gepa_ex

# GEPA Elixir Implementation Roadmap

**Version:** 0.1.0-dev → 1.0.0
**Last Updated:** October 29, 2025
**Current Status:** MVP Complete (63/63 tests passing)

## Vision

Build a production-ready, high-performance Elixir implementation of GEPA that:
- ✅ Maintains feature parity with Python GEPA
- ✅ Leverages BEAM concurrency for 5-10x speedup
- ✅ Provides excellent developer experience
- ✅ Integrates with major AI/ML frameworks
- ✅ Offers comprehensive observability

---

## Roadmap Overview

```
MVP (Complete) → v0.2 (Production) → v0.5 (Feature Parity) → v1.0 (Optimized)
     ✅              2-3 weeks          4-6 weeks             8-10 weeks
```

---

## Phase 1: Production Viability (v0.2.0)
**Timeline:** 2-3 weeks
**Goal:** Make system usable in production environments

### Critical Path

#### 1.1 Real LLM Integration ⚡ PRIORITY 1
**Effort:** 3-4 days | **Impact:** Critical | **Status:** Not started

**Objective:** Enable actual LLM API calls for production use

**Tasks:**
- [ ] Design LLM behavior interface
  - [ ] Define `GEPA.LLM` behavior callbacks
  - [ ] Specify error handling patterns
  - [ ] Document retry and timeout strategies

- [ ] Implement OpenAI client
  - [ ] Add `ex_openai` dependency
  - [ ] Create `GEPA.LLM.OpenAI` module
  - [ ] Implement `complete/2` with options
  - [ ] Add streaming support (optional)
  - [ ] Handle rate limiting
  - [ ] Add token counting
  - [ ] Write unit tests (mock HTTP)

- [ ] Implement Anthropic client
  - [ ] Research available Elixir libraries
  - [ ] Create `GEPA.LLM.Anthropic` module
  - [ ] Implement Claude API calls
  - [ ] Handle streaming
  - [ ] Write unit tests

- [ ] Add configuration management
  - [ ] API key from environment
  - [ ] Model selection
  - [ ] Temperature/top_p settings
  - [ ] Timeout configuration

- [ ] Integration testing
  - [ ] Test with real API (optional, gated)
  - [ ] Mock-based integration tests
  - [ ] Error scenario testing

**Deliverable:**
```elixir
# Usage
{:ok, response} = GEPA.LLM.OpenAI.complete(prompt,
  model: "gpt-4",
  temperature: 0.7,
  max_tokens: 1000
)
```

**Acceptance Criteria:**
- OpenAI and Anthropic clients working
- Error handling for API failures
- Rate limiting respected
- Tests passing
- Documentation complete

**Dependencies:** None
**Blocks:** All production use cases

---

#### 1.2 EpochShuffledBatchSampler
**Effort:** 1 day | **Impact:** Medium | **Status:** Not started

**Objective:** Enable epoch-based training with shuffling

**Tasks:**
- [ ] Create `GEPA.Strategies.EpochShuffledBatchSampler`
  - [ ] Implement epoch tracking
  - [ ] Add shuffling per epoch
  - [ ] Configurable minibatch size
  - [ ] State management for current position

- [ ] Update `GEPA.optimize/1` to accept batch sampler config
- [ ] Write unit tests
- [ ] Add documentation with examples

**Deliverable:**
```elixir
GEPA.optimize(
  seed_candidate: seed,
  trainset: trainset,
  batch_sampler: {:epoch_shuffled, minibatch_size: 5}
)
```

**Acceptance Criteria:**
- Epochs work correctly
- Shuffling happens each epoch
- Tests passing (including property tests)
- Documented

**Dependencies:** None

---

#### 1.3 Quick Start Examples
**Effort:** 2-3 days | **Impact:** High | **Status:** Not started

**Objective:** Enable users to get started quickly

**Tasks:**
- [ ] Create `examples/` directory structure

- [ ] **Example 1: Quick Start** (10 lines)
  - [ ] Simple Q&A optimization
  - [ ] Inline documentation
  - [ ] Expected output
  - [ ] Run instructions

