Merge remote-tracking branch 'origin/main' into fix/codex-install-docs
# Conflicts: # mem0-plugin/README.md
This commit is contained in:
gabrielstein-mem0
@@ -12,7 +12,7 @@
|
||||
"name": "mem0",
|
||||
"source": "./mem0-plugin",
|
||||
"description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search to Claude workflows.",
|
||||
"version": "0.1.0"
|
||||
"version": "0.1.1"
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
@@ -12,7 +12,7 @@
|
||||
"name": "mem0",
|
||||
"source": "./mem0-plugin",
|
||||
"description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search.",
|
||||
"version": "0.1.0"
|
||||
"version": "0.1.1"
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
@@ -39,7 +39,7 @@
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<a href="https://mem0.ai/research"><strong>📄 Building Production-Ready AI Agents with Scalable Long-Term Memory →</strong></a>
|
||||
<a href="https://mem0.ai/research"><strong>📄 Benchmarking Mem0's token-efficient memory algorithm →</strong></a>
|
||||
</p>
|
||||
|
||||
## New Memory Algorithm (April 2026)
|
||||
|
||||
@@ -7,6 +7,23 @@ mode: "wide"
|
||||
<Tabs>
|
||||
<Tab title="Python">
|
||||
|
||||
<Update label="2026-04-25" description="v2.0.1">
|
||||
|
||||
**Bug Fixes:**
|
||||
- **Client:** Map `user_id`, `agent_id`, `run_id` entity params to filters in `GET /memories` ([#4960](https://github.com/mem0ai/mem0/pull/4960))
|
||||
- **Memory:** Honor `prompt` param in vector store extraction pipeline ([#4914](https://github.com/mem0ai/mem0/pull/4914))
|
||||
- **Memory:** Add missing `text_lemmatized` field in `AsyncMemory._create_memory` ([#4886](https://github.com/mem0ai/mem0/pull/4886))
|
||||
- **Memory:** Merge same-key operator dicts in AND metadata filters ([#4853](https://github.com/mem0ai/mem0/pull/4853))
|
||||
- **LLMs:** Narrow `_is_reasoning_model` check to not match `gpt-5.x` variants ([#4746](https://github.com/mem0ai/mem0/pull/4746))
|
||||
- **Vector Stores:** Add `ca_certs` config option for Elasticsearch vector store ([#3993](https://github.com/mem0ai/mem0/pull/3993))
|
||||
- **Vector Stores:** Add `agent_id` and `run_id` to Elasticsearch/OpenSearch default mappings ([#4906](https://github.com/mem0ai/mem0/pull/4906))
|
||||
- **Embeddings:** Set FastEmbed `embedding_dims` from model metadata at init ([#4711](https://github.com/mem0ai/mem0/pull/4711))
|
||||
|
||||
**Security:**
|
||||
- Bump vulnerable dependencies to patched versions ([#4835](https://github.com/mem0ai/mem0/pull/4835))
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="2026-04-14" description="v2.0.0">
|
||||
|
||||
**Major Release** — Python SDK with V3 memory pipeline, ADD-only extraction, and cleaned-up API surface.
|
||||
@@ -893,6 +910,17 @@ See the [OSS v1 to v2 migration guide](https://docs.mem0.ai/migration/oss-v1-to-
|
||||
</Tab>
|
||||
|
||||
<Tab title="TypeScript">
|
||||
<Update label="2026-04-25" description="v3.0.2">
|
||||
|
||||
**Bug Fixes:**
|
||||
- **LLMs:** Forward `timeout` config to OpenAI client in JS OSS LLM providers ([#4770](https://github.com/mem0ai/mem0/pull/4770))
|
||||
|
||||
**Improvements:**
|
||||
- **Telemetry:** Harden TS telemetry version injection and require changelog entry on version bump ([#4900](https://github.com/mem0ai/mem0/pull/4900))
|
||||
- **Docs:** Update memory tool list, CLI usage, and config file reading logic ([#4861](https://github.com/mem0ai/mem0/pull/4861))
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="2026-04-20" description="v3.0.1">
|
||||
|
||||
**Bug Fixes:**
|
||||
|
||||
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@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "mem0",
|
||||
"version": "0.1.0",
|
||||
"version": "0.1.1",
|
||||
"description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search to Claude workflows using the Mem0 Platform MCP server.",
|
||||
"author": {
|
||||
"name": "Mem0",
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "mem0",
|
||||
"version": "0.1.0",
|
||||
"version": "0.1.1",
|
||||
"description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search to Codex workflows using the Mem0 Platform MCP server.",
|
||||
"author": {
|
||||
"name": "Mem0",
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "mem0",
|
||||
"version": "0.1.0",
|
||||
"version": "0.1.1",
|
||||
"description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search using the Mem0 Platform MCP server.",
|
||||
"author": {
|
||||
"name": "Mem0",
|
||||
|
||||
+13
-3
@@ -74,20 +74,30 @@ This points Codex at the repo's `.agents/plugins/marketplace.json`, which refere
|
||||
|
||||
> **Don't combine with Option A.** The plugin manifest auto-registers `mem0` as an MCP server via `mem0-plugin/.codex-mcp.json` — adding a manual `[mcp_servers.mem0]` block would duplicate the registration.
|
||||
|
||||
**Optional — enable lifecycle hooks.** Codex doesn't auto-wire hooks from plugin manifests; it only reads `~/.codex/hooks.json` (or `<repo>/.codex/hooks.json`). Run the bundled installer once to merge Mem0's entries:
|
||||
**Optional — enable lifecycle hooks.** Codex doesn't auto-wire hooks from plugin manifests; it only reads `~/.codex/hooks.json` (or `<repo>/.codex/hooks.json`) ([docs](https://developers.openai.com/codex/hooks)). Run the bundled installer once to merge Mem0's entries:
|
||||
|
||||
```bash
|
||||
python3 ~/codex-plugins/mem0-source/mem0-plugin/scripts/install_codex_hooks.py
|
||||
```
|
||||
|
||||
Then enable the feature flag in `~/.codex/config.toml`:
|
||||
This merges three entries into `~/.codex/hooks.json` with absolute paths pointing into your clone:
|
||||
|
||||
| Event | What it does |
|
||||
|-------|--------------|
|
||||
| `SessionStart` | Loads prior memories as bootstrap context |
|
||||
| `UserPromptSubmit` | Injects relevant memories into the prompt |
|
||||
| `Stop` | Reminds the agent to persist learnings at turn end |
|
||||
|
||||
Re-running the installer is idempotent (replaces the Mem0 entries rather than duplicating) and preserves any other hooks you have. To remove: `python3 .../install_codex_hooks.py --uninstall`. If you move or delete the clone directory, re-run the installer from the new location — the hooks file stores absolute paths.
|
||||
|
||||
Codex hooks also require the `codex_hooks` feature flag in `~/.codex/config.toml`:
|
||||
|
||||
```toml
|
||||
[features]
|
||||
codex_hooks = true
|
||||
```
|
||||
|
||||
Restart Codex. This registers `SessionStart` (loads prior memories), `UserPromptSubmit` (injects relevant memories before each prompt), and `Stop` (reminds the agent to persist learnings at turn end). The installer is idempotent. To remove: `python3 .../install_codex_hooks.py --uninstall`. If you move or delete the clone directory, re-run the installer from the new location — the hooks file stores absolute paths into your clone.
|
||||
The installer prints a reminder if the flag isn't set. Restart Codex after editing the config.
|
||||
|
||||
**Managing the plugin:**
|
||||
|
||||
|
||||
@@ -0,0 +1,39 @@
|
||||
{
|
||||
"hooks": {
|
||||
"SessionStart": [
|
||||
{
|
||||
"matcher": "startup|resume",
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "${CODEX_PLUGIN_ROOT}/scripts/on_session_start.sh",
|
||||
"statusMessage": "Loading mem0 context..."
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"UserPromptSubmit": [
|
||||
{
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "${CODEX_PLUGIN_ROOT}/scripts/on_user_prompt.sh",
|
||||
"statusMessage": "Searching mem0 memories...",
|
||||
"timeout": 5
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"Stop": [
|
||||
{
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "${CODEX_PLUGIN_ROOT}/scripts/on_stop_codex.sh",
|
||||
"timeout": 10
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
Executable
+149
@@ -0,0 +1,149 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Install Mem0 lifecycle hooks into ~/.codex/hooks.json.
|
||||
|
||||
Codex discovers hooks only at ~/.codex/hooks.json or <repo>/.codex/hooks.json,
|
||||
and has no plugin-host mechanism for auto-wiring hooks from an installed
|
||||
plugin. This installer reads the template at hooks/codex-hooks.json, rewrites
|
||||
the ${CODEX_PLUGIN_ROOT} placeholder to the absolute install path of this
|
||||
plugin, then merges the entries into ~/.codex/hooks.json.
|
||||
|
||||
Re-running is idempotent: existing Mem0 entries (identified by the plugin
|
||||
directory name in the command string) are removed before fresh entries are
|
||||
added, so upgrades don't leave duplicates.
|
||||
|
||||
Usage:
|
||||
python3 install_codex_hooks.py # install or update
|
||||
python3 install_codex_hooks.py --uninstall # remove Mem0 entries
|
||||
|
||||
After installing, Codex requires the hooks feature flag in ~/.codex/config.toml:
|
||||
|
||||
[features]
|
||||
codex_hooks = true
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
SCRIPT_DIR = Path(__file__).resolve().parent
|
||||
PLUGIN_ROOT = SCRIPT_DIR.parent
|
||||
|
||||
CODEX_DIR = Path.home() / ".codex"
|
||||
HOOKS_FILE = CODEX_DIR / "hooks.json"
|
||||
CONFIG_FILE = CODEX_DIR / "config.toml"
|
||||
|
||||
TEMPLATE_FILE = PLUGIN_ROOT / "hooks" / "codex-hooks.json"
|
||||
|
||||
# Substring we look for when identifying entries this installer owns.
|
||||
# Matches the plugin directory name, which stays stable across install paths.
|
||||
OWNER_MARKER = "mem0-plugin"
|
||||
|
||||
|
||||
def load_template() -> dict:
|
||||
raw = TEMPLATE_FILE.read_text()
|
||||
raw = raw.replace("${CODEX_PLUGIN_ROOT}", str(PLUGIN_ROOT))
|
||||
return json.loads(raw)
|
||||
|
||||
|
||||
def load_existing() -> dict:
|
||||
if not HOOKS_FILE.exists():
|
||||
return {"hooks": {}}
|
||||
try:
|
||||
return json.loads(HOOKS_FILE.read_text())
|
||||
except (json.JSONDecodeError, OSError) as e:
|
||||
print(f"error: failed to read {HOOKS_FILE}: {e}", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
def is_owned_entry(entry: dict) -> bool:
|
||||
for hook in entry.get("hooks", []):
|
||||
if OWNER_MARKER in hook.get("command", ""):
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def strip_owned_entries(config: dict) -> dict:
|
||||
hooks = config.get("hooks", {}) or {}
|
||||
for event in list(hooks.keys()):
|
||||
hooks[event] = [e for e in hooks[event] if not is_owned_entry(e)]
|
||||
if not hooks[event]:
|
||||
del hooks[event]
|
||||
config["hooks"] = hooks
|
||||
return config
|
||||
|
||||
|
||||
def merge_template(config: dict, template: dict) -> dict:
|
||||
hooks = config.setdefault("hooks", {})
|
||||
for event, entries in template.get("hooks", {}).items():
|
||||
hooks.setdefault(event, []).extend(entries)
|
||||
return config
|
||||
|
||||
|
||||
def write_config(config: dict) -> None:
|
||||
CODEX_DIR.mkdir(parents=True, exist_ok=True)
|
||||
HOOKS_FILE.write_text(json.dumps(config, indent=2) + "\n")
|
||||
|
||||
|
||||
def feature_flag_enabled() -> bool:
|
||||
if not CONFIG_FILE.exists():
|
||||
return False
|
||||
content = CONFIG_FILE.read_text()
|
||||
for line in content.splitlines():
|
||||
stripped = line.split("#", 1)[0].strip().replace(" ", "")
|
||||
if stripped == "codex_hooks=true":
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def print_feature_flag_hint() -> None:
|
||||
print()
|
||||
print("Codex hooks feature flag is not enabled.")
|
||||
print(f"Add this to {CONFIG_FILE}:")
|
||||
print()
|
||||
print(" [features]")
|
||||
print(" codex_hooks = true")
|
||||
print()
|
||||
print("Then restart Codex.")
|
||||
|
||||
|
||||
def main() -> int:
|
||||
parser = argparse.ArgumentParser(description="Install or remove Mem0 Codex hooks.")
|
||||
parser.add_argument(
|
||||
"--uninstall",
|
||||
action="store_true",
|
||||
help="Remove Mem0 entries from ~/.codex/hooks.json and exit.",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
config = load_existing()
|
||||
|
||||
if args.uninstall:
|
||||
config = strip_owned_entries(config)
|
||||
write_config(config)
|
||||
print(f"Removed Mem0 hooks from {HOOKS_FILE}")
|
||||
return 0
|
||||
|
||||
if not TEMPLATE_FILE.exists():
|
||||
print(f"error: template not found at {TEMPLATE_FILE}", file=sys.stderr)
|
||||
return 1
|
||||
|
||||
template = load_template()
|
||||
config = strip_owned_entries(config)
|
||||
config = merge_template(config, template)
|
||||
write_config(config)
|
||||
|
||||
print(f"Installed Mem0 hooks into {HOOKS_FILE}")
|
||||
print(f"Plugin path: {PLUGIN_ROOT}")
|
||||
print("Events: SessionStart, UserPromptSubmit, Stop")
|
||||
|
||||
if not feature_flag_enabled():
|
||||
print_feature_flag_hint()
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
Executable
+44
@@ -0,0 +1,44 @@
|
||||
#!/usr/bin/env bash
|
||||
# Hook: Stop (Codex)
|
||||
#
|
||||
# Fires when Codex finishes a turn. Reminds the agent to persist any
|
||||
# important learnings via the mem0 MCP tools before the turn closes.
|
||||
#
|
||||
# Input: JSON on stdin with session_id, turn_id, stop_hook_active,
|
||||
# last_assistant_message, transcript_path, cwd,
|
||||
# hook_event_name, model
|
||||
# Output: JSON on stdout (Codex rejects plain text on Stop).
|
||||
# - stop_hook_active=true -> {"continue": true} (let the turn end)
|
||||
# - stop_hook_active=false -> {"decision":"block","reason":"..."}
|
||||
# (continue the turn with the reminder as context)
|
||||
#
|
||||
# We must respect stop_hook_active or we'd loop forever: every "block"
|
||||
# reopens the turn, which triggers Stop again when the agent settles.
|
||||
|
||||
set -uo pipefail
|
||||
|
||||
INPUT=$(cat)
|
||||
STOP_HOOK_ACTIVE=$(echo "$INPUT" | jq -r '.stop_hook_active // false' 2>/dev/null || echo "false")
|
||||
|
||||
if [ "$STOP_HOOK_ACTIVE" = "true" ]; then
|
||||
printf '{"continue":true}\n'
|
||||
exit 0
|
||||
fi
|
||||
|
||||
REASON=$(cat <<'EOF'
|
||||
Before finishing, check if there are important learnings from this interaction that should be persisted using the mem0 `add_memory` tool:
|
||||
|
||||
1. Were any significant decisions made? -> Store with metadata `{"type": "decision"}`
|
||||
2. Were any new patterns or strategies discovered? -> Store with metadata `{"type": "task_learning"}`
|
||||
3. Did any approach fail? -> Store with metadata `{"type": "anti_pattern"}`
|
||||
4. Did you learn anything about the user's preferences? -> Store with metadata `{"type": "user_preference"}`
|
||||
5. Were there environment/setup discoveries? -> Store with metadata `{"type": "environmental"}`
|
||||
|
||||
Memories can be as detailed as needed — include full context, reasoning, code snippets, file paths, and examples. Longer, searchable memories are more valuable than vague one-liners.
