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* feat(open-swe): add Jira tool plane (Phase 1)
Curated Jira Cloud REST v3 toolset for the agent, mirroring the Linear
tools:
- utils/jira.py: service-account REST client (Basic auth) with get/
create/update issue, comments, list projects, trace comment; issue and
comment bodies normalized to markdown.
- utils/adf.py: minimal ADF <-> markdown conversion (read paths convert
Jira ADF to markdown; agent comments convert prose to ADF).
- tools/jira_{comment,get_issue,get_issue_comments,create_issue,
update_issue,list_projects}.py wired into the tool registry and the
main agent tool list.
- tests/test_jira_utils.py: ADF conversion + mocked-transport util tests.
Reads JIRA_BASE_URL / JIRA_SERVICE_EMAIL / JIRA_API_TOKEN; unset env
returns a clean error, so this is safe to land dark. Trigger plane,
prompt guidance, and config plumbing follow in Phase 2.
* feat(open-swe): add Confluence tool plane (Phase 3)
Curated Confluence Cloud REST toolset for the agent, mirroring the Jira
tools:
- utils/confluence.py: service-account REST client (Basic auth) with
get/create/update page, add comment, CQL search. Page bodies are XHTML
storage format (not ADF), with minimal storage<->text converters;
update_page reads the current version and bumps it, as Confluence
requires.
- tools/confluence_{get_page,create_page,update_page,comment,search}.py
registered in the tool registry.
- tests/test_confluence_utils.py: converter + mocked-transport tests
including the version-bump path.
Reads CONFLUENCE_BASE_URL / CONFLUENCE_EMAIL / CONFLUENCE_API_TOKEN;
unset env returns a clean error. Activation in the agent tool list lands
with the Phase 2 server.py wiring.
* feat(open-swe): add Jira trigger plane (Phase 2)
Make an @openswe comment on a Jira issue spawn an agent run, mirroring
the Linear trigger plane:
- webhooks/jira.py: process_jira_issue clones process_linear_issue —
deterministic thread id, full-issue fetch, actor accountId->email
attribution feeding resolve_login_from_email_async (PRs open as the
human), multimodal image handling, source="jira" + jira_issue config.
- webapp.py: POST/GET /webhooks/jira, verify_jira_secret (constant-time
X-Automation-Webhook-Token check, fails closed), repo-resolution
cascade, get_repo_config_from_jira_mapping.
- utils/jira_project_repo_map.py: JIRA_PROJECT_TO_REPO (placeholder
entry — real project->repo mappings still needed).
- utils/jira.py: get_user_email (accountId -> email) for attribution.
- completion.py: source=="jira" failure-reply branch.
- prompt.py: Jira-triggered notify guidance + Refs:/branch key from
{jira_project_key}-{jira_issue_number}.
- server.py: read jira_issue config + pass jira key to the system
prompt; also activates the Phase 3 Confluence tools in the agent list.
Jira Automation lacks native webhook HMAC signing, so trust is a shared
secret header (decision D2); replay protection is weaker than Linear's
HMAC+timestamp. /sh-security-review + an Atlassian IP allowlist are the
outstanding gate/hardening before push.
* fix(open-swe): harden Jira webhook trust (sh-security-review)
Resolves findings from the Phase 2 security review (detector fan-out +
proof-or-kill verifier). The unsigned Jira Automation webhook body was
trusted for identity, comment content, repo routing, and issue
existence; a JIRA_WEBHOOK_SECRET holder could forge those fields.
- Corroborate against the real Jira record: the webhook body is now only
a pointer (issue_key + required comment_id). The triggering comment's
author and text are re-fetched server-side via get_comment/fetch_jira_
comment, and identity, the @openswe check, prompt text, and project
key are derived from that authoritative record — never payload author/
body fields. An uncorroborated comment is rejected. (closes the
account-id impersonation, unsigned-body prompt injection, and
fabricated-issue findings)
- Validate issue_key against the Jira key format and percent-encode all
untrusted path segments (_seg) so a crafted key can't traverse to a
different Jira REST endpoint or inject query params. (closes the path-
traversal / query-injection findings)
- Route source=="jira" through the bot-token-default / author_prs_as_
user opt-in path in resolve_github_token, matching Linear, instead of
unconditionally resolving a per-user OAuth token from a payload email.
- Gate attribution on an active user mapping (is_login_mapped) so a
pending/unconfirmed mapping can't drive PR authorship.
Adds regression tests: server-corroboration wins over payload, malformed
issue_key rejected, uncorroborated comment rejected, path-segment
encoding, project-key derivation, active-mapping gate.
