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merge main into pr5 after #7322 was squash-merged

Browse Source 
Same squash divergence as pr2 and pr4. Nine conflicts, three of them real and
six generated.

build.py: kept this branch's side, which carries the portable-bundle fix main
does not have. Regenerated all six _harness_id.py from it rather than resolving
them by hand, and verified the outcome: the portable bundle declares no
application and each native one still names its host.

deepseek-plugin/src/index.ts: kept this branch's side. Main has the comment
claiming the backend allowlist already recognizes DEEPSEEK_HARNESS, which is not
true until mem0ai/platform#3602 ships; this branch carries the correction.

test_uninitialised_identity.py: append-only, as on pr4. Our side kept whole.

Bundles clean for all six hosts, 318 passed 8 skipped, deepseek and pi-agent
suites green.
This commit is contained in:
Saket Aryan
2026-09-18 13:47:56 +05:30
parent 6aa1f60503 4e38b057fd
commit af7dfc8f64
60 changed files with 402 additions and 218 deletions
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docs/api-reference.mdx
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---
title: "Overview"
seo:
title: "API Reference Overview - Mem0"
title: "API Reference Overview"
sidebarTitle: "Overview"
icon: "terminal"
iconType: "solid"
description: "REST APIs for memory management, search, and entity operations"
docs/api-reference/memory/delete-memory.mdx
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---
title: 'Delete Memory'
seo:
title: "Delete Memory API Endpoint - Mem0"
title: "Delete Memory API Endpoint"
sidebarTitle: "Delete Memory"
description: "Delete a single memory by its unique memory ID from the Mem0 platform using the DELETE endpoint."
openapi: delete /v1/memories/{memory_id}/
---
docs/api-reference/memory/update-memory.mdx
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---
title: 'Update Memory'
seo:
title: "Update Memory API Endpoint - Mem0"
title: "Update Memory API Endpoint"
sidebarTitle: "Update Memory"
description: "Update the content, metadata, timestamp, or expiration date of a single memory by its unique ID using the PUT endpoint."
openapi: put /v1/memories/{memory_id}/
---
docs/api-reference/organization/add-org-member.mdx
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---
title: 'Add Member'
seo:
title: "Add Organization Member API Endpoint - Mem0"
title: "Add Organization Member API Endpoint"
sidebarTitle: "Add Member"
description: "Add a new member to an organization with a specified role such as READER or OWNER access level."
openapi: post /api/v1/orgs/organizations/{org_id}/members/
---
docs/api-reference/organization/get-org-members.mdx
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---
title: 'Get Members'
seo:
title: "Get Organization Members API Endpoint - Mem0"
title: "Get Organization Members API Endpoint"
sidebarTitle: "Get Members"
description: "Retrieve a list of all members belonging to a specific organization on the Mem0 platform."
openapi: get /api/v1/orgs/organizations/{org_id}/members/
---
docs/api-reference/project/add-project-member.mdx
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---
title: 'Add Member'
seo:
title: "Add Project Member API Endpoint - Mem0"
title: "Add Project Member API Endpoint"
sidebarTitle: "Add Member"
description: "Add a new member to a project with a specified role such as READER or OWNER access level."
openapi: post /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/
---
docs/api-reference/project/get-project-members.mdx
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---
title: 'Get Members'
seo:
title: "Get Project Members API Endpoint - Mem0"
title: "Get Project Members API Endpoint"
sidebarTitle: "Get Members"
description: "Retrieve a list of all members belonging to a specific project on the Mem0 platform."
openapi: get /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/
---
docs/components/embedders/config.mdx
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---
title: Configurations
seo:
title: "Embedder Configuration Reference - Mem0"
title: "Embedder Configuration Reference"
sidebarTitle: "Configurations"
description: "Reference for embedder configuration options in Mem0, including provider selection and model settings."
---
docs/components/embedders/models/aws-bedrock.mdx
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@@ -1,7 +1,6 @@
---
title: AWS Bedrock
seo:
title: "AWS Bedrock as Embedding Provider - Mem0"
title: "AWS Bedrock as Embedding Provider"
sidebarTitle: "AWS Bedrock"
description: "Configure AWS Bedrock as an embedding provider in Mem0 with IAM credentials and boto3 authentication."
---
docs/components/embedders/models/azure-openai.mdx
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@@ -1,7 +1,6 @@
---
title: Azure OpenAI
seo:
title: "Azure OpenAI as Embedding Provider - Mem0"
title: "Azure OpenAI as Embedding Provider"
sidebarTitle: "Azure OpenAI"
description: "Configure Azure OpenAI as an embedding provider in Mem0 with API key, deployment, and endpoint settings."
---
docs/components/embedders/models/google-ai.mdx
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@@ -1,7 +1,6 @@
---
title: Google AI
seo:
title: "Google AI as Embedding Provider - Mem0"
title: "Google AI as Embedding Provider"
sidebarTitle: "Google AI"
description: "Configure Google AI as an embedding provider in Mem0 using Gemini models and the GOOGLE_API_KEY variable."
---
docs/components/embedders/models/langchain.mdx
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@@ -1,7 +1,6 @@
---
title: LangChain
seo:
title: "LangChain as Embedding Provider - Mem0"
title: "LangChain as Embedding Provider"
sidebarTitle: "LangChain"
description: "Use LangChain as an embedding provider in Mem0 to access a wide range of models through a unified interface."
---
docs/components/embedders/models/lmstudio.mdx
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---
title: "LM Studio"
seo:
title: "LM Studio as Embedding Provider - Mem0"
title: "LM Studio as Embedding Provider"
sidebarTitle: "LM Studio"
description: "Configure LM Studio as an embedding provider in Mem0 for local embedding generation with models like nomic-embed-text."
---
You can use embedding models from LM Studio to run Mem0 locally.
docs/components/embedders/models/ollama.mdx
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---
title: "Ollama"
seo:
title: "Ollama as Embedding Provider - Mem0"
title: "Ollama as Embedding Provider"
sidebarTitle: "Ollama"
description: "Configure Ollama as an embedding provider in Mem0 to generate embeddings locally using open-source models."