|
||||
|
||||
If nothing notable happened in this interaction, it's fine to skip. Only store genuinely useful learnings.
|
||||
EOF
|
||||
)
|
||||
|
||||
jq -cn --arg reason "$REASON" '{decision:"block", reason:$reason}'
|
||||
exit 0
|
||||
@@ -1,25 +1,34 @@
|
||||
---
|
||||
name: mem0
|
||||
description: >
|
||||
Integrate Mem0 Platform into AI applications for persistent memory, personalization, and semantic search.
|
||||
Use this skill when the user mentions "mem0", "memory layer", "remember user preferences",
|
||||
"persistent context", "personalization", or needs to add long-term memory to chatbots, agents,
|
||||
or AI apps. Covers Python and TypeScript SDKs, framework integrations (LangChain, CrewAI,
|
||||
Vercel AI SDK, OpenAI Agents SDK, Pipecat), and the full Platform API. Use even when the user
|
||||
doesn't explicitly say "mem0" but describes needing conversation memory, user context retention,
|
||||
or knowledge retrieval across sessions.
|
||||
Mem0 Platform SDK for adding persistent memory to AI applications.
|
||||
TRIGGER when: user mentions "mem0", "MemoryClient", "memory layer",
|
||||
"remember user preferences", "persistent context", "personalization",
|
||||
or needs to add long-term memory to chatbots, agents, or AI apps.
|
||||
Covers Python SDK (mem0ai), TypeScript SDK (mem0ai), and framework integrations
|
||||
(LangChain, CrewAI, OpenAI Agents SDK, Pipecat, LlamaIndex, AutoGen, LangGraph).
|
||||
Also covers the open-source self-hosted Memory class.
|
||||
This is the DEFAULT mem0 skill for ambiguous queries.
|
||||
DO NOT TRIGGER when: user asks about CLI commands, terminal usage, or shell
|
||||
scripts (use mem0-cli), or Vercel AI SDK / @mem0/vercel-ai-provider / createMem0
|
||||
(use mem0-vercel-ai-sdk).
|
||||
license: Apache-2.0
|
||||
metadata:
|
||||
author: mem0ai
|
||||
version: "0.1.0"
|
||||
version: "0.1.1"
|
||||
category: ai-memory
|
||||
tags: "memory, personalization, ai, python, typescript, vector-search"
|
||||
compatibility: Requires Python 3.10+ or Node.js 18+, pip install mem0ai or npm install mem0ai, MEM0_API_KEY env var, and internet access to api.mem0.ai
|
||||
compatibility: Requires Python 3.10+ or Node.js 18+, pip install mem0ai or npm install mem0ai, MEM0_API_KEY env var (Platform), and internet access to api.mem0.ai. Uses Mem0 v3 API.
|
||||
---
|
||||
|
||||
# Mem0 Platform Integration
|
||||
|
||||
Mem0 is a managed memory layer for AI applications. It stores, retrieves, and manages user memories via API — no infrastructure to deploy.
|
||||
> **Skill Graph:** This skill is part of the Mem0 skill graph:
|
||||
> - **mem0** (this skill) -- Platform Client SDK + OSS (Python + TypeScript)
|
||||
> - **[mem0-cli](https://github.com/mem0ai/mem0/tree/main/skills/mem0-cli)** -- Command-line interface
|
||||
> - **[mem0-vercel-ai-sdk](https://github.com/mem0ai/mem0/tree/main/skills/mem0-vercel-ai-sdk)** -- Vercel AI SDK provider
|
||||
|
||||
Mem0 is a managed memory layer for AI applications. It stores, retrieves, and manages user memories via API — no infrastructure to deploy. For self-hosted usage, see the OSS section in the client references below.
|
||||
|
||||
## Step 1: Install and authenticate
|
||||
|
||||
@@ -68,14 +77,14 @@ client.add(messages, user_id="alice")
|
||||
|
||||
### Search memories
|
||||
```python
|
||||
results = client.search("dietary preferences", user_id="alice")
|
||||
results = client.search("dietary preferences", filters={"user_id": "alice"})
|
||||
for mem in results.get("results", []):
|
||||
print(mem["memory"])
|
||||
```
|
||||
|
||||
### Get all memories
|
||||
```python
|
||||
all_memories = client.get_all(user_id="alice")
|
||||
all_memories = client.get_all(filters={"user_id": "alice"})
|
||||
```
|
||||
|
||||
### Update a memory
|
||||
@@ -100,12 +109,12 @@ openai = OpenAI()
|
||||
|
||||
def chat(user_input: str, user_id: str) -> str:
|
||||
# 1. Retrieve relevant memories
|
||||
memories = mem0.search(user_input, user_id=user_id)
|
||||
memories = mem0.search(user_input, filters={"user_id": user_id})
|
||||
context = "\n".join([m["memory"] for m in memories.get("results", [])])
|
||||
|
||||
# 2. Generate response with memory context
|
||||
response = openai.chat.completions.create(
|
||||
model="gpt-4.1-nano-2025-04-14",
|
||||
model="gpt-5-mini",
|
||||
messages=[
|
||||
{"role": "system", "content": f"User context:\n{context}"},
|
||||
{"role": "user", "content": user_input},
|
||||
@@ -123,11 +132,20 @@ def chat(user_input: str, user_id: str) -> str:
|
||||
|
||||
## Common edge cases
|
||||
|
||||
- **Search returns empty:** Memories process asynchronously. Wait 2-3s after `add()` before searching. Also verify `user_id` matches exactly (case-sensitive).
|
||||
- **Search returns empty:** Memories process asynchronously. Wait 2-3s after `add()` before searching. Also verify `user_id` matches exactly (case-sensitive) and use `filters={"user_id": "..."}` syntax.
|
||||
- **AND filter with user_id + agent_id returns empty:** Entities are stored separately. Use `OR` instead, or query separately.
|
||||
- **Duplicate memories:** Don't mix `infer=True` (default) and `infer=False` for the same data. Stick to one mode.
|
||||
- **Wrong import:** Always use `from mem0 import MemoryClient` (or `AsyncMemoryClient` for async). Do not use `from mem0 import Memory`.
|
||||
- **Immutable memories:** Cannot be updated or deleted once created. Use `client.history(memory_id)` to track changes over time.
|
||||
- **v3 defaults:** `top_k=20`, `threshold=0.1`, `rerank=False`. Adjust as needed for your use case.
|
||||
|
||||
## v2 Compatibility
|
||||
|
||||
If you're using SDK v2.x, note these differences:
|
||||
- **Entity IDs:** Pass `user_id` as top-level kwarg to `search()` instead of inside `filters`
|
||||
- **Defaults:** `top_k=100`, no threshold, `rerank=True`
|
||||
- **Graph memory:** Available via `enable_graph=True`
|
||||
|
||||
See the [migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) for details.
|
||||
|
||||
## Live documentation search
|
||||
|
||||
@@ -141,7 +159,17 @@ python ${CLAUDE_SKILL_DIR}/scripts/mem0_doc_search.py --index
|
||||
|
||||
No API key needed — searches docs.mem0.ai directly.
|
||||
|
||||
## References
|
||||
## Client SDK References
|
||||
|
||||
Language-specific deep references (Platform + OSS):
|
||||
|
||||
| Language | File |
|
||||
|----------|------|
|
||||
| Python (MemoryClient + AsyncMemoryClient + Memory OSS) | [client/python.md](client/python.md) |
|
||||
| TypeScript/Node.js (MemoryClient + Memory OSS) | [client/node.md](client/node.md) |
|
||||
| Python vs TypeScript differences | [client/differences.md](client/differences.md) |
|
||||
|
||||
## Platform References
|
||||
|
||||
Load these on demand for deeper detail:
|
||||
|
||||
@@ -152,5 +180,12 @@ Load these on demand for deeper detail:
|
||||
| API reference (endpoints, filters, object schema) | [references/api-reference.md](references/api-reference.md) |
|
||||
| Architecture (pipeline, lifecycle, scoping, performance) | [references/architecture.md](references/architecture.md) |
|
||||
| Platform features (retrieval, graph, categories, MCP, etc.) | [references/features.md](references/features.md) |
|
||||
| Framework integrations (LangChain, CrewAI, Vercel AI, etc.) | [references/integration-patterns.md](references/integration-patterns.md) |
|
||||
| Framework integrations (LangChain, CrewAI, OpenAI Agents, etc.) | [references/integration-patterns.md](references/integration-patterns.md) |
|
||||
| Use cases & examples (real-world patterns with code) | [references/use-cases.md](references/use-cases.md) |
|
||||
|
||||
## Related Mem0 Skills
|
||||
|
||||
| Skill | When to use | Link |
|
||||
|-------|-------------|------|
|
||||
| mem0-cli | Terminal commands, scripting, CI/CD, agent tool loops | [GitHub](https://github.com/mem0ai/mem0/tree/main/skills/mem0-cli) |
|
||||
| mem0-vercel-ai-sdk | Vercel AI SDK provider with automatic memory | [GitHub](https://github.com/mem0ai/mem0/tree/main/skills/mem0-vercel-ai-sdk) |
|
||||
|
||||
@@ -0,0 +1,129 @@
|
||||
# Python vs TypeScript SDK Differences
|
||||
|
||||
Quick-reference cheatsheet for developers working across both Mem0 SDKs.
|
||||
|
||||
## Constructor
|
||||
|
||||
| Aspect | Python | TypeScript |
|
||||
|--------|--------|------------|
|
||||
| Import (Platform) | `from mem0 import MemoryClient` | `import MemoryClient from 'mem0ai'` |
|
||||
| Import (OSS) | `from mem0 import Memory` | `import { Memory } from 'mem0ai/oss'` |
|
||||
| Constructor | `MemoryClient(api_key="m0-xxx")` | `new MemoryClient({ apiKey: 'm0-xxx' })` |
|
||||
| Required param | `api_key` (positional or kwarg) | `apiKey` (in options object) |
|
||||
|
||||
Both read from `MEM0_API_KEY` env var if no key provided.
|
||||
|
||||
## Method Naming
|
||||
|
||||
| Operation | Python | TypeScript |
|
||||
|-----------|--------|------------|
|
||||
| Add | `add()` | `add()` |
|
||||
| Search | `search()` | `search()` |
|
||||
| Get | `get()` | `get()` |
|
||||
| Get all | `get_all()` | `getAll()` |
|
||||
| Update | `update()` | `update()` |
|
||||
| Delete | `delete()` | `delete()` |
|
||||
| Delete all | `delete_all()` | `deleteAll()` |
|
||||
| History | `history()` | `history()` |
|
||||
| Batch update | `batch_update()` | `batchUpdate()` |
|
||||
| Batch delete | `batch_delete()` | `batchDelete()` |
|
||||
| List users | `users()` | `users()` |
|
||||
| Delete users | `delete_users()` | `deleteUsers()` |
|
||||
| Get project | `project.get()` | `getProject()` |
|
||||
| Update project | `project.update()` | `updateProject()` |
|
||||
| Create webhook | `create_webhook()` | `createWebhook()` |
|
||||
| Get webhooks | `get_webhooks()` | `getWebhooks()` |
|
||||
| Update webhook | `update_webhook()` | `updateWebhook()` |
|
||||
| Delete webhook | `delete_webhook()` | `deleteWebhook()` |
|
||||
| Create export | `create_memory_export()` | `createMemoryExport()` |
|
||||
| Get export | `get_memory_export()` | `getMemoryExport()` |
|
||||
| Feedback | `feedback()` | `feedback()` |
|
||||
|
||||
**Rule:** Python uses `snake_case`, TypeScript uses `camelCase` for method names.
|
||||
|
||||
## Parameter Passing
|
||||
|
||||
```python
|
||||
# Python: kwargs
|
||||
client.add(messages, user_id="alice", metadata={"source": "chat"})
|
||||
client.search("query", filters={"user_id": "alice"}, top_k=5, rerank=True)
|
||||
```
|
||||
|
||||
```typescript
|
||||
// TypeScript: options object with camelCase for top-level params, snake_case for filter keys
|
||||
await client.add(messages, { userId: 'alice', metadata: { source: 'chat' } });
|
||||
await client.search('query', { filters: { user_id: 'alice' }, topK: 5, rerank: true });
|
||||
```
|
||||
|
||||
**v3:** Python uses `snake_case` everywhere. TypeScript uses `camelCase` for top-level params (`userId`, `topK`) but `snake_case` for filter keys (`user_id`, `agent_id`).
|
||||
|
||||
## Architectural Differences
|
||||
|
||||
| Aspect | Python | TypeScript |
|
||||
|--------|--------|------------|
|
||||
| HTTP library | httpx | axios |
|
||||
| Default timeout | 300s | 60s |
|
||||
| Sync support | Yes (`MemoryClient`) | No (all async) |
|
||||
| Async support | Yes (`AsyncMemoryClient`) | All methods are async |
|
||||
| Project management | `client.project.*` (separate class) | `client.getProject()` / `client.updateProject()` |
|
||||
| Context manager | `async with AsyncMemoryClient()` | Not supported |
|
||||
|
||||
## Platform Features: Python-only
|
||||
|
||||
These methods exist in Python but not TypeScript:
|
||||
|
||||
| Method | Description |
|
||||
|--------|-------------|
|
||||
| `get_summary(filters)` | Get summary of memories |
|
||||
| `reset()` | Delete ALL data (users + memories) |
|
||||
| `project.create(name)` | Create a new project |
|
||||
| `project.delete()` | Delete current project |
|
||||
| `project.get_members()` | List project members |
|
||||
| `project.add_member(email, role)` | Add member to project |
|
||||
| `project.update_member(email, role)` | Change member role |
|
||||
| `project.remove_member(email)` | Remove member |
|
||||
|
||||
## Platform Features: TypeScript-only
|
||||
|
||||
| Method | Description |
|
||||
|--------|-------------|
|
||||
| `deleteUser(data)` | Convenience method for single entity deletion |
|
||||
| `ping()` | Health check endpoint |
|
||||
|
||||
## OSS Config Naming
|
||||
|
||||
| Python config key | TypeScript config key |
|
||||
|-------------------|----------------------|
|
||||
| `vector_store` | `vectorStore` |
|
||||
| `history_db_path` | `historyDbPath` |
|
||||
| `custom_instructions` | `customInstructions` |
|
||||
|
||||
## OSS Scope Parameter Naming
|
||||
|
||||
| Python | TypeScript |
|
||||
|--------|------------|
|
||||
| `user_id="alice"` | `userId: 'alice'` |
|
||||
| `agent_id="bot"` | `agentId: 'bot'` |
|
||||
| `run_id="session"` | `runId: 'session'` |
|
||||
|
||||
## Entity ID Passing (v3)
|
||||
|
||||
| Method | Python | TypeScript |
|
||||
|--------|--------|------------|
|
||||
| add() | Top-level: `user_id="alice"` | Top-level: `{ userId: 'alice' }` |
|
||||
| search() | In filters: `filters={"user_id": "alice"}` | In filters: `{ filters: { user_id: 'alice' } }` |
|
||||
| get_all() | In filters: `filters={"user_id": "alice"}` | In filters: `{ filters: { user_id: 'alice' } }` |
|
||||
|
||||
## Common Gotcha
|
||||
|
||||
When searching/filtering, both Python and TypeScript use `snake_case` for filter keys. TypeScript only uses `camelCase` for top-level method parameters:
|
||||
|
||||
```python
|
||||
# Python - snake_case in filters
|
||||
results = client.search("query", filters={"user_id": "alice"})
|
||||
```
|
||||
|
||||
```typescript
|
||||
// TypeScript - snake_case in filters, camelCase for top-level params
|
||||
const results = await client.search('query', { filters: { user_id: 'alice' }, topK: 20 });
|
||||
```
|
||||
@@ -0,0 +1,418 @@
|
||||
# Mem0 Node.js / TypeScript SDK Reference
|
||||
|
||||
Complete reference for the `mem0ai` npm package. Covers both the Platform client (managed API) and the Open Source self-hosted variant.
