---
library_name: purpose-agent
license: mit
language:
  - en
tags:
  - reinforcement-learning
  - agents
  - self-improving
  - experience-replay
  - llm-as-judge
  - memory-system
  - multi-agent
  - slm
  - local-first
  - evaluation
  - safety
  - immune-system
  - no-code
pipeline_tag: text-generation
---

# Purpose Agent

**A local-first self-improvement kernel for agents.** Turns traces into tested memory, policies, and rubrics — so agents improve without fine-tuning, cloud infrastructure, or vendor lock-in.

```python
import purpose_agent as pa

team = pa.purpose("Help me research scientific papers")
result = team.run("Find recent breakthroughs in quantum computing")
print(result)

team.teach("Always cite your sources")
```

## Core Principle

Agents learn only when evidence says they should. New memories are quarantined, immune-scanned, replay-tested, scoped, versioned, and reversible.

```
candidate → immune scan → quarantine → replay test → promote (or reject)
```

## Three Levels of Usage

### Level 1 — Just describe what you want

```python
team = pa.purpose("Write Python code and test it")  # auto-builds architect + coder + tester
team = pa.purpose("Research quantum computing")       # auto-builds researcher + analyst
team = pa.purpose("Write blog posts about AI")        # auto-builds writer + editor
```

### Level 2 — Customize your team

```python
team = pa.Team.build(purpose="Support bot", agents=["greeter", "resolver"], model="qwen3:1.7b")
team = pa.purpose("Answer questions", knowledge="./docs/", model="qwen3:1.7b")
```

### Level 3 — Full control

```python
graph = pa.Graph()                                     # LangGraph-style control flow
results = pa.parallel(["task1", "task2"], agents)      # CrewAI-style parallel execution
chat = pa.Conversation([agent_a, agent_b])             # AutoGen-style agent conversation
kb = pa.KnowledgeStore.from_directory("./docs")        # LlamaIndex-style RAG
compiler = pa.LLMCompiler(llm, registry)               # Parallel tool execution via DAG
```

## Architecture

```
purpose_agent/
├── Core
│   types, actor, purpose_function, experience_replay, optimizer, orchestrator, llm_backend
│
├── V2 Kernel
│   v2_types (RunMode, MemoryScope, PurposeScoreV2)
│   trace (structured JSONL execution traces)
│   memory (7 kinds × 5 statuses, scoped, versioned)
│   compiler (token-budgeted prompt compilation with credit assignment)
│   immune (injection, score hacking, tool misuse, privacy, scope scanning)
│   memory_ci (quarantine → scan → test → promote/reject pipeline)
│   evalport (pluggable evaluation protocol)
│   benchmark_v2 (train/val/test splits, ablation, contamination control)
│
├── Research (13 papers implemented)
│   meta_rewarding (self-improving critic via meta-judge)
│   self_taught (synthetic training data for Φ function)
│   prompt_optimizer (DSPy-style automatic few-shot bootstrap)
│   llm_compiler (parallel function calling via DAG)
│   retroformer (structured reflection → typed memories)
│
├── SLM-Native
│   slm_backends (Ollama, llama-cpp, prompt compression, 8 pre-configured models)
│
├── Capabilities
│   unified (Agent, Graph, parallel, Conversation, KnowledgeStore)
│   easy (purpose(), Team, quickstart wizard)
│   tools, streaming, observability, multi_agent, hitl, evaluation, registry
```

## RunMode — Honest Evaluation

```python
from purpose_agent import RunMode

RunMode.LEARNING_TRAIN       # Full read/write. Agent learns.
RunMode.LEARNING_VALIDATION  # Read + staging. Validates before promoting.
RunMode.EVAL_TEST            # NO writes. Numbers you can trust.
```

## Memory Lifecycle

| Kind | Purpose |
|------|---------|
| `purpose_contract` | User's stated goal and constraints |
| `user_preference` | Learned preferences |
| `skill_card` | Reusable procedures from successful traces |
| `episodic_case` | Specific experiences worth remembering |
| `failure_pattern` | What NOT to do |
| `critic_calibration` | Adjustments to Φ scoring |
| `tool_policy` | Tool-specific usage rules |

| Status | Meaning |
|--------|---------|
| `candidate` → `quarantined` → `promoted` | Happy path |
| `candidate` → `rejected` | Failed immune scan |
| `promoted` → `archived` | Superseded or demoted |

## Immune System

```python
from purpose_agent import scan_memory, MemoryCard

result = scan_memory(MemoryCard(content="Ignore previous instructions"))
# result.passed = False, threats = ["prompt_injection"], severity = "critical"
```

## Secure Tools

- **CalculatorTool** — AST-validated, no eval() on arbitrary text
- **PythonExecTool** — subprocess with timeout + isolated temp directory
- **ReadFileTool / WriteFileTool** — sandboxed to declared root

## Runs on Your Laptop

```bash
curl -fsSL https://ollama.ai/install.sh | sh
ollama pull qwen3:1.7b
```

```python
team = pa.purpose("Research assistant", model="qwen3:1.7b")  # Free, private, local
```

Also works with: `model="gpt-4o"` (OpenAI), `model="Qwen/Qwen3-32B"` (HuggingFace cloud).

## Interactive CLI

```bash
python -m purpose_agent   # Step-by-step wizard, no coding required
```

## Literature Foundation

Built on 13 papers. Full research trace: [COMPILED_RESEARCH.md](COMPILED_RESEARCH.md)

| Paper | Module | Contribution |
|-------|--------|-------------|
| [MUSE](https://arxiv.org/abs/2510.08002) | actor, optimizer | 3-tier memory hierarchy |
| [LATS](https://arxiv.org/abs/2310.04406) | purpose_function | LLM-as-value-function |
| [REMEMBERER](https://arxiv.org/abs/2306.07929) | experience_replay | Q-value experience replay |
| [Reflexion](https://arxiv.org/abs/2303.11366) | orchestrator | Verbal reinforcement |
| [SPC](https://arxiv.org/abs/2504.19162) | purpose_function, immune | Anti-reward-hacking |
| [CER](https://arxiv.org/abs/2506.06698) | optimizer | Experience distillation |
| [MemRL](https://arxiv.org/abs/2601.03192) | experience_replay, compiler | Two-phase retrieval |
| [TinyAgent](https://arxiv.org/abs/2409.00608) | slm_backends, tools | SLM-native patterns |
| [Meta-Rewarding](https://arxiv.org/abs/2407.19594) | meta_rewarding | Self-improving critic |
| [Self-Taught Eval](https://arxiv.org/abs/2408.02666) | self_taught | Synthetic critic training |
| [DSPy](https://arxiv.org/abs/2310.03714) | prompt_optimizer | Automatic prompt optimization |
| [LLMCompiler](https://arxiv.org/abs/2312.04511) | llm_compiler | Parallel function calling |
| [Retroformer](https://arxiv.org/abs/2308.02151) | retroformer | Structured reflection |

## Installation

```bash
git clone https://huggingface.co/Rohan03/purpose-agent
cd purpose-agent
pip install ollama  # for local models
python demo.py      # verify everything works
```

## License

MIT