---
You can use embedding models from Ollama to run Mem0 locally.
docs/components/embedders/models/openai.mdx
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---
title: OpenAI
seo:
title: "OpenAI as Embedding Provider - Mem0"
title: "OpenAI as Embedding Provider"
sidebarTitle: "OpenAI"
description: "Configure OpenAI as an embedding provider in Mem0 using models like text-embedding-3-large for vector generation."
---
docs/components/embedders/models/together.mdx
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---
title: Together
seo:
title: "Together AI as Embedding Provider - Mem0"
title: "Together AI as Embedding Provider"
sidebarTitle: "Together"
description: "Configure Together AI as an embedding provider in Mem0 with support for 1024-dimensional embedding models."
---
docs/components/embedders/overview.mdx
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---
title: Overview
seo:
title: "Embedding Providers Overview - Mem0"
title: "Embedding Providers Overview"
sidebarTitle: Overview
description: "Overview of all supported embedding model providers in Mem0, including OpenAI, Azure, Ollama, and more."
---
docs/components/llms/config.mdx
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---
title: Configurations
seo:
title: "LLM Configuration Reference - Mem0"
title: "LLM Configuration Reference"
sidebarTitle: "Configurations"
description: "Reference for LLM configuration options in Mem0 for Python and TypeScript, including value precedence rules."
---
docs/components/llms/models/aws-bedrock.mdx
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---
title: AWS Bedrock
seo:
title: "AWS Bedrock as LLM Provider - Mem0"
title: "AWS Bedrock as LLM Provider"
sidebarTitle: "AWS Bedrock"
description: "Configure AWS Bedrock as an LLM provider in Mem0 with IAM authentication and Claude model support."
---
docs/components/llms/models/azure-openai.mdx
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---
title: Azure OpenAI
seo:
title: "Azure OpenAI as LLM Provider - Mem0"
title: "Azure OpenAI as LLM Provider"
sidebarTitle: "Azure OpenAI"
description: "Configure Azure OpenAI as an LLM provider in Mem0 with Azure Identity authentication and deployment settings."
---
docs/components/llms/models/deepseek.mdx
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description: "Configure DeepSeek as an LLM provider in Mem0 with API key setup and optional custom endpoint configuration."
---
To use DeepSeek LLM models, you have to set the `DEEPSEEK_API_KEY` environment variable. You can also optionally set `DEEPSEEK_API_BASE` if you need to use a different API endpoint (defaults to "https://api.deepseek.com").
To use DeepSeek LLM models, you have to set the `DEEPSEEK_API_KEY` environment variable. You can also optionally set `DEEPSEEK_API_BASE` if you need to use a different API endpoint (defaults to `https://api.deepseek.com`).
## Usage
docs/components/llms/models/google-ai.mdx
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---
title: Google AI
seo:
title: "Google AI as LLM Provider - Mem0"
title: "Google AI as LLM Provider"
sidebarTitle: "Google AI"
description: "Configure Google Gemini as an LLM provider in Mem0 using the google.genai SDK and GOOGLE_API_KEY variable."
---
docs/components/llms/models/langchain.mdx
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@@ -1,7 +1,6 @@
---
title: LangChain
seo:
title: "LangChain as LLM Provider - Mem0"
title: "LangChain as LLM Provider"
sidebarTitle: "LangChain"
description: "Use LangChain as an LLM provider in Mem0 to integrate with various chat models through a unified interface."
---
docs/components/llms/models/lmstudio.mdx
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---
title: LM Studio
seo:
title: "LM Studio as LLM Provider - Mem0"
title: "LM Studio as LLM Provider"
sidebarTitle: "LM Studio"
description: "Configure LM Studio as an LLM provider in Mem0 for running local language models via an OpenAI-compatible API."
---
@@ -78,7 +77,7 @@ m.add(messages, user_id="alice123", metadata={"category": "movies"})
To use LM Studio, you need to:
1. Download and install [LM Studio](https://lmstudio.ai/)
2. Start a local server from the "Server" tab
3. Set the appropriate `lmstudio_base_url` in your configuration (default is usually http://localhost:1234/v1)
3. Set the appropriate `lmstudio_base_url` in your configuration (default is usually `http://localhost:1234/v1`)
</Note>
## Config
docs/components/llms/models/minimax.mdx
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description: "Configure MiniMax as an LLM provider in Mem0 with API key setup and optional custom endpoint configuration."
---
To use MiniMax LLM models, you have to set the `MINIMAX_API_KEY` environment variable. You can also optionally set `MINIMAX_API_BASE` if you need to use a different API endpoint (defaults to "https://api.minimax.io/v1").
To use MiniMax LLM models, you have to set the `MINIMAX_API_KEY` environment variable. You can also optionally set `MINIMAX_API_BASE` if you need to use a different API endpoint (defaults to `https://api.minimax.io/v1`).
## Usage
docs/components/llms/models/ollama.mdx
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---
title: Ollama
seo:
title: "Ollama as LLM Provider - Mem0"
title: "Ollama as LLM Provider"
sidebarTitle: "Ollama"
description: "Configure Ollama as an LLM provider in Mem0 for running local language models with tool-calling support."
---
docs/components/llms/models/openai.mdx
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---
title: OpenAI
seo:
title: "OpenAI as LLM Provider - Mem0"
title: "OpenAI as LLM Provider"
sidebarTitle: "OpenAI"
description: "Configure OpenAI as an LLM provider in Mem0 with support for GPT models and Openrouter compatibility."
---
docs/components/llms/models/together.mdx
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---
title: Together
seo:
title: "Together AI as LLM Provider - Mem0"
title: "Together AI as LLM Provider"
sidebarTitle: "Together"
description: "Configure Together AI as an LLM provider in Mem0 with API key setup and optional custom endpoint configuration."
