soul-agent 0.1.1

Persistent identity and memory for any LLM agent — markdown-native, provider-agnostic

soul.py 🧠

Your AI forgets everything when the conversation ends. soul.py fixes that.

from hybrid_agent import HybridAgent

agent = HybridAgent()
agent.ask("My name is Prahlad and I'm building an AI research lab.")

# New process. New session. Memory persists.
agent = HybridAgent()
result = agent.ask("What do you know about me?")
print(result["answer"])
# → "You're Prahlad, building an AI research lab."

No database. No server. Just markdown files and smart retrieval.

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▶ Live Demos

Version	Demo	What it shows
v0.1	soul.themenonlab.com	Memory persists across sessions
v1.0	soulv1.themenonlab.com	Semantic RAG retrieval
v2.0	soulv2.themenonlab.com	Auto query routing: RAG + RLM

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Install

pip install soul-agent          # core (zero deps)
pip install soul-agent[anthropic]
pip install soul-agent[openai]

Quickstart

soul init   # creates SOUL.md and MEMORY.md

# v0.1 — simple markdown memory (great starting point)
from soul import Agent
agent = Agent(provider="anthropic")
agent.ask("Remember this.")

# v2.0 — automatic RAG + RLM routing (this repo's default)
from hybrid_agent import HybridAgent
agent = HybridAgent()  # auto-detects best retrieval per query
result = agent.ask("What do you know about me?")
print(result["answer"])
print(result["route"])   # "RAG" or "RLM"

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How it works

soul.py uses two markdown files as persistent state:

File	Purpose
SOUL.md	Identity — who the agent is, how it behaves
MEMORY.md	Memory — timestamped log of every exchange

v2.0 adds a query router that automatically dispatches to the right retrieval strategy:

Your query
    ↓
Router (fast LLM call)
    ├── FOCUSED  (~90%) → RAG — vector search, sub-second
    └── EXHAUSTIVE (~10%) → RLM — recursive synthesis, thorough

Architecture based on: RAG + RLM: The Complete Knowledge Base Architecture

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Branches

Branch	Description	Best for
main	v2.0 — RAG + RLM hybrid (default)	Production use
v2.0-rag-rlm	Same as main, versioned	Pinning to v2
v1.0-rag	RAG only, no RLM	Simpler setup
v0.1-stable	Pure markdown, zero deps	Learning / prototyping

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v2.0 API

result = agent.ask("What is my name?")

result["answer"]        # the response
result["route"]         # "RAG" or "RLM"
result["router_ms"]     # router latency
result["retrieval_ms"]  # retrieval latency
result["total_ms"]      # total latency
result["rag_context"]   # retrieved chunks (RAG path)
result["rlm_meta"]      # chunk stats (RLM path)

v2.0 Setup

agent = HybridAgent(
    soul_path="SOUL.md",
    memory_path="MEMORY.md",
    mode="auto",                    # "auto" | "rag" | "rlm"
    qdrant_url="...",               # or set QDRANT_URL env var
    qdrant_api_key="...",           # or QDRANT_API_KEY
    azure_embedding_endpoint="...", # or AZURE_EMBEDDING_ENDPOINT
    azure_embedding_key="...",      # or AZURE_EMBEDDING_KEY
    k=5,                            # RAG retrieval count
)

Falls back to BM25 (keyword) if Qdrant/Azure not configured.

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Why not LangChain / LlamaIndex / MemGPT?

Those are orchestration frameworks. soul.py is a primitive — persistent identity and memory you can drop into anything you're building.

• No framework lock-in — works with any LLM provider
• Human-readable — SOUL.md and MEMORY.md are plain text
• Version-controllable — git diff your agent's memories
• Composable — use just the parts you need

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License

MIT

Citation

@software{menon2026soul,
  author = {Menon, Prahlad G.},
  title  = {soul.py: Persistent Identity and Memory for LLM Agents},
  year   = {2026},
  url    = {https://github.com/menonpg/soul.py}
}