- [ ] **Example 2: Math Problems**
  - [ ] AIME-style dataset
  - [ ] Seed prompt
  - [ ] Optimization loop
  - [ ] Results analysis

- [ ] **Example 3: Custom Adapter**
  - [ ] Step-by-step adapter implementation
  - [ ] Evaluation function
  - [ ] Trace extraction
  - [ ] Integration example

- [ ] **Example 4: State Persistence**
  - [ ] Save optimization state
  - [ ] Resume from checkpoint
  - [ ] Result comparison

- [ ] Update main README with examples
- [ ] Create `EXAMPLES.md` guide
- [ ] Add to documentation generation

**Deliverable:**
- 4+ working examples
- README updated
- Examples documented in HexDocs

**Acceptance Criteria:**
- All examples run successfully
- Clear, commented code
- Expected outputs documented
- Works with mock LLM and real LLM

**Dependencies:** 1.1 (LLM Integration) for realistic examples

---

#### 1.4 Basic Documentation
**Effort:** 1-2 days | **Impact:** High | **Status:** Not started

**Objective:** Comprehensive getting started guide

**Tasks:**
- [ ] Write "Getting Started" guide
  - [ ] Installation
  - [ ] Configuration
  - [ ] First optimization
  - [ ] Understanding results

- [ ] Document public API
  - [ ] `GEPA.optimize/1` options
  - [ ] Adapter behavior
  - [ ] Proposer behavior
  - [ ] Strategy behaviors

- [ ] Add module documentation
  - [ ] Overview for each module
  - [ ] Usage examples
  - [ ] Configuration options

- [ ] Create troubleshooting guide
  - [ ] Common errors
  - [ ] Debug strategies
  - [ ] Performance tips

**Deliverable:**
- Comprehensive HexDocs
- Getting started guide
- API reference
- Troubleshooting guide

**Acceptance Criteria:**
- `mix docs` generates complete documentation
- All public functions documented
- Examples in documentation work
- Published to HexDocs (optional)

---

### Phase 1 Success Metrics
- ✅ LLM integration working with OpenAI + Anthropic
- ✅ At least 3 examples running
- ✅ Documentation covers 90%+ of public API
- ✅ Users can successfully run first optimization
- ✅ Version 0.2.0 published to Hex.pm

**Risk:** LLM API rate limits, authentication issues

---

## Phase 2: Core Completeness (v0.3.0 - v0.4.0)
**Timeline:** 4-6 weeks
**Goal:** Feature parity with Python core functionality

### Major Features

#### 2.1 Merge Proposer ⚡ PRIORITY 2
**Effort:** 7-10 days | **Impact:** High | **Status:** Not started

**Objective:** Implement genealogy-based candidate merging

**Tasks:**
- [ ] Design genealogy tracking
  - [ ] Graph representation
  - [ ] Ancestor traversal algorithms
  - [ ] Common ancestor detection

- [ ] Implement utility functions
  - [ ] `find_dominator_programs/2`
  - [ ] `find_common_ancestor_pair/6`
  - [ ] `filter_ancestors/5`
  - [ ] `does_triplet_have_desirable_predictors/4`

- [ ] Create `GEPA.Proposer.Merge`
  - [ ] Merge scheduling logic
  - [ ] Parent selection
  - [ ] Predictor merging strategy
  - [ ] Subsample evaluation
  - [ ] Merge deduplication

- [ ] Integrate with Engine
  - [ ] Proposer scheduling
  - [ ] State updates
  - [ ] Pareto front integration

- [ ] Write comprehensive tests
  - [ ] Unit tests for utilities
  - [ ] Integration tests
  - [ ] Property tests for merge correctness

- [ ] Documentation
  - [ ] Module documentation
  - [ ] Merge algorithm explanation
  - [ ] Configuration options