|
||||
|
||||
---
|
||||
|
||||
## Platform Client
|
||||
|
||||
### Installation
|
||||
|
||||
```bash
|
||||
npm install mem0ai
|
||||
export MEM0_API_KEY="m0-your-api-key"
|
||||
```
|
||||
|
||||
### MemoryClient
|
||||
|
||||
```typescript
|
||||
import MemoryClient from 'mem0ai';
|
||||
|
||||
const client = new MemoryClient({ apiKey: 'm0-xxx' });
|
||||
```
|
||||
|
||||
**Constructor:** `new MemoryClient({ apiKey })`. If `apiKey` is not provided, reads from `MEM0_API_KEY` environment variable.
|
||||
|
||||
- HTTP library: `axios`
|
||||
- Timeout: 60 seconds
|
||||
- Base URL: `https://api.mem0.ai`
|
||||
- All methods are async (return `Promise`)
|
||||
|
||||
---
|
||||
|
||||
### Memory Methods
|
||||
|
||||
#### add(messages, options?)
|
||||
|
||||
Store new memories from messages.
|
||||
|
||||
```typescript
|
||||
const messages = [
|
||||
{ role: 'user', content: "I'm a vegetarian and allergic to nuts." },
|
||||
{ role: 'assistant', content: "Got it! I'll remember that." },
|
||||
];
|
||||
await client.add(messages, { userId: 'alice' });
|
||||
```
|
||||
|
||||
| Parameter | Type | Description |
|
||||
|-----------|------|-------------|
|
||||
| `messages` | `Message[]` | Array of `{role, content}` objects |
|
||||
| `options.userId` | string | User identifier |
|
||||
| `options.agentId` | string | Agent identifier |
|
||||
| `options.appId` | string | Application identifier |
|
||||
| `options.runId` | string | Session identifier |
|
||||
| `options.metadata` | object | Custom key-value pairs |
|
||||
| `options.infer` | boolean | If false, store raw text (default: true) |
|
||||
|
||||
**Returns:** `Promise<any>` -- list of events
|
||||
|
||||
#### search(query, options?)
|
||||
|
||||
Search memories by semantic similarity.
|
||||
|
||||
```typescript
|
||||
const results = await client.search('dietary preferences', { filters: { user_id: 'alice' }, topK: 20 });
|
||||
for (const mem of results.results) {
|
||||
console.log(mem.memory, mem.score);
|
||||
}
|
||||
```
|
||||
|
||||
| Parameter | Type | Description |
|
||||
|-----------|------|-------------|
|
||||
| `query` | string | Natural language search query |
|
||||
| `options.filters` | object | Filter object with entity IDs (`user_id`, `agent_id`, etc.) and/or `AND`/`OR`/`NOT` conditions |
|
||||
| `options.topK` | number | Number of results (default: 20) |
|
||||
| `options.rerank` | boolean | Enable semantic reranking (default: false) |
|
||||
| `options.threshold` | number | Minimum similarity (default: 0.1) |
|
||||
|
||||
**Returns:** `Promise<SearchResult>` -- `{results: [{id, memory, score, ...}]}`
|
||||
|
||||
#### get(memoryId)
|
||||
|
||||
```typescript
|
||||
const memory = await client.get('ea925981-...');
|
||||
```
|
||||
|
||||
#### getAll(options?)
|
||||
|
||||
Retrieve all memories. Requires at least one entity identifier in filters.
|
||||
|
||||
```typescript
|
||||
const memories = await client.getAll({ filters: { user_id: 'alice' } });
|
||||
// With filters
|
||||
const filtered = await client.getAll({
|
||||
filters: { AND: [{ user_id: 'alice' }, { categories: { contains: 'health' } }] },
|
||||
});
|
||||
```
|
||||
|
||||
| Parameter | Type | Description |
|
||||
|-----------|------|-------------|
|
||||
| `options.filters` | object | Filter object with entity IDs (`user_id`, `agent_id`, etc.) and/or `AND`/`OR`/`NOT` conditions |
|
||||
| `options.page` | number | Page number |
|
||||
| `options.pageSize` | number | Results per page |
|
||||
|
||||
#### update(memoryId, data)
|
||||
|
||||
```typescript
|
||||
await client.update('ea925981-...', { text: 'Updated: vegan since 2024' });
|
||||
await client.update('ea925981-...', { text: 'Updated', metadata: { verified: true } });
|
||||
```
|
||||
|
||||
| Parameter | Type | Description |
|
||||
|-----------|------|-------------|
|
||||
| `memoryId` | string | Memory ID |
|
||||
| `data.text` | string | New content |
|
||||
| `data.metadata` | object | New metadata |
|
||||
| `data.timestamp` | string | New timestamp |
|
||||
|
||||
#### delete(memoryId)
|
||||
|
||||
```typescript
|
||||
await client.delete('ea925981-...');
|
||||
```
|
||||
|
||||
#### deleteAll(options?)
|
||||
|
||||
```typescript
|
||||
await client.deleteAll({ userId: 'alice' });
|
||||
```
|
||||
|
||||
#### history(memoryId)
|
||||
|
||||
```typescript
|
||||
const history = await client.history('ea925981-...');
|
||||
// Returns: [{previousValue, newValue, action, timestamps}]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Batch Methods
|
||||
|
||||
#### batchUpdate(memories)
|
||||
|
||||
```typescript
|
||||
await client.batchUpdate([
|
||||
{ memoryId: 'uuid-1', text: 'Updated text' },
|
||||
{ memoryId: 'uuid-2', text: 'Another update' },
|
||||
]);
|
||||
```
|
||||
|
||||
#### batchDelete(memories)
|
||||
|
||||
```typescript
|
||||
await client.batchDelete(['uuid-1', 'uuid-2', 'uuid-3']);
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### User/Entity Management
|
||||
|
||||
#### users()
|
||||
|
||||
```typescript
|
||||
const users = await client.users();
|
||||
// Returns: {results: [{type: "user", name: "alice"}, ...]}
|
||||
```
|
||||
|
||||
#### deleteUser(data) / deleteUsers(data)
|
||||
|
||||
```typescript
|
||||
await client.deleteUser({ userId: 'alice' }); // Single entity
|
||||
await client.deleteUsers({ agentId: 'bot-1' }); // Flexible
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Project Management
|
||||
|
||||
```typescript
|
||||
// Get project config
|
||||
const config = await client.getProject({ fields: ['customCategories'] });
|
||||
|
||||
// Update project settings
|
||||
await client.updateProject({
|
||||
customInstructions: 'Extract dietary preferences and health info',
|
||||
customCategories: [{ health: 'Medical and dietary info' }],
|
||||
});
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Webhooks
|
||||
|
||||
```typescript
|
||||
// List
|
||||
const webhooks = await client.getWebhooks({ projectId: 'proj_123' });
|
||||
|
||||
// Create
|
||||
const webhook = await client.createWebhook({
|
||||
url: 'https://your-app.com/webhook',
|
||||
name: 'Memory Logger',
|
||||
projectId: 'proj_123',
|
||||
eventTypes: ['memory_add', 'memory_update'],
|
||||
});
|
||||
|
||||
// Update
|
||||
await client.updateWebhook({
|
||||
webhookId: 'wh_123',
|
||||
name: 'Updated Logger',
|
||||
url: 'https://new-url.com',
|
||||
});
|
||||
|
||||
// Delete
|
||||
await client.deleteWebhook({ webhookId: 'wh_123' });
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Feedback
|
||||
|
||||
```typescript
|
||||
await client.feedback({
|
||||
memoryId: 'mem-123',
|
||||
feedback: 'POSITIVE',
|
||||
feedbackReason: 'Accurately captured preference',
|
||||
});
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Export
|
||||
|
||||
```typescript
|
||||
const exportReq = await client.createMemoryExport({
|
||||
schema: JSON.stringify({ type: 'object', properties: { name: { type: 'string' } } }),
|
||||
filters: { user_id: 'alice' },
|
||||
});
|
||||
|
||||
const result = await client.getMemoryExport({ memoryExportId: exportReq.id });
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### TypeScript Types
|
||||
|
||||
Key interfaces from `mem0.types.ts`:
|
||||
|
||||
```typescript
|
||||
interface Message { role: string; content: string; }
|
||||
interface Memory { id: string; memory: string; userId: string; categories: string[]; score?: number; /* ... */ }
|
||||
interface MemoryOptions { userId?: string; agentId?: string; appId?: string; runId?: string; metadata?: object; /* ... */ }
|
||||
interface SearchOptions { filters?: object; topK?: number; rerank?: boolean; threshold?: number; /* ... */ }
|
||||
interface MemoryHistory { id: string; memoryId: string; previousValue: string; newValue: string; action: string; /* ... */ }
|
||||
interface FeedbackPayload { memoryId: string; feedback: string; feedbackReason?: string; }
|
||||
interface WebhookCreatePayload { url: string; name: string; projectId: string; eventTypes: string[]; }
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Open Source / Self-Hosted
|
||||
|
||||
### Installation
|
||||
|
||||
```bash
|
||||
npm install mem0ai
|
||||
```
|
||||
|
||||
### Memory Class
|
||||
|
||||
```typescript
|
||||
import { Memory } from 'mem0ai/oss';
|
||||
|
||||
const m = new Memory(); // Uses default config
|
||||
```
|
||||
|
||||
**Import:** `from 'mem0ai/oss'` (NOT the default export -- that is `MemoryClient` for Platform)
|
||||
|
||||
### Configuration
|
||||
|
||||
```typescript
|
||||
const config = {
|
||||
llm: {
|
||||
provider: 'openai', // openai, groq, anthropic, google, ollama, lmstudio, mistral, azure
|
||||
config: {
|
||||
model: 'gpt-5-mini',
|
||||
apiKey: 'sk-xxx',
|
||||
},
|
||||
},
|
||||
embedder: {
|
||||
provider: 'openai', // openai, ollama, lmstudio, google, azure, langchain, anthropic
|
||||
config: {
|
||||
model: 'text-embedding-3-small',
|
||||
apiKey: 'sk-xxx',
|
||||
},
|
||||
},
|
||||
vectorStore: {
|
||||
provider: 'qdrant', // memory, qdrant, redis, supabase, langchain, azure_ai_search, pgvector
|
||||
config: {
|
||||
collectionName: 'my_memories',
|
||||
host: 'localhost',
|
||||
port: 6333,
|
||||
},
|
||||
},
|
||||
historyDbPath: 'history.db',
|
||||
customInstructions: '...',
|
||||
disableHistory: false,
|
||||
};
|
||||
|
||||
const m = new Memory(config);
|
||||
// Or from dict with validation:
|
||||
const m2 = Memory.fromConfig(config);
|
||||
```
|
||||
|
||||
### Methods
|
||||
|
||||
All methods are async (return `Promise`):
|
||||
|
||||
#### add(messages, config)
|
||||
|
||||
```typescript
|
||||
await m.add('I prefer dark mode', { userId: 'alice' });
|
||||
await m.add([
|
||||
{ role: 'user', content: 'I like hiking' },
|
||||
{ role: 'assistant', content: 'Great outdoor activity!' },
|
||||
], { userId: 'alice' });
|
||||
```
|
||||
|
||||
| Parameter | Type | Description |
|
||||
|-----------|------|-------------|
|
||||
| `messages` | `string \| Message[]` | Content to store |
|
||||
| `config.userId` | string | User identifier (at least one scope required) |
|
||||
| `config.agentId` | string | Agent identifier |
|
||||
| `config.runId` | string | Session identifier |
|
||||
| `config.metadata` | object | Custom key-value pairs |
|
||||
| `config.filters` | object | Additional filters |
|
||||
| `config.infer` | boolean | LLM inference (default: true) |
|
||||
|
||||
**Returns:** `Promise<{results: [...], relations?: [...]}>`
|
||||
|
||||
#### search(query, config)
|
||||
|
||||
```typescript
|
||||
const results = await m.search('dietary preferences', { filters: { user_id: 'alice' }, topK: 5 });
|
||||
```
|
||||
|
||||
| Parameter | Type | Description |
|
||||
|-----------|------|-------------|
|
||||
| `query` | string | Search query |
|
||||
| `config.filters` | object | Filter object with entity IDs (`user_id`, `agent_id`, `run_id`, etc.) |
|
||||
| `config.topK` | number | Max results (default: 20) |
|
||||
|
||||
#### get(memoryId) / getAll(config) / update(memoryId, data) / delete(memoryId) / deleteAll(config) / history(memoryId)
|
||||
|
||||
Same interface patterns. Note: OSS `update` takes a string for data, not an object.
|
||||
|
||||
```typescript
|
||||
await m.update('mem-id', 'new content');
|
||||
```
|
||||
|
||||
#### reset()
|
||||
|
||||
Clear the entire vector store and history.
|
||||
|
||||
```typescript
|
||||
await m.reset();
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Key Differences: Platform vs OSS
|
||||
|
||||
| Aspect | Platform (`MemoryClient`) | OSS (`Memory`) |
|
||||
|--------|--------------------------|----------------|
|
||||
| **Import** | `import MemoryClient from 'mem0ai'` | `import { Memory } from 'mem0ai/oss'` |
|
||||
| **Auth** | API key required (`MEM0_API_KEY`) | No API key -- config-based |
|
||||
| **Execution** | API calls to `api.mem0.ai` | Local execution |
|
||||
| **Infrastructure** | Fully managed | Self-managed vector DB, embedder, LLM |
|
||||
| **Param style** | Top-level: `camelCase` (`userId`, `topK`), filter keys: `snake_case` (`user_id`) | Top-level: `camelCase` (`userId`, `topK`), filter keys: `snake_case` (`user_id`) |
|
||||
| **Batch ops** | `batchUpdate`, `batchDelete` | Not available |
|
||||
| **Webhooks** | Full CRUD | Not available |
|
||||
| **Export** | `createMemoryExport` | Not available |
|
||||
| **Feedback** | `feedback()` | Not available |
|
||||
| **Project mgmt** | `getProject`, `updateProject` | Not available |
|
||||
| **User listing** | `users()`, `deleteUser()` | Not available |
|
||||
| **History** | Platform-managed | SQLite (configurable) |
|
||||
|
||||
---
|
||||
|
||||
## v2 Compatibility
|
||||
|
||||
If you're using SDK v2.x:
|
||||
|
||||
**Naming Changes:**
|
||||
- Top-level params now use camelCase: `topK`, `rerank` (not `top_k`)
|
||||
- Filter keys use snake_case: `user_id`, `agent_id`
|
||||
- OSS: `limit` renamed to `topK`
|
||||
|
||||
**API Changes:**
|
||||
```typescript
|
||||
// v2 - top-level entity IDs, snake_case
|
||||
await client.search("query", { user_id: "alice", top_k: 20 });
|
||||
|
||||
// v3 - filters object with snake_case keys, camelCase top-level params
|
||||
await client.search("query", { filters: { user_id: "alice" }, topK: 20 });
|
||||
```
|
||||
|
||||
**Default Changes:**
|
||||
| Param | v2 | v3 |
|
||||
|-------|----|----|
|
||||
| `topK` | 100 | 20 |
|
||||
| `threshold` | none | 0.1 |
|
||||
| `rerank` | true | false |
|
||||
|
||||
**Removed:**
|
||||
- `OutputFormat` and `API_VERSION` enums
|
||||
- `organizationId`, `projectId` from constructor
|
||||
- `enableGraph`, `asyncMode`, `outputFormat`, `immutable`, `expirationDate`, `filterMemories`, `batchSize`, `forceAddOnly`, `includes`, `excludes`, `keywordSearch`
|
||||
|
||||
See the [v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) for details.
