SWE-AF

Autonomous Engineering Team Runtime Built on AgentField

Pronounced: "swee-AF" (one word)

One API call → full engineering team → shipped code.

Quick Start • Why SWE-AF • In Action • Factory Control • Benchmark • Modes • API • Architecture

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.

One-Call DX

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:

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:

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

Measurement methodology

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.

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

Details: 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).
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 — 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

Cost breakdown by agent role

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.

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)

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 (uses Pro/Max subscription credits)
GH_TOKEN — GitHub personal access token with repo scope for PR creation

Once deployed, trigger a build:

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

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

3. Run

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

4. Trigger a build

# 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.

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

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	?	?	?

Full benchmark details and reproduction

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

# 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 of measurable metrics across all agents.

Benchmark assets, logs, evaluator, and generated projects live in examples/agent-comparison/.

Ship code, then audit it: SEC-AF runs the same multi-agent architecture against your codebase — 250 agents, 94% noise reduction, every finding proven.

Docker

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:

# 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:

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

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

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.

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

Agent endpoints

Core async endpoints (returns an execution_id immediately):

# 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:

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

Every specialist is also callable directly:

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

Agent execution flow

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

Configuration

Pass config to build or execute. Full schema: 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

Model Role Keys

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

Resolution order

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

Config examples

Minimal:

{
  "runtime": "claude_code"
}

Codex:

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

Fully customized:

{
  "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
}

Artifacts

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

Development

make test
make check
make clean
make clean-examples

Security and Community

Contribution guide: docs/CONTRIBUTING.md
Code of conduct: CODE_OF_CONDUCT.md
Security policy: SECURITY.md
Changelog: CHANGELOG.md
License: Apache-2.0

Also built on AgentField

SEC-AF — AI-native security auditor. 250 agents per audit, 94% noise reduction, every finding proven exploitable.

Contract-AF — Legal contract risk analyzer. Agents spawn agents at runtime. Adversarial review catches what solo LLMs miss.

All repos →

SWE-AF is built on AgentField as a first step from single-agent harnesses to autonomous software engineering factories. See what else we're building →

Name		Name	Last commit message	Last commit date
Latest commit History 184 Commits
.github		.github
assets		assets
docs		docs
examples		examples
swe_af		swe_af
tests		tests
.dockerignore		.dockerignore
.env.example		.env.example
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CODEOWNERS		CODEOWNERS
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
Dockerfile		Dockerfile
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
SECURITY.md		SECURITY.md
docker-compose.local.yml		docker-compose.local.yml
docker-compose.yml		docker-compose.yml
pyproject.toml		pyproject.toml
railway.toml		railway.toml
requirements-docker.txt		requirements-docker.txt
requirements.txt		requirements.txt

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SWE-AF

Autonomous Engineering Team Runtime Built on AgentField

One-Call DX

Operating Modes

Single-Repository Mode

Multi-Repository Mode

Autonomous Build Spotlight

Why SWE-AF

In Action

Adaptive Factory Control

Quick Start

Deploy with Railway (fastest)

1. Requirements (local)

2. Install

3. Run

4. Trigger a build

Optional: web search

What Happens In One Build

Benchmark

Scoring framework

Reproduction

Docker

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

Post-PR CI gate

API Reference

Also built on AgentField

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SWE-AF

Autonomous Engineering Team Runtime Built on AgentField

One-Call DX

Operating Modes

Single-Repository Mode

Multi-Repository Mode

Autonomous Build Spotlight

Why SWE-AF

In Action

Adaptive Factory Control

Quick Start

Deploy with Railway (fastest)

1. Requirements (local)

2. Install

3. Run

4. Trigger a build

Optional: web search

What Happens In One Build

Benchmark

Scoring framework

Reproduction

Docker

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

Post-PR CI gate

API Reference

Also built on AgentField

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