**Deliverable:**
```elixir
GEPA.optimize(
  seed_candidate: seed,
  trainset: trainset,
  use_merge: true,
  max_merge_invocations: 5
)
```

**Acceptance Criteria:**
- Merge proposer creates valid candidates
- Merges improve over parents (on average)
- All tests passing
- Performance acceptable
- Documented with examples

**Dependencies:** None (uses existing State structure)
**Complexity:** High (genealogy tracking, merge logic)

---

#### 2.2 IncrementalEvaluationPolicy
**Effort:** 3-4 days | **Impact:** Medium | **Status:** Not started

**Objective:** Progressive validation set evaluation

**Tasks:**
- [ ] Design incremental evaluation strategy
  - [ ] Sample selection algorithm
  - [ ] Budget management
  - [ ] Score tracking

- [ ] Create `GEPA.Strategies.IncrementalEvaluationPolicy`
  - [ ] Implement `select_samples/2`
  - [ ] Track evaluated samples
  - [ ] Estimate scores from subsamples

- [ ] Update State management
  - [ ] Track which samples evaluated
  - [ ] Score estimation logic

- [ ] Integration with Engine
  - [ ] Conditional full evaluation
  - [ ] Early stopping for poor candidates

- [ ] Tests and documentation

**Deliverable:**
```elixir
GEPA.optimize(
  seed_candidate: seed,
  valset: large_valset,
  val_evaluation_policy: {:incremental, initial_sample: 10, increment: 5}
)
```

**Acceptance Criteria:**
- Reduces validation evaluations
- Maintains optimization quality
- Tests passing
- Documented

**Dependencies:** None

---

#### 2.3 InstructionProposal Template System
**Effort:** 2-3 days | **Impact:** Medium | **Status:** Not started

**Objective:** Flexible prompt template system

**Tasks:**
- [ ] Create `GEPA.Strategies.InstructionProposal`
  - [ ] Default template (port from Python)
  - [ ] Template validation
  - [ ] Placeholder replacement
  - [ ] Markdown rendering for feedback

- [ ] Implement output parsing
  - [ ] Extract instruction from LLM response
  - [ ] Handle code blocks
  - [ ] Error handling

- [ ] Integration with Reflective proposer
  - [ ] Use template system
  - [ ] Custom template support

- [ ] Tests
  - [ ] Template validation tests
  - [ ] Parsing tests
  - [ ] Integration tests

- [ ] Documentation and examples

**Deliverable:**
```elixir
GEPA.optimize(
  seed_candidate: seed,
  reflection_prompt_template: custom_template
)
```

**Acceptance Criteria:**
- Default template works
- Custom templates supported
- Output parsing robust
- Tests passing

**Dependencies:** 1.1 (LLM Integration)

---

#### 2.4 Additional Stop Conditions
**Effort:** 1-2 days | **Impact:** Low | **Status:** Not started

**Objective:** More stop condition options

**Tasks:**
- [ ] Create `GEPA.StopCondition.Timeout`
  - [ ] Time-based stopping
  - [ ] Wall clock and CPU time options

- [ ] Create `GEPA.StopCondition.NoImprovement`
  - [ ] Track iterations without improvement
  - [ ] Configurable patience

- [ ] Create `GEPA.StopCondition.Signal` (optional)
  - [ ] Graceful shutdown on SIGINT/SIGTERM
  - [ ] Cleanup logic

- [ ] Update documentation
- [ ] Write tests

**Deliverable:**
```elixir
GEPA.optimize(
  seed_candidate: seed,
  stop_conditions: [
    {:timeout, hours: 2},
    {:no_improvement, patience: 10}
  ]
)
```

**Acceptance Criteria:**
- Stop conditions work reliably
- Tests passing
- Documented

**Dependencies:** None

---

### Phase 2 Success Metrics
- ✅ Merge proposer working and tested
- ✅ Incremental evaluation reduces computation
- ✅ Template system flexible and robust
- ✅ 80%+ feature parity with Python core
- ✅ Version 0.4.0 released