---
docs/components/llms/models/xAI.mdx
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---
title: xAI
seo:
title: "xAI Grok as LLM Provider - Mem0"
title: "xAI Grok as LLM Provider"
sidebarTitle: "xAI"
description: "Configure xAI Grok models as an LLM provider in Mem0 with API key setup and usage examples."
---
docs/components/llms/overview.mdx
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---
title: Overview
seo:
title: "LLM Providers Overview - Mem0"
title: "LLM Providers Overview"
sidebarTitle: Overview
description: "Overview of all supported LLM providers in Mem0, including OpenAI, Anthropic, Groq, Ollama, and more."
---
docs/components/rerankers/overview.mdx
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---
title: Overview
seo:
title: "Reranker Providers Overview - Mem0"
title: "Reranker Providers Overview"
sidebarTitle: "Overview"
description: 'Pick the right reranker path to boost Mem0 search relevance.'
---
docs/components/vectordbs/config.mdx
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---
title: Configurations
seo:
title: "Vector Store Configuration Reference - Mem0"
title: "Vector Store Configuration Reference"
sidebarTitle: "Configurations"
description: "Reference for vector database configuration options in Mem0, including provider selection and connection settings."
---
docs/components/vectordbs/dbs/langchain.mdx
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---
title: LangChain
seo:
title: "LangChain as Vector Store Provider - Mem0"
title: "LangChain as Vector Store Provider"
sidebarTitle: "LangChain"
description: "Use LangChain as a unified vector store provider in Mem0 to access multiple vector databases through one interface."
---
docs/components/vectordbs/dbs/upstash-vector.mdx
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@@ -102,7 +102,7 @@ Here are the parameters available for configuring Upstash Vector:
| `url` | URL for the Upstash Vector index | `None` |
| `token` | Token for the Upstash Vector index | `None` |
| `client` | An `upstash_vector.Index` instance | `None` |
| `collection_name` | The default namespace used | `""` |
| `collection_name` | The default namespace used | `"mem0"` |
| `enable_embeddings` | Whether to use Upstash embeddings | `False` |
<Note>
docs/components/vectordbs/overview.mdx
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---
title: Overview
seo:
title: "Vector Store Providers Overview - Mem0"
title: "Vector Store Providers Overview"
sidebarTitle: "Overview"
description: "Overview of all supported vector databases in Mem0, including Qdrant, Chroma, PGVector, Pinecone, Oracle, and more."
---
docs/cookbooks/overview.mdx
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---
title: Overview
seo:
title: "Cookbooks and Tutorials - Mem0"
title: "Cookbooks and Tutorials"
sidebarTitle: "Overview"
description: "Browse cookbook examples and tutorials for building AI applications with Mem0, from companion chatbots to AI agents."
---
docs/core-concepts/memory-operations/delete.mdx
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---
title: Delete Memory
seo:
title: "Delete Memory Operation - Mem0"
title: "Delete Memory Operation"
sidebarTitle: "Delete Memory"
description: Remove memories from Mem0 either individually, in bulk, or via filters.
icon: "trash"
iconType: "solid"
docs/core-concepts/memory-operations/update.mdx
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---
title: Update Memory
seo:
title: "Update Memory Operation - Mem0"
title: "Update Memory Operation"
sidebarTitle: "Update Memory"
description: Modify an existing memory by updating its content or metadata.
icon: "pen-to-square"
iconType: "solid"
docs/docs.json
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@@ -41,6 +41,7 @@
"pages": [
"platform/quickstart",
"platform/overview",
"platform/copilot",
"platform/agent-signup",
"vibecoding",
"platform/cli",
docs/integrations.mdx
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---
title: Overview
seo:
title: "Integrations Overview - Mem0"
title: "Integrations Overview"
sidebarTitle: "Overview"
description: "Overview of Mem0 integrations with popular AI frameworks and tools for persistent memory and context management."
---
docs/integrations/aws-bedrock.mdx
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---
title: AWS Bedrock
seo:
title: "AWS Bedrock Integration with Mem0"
title: "AWS Bedrock Integration"
sidebarTitle: "AWS Bedrock"
description: "Use Mem0 with AWS Bedrock and OpenSearch Service for cloud-native persistent semantic memory storage."
---
docs/integrations/codex.mdx
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@@ -3,7 +3,7 @@ title: Codex
description: "Add persistent memory to OpenAI Codex with automatic capture, automatic recall, a search tool, and six memory skills."
---
Add persistent memory to [**OpenAI Codex**](https://openai.com/index/codex/) with the Mem0 plugin. Codex forgets everything between tasks. This plugin fixes that by connecting to Mem0's cloud memory layer via MCP, automatically capturing learnings at key lifecycle points, and retrieving relevant context on the first prompt of a session. Codex can use the search tool for recall later in the session.
Add persistent memory to [**OpenAI Codex**](https://openai.com/codex/) with the Mem0 plugin. Codex forgets everything between tasks. This plugin fixes that by connecting to Mem0's cloud memory layer via MCP, automatically capturing learnings at key lifecycle points, and retrieving relevant context on the first prompt of a session. Codex can use the search tool for recall later in the session.
<Info>Current plugin version: `0.3.1`.</Info>
docs/integrations/flowise.mdx
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@@ -23,7 +23,7 @@ npm install -g flowise
npx flowise start
```
2. Access to the Flowise UI at http://localhost:3000
2. Access to the Flowise UI at `http://localhost:3000`
3. Basic familiarity with [Flowise's LLM orchestration](https://flowiseai.com/#features) concepts
## Setup and Configuration
docs/integrations/hermes.mdx
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---
title: Hermes Agent
description: "Add long-term memory to Hermes agents with Mem0, on managed Mem0 Cloud or fully self-hosted (OSS), with automatic background sync and zero-latency prefetch."
description: "Add long-term memory to Hermes agents using Mem0 Platform, a self-hosted server, or local OSS mode with background fact extraction."