Remaining (non-blocking, deployment/hardening): set ALLOWED_GITHUB_ORGS/
REPOS so the shared allowlist isn't fail-open; consider HMAC-over-body +
timestamp on the Automation payload to close the residual replay gap.
* harden(open-swe): opt-in Jira webhook replay/IP + fail-closed allowlist
Folds the two deployment-hardening items from the Phase 2 security review
into code (all opt-in / default-off, so existing and upstream deployments
are unaffected):
- JIRA_WEBHOOK_REQUIRE_SIGNATURE: when set, the Automation payload must
carry X-Openswe-Signature (hex HMAC-SHA256 of the raw body keyed by
JIRA_WEBHOOK_SECRET) plus a fresh timestamp, verified by
verify_jira_signature / _jira_timestamp_is_fresh (mirrors the Linear
HMAC+freshness model). Closes the static-token model's replay/forgery
gap when enabled.
- JIRA_WEBHOOK_IP_ALLOWLIST: optional CIDR allowlist on the webhook's
direct client IP (verify_jira_source_ip). Documented as direct-peer
only; behind a proxy/LB, allowlist Atlassian's ranges at that layer.
- REQUIRE_REPO_ALLOWLIST: makes an empty ALLOWED_GITHUB_ORGS/REPOS fail
CLOSED instead of the back-compat allow-all, plus a startup fail-open
warning. Applies to all channels for consistency.
Documents all new vars (and a Jira section) in .env.example. Adds tests
for signature on/off + valid/missing/wrong/stale, IP allow/deny/off, and
the fail-closed allowlist.
* feat(open-swe): Confluence Atlassian Connect trigger (Phase 4)
Adds the @openswe-on-a-Confluence-comment trigger via a private Atlassian
Connect app. Designed and adversarially verified with the ultracode
workflow (3 divergent Opus designs + judge; 3 proof-or-kill Opus
skeptics on the implemented crypto).
- utils/atlassian_connect.py: hand-rolled qsh (pinned to Atlassian's
official test vector), PyJWT HS256 webhook verifier with alg-pinning,
issuer binding, and qsh-verified-last ordering; RS256 signed-install
lifecycle verifier against Atlassian's published keys; installation
store keyed by clientKey with the sharedSecret encrypted at rest
(TOKEN_ENCRYPTION_KEY / Fernet). No new dependency (PyJWT already pinned).
- webhooks/confluence.py: install/uninstall lifecycle + comment handler.
The JWT-signed webhook body is only a pointer; the comment's real
author/text/container are re-fetched server-side via the Basic-auth
service account (Phase-2 corroboration lesson), with active-only login
attribution and the repo allowlist.
- utils/confluence.py: get_comment / get_user_email (path-encoded).
- webapp.py: GET /connect/atlassian-connect.json (served dynamically),
POST /connect/{installed,uninstalled,webhook/comment-created}, the
space->repo resolver, thread-id, and fetch helpers.
- completion.py: source=="confluence" failure-reply branch.
Security: the sh-security-review verify pass confirmed one HIGH — the
symmetric signed-install=false first-install was trust-on-first-use gated
only by the public Confluence hostname (webhook-auth bypass). Fixed by
switching to signed-install=true + RS256 verification of lifecycle
callbacks, which cryptographically authenticates the first install. All
other attack lenses (forgery/replay/alg-confusion/overwrite/uninstall
DoS/corroboration/injection) were defeated; residuals are deployment
config (REQUIRE_REPO_ALLOWLIST) or accepted-by-design (qsh cannot cover
bodies; comment-trigger prompt injection, shared with all sources).
New env (documented in .env.example): CONFLUENCE_BASE_URL/EMAIL/API_TOKEN,
CONNECT_BASE_URL, CONNECT_EXPECTED_BASE_URL (optional). Install secrets
require the durable Postgres LangGraph store in prod.
Outstanding before push: /sh-security-review on the real diff and the
GPT-4.1 cross-family review (auth boundary); README/CLAUDE.md + memory.
* docs(open-swe): Phase 5 — Confluence prompt guidance + architecture docs
- prompt.py: Confluence-triggered runs notify via confluence_comment on
the triggering page; add Confluence to the shared-base source list.
- CLAUDE.md: document the Jira + Confluence tool planes and the Atlassian
triggers (Jira Automation shared-secret webhook; Confluence Connect app
with HS256 webhook + qsh and RS256 signed-install lifecycle), plus the
server-side corroboration + encrypted install store.