|
||||
@@ -0,0 +1,487 @@
|
||||
# Mem0 Python SDK Reference
|
||||
|
||||
Complete reference for the `mem0ai` Python package. Covers both the Platform client (managed API) and the Open Source self-hosted variant.
|
||||
|
||||
---
|
||||
|
||||
## Platform Client
|
||||
|
||||
### Installation
|
||||
|
||||
```bash
|
||||
pip install mem0ai
|
||||
export MEM0_API_KEY="m0-your-api-key"
|
||||
```
|
||||
|
||||
### MemoryClient (Synchronous)
|
||||
|
||||
```python
|
||||
from mem0 import MemoryClient
|
||||
|
||||
client = MemoryClient(api_key="m0-xxx")
|
||||
```
|
||||
|
||||
**Constructor:** `MemoryClient(api_key=None)`. If `api_key` is not provided, reads from `MEM0_API_KEY` environment variable. Raises `ValueError` if no key found.
|
||||
|
||||
- HTTP library: `httpx`
|
||||
- Timeout: 300 seconds
|
||||
- Base URL: `https://api.mem0.ai`
|
||||
|
||||
### AsyncMemoryClient (Asynchronous)
|
||||
|
||||
```python
|
||||
from mem0 import AsyncMemoryClient
|
||||
|
||||
client = AsyncMemoryClient(api_key="m0-xxx")
|
||||
|
||||
# Or use as context manager
|
||||
async with AsyncMemoryClient(api_key="m0-xxx") as client:
|
||||
results = await client.search("query", filters={"user_id": "alice"})
|
||||
```
|
||||
|
||||
Same methods as `MemoryClient`, all `async`/`await`. Supports async context manager.
|
||||
|
||||
---
|
||||
|
||||
### Memory Methods
|
||||
|
||||
#### add(messages, **kwargs)
|
||||
|
||||
Store new memories from messages.
|
||||
|
||||
```python
|
||||
messages = [
|
||||
{"role": "user", "content": "I'm a vegetarian and allergic to nuts."},
|
||||
{"role": "assistant", "content": "Got it! I'll remember that."}
|
||||
]
|
||||
client.add(messages, user_id="alice")
|
||||
```
|
||||
|
||||
| Parameter | Type | Default | Description |
|
||||
|-----------|------|---------|-------------|
|
||||
| `messages` | str \| dict \| list[dict] | required | Message content. Strings auto-convert to user messages |
|
||||
| `user_id` | str | None | User identifier |
|
||||
| `agent_id` | str | None | Agent identifier |
|
||||
| `app_id` | str | None | Application identifier |
|
||||
| `run_id` | str | None | Session/run identifier |
|
||||
| `metadata` | dict | None | Custom key-value pairs |
|
||||
| `infer` | bool | True | If False, store raw text without LLM inference |
|
||||
| `custom_categories` | list | None | Override project categories |
|
||||
| `custom_instructions` | str | None | Override extraction instructions |
|
||||
| `timestamp` | int \| float \| str | None | Custom timestamp (Unix epoch or ISO 8601) |
|
||||
|
||||
**Returns:** `dict` -- list of events: `[{"id": "...", "event": "ADD", "data": {"memory": "..."}}]`
|
||||
|
||||
#### search(query, **kwargs)
|
||||
|
||||
Search memories by semantic similarity.
|
||||
|
||||
```python
|
||||
results = client.search("dietary preferences", filters={"user_id": "alice"})
|
||||
for mem in results.get("results", []):
|
||||
print(mem["memory"], mem["score"])
|
||||
```
|
||||
|
||||
| Parameter | Type | Default | Description |
|
||||
|-----------|------|---------|-------------|
|
||||
| `query` | str | required | Natural language search query |
|
||||
| `filters` | dict | None | Filter object with entity IDs and/or `AND`/`OR`/`NOT` conditions (e.g., `{"user_id": "alice"}`) |
|
||||
| `top_k` | int | 10 | Number of results |
|
||||
| `rerank` | bool | False | Enable deep semantic reranking (+150-200ms) |
|
||||
| `threshold` | float | 0.1 | Minimum similarity score |
|
||||
| `fields` | list | None | Specific fields to return |
|
||||
| `categories` | list | None | Filter by category |
|
||||
|
||||
**Returns:** `dict` -- `{"results": [{id, memory, user_id, categories, score, created_at, ...}]}`
|
||||
|
||||
#### get(memory_id)
|
||||
|
||||
Retrieve a single memory by ID.
|
||||
|
||||
```python
|
||||
memory = client.get(memory_id="ea925981-...")
|
||||
```
|
||||
|
||||
**Returns:** `dict` -- full memory object
|
||||
|
||||
#### get_all(**kwargs)
|
||||
|
||||
Retrieve all memories with optional filtering. Requires at least one entity identifier.
|
||||
|
||||
```python
|
||||
memories = client.get_all(filters={"user_id": "alice"})
|
||||
# With compound filters
|
||||
memories = client.get_all(filters={"AND": [{"user_id": "alice"}, {"categories": {"contains": "health"}}]})
|
||||
```
|
||||
|
||||
| Parameter | Type | Default | Description |
|
||||
|-----------|------|---------|-------------|
|
||||
| `filters` | dict | None | Filter object with entity IDs and/or `AND`/`OR`/`NOT` conditions |
|
||||
| `top_k` | int | None | Limit results |
|
||||
| `page` | int | None | Page number |
|
||||
| `page_size` | int | None | Results per page |
|
||||
|
||||
**Returns:** `dict` -- `{"results": [...]}`
|
||||
|
||||
#### update(memory_id, text=None, metadata=None, timestamp=None)
|
||||
|
||||
Update a memory's content, metadata, or timestamp. At least one parameter required.
|
||||
|
||||
```python
|
||||
client.update("ea925981-...", text="Updated: vegan since 2024")
|
||||
client.update("ea925981-...", metadata={"verified": True})
|
||||
```
|
||||
|
||||
**Returns:** `dict` -- updated memory
|
||||
|
||||
#### delete(memory_id)
|
||||
|
||||
Permanently delete a single memory.
|
||||
|
||||
```python
|
||||
client.delete("ea925981-...")
|
||||
```
|
||||
|
||||
#### delete_all(**kwargs)
|
||||
|
||||
Delete all memories matching filters. Irreversible.
|
||||
|
||||
```python
|
||||
client.delete_all(user_id="alice")
|
||||
```
|
||||
|
||||
#### history(memory_id)
|
||||
|
||||
Get the change history of a memory.
|
||||
|
||||
```python
|
||||
history = client.history("ea925981-...")
|
||||
# Returns: [{previous_value, new_value, action, timestamps}]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Batch Methods
|
||||
|
||||
#### batch_update(memories)
|
||||
|
||||
Update up to 1000 memories in a single request.
|
||||
|
||||
```python
|
||||
client.batch_update([
|
||||
{"memory_id": "uuid-1", "text": "Updated text"},
|
||||
{"memory_id": "uuid-2", "text": "Another update", "metadata": {"verified": True}},
|
||||
])
|
||||
```
|
||||
|
||||
#### batch_delete(memories)
|
||||
|
||||
Delete up to 1000 memories in a single request.
|
||||
|
||||
```python
|
||||
client.batch_delete([
|
||||
{"memory_id": "uuid-1"},
|
||||
{"memory_id": "uuid-2"},
|
||||
])
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### User/Entity Management
|
||||
|
||||
#### users()
|
||||
|
||||
List all users, agents, and sessions that have memories.
|
||||
|
||||
```python
|
||||
users = client.users()
|
||||
# Returns: {"results": [{"type": "user", "name": "alice"}, ...]}
|
||||
```
|
||||
|
||||
#### delete_users(user_id=None, agent_id=None, app_id=None, run_id=None)
|
||||
|
||||
Delete a specific entity and all its memories.
|
||||
|
||||
```python
|
||||
client.delete_users(user_id="alice")
|
||||
```
|
||||
|
||||
#### reset()
|
||||
|
||||
Delete ALL users, agents, sessions, and memories. Complete data reset.
|
||||
|
||||
```python
|
||||
client.reset()
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Export & Summary
|
||||
|
||||
#### create_memory_export(schema, **kwargs)
|
||||
|
||||
Create a structured export of memories.
|
||||
|
||||
```python
|
||||
import json
|
||||
|
||||
schema = json.dumps({
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"name": {"type": "string"},
|
||||
"preferences": {"type": "array", "items": {"type": "string"}},
|
||||
}
|
||||
})
|
||||
export = client.create_memory_export(schema=schema, user_id="alice")
|
||||
```
|
||||
|
||||
#### get_memory_export(**kwargs)
|
||||
|
||||
Retrieve a previously created export.
|
||||
|
||||
```python
|
||||
result = client.get_memory_export(memory_export_id=export["id"])
|
||||
```
|
||||
|
||||
#### get_summary(filters=None)
|
||||
|
||||
Get a summary of memories.
|
||||
|
||||
```python
|
||||
summary = client.get_summary(filters={"user_id": "alice"})
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Feedback
|
||||
|
||||
#### feedback(memory_id, feedback=None, feedback_reason=None)
|
||||
|
||||
Provide quality feedback on a memory.
|
||||
|
||||
```python
|
||||
client.feedback(
|
||||
memory_id="mem-123",
|
||||
feedback="POSITIVE", # POSITIVE | NEGATIVE | VERY_NEGATIVE | None (clear)
|
||||
feedback_reason="Accurately captured preference"
|
||||
)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Webhooks
|
||||
|
||||
```python
|
||||
# List
|
||||
webhooks = client.get_webhooks(project_id="proj_123")
|
||||
|
||||
# Create
|
||||
webhook = client.create_webhook(
|
||||
url="https://your-app.com/webhook",
|
||||
name="Memory Logger",
|
||||
project_id="proj_123",
|
||||
event_types=["memory_add", "memory_update"]
|
||||
)
|
||||
|
||||
# Update
|
||||
client.update_webhook(webhook_id=123, name="Updated", url="https://new-url.com")
|
||||
|
||||
# Delete
|
||||
client.delete_webhook(webhook_id=123)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Project Management
|
||||
|
||||
Access via `client.project.*`:
|
||||
|
||||
```python
|
||||
# Get project config
|
||||
config = client.project.get(fields=["custom_categories", "custom_instructions"])
|
||||
|
||||
# Update project settings
|
||||
client.project.update(
|
||||
custom_instructions="Extract dietary preferences and health info",
|
||||
custom_categories=[{"health": "Medical and dietary info"}],
|
||||
multilingual=True,
|
||||
)
|
||||
|
||||
# Create/delete project
|
||||
client.project.create(name="My Project", description="...")
|
||||
client.project.delete()
|
||||
|
||||
# Member management
|
||||
members = client.project.get_members()
|
||||
client.project.add_member(email="user@example.com", role="READER") # READER or OWNER
|
||||
client.project.update_member(email="user@example.com", role="OWNER")
|
||||
client.project.remove_member(email="user@example.com")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Open Source / Self-Hosted
|
||||
|
||||
### Installation
|
||||
|
||||
```bash
|
||||
pip install mem0ai
|
||||
```
|
||||
|
||||
### Memory Class
|
||||
|
||||
```python
|
||||
from mem0 import Memory
|
||||
|
||||
m = Memory() # Uses default config (OpenAI embedder + in-memory vector store)
|
||||
```
|
||||
|
||||
**Import:** `from mem0 import Memory` (NOT `MemoryClient` -- that is the Platform client)
|
||||
|
||||
### Configuration
|
||||
|
||||
```python
|
||||
config = {
|
||||
"llm": {
|
||||
"provider": "openai", # openai, groq, azure, ollama, lmstudio, google, anthropic, mistral
|
||||
"config": {
|
||||
"model": "gpt-5-mini",
|
||||
"api_key": "sk-xxx",
|
||||
}
|
||||
},
|
||||
"embedder": {
|
||||
"provider": "openai", # openai, ollama, azure, lmstudio, google, huggingface
|
||||
"config": {
|
||||
"model": "text-embedding-3-small",
|
||||
"api_key": "sk-xxx",
|
||||
}
|
||||
},
|
||||
"vector_store": {
|
||||
"provider": "qdrant", # faiss, qdrant, pgvector, redis, supabase, azure_ai_search, memory
|
||||
"config": {
|
||||
"collection_name": "my_memories",
|
||||
"host": "localhost",
|
||||
"port": 6333,
|
||||
}
|
||||
},
|
||||
"history_db_path": "history.db", # SQLite path for change history
|
||||
"custom_instructions": "...", # Custom LLM prompt for extraction
|
||||
}
|
||||
|
||||
m = Memory.from_config(config)
|
||||
```
|
||||
|
||||
### Context Manager
|
||||
|
||||
```python
|
||||
with Memory(config) as m:
|
||||
m.add("I prefer dark mode", user_id="alice")
|
||||
results = m.search("preferences", filters={"user_id": "alice"})
|
||||
# SQLite connections released automatically
|
||||
```
|
||||
|
||||
### Methods
|
||||
|
||||
All methods mirror the Platform client but run locally:
|
||||
|
||||
#### add(messages, *, user_id, agent_id, run_id, metadata, infer=True)
|
||||
|
||||
```python
|
||||
m.add("I'm a vegetarian", user_id="alice")
|
||||
m.add([
|
||||
{"role": "user", "content": "I like hiking"},
|
||||
{"role": "assistant", "content": "Great outdoor activity!"}
|
||||
], user_id="alice")
|
||||
```
|
||||
|
||||
At least one of `user_id`, `agent_id`, `run_id` required.
|
||||
|
||||
**Returns:** `{"results": [...], "relations": [...]}`
|
||||
|
||||
#### search(query, *, filters=None, top_k=20, threshold=0.1, rerank=False)
|
||||
|
||||
```python
|
||||
results = m.search("dietary preferences", filters={"user_id": "alice"}, top_k=5)
|
||||
```
|
||||
|
||||
Entity IDs (`user_id`, `agent_id`, `run_id`) must be passed inside the `filters` dict.
|
||||
|
||||
Supports filter operators: `eq`, `ne`, `in`, `nin`, `gt`, `gte`, `lt`, `lte`, `contains`, `not_contains`.
|
||||
|
||||
#### get(memory_id) / get_all(**kwargs) / update(memory_id, data, metadata=None) / delete(memory_id) / delete_all(**kwargs) / history(memory_id)
|
||||
|
||||
Same interface as Platform client.
|
||||
|
||||
#### reset()
|
||||
|
||||
Clear the entire vector store collection and history database. Recreates the vector store.
|
||||
|
||||
```python
|
||||
m.reset()
|
||||
```
|
||||
|
||||
#### close()
|
||||
|
||||
Release SQLite connections. Called automatically when using context manager.
