Clean up release highlights copy and removing emdash from the docs (#5984)
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Kartik
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---
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title: ChatDev
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description: "Add persistent, cloud-managed memory to ChatDev multi-agent workflows with Mem0 — no code required, just YAML configuration."
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description: "Add persistent, cloud-managed memory to ChatDev multi-agent workflows with Mem0: no code required, just YAML configuration."
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---
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Build multi-agent workflows in [ChatDev](https://github.com/OpenBMB/ChatDev) with persistent memory powered by Mem0. ChatDev is a zero-code multi-agent platform where agents, tools, and workflows are defined entirely in YAML. Mem0 integrates as a built-in memory store (`type: mem0`), giving your agents cloud-managed semantic search and cross-session persistence — all without writing any code.
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Build multi-agent workflows in [ChatDev](https://github.com/OpenBMB/ChatDev) with persistent memory powered by Mem0. ChatDev is a zero-code multi-agent platform where agents, tools, and workflows are defined entirely in YAML. Mem0 integrates as a built-in memory store (`type: mem0`), giving your agents cloud-managed semantic search and cross-session persistence, all without writing any code.
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## Overview
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@@ -16,8 +16,8 @@ In this guide, you'll:
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## Prerequisites
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- **Python 3.12+**
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- **[uv](https://docs.astral.sh/uv/)** — Python package manager
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- **Node.js 18+** and **npm** — only needed if using the web console
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- **[uv](https://docs.astral.sh/uv/)**: Python package manager
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- **Node.js 18+** and **npm**: only needed if using the web console
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- A **Mem0 API key** from <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-chatdev" rel="nofollow">app.mem0.ai</a>
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- An **OpenAI API key** (or another LLM provider supported by ChatDev)
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@@ -61,7 +61,7 @@ memory:
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agent_id: my-agent # optional: scope memories to an agent
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```
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Mem0 handles all storage, embeddings, and search server-side — no local vector databases or embedding models are needed.
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Mem0 handles all storage, embeddings, and search server-side. No local vector databases or embedding models are needed.
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## Attach Memory to an Agent
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write: true
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```
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- **`read: true`** — Agent retrieves relevant memories before generating a response
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- **`write: true`** — Agent stores new memories from user input after each interaction
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- **`top_k`** — Number of memories to retrieve per query
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- **`similarity_threshold`** — Minimum relevance score for retrieved memories. Set to `-1.0` to return all results regardless of score
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- **`retrieve_stage`** — When to retrieve memories. Options: `pre_gen_thinking` (before generation), `gen` (during generation), `post_gen_thinking` (after generation), `finished` (after completion)
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- **`read: true`**: Agent retrieves relevant memories before generating a response
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- **`write: true`**: Agent stores new memories from user input after each interaction
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- **`top_k`**: Number of memories to retrieve per query
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- **`similarity_threshold`**: Minimum relevance score for retrieved memories. Set to `-1.0` to return all results regardless of score
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- **`retrieve_stage`**: When to retrieve memories. Options: `pre_gen_thinking` (before generation), `gen` (during generation), `post_gen_thinking` (after generation), `finished` (after completion)
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## Full Example Workflow
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@@ -154,7 +154,7 @@ To use the web console, open `http://localhost:5173`, create a new workflow, and
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When an agent with Mem0 memory receives input, the following cycle runs automatically:
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**1. Retrieve** — Before generating a response, ChatDev queries Mem0 with the user's input using semantic search. Relevant memories are injected into the agent's context in this format:
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**1. Retrieve**: Before generating a response, ChatDev queries Mem0 with the user's input using semantic search. Relevant memories are injected into the agent's context in this format:
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```
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===== Related Memories =====
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@@ -164,11 +164,11 @@ When an agent with Mem0 memory receives input, the following cycle runs automati
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===== End of Memory =====
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```
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This is why the role prompt in the example references `===== Related Memories =====` — the agent needs to know how to use this injected context.
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This is why the role prompt in the example references `===== Related Memories =====`: the agent needs to know how to use this injected context.
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**2. Generate** — The agent produces a response using the retrieved memories as additional context.
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**2. Generate**: The agent produces a response using the retrieved memories as additional context.
