Metadata-Version: 2.4
Name: mimir-learn
Version: 0.1.1
Summary: Experience-driven memory for autonomous agents: learn from past successes and failures.
Project-URL: Homepage, https://github.com/AshNicolus/mimir
Project-URL: Repository, https://github.com/AshNicolus/mimir
Author: AshNicolus
License: MIT License
        
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License-File: LICENSE
Keywords: agents,experience,learning,llm,memory
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.11
Requires-Dist: pydantic>=2.5
Provides-Extra: dev
Requires-Dist: pytest>=8.0; extra == 'dev'
Requires-Dist: ruff>=0.5; extra == 'dev'
Provides-Extra: embeddings
Requires-Dist: numpy>=1.24; extra == 'embeddings'
Requires-Dist: sentence-transformers>=2.2; extra == 'embeddings'
Description-Content-Type: text/markdown

# Mimir

[![PyPI](https://img.shields.io/pypi/v/mimir-learn.svg)](https://pypi.org/project/mimir-learn/)
[![Python](https://img.shields.io/pypi/pyversions/mimir-learn.svg)](https://pypi.org/project/mimir-learn/)
[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)

**Experience-driven memory for autonomous agents.** Mimir helps agents learn from their past successes and failures instead of starting from scratch on every task.

> Named after Mímir, the keeper of wisdom in Norse mythology.

---

## The problem

Today's agents have memory, but they don't really *learn*.

Most frameworks store one of two things:

- **Conversation history** (LangGraph memory, buffer memory)
- **Vector embeddings of documents** (RAG, AGENTS.md, CLAUDE.md)

Both let an agent **remember information**. Neither lets it **remember experience**.

```
Task:     Fix authentication latency
Action:   Added Redis cache
Outcome:  Success
```

A month later, the agent has no meaningful understanding that this strategy worked. It solves the same class of problem from zero, every time.

## The idea

Instead of storing documents, embeddings, and metadata, Mimir stores **experiences**:

```
Problem  →  Action  →  Outcome  →  Confidence  →  Context  →  Time
```

From a stream of experiences, Mimir reflects, extracts reusable strategies, and recommends actions for new tasks, so the agent gets measurably better over time.

```python
from mimir import Mimir

memory = Mimir()

# Record what happened
memory.record(
    task="Fix authentication timeout",
    action="Implemented Redis caching",
    outcome="success",
    score=0.95,
)

# Recall relevant past experience
past = memory.recall("authentication latency")

# Get a recommended strategy with confidence
strategy = memory.recommend("login timeout")
# → Strategy: "Redis caching"  |  confidence: 0.87  |  based on 23 successes / 2 failures
```

## How it differs from AGENTS.md / CLAUDE.md

| | AGENTS.md / CLAUDE.md | Mimir |
|---|---|---|
| Knowledge type | Static, hand-written rules | Dynamic, learned from outcomes |
| Updates | Manually edited | Updates itself from results |
| Example | "Use FastAPI. Use PostgreSQL." | "Redis caching solved auth latency 23/25 times (92%)." |
| Failures | Not tracked | First-class: agents stop repeating mistakes |

AGENTS.md answers *"What should the agent remember?"*
Mimir answers *"How does an agent accumulate experience and become wiser over time?"*

## Design principles

Mimir is built as a **modular monolith Python library**, not a microservice swarm or managed cloud product. The library is the product.

- **No LLM and no web server required for v1.** Storage, retrieval, and ranking come first. Reflection via an LLM is added later, behind an interface.
- **Pluggable seams.** Storage, embeddings, and the write path are interfaces, so scaling up (SQLite → Postgres → async reflection → Redis cache) is a swap, never a rewrite.
- **Derived knowledge is rebuildable.** Strategies and reflections are computed from raw experiences and can always be regenerated.
- **Failures are first-class.** Learning from what *didn't* work is treated as importantly as what did.

## Architecture

```
┌────────────────────────────────────────────────────────────┐
│  Public API   Mimir()  .record() .recall() .recommend()     │
├────────────────────────────────────────────────────────────┤
│  Write chokepoint   ──►  [validation / provenance hook]      │   single write path
├──────────────┬───────────────┬─────────────────────────────┤
│  Episodic    │  Reflection   │  Recommendation             │
│  Engine      │  Engine       │  Engine                     │
│  (record/    │  (reflect/    │  (recommend / rank /         │
│   recall)    │   extract)    │   confidence)               │
├──────────────┴───────────────┴─────────────────────────────┤
│  Retrieval layer        (keyword + optional vector hybrid)   │
├────────────────────────────────────────────────────────────┤
│  Storage interface      SQLite (v1) · Postgres (v2) · …      │   pluggable
├────────────────────────────────────────────────────────────┤
│  Embedding provider     none (default) · local · API        │   pluggable
└────────────────────────────────────────────────────────────┘
```

### Data model

```
Experience
  id, task, action, outcome (success|failure|partial),
  score (0..1), context (json), embedding (nullable),
  created_at, superseded_by (nullable)

Strategy   (derived)  problem_pattern, recommended_action, confidence,
                      success_count, failure_count, source_experience_ids
Reflection (derived)  summary, pattern, supporting_experience_ids, created_at
```

## Installation

```bash
pip install mimir-learn
```

The distribution is named `mimir-learn` on PyPI, but you import it as `mimir`:

```python
from mimir import Mimir
```

For development:

```bash
git clone https://github.com/AshNicolus/mimir.git
cd mimir
pip install -e ".[dev]"
```

**Requirements:** Python 3.11+. v1 has no required external services: storage is a local SQLite file. Semantic search and reflection are optional extras.

## Quick start

```python
from mimir import Mimir

memory = Mimir(db_path="mimir.db")

memory.record(
    task="Fix login latency",
    action="Added Redis cache in front of session lookups",
    outcome="success",
    score=0.9,
    context={"service": "auth", "language": "python"},
)

memory.record_failure(
    task="Throttle abusive clients",
    action="Added a fixed-window rate limiter",
    reason="WebSocket traffic wasn't handled; limiter only saw HTTP",
)

for exp in memory.recall("authentication is slow", k=5):
    print(exp.action, exp.outcome, exp.score)

print(memory.recommend("login times out under load"))
```

## Roadmap

| Phase | Goal | Status |
|---|---|---|
| **1: Episodic memory** | `record()` / `recall()`, outcome tracking, SQLite backend | ✅ Done |
| **2: Failure memory** | `record_failure()`, failures queried separately | ✅ Done |
| **3: Reflection engine** | `reflect()`: cluster experiences, synthesize patterns (LLM) | Planned |
| **4: Strategy extraction** | Turn experiences into reusable strategies with confidence | Planned |
| **5: Recommendation engine** | `recommend()`: rank strategies for a new task | 🛠️ Basic (non-LLM aggregation) |
| **6: Shared org memory** | Multiple agents learn from a shared store | Future |

## Scaling path

Mimir starts as a single SQLite file and grows by swapping seams, no rewrites:

1. **v1**: SQLite, in-process, single agent.
2. **v2**: Postgres + pgvector backend for concurrent multi-agent writes.
3. **v3**: extract the (slow, batch) reflection engine into an async worker.
4. **v4**: Redis cache for hot/recent experiences on the read path.

## Status

Alpha (`0.1.1`): **published on PyPI** as [`mimir-learn`](https://pypi.org/project/mimir-learn/). Phase 1 (episodic + failure memory) is complete and tested; the recommendation engine works today via outcome aggregation (no LLM). APIs may still change before `1.0`. Feedback and ideas welcome.

## License

MIT