---

## Phase 3: Production Readiness (v0.5.0)
**Timeline:** 2-3 weeks
**Goal:** Enterprise-grade observability and reliability

### Features

#### 3.1 Telemetry Integration
**Effort:** 4-5 days | **Impact:** High | **Status:** Not started

**Objective:** Comprehensive observability

**Tasks:**
- [ ] Define telemetry events
  - [ ] Optimization lifecycle events
  - [ ] Iteration events
  - [ ] Evaluation events
  - [ ] Proposer events

- [ ] Emit telemetry from Engine
  - [ ] Start/stop optimization
  - [ ] Iteration start/complete
  - [ ] Candidate evaluation
  - [ ] Score updates

- [ ] Create default telemetry handler
  - [ ] Console logging
  - [ ] Structured output
  - [ ] Configurable verbosity

- [ ] Document telemetry events
  - [ ] Event reference
  - [ ] Metadata included
  - [ ] Example handlers

- [ ] Integration tests

**Deliverable:**
```elixir
:telemetry.attach(
  "gepa-logger",
  [:gepa, :iteration, :complete],
  &MyApp.handle_gepa_event/4,
  nil
)

GEPA.optimize(seed_candidate: seed, telemetry: true)
```

**Acceptance Criteria:**
- All major events emit telemetry
- Default handler works
- Custom handlers can be attached
- Documented

**Dependencies:** None

---

#### 3.2 Experiment Tracking (WandB/MLflow) [OPTIONAL]
**Effort:** 5-7 days | **Impact:** Medium | **Status:** Not started

**Objective:** Integration with experiment tracking platforms

**Tasks:**
- [ ] Design reporter interface
  - [ ] Reporter behavior
  - [ ] Configuration
  - [ ] Lifecycle management

- [ ] Create WandB reporter
  - [ ] API client (HTTP)
  - [ ] Metric logging
  - [ ] Run initialization
  - [ ] Error handling

- [ ] Create MLflow reporter
  - [ ] API client (HTTP)
  - [ ] Metric logging
  - [ ] Experiment management

- [ ] Integrate with telemetry
  - [ ] Telemetry → Reporter pipeline
  - [ ] Automatic metric extraction

- [ ] Tests and documentation

**Deliverable:**
```elixir
GEPA.optimize(
  seed_candidate: seed,
  experiment_tracking: [
    {:wandb, api_key: "...", project: "my-project"},
    {:mlflow, tracking_uri: "http://localhost:5000"}
  ]
)
```

**Acceptance Criteria:**
- WandB integration works
- MLflow integration works
- Can use both simultaneously
- Documented

**Dependencies:** 3.1 (Telemetry)
**Note:** Can be deferred to Phase 4 if time-constrained

---

#### 3.3 Progress Tracking
**Effort:** 1 day | **Impact:** Low | **Status:** Not started

**Objective:** User-friendly progress display

**Tasks:**
- [ ] Add `progress_bar` dependency
- [ ] Integrate with Engine loop
- [ ] Show current iteration, best score, time remaining
- [ ] Make progress display optional
- [ ] Tests

**Deliverable:**
```elixir
GEPA.optimize(seed_candidate: seed, show_progress: true)
# Output:
# Optimizing: [████████░░] 45/100 (45%) | Best: 0.85 | ETA: 2m 45s
```

**Acceptance Criteria:**
- Progress bar displays correctly
- Updates in real-time
- Can be disabled
- Doesn't interfere with logging

**Dependencies:** None

---

#### 3.4 Robust Error Handling
**Effort:** 2-3 days | **Impact:** Medium | **Status:** Not started

**Objective:** Graceful handling of failures

**Tasks:**
- [ ] Audit error paths
  - [ ] LLM API failures
  - [ ] Adapter errors
  - [ ] Proposer errors
  - [ ] Evaluation errors

