telemem 1.1.0

High-performance drop-in replacement of Mem0

TeleMem: Building Long-Term and Multimodal Memory for Agentic AI

If you find this project helpful, please give us a ⭐️ on GitHub for the latest update.

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English | 简体中文

📄 Awesome-Agent-Memory →

TeleMem is an agent memory management layer that can be used as a drop-in replacement for Mem0 with one line of code (import vendor.TeleMem as mem0), deeply optimized for complex scenarios involving multi-turn dialogues, character modeling, long-term information storage, and semantic retrieval.

Through its unique context-aware enhancement mechanism, TeleMem provides conversational AI with core infrastructure offering higher accuracy, faster performance, and stronger character memory capabilities.

Building upon this foundation, TeleMem implements video understanding, multimodal reasoning, and visual question answering capabilities. Through a complete pipeline of video frame extraction, caption generation, and vector database construction, AI Agents can effortlessly store, retrieve, and reason over video content just like handling text memories.

The ultimate goal of the TeleMem project is to use an agent's hindsight to improve its foresignt.

TeleMem, where memory lives on and intelligence grows strong.

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📢 Latest Updates

• [2025-12-31] 🎉 TeleMem v1.1.0 Released!
• [2025-12-05] 🎉 TeleMem v1.0.0 Released!

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🔥 Research Highlights

• Significantly improved memory accuracy: Achieved 86.33% accuracy on the ZH-4O Chinese multi-character long-dialogue benchmark, 19% higher than Mem0.
• Doubled speed performance: Millisecond-level semantic retrieval enabled by efficient buffering and batch writing.
• Greatly reduced token cost: Optimized token usage delivers the same performance with significantly lower LLM overhead.
• Precise character memory preservation: Automatically builds independent memory profiles for each character, eliminating confusion.
• Automated Video Processing Pipeline: From raw video → frame extraction → caption generation → vector database, fully automated
• ReAct-Style Video QA: Multi-step reasoning + tool calling for precise video content understanding

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📌 Table of Contents

• Project Introduction
• TeleMem vs Mem0: Core Advantages
• Experimental Results
• Quick Start
• Project Structure
• Core Functions
• Multimodal Extensions
• Data Storage Explanation
• Development and Contribution
• Acknowledgements

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Project Introduction

TeleMem enables conversational AI to maintain stable, natural, and continuous worldviews and character settings during long-term interactions through a deeply optimized pipeline of character-aware summarization → semantic clustering deduplication → efficient storage → precise retrieval.

Features

• Automatic memory extraction: Extracts and structures key facts from dialogues.
• Semantic clustering & deduplication: Uses LLMs to semantically merge similar memories, reducing conflicts and improving consistency.
• Character-profiled memory management: Builds independent memory archives for each character in a dialogue, ensuring precise isolation and personalized management.
• Efficient asynchronous writing: Employs a buffer + batch-flush mechanism for high-performance, stable persistence.
• Precise semantic retrieval: Combines FAISS + JSON dual storage for fast recall and human-readable auditability.

Applicable Scenarios

• Multi-character virtual agent systems

• Long-memory AI assistants (e.g., customer service, companionship, creative co-pilots)

• Complex narrative/world-building in virtual environments

• Dialogue scenarios with strong contextual dependencies

• Video content QA and reasoning

• Multimodal agent memory management

• Long video understanding and information retrieval

  

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TeleMem vs Mem0: Core Advantages

TeleMem deeply refactors Mem0 to address characterization, long-term memory, and high performance. Key differences:

Capability Dimension	Mem0	TeleMem
Multi-character separation	❌ Not supported	✅ Automatically creates independent memory profiles per character
Summary quality	Basic summarization	✅ Context-aware + character-focused prompts covering key entities, actions, and timestamps
Deduplication mechanism	Vector similarity filtering	✅ LLM-based semantic clustering: merges similar memories via LLM
Write performance	Streaming, single writes	✅ Batch flush + concurrency: 2–3× faster writes
Storage format	SQLite / vector DB	✅ FAISS + JSON metadata dual-write: fast retrieval + human-readable
Multimodal Capability	Single image to text only	✅ Video Multimodal Memory: Full video processing pipeline + ReAct multi-step reasoning QA

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Experimental Results

Dataset

We evaluate the ZH-4O Chinese long-character dialogue dataset constructed in the paper MOOM: Maintenance, Organization and Optimization of Memory in Ultra-Long Role-Playing Dialogues:

• Average dialogue length: 600 turns per conversation
• Scenarios: daily interactions, plot progression, evolving character relationships

Memory capability was assessed via QA benchmarks, e.g.:

{
"question": "What is Zhao Qi's nickname for Bai Yulan? A Xiaobai B Xiaoyu C Lanlan D Yuyu",
"answer": "A"
},
{
"question": "What is the relationship between Zhao Qi and Bai Yulan? A Classmates B Teacher and student C Enemies D Neighbors",
"answer": "B"
}

Experimental Configuration

• LLM: Qwen3-8B (thinking mode disabled)

• Embedding model: Qwen3-Embedding-8B

• Metric: QA accuracy

  Method	Overall(%)
  RAG	62.45
  Mem0	70.20
  MOOM	72.60
  A-mem	73.78
  Memobase	76.78
  TeleMem	86.33

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Quick Start

Environment Preparation

# Create and activate virtual environment
conda create -n telemem python=3.10
conda activate telemem
# Install dependencies
pip install -r requirements.txt

Apply Patches

# Run patch application script (see TeleMem-Overlay.md for details)
bash scripts/apply_patches.sh

Example

Set your OpenAI API key:

export OPENAI_API_KEY="your-openai-api-key"

# python examples/quickstart.py
import vendor.TeleMem as mem0

memory = mem0.Memory()

messages = [
    {"role": "user", "content": "Jordan, did you take the subway to work again today?"},
    {"role": "assistant", "content": "Yes, James. The subway is much faster than driving. I leave at 7 o'clock and it's just not crowded."},
    {"role": "user", "content": "Jordan, I want to try taking the subway too. Can you tell me which station is closest?"},
    {"role": "assistant", "content": "Of course, James. You take Line 2 to Civic Center Station, exit from Exit A, and walk 5 minutes to the company."}
]

memory.add(messages=messages, user_id="Jordan")
results = memory.search("What transportation did Jordan use to go to work today?", user_id="Jordan")
print(results)

By default Memory() wires up:

• OpenAI gpt-4.1-nano-2025-04-14 for summary extraction and updates
• OpenAI text-embedding-3-small embeddings (1536 dimensions)
• Faiss vector store with on-disk data

If you want to customize the configuration, please modify config/config.yaml.

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Project Structure

Expand/Collapse Directory Structure

telemem/
├── assets/                 # Documentation assets and figures
├── vendor/
│ └── mem0/                 # Upstream repository source code
├── overlay/
│ └── patches/              # TeleMem custom patch files (.patch)
├── scripts/                # Overlay management scripts
│ ├── init_upstream.sh      # Initialize upstream subtree
│ ├── update_upstream.sh    # Sync upstream and reapply patches
│ ├── record_patch.sh       # Record local modifications as patches
│ └── apply_patches.sh      # Apply patches
├── baselines/              # Baseline implementations for comparative evaluation
│ ├── RAG                   # Retrieval-Augmented Generation baseline
│ ├── MemoBase              # MemoBase memory management system
│ ├── MOOM                  # MOOM dual-branch narrative memory framework
│ ├── A-mem                 # A-mem agent memory baseline
│ └── Mem0                  # Mem0 baseline implementation
├── config/               
| └── config.yaml           # TeleMem configuration
├── data/                   # Small sample datasets for evaluation or demonstration
├── examples/               # Code examples and tutorial demos
│ ├── quickstart.py         # Quick start
│ └── quickstart_mm.py      # Quick start(Multimodel)
├── docs/
│ ├── TeleMem-Overlay.md    # Overlay development guide (English)
│ ├── TeleMem-Overlay-ZH.md # Overlay development guide (Chinese)
│ └── README-ZH.md          # Chinese README
├── PATCHES.md              # Patch list and descriptions
└── README.md               # This file

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Core Functions

Add Memory (add)

The add() method injects one or more dialogue turns into the memory system.

def add(
 self,
 messages,
 *,
 user_id: Optional[str] = None,
 agent_id: Optional[str] = None,
 run_id: Optional[str] = None,
 metadata: Optional[Dict[str, Any]] = None,
 infer: bool = True,
 memory_type: Optional[str] = None,
 prompt: Optional[str] = None,
)