---
Add long-term memory to [Hermes Agent](https://github.com/NousResearch/hermes-agent), a self-improving AI agent CLI by Nous Research. Hermes has a pluggable memory system, and Mem0 is one of the supported providers. Once enabled, Mem0 learns facts from your conversations and surfaces relevant ones before each turn, without slowing down the chat.
Add long-term memory to [Hermes Agent](https://github.com/NousResearch/hermes-agent), a self-improving AI agent CLI by Nous Research. Hermes has a pluggable memory system, and Mem0 is one of the supported providers. Once enabled, Mem0 learns facts from your conversations and surfaces relevant ones for the current question, without slowing down the chat.
You can run Mem0 in two ways:
You can run Mem0 in three ways:
- **Platform mode** (default): managed Mem0 Cloud. Add your API key and you are ready.
- **OSS mode**: fully self-hosted with your own LLM, embedder, and vector store. No data leaves your machine.
- **Self-hosted server mode**: point the plugin at a Mem0 server you run yourself (the Docker-shipped server). The plugin only talks HTTP to your server.
- **OSS mode**: run Mem0 in-process with your own LLM, embedder, and vector store. No Mem0 server required.
## How It Works
Hermes runs a built-in memory system (file-based `MEMORY.md` and `USER.md`) alongside one external provider. When Mem0 is active, it works additively with the built-in system at three points in every conversation turn.
Hermes runs a built-in memory system (file-based `MEMORY.md` and `USER.md`) alongside one external provider. When Mem0 is active, it works additively with the built-in system at two points in every conversation turn.
### 1. Before the agent responds (prefetch)
### 1. Current-turn recall (bounded wait)
When you send a message, Hermes checks for cached Mem0 search results from the previous turn. If they exist, those memories are injected into the system prompt so the model can see them. This is zero-latency, with no waiting on an API call.
When you send a message, Hermes searches your stored memories for the current question and waits up to 3 seconds for results. If they arrive in time, they are injected into the system prompt so the model can see them. If the backend is slower, Hermes skips the injection and the model can still call `mem0_search` itself — so a slow backend never blocks a turn.
### 2. After the agent responds (sync)
### 2. Background fact extraction (sync)
Once the model finishes, Hermes sends the `(user message, assistant response)` pair to Mem0 in a background thread. Mem0 extracts facts automatically (for example, "user prefers Python" or "user works at Acme Corp"), so you never have to tell it what to remember. Each write is tagged with the gateway channel it came from.
### 3. Background prefetch for the next turn
At the same time, Hermes runs a background search to pre-load relevant memories for your next message. By the time you type, the results are already cached.
## Agent Tools
When Mem0 is active, the model gets five tools it can call during a conversation:
When Mem0 is active, the model gets four tools it can call during a conversation:
| Tool | Description | Parameters |
|------|-------------|------------|
| `mem0_list` | List all stored memories, for a full overview | `page`, `page_size` (default 100, max 200) |
| `mem0_search` | Semantic search by meaning, ranked by relevance | `query` (required), `top_k` (default 10, max 50), `rerank` (default `true`, Platform mode only) |
| `mem0_search` | Semantic search by meaning, ranked by relevance | `query` (required), `top_k` (default 10, max 50), `rerank` (default `false`, Platform mode only) |
| `mem0_add` | Store a fact verbatim, with no LLM extraction | `content` (required) |
| `mem0_update` | Update a memory's text by ID | `memory_id`, `text` (both required) |
| `mem0_delete` | Delete a memory by ID | `memory_id` (required) |
@@ -79,6 +75,36 @@ memory:
That's it. Mem0 runs automatically from here.
## Self-Hosted Server Setup
Run the [Mem0 server](https://github.com/mem0ai/mem0/tree/main/server) (FastAPI + pgvector) from its Docker image and point the plugin at it. Unlike OSS mode, the plugin just talks HTTP to your server.
### Interactive
```bash
hermes memory setup
# Select "mem0", then "Self-hosted server", and enter the server URL
```
### With flags
```bash
hermes memory setup mem0 --mode selfhosted \
--host http://localhost:8888 \
--api-key your-admin-api-key
```
### With environment variables
```bash
echo "MEM0_HOST=http://localhost:8888" >> ~/.hermes/.env
echo "MEM0_API_KEY=your-admin-api-key" >> ~/.hermes/.env
```
Then start a fresh Hermes session and call `mem0_search` — it connects to your server. The plugin authenticates with `X-API-Key` and uses the server's `/search` and `/memories` routes. The API key is optional only for servers running with `AUTH_DISABLED`.
<Note>Setting `host` routes to the self-hosted server automatically. Don't combine it with `mode: oss` — OSS takes precedence and ignores `host`.</Note>
## OSS (Self-Hosted) Setup
OSS mode runs Mem0 entirely on your own infrastructure: your LLM, your embedder, and your vector store. No data is sent to Mem0 Cloud, and no Mem0 API key is required.
@@ -111,15 +137,20 @@ hermes memory setup mem0 --mode oss \
| Flag | Description |
|------|-------------|
| `--mode` | `platform` or `oss` |
| `--mode` | `platform`, `selfhosted`, or `oss` |
| `--api-key` | Platform API key, or the admin key of a self-hosted server |
| `--host` | Self-hosted server URL (with `--mode selfhosted`) |
| `--oss-llm` | LLM provider (`openai` or `ollama`, default `openai`) |
| `--oss-llm-key` | LLM API key (for `openai`) |
| `--oss-llm-model` | Override the LLM model |
| `--oss-llm-url` | LLM base URL (for `ollama` or a custom endpoint) |
| `--oss-embedder` | Embedder provider (default `openai`) |
| `--oss-embedder-key` | Embedder API key |
| `--oss-embedder-model` | Override the embedder model |
| `--oss-embedder-url` | Embedder base URL (for `ollama` or a custom endpoint) |
| `--oss-vector` | Vector store (`qdrant` or `pgvector`, default `qdrant`) |
| `--oss-vector-path` | Local Qdrant storage path |
| `--oss-vector-url` | Qdrant server URL |
| `--oss-vector-host`, `--oss-vector-port` | PGVector or remote Qdrant host and port |
| `--oss-vector-user`, `--oss-vector-password`, `--oss-vector-dbname` | PGVector connection details |
| `--user-id` | Canonical user identifier |
@@ -127,7 +158,7 @@ hermes memory setup mem0 --mode oss \
## Switching Modes
You can move between Platform and OSS at any time. Run the setup command again, or edit `~/.hermes/mem0.json` directly.
