dasein-core 0.1.2

Universal memory for agentic AI. Attach a brain to any LangChain/LangGraph agent in a single line.

Dasein

Universal memory for agentic AI. Attach a brain to any LangChain/LangGraph agent in a single line.

Dasein learns from your agent's execution history and automatically injects learned rules to improve performance, reduce costs, and increase reliability across runs.

Features

✨ Zero-friction integration - Wrap any LangChain or LangGraph agent in one line
🧠 Automatic learning - Agents learn from successes and failures
📊 Performance tracking - Built-in token usage, timing, and success metrics
🔄 Retry logic - Intelligent retry with learned optimizations
🔍 Execution traces - Detailed step-by-step visibility into agent behavior
☁️ Cloud-powered - Distributed rule synthesis and storage

Installation

pip install dasein-core

Or install from source:

git clone https://github.com/nickswami/dasein-core.git
cd dasein-core
pip install -e .

📓 Try It Now in Colab

🚀 Zero setup required! Try all three examples in your browser:

Three complete examples with automatic learning:

🗄️ SQL Agent • 🌐 Browser Agent • 🔍 Deep Research

30-50% token reduction • Optimized navigation • 20-40% multi-agent savings

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Quick Start

Basic Usage

from dasein import cognate
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.agents import create_sql_agent
from langchain_community.agent_toolkits import SQLDatabaseToolkit

# Create your agent as usual
llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash")
toolkit = SQLDatabaseToolkit(db=your_database, llm=llm)
agent = create_sql_agent(llm=llm, toolkit=toolkit, agent_type="tool-calling")

# Wrap with Dasein - that's it!
agent = cognate(agent)

# Use exactly like the original
result = agent.run("Show me the top 5 customers by revenue")

With Performance Tracking

from dasein import cognate

# Enable automatic retry and performance comparison
agent = cognate(
    your_agent,
    retry=2,  # Run twice to learn and improve
    performance_tracking=True  # Show before/after metrics
)

result = agent.run("your query")
# 🎯 Dasein automatically shows improvement metrics

Advanced: Custom Optimization Weights

from dasein import cognate

# Customize what Dasein optimizes for
agent = cognate(
    your_agent,
    weights={
        "w1": 2.0,  # Heavily favor successful rules
        "w2": 0.5,  # Less emphasis on turn count
        "w3": 1.0,  # Standard uncertainty penalty
        "w4": 3.0,  # Heavily optimize for token efficiency
        "w5": 0.1   # Minimal time emphasis
    }
)

Architecture

Dasein uses a cloud-first architecture for rule learning and synthesis:

┌─────────────────┐
│  Your Agent     │
│  (LangChain/    │
│   LangGraph)    │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│ Dasein Wrapper  │  ◄── cognate()
│ - Trace Capture │
│ - Rule Injection│
└────────┬────────┘
         │
    ┌────┴────┐
    ▼         ▼
┌────────┐ ┌────────┐
│Pre-Run │ │Post-Run│
│Service │ │Service │
│        │ │        │
│Recalls │ │Learns  │
│Rules   │ │Rules   │
└────────┘ └────────┘

How It Works

1. Pre-Run: Dasein queries cloud services for relevant learned rules based on the task
2. Execution: Rules are injected into the agent's prompts/tools at optimal injection points
3. Trace Capture: Every LLM call, tool invocation, and decision is captured
4. Post-Run: Traces are sent to cloud services for rule synthesis and learning
5. Next Run: Improved rules are automatically available

API Reference

Core Functions

cognate(agent, weights=None, verbose=False, retry=0, performance_tracking=False, rule_trace=False)

Wrap any LangChain/LangGraph agent with Dasein's learning capabilities.

Parameters:

• agent - LangChain or LangGraph agent instance
• weights (dict) - Custom optimization weights for rule selection (w1-w5)
• verbose (bool) - Enable detailed debug logging
• retry (int) - Number of retries with learning (0 = single run, 2 = run twice with improvement)
• performance_tracking (bool) - Show before/after performance metrics
• rule_trace (bool) - Show detailed rule application trace

Returns: Wrapped agent with identical interface to the original

print_trace()

Display the execution trace of the last agent run.

get_trace()

Retrieve the execution trace as a list of dictionaries.

Returns: List[Dict] - Trace steps with timestamps, tokens, and decisions

clear_trace()

Clear the current execution trace.

inject_hint(hint: str)

Manually inject a hint/rule for the next agent run.

Parameters:

• hint (str) - The hint text to inject

reset_brain()

Clear all local state and event storage.

Supported Frameworks

• ✅ LangChain Agents (all agent types)
• ✅ LangGraph Agents (CompiledStateGraph)
• ✅ Custom agents implementing standard interfaces

Examples

See the examples/ directory for complete examples:

• SQL Agent - Learn query patterns for a Chinook database
• Browser Agent - Learn web scraping strategies
• Research Agent - Multi-agent research coordination

Verbose Mode

For debugging, enable verbose logging:

agent = cognate(your_agent, verbose=True)

This shows detailed information about:

• Rule retrieval from cloud services
• Rule injection points and content
• Trace capture steps
• Post-run learning triggers

Requirements

• Python 3.8+
• LangChain 0.1.0+
• LangChain Community 0.1.0+
• LangChain Google GenAI 0.0.6+

See pyproject.toml for complete dependency list.

Configuration

Dasein uses cloud services for rule synthesis and storage. Configure service endpoints via environment variables:

export DASEIN_PRE_RUN_URL="https://your-pre-run-service.com"
export DASEIN_POST_RUN_URL="https://your-post-run-service.com"

Contact the Dasein team for cloud service access.

Performance

Dasein is designed for minimal overhead:

• Pre-run: ~100-200ms for rule retrieval
• Runtime: <1% overhead for trace capture
• Post-run: Async - doesn't block your code

The benefits far outweigh the costs:

• 🎯 30-50% token reduction on repeated tasks
• 🎯 Fewer failed runs through learned error handling
• 🎯 Faster execution with optimized tool usage

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

License

MIT License - see LICENSE file for details.

Troubleshooting

Common Issues in Colab/Jupyter

Q: I see timeout warnings for dasein-pre-run and dasein-post-run services

A: These warnings can appear on first connection while the cloud services wake up (cold start). The services are fully public and will work after a brief initialization period. Your agent will continue running and learning will activate automatically once the services respond.

Q: I see dependency conflict warnings

A: These are safe to ignore in Colab. The package will work correctly despite version mismatches with Colab's pre-installed packages.

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Support

• 🐛 Issues: GitHub Issues

Citation

If you use Dasein in your research, please cite:

@software{dasein2025,
  title={Dasein: Universal Memory for Agentic AI},
  author={Dasein Team},
  year={2025},
  url={https://github.com/nickswami/dasein-core}
}

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