- [ ] Implement retry logic
  - [ ] Exponential backoff for API calls
  - [ ] Configurable retry limits

- [ ] Add circuit breaker (optional)
  - [ ] For repeated API failures

- [ ] Improve error messages
  - [ ] Clear, actionable messages
  - [ ] Suggestions for fixes

- [ ] Add error recovery
  - [ ] Save state on crash
  - [ ] Resume capability

- [ ] Tests for error scenarios

**Deliverable:**
- Robust error handling throughout
- Automatic retries
- State saved on failure
- Can resume after crash

**Acceptance Criteria:**
- LLM failures don't crash optimization
- State preserved on error
- Clear error messages
- Tests for error paths

**Dependencies:** None

---

### Phase 3 Success Metrics
- ✅ Telemetry events comprehensive
- ✅ Experiment tracking working (optional)
- ✅ Progress display helpful
- ✅ Error handling robust
- ✅ Production-ready quality
- ✅ Version 0.5.0 released

---

## Phase 4: Ecosystem Expansion (v0.6.0 - v1.0.0)
**Timeline:** 4-8 weeks
**Goal:** Broader use cases and community adoption

### Features

#### 4.1 Generic Adapter Framework
**Effort:** 5-7 days | **Impact:** High | **Status:** Not started

**Objective:** Easy adapter creation for any system

**Tasks:**
- [ ] Design simplified adapter API
  - [ ] Minimal required implementation
  - [ ] Helper functions
  - [ ] Common patterns

- [ ] Create `GEPA.Adapters.Generic`
  - [ ] Configurable evaluation function
  - [ ] Flexible trace extraction
  - [ ] Default implementations

- [ ] Documentation and examples
  - [ ] Adapter creation guide
  - [ ] Common patterns
  - [ ] Best practices

- [ ] Example adapters
  - [ ] HTTP API adapter
  - [ ] CLI tool adapter
  - [ ] Database query adapter

**Deliverable:**
```elixir
adapter = GEPA.Adapters.Generic.new(
  evaluate_fn: &MySystem.evaluate/2,
  extract_traces_fn: &MySystem.extract_traces/2
)
```

**Acceptance Criteria:**
- Easy to create custom adapters
- Clear documentation
- Examples work
- Community can contribute adapters

**Dependencies:** None

---

#### 4.2 RAG Adapter
**Effort:** 10-14 days | **Impact:** High | **Status:** Not started

**Objective:** Optimize RAG systems

**Tasks:**
- [ ] Design vector store interface
  - [ ] Behavior definition
  - [ ] Common operations

- [ ] Implement vector store clients
  - [ ] Qdrant (Elixir client exists)
  - [ ] Weaviate
  - [ ] Chroma (HTTP API)

- [ ] Create RAG pipeline
  - [ ] Query reformulation
  - [ ] Retrieval
  - [ ] Context synthesis
  - [ ] Answer generation
  - [ ] Reranking

- [ ] Implement `GEPA.Adapters.RAG`
  - [ ] Multi-component optimization
  - [ ] Pipeline orchestration
  - [ ] Evaluation metrics

- [ ] Tests and examples
  - [ ] Unit tests
  - [ ] Integration tests
  - [ ] Complete RAG optimization example

- [ ] Documentation
  - [ ] RAG guide (port from Python)
  - [ ] Vector store setup
  - [ ] Optimization strategies

**Deliverable:**
```elixir
adapter = GEPA.Adapters.RAG.new(
  vector_store: {:qdrant, url: "http://localhost:6333"},
  retrieval_k: 5
)

GEPA.optimize(
  seed_candidate: %{
    "query_reformulation" => "...",
    "context_synthesis" => "...",
    "answer_generation" => "..."
  },
  adapter: adapter
)
```

**Acceptance Criteria:**
- RAG pipeline works end-to-end
- Can optimize multiple RAG components
- Vector stores integrate cleanly
- Example demonstrates value
- Documented