You can move between the three modes at any time. Run the setup command again, or edit `~/.hermes/mem0.json` directly.
```bash
# Platform to OSS
@@ -136,6 +167,9 @@ hermes memory setup mem0 --mode oss --oss-llm-key sk-...
# OSS to Platform
hermes memory setup mem0 --mode platform --api-key sk-...
# Platform to a self-hosted server
hermes memory setup mem0 --mode selfhosted --host http://localhost:8888
# Preview without writing anything
hermes memory setup mem0 --mode oss --oss-llm-key sk-... --dry-run
```
@@ -146,7 +180,7 @@ A self-hosted `~/.hermes/mem0.json` looks like this:
{
"mode": "oss",
"oss": {
"llm": {"provider": "openai", "config": {"model": "gpt-5-mini"}},
"llm": {"provider": "openai", "config": {"model": "gpt-5-mini", "is_reasoning_model": true}},
"embedder": {"provider": "openai", "config": {"model": "text-embedding-3-small"}},
"vector_store": {"provider": "qdrant", "config": {"path": "~/.hermes/mem0_qdrant"}}
}
@@ -159,11 +193,12 @@ Behavioral settings live in `~/.hermes/mem0.json` and are written for you by `he
| Key | Default | Description |
|-----|---------|-------------|
| `mode` | `platform` | `platform` (Mem0 Cloud) or `oss` (self-hosted) |
| `api_key` | none | Mem0 Platform API key, required in Platform mode. Stored in `.env` as `MEM0_API_KEY` |
| `mode` | `platform` | `platform` (Mem0 Cloud) or `oss` (self-managed, in-process). Self-hosted server routing is set via `host` |
| `host` | none | Self-hosted Mem0 server URL. When set, the plugin talks HTTP to your server instead of the cloud |
| `api_key` | none | Mem0 Platform API key, or the admin key of a self-hosted server. Stored in `.env` as `MEM0_API_KEY` |
| `user_id` | `hermes-user` | Identifier that scopes memories. See cross-channel behavior below |
| `agent_id` | `hermes` | Agent identifier attached to writes |
| `rerank` | `true` | Rerank search results for relevance (Platform mode only) |
| `rerank` | `false` | Rerank search results for relevance (Platform mode only) |
### Cross-channel memories
@@ -174,12 +209,11 @@ Hermes can run from the CLI and from gateways like Telegram, Slack, and Discord.
Either way, every write is tagged with `metadata.channel` (for example `telegram` or `cli`), so per-channel views are still possible at query time.
## Reliability
- **Circuit breaker**: if Mem0 fails five times in a row, Hermes pauses calls for two minutes, then retries. The agent keeps working without memory during that window. Expected client errors, like a 404 on a missing memory id, do not count toward tripping the breaker.
- **Non-blocking**: every Mem0 call runs in a background daemon thread, so a slow or failed call never blocks your conversation.
- **Thread-safe**: the client uses lazy initialization with locking, and the background sync and prefetch threads are guarded so concurrent gateway messages cannot produce duplicate memories.
- **Non-blocking**: fact extraction runs in a background daemon thread, and current-turn recall waits at most 3 seconds, so a slow or failed call never blocks your conversation.
- **Thread-safe**: the client uses lazy initialization with locking, and the background sync and recall threads are guarded so concurrent gateway messages cannot produce duplicate memories.
## Troubleshooting
@@ -188,6 +222,7 @@ Either way, every write is tagged with `metadata.channel` (for example `telegram
The circuit breaker tripped after five consecutive failures and resets after two minutes.
- **Platform mode**: check your API key and internet connection.
- **Self-hosted server mode**: check that the server is running and reachable at the configured `host` URL.
- **OSS mode**: make sure your vector store (Qdrant or PGVector) is running and reachable.
### OSS: vector store connection refused
@@ -217,8 +252,8 @@ curl http://localhost:11434/api/tags
## Key Features
1. **Two ways to run**: managed Platform or fully self-hosted OSS, switchable at any time.
2. **Zero-latency recall**: memories are prefetched in the background and cached before you type.
1. **Three ways to run**: managed Platform, a self-hosted server, or fully local OSS, switchable at any time.
2. **Current-turn recall**: memories for the current question are injected within a 3-second window, with `mem0_search` as the model's own backstop.
3. **Automatic extraction**: Mem0 extracts and deduplicates facts from each exchange for you.
4. **Non-blocking and fault tolerant**: background threads plus a circuit breaker keep the agent responsive even when Mem0 is unreachable.
5. **Additive memory**: works alongside Hermes' built-in file memory (`MEMORY.md`, `USER.md`).
docs/integrations/langchain.mdx
+2 -3
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@@ -1,7 +1,6 @@
---
title: Langchain
seo:
title: "LangChain Integration with Mem0"
title: "LangChain Integration"
sidebarTitle: "Langchain"
description: "Build personalized AI agents using LangChain for conversation flow and Mem0 for long-term memory retention."
---
docs/integrations/n8n.mdx
+2 -3
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@@ -1,7 +1,6 @@
---
title: n8n
seo:
title: "n8n Integration with Mem0"
title: "n8n Integration"
sidebarTitle: "n8n"
description: "Add long-term memory to n8n workflows and AI Agents with the Mem0 community node, no code required."
---
docs/llms.txt
+1
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@@ -171,6 +171,7 @@ If the user is on a pre-current major (Python < 2, TS < 3, or a Platform call st
- [Introduction](https://docs.mem0.ai/introduction) [Both]: Use when the user wants a one-page overview of how memory fits between the LLM and the app.