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**3. Store** — After generation, the user's input is sent to Mem0 via `client.add()`. Mem0's extraction model automatically identifies and stores facts, preferences, and key information. Only user input is stored — agent output is excluded to keep memories clean.
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**3. Store**: After generation, the user's input is sent to Mem0 via `client.add()`. Mem0's extraction model automatically identifies and stores facts, preferences, and key information. Only user input is stored. Agent output is excluded to keep memories clean.
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Memories persist in Mem0's cloud across all sessions. The next time the same `user_id` or `agent_id` is used, previous memories are automatically retrieved.
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@@ -211,23 +211,23 @@ This means retrieval returns memories from **both** the user's scope and the age
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## Tips and Common Pitfalls
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<Info>
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**Indexing delay** — Freshly stored memories may take a few seconds to become searchable. If a memory isn't retrieved immediately after being stored, wait a moment and try again.
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**Indexing delay**: Freshly stored memories may take a few seconds to become searchable. If a memory isn't retrieved immediately after being stored, wait a moment and try again.
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</Info>
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- **No memories returned on first run** — This is expected. Memories are stored *after* the agent responds, so the first interaction has no prior context. Memories appear starting from the second interaction onward.
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- **`mem0ai` not installed** — If you see `ImportError: mem0ai is required for Mem0Memory`, run `uv add mem0ai` or `pip install mem0ai` to add the dependency.
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- **Invalid API key** — A wrong or expired `MEM0_API_KEY` will log errors like `Mem0 search failed` or `Mem0 add failed` but won't crash the agent. Check your key at <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-chatdev" rel="nofollow">app.mem0.ai</a>.
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- **Pipeline headers in memories** — ChatDev automatically strips internal pipeline headers (e.g., `=== INPUT FROM TASK (user) ===`) before sending text to Mem0, so your memories stay clean.
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- **Clearing test memories** — To delete memories created during testing, use the Mem0 dashboard at <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-chatdev" rel="nofollow">app.mem0.ai</a> or the Python SDK: `MemoryClient().delete_all(user_id="your-test-user")`.
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- **No memories returned on first run**: This is expected. Memories are stored *after* the agent responds, so the first interaction has no prior context. Memories appear starting from the second interaction onward.
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- **`mem0ai` not installed**: If you see `ImportError: mem0ai is required for Mem0Memory`, run `uv add mem0ai` or `pip install mem0ai` to add the dependency.
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- **Invalid API key**: A wrong or expired `MEM0_API_KEY` will log errors like `Mem0 search failed` or `Mem0 add failed` but won't crash the agent. Check your key at <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-chatdev" rel="nofollow">app.mem0.ai</a>.
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- **Pipeline headers in memories**: ChatDev automatically strips internal pipeline headers (e.g., `=== INPUT FROM TASK (user) ===`) before sending text to Mem0, so your memories stay clean.
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- **Clearing test memories**: To delete memories created during testing, use the Mem0 dashboard at <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-chatdev" rel="nofollow">app.mem0.ai</a> or the Python SDK: `MemoryClient().delete_all(user_id="your-test-user")`.
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## Key Features
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1. **Zero-Code Integration** — Configure Mem0 entirely through YAML, no Python code required
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2. **Cloud-Managed Storage** — Mem0 handles embeddings, persistence, and search server-side
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3. **Semantic Search** — Retrieve contextually relevant memories, not just keyword matches
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4. **Cross-Session Persistence** — Memories survive across runs, sessions, and restarts
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5. **Multi-Agent Memory Sharing** — Multiple agents can share memories through common `user_id` or `agent_id` scopes
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6. **Intelligent Input Processing** — Only user input is stored; agent output is excluded to prevent noisy memories
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1. **Zero-Code Integration**: Configure Mem0 entirely through YAML, no Python code required
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2. **Cloud-Managed Storage**: Mem0 handles embeddings, persistence, and search server-side
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3. **Semantic Search**: Retrieve contextually relevant memories, not just keyword matches
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4. **Cross-Session Persistence**: Memories survive across runs, sessions, and restarts
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5. **Multi-Agent Memory Sharing**: Multiple agents can share memories through common `user_id` or `agent_id` scopes
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6. **Intelligent Input Processing**: Only user input is stored; agent output is excluded to prevent noisy memories
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## Conclusion
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