Phase 5 also verified the trigger surface end-to-end against a running
uvicorn app (descriptor served; /connect/* and /webhooks/jira fail closed
without valid auth) and recorded the integration in project memory.
* fix(open-swe): resolve /sh-security-review findings on the Atlassian surface
Formal sh-security-review (detector fan-out + verifier) over the Phase-4
Connect surface (esp. the new RS256 signed-install code, unseen by the
earlier adversarial verify) and the Phase-2 opt-in hardening.
CRITICAL — cross-tenant install (origin validation, CWE-346): signed-
install proves the caller is *an* Atlassian tenant, not *ours*, and the
descriptor is served publicly, so any attacker could install the app on
their own Confluence site and drive agent runs against our allowlisted
repos. The baseUrl body field is attacker-controlled and cannot bind the
tenant; only the signature-verified clientKey (JWT iss) can. Added a
MANDATORY, fail-closed CONNECT_EXPECTED_CLIENT_KEYS allowlist checked in
process_install after signature+iss verification.
HIGH — cross-tenant thread-id collision (CWE-330/863): Confluence comment
ids are per-instance, so generate_thread_id_from_confluence_comment now
salts the hash with the verified clientKey (plumbed from the webhook JWT
iss) to prevent thread hijack across tenants.
HIGH/MEDIUM — path/query injection (CWE-22/88): get_page and update_page
interpolated page_id into the REST path unencoded (update_page on a
mutating PUT with no params= backstop). Now _seg()-encoded, matching the
rest of the module.
MEDIUM — self-trigger loop (CWE-405): process_confluence_comment had no
bot-authorship early-out. Added an optional CONFLUENCE_BOT_ACCOUNT_ID
guard mirroring the Linear botActor / Jira comment_author_is_bot checks.
LOW — corrected the CONNECT_EXPECTED_BASE_URL comment to document it as
opt-in defense-in-depth (the clientKey allowlist is the real gate).
Verified clean by the detectors: RS256/HS256 alg-pinning, aud/iss/exp,
kid-fetch SSRF (host-pinned + quote-encoded), at-rest secret encryption,
constant-time comparisons, and the Phase-2 hardening. New regression
tests for each fix; full suite green (1602).
* harden(open-swe): GPT-4.1 cross-family review follow-ups
Cross-family review (GPT-4.1 via orchestrator cross_reviewer) found no
critical/high issues and confirmed the auth boundary is fail-closed and
correct. Two low-cost defense-in-depth items applied:
- Validate the signed-install JWT 'kid' against a strict charset before
the public-key fetch, so a malformed kid fails fast with no network
call (on top of the existing fixed host + percent-encoding).
- Make JWT nbf verification explicit (verify_nbf) on both the RS256
lifecycle and HS256 webhook decodes.
Other suggestions triaged as already-handled (aud cross-app replay is
blocked by the per-tenant iss->secret lookup; documented static-token/IP/
baseUrl tradeoffs; qsh pinned to Atlassian's vector) or ops/infra
(Fernet rotation via MultiFernet; rate limiting at the gateway).
* docs(open-swe): document Jira + Confluence in installation & customization guides
- INSTALLATION.md §5: add Jira (Automation-rule webhook + shared secret,
service account, JIRA_PROJECT_TO_REPO) and Confluence (Atlassian Connect
app install, CONNECT_EXPECTED_CLIENT_KEYS bootstrap, durable-store note,
CONFLUENCE_SPACE_TO_REPO) trigger setup; §6: add the new env vars +
REQUIRE_REPO_ALLOWLIST.
- CUSTOMIZATION.md: jira_*/confluence_* in the tools table; repo-extraction
note covers all four sources.
- AGENTS.md: match CLAUDE.md (triggers, webhooks, tool list, auth).
- README.md: invocation section, tools table, and overview line.