- [Vibe Code with Mem0](https://docs.mem0.ai/vibecoding) [Both]: Use when the user is in Claude Code, Cursor, or Windsurf and wants memory wired into their editor.
- [Platform Overview](https://docs.mem0.ai/platform/overview) [Platform]: Use when the user picks the managed product - 4-line integration, hosted API, dashboard.
- [Mem0 Copilot](https://docs.mem0.ai/platform/copilot) [Platform]: Use when inspecting project memories, reviewing configuration changes, or testing extraction in the dashboard.
- [Sign up as an agent](https://docs.mem0.ai/platform/agent-signup) [Platform]: Use when an AI agent needs to mint a Mem0 API key autonomously - four commands, no email or dashboard, human claims ownership later.
- [Platform vs Open Source](https://docs.mem0.ai/platform/platform-vs-oss) [Both]: Use when the user is deciding between managed and self-hosted.
- [Platform Quickstart](https://docs.mem0.ai/platform/quickstart) [Platform]: Use for the first Platform integration - API key plus `MemoryClient.add/search`.
docs/open-source/features/custom-instructions.mdx
+2 -3
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@@ -1,7 +1,6 @@
---
title: Custom Instructions
seo:
title: "Open Source Custom Instructions - Mem0"
title: "Open Source Custom Instructions"
sidebarTitle: "Custom Instructions"
description: Tailor fact extraction so Mem0 stores only the details you care about.
icon: "wand-magic-sparkles"
---
docs/open-source/features/multimodal-support.mdx
+2 -3
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@@ -1,7 +1,6 @@
---
title: Multimodal Support
seo:
title: "Open Source Multimodal Support - Mem0"
title: "Open Source Multimodal Support"
sidebarTitle: "Multimodal Support"
description: Capture and recall memories from both text and images.
icon: "image"
---
docs/open-source/features/overview.mdx
+2 -3
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@@ -1,7 +1,6 @@
---
title: "Overview"
seo:
title: "Open Source Features Overview - Mem0"
title: "Open Source Features Overview"
sidebarTitle: "Overview"
description: "Self-hosting features that extend Mem0 beyond basic memory storage"
icon: "list"
---
docs/open-source/features/rest-api.mdx
+1 -1
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@@ -56,7 +56,7 @@ The Mem0 REST API server exposes every OSS memory operation over HTTP. Run it al
make bootstrap # starts Compose, creates an admin, issues the first API key
```
Or to start the stack only and finish setup via the browser wizard at http://localhost:3000:
Or to start the stack only and finish setup via the browser wizard at `http://localhost:3000`:
```bash
cd server
docs/open-source/overview.mdx
+2 -3
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@@ -1,7 +1,6 @@
---
title: "Overview"
seo:
title: "Mem0 Open Source Overview"
title: "Open Source Overview"
sidebarTitle: "Overview"
description: "Self-host Mem0 with full control over your infrastructure and data"
icon: "house"
---
docs/platform/cli.mdx
+2 -3
View File
@@ -1,7 +1,6 @@
---
title: CLI
seo:
title: "Mem0 CLI for Terminal Memory Management"
title: "CLI for Terminal Memory Management"
sidebarTitle: "CLI"
description: "Manage memories from your terminal, for both humans and AI agents."
icon: "terminal"
iconType: "solid"
docs/platform/copilot.mdx
+194
View File
@@ -0,0 +1,194 @@
---
title: "Mem0 Copilot"
description: "Inspect project memories, review configuration changes, and test extraction from the Mem0 dashboard."
icon: "message"
---
Copilot is an AI assistant in the [Mem0 dashboard](https://app.mem0.ai/dashboard/copilot). Ask it to inspect stored memories, suggest extraction rules and categories, change project settings, or explain the platform SDKs and APIs.
You describe the task in chat. Copilot uses tools to read project data or propose changes. In **Review changes** mode, you approve each change before it runs.
## Start with your project
1. Sign in to the dashboard and select the organization and project you want to work on.
2. Open **Copilot** in the sidebar.
3. Select **Review changes** below the message box.
4. Ask: “Show my current project settings and explain what each one does.”
5. Expand an activity card, such as **Read project config**, to see its **Input** and **Result**. Check these results when reviewing an answer or confirming a change.
You can ask about settings or SDK usage with an empty project. Suggestions based on stored data need enough project history to analyze.
## Inspect stored memories
Start with a project overview, then narrow the question:
| Prompt | What to inspect |
| --- | --- |
| “Summarize what this project has stored and how it is categorized.” | The memories and category patterns Copilot reads. Check which records support its summary. |
| “Analyze the memories for user alice.” | Facts stored for `alice`, their categories, and unwanted information. To investigate a missing fact, also provide the original input. |
| “Search alice's memories for dietary preferences.” | Memories relevant to the query within that user's scope. |
Copilot can list memories across the project. Searching by meaning needs a specific User, Agent, App, or Run ID. Select one in **Scope** or name it in your prompt.
Memory lists are paginated, and suggestions use samples. Check the activity results to see which records were read. Ask for more pages when you need to inspect the rest.
### Choose the memory scope
Open **Scope** below the message box. Select an existing ID or type one and choose it.
| Control | Identifies |
| --- | --- |
| **User** | The end user whose memories you want to inspect, such as `alice`. |
| **Agent** | An AI agent associated with the memories. |
| **App** | An application associated with the memories. |
| **Run** | A particular execution or session associated with the memories. |
A field set to **any** adds no filter for that entity type. **Clear scope** removes the selections. Scope gives Copilot default IDs to use; you can request a different entity in a message. Check the activity's **Input** to confirm which IDs it used. Adding a test memory requires a **User** ID, even when another entity is selected.
<Note>
Scope selects memories, not separate settings. Categories, extraction instructions, memory depth, and multilingual settings apply to the selected project. Category and extraction suggestions also analyze project data, regardless of the selected entity.
</Note>
See [Entity-scoped memory](/platform/features/entity-scoped-memory) for how these IDs organize memories in your application.