**Dependencies:** 1.1 (LLM Integration), 4.1 (Generic Adapter)

---

#### 4.3 Advanced Examples
**Effort:** 5-7 days | **Impact:** Medium | **Status:** Not started

**Objective:** Showcase advanced use cases

**Tasks:**
- [ ] **Multi-turn Agent Example**
  - [ ] Conversation optimization
  - [ ] State management
  - [ ] Multi-step reasoning

- [ ] **RAG System Example**
  - [ ] Document retrieval optimization
  - [ ] Answer quality improvement
  - [ ] Metrics tracking

- [ ] **Code Generation Example**
  - [ ] Prompt optimization for coding
  - [ ] Test-driven evaluation
  - [ ] Output parsing

- [ ] **Domain-Specific Example**
  - [ ] Math problems (AIME-style)
  - [ ] Or legal document analysis
  - [ ] Or medical question answering

- [ ] Create Livebook notebooks
  - [ ] Interactive examples
  - [ ] Visual results
  - [ ] Shareable

**Deliverable:**
- 4+ advanced examples
- At least 2 Livebook notebooks
- Clear documentation
- Real-world inspired

**Acceptance Criteria:**
- Examples demonstrate advanced features
- Clear value proposition
- Reproducible results
- Documented

**Dependencies:** 4.1, 4.2

---

#### 4.4 Performance Optimization
**Effort:** 7-10 days | **Impact:** Medium | **Status:** Not started

**Objective:** Leverage BEAM concurrency for speedup

**Tasks:**
- [ ] Profile current implementation
  - [ ] Identify bottlenecks
  - [ ] Measure baseline performance

- [ ] Implement parallel evaluation
  - [ ] Use `Task.async` for batch evaluation
  - [ ] Concurrent LLM API calls
  - [ ] Configurable concurrency limit

- [ ] Optimize State updates
  - [ ] Reduce copying
  - [ ] Efficient Pareto calculations

- [ ] Add streaming support (optional)
  - [ ] Stream LLM responses
  - [ ] Progressive evaluation

- [ ] Benchmark improvements
  - [ ] Compare with Python
  - [ ] Measure speedup

- [ ] Document performance characteristics
  - [ ] Concurrency settings
  - [ ] Resource usage
  - [ ] Best practices

**Deliverable:**
```elixir
GEPA.optimize(
  seed_candidate: seed,
  concurrent_evaluations: 5,  # Evaluate 5 in parallel
  streaming: true              # Stream LLM responses
)
```

**Acceptance Criteria:**
- 3-5x speedup on batch evaluation
- Configurable concurrency
- No race conditions
- Resource usage acceptable
- Benchmarks documented

**Dependencies:** 1.1 (LLM Integration)

---

#### 4.5 Community Infrastructure
**Effort:** 3-5 days | **Impact:** High | **Status:** Not started

**Objective:** Enable community contributions

**Tasks:**
- [ ] Create CONTRIBUTING.md
  - [ ] How to contribute
  - [ ] Code style guide
  - [ ] Testing requirements
  - [ ] PR process

- [ ] Set up CI/CD
  - [ ] GitHub Actions
  - [ ] Run tests on PR
  - [ ] Check formatting
  - [ ] Run Dialyzer
  - [ ] Coverage reporting

- [ ] Create issue templates
  - [ ] Bug report
  - [ ] Feature request
  - [ ] Adapter contribution

- [ ] Add CODE_OF_CONDUCT.md

- [ ] Create PR template

- [ ] Set up discussions/Discord

- [ ] Badge updates
  - [ ] CI status
  - [ ] Coverage
  - [ ] Hex version
  - [ ] Documentation

**Deliverable:**
- Complete contribution infrastructure
- Welcoming community
- Easy to get started

**Acceptance Criteria:**
- CI/CD working
- Documentation clear
- Templates helpful
- Community-friendly

**Dependencies:** None

---

### Phase 4 Success Metrics
- ✅ 3+ adapters available (Basic, Generic, RAG)
- ✅ 10+ examples covering common use cases
- ✅ 3-5x performance improvement
- ✅ Active community contributions
- ✅ Version 1.0.0 released to Hex.pm
- ✅ Feature parity with Python GEPA

