<div align="center">

# SWE-AF

### Autonomous Engineering Team Runtime Built on [AgentField](https://github.com/Agent-Field/agentfield)

**Pronounced:** _"swee-AF"_ (one word)

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**One API call → full engineering team → shipped code.**

<p>
  <a href="#quick-start">Quick Start</a> •
  <a href="#why-swe-af">Why SWE-AF</a> •
  <a href="#in-action">In Action</a> •
  <a href="#adaptive-factory-control">Factory Control</a> •
  <a href="#benchmark">Benchmark</a> •
  <a href="#operating-modes">Modes</a> •
  <a href="#api-reference">API</a> •
  <a href="docs/ARCHITECTURE.md">Architecture</a>
</p>

</div>

One API call spins up a full autonomous engineering team — product managers, architects, coders, reviewers, testers — that scopes, builds, adapts, and ships complex software end to end.
SWE-AF is a first step toward **autonomous software engineering factories**, scaling from simple goals to hard multi-issue programs with hundreds to thousands of agent invocations.

<p align="center">
  <img src="assets/banner.jpg" alt="SWE-AF autonomous engineering fleet banner" width="100%" />
</p>

## One-Call DX

```bash
curl -X POST http://localhost:8080/api/v1/execute/async/swe-planner.build \
  -H "Content-Type: application/json" \
  -d @- <<'JSON'
{
  "input": {
    "goal": "Refactor and harden auth + billing flows",
    "repo_url": "https://github.com/user/my-project",
    "config": {
      "runtime": "claude_code",
      "models": {
        "default": "sonnet",
        "coder": "opus",
        "qa": "opus"
      },
      "enable_learning": true
    }
  }
}
JSON
```

Swap `models.default` and any role key (`coder`, `qa`, `architect`, etc.) to any model your runtime supports.

## Operating Modes

SWE-AF works in two modes: point it at a single repository, or orchestrate coordinated changes across multiple repos in one build.

### Single-Repository Mode

The default. Pass `repo_url` (remote) or `repo_path` (local) and SWE-AF handles everything:

```bash
curl -X POST http://localhost:8080/api/v1/execute/async/swe-planner.build \
  -H "Content-Type: application/json" \
  -d '{
    "input": {
      "goal": "Add JWT auth",
      "repo_url": "https://github.com/user/my-project"
    }
  }'
```

### Multi-Repository Mode

When your work spans multiple codebases — a primary app plus shared libraries, monorepo sub-projects, or dependent microservices — pass `config.repos` as an array with roles:

```bash
curl -X POST http://localhost:8080/api/v1/execute/async/swe-planner.build \
  -H "Content-Type: application/json" \
  -d '{
    "input": {
      "goal": "Add JWT auth across API and shared-lib",
      "config": {
        "repos": [
          {
            "repo_url": "https://github.com/org/main-app",
            "role": "primary"
          },
          {
            "repo_url": "https://github.com/org/shared-lib",
            "role": "dependency"
          }
        ],
        "runtime": "claude_code",
        "models": {
          "default": "sonnet"
        }
      }
    }
  }'
```

**Roles:**
- `primary` — The main application. Changes here drive the build; failures block progress.
- `dependency` — Libraries or services modified to support the primary repo. Failures are captured but don't block.

**Use cases:**
- Primary app + shared SDK or utilities library
- Monorepo sub-projects that live in separate repos
- Feature spanning multiple microservices (e.g., API + worker queue)

## Autonomous Build Spotlight

Rust-based Python compiler benchmark (built autonomously):

| Metric                 | CPython (subprocess) | RustPython (SWE-AF)          | Improvement             |
| ---------------------- | -------------------- | ---------------------------- | ----------------------- |
| Steady-state execution | Baseline (~19ms)     | Optimized in-process runtime | **88.3x-602.3x faster** |
| Geometric mean         | 1.0x baseline        | 253.8x                       | **253.8x**              |
| Peak throughput        | ~52 ops/s            | 31,807 ops/s                 | **~612x**               |

<details>
<summary>Measurement methodology</summary>

Throughput comparison measures different execution models: CPython subprocess spawn (~19ms per call → ~52 ops/s) vs RustPython pre-warmed interpreter pool (in-process). This is the real-world tradeoff the system was built to optimize — replacing repeated subprocess invocations with a persistent pool for short-snippet execution.