## Review and apply changes
The mode control below the message box determines when changes run:
- **Review changes**: Copilot pauses before changing project configuration or adding a test memory. Read the proposal and choose whether to apply it.
- **Auto-apply**: Copilot can change settings and add test memories without asking for approval. Check the selected project and scope before using it.
Ask Copilot to show the current settings before requesting an update. In the approval card, review every listed field. Approving the card applies the whole proposal, including fields you did not edit.
| Action | Effect |
| --- | --- |
| **Apply change** | Approve the proposed write. Inspect the subsequent result to confirm it succeeded. |
| **Edit** | When offered, edit extraction instructions or category names and descriptions. Choose **Apply with edits** to submit your revision. |
| **Discard edits** | Return to the original proposal without applying it. |
| **Decline** | Reject this proposal without applying it. |
| **Ask for something else** | Enter feedback and choose **Send**. This declines the current proposal and sends your feedback as a new message. Review the next proposal before applying it. |
Not every field has an inline editor. If a proposal includes both extraction instructions and categories, only the instructions get an editor. The editor cannot apply blank instructions or an empty category list. Use **Ask for something else** to change other fields or request separate proposals.
<Warning>
A generated prompt profile contains separate prompts for extraction and summaries. If your project uses one, saving extraction instructions through Copilot deactivates it. Review the replacement rules and test the affected behavior before using them in production.
</Warning>
## What Copilot cannot do
Copilot cannot perform these actions, even in **Auto-apply** mode:
- Create or delete API keys.
- Directly edit or delete existing memories.
- Export project data.
- Invite or remove organization or project members, or change their roles and permissions.
- Create or delete organizations or projects.
- Change billing details or subscription plans.
Use the dashboard or the relevant platform API for these tasks. Copilot can explain the steps or point you to documentation, but it cannot carry out the actions.
Copilot supports the managed Mem0 platform. It does not help configure or use the [self-hosted open-source library](/open-source/overview).
## Example 1: Stop storing small talk, then test extraction
This walkthrough uses a project where you have noticed greetings or small talk in stored memories. Use a development project when trying configuration changes, since a test user does not isolate project settings.
<Steps>
<Step title="Inspect the problem">
Select the project, set **User** to `alice`, and ask:
> Analyze the memories for user alice.
Inspect the returned memories. Identify examples of small talk you want to exclude and useful facts you still want to keep.
</Step>
<Step title="Request a targeted change">
With **Review changes** selected, ask:
> Update my extraction instructions to ignore greetings and small talk. Keep the existing rules for durable user preferences.
Check for a **Read project config** activity before reviewing the update. If it is missing, ask Copilot to read the current instructions first. If analysis reports insufficient data, ask it to use the rule you provided. You can also set the rule directly through [Custom instructions](/platform/features/custom-instructions).
</Step>
<Step title="Review the proposal">
Review the full replacement text. Keep existing rules your application needs. For this test, the relevant rules could look like:
```text
Remember the following:
* The user's dietary preferences and food restrictions.
Don't remember the following:
* Greetings and small talk.
```
Use **Edit** to adjust the wording, or **Ask for something else** to request a revision.
Choose **Apply change** or **Apply with edits**, then inspect the result to confirm the instructions were saved.
</Step>
<Step title="Add a test conversation">
Choose a new **User** ID for this test, such as `copilot-extraction-test-01`, and clear any other entity selections. Ask:
> Add this as a user message for copilot-extraction-test-01: “Hi! How's your day? I prefer vegetarian meals and avoid peanuts.”
Review the test-memory proposal and choose **Apply change**.
<Warning>
Adding a test memory writes to the selected project. It is not a dry run. Use a dedicated test user so you can find and remove the test data afterward.
</Warning>
</Step>
<Step title="Wait for extraction, then check the result">
Expand **Add test memory** and inspect **Result**. A `PENDING` status means extraction is still running. Use the returned `event_id` with the [Get Event API](/api-reference/events/get-event) to check progress. Wait for `SUCCEEDED` before evaluating the output. If the event is `FAILED`, inspect its details before retrying.
Then ask:
> List all memories for user copilot-extraction-test-01. Then search that user's memories for dietary preferences.
Check that the memories retain the vegetarian preference and peanut restriction, and exclude the greeting. Inspect the full list as well as search results: a relevant search can hide unwanted small-talk records.
If the output is wrong, describe the mismatch and review another instruction change. Use a new test user for the next attempt so earlier memories do not affect the result. Remove test data afterward through the dashboard or [memory deletion API](/api-reference/memory/delete-memories).
</Step>
</Steps>
Test with new inputs after changing settings. Updating configuration is not a cleanup operation for existing memories. See [Custom instructions](/platform/features/custom-instructions) for guidance on writing extraction rules.
## Example 2: Suggest categories and extraction instructions
These workflows sample the selected project's data. Generating a suggestion does not update settings by itself. Copilot can then propose applying it, which follows the selected review mode. Use **Review changes** to inspect suggestions before they are saved.
| Workflow | Data used | Example prompt |
| --- | --- | --- |
| Custom categories | Stored memories, excluding deleted memories. | “Suggest custom categories from this project's memories.” |
| Extraction instructions | Successful requests to add conversation data, including requests that produced no memories. | “Compare recent add requests with the extracted memories and suggest better extraction instructions.” |
Category suggestions identify recurring themes. Review the names, descriptions, and examples. Applying a category list replaces the project's previous list; it does not add to it or re-tag existing memories. See [Custom categories](/platform/features/custom-categories) for project and per-call behavior.
Extraction suggestions compare conversation inputs with what was extracted. One add request can produce several memories or none, so the number of add requests is different from the number of stored memories. Keep your application's existing requirements when reviewing the proposal.
Both workflows require enough project data. If a suggestion fails because there is too little data, check the activity's **Result** for the current and required counts. You can still inspect memories, ask SDK/API questions, or provide your own rules instead of requesting a data-based suggestion.