---

## Version Milestones

### v0.2.0 - Production Viable
**ETA:** 2-3 weeks from now
- [x] MVP Complete (✅ Done!)
- [ ] LLM Integration (OpenAI + Anthropic)
- [ ] EpochShuffledBatchSampler
- [ ] Quick Start Examples
- [ ] Basic Documentation
- [ ] Published to Hex.pm

**Success Criteria:**
- Users can run real optimizations
- Documentation clear
- Examples work

---

### v0.3.0 - Core Features
**ETA:** 4-5 weeks from now
- [ ] Merge Proposer
- [ ] IncrementalEvaluationPolicy
- [ ] InstructionProposal Templates
- [ ] Additional Stop Conditions

**Success Criteria:**
- Merge proposer improves results
- Core feature parity at 80%

---

### v0.4.0 - Enhanced Functionality
**ETA:** 6-7 weeks from now
- [ ] Telemetry Integration
- [ ] Progress Tracking
- [ ] Robust Error Handling
- [ ] Improved Documentation

**Success Criteria:**
- Production-ready quality
- Good observability
- Reliable error handling

---

### v0.5.0 - Ecosystem Integration
**ETA:** 8-10 weeks from now
- [ ] Generic Adapter Framework
- [ ] RAG Adapter
- [ ] Advanced Examples
- [ ] Community Infrastructure

**Success Criteria:**
- 3+ adapters
- 10+ examples
- Community can contribute

---

### v1.0.0 - Production Release
**ETA:** 12-14 weeks from now
- [ ] Performance Optimization
- [ ] Complete Documentation
- [ ] Full Test Coverage (>90%)
- [ ] Comprehensive Examples
- [ ] Migration Guide from Python

**Success Criteria:**
- Feature parity with Python GEPA
- Performance 3-5x better
- Production deployments
- Community adoption
- HexDocs published
- Stable API

---

## Deferred Features (Post v1.0)

### v1.1+
- [ ] DSPy Adapter (requires DSPy Elixir port)
- [ ] Full WandB/MLflow Integration
- [ ] Distributed Optimization (multi-node BEAM)
- [ ] GPU Acceleration (if applicable)
- [ ] Advanced Merge Strategies
- [ ] Adaptive Batch Sampling
- [ ] Neural Architecture Search Adapter
- [ ] Multi-Objective Optimization UI
- [ ] Real-time Dashboard
- [ ] Benchmark Suite

---

## Success Metrics by Phase

### Phase 1 (v0.2.0)
- [ ] 1,000+ total downloads from Hex.pm
- [ ] 50+ stars on GitHub
- [ ] 5+ external users
- [ ] Documentation views: 500+/month

### Phase 2 (v0.4.0)
- [ ] 5,000+ downloads
- [ ] 100+ stars
- [ ] 20+ external users
- [ ] 1+ community contribution

### Phase 3 (v0.5.0)
- [ ] 10,000+ downloads
- [ ] 200+ stars
- [ ] 50+ external users
- [ ] 5+ community contributions

### Phase 4 (v1.0.0)
- [ ] 25,000+ downloads
- [ ] 500+ stars
- [ ] 100+ external users
- [ ] 10+ community contributions
- [ ] Mentioned in Elixir community

---

## Risk Mitigation

### Technical Risks
| Risk | Probability | Impact | Mitigation |
|------|-------------|---------|------------|
| LLM API rate limits | Medium | High | Implement robust retry, backoff, circuit breaker |
| Merge proposer complexity | Medium | Medium | Incremental implementation, extensive testing |
| Performance bottlenecks | Low | Medium | Early profiling, benchmarking |
| Vector store integration issues | Medium | Low | Start with well-documented stores (Qdrant) |

