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220d62d79a
* feat(memory): add scoped fact lookup by ID * docs(memory): synchronize tool-mode lookup guidance * docs(memory): clarify named-agent read capability requirement
75 lines
2.2 KiB
Plaintext
75 lines
2.2 KiB
Plaintext
---
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title: Work with Memory
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description: This tutorial shows you how to enable and use DeerFlow's memory system so the agent remembers important information about you across multiple sessions.
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---
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import { Callout } from "nextra/components";
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# Work with Memory
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This tutorial shows you how to enable and use DeerFlow's memory system so the agent remembers important information about you across multiple sessions.
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## Enable memory
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In `config.yaml`:
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```yaml
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memory:
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enabled: true
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mode: middleware
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injection_enabled: true
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max_injection_tokens: 2000
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debounce_seconds: 30
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```
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## How memory works
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By default, memory works automatically through `MemoryMiddleware`:
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1. **First conversation**: tell the agent about your preferences, project, or background.
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2. **Automatic learning**: the agent extracts and saves important facts in the background.
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3. **Future conversations**: memory facts are automatically injected into the system prompt — the agent does not need you to repeat context.
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DeerFlow also has an experimental `mode: tool` option. In tool mode, the agent receives `memory_search`, `memory_get`, `memory_add`, `memory_update`, and `memory_delete` tools and decides when to call them. Use `memory_get(fact_id)` to reread a fact whose ID is already known, or `memory_search` when the ID is unknown. Keep `mode: middleware` unless you specifically want to evaluate model-driven memory behavior.
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## Example
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**First conversation:**
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```
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I am a Python backend developer primarily using FastAPI and PostgreSQL.
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My team follows PEP 8 and prefers type annotations everywhere.
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Please remember this for future code suggestions.
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```
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**Later conversation** (no need to repeat background):
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```
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Help me write a user authentication module
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```
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The agent will automatically produce FastAPI-style code with type annotations.
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## Inspect memory
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Memory is stored in `backend/.deer-flow/memory.json`:
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```bash
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cat backend/.deer-flow/memory.json
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```
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## Per-agent memory
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When a custom agent is active, it maintains its own memory file at:
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```
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backend/.deer-flow/agents/{agent_name}/memory.json
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```
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This keeps each agent's learned knowledge separate.
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## Next steps
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- [Deploy Your Own DeerFlow](/docs/tutorials/deploy-your-own-deerflow)
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- [Memory System Reference](/docs/harness/memory)
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