🔎 Parameter Description

Parameter	Type	Required	Description
messages	List[Dict[str, str]]	✅ Yes	List of dialogue messages, each with role (user/assistant) and content
metadata	Dict[str, Any]	✅ Yes	Must include: ・sample_id: unique session ID ・user: list of character names
user_id / agent_id / run_id	Optional[str]	❌ No	Mem0-compatible parameters (ignored in TeleMem)
infer	bool	❌ No	Whether to auto-generate memory summaries (default: True)
memory_type	Optional[str]	❌ No	Memory category (auto-classified if omitted)
prompt	Optional[str]	❌ No	Custom prompt for summarization (uses optimized default if omitted)

🔁 Internal Workflow of add()

1. Message preprocessing: Merge consecutive messages from the same speaker; normalize turn structure.
2. Multi-perspective summarization:
   • Global event summary
   • Character 1’s perspective (actions, preferences, relationships)
   • Character 2’s perspective
3. Vectorization & similarity search: Generate embeddings and retrieve existing similar memories.
4. Batch processing: When buffer threshold is reached, invoke LLM to semantically merge similar memories.
5. Persistence: Dual-write to FAISS (for retrieval) and JSON (for metadata).

---

Search Memory (search)

Performs semantic vector-based retrieval of relevant memories with context-aware recall.

def search(
 self,
 query: str,
 *,
 user_id: Optional[str] = None,
 agent_id: Optional[str] = None,
 run_id: Optional[str] = None,
 limit: int = 5,
 filters: Optional[Dict[str, Any]] = None,
 threshold: Optional[float] = None,
 rerank: bool = True,
)

🔎 Parameter Description

Parameter	Type	Required	Description
query	str	✅ Yes	Natural language query
run_id	str	✅ Yes	Session ID (must match sample_id used in add)
limit	int	❌ No	Max number of results (default: 5)
threshold	float	❌ No	Similarity threshold (0–1; auto-tuned if omitted)
filters	Dict[str, Any]	❌ No	Custom filters (e.g., by character, time range)
rerank	bool	❌ No	Whether to rerank results (default: True)
user_id / agent_id	Optional[str]	❌ No	Mem0-compatible (no effect in TeleMem)

🔍 Search is based on FAISS vector retrieval, supporting millisecond-level responses.

---

Multimodal Extensions

Beyond text memory, TeleMem further extends multimodal capabilities. Drawing inspiration from Deep Video Discovery's Agentic Search and Tool Use approach, we implemented two core methods in the TeleMemory class to support intelligent storage and semantic retrieval of video content.

Method	Description
add_mm()	Process video into retrievable memory (frame extraction → caption generation → vector database)
search_mm()	Query video content using natural language, supporting ReAct-style multi-step reasoning

Add Multimodal Memory (add_mm)

def add_mm(
    self,
    video_path: str,
    *,
    frames_root: str = "video/frames",
    captions_root: str = "video/captions",
    vdb_root: str = "video/vdb",
    clip_secs: int = None,
    emb_dim: int = None,
    subtitle_path: str | None = None,
)

🔎 Parameter Description

Parameter	Type	Required	Description
video_path	str	✅ Yes	Source video file path, e.g., "video/3EQLFHRHpag.mp4"
frames_root	str	❌ No	Frame output root directory (default "video/frames")
captions_root	str	❌ No	Caption JSON output root directory (default "video/captions")
vdb_root	str	❌ No	Vector database output root directory (default "video/vdb")
clip_secs	int	❌ No	Seconds per clip, overrides config.CLIP_SECS
emb_dim	int	❌ No	Embedding dimension, reads from config by default
subtitle_path	str	❌ No	Subtitle file path (.srt), optional

🔁 add_mm() Internal Flow

1. Frame Extraction: decode_video_to_frames - Decodes video to JPEG frames at configured FPS
2. Caption Generation: process_video - Uses VLM (e.g., Qwen3-Omni) to generate detailed descriptions for each clip
3. Vector Database Construction: init_single_video_db - Generates embeddings for semantic retrieval

💡 Smart Caching: If the target file for a stage already exists, that stage is automatically skipped to save computational resources.