|
||||
|
||||
### AsyncMemory
|
||||
|
||||
```python
|
||||
from mem0 import AsyncMemory
|
||||
|
||||
m = AsyncMemory(config)
|
||||
await m.add("text", user_id="alice")
|
||||
results = await m.search("query", filters={"user_id": "alice"})
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Key Differences: Platform vs OSS
|
||||
|
||||
| Aspect | Platform (`MemoryClient`) | OSS (`Memory`) |
|
||||
|--------|--------------------------|----------------|
|
||||
| **Import** | `from mem0 import MemoryClient` | `from mem0 import Memory` |
|
||||
| **Auth** | API key required (`MEM0_API_KEY`) | No API key -- config-based |
|
||||
| **Execution** | API calls to `api.mem0.ai` | Local execution |
|
||||
| **Infrastructure** | Fully managed | Self-managed vector DB, embedder, LLM |
|
||||
| **Entity filtering** | `filters={"user_id": "..."}` | `filters={"user_id": "..."}` |
|
||||
| **Batch ops** | `batch_update`, `batch_delete` | Not available |
|
||||
| **Webhooks** | Full CRUD | Not available |
|
||||
| **Export** | `create_memory_export`, `get_memory_export` | Not available |
|
||||
| **Feedback** | `feedback()` | Not available |
|
||||
| **Project mgmt** | `client.project.*` | Not available |
|
||||
| **User listing** | `users()`, `delete_users()` | Not available |
|
||||
| **Custom prompts** | Via project settings | Direct config (`custom_instructions`) |
|
||||
| **History** | Platform-managed | SQLite (configurable) |
|
||||
| **Async** | `AsyncMemoryClient` | `AsyncMemory` |
|
||||
|
||||
---
|
||||
|
||||
## v2 Compatibility
|
||||
|
||||
If you're using SDK v2.x or the v2 API:
|
||||
|
||||
**API Changes:**
|
||||
- **Entity IDs in search/get_all:** Pass `user_id`, `agent_id` as top-level kwargs instead of inside `filters`
|
||||
```python
|
||||
# v2
|
||||
results = client.search("query", user_id="alice")
|
||||
# v3
|
||||
results = client.search("query", filters={"user_id": "alice"})
|
||||
```
|
||||
- **add() returns:** v2 returns ADD, UPDATE, DELETE events; v3 returns ADD only
|
||||
|
||||
**Default Changes:**
|
||||
| Param | v2 | v3 |
|
||||
|-------|----|----|
|
||||
| `top_k` | 100 | 20 |
|
||||
| `threshold` | None | 0.1 |
|
||||
| `rerank` | True | False |
|
||||
|
||||
**Removed Parameters:**
|
||||
- Constructor: `org_id`, `project_id`
|
||||
- add(): `async_mode`, `output_format`, `enable_graph`, `immutable`, `expiration_date`, `filter_memories`, `batch_size`, `force_add_only`, `includes`, `excludes`, `keyword_search`
|
||||
- search()/get_all(): `enable_graph`
|
||||
- Config: `enable_graph`, `graph_store`, `custom_fact_extraction_prompt` (renamed to `custom_instructions`)
|
||||
|
||||
See the [v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) for full details.
|
||||
@@ -8,13 +8,15 @@ All endpoints require: `Authorization: Token <MEM0_API_KEY>`
|
||||
|
||||
| Operation | Method | URL |
|
||||
|-----------|--------|-----|
|
||||
| Add Memories | `POST` | `/v1/memories/` |
|
||||
| Search Memories | `POST` | `/v2/memories/search/` |
|
||||
| Get All Memories | `POST` | `/v2/memories/` |
|
||||
| Add Memories | `POST` | `/v3/memories/add/` |
|
||||
| Search Memories | `POST` | `/v3/memories/search/` |
|
||||
| Get All Memories | `POST` | `/v3/memories/` |
|
||||
| Get Single Memory | `GET` | `/v1/memories/{memory_id}/` |
|
||||
| Update Memory | `PUT` | `/v1/memories/{memory_id}/` |
|
||||
| Delete Memory | `DELETE` | `/v1/memories/{memory_id}/` |
|
||||
|
||||
Note: v1/v2 endpoints still work (backward compatible).
|
||||
|
||||
## Memory Object Structure
|
||||
|
||||
| Field | Type | Description |
|
||||
@@ -27,8 +29,6 @@ All endpoints require: `Authorization: Token <MEM0_API_KEY>`
|
||||
| `run_id` | string (nullable) | Run/session identifier |
|
||||
| `metadata` | object | Custom key-value pairs |
|
||||
| `categories` | array of strings | Auto-assigned category tags |
|
||||
| `immutable` | boolean | If true, prevents modification |
|
||||
| `expiration_date` | datetime (nullable) | Auto-expiry date |
|
||||
| `hash` | string | Content hash |
|
||||
| `created_at` | datetime | Creation timestamp |
|
||||
| `updated_at` | datetime | Last modification timestamp |
|
||||
@@ -50,10 +50,9 @@ Memories can be scoped to different levels:
|
||||
|
||||
## Processing Model
|
||||
|
||||
- Memories are processed **asynchronously by default** (`async_mode=true`)
|
||||
- Add responses return queued events (`ADD`, `UPDATE`, `DELETE`) for tracking
|
||||
- Set `async_mode=false` for synchronous processing when needed
|
||||
- Graph metadata is processed asynchronously -- use `get_all()` for complete graph data
|
||||
- Memories are processed **asynchronously** (v3 default)
|
||||
- Add responses return queued `ADD` events only (v3 is ADD-only, no UPDATE/DELETE)
|
||||
- Poll status via `GET /v1/event/{event_id}/`
|
||||
|
||||
## Filter System
|
||||
|
||||
@@ -106,19 +105,17 @@ Root must be `AND`, `OR`, or `NOT`. Simple shorthand `{"user_id": "alice"}` also
|
||||
|
||||
## Response Formats
|
||||
|
||||
### Add Response
|
||||
### Add Response (v3)
|
||||
|
||||
```json
|
||||
[
|
||||
{
|
||||
"id": "mem_01JF8ZS4Y0R0SPM13R5R6H32CJ",
|
||||
"event": "ADD",
|
||||
"data": { "memory": "The user moved to Austin in 2025." }
|
||||
}
|
||||
]
|
||||
{
|
||||
"message": "Memory processing has been queued for background execution",
|
||||
"status": "PENDING",
|
||||
"event_id": "evt-uuid"
|
||||
}
|
||||
```
|
||||
|
||||
Event types: `ADD`, `UPDATE`, `DELETE`. A single add can trigger multiple events.
|
||||
v3 is ADD-only. No UPDATE or DELETE events.
|
||||
|
||||
### Search Response
|
||||
|
||||
@@ -137,4 +134,17 @@ Event types: `ADD`, `UPDATE`, `DELETE`. A single add can trigger multiple events
|
||||
}
|
||||
```
|
||||
|
||||
With `enable_graph=true`, includes additional `relations` array with entity relationships.
|
||||
In v3, `score` is a combined multi-signal relevance score.
|
||||
|
||||
### Get All Response (v3)
|
||||
|
||||
```json
|
||||
{
|
||||
"count": 123,
|
||||
"next": "https://api.mem0.ai/v3/memories/?page=2&page_size=50",
|
||||
"previous": null,
|
||||
"results": [...]
|
||||
}
|
||||
```
|
||||
|
||||
v3 returns paginated envelope. Use `page` and `page_size` query params.
|
||||
|
||||
@@ -25,9 +25,9 @@ User Input → Retrieve relevant memories → Enrich LLM prompt → Generate res
|
||||
|
||||
Mem0 handles the complexity of extraction, deduplication, conflict resolution, and semantic retrieval so your application only needs to call `search()` and `add()`.
|
||||
|
||||
**Dual storage architecture:**
|
||||
**Storage architecture:**
|
||||
- **Vector store**: Embeddings for semantic similarity search
|
||||
- **Graph store** (optional): Entity nodes and relationship edges for structured knowledge
|
||||
- **Entity store**: Automatic entity linking for relationship-aware retrieval
|
||||
|
||||
---
|
||||
|
||||
@@ -40,41 +40,32 @@ Messages In
|
||||
│
|
||||
▼
|
||||
┌─────────────────────┐
|
||||
│ 1. EXTRACTION │ LLM analyzes messages, extracts key facts
|
||||
│ 1. EXTRACTION │ Single LLM call extracts all distinct new facts
|
||||
│ (infer=True) │ If infer=False, stores raw text as-is
|
||||
└─────────┬───────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────┐
|
||||
│ 2. CONFLICT │ Checks existing memories for duplicates
|
||||
│ RESOLUTION │ Latest truth wins (newer overrides older)
|
||||
│ │ Only runs when infer=True
|
||||
│ 2. DEDUPLICATION │ Hash-based dedup (MD5 prevents exact duplicates)
|
||||
│ │ No UPDATE/DELETE - v3 is ADD-only
|
||||
└─────────┬───────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────┐
|
||||
│ 3. STORAGE │ Generates embeddings → vector store
|
||||
│ │ Optional: entity extraction → graph store
|
||||
│ │ Indexes metadata, categories, timestamps
|
||||
│ 3. STORAGE │ Batch embed → vector store
|
||||
│ │ Entity extraction → entity store
|
||||
└─────────┬───────────┘
|
||||
│
|
||||
▼
|
||||
Memory Object
|
||||
(id, memory, categories, structured_attributes)
|
||||
```
|
||||
|
||||
### Processing modes
|
||||
### Processing (v3)
|
||||
|
||||
**Async (default, `async_mode=True`):**
|
||||
- API returns immediately: `{"status": "PENDING", "event_id": "..."}`
|
||||
- Processing happens in background
|
||||
v3 processes memories asynchronously by default:
|
||||
- API returns immediately: `{"status": "PENDING", "event_id": "evt-..."}`
|
||||
- Poll status via `GET /v1/event/{event_id}/`
|
||||
- Use webhooks for completion notifications
|
||||
- Best for: high-throughput, non-blocking workflows
|
||||
|
||||
**Sync (`async_mode=False`):**
|
||||
- API waits for full processing
|
||||
- Returns complete memory object with `id`, `event`, `memory`
|
||||
- Best for: real-time access immediately after add
|
||||
|
||||
### Extraction modes
|
||||
|
||||
@@ -93,7 +84,7 @@ Messages In
|
||||
|
||||
---
|
||||
|
||||
## Retrieval Pipeline
|
||||
## Retrieval Pipeline (v3)
|
||||
|
||||
### What happens when you call `client.search()`
|
||||
|
||||
@@ -102,45 +93,37 @@ Query In
|
||||
│
|
||||
▼
|
||||
┌─────────────────────┐
|
||||
│ 1. QUERY EMBEDDING │ Convert query to vector representation
|
||||
│ 1. PREPROCESSING │ Lemmatize keywords, extract entities
|
||||
└─────────┬───────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────┐
|
||||
│ 2. VECTOR SEARCH │ Cosine similarity across stored embeddings
|
||||
│ │ Scoped by filters (user_id, agent_id, etc.)
|
||||
└─────────┬───────────┘
|
||||
│
|
||||
▼ (optional enhancements)
|
||||
┌─────────────────────┐
|
||||
│ 3a. KEYWORD SEARCH │ Expands results with specific terms (+10ms)
|
||||
│ 3b. RERANKING │ Deep semantic reordering (+150-200ms)
|
||||
│ 3c. FILTER MEMORIES │ Precision filtering, removes low-relevance (+200-300ms)
|
||||
└─────────┬───────────┘
|
||||
│
|
||||
▼ (if enable_graph=True)
|
||||
┌─────────────────────┐
|
||||
│ 4. GRAPH LOOKUP │ Finds entity relationships
|
||||
│ │ Appends relations WITHOUT reranking vector results
|
||||
│ 2. PARALLEL SCORING │ Semantic search (vector similarity)
|
||||
│ │ BM25 keyword search (term matching)
|
||||
│ │ Entity matching (entity graph boost)
|
||||
└─────────┬───────────┘
|
||||
│
|
||||
▼
|
||||
Results + Relations
|
||||
┌─────────────────────┐
|
||||
│ 3. SCORE FUSION │ Combine signals into single score
|
||||
│ │ Optional: rerank=True for deep reordering
|
||||
└─────────┬───────────┘
|
||||
│
|
||||
▼
|
||||
Results (combined score per memory)
|
||||
```
|
||||
|
||||
### Retrieval enhancement combinations
|
||||
### v3 Search Defaults
|
||||
|
||||
| Configuration | Latency | Best for |
|
||||
|--------------|---------|----------|
|
||||
| Base search only | ~100ms | Simple lookups |
|
||||
| `keyword_search=True` | ~110ms | Entity-heavy queries, broad coverage |
|
||||
| `rerank=True` | ~250-300ms | User-facing results, top-N precision |
|
||||
| `keyword_search=True` + `rerank=True` | ~310ms | Balanced (recommended for most apps) |
|
||||
| `rerank=True` + `filter_memories=True` | ~400-500ms | Safety-critical, production systems |
|
||||
| Parameter | Default | Notes |
|
||||
|-----------|---------|-------|
|
||||
| `top_k` | 20 | Was 100 in v2 |
|
||||
| `threshold` | 0.1 | Was None in v2 |
|
||||
| `rerank` | False | Was True in v2 |
|
||||
|
||||
### Implicit null scoping
|
||||
|
||||
When you search with `user_id="alice"` only, Mem0 returns memories where `agent_id`, `app_id`, and `run_id` are all null. This prevents cross-scope leakage by default.
|
||||
When you search with `filters={"user_id": "alice"}` only, Mem0 returns memories where `agent_id`, `app_id`, and `run_id` are all null. This prevents cross-scope leakage by default.
|
||||
|
||||
To include memories with non-null fields, use explicit filters:
|
||||
```python
|
||||
@@ -150,53 +133,23 @@ filters={"OR": [{"user_id": "alice"}]}
|
||||
|
||||
---
|
||||
|
||||
## Memory Lifecycle
|
||||
## Memory Lifecycle (v3)
|
||||
|
||||
```
|
||||
CREATE ──→ ACTIVE ──→ UPDATE ──→ ACTIVE
|
||||
│ │ │
|
||||
│ ▼ ▼
|
||||
│ EXPIRED EXPIRED
|
||||
│ (still stored, (still stored,
|
||||
│ not retrieved) not retrieved)
|
||||
│ │ │
|
||||
▼ ▼ ▼
|
||||
DELETE DELETE DELETE
|
||||
(permanent)
|
||||
```
|
||||
v3 uses ADD-only extraction. Memories accumulate over time rather than being consolidated.
