Metadata-Version: 2.4
Name: mimir-learn
Version: 0.1.4
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 :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.10
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'
Provides-Extra: vector
Requires-Dist: sqlite-vec>=0.1.6; extra == 'vector'
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
```

Optional extras (keyword recall works without either):

```bash
pip install "mimir-learn[embeddings]"  # local embeddings for semantic recall
pip install "mimir-learn[vector]"      # sqlite-vec ANN index for fast vector search
```

For development:

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

**Requirements:** Python 3.10+, tested on 3.10, 3.11, and 3.12 (Linux, macOS, Windows). This matters for agents, which often run on the Python version their host ships, and 3.10 is still the default on several current Linux distributions. 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"))
```

## Recommendations

`recommend()` aggregates past experiences for a task and returns the best action.
Its `confidence` is the lower bound of a Beta posterior on the action's success
rate from its raw counts (a Jeffreys prior keeps small samples honest), so it
reads as a success rate: a 9/10 action beats a lucky 1/1, and the number means the
same whatever the query. Relevance and recency steer only which action wins, not
the confidence: with weighting on (the default), an action proven on closely
matching tasks outranks an equally confident one proven on loosely related ones.
Turn weighting off to rank on track record alone:

```python
memory.recommend("login times out under load", weight_by_relevance=False)
```

By default actions are grouped by normalized text, so "Added Redis cache" and
"use redis caching" count as separate strategies. Plug in a clusterer to merge
equivalent phrasings and pool their evidence:

```python
from mimir import Mimir, EmbeddingClusterer

memory = Mimir(clusterer=EmbeddingClusterer(my_embedder))
```

`ExactClusterer` is the default and needs no embeddings. Any other strategy can
implement `ActionClusterer`.

## Hybrid recall and the vector index

Configure an embedder and recall becomes hybrid: keyword (SQLite FTS5) and vector
candidates are fused with reciprocal-rank fusion, so an experience can be found by
meaning even when it shares no words with the query. Install the `vector` extra to
back vector search with a [sqlite-vec](https://github.com/asg017/sqlite-vec) ANN
index; without it, recall falls back to a Python cosine scan (numpy-accelerated
when numpy is installed), so the behavior is identical and only the speed differs.

Query embeddings are cached in a small per-instance LRU, so an agent retrying the
same query doesn't pay to re-embed it. Tune it with `Mimir(query_cache_size=...)`,
or pass `0` to disable.

## Superseding stale experiences

Knowledge goes stale. Mark an old experience as replaced, and it drops out of
recall and recommendation by default while staying retrievable by id:

```python
# record a replacement and link it in one call
new = memory.record("auth is slow", "add a read cache", supersedes=old_id)

# or link two experiences that already exist
memory.supersede(old_id, new.id)
```

Pass `include_superseded=True` to `recall()` or `recommend()` to see superseded
rows anyway, which is useful for studying concept drift and decay.

## Time decay

Superseding is manual. For gradual staleness, set a half-life so `recommend()`
discounts older evidence: an experience's weight halves every `half_life_days`,
so a recent result outweighs an equally successful but older one. Off by default,
which keeps all evidence equal.

```python
memory = Mimir(half_life_days=30)  # evidence from 30 days ago counts for half
```

The same half-life also reweights `recall()`: a fresh experience outranks an
equally relevant but staler one. Reported counts stay exact; decay only affects
ranking.

## 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 | ✅ Beta-posterior confidence over relevance/recency-weighted evidence, pluggable action clustering (non-LLM) |
| **6: Shared org memory** | Multiple agents learn from a shared store | Future |
| **Hybrid retrieval** | Keyword + vector recall, optional sqlite-vec ANN index | ✅ Done |
| **Reliability** | Versioned schema with a migration runner; staleness via `superseded_by` and time decay | ✅ Done |
| **Quality eval** | Labeled recall@k / MRR / recommendation-accuracy gate in CI | ✅ Done |
| **Concurrency** | Per-thread connections so reads scale under WAL, writes serialized | ✅ Done |
| **Runtime support** | Run on the Python versions agent hosts actually ship, across Linux, macOS, and Windows |  Python 3.10–3.12 |

## Scaling path

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

1. **v1**: SQLite, in-process; WAL with per-thread connections so reads scale across threads while writes serialize.
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 (`0.1.4`): **published on PyPI** as [`mimir-learn`](https://pypi.org/project/mimir-learn/). Episodic and failure memory are complete and tested, and the recommendation engine works today via Beta-posterior confidence over relevance/recency-weighted evidence, with pluggable action clustering (no LLM). Recall is hybrid keyword + vector (optional sqlite-vec ANN index), backed by schema versioning with a migration runner, concurrent reads under WAL, experience superseding and time decay for staleness, a query-embedding cache, and a recall@k / recommendation-accuracy eval gate in CI. See the [Playbook](PLAYBOOK.md) for a basic-to-advanced guide. APIs may still change before `1.0`. Feedback and ideas welcome.

## License

MIT
