the mode toggle in the chat interface. Use Chat Mode for quick questions and Agent Mode when you need the AI to take action.

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Built-in Agents

EnConvo ships with several pre-configured agents for common tasks:

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Mavis — Your AI Assistant

Mavis is EnConvo’s default general-purpose agent. It has access to a broad set of tools and can handle most everyday tasks:
  • Answer questions using web search
  • Read and analyze files on your Mac
  • Generate and edit images
  • Manage your knowledge base
  • Execute MCP server tools
  • Run workflows

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Deep Research Agent

The Deep Research Agent is designed for in-depth investigation of complex topics:
1

Define Your Research Question

Provide a clear research question or topic
2

Agent Plans Research

The agent creates a research plan, identifying key areas to investigate
3

Multi-Source Search

Searches across multiple web sources, academic databases, and your knowledge base
4

Analysis & Synthesis

Analyzes findings, identifies patterns, compares perspectives
5

Comprehensive Report

Delivers a structured report with citations and key takeaways
Best for: Market research, competitive analysis, literature reviews, technical deep-dives, fact-checking complex claims.

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Other Built-in Agents

AgentPurposeKey Tools
Code AssistantProgramming help with file system accessFilesystem, code execution
Writing CoachLong-form writing with researchWeb search, knowledge base
Data AnalystData processing and visualizationDatabase tools, file system
Task ManagerProject and task managementCalendar, todo, integrations

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Creating Custom Agents (Bots)

Custom agents let you build specialized AI assistants with exactly the capabilities you need.

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Using the Bot Builder

1

Open Bot Builder

Go to Settings and navigate to the Bots section, or use the command /create-bot in SmartBar
2

Define Identity

Give your bot a name, icon, and description that clearly convey its purpose
3

Write System Prompt

Define the bot’s personality, expertise, and behavior rules in the system prompt. This is the most important step — it shapes every response.
4

Select Tools

Choose which tools your bot can access: MCP servers, web search, file system, knowledge base, and more
5

Configure Model

Select the AI model and parameters (temperature, max tokens) for this bot
6

Test & Refine

Test your bot with sample queries and refine its behavior

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System Prompt Best Practices

The system prompt defines your agent’s behavior. A well-written prompt makes the difference between a useful agent and a frustrating one. 
# Role
You are a senior code reviewer specializing in TypeScript and React applications.

# Expertise
- React component architecture and best practices
- TypeScript type safety and advanced patterns
- Performance optimization
- Accessibility compliance (WCAG 2.1)

# Behavior
- Always explain WHY something is a problem, not just WHAT
- Suggest specific fixes with code examples
- Prioritize issues by severity: critical > warning > suggestion
- Be constructive and educational in tone

# Constraints
- Do not rewrite entire files -- focus on specific issues
- Always check for security vulnerabilities first
- Reference official documentation when suggesting patterns
Instead of “You are a helpful assistant”, write “You are a senior marketing strategist specializing in B2B SaaS content”. Specificity leads to better, more focused responses.
Clearly state what the agent should and should not do. Include explicit constraints like “Do not provide medical advice” or “Always cite sources”.
If you want structured output, include examples: “Format your response as: Summary (2-3 sentences), Key Points (bullet list), Action Items (numbered list)”.
Include relevant terminology, frameworks, or standards your agent should know. This helps it respond with appropriate expertise.

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Selecting Agent Tools

Tools give your agent the ability to take action beyond generating text. Choose tools based on what your agent needs to accomplish:
Tool CategoryExamplesUse When
Web SearchGoogle, Brave SearchAgent needs real-time information
File SystemRead/write files, list directoriesAgent works with local files
Knowledge BaseSearch your documentsAgent needs your personal data
MCP ServersGitHub, Notion, Slack, databasesAgent interacts with external services
Code ExecutionRun scripts, terminal commandsAgent needs to compute or automate
Image GenerationDALL-E, Stable DiffusionAgent creates visual content
Be selective with tools. An agent with too many tools may take longer to respond and may choose suboptimal tools. Give each agent only the tools it genuinely needs.

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Using Agents

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From SmartBar

Invoke any agent from SmartBar using the @ prefix: 
@deep-research What are the current trends in AI regulation?
@code-reviewer Review this pull request for security issues
@my-custom-bot Analyze the Q4 sales report

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From the Chat Interface

  1. Open the chat interface
  2. Click the agent/bot selector at the top
  3. Choose your desired agent
  4. Start your conversation

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From Workflows

Agents can be used as nodes in workflows, allowing you to chain agent tasks together:
  1. Add an Agent Node to your workflow
  2. Select which agent to use
  3. Define the input from the previous node
  4. The agent’s output feeds into the next step

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Agent Memory

Agents can maintain memory across conversations:
  • Conversation Memory: The agent remembers the current conversation context
  • Persistent Memory: Key facts and preferences are stored and recalled in future conversations
  • Knowledge Base Integration: The agent can reference your stored documents
See Memory for more details on how memory works.

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Example Custom Agents

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Email Composer

# Role
Professional email writer for business communication.

# Behavior
- Ask clarifying questions before writing (recipient, purpose, tone)
- Default to professional but friendly tone
- Keep emails concise -- under 200 words when possible
- Include a clear subject line suggestion
- Offer formal and casual variants when tone is ambiguous

# Output Format
**Subject:** [suggested subject line]

[email body]

---
**Tone:** [formal/casual/neutral]
**Word count:** [count]

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Meeting Summarizer

# Role
Meeting notes specialist who turns transcripts into structured, actionable summaries.

# Behavior
1. Identify all participants mentioned
2. Extract key decisions made
3. List action items with owners and deadlines
4. Note any unresolved questions
5. Keep summary under 500 words

# Output Format
## Meeting Summary
**Date:** [extracted date]
**Participants:** [list]

### Key Decisions
- [decision 1]
- [decision 2]

### Action Items
| Owner | Task | Deadline |
|-------|------|----------|

### Open Questions
- [question 1]

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Technical Documentation Writer

# Role
Technical writer who creates clear, developer-friendly documentation.

# Tools
- File system (to read source code)
- Web search (to check API references)

# Behavior
- Start with a one-sentence overview
- Include code examples for every concept
- Use consistent heading hierarchy
- Add a "Quick Start" section for new users
- Cross-reference related documentation
- Avoid jargon without explanation

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Sharing Agents

You can share your custom agents with others:

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Export

  1. Go to Settings -> Bots
  2. Select the bot you want to share
  3. Click Export to save the configuration

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Import

  1. Go to Settings -> Bots
  2. Click Import
  3. Select the exported bot configuration file

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Tips for Effective Agents

Begin with a minimal system prompt and a few tools. Test thoroughly, then add complexity as needed. Over-engineering from the start leads to unpredictable behavior.
Try unusual inputs, ambiguous requests, and tasks outside the agent’s scope. A good agent handles these gracefully.
Use powerful models (GPT-4o, Claude Sonnet) for complex reasoning tasks. Use faster models (GPT-4o mini) for simple, high-frequency tasks.
Low temperature (0-0.3) for factual tasks, code review, data analysis. Higher temperature (0.7-1.0) for creative writing, brainstorming, marketing copy.
Pay attention to where your agent falls short. Update the system prompt and tool selection based on real usage patterns.

MCP Servers

Connect agents to external tools and data

Memory

How agents remember across conversations

Skills

Extend agent capabilities with skills

Workflows

Chain agents together in automated workflows