|
||||
|
||||
### Creation
|
||||
- Triggered by `client.add(messages, user_id="...")`
|
||||
- Messages processed through extraction → conflict resolution → storage
|
||||
- Gets unique UUID, `created_at` timestamp
|
||||
- Optional: custom `timestamp`, `expiration_date`, `metadata`, `immutable`
|
||||
- `client.add(messages, user_id="...")`
|
||||
- Single-pass extraction → deduplication → storage
|
||||
- Returns `{"event_id": "...", "status": "PENDING"}`
|
||||
|
||||
### Updates
|
||||
- `client.update(memory_id, text="...")` replaces text and reindexes
|
||||
- `client.batch_update([...])` for up to 1000 memories at once
|
||||
- Immutable memories (`immutable=True`) cannot be updated — must delete and re-add
|
||||
|
||||
### Deduplication
|
||||
- Automatic during `add()` with `infer=True`
|
||||
- Conflict resolution merges duplicate facts
|
||||
- Latest truth wins when contradictions detected
|
||||
- Prevents memory bloat from repeated information
|
||||
|
||||
### Expiration
|
||||
- Optional `expiration_date` parameter (ISO 8601 or `YYYY-MM-DD`)
|
||||
- After expiration: memory NOT returned in searches but remains in storage
|
||||
- Useful for time-sensitive info (events, temporary preferences, session state)
|
||||
- `client.update(memory_id, text="...")` replaces text
|
||||
- Batch: `client.batch_update([...])`
|
||||
|
||||
### Deletion
|
||||
- Single: `client.delete(memory_id)` — permanent, no recovery
|
||||
- Batch: `client.batch_delete([memory_ids])` — up to 1000
|
||||
- Bulk: `client.delete_all(user_id="alice")` — all memories for entity
|
||||
- `delete_all()` without filters raises error to prevent accidental data loss
|
||||
|
||||
### History tracking
|
||||
- `client.history(memory_id)` returns version timeline
|
||||
- Shows all changes: `{previous_value, new_value, action, timestamps}`
|
||||
- Useful for audit trails and debugging
|
||||
- Single: `client.delete(memory_id)`
|
||||
- Batch: `client.batch_delete([...])`
|
||||
- Bulk: `client.delete_all(filters={"user_id": "alice"})`
|
||||
|
||||
---
|
||||
|
||||
@@ -214,8 +167,6 @@ DELETE DELETE DELETE
|
||||
"categories": ["health", "preferences"],
|
||||
"created_at": "2025-03-12T12:34:56Z",
|
||||
"updated_at": "2025-03-12T12:34:56Z",
|
||||
"expiration_date": null,
|
||||
"immutable": false,
|
||||
"structured_attributes": {
|
||||
"day": 12, "month": 3, "year": 2025,
|
||||
"hour": 12, "minute": 34,
|
||||
@@ -239,8 +190,6 @@ DELETE DELETE DELETE
|
||||
| `categories` | array | Auto-assigned or custom category tags |
|
||||
| `created_at` | datetime | Creation timestamp |
|
||||
| `updated_at` | datetime | Last modification timestamp |
|
||||
| `expiration_date` | datetime | Auto-expiry date (stops retrieval, data persists) |
|
||||
| `immutable` | boolean | If true, prevents modification |
|
||||
| `structured_attributes` | object | Temporal breakdown for time-based queries |
|
||||
| `score` | float | Semantic similarity (search results only, 0-1) |
|
||||
|
||||
@@ -322,7 +271,7 @@ Mem0 supports three layers of memory, from shortest to longest lived:
|
||||
```python
|
||||
def chat(user_input: str, user_id: str, session_id: str) -> str:
|
||||
# 1. Retrieve user memories (long-term preferences)
|
||||
user_mems = mem0.search(user_input, user_id=user_id)
|
||||
user_mems = mem0.search(user_input, filters={"user_id": user_id})
|
||||
|
||||
# 2. Retrieve session memories (current task context)
|
||||
session_mems = mem0.search(user_input, filters={
|
||||
@@ -350,18 +299,13 @@ def chat(user_input: str, user_id: str, session_id: str) -> str:
|
||||
|
||||
| Operation | Typical Latency |
|
||||
|-----------|----------------|
|
||||
| Base vector search | ~100ms |
|
||||
| + keyword_search | +10ms |
|
||||
| Hybrid search (v3 default) | ~100-150ms |
|
||||
| + reranking | +150-200ms |
|
||||
| + filter_memories | +200-300ms |
|
||||
| Add (async, default) | < 50ms response, background processing |
|
||||
| Add (sync) | 500ms-2s depending on extraction complexity |
|
||||
| Graph operations | Slight overhead for large stores |
|
||||
| Add (async) | < 50ms response |
|
||||
|
||||
### Processing
|
||||
|
||||
- **Async mode (default):** Returns immediately, processes in background
|
||||
- **Sync mode:** Waits for full extraction + storage pipeline
|
||||
- **Async (default):** Returns immediately, processes in background
|
||||
- **Batch operations:** Up to 1000 memories per batch_update/batch_delete
|
||||
- **Webhooks:** Real-time notifications when async processing completes
|
||||
|
||||
|
||||
@@ -5,7 +5,7 @@ Additional platform capabilities beyond core CRUD operations.
|
||||
## Table of Contents
|
||||
|
||||
- [Advanced Retrieval](#advanced-retrieval)
|
||||
- [Graph Memory](#graph-memory)
|
||||
- [Entity Linking](#entity-linking)
|
||||
- [Custom Categories](#custom-categories)
|
||||
- [Custom Instructions](#custom-instructions)
|
||||
- [Criteria Retrieval](#criteria-retrieval)
|
||||
@@ -18,124 +18,58 @@ Additional platform capabilities beyond core CRUD operations.
|
||||
|
||||
## Advanced Retrieval
|
||||
|
||||
Three enhancement options for tuning search precision, recall, and latency.
|
||||
### Hybrid Search (v3 Default)
|
||||
|
||||
### Keyword Search (`keyword_search=True`)
|
||||
v3 uses multi-signal hybrid search combining:
|
||||
- **Semantic search** (vector similarity)
|
||||
- **BM25 keyword search** (normalized term matching)
|
||||
- **Entity matching** (entity graph boost)
|
||||
|
||||
Expands results to include memories with specific terms, names, and technical keywords.
|
||||
|
||||
- Latency: +10ms
|
||||
- Recall: Significantly increased
|
||||
- Best for: entity-heavy queries, comprehensive coverage
|
||||
This is automatic — no configuration needed.
|
||||
|
||||
### Reranking (`rerank=True`)
|
||||
|
||||
Deep semantic reordering of results — most relevant first.
|
||||
|
||||
- Latency: +150-200ms
|
||||
- Accuracy: Significantly improved
|
||||
- Default: `False` (was `True` in v2)
|
||||
- Best for: user-facing results, top-N precision
|
||||
|
||||
### Filter Memories (`filter_memories=True`)
|
||||
|
||||
Precision filtering — removes low-relevance results entirely.
|
||||
|
||||
- Latency: +200-300ms
|
||||
- Precision: Maximized
|
||||
- Best for: safety-critical applications, production systems
|
||||
|
||||
### Recommended Combinations
|
||||
|
||||
**Python:**
|
||||
```python
|
||||
# Fast & broad
|
||||
results = client.search(query, keyword_search=True, user_id="user123")
|
||||
|
||||
# Balanced (recommended for most apps)
|
||||
results = client.search(query, keyword_search=True, rerank=True, user_id="user123")
|
||||
|
||||
# High precision (critical apps)
|
||||
results = client.search(query, rerank=True, filter_memories=True, user_id="user123")
|
||||
results = client.search(query, filters={"user_id": "user123"}, rerank=True)
|
||||
```
|
||||
|
||||
**TypeScript:**
|
||||
```typescript
|
||||
const results = await client.search(query, {
|
||||
user_id: 'user123',
|
||||
keyword_search: true,
|
||||
filters: { user_id: 'user123' },
|
||||
rerank: true,
|
||||
});
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Graph Memory
|
||||
## Entity Linking
|
||||
|
||||
Entity-level knowledge graph that creates relationships between memories.
|
||||
v3 replaces graph memory with built-in entity linking. Entities (proper nouns, quoted text, compound noun phrases) are automatically extracted and linked across memories.
|
||||
|
||||
### How It Works
|
||||
|
||||
1. **Extraction**: LLM analyzes conversation and identifies entities and relationships
|
||||
2. **Storage**: Embeddings go to vector store; entity nodes and edges go to graph store
|
||||
3. **Retrieval**: Vector search returns semantic matches; graph relations are appended to results
|
||||
1. **Extraction**: During `add()`, entities are automatically extracted from memory text
|
||||
2. **Storage**: Entities are stored in a parallel collection (`{collection}_entities`)
|
||||
3. **Retrieval**: During `search()`, query entities are matched and used to boost relevant memories
|
||||
|
||||
Graph relations **augment** vector results without reordering them. Vector similarity always determines hit sequence.
|
||||
Entity linking is automatic — no configuration required. The boost is folded into the combined `score` on each result.
|
||||
|
||||
### Enabling Graph Memory
|
||||
### v2 Migration Note
|
||||
|
||||
**Per request:**
|
||||
```python
|
||||
client.add(messages, user_id="alice", enable_graph=True)
|
||||
client.search("query", user_id="alice", enable_graph=True)
|
||||
client.get_all(filters={"AND": [{"user_id": "alice"}]}, enable_graph=True)
|
||||
```
|
||||
If you were using `enable_graph=True` in v2:
|
||||
- Remove `enable_graph` from all API calls
|
||||
- Remove `graph_store` from OSS configuration
|
||||
- Entity relationships are now consumed through retrieval ranking, not exposed as a separate `relations` array
|
||||
|
||||
**Project-level (default for all operations):**
|
||||
```python
|
||||
client.project.update(enable_graph=True)
|
||||
```
|
||||
|
||||
```javascript
|
||||
await client.updateProject({ enable_graph: true });
|
||||
```
|
||||
|
||||
### Relation Structure
|
||||
|
||||
Each relation in the response contains:
|
||||
|
||||
| Field | Type | Description |
|
||||
|-------|------|-------------|
|
||||
| `source` | string | Source entity name |
|
||||
| `source_type` | string | Source entity type (e.g., "Person") |
|
||||
| `relationship` | string | Relationship label (e.g., "lives_in") |
|
||||
| `target` | string | Target entity name |
|
||||
| `target_type` | string | Target entity type (e.g., "City") |
|
||||
| `score` | number | Confidence score |
|
||||
|
||||
**Example:**
|
||||
```json
|
||||
{
|
||||
"relations": [
|
||||
{
|
||||
"source": "Joseph",
|
||||
"source_type": "Person",
|
||||
"relationship": "lives_in",
|
||||
"target": "Seattle",
|
||||
"target_type": "City",
|
||||
"score": 0.92
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
### Technical Notes
|
||||
|
||||
- Graph Memory adds processing time; see docs for current plan availability
|
||||
- Works optimally with rich conversation histories containing entity relationships
|
||||
- Best suited for long-running assistants tracking evolving information
|
||||
- Graph writes and reads toggle independently per request
|
||||
- Multi-agent context supported via `user_id`, `agent_id`, `run_id` scoping
|
||||
- Add operations are asynchronous; graph metadata may not be immediately available
|
||||
See the [v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) for details.
|
||||
|
||||
---
|
||||
|
||||
@@ -160,7 +94,7 @@ client.project.update(custom_categories=new_categories)
|
||||
```
|
||||
|
||||
```javascript
|
||||
await client.updateProject({ custom_categories: new_categories });
|
||||
await client.updateProject({ customCategories: newCategories });
|
||||
```
|
||||
|
||||
**Retrieve active categories:**
|
||||
@@ -185,7 +119,7 @@ client.project.update(custom_instructions="Your guidelines here...")
|
||||
```
|
||||
|
||||
```javascript
|
||||
await client.updateProject({ custom_instructions: "Your guidelines here..." });
|
||||
await client.updateProject({ customInstructions: "Your guidelines here..." });
|
||||
```
|
||||
|
||||
### Template Structure
|
||||
@@ -229,7 +163,7 @@ client.project.update(retrieval_criteria=retrieval_criteria)
|
||||
|
||||
```typescript
|
||||
await client.updateProject({
|
||||
retrieval_criteria: [
|
||||
retrievalCriteria: [
|
||||
{ name: 'joy', description: 'Positive emotions', weight: 3 },
|
||||
{ name: 'urgency', description: 'Time-sensitive items', weight: 4 },
|
||||
],
|
||||
@@ -281,7 +215,7 @@ for item in feedback_data:
|
||||
```typescript
|
||||
await client.feedback('mem-123', {
|
||||
feedback: 'POSITIVE',
|
||||
feedback_reason: 'Accurately captured dietary preference',
|
||||
feedbackReason: 'Accurately captured dietary preference',
|
||||
});
|
||||
```
|
||||
|
||||
@@ -349,23 +283,18 @@ Use the `name` field in messages to identify speakers. Mem0 maps names to entity
|
||||
|
||||
## MCP Integration
|
||||
|
||||
Model Context Protocol integration enables AI clients (Claude Desktop, Cursor, custom agents) to manage Mem0 memory autonomously.
|
||||
Model Context Protocol integration enables AI clients (Claude, Claude Code, Cursor, Windsurf, VS Code, OpenCode) to manage Mem0 memory autonomously.
|
||||
|
||||
### Configuration
|
||||
### Setup
|
||||
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"mem0": {
|
||||
"command": "uvx",
|
||||
"args": ["mem0-mcp-server"],
|
||||
"env": {
|
||||
"MEM0_API_KEY": "m0-your-api-key",
|
||||
"MEM0_DEFAULT_USER_ID": "your-user-id"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
Add Mem0 MCP to your clients with a single command:
|
||||
|
||||
```bash
|
||||
npx mcp-add \
|
||||
--name mem0-mcp \
|
||||
--type http \
|
||||
--url "https://mcp.mem0.ai/mcp" \
|
||||
--clients "claude,claude code,cursor,windsurf,vscode,opencode"
|
||||
```
|
||||
|
||||
### Available MCP Tools
|
||||
@@ -377,7 +306,7 @@ The MCP server exposes 9 memory tools that AI agents can use autonomously:
|
||||
|
||||
### How It Works
|
||||
|
||||
1. Configure the MCP server in your AI client
|
||||
1. Add Mem0 MCP to your AI client using the setup command above
|
||||
2. The agent autonomously decides when to store/retrieve memories
|
||||
3. No manual API calls needed — the agent manages memory as part of its reasoning
|
||||
|
||||
|
||||
@@ -27,7 +27,7 @@ from langchain_core.messages import SystemMessage, HumanMessage
|
||||
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
|
||||
from mem0 import MemoryClient
|
||||
|
||||
llm = ChatOpenAI(model="gpt-4.1-nano-2025-04-14")
|
||||
llm = ChatOpenAI(model="gpt-5-mini")
|
||||
mem0 = MemoryClient()
|
||||
|
||||
prompt = ChatPromptTemplate.from_messages([
|
||||
@@ -38,7 +38,7 @@ prompt = ChatPromptTemplate.from_messages([
|
||||
|
||||
def retrieve_context(query: str, user_id: str):
|
||||
"""Retrieve relevant memories from Mem0"""
|
||||
memories = mem0.search(query, user_id=user_id)
|
||||
memories = mem0.search(query, filters={"user_id": user_id})
|
||||
memory_list = memories['results']
|
||||
serialized = ' '.join([m["memory"] for m in memory_list])
|
||||
return [
|
||||
@@ -116,73 +116,24 @@ result = crew.kickoff()
|
||||
|
||||
## Vercel AI SDK
|
||||
|
||||
Source: [docs.mem0.ai/integrations/vercel-ai-sdk](https://docs.mem0.ai/integrations/vercel-ai-sdk)
|
||||
> **Dedicated skill available.** For comprehensive Vercel AI SDK documentation, see the [mem0-vercel-ai-sdk skill](https://github.com/mem0ai/mem0/tree/main/skills/mem0-vercel-ai-sdk).