### Project Risks
| Risk | Probability | Impact | Mitigation |
|------|-------------|---------|------------|
| Scope creep | Medium | Medium | Strict phase boundaries, MVP-first |
| Insufficient testing | Low | High | Maintain >80% coverage, property tests |
| Poor documentation | Low | High | Doc-first approach, examples mandatory |
| No community adoption | Medium | High | Active engagement, clear value prop |

---

## Resource Requirements

### Development Time
- **Phase 1:** 80-100 hours (2-3 weeks)
- **Phase 2:** 120-160 hours (4-6 weeks)
- **Phase 3:** 80-100 hours (2-3 weeks)
- **Phase 4:** 160-200 hours (4-8 weeks)
- **Total to v1.0:** 440-560 hours (12-14 weeks)

### External Dependencies
- `ex_openai` - OpenAI API client
- `req` or `tesla` - HTTP client
- `progress_bar` - Terminal progress bars
- `telemetry` - Already included (standard)
- Vector store clients (Qdrant, etc.)

### Infrastructure
- GitHub repository (exists)
- CI/CD (GitHub Actions)
- Hex.pm account
- HexDocs hosting
- (Optional) Discord/Discussions

---

## Communication Plan

### Regular Updates
- [ ] Weekly progress updates (GitHub Discussions)
- [ ] Monthly blog post (dev.to or similar)
- [ ] Release notes for each version
- [ ] Changelog maintained

### Community Engagement
- [ ] Respond to issues within 48 hours
- [ ] Welcome PRs with clear guidelines
- [ ] Monthly community call (optional, if adoption grows)
- [ ] Showcase user projects

### Marketing
- [ ] Announce v0.2.0 on Elixir Forum
- [ ] Post to Reddit (r/elixir, r/MachineLearning)
- [ ] Tweet from project account (if created)
- [ ] Submit to Elixir Radar
- [ ] Present at local Elixir meetup (optional)

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## Open Questions

### Technical
- [ ] Should we use GenServer for state management? (Tradeoffs: complexity vs hot-reload)
- [ ] Streaming vs batch LLM calls? (Impacts UX and error handling)
- [ ] Support for local LLMs (Ollama, llama.cpp)? (Defer to v1.1?)

### Product
- [ ] Target audience: Researchers vs Practitioners vs Both?
- [ ] Pricing/licensing for commercial use? (Current: MIT, keep?)
- [ ] Integration with Livebook as primary interface?

### Community
- [ ] Accept all adapter contributions or curate quality?
- [ ] How to handle support requests?
- [ ] Create separate repo for adapters? (monorepo vs multi-repo)

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## Dependencies & Prerequisites

### Required Knowledge
- Elixir/OTP fundamentals
- LLM API usage (OpenAI, Anthropic)
- HTTP client usage
- Testing strategies
- Documentation practices

### Development Environment
- Elixir 1.14+
- Erlang/OTP 25+
- Git
- OpenAI/Anthropic API keys (for testing)
- (Optional) Docker for vector stores

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## Conclusion

This roadmap provides a clear path from the current MVP (v0.1.0) to a production-ready, feature-complete v1.0.0 release in approximately 12-14 weeks of focused development.

**Immediate Next Steps:**
1. ✅ Review and approve this roadmap
2. Create GitHub project board with these phases
3. Create issues for Phase 1 tasks
4. Begin work on LLM Integration (Priority 1)
5. Set up CI/CD pipeline
6. Draft v0.2.0 release plan

**Key Success Factors:**
- Focus on user value first (LLM, examples, docs)
- Maintain test quality (>80% coverage)
- Ship frequently (2-3 week releases)
- Engage community early
- Document everything

The foundation is solid. The path is clear. Let's build something great! 🚀

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**Questions or Feedback?**
- Open a GitHub issue
- Start a discussion
- Reach out to maintainers

**Want to Contribute?**
See CONTRIBUTING.md (coming in Phase 4) or open an issue asking where to start!