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<div align="center">
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<a href="https://github.com/langchain-ai/open-swe">
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<source media="(prefers-color-scheme: dark)" srcset="static/dark.svg">
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<source media="(prefers-color-scheme: light)" srcset="static/light.svg">
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<img alt="Open SWE Logo" src="static/dark.svg" width="35%">
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</picture>
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</a>
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</div>
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<div align="center">
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<h3>Open-source framework for building your org's internal coding agent.</h3>
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</div>
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<div align="center">
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<a href="https://github.com/Sea-Haven-Industries/open-swe/actions/workflows/ci.yml" target="_blank"><img src="https://github.com/Sea-Haven-Industries/open-swe/actions/workflows/ci.yml/badge.svg?branch=dev" alt="CI"></a>
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<a href="https://opensource.org/licenses/MIT" target="_blank"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License: MIT"></a>
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<a href="https://www.python.org/" target="_blank"><img src="https://img.shields.io/badge/Python-3.11+-3776AB.svg" alt="Python 3.11+"></a>
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<a href="https://www.typescriptlang.org/" target="_blank"><img src="https://img.shields.io/badge/TypeScript-6.0+-3178C6.svg" alt="TypeScript 6.0+"></a>
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<a href="https://github.com/langchain-ai/langgraph" target="_blank"><img src="https://img.shields.io/badge/Built%20on-LangGraph-blue" alt="Built on LangGraph"></a>
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<a href="https://github.com/langchain-ai/deepagents" target="_blank"><img src="https://img.shields.io/badge/Built%20on-Deep%20Agents-blue" alt="Built on Deep Agents"></a>
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</div>
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<br>
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Elite engineering orgs like Stripe, Ramp, and Coinbase are building their own internal coding agents — Slackbots, CLIs, and web apps that meet engineers where they already work. These agents are connected to internal systems with the right context, permissioning, and safety boundaries to operate with minimal human oversight.
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Open SWE is the open-source version of this pattern. Built on [LangGraph](https://langchain-ai.github.io/langgraph/) and [Deep Agents](https://github.com/langchain-ai/deepagents), it gives you the same architecture those companies built internally: cloud sandboxes, Slack / Linear / Jira / Confluence / GitHub invocation, subagent orchestration, and automatic PR creation — ready to customize for your own codebase and workflows.
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> [!NOTE]
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> Read the **announcement blog post [here](https://blog.langchain.com/open-swe-an-open-source-framework-for-internal-coding-agents/)**
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---
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## Architecture
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Open SWE makes the same core architectural decisions as the best internal coding agents. Here's how it maps to the patterns described in [this overview](https://x.com/kishan_dahya/status/2028971339974099317) of Stripe's Minions, Ramp's Inspect, and Coinbase's Cloudbot:
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### 1. Agent Harness — Composed on Deep Agents
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Rather than forking an existing agent or building from scratch, Open SWE **composes** on the [Deep Agents](https://github.com/langchain-ai/deepagents) framework — similar to how Ramp built on top of OpenCode. This gives you an upgrade path (pull in upstream improvements) while letting you customize the orchestration, tools, and middleware for your org.
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```python
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create_deep_agent(
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model="openai:gpt-5.5",
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system_prompt=construct_system_prompt(...),
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tools=[http_request, fetch_url, linear_comment, slack_thread_reply],
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backend=sandbox_backend,
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middleware=[ToolErrorMiddleware(), check_message_queue_before_model, ...],
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)
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```
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### 2. Sandbox — Isolated Cloud Environments
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Every task runs in its own **isolated cloud sandbox** — a remote Linux environment with full shell access. The repo is cloned in, the agent gets full permissions, and the blast radius of any mistake is fully contained. No production access, no confirmation prompts.
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Open SWE supports multiple sandbox providers out of the box — [Modal](https://modal.com/), [Daytona](https://www.daytona.io/), [Runloop](https://www.runloop.ai/), and [LangSmith](https://smith.langchain.com/) — and you can plug in your own. See the [Customization Guide](CUSTOMIZATION.md#1-sandbox) for details.
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This follows the principle all three companies converge on: **isolate first, then give full permissions inside the boundary.**
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- Each thread gets a persistent sandbox (reused across follow-up messages)
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- Sandboxes auto-recreate if they become unreachable
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- Multiple tasks run in parallel — each in its own sandbox, no queuing
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### 3. Tools — Curated, Not Accumulated
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Stripe's key insight: *tool curation matters more than tool quantity.* Open SWE follows this principle with a small, focused toolset:
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| Tool | Purpose |
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| `execute` | Shell commands in the sandbox |
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| `fetch_url` | Fetch web pages as markdown |
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| `http_request` | API calls (GET, POST, etc.) |
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| `linear_comment` | Post updates to Linear tickets |
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| `jira_*` | Read/comment/create/update Jira issues |
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| `confluence_*` | Read/write Confluence pages + comments |
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| `slack_add_reaction` | React to Slack messages |
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| `slack_thread_reply` | Reply in Slack threads |
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GitHub operations are performed with `GH_TOKEN=dummy gh` inside the sandbox, backed by the LangSmith proxy. Plus the built-in Deep Agents tools: `read_file`, `write_file`, `edit_file`, `ls`, `glob`, `grep`, `write_todos`, and `task` (subagent spawning).
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**Optional observability tools (server-side):** Admins can connect Datadog and LangSmith from team settings (Admin → Observability credentials). When connected, the agent gains Datadog tools (via Datadog's hosted MCP server, default `toolsets=core`) and read-only LangSmith tools (`langsmith_get_trace`, `langsmith_list_runs`). These run in the LangGraph server process using credentials encrypted at rest — the sandbox never holds Datadog or LangSmith keys. They are loaded **only for runs triggered by an authorized user** (admins, plus any emails in `OBSERVABILITY_AUTHORIZED_EMAILS`), so a prompt-injected run from an untrusted contributor cannot reach team observability data. Use scoped, read-oriented keys regardless: observability data (logs, traces) is attacker-influenced content that can carry prompt injection, and the agent has network egress — the same residual-risk class as `web_search` / `fetch_url`.
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**Optional Corridor guardrails (server-side MCP):** Set `CORRIDOR_API_TOKEN` (or `CORRIDOR_MCP_TOKEN` / `CORRIDOR_TOKEN`) to load Corridor's hosted MCP server for each agent run. Open SWE exposes only Corridor's `analyzePlan` tool. `CORRIDOR_MCP_URL` defaults to `https://app.corridor.dev/api/mcp`; if set explicitly, Open SWE only accepts the same HTTPS host and `/api/mcp` path. Tokens are sent via `Authorization: Bearer ...` from the LangGraph server process and are never placed in the sandbox. A legacy `?token=...` URL is accepted and normalized into the header form.
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### 4. Context Engineering — AGENTS.md + Source Context
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Open SWE gathers context from two sources:
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- **`AGENTS.md`** — If the repo contains an `AGENTS.md` file at the root, it's read from the sandbox and injected into the system prompt. This is your repo-level equivalent of Stripe's rule files: encoding conventions, testing requirements, and architectural decisions that every agent run should follow. Reference templates for stack-specific conventions (AWS, SAM, CDK, EC2) live in [`docs/repo-conventions/`](docs/repo-conventions/).
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- **Source context** — The full Linear issue (title, description, comments) or Slack thread history is assembled and passed to the agent, so it starts with rich context rather than discovering everything through tool calls.
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### 5. Orchestration — Subagents + Middleware
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Open SWE's orchestration has two layers:
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**Subagents:** The Deep Agents framework natively supports spawning child agents via the `task` tool. The main agent can fan out independent subtasks to isolated subagents — each with its own middleware stack, todo list, and file operations. This is similar to Ramp's child sessions for parallel work.
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**Middleware:** Deterministic middleware hooks run around the agent loop:
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- **`check_message_queue_before_model`** — Injects follow-up messages (Linear comments or Slack messages that arrive mid-run) before the next model call. You can message the agent while it's working and it'll pick up your input at its next step.
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- **`notify_step_limit_reached`** — After-agent hook that posts a Slack reply when the agent hits the model-call limit, so users get a clear signal instead of silence.
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- **`ToolErrorMiddleware`** — Catches and handles tool errors gracefully.
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### 6. Invocation — Slack, Linear, Jira, Confluence, and GitHub
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All three companies in the article converge on **Slack as the primary invocation surface**. Open SWE does the same:
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- **Slack** — Mention the bot in any thread. Supports `repo:owner/name` syntax to specify which repo to work on. The agent replies in-thread with status updates and PR links.
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- **Linear** — Comment `@openswe` on any issue. The agent reacts with 👀 to acknowledge, reads the full issue context, and posts results back as comments.
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- **Jira** — Comment `@openswe` on any issue (fronted by a Jira Automation rule → `/webhooks/jira`). The agent reads the issue and posts results back as a comment.
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- **Confluence** — Comment `@openswe` on a page. A private Atlassian Connect app delivers the `comment_created` event; the agent acts and replies on the page.
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- **GitHub** — Tag `@openswe` in PR comments on agent-created PRs to have it address review feedback and push fixes to the same branch.
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See **[INSTALLATION.md](./INSTALLATION.md) §5** for per-surface trigger setup.
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Each invocation creates a deterministic thread ID, so follow-up messages on the same issue or thread route to the same running agent.
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**Trigger tags (Sea Haven fork):** a mention is a case-insensitive substring match on the comment body — `@openswe`, `@open-swe`, `@openswe-dev`, or `@seahaven-openswe` (the deployed App slug). GitHub won't linkify `@