|
||||
|
||||
Install: `npm install @mem0/vercel-ai-provider`
|
||||
|
||||
### Basic Text Generation with Memory
|
||||
Quick example (wrapped model with automatic memory):
|
||||
|
||||
```typescript
|
||||
import { generateText } from "ai";
|
||||
import { createMem0 } from "@mem0/vercel-ai-provider";
|
||||
|
||||
const mem0 = createMem0({
|
||||
provider: "openai",
|
||||
mem0ApiKey: "m0-xxx",
|
||||
apiKey: "openai-api-key",
|
||||
});
|
||||
|
||||
const { text } = await generateText({
|
||||
model: mem0("gpt-4-turbo", { user_id: "borat" }),
|
||||
prompt: "Suggest me a good car to buy!",
|
||||
});
|
||||
```
|
||||
|
||||
### Streaming with Memory
|
||||
|
||||
```typescript
|
||||
import { streamText } from "ai";
|
||||
import { createMem0 } from "@mem0/vercel-ai-provider";
|
||||
|
||||
const mem0 = createMem0();
|
||||
|
||||
const { textStream } = streamText({
|
||||
model: mem0("gpt-4-turbo", { user_id: "borat" }),
|
||||
const { text } = await generateText({
|
||||
model: mem0("gpt-5-mini", { user_id: "borat" }),
|
||||
prompt: "Suggest me a good car to buy!",
|
||||
});
|
||||
|
||||
for await (const textPart of textStream) {
|
||||
process.stdout.write(textPart);
|
||||
}
|
||||
```
|
||||
|
||||
### Using Memory Utilities Standalone
|
||||
|
||||
```typescript
|
||||
import { openai } from "@ai-sdk/openai";
|
||||
import { generateText } from "ai";
|
||||
import { retrieveMemories, addMemories } from "@mem0/vercel-ai-provider";
|
||||
|
||||
// Retrieve memories and inject into any provider
|
||||
const prompt = "Suggest me a good car to buy.";
|
||||
const memories = await retrieveMemories(prompt, { user_id: "borat", mem0ApiKey: "m0-xxx" });
|
||||
|
||||
const { text } = await generateText({
|
||||
model: openai("gpt-4-turbo"),
|
||||
prompt: prompt,
|
||||
system: memories,
|
||||
});
|
||||
|
||||
// Store new memories
|
||||
await addMemories(
|
||||
[{ role: "user", content: [{ type: "text", text: "I love red cars." }] }],
|
||||
{ user_id: "borat", mem0ApiKey: "m0-xxx" }
|
||||
);
|
||||
```
|
||||
|
||||
### Supported Providers
|
||||
|
||||
`openai`, `anthropic`, `google`, `groq`
|
||||
Supported providers: `openai`, `anthropic`, `google`, `groq`, `cohere`
|
||||
|
||||
---
|
||||
|
||||
@@ -199,7 +150,7 @@ mem0 = MemoryClient()
|
||||
@function_tool
|
||||
def search_memory(query: str, user_id: str) -> str:
|
||||
"""Search through past conversations and memories"""
|
||||
memories = mem0.search(query, user_id=user_id, top_k=3)
|
||||
memories = mem0.search(query, filters={"user_id": user_id}, top_k=3)
|
||||
if memories and memories.get('results'):
|
||||
return "\n".join([f"- {mem['memory']}" for mem in memories['results']])
|
||||
return "No relevant memories found."
|
||||
@@ -216,7 +167,7 @@ agent = Agent(
|
||||
Use search_memory to recall past conversations.
|
||||
Use save_memory to store important information.""",
|
||||
tools=[search_memory, save_memory],
|
||||
model="gpt-4.1-nano-2025-04-14"
|
||||
model="gpt-5-mini"
|
||||
)
|
||||
|
||||
result = Runner.run_sync(agent, "I love Italian food and I'm planning a trip to Rome next month")
|
||||
@@ -232,21 +183,21 @@ travel_agent = Agent(
|
||||
name="Travel Planner",
|
||||
instructions="You are a travel planning specialist. Use search_memory and save_memory tools.",
|
||||
tools=[search_memory, save_memory],
|
||||
model="gpt-4.1-nano-2025-04-14"
|
||||
model="gpt-5-mini"
|
||||
)
|
||||
|
||||
health_agent = Agent(
|
||||
name="Health Advisor",
|
||||
instructions="You are a health and wellness advisor. Use search_memory and save_memory tools.",
|
||||
tools=[search_memory, save_memory],
|
||||
model="gpt-4.1-nano-2025-04-14"
|
||||
model="gpt-5-mini"
|
||||
)
|
||||
|
||||
triage_agent = Agent(
|
||||
name="Personal Assistant",
|
||||
instructions="""Route travel questions to Travel Planner, health questions to Health Advisor.""",
|
||||
handoffs=[travel_agent, health_agent],
|
||||
model="gpt-4.1-nano-2025-04-14"
|
||||
model="gpt-5-mini"
|
||||
)
|
||||
|
||||
result = Runner.run_sync(triage_agent, "Plan a healthy meal for my Italy trip")
|
||||
@@ -303,7 +254,7 @@ from langchain_openai import ChatOpenAI
|
||||
from mem0 import MemoryClient
|
||||
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
|
||||
|
||||
llm = ChatOpenAI(model="gpt-4")
|
||||
llm = ChatOpenAI(model="gpt-5-mini")
|
||||
mem0 = MemoryClient()
|
||||
|
||||
class State(TypedDict):
|
||||
@@ -315,7 +266,7 @@ def chatbot(state: State):
|
||||
user_id = state["mem0_user_id"]
|
||||
|
||||
# Retrieve relevant memories
|
||||
memories = mem0.search(messages[-1].content, user_id=user_id)
|
||||
memories = mem0.search(messages[-1].content, filters={"user_id": user_id})
|
||||
context = "Relevant context:\n"
|
||||
for memory in memories["results"]:
|
||||
context += f"- {memory['memory']}\n"
|
||||
@@ -368,7 +319,7 @@ memory = Mem0Memory.from_client(
|
||||
from llama_index.core.agent import FunctionCallingAgent
|
||||
from llama_index.llms.openai import OpenAI
|
||||
|
||||
llm = OpenAI(model="gpt-4")
|
||||
llm = OpenAI(model="gpt-5-mini")
|
||||
agent = FunctionCallingAgent.from_tools(
|
||||
tools=[],
|
||||
llm=llm,
|
||||
@@ -401,14 +352,14 @@ USER_ID = "alice"
|
||||
|
||||
agent = ConversableAgent(
|
||||
"chatbot",
|
||||
llm_config={"config_list": [{"model": "gpt-4", "api_key": os.environ["OPENAI_API_KEY"]}]},
|
||||
llm_config={"config_list": [{"model": "gpt-5-mini", "api_key": os.environ["OPENAI_API_KEY"]}]},
|
||||
code_execution_config=False,
|
||||
human_input_mode="NEVER",
|
||||
)
|
||||
|
||||
def get_context_aware_response(question: str) -> str:
|
||||
# Retrieve memories for context
|
||||
relevant_memories = memory_client.search(question, user_id=USER_ID)
|
||||
relevant_memories = memory_client.search(question, filters={"user_id": USER_ID})
|
||||
context = "\n".join([m["memory"] for m in relevant_memories.get("results", [])])
|
||||
|
||||
prompt = f"""Answer considering previous interactions:
|
||||
|
||||
@@ -27,7 +27,7 @@ messages = [
|
||||
client.add(messages, user_id="user123")
|
||||
|
||||
# Search memories
|
||||
results = client.search("What are my dietary restrictions?", user_id="user123")
|
||||
results = client.search("What are my dietary restrictions?", filters={"user_id": "user123"})
|
||||
print(results)
|
||||
```
|
||||
|
||||
@@ -39,7 +39,7 @@ from mem0 import AsyncMemoryClient
|
||||
client = AsyncMemoryClient(api_key="your-api-key")
|
||||
|
||||
await client.add(messages, user_id="user123")
|
||||
results = await client.search("query", user_id="user123")
|
||||
results = await client.search("query", filters={"user_id": "user123"})
|
||||
```
|
||||
|
||||
## TypeScript / JavaScript Setup
|
||||
@@ -59,11 +59,11 @@ const messages = [
|
||||
{"role": "user", "content": "I'm a vegetarian and allergic to nuts."},
|
||||
{"role": "assistant", "content": "Got it! I'll remember your dietary preferences."}
|
||||
];
|
||||
await client.add(messages, { user_id: "user123" });
|
||||
await client.add(messages, { userId: "user123" });
|
||||
|
||||
// Search memories
|
||||
const results = await client.search("What are my dietary restrictions?", {
|
||||
user_id: "user123"
|
||||
filters: { user_id: "user123" }
|
||||
});
|
||||
console.log(results);
|
||||
```
|
||||
@@ -86,7 +86,7 @@ curl -X POST https://api.mem0.ai/v1/memories/ \
|
||||
}'
|
||||
|
||||
# Search memories
|
||||
curl -X POST https://api.mem0.ai/v2/memories/search/ \
|
||||
curl -X POST https://api.mem0.ai/v3/memories/search/ \
|
||||
-H "Authorization: Token $MEM0_API_KEY" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
|
||||
@@ -2,6 +2,8 @@
|
||||
|
||||
Complete SDK reference for Python and TypeScript. All methods use `MemoryClient` (Platform API).
|
||||
|
||||
> **For language-specific deep references (including OSS):** See [client/python.md](../client/python.md) and [client/node.md](../client/node.md). For Python vs TypeScript differences: [client/differences.md](../client/differences.md).
|
||||
|
||||
## Initialization
|
||||
|
||||
**Python:**
|
||||
@@ -38,16 +40,12 @@ client.add(messages, user_id="alice")
|
||||
|
||||
# With metadata
|
||||
client.add(messages, user_id="alice", metadata={"source": "onboarding"})
|
||||
|
||||
# With graph memory
|
||||
client.add(messages, user_id="alice", enable_graph=True)
|
||||
```
|
||||
|
||||
**TypeScript:**
|
||||
```typescript
|
||||
await client.add(messages, { user_id: "alice" });
|
||||
await client.add(messages, { user_id: "alice", metadata: { source: "onboarding" } });
|
||||
await client.add(messages, { user_id: "alice", enable_graph: true });
|
||||
await client.add(messages, { userId: "alice" });
|
||||
await client.add(messages, { userId: "alice", metadata: { source: "onboarding" } });
|
||||
```
|
||||
|
||||
### Parameters
|
||||
@@ -59,32 +57,14 @@ await client.add(messages, { user_id: "alice", enable_graph: true });
|
||||
| `agent_id` | string | Agent identifier |
|
||||
| `run_id` | string | Session identifier |
|
||||
| `metadata` | object | Custom key-value pairs |
|
||||
| `enable_graph` | boolean | Activate knowledge graph |
|
||||
| `infer` | boolean | If `false`, store raw text without inference (default: `true`) |
|
||||
| `immutable` | boolean | Prevents modification after creation |
|
||||
| `expiration_date` | string | Auto-expiry date (`YYYY-MM-DD`) |
|
||||
| `includes` | string | Preference filters for inclusion |
|
||||
| `excludes` | string | Preference filters for exclusion |
|
||||
| `async_mode` | boolean | Async processing (default: `true`). Set `false` to wait |
|
||||
|
||||
### Advanced Add Options
|
||||
|
||||
```python
|
||||
# Immutable -- cannot be modified or overwritten
|
||||
client.add(messages, user_id="alice", immutable=True)
|
||||
|
||||
# Expiring memory
|
||||
client.add(messages, user_id="alice", expiration_date="2025-12-31")
|
||||
|
||||
# Selective extraction
|
||||
client.add(messages, user_id="alice", includes="dietary preferences", excludes="payment info")
|
||||
|
||||
# Agent + session scoping
|
||||
client.add(messages, user_id="alice", agent_id="nutrition-agent", run_id="session-456")
|
||||
|
||||
# Synchronous processing (wait for completion)
|
||||
client.add(messages, user_id="alice", async_mode=False)
|
||||
|
||||
# Raw text -- skip LLM inference
|
||||
client.add(
|
||||
[{"role": "user", "content": "User prefers dark mode."}],
|
||||
@@ -99,7 +79,7 @@ client.add(
|
||||
|
||||
**Python:**
|
||||
```python
|
||||
results = client.search("dietary preferences?", user_id="alice")
|
||||
results = client.search("dietary preferences?", filters={"user_id": "alice"})
|
||||
|
||||
# With filters and reranking
|
||||
results = client.search(
|
||||
@@ -109,20 +89,14 @@ results = client.search(
|
||||
rerank=True,
|
||||
threshold=0.5
|
||||
)
|
||||
|
||||
# With graph relations
|
||||
results = client.search("colleagues", user_id="alice", enable_graph=True)
|
||||
|
||||
# Keyword search
|
||||
results = client.search("vegetarian", user_id="alice", keyword_search=True)
|
||||
```
|
||||
|
||||
**TypeScript:**
|
||||
```typescript
|
||||
const results = await client.search("dietary preferences", { user_id: "alice" });
|
||||
const results = await client.search("dietary preferences", { filters: { user_id: "alice" } });
|
||||
const results = await client.search("work experience", {
|
||||
filters: { AND: [{ user_id: "alice" }, { categories: { contains: "professional_details" } }] },
|
||||
top_k: 5,
|
||||
topK: 5,
|
||||
rerank: true,
|
||||
});
|
||||
```
|
||||
@@ -132,19 +106,17 @@ const results = await client.search("work experience", {
|
||||
| Name | Type | Description |
|
||||
|------|------|-------------|
|
||||
| `query` | string | Natural language search query |
|
||||
| `user_id` | string | Filter by user |
|
||||
| `filters` | object | V2 filter object (AND/OR operators) |
|
||||
| `top_k` | number | Number of results (default: 10) |
|
||||
| `rerank` | boolean | Enable reranking for better relevance |
|
||||
| `threshold` | number | Minimum similarity score (default: 0.3) |
|
||||
| `keyword_search` | boolean | Use keyword-based search |
|
||||
| `enable_graph` | boolean | Include graph relations |
|
||||
| `filters` | object | Filter object (AND/OR operators). Use `{"user_id": "..."}` to filter by user |
|
||||
| `top_k` | number | Number of results (default: 10 for Platform) |
|
||||
| `rerank` | boolean | Enable reranking for better relevance (default: `false`) |
|
||||
| `threshold` | number | Minimum similarity score (default: 0.1) |
|
||||
|
||||
### Common Filter Patterns
|
||||
|
||||
**Python:**
|
||||
```python
|
||||
# Single user (shorthand)
|
||||
client.search("query", user_id="alice")
|
||||
# Single user filter
|
||||
filters={"user_id": "alice"}
|
||||
|
||||
# OR across agents
|
||||
filters={"OR": [{"user_id": "alice"}, {"agent_id": {"in": ["travel-agent", "sports-agent"]}}]}
|
||||
@@ -177,6 +149,21 @@ filters={"AND": [
|
||||
]}
|
||||
```
|
||||
|
||||
**TypeScript:**
|
||||
```typescript
|
||||
// Single user filter
|
||||
filters: { user_id: "alice" }
|
||||
|
||||
// OR across agents
|
||||
filters: { OR: [{ user_id: "alice" }, { agent_id: { in: ["travel-agent", "sports-agent"] } }] }
|
||||
|
||||
// Category filtering (partial match)
|
||||
filters: { AND: [{ user_id: "alice" }, { categories: { contains: "finance" } }] }
|
||||
|
||||
// Category filtering (exact match)
|
||||
filters: { AND: [{ user_id: "alice" }, { categories: { in: ["personal_information"] } }] }
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## get() / getAll() -- Retrieve Memories
|
||||
@@ -187,7 +174,7 @@ filters={"AND": [
|
||||
memory = client.get(memory_id="ea925981-...")