## Example 3: Adjust detail and language
Ask Copilot to read the current settings, then request the change you need:
- **Memory depth** controls the level of detail: **Less**, **Medium**, or **More detailed**. Try “Show my current memory depth, then propose More detailed memories.” For deciding *which facts* to retain, use extraction instructions.
- **Multilingual** behavior preserves the user's original language. Try “Enable multilingual behavior so memories preserve the language of the input.” Test it with a new conversation in the language your application uses.
Both are project settings. Review the proposed values and test a fresh input after applying them. See [Organization and project settings](/api-reference/organizations-projects) for configuration through the API.
## Example 4: Ask SDK and API questions
Include your language and the task, for example:
> Show me how to search memories for user alice with the managed Mem0 Python SDK. Link the documentation you used.
Copilot can look up the official platform documentation. Check the **Browse mem0 docs** and **Read docs page** activities and open the cited pages. If an answer has no sources, ask for them before using the example. The [Platform quickstart](/platform/quickstart) covers adding and searching memories in code.
## Resume a conversation and check message limits
Use **History** to reopen your 50 most recently active chats for the selected project. Chats belong to the person who created them; other project members cannot open them. Use **New chat** to start a separate conversation, or **Delete chat** in history to remove one. Deleting a chat does not undo settings changes or remove test memories.
Message allowances depend on the organization's plan and are shared across its projects and members. They reset at the start of each calendar month in UTC.
For plans with a limit, a usage notice appears once 80% of the allowance is used. Below that point, no counter is shown. At the limit, new messages are disabled until the allowance resets or the plan is upgraded. Follow the notice's upgrade link to review plan options.
Sending feedback through **Ask for something else** counts as a new message. Approving or declining a saved proposal without feedback does not use another message. Test additions and searches also use the platform APIs and remain subject to their normal quotas.
docs/platform/features/custom-instructions.mdx
+2 -3
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@@ -1,7 +1,6 @@
---
title: Custom Instructions
seo:
title: "Platform Custom Instructions - Mem0"
title: "Platform Custom Instructions"
sidebarTitle: "Custom Instructions"
description: 'Control how Mem0 extracts and stores memories using natural language guidelines'
---
docs/platform/features/multimodal-support.mdx
+2 -3
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@@ -1,7 +1,6 @@
---
title: Multimodal Support
seo:
title: "Platform Multimodal Support - Mem0"
title: "Platform Multimodal Support"
sidebarTitle: "Multimodal Support"
description: Integrate images and documents into your interactions with Mem0
---
docs/platform/overview.mdx
+2 -3
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@@ -1,7 +1,6 @@
---
title: "Overview"
seo:
title: "Mem0 Platform Overview"
title: "Platform Overview"
sidebarTitle: "Overview"
description: "Managed memory layer for AI agents, production-ready in minutes"
icon: "cloud"
---
examples/mem0-demo/package.json
+1 -1
View File
@@ -28,7 +28,7 @@
"clsx": "^2.1.1",
"js-cookie": "^3.0.6",
"lucide-react": "^0.477.0",
"next": "15.5.21",
"next": "15.5.24",
"react": "^19.0.0",
"react-dom": "^19.0.0",
"react-markdown": "^10.0.1",
server/dashboard/package.json
+1 -1
View File
@@ -45,7 +45,7 @@
"framer-motion": "^12.23.12",
"lodash": "^4.18.1",
"lucide-react": "^0.542.0",
"next": "15.5.21",
"next": "15.5.24",
"next-themes": "^0.4.4",
"react": "^19.0.0",
"react-copy-to-clipboard": "^5.1.0",
server/dashboard/pnpm-lock.yaml
Generated
+42 -42
View File
@@ -122,8 +122,8 @@ importers:
specifier: ^0.542.0
version: 0.542.0(react@19.2.7)
next:
specifier: 15.5.21
version: 15.5.21(@types/node@22.5.4)(react-dom@19.2.7(react@19.2.7))(react@19.2.7)
specifier: 15.5.24
version: 15.5.24(@types/node@22.5.4)(react-dom@19.2.7(react@19.2.7))(react@19.2.7)
next-themes:
specifier: ^0.4.4
version: 0.4.6(react-dom@19.2.7(react@19.2.7))(react@19.2.7)
@@ -476,60 +476,60 @@ packages:
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'@emnapi/runtime': ^1.7.1
'@next/env@15.5.21':
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os: [darwin]
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engines: {node: '>= 10'}
cpu: [x64]
os: [linux]
libc: [musl]
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engines: {node: '>= 10'}
cpu: [arm64]
os: [win32]
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cpu: [x64]
os: [win32]
@@ -2417,8 +2417,8 @@ packages:
react: ^16.8 || ^17 || ^18 || ^19 || ^19.0.0-rc
react-dom: ^16.8 || ^17 || ^18 || ^19 || ^19.0.0-rc
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peerDependencies:
@@ -3343,34 +3343,34 @@ snapshots:
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optional: true
'@next/env@15.5.21': {}
'@next/env@15.5.24': {}
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react-dom: 19.2.7(react@19.2.7)
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caniuse-lite: 1.0.30001810
postcss: 8.5.23
react: 19.2.7
react-dom: 19.2.7(react@19.2.7)
styled-jsx: 5.1.6(react@19.2.7)
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'@next/swc-linux-x64-musl': 15.5.21
'@next/swc-win32-arm64-msvc': 15.5.21
'@next/swc-win32-x64-msvc': 15.5.21
'@next/swc-darwin-arm64': 15.5.24
'@next/swc-darwin-x64': 15.5.24
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'@next/swc-linux-arm64-musl': 15.5.24
'@next/swc-linux-x64-gnu': 15.5.24
'@next/swc-linux-x64-musl': 15.5.24
'@next/swc-win32-arm64-msvc': 15.5.24
'@next/swc-win32-x64-msvc': 15.5.24
sharp: 0.35.3(@types/node@22.5.4)
transitivePeerDependencies:
- '@babel/core'