</details>

Artifact trail includes **175 tracked autonomous agents** across planning, coding, review, merge, and verification.

Details: [`examples/llm-rust-python-compiler-sonnet/README.md`](examples/llm-rust-python-compiler-sonnet/README.md)

## Why SWE-AF

Most agent frameworks wrap a single coder loop. SWE-AF is a coordinated engineering factory — planning, execution, and governance agents run as a control stack that adapts in real time.

- **Hardness-aware execution** — easy issues pass through quickly, while hard issues trigger deeper adaptation and DAG-level replanning instead of blind retries.
- **Factory architecture** — not a single-agent wrapper. Planning, execution, and governance agents run as a coordinated control stack — the architecture encodes the engineering strategy, not the prompts (see [The Atomic Unit of Intelligence](https://www.santoshkumarradha.com/writing/atomic-unit-of-intelligence)).
- **Multi-model, multi-provider** — assign different models per role (`coder: opus`, `qa: haiku`). Works with Claude, OpenRouter, OpenAI, and Google.
- **Continual learning** — with `enable_learning=true`, conventions and failure patterns discovered early are injected into downstream issues.
- **Agent-scale parallelism** — dependency-level scheduling + isolated git worktrees allow large fan-out without branch collisions.
- **Fleet-scale orchestration** — many SWE-AF nodes can run continuously in parallel via AgentField, driving thousands of agent invocations across concurrent builds.
- **Explicit compromise tracking** — when scope is relaxed, debt is typed, severity-rated, and propagated.
- **Long-run reliability** — checkpointed execution supports `resume_build` after crashes or interruptions.

## In Action

[PR #179: Go SDK DID/VC Registration](https://github.com/Agent-Field/agentfield/pull/179) — built entirely by SWE-AF (Claude runtime with haiku-class models). One API call, zero human code.

| Metric              | Value              |
| ------------------- | ------------------ |
| Issues completed    | 10/10              |
| Tests passing       | 217                |
| Acceptance criteria | 34/34              |
| Agent invocations   | 79                 |
| Model               | `claude-haiku-4-5` |
| **Total cost**      | **$19.23**         |

<details>
<summary>Cost breakdown by agent role</summary>

| Role                               | Cost  | %     |
| ---------------------------------- | ----- | ----- |
| Coder                              | $5.88 | 30.6% |
| Code Reviewer                      | $3.48 | 18.1% |
| QA                                 | $1.78 | 9.2%  |
| GitHub PR                          | $1.66 | 8.6%  |
| Integration Tester                 | $1.59 | 8.3%  |
| Merger                             | $1.22 | 6.3%  |
| Workspace Ops                      | $1.77 | 9.2%  |
| Planning (PM + Arch + TL + Sprint) | $0.79 | 4.1%  |
| Verifier + Finalize                | $0.34 | 1.8%  |
| Synthesizer                        | $0.05 | 0.2%  |

79 invocations, 2,070 conversation turns. Planning agents scope and decompose; coders work in parallel isolated worktrees; reviewers and QA validate each issue; merger integrates branches; verifier checks acceptance criteria against the PRD.

</details>

**Claude, open-source, and Codex models supported**: Run builds with any runtime and tune models per role in one flat config map.
- `runtime: "claude_code"` maps to Claude backend.
- `runtime: "open_code"` maps to OpenCode backend (OpenRouter/OpenAI/Google/Anthropic model IDs).
- `runtime: "codex"` maps to the OpenAI Codex CLI backend.

## Adaptive Factory Control

SWE-AF uses three nested control loops to adapt to task difficulty in real time:

| Loop        | Scope         | Trigger              | Action                                                                             |
| ----------- | ------------- | -------------------- | ---------------------------------------------------------------------------------- |
| Inner loop  | Single issue  | QA/review fails      | Coder retries with feedback                                                        |
| Middle loop | Single issue  | Inner loop exhausted | `run_issue_advisor` retries with a new approach, splits work, or accepts with debt |
| Outer loop  | Remaining DAG | Escalated failures   | `run_replanner` restructures remaining issues and dependencies                     |

This is the core factory-control behavior: control agents supervise worker agents and continuously reshape the plan as reality changes.

## Quick Start

### Deploy with Railway (fastest)

[![Deploy on Railway](https://railway.com/button.svg)](https://railway.com/deploy/swe-af)

One click deploys SWE-AF + AgentField control plane + PostgreSQL. Set two environment variables in Railway:

- `CLAUDE_CODE_OAUTH_TOKEN` — run `claude setup-token` in [Claude Code CLI](https://docs.anthropic.com/en/docs/claude-code) (uses Pro/Max subscription credits)
- `GH_TOKEN` — GitHub personal access token with `repo` scope for PR creation

Once deployed, trigger a build:

```bash
curl -X POST https://<control-plane>.up.railway.app/api/v1/execute/async/swe-planner.build \
  -H "Content-Type: application/json" \
  -H "X-API-Key: this-is-a-secret" \
  -d '{"input": {"goal": "Add JWT auth", "repo_url": "https://github.com/user/my-repo"}}'
```

### 1. Requirements (local)

- Python 3.12+
- AgentField control plane (`af`)
- AI provider API key (Anthropic, OpenRouter, OpenAI, or Google)

### 2. Install

```bash
python3.12 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -e ".[dev]"
```

### 3. Run

```bash
af                 # starts AgentField control plane on :8080
python -m swe_af   # registers node id "swe-planner"
```

### 4. Trigger a build

```bash
# Default (uses Claude)
curl -X POST http://localhost:8080/api/v1/execute/async/swe-planner.build \
  -H "Content-Type: application/json" \
  -d @- <<'JSON'
{
  "input": {
    "goal": "Add JWT auth to all API endpoints",
    "repo_url": "https://github.com/user/my-project"
  }
}
JSON

# With open-source runtime + flat role map
curl -X POST http://localhost:8080/api/v1/execute/async/swe-planner.build \
  -H "Content-Type: application/json" \
  -d @- <<'JSON'
{
  "input": {
    "goal": "Add JWT auth",
    "repo_url": "https://github.com/user/my-project",
    "config": {
      "runtime": "open_code",
      "models": {
        "default": "openrouter/minimax/minimax-m2.5"
      }
    }
  }
}
JSON

# With Codex CLI runtime
curl -X POST http://localhost:8080/api/v1/execute/async/swe-planner.build \
  -H "Content-Type: application/json" \
  -d @- <<'JSON'
{
  "input": {
    "goal": "Add JWT auth",
    "repo_url": "https://github.com/user/my-project",
    "config": {
      "runtime": "codex",
      "models": {
        "default": "gpt-5.3-codex"
      }
    }
  }
}
JSON

# Fast mode with Codex CLI runtime
curl -X POST http://localhost:8080/api/v1/execute/async/swe-fast.build \
  -H "Content-Type: application/json" \
  -d @- <<'JSON'
{
  "input": {
    "goal": "Add a focused bug fix",
    "repo_url": "https://github.com/user/my-project",
    "config": {
      "runtime": "codex",
      "models": {
        "default": "gpt-5.3-codex"
      }
    }
  }
}
JSON

# Local workspace mode (repo_path) + targeted role override
curl -X POST http://localhost:8080/api/v1/execute/async/swe-planner.build \
  -H "Content-Type: application/json" \
  -d @- <<'JSON'
{
  "input": {
    "goal": "Refactor and harden auth + billing flows",
    "repo_path": "/path/to/repo",
    "config": {
      "runtime": "claude_code",
      "models": {
        "default": "sonnet",
        "coder": "opus",
        "qa": "opus"
      },
      "enable_learning": true
    }
  }
}
JSON
```

For OpenRouter with `open_code`, use model IDs in `openrouter/<provider>/<model>` format (for example `openrouter/minimax/minimax-m2.5`).

For Codex with ChatGPT subscription auth, install the Codex CLI on the host, run `codex login`, leave `OPENAI_API_KEY` unset for this process, and set `SWE_CODEX_AUTH_MODE=chatgpt` or `auto`. For OpenAI API-platform billing, set `SWE_CODEX_AUTH_MODE=api_key` and `OPENAI_API_KEY`.

> **Codex deployments using the Docker image must set `SWE_DEFAULT_MODEL=gpt-5.3-codex` on the environment** (or pass `models: {"default": "gpt-5.3-codex"}` in every build's `config`). The image bakes `HARNESS_MODEL=openrouter/moonshotai/kimi-k2.6` as an OpenCode fallback, and SWE-AF's model-resolution env cascade reads `HARNESS_MODEL` — so without `SWE_DEFAULT_MODEL` set, the Codex CLI receives an OpenRouter model id it can't handle and the Product Manager reasoner fails in ~13s. Setting `SWE_DEFAULT_MODEL` makes the cascade pin every role to the Codex model.

> Codex CLI's `workspace-write` sandbox uses bubblewrap (`bwrap`) and needs Linux user namespaces enabled on the host. Most production Linux hosts and managed container runtimes (Railway, etc.) allow this by default, but local Docker on WSL2 or hardened environments may refuse with `bwrap: No permissions to create a new namespace`. If the verifier reports that error, the coder ran but couldn't write files — enable user namespaces on the host before relying on the codex runtime there.

### Optional: web search

Coding and review agents can look up external documentation, library APIs, error messages, and version/deprecation status during a build. This is opt-in via two env vars on the deployment:

```
OPENCODE_ENABLE_EXA=1
EXA_API_KEY=...
```

When set, opencode's built-in `websearch` and `webfetch` tools become available to every reasoner running through the open runtime — the model decides when to use them based on the task. Get a key at [exa.ai](https://exa.ai/).

The coder reasoner additionally gets a brief restraint guideline appended to its system prompt, so a long coding loop doesn't rabbit-hole on searches it could answer by reading the codebase. No setup required beyond the env vars; the wiring inherits parent env naturally through agentfield's CLI harness.

This works on the open runtime (opencode). The Claude runtime uses Anthropic's first-party `WebSearch`/`WebFetch` and is currently not wired here — file an issue if you want it.

## What Happens In One Build

- Architecture is generated and reviewed before coding starts
- Issues are dependency-sorted and run in parallel across isolated worktrees
- Each issue gets dedicated coder, tester, and reviewer passes
- Failed issues trigger advisor-driven adaptation (split, re-scope, or escalate)
- Escalations trigger replanning of the remaining DAG
- End result is merged, integration-tested, and verified against acceptance criteria

<p align="center">
  <img src="assets/archi.png" alt="SWE-AF architecture" width="100%" />
</p>

> Typical runs spin up 400-500+ agent instances across planning, execution, QA, and verification. For larger DAGs and repeated adaptation/replanning cycles, SWE-AF can scale into the high hundreds to thousands of agent invocations in a single build.

## Benchmark

**95/100 with haiku and MiniMax**: SWE-AF scored 95/100 with both Claude haiku-class routing ($20) and MiniMax M2.5 via open runtime ($6), outperforming Claude Code sonnet (73), Codex o3 (62), and Claude Code haiku (59) on the same prompt.

| Dimension       | SWE-AF (haiku) | SWE-AF (MiniMax) | CC Sonnet | Codex (o3) | CC Haiku |
| --------------- | -------------- | ---------------- | --------- | ---------- | -------- |
| Functional (30) | **30**         | **30**           | **30**    | **30**     | **30**   |
| Structure (20)  | **20**         | **20**           | 10        | 10         | 10       |
| Hygiene (20)    | **20**         | **20**           | 16        | 10         | 7        |
| Git (15)        | **15**         | **15**           | 2         | 2          | 2        |
| Quality (15)    | 10             | 10               | **15**    | 10         | 10       |
| Total           | **95**         | **95**           | **73**    | **62**     | **59**   |
| **Cost**        | **~$20**       | **~$6**          | ?         | ?          | ?        |
| **Time**        | ~30-40 min     | 43 min           | ?         | ?          | ?        |

<details>
<summary><strong>Full benchmark details and reproduction</strong></summary>

Same prompt tested across multiple agents. SWE-AF with Claude runtime (haiku-class model mapping) used 400+ agent instances; SWE-AF with MiniMax M2.5 via open runtime achieved identical quality at 70% cost savings.

**Prompt used for all agents:**

> Build a Node.js CLI todo app with add, list, complete, and delete commands. Data should persist to a JSON file. Initialize git, write tests, and commit your work.

### Scoring framework

| Dimension  | Points | What it measures                                 |
| ---------- | ------ | ------------------------------------------------ |
| Functional | 30     | CLI behavior and passing tests                   |
| Structure  | 20     | Modular source layout and test organization      |
| Hygiene    | 20     | `.gitignore`, clean status, no junk artifacts    |
| Git        | 15     | Commit discipline and message quality            |
| Quality    | 15     | Error handling, package metadata, README quality |

### Reproduction

```bash
# SWE-AF (Claude runtime, haiku-class mapping) - $20, 30-40 min
curl -X POST http://localhost:8080/api/v1/execute/async/swe-planner.build \
  -H "Content-Type: application/json" \
  -d @- <<'JSON'
{
  "input": {
    "goal": "Build a Node.js CLI todo app with add, list, complete, and delete commands. Data should persist to a JSON file. Initialize git, write tests, and commit your work.",
    "repo_path": "/tmp/swe-af-output",
    "config": {
      "runtime": "claude_code",
      "models": {
        "default": "haiku"
      }
    }
  }
}
JSON

# SWE-AF (MiniMax M2.5 via OpenRouter runtime) - $6, 43 min
curl -X POST http://localhost:8080/api/v1/execute/async/swe-planner.build \
  -H "Content-Type: application/json" \
  -d @- <<'JSON'
{
  "input": {
    "goal": "Build a Node.js CLI todo app with add, list, complete, and delete commands. Data should persist to a JSON file. Initialize git, write tests, and commit your work.",
    "repo_path": "/workspaces/todo-app-benchmark",
    "config": {
      "runtime": "open_code",
      "models": {
        "default": "openrouter/minimax/minimax-m2.5"
      }
    }
  }
}
JSON

# Claude Code (haiku)
claude -p "Build a Node.js CLI todo app with add, list, complete, and delete commands. Data should persist to a JSON file. Initialize git, write tests, and commit your work." --model haiku --dangerously-skip-permissions

# Claude Code (sonnet)
claude -p "Build a Node.js CLI todo app with add, list, complete, and delete commands. Data should persist to a JSON file. Initialize git, write tests, and commit your work." --model sonnet --dangerously-skip-permissions

# Codex (gpt-5.3-codex)
codex exec "Build a Node.js CLI todo app with add, list, complete, and delete commands. Data should persist to a JSON file. Initialize git, write tests, and commit your work." --full-auto
```

**MiniMax M2.5 Measured Metrics (Feb 2026):**
- 99.22% code coverage (only agent with measured coverage)
- 4 custom error types (TodoError, ValidationError, NotFoundError, StorageError)
- 999 LOC, 4 modules, 74 tests, 9 commits

**Production Quality Analysis:** [Objective comparison](examples/agent-comparison/PRODUCTION_QUALITY_ANALYSIS.md) of measurable metrics across all agents.

Benchmark assets, logs, evaluator, and generated projects live in [`examples/agent-comparison/`](examples/agent-comparison/).

</details>

> **Ship code, then audit it:** [SEC-AF](https://github.com/Agent-Field/sec-af) runs the same multi-agent architecture against your codebase — 250 agents, 94% noise reduction, every finding proven.

## Docker

```bash
cp .env.example .env
# Add your API key: ANTHROPIC_API_KEY, OPENROUTER_API_KEY, OPENAI_API_KEY, or GOOGLE_API_KEY
# Optionally add GH_TOKEN for PR workflow

docker compose up -d
```

Submit a build:

```bash
# Default (Claude)
curl -X POST http://localhost:8080/api/v1/execute/async/swe-planner.build \
  -H "Content-Type: application/json" \
  -d @- <<'JSON'
{
  "input": {
    "goal": "Add JWT auth",
    "repo_url": "https://github.com/user/my-repo"
  }
}
JSON

# With open-source runtime (set OPENROUTER_API_KEY in .env)
curl -X POST http://localhost:8080/api/v1/execute/async/swe-planner.build \
  -H "Content-Type: application/json" \
  -d @- <<'JSON'
{
  "input": {
    "goal": "Add JWT auth",
    "repo_url": "https://github.com/user/my-repo",
    "config": {
      "runtime": "open_code",
      "models": {
        "default": "openrouter/minimax/minimax-m2.5"
      }
    }
  }
}
JSON

# Local workspace mode (repo_path)
curl -X POST http://localhost:8080/api/v1/execute/async/swe-planner.build \
  -H "Content-Type: application/json" \
  -d @- <<'JSON'
{
  "input": {
    "goal": "Add JWT auth",
    "repo_path": "/workspaces/my-repo"
  }
}
JSON
```

Scale workers:

```bash
docker compose up --scale swe-agent=3 -d
```

Use a host control plane instead of Docker control-plane service:

```bash
docker compose -f docker-compose.local.yml up -d
```

## GitHub Repo Workflow (Clone -> Build -> PR)

Pass `repo_url` instead of `repo_path` to let SWE-AF clone and open a PR after execution.

```bash
curl -X POST http://localhost:8080/api/v1/execute/async/swe-planner.build \
  -H "Content-Type: application/json" \
  -d @- <<'JSON'
{
  "input": {
    "repo_url": "https://github.com/user/my-project",
    "goal": "Add comprehensive test coverage",
    "config": {
      "runtime": "claude_code",
      "models": {
        "default": "sonnet",
        "coder": "opus",
        "qa": "opus"
      }
    }
  }
}
JSON
```

Requirements:

- `GH_TOKEN` in `.env` with `repo` scope
- Repo access for that token

### Post-PR CI gate

After SWE-AF pushes the integration branch and opens a PR (ready for review,
not draft), it watches GitHub Actions on that PR until checks are
conclusive. If they fail, a bounded fix-and-repush loop runs an agent that
is explicitly forbidden from silencing tests (no `pytest.skip`, no `xfail`,
no commenting tests out, no loosening assertions) — it must produce a
legitimate fix in the production code and push a new commit. When CI is
green, the gate returns success; when CI fails after fix attempts, the PR
stays open with visible failing checks so a human reviewer can step in.

Configuration on `BuildConfig`:

| Field | Default | Purpose |
|---|---|---|
| `check_ci` | `true` | Run the post-PR CI gate. Set `false` to return immediately after the PR is created. |
| `max_ci_fix_cycles` | `2` | Cap on watch → fix → repush iterations after the initial push. |
| `ci_wait_seconds` | `1500` | Wall-clock cap per `gh pr checks` watch (25 min). |
| `ci_poll_seconds` | `30` | Poll interval for `gh pr checks`. |

## API Reference

<details>
<summary><strong>Agent endpoints</strong></summary>

Core async endpoints (returns an `execution_id` immediately):

```bash
# Full build: plan -> execute -> verify
POST /api/v1/execute/async/swe-planner.build

# Plan only
POST /api/v1/execute/async/swe-planner.plan

# Execute a prebuilt plan
POST /api/v1/execute/async/swe-planner.execute

# Resume after interruption
POST /api/v1/execute/async/swe-planner.resume_build
```

Monitoring:

```bash
curl http://localhost:8080/api/v1/executions/<execution_id>
```

Every specialist is also callable directly:

`POST /api/v1/execute/async/swe-planner.<agent>`

</details>

<details>
<summary><strong>Agent execution flow</strong></summary>

| Agent                    | In -> Out                                            |
| ------------------------ | ---------------------------------------------------- |
| `run_product_manager`    | goal -> PRD                                          |
| `run_architect`          | PRD -> architecture                                  |
| `run_tech_lead`          | architecture -> review                               |
| `run_sprint_planner`     | architecture -> issue DAG                            |
| `run_issue_writer`       | issue spec -> detailed issue                         |
| `run_coder`              | issue + worktree -> code + tests + commit            |
| `run_qa`                 | worktree -> test results                             |
| `run_code_reviewer`      | worktree -> quality/security review                  |
| `run_qa_synthesizer`     | QA + review -> FIX / APPROVE / BLOCK                 |
| `run_issue_advisor`      | failure context -> adapt / split / accept / escalate |
| `run_replanner`          | build state + failures -> restructured plan          |
| `run_merger`             | branches -> merged output                            |
| `run_integration_tester` | merged repo -> integration results                   |
| `run_verifier`           | repo + PRD -> acceptance pass/fail                   |
| `generate_fix_issues`    | failed criteria -> targeted fix issues               |
| `run_github_pr`          | branch -> push + PR                            |

</details>

<details>
<summary><strong>Configuration</strong></summary>

Pass `config` to `build` or `execute`. Full schema: [`swe_af/execution/schemas.py`](swe_af/execution/schemas.py)

| Key                       | Default         | Description                                           |
| ------------------------- | --------------- | ----------------------------------------------------- |
| `runtime`                 | `"claude_code"` | Model runtime: `"claude_code"`, `"open_code"`, or `"codex"`. The default also honors the `SWE_DEFAULT_RUNTIME` env var when no `runtime` is passed in `config` — set it on the deployment so callers don't need to plumb a config through. |
| `models`                  | `null`          | Flat role-model map (`default` + role keys below). Without a caller-supplied value, the `SWE_DEFAULT_MODEL` env var is used as the default for all roles — set it on the deployment to pin a model without code changes. Caller `models.default` or per-role keys still win. |
| `max_coding_iterations`   | `5`             | Inner-loop retry budget                               |
| `max_advisor_invocations` | `2`             | Middle-loop advisor budget                            |
| `max_replans`             | `2`             | Build-level replanning budget                         |
| `enable_issue_advisor`    | `true`          | Enable issue adaptation                               |
| `enable_replanning`       | `true`          | Enable global replanning                              |
| `enable_learning`         | `false`         | Enable cross-issue shared memory (continual learning) |
| `agent_timeout_seconds`   | `2700`          | Per-agent timeout                                     |
| `agent_max_turns`         | `150`           | Tool-use turn budget                                  |

</details>

<details>
<summary><strong>Model Role Keys</strong></summary>

`models` supports:

- `default`
- `pm`, `architect`, `tech_lead`, `sprint_planner`
- `coder`, `qa`, `code_reviewer`, `qa_synthesizer`
- `replan`, `retry_advisor`, `issue_writer`, `issue_advisor`
- `verifier`, `git`, `merger`, `integration_tester`

</details>

<details>
<summary><strong>Resolution order</strong></summary>

`runtime defaults` < `models.default` < `models.<role>`

</details>

<details>
<summary><strong>Config examples</strong></summary>

Minimal:

```json
{
  "runtime": "claude_code"
}
```

Codex:

```json
{
  "runtime": "codex",
  "models": {
    "default": "gpt-5.3-codex"
  }
}
```

Fully customized:

```json
{
  "runtime": "open_code",
  "models": {
    "default": "openrouter/minimax/minimax-m2.5",
    "pm": "openrouter/qwen/qwen-2.5-72b-instruct",
    "architect": "openrouter/qwen/qwen-2.5-72b-instruct",
    "coder": "openrouter/deepseek/deepseek-chat",
    "qa": "openrouter/deepseek/deepseek-chat",
    "verifier": "openrouter/qwen/qwen-2.5-72b-instruct"
  },
  "max_coding_iterations": 6,
  "enable_learning": true
}
```

</details>

<details>
<summary><strong>Artifacts</strong></summary>

```text
.artifacts/
├── plan/           # PRD, architecture, issue specs
├── execution/      # checkpoints, per-issue logs, agent outputs
└── verification/   # acceptance criteria results
```

</details>

<details>
<summary><strong>Development</strong></summary>

```bash
make test
make check
make clean
make clean-examples
```

</details>

<details>
<summary><strong>Security and Community</strong></summary>

- Contribution guide: [`docs/CONTRIBUTING.md`](docs/CONTRIBUTING.md)
- Code of conduct: [`CODE_OF_CONDUCT.md`](CODE_OF_CONDUCT.md)
- Security policy: [`SECURITY.md`](SECURITY.md)
- Changelog: [`CHANGELOG.md`](CHANGELOG.md)
- License: [`Apache-2.0`](LICENSE)

</details>

---

### Also built on AgentField

> **[SEC-AF](https://github.com/Agent-Field/sec-af)** — AI-native security auditor. 250 agents per audit, 94% noise reduction, every finding proven exploitable.
>
> **[Contract-AF](https://github.com/Agent-Field/contract-af)** — Legal contract risk analyzer. Agents spawn agents at runtime. Adversarial review catches what solo LLMs miss.

[All repos →](https://github.com/Agent-Field)

---

SWE-AF is built on [AgentField](https://github.com/Agent-Field/agentfield) as a first step from single-agent harnesses to autonomous software engineering factories. [See what else we're building →](https://github.com/Agent-Field)