|
||||
|
||||
# All memories for a user
|
||||
memories = client.get_all(filters={"AND": [{"user_id": "alice"}]})
|
||||
memories = client.get_all(filters={"user_id": "alice"})
|
||||
|
||||
# With date range
|
||||
memories = client.get_all(
|
||||
@@ -196,15 +183,12 @@ memories = client.get_all(
|
||||
{"created_at": {"gte": "2024-07-01", "lte": "2024-07-31"}}
|
||||
]}
|
||||
)
|
||||
|
||||
# With graph data
|
||||
memories = client.get_all(filters={"AND": [{"user_id": "alice"}]}, enable_graph=True)
|
||||
```
|
||||
|
||||
**TypeScript:**
|
||||
```typescript
|
||||
const memory = await client.get("ea925981-...");
|
||||
const memories = await client.getAll({ filters: { AND: [{ user_id: "alice" }] } });
|
||||
const memories = await client.getAll({ filters: { user_id: "alice" } });
|
||||
```
|
||||
|
||||
**Note:** `get_all` requires at least one of `user_id`, `agent_id`, `app_id`, or `run_id` in filters.
|
||||
@@ -224,8 +208,6 @@ client.update(memory_id="ea925981-...", text="Updated", metadata={"verified": Tr
|
||||
await client.update("ea925981-...", { text: "Updated: vegan since 2024" });
|
||||
```
|
||||
|
||||
Cannot update immutable memories.
|
||||
|
||||
---
|
||||
|
||||
## delete() / deleteAll() -- Remove Memories
|
||||
@@ -239,7 +221,7 @@ client.delete_all(user_id="alice") # Irreversible bulk delete
|
||||
**TypeScript:**
|
||||
```typescript
|
||||
await client.delete("ea925981-...");
|
||||
await client.deleteAll({ user_id: "alice" });
|
||||
await client.deleteAll({ userId: "alice" });
|
||||
```
|
||||
|
||||
---
|
||||
@@ -299,10 +281,73 @@ data = client.get_memory_export(memory_export_id=export["id"])
|
||||
2. **SQL operators rejected** -- use `gte`, `lt`, etc. Not `>=`, `<`.
|
||||
3. **Metadata filtering is limited** -- only top-level keys with `eq`, `contains`, `ne`.
|
||||
4. **Wildcard `*` excludes null** -- only matches non-null values.
|
||||
5. **Default threshold is 0.3** -- increase for stricter matching.
|
||||
5. **Default threshold is 0.1** -- increase for stricter matching.
|
||||
6. **Async processing** -- memories process asynchronously. Wait 2-3s after `add()` before searching.
|
||||
7. **Immutable memories** -- cannot be updated or deleted once created.
|
||||
|
||||
## Naming Conventions
|
||||
|
||||
Python uses `snake_case` (`user_id`, `memory_id`, `get_all`). TypeScript uses `camelCase` for methods (`getAll`, `deleteAll`, `batchUpdate`) but `snake_case` for API parameters (`user_id`, `agent_id`).
|
||||
Python uses `snake_case` everywhere (`user_id`, `memory_id`, `get_all`). TypeScript uses `camelCase` for methods (`getAll`, `deleteAll`, `batchUpdate`) and top-level parameters (`userId`, `topK`, `pageSize`), but filter keys use `snake_case` (`user_id`, `agent_id`).
|
||||
|
||||
---
|
||||
|
||||
## v2 to v3 Migration
|
||||
|
||||
### Breaking Changes in v3
|
||||
|
||||
**1. Entity IDs in search() and getAll()**
|
||||
|
||||
v3 requires entity IDs (`user_id`, `agent_id`, `run_id`) inside `filters` instead of as top-level parameters:
|
||||
|
||||
```python
|
||||
# v2 (deprecated)
|
||||
client.search("query", user_id="alice")
|
||||
client.get_all(user_id="alice")
|
||||
|
||||
# v3
|
||||
client.search("query", filters={"user_id": "alice"})
|
||||
client.get_all(filters={"user_id": "alice"})
|
||||
```
|
||||
|
||||
```typescript
|
||||
// v2 (deprecated)
|
||||
await client.search("query", { user_id: "alice" });
|
||||
await client.getAll({ user_id: "alice" });
|
||||
|
||||
// v3
|
||||
await client.search("query", { filters: { user_id: "alice" } });
|
||||
await client.getAll({ filters: { user_id: "alice" } });
|
||||
```
|
||||
|
||||
**2. TypeScript Parameter Naming**
|
||||
|
||||
v3 TypeScript uses camelCase for all parameters:
|
||||
|
||||
| v2 | v3 |
|
||||
|----|-----|
|
||||
| `user_id` | `userId` |
|
||||
| `agent_id` | `agentId` |
|
||||
| `run_id` | `runId` |
|
||||
| `top_k` | `topK` |
|
||||
| `page_size` | `pageSize` |
|
||||
|
||||
**3. Default Values Changed**
|
||||
|
||||
| Parameter | v2 Default | v3 Default |
|
||||
|-----------|------------|------------|
|
||||
| `threshold` | 0.3 | 0.1 |
|
||||
| `rerank` | (not specified) | `false` |
|
||||
|
||||
**4. Removed Parameters**
|
||||
|
||||
The following parameters are no longer supported:
|
||||
|
||||
| Parameter | Status |
|
||||
|-----------|--------|
|
||||
| `enable_graph` | Removed from add/search/getAll |
|
||||
| `keyword_search` | Removed from search |
|
||||
| `filter_memories` | Removed |
|
||||
| `immutable` | Removed from add |
|
||||
| `expiration_date` | Removed from add |
|
||||
| `includes` | Removed from add |
|
||||
| `excludes` | Removed from add |
|
||||
| `async_mode` | Removed from add |
|
||||
|
||||
@@ -39,7 +39,7 @@ Use these known facts about the user to personalize your response:
|
||||
{context if context else 'No prior context yet.'}"""
|
||||
|
||||
response = openai_client.chat.completions.create(
|
||||
model="gpt-4.1-nano-2025-04-14",
|
||||
model="gpt-5-mini",
|
||||
messages=[
|
||||
{"role": "system", "content": system_prompt},
|
||||
{"role": "user", "content": user_input},
|
||||
@@ -72,14 +72,14 @@ const openai = new OpenAI();
|
||||
|
||||
async function chat(userInput: string, userId: string): Promise<string> {
|
||||
// 1. Retrieve relevant memories
|
||||
const memories = await mem0.search(userInput, { user_id: userId });
|
||||
const memories = await mem0.search(userInput, { filters: { user_id: userId } });
|
||||
const context = memories.results
|
||||
?.map((m: any) => `- ${m.memory}`)
|
||||
.join('\n') || 'No prior context yet.';
|
||||
|
||||
// 2. Generate response with memory context
|
||||
const response = await openai.chat.completions.create({
|
||||
model: 'gpt-4.1-nano-2025-04-14',
|
||||
model: 'gpt-5-mini',
|
||||
messages: [
|
||||
{ role: 'system', content: `You are Ray, a personal fitness coach.\nUser context:\n${context}` },
|
||||
{ role: 'user', content: userInput },
|
||||
@@ -90,7 +90,7 @@ async function chat(userInput: string, userId: string): Promise<string> {
|
||||
// 3. Store interaction
|
||||
await mem0.add(
|
||||
[{ role: 'user', content: userInput }, { role: 'assistant', content: reply }],
|
||||
{ user_id: userId }
|
||||
{ userId: userId }
|
||||
);
|
||||
return reply;
|
||||
}
|
||||
@@ -182,7 +182,7 @@ await client.updateProject({
|
||||
async function logInteraction(userId: string, message: string, priority = 'normal') {
|
||||
await client.add(
|
||||
[{ role: 'user', content: message }],
|
||||
{ user_id: userId, metadata: { priority, source: 'support_chat' } }
|
||||
{ userId: userId, metadata: { priority, source: 'support_chat' } }
|
||||
);
|
||||
}
|
||||
|
||||
@@ -230,7 +230,7 @@ def consult(user_id: str, question: str) -> str:
|
||||
context = "\n".join([f"- {m['memory']}" for m in memories.get("results", [])])
|
||||
|
||||
response = openai_client.chat.completions.create(
|
||||
model="gpt-4.1-nano-2025-04-14",
|
||||
model="gpt-5-mini",
|
||||
messages=[
|
||||
{"role": "system", "content": f"You are a health coach. Patient context:\n{context}"},
|
||||
{"role": "user", "content": question},
|
||||
@@ -264,20 +264,20 @@ const openai = new OpenAI();
|
||||
async function savePatientInfo(userId: string, info: string) {
|
||||
await mem0.add(
|
||||
[{ role: 'user', content: info }],
|
||||
{ user_id: userId, run_id: 'healthcare_session', metadata: { type: 'patient_information' } }
|
||||
{ userId: userId, runId: 'healthcare_session', metadata: { type: 'patient_information' } }
|
||||
);
|
||||
}
|
||||
|
||||
async function consult(userId: string, question: string): Promise<string> {
|
||||
const memories = await mem0.search(question, {
|
||||
user_id: userId,
|
||||
top_k: 5,
|
||||
filters: { user_id: userId },
|
||||
topK: 5,
|
||||
threshold: 0.7,
|
||||
});
|
||||
const context = memories.results?.map((m: any) => `- ${m.memory}`).join('\n') || '';
|
||||
|
||||
const response = await openai.chat.completions.create({
|
||||
model: 'gpt-4.1-nano-2025-04-14',
|
||||
model: 'gpt-5-mini',
|
||||
messages: [
|
||||
{ role: 'system', content: `You are a health coach. Patient context:\n${context}` },
|
||||
{ role: 'user', content: question },
|
||||
@@ -287,7 +287,7 @@ async function consult(userId: string, question: string): Promise<string> {
|
||||
|
||||
await mem0.add(
|
||||
[{ role: 'user', content: question }, { role: 'assistant', content: reply }],
|
||||
{ user_id: userId, run_id: 'healthcare_session' }
|
||||
{ userId: userId, runId: 'healthcare_session' }
|
||||
);
|
||||
return reply;
|
||||
}
|
||||
@@ -333,7 +333,7 @@ def draft_content(user_id: str, topic: str) -> str:
|
||||
style_context = "\n".join([f"- {m['memory']}" for m in prefs.get("results", [])])
|
||||
|
||||
response = openai_client.chat.completions.create(
|
||||
model="gpt-4.1-nano-2025-04-14",
|
||||
model="gpt-5-mini",
|
||||
messages=[
|
||||
{"role": "system", "content": f"Write content matching these style preferences:\n{style_context}"},
|
||||
{"role": "user", "content": f"Write a blog post about: {topic}"},
|
||||
@@ -359,7 +359,7 @@ const openai = new OpenAI();
|
||||
async function storePreferences(userId: string, preferences: string) {
|
||||
await mem0.add(
|
||||
[{ role: 'user', content: preferences }],
|
||||
{ user_id: userId, run_id: 'editing_session', metadata: { type: 'preferences' } }
|
||||
{ userId: userId, runId: 'editing_session', metadata: { type: 'preferences' } }
|
||||
);
|
||||
}
|
||||
|
||||
@@ -370,7 +370,7 @@ async function draftContent(userId: string, topic: string): Promise<string> {
|
||||
const styleContext = prefs.results?.map((m: any) => `- ${m.memory}`).join('\n') || '';
|
||||
|
||||
const response = await openai.chat.completions.create({
|
||||
model: 'gpt-4.1-nano-2025-04-14',
|
||||
model: 'gpt-5-mini',
|
||||
messages: [
|
||||
{ role: 'system', content: `Write content matching these preferences:\n${styleContext}` },
|
||||
{ role: 'user', content: `Write a blog post about: ${topic}` },
|
||||
@@ -465,10 +465,10 @@ async function storeScopedMemory(
|
||||
userId: string, agentId: string, runId: string, appId: string
|
||||
) {
|
||||
await client.add(messages, {
|
||||
user_id: userId,
|
||||
agent_id: agentId,
|
||||
run_id: runId,
|
||||
app_id: appId,
|
||||
userId: userId,
|
||||
agentId: agentId,
|
||||
runId: runId,
|
||||
appId: appId,
|
||||
});
|
||||
}
|
||||
|
||||
@@ -520,7 +520,7 @@ def personalized_search(user_id: str, query: str, search_results: list) -> str:
|
||||
user_context = "\n".join([f"- {m['memory']}" for m in memories.get("results", [])])
|
||||
|
||||
response = openai_client.chat.completions.create(
|
||||
model="gpt-4.1-nano-2025-04-14",
|
||||
model="gpt-5-mini",
|
||||
messages=[
|
||||
{"role": "system", "content": f"Personalize search results using user context:\n{user_context}"},
|
||||
{"role": "user", "content": f"Query: {query}\n\nSearch results:\n{search_results}"},
|
||||
@@ -551,11 +551,11 @@ const mem0 = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! });
|
||||
const openai = new OpenAI();
|
||||
|
||||
async function personalizedSearch(userId: string, query: string, searchResults: string[]): Promise<string> {
|
||||
const memories = await mem0.search(query, { user_id: userId, top_k: 5 });
|
||||
const memories = await mem0.search(query, { filters: { user_id: userId }, topK: 5 });
|
||||
const context = memories.results?.map((m: any) => `- ${m.memory}`).join('\n') || '';
|
||||
|
||||
const response = await openai.chat.completions.create({
|
||||
model: 'gpt-4.1-nano-2025-04-14',
|
||||
model: 'gpt-5-mini',
|
||||
messages: [
|
||||
{ role: 'system', content: `Personalize results using user context:\n${context}` },
|
||||
{ role: 'user', content: `Query: ${query}\nResults: ${searchResults.join(', ')}` },
|
||||
@@ -563,7 +563,7 @@ async function personalizedSearch(userId: string, query: string, searchResults:
|
||||
});
|
||||
const reply = response.choices[0].message.content!;
|
||||
|
||||
await mem0.add([{ role: 'user', content: query }], { user_id: userId });
|
||||
await mem0.add([{ role: 'user', content: query }], { userId: userId });
|
||||
return reply;
|
||||
}
|
||||
```
|
||||
@@ -631,14 +631,14 @@ const client = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! });
|
||||
async function storeEmail(userId: string, sender: string, subject: string, body: string, date: string) {
|
||||
await client.add(
|
||||
[{ role: 'user', content: `Email from ${sender}: ${subject}\n\n${body}` }],
|
||||
{ user_id: userId, metadata: { email_type: 'incoming', sender, subject, date } }
|
||||
{ userId: userId, metadata: { email_type: 'incoming', sender, subject, date } }
|
||||
);
|
||||
}
|
||||
|
||||
async function searchEmails(userId: string, query: string) {
|
||||
return client.search(query, {
|
||||
filters: { AND: [{ user_id: userId }, { categories: { contains: 'email' } }] },
|
||||
top_k: 10,
|
||||
topK: 10,
|
||||
});
|
||||
}
|
||||
```
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "mem0ai",
|
||||
"version": "3.0.1",
|
||||
"version": "3.0.2",
|
||||
"description": "The Memory Layer For Your AI Apps",
|
||||
"main": "./dist/index.js",
|
||||
"module": "./dist/index.mjs",
|
||||
|
||||
+1
-1
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
|
||||
|
||||
[project]
|
||||
name = "mem0ai"
|
||||
version = "2.0.0"
|
||||
version = "2.0.1"
|
||||
description = "Long-term memory for AI Agents"
|
||||
authors = [
|
||||
{ name = "Mem0", email = "support@mem0.ai" }
|
||||
|
||||
Reference in New Issue
Block a user