Return Value Example

{
    "video_name": "3EQLFHRHpag",
    "frames_dir": "video/frames/3EQLFHRHpag/frames",
    "caption_json": "video/captions/3EQLFHRHpag/captions.json",
    "vdb_json": "video/vdb/3EQLFHRHpag/3EQLFHRHpag_vdb.json"
}

---

Search Multimodal Memory (search_mm)

def search_mm(
    self,
    question: str,
    video_db_path: str = "video/vdb/3EQLFHRHpag_vdb.json",
    video_caption_path: str = "video/captions/captions.json",
    max_iterations: int = 15,
)

🔎 Parameter Description

Parameter	Type	Required	Description
question	str	✅ Yes	Question string (supports A/B/C/D multiple choice format)
video_db_path	str	❌ No	Video vector database path
video_caption_path	str	❌ No	Video caption JSON path
max_iterations	int	❌ No	Maximum MMCoreAgent reasoning iterations (default 15)

🛠️ ReAct-Style Reasoning Tools

search_mm internally uses MMCoreAgent, employing a THINK → ACTION → OBSERVATION loop with three specialized tools:

Tool Name	Function
global_browse_tool	Get global overview of video events and themes
clip_search_tool	Search for specific content using semantic queries
frame_inspect_tool	Inspect frame details within a specific time range

---

Multimodal Example

Run the multimodal demo:

python examples/quickstart_mm.py

On the first run, all frames, captions and VDB JSON will be generated under output_dir (default data/samples/video/). For reproducibility, we also ship these intermediates in the repo so you can run queries directly without recomputing.

Complete code example:

import vendor.TeleMem as mem0
import os

# Initialize
memory = mem0.Memory()

# Define paths
video_path = "data/samples/video/3EQLFHRHpag.mp4"
video_name = os.path.splitext(os.path.basename(video_path))[0]
output_dir = "data/samples/video"
os.makedirs(output_dir, exist_ok=True)


# Step 1: Add video to memory (auto-processing)
vdb_json_path = f"{output_dir}/vdb/{video_name}/{video_name}_vdb.json"
if not os.path.exists(vdb_json_path):
    result = memory.add_mm(
        video_path=video_path,
        output_dir=output_dir,
    )
    print(f"Video processing complete: {result}")
else:
    print(f"VDB already exists: {vdb_json_path}")

# Step 2: Query video content
question = """The problems people encounter in the video are caused by what?
(A) Catastrophic weather.
(B) Global warming.
(C) Financial crisis.
(D) Oil crisis.
"""

messages = memory.search_mm(
    question=question,
    output_dir=output_dir,
    max_iterations=15,
)

# Extract final answer
from core import extract_choice_from_msg
answer = extract_choice_from_msg(messages)
print(f"Answer: ({answer})")

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Data Storage

Text Memory Storage

TeleMem automatically creates a structured storage layout under ./faiss_db/, organized by session and character:

faiss_db/
├── session_001_events.index
├── session_001_events_meta.json 
├── session_001_person_1.index 
├── session_001_person_1_meta.json 
├── session_001_person_2.index 
└── session_001_person_2_meta.json 

📄 Metadata Example (_meta.json)

{
 "summary": "Characters discussed the upcoming action plan.",
 "sample_id": "session_001",
 "round_index": 3,
 "timestamp": "2024-01-01T00:00:00Z",
 "user": "Jordan" // Only present in person_*.json
}

All memories include summary, round number, timestamp, and character, facilitating auditing and debugging.

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Multimodal Memory Storage

TeleMem generates video-related storage files in the .data/samples/video/ directory:

video/
├── frames/
│   └── <video_name>/
│       └── frames/
│           ├── frame_000001_n0.00.jpg
│           ├── frame_000002_n0.50.jpg
│           └── ...
├── captions/
│   └── <video_name>/
│       ├── captions.json          # Clip descriptions + subject registry
│       └── ckpt/                  # Checkpoint for resume
│           ├── 0_10.json
│           └── 10_20.json
└── vdb/
    └── <video_name>/
        └── <video_name>_vdb.json  # Semantic retrieval vector database

📄 captions.json Structure

{
    "0_10": {
        "caption": "The narrator discusses climate data, showing melting glaciers..."
    },
    "10_20": {
        "caption": "Scene shifts to coastal communities affected by rising sea levels..."
    },
    "subject_registry": {
        "narrator": {
            "name": "narrator",
            "appearance": ["professional attire"],
            "identity": ["climate scientist"],
            "first_seen": "00:00:00"
        }
    }
}

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Development and Contribution

• Overlay development process: TeleMem-Overlay.md
• Chinese documentation: README-ZH.md

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License

Apache 2.0 License

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Acknowledgements

TeleMem’s development has been deeply inspired by open-source communities and cutting-edge research. We extend our sincere gratitude to the following projects and teams:

• Mem0
• Memobase
• MOOM
• DVD
• Memento

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Star History

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If you find this project helpful, please give us a ⭐️.

Made with ❤️ by the Ubiquitous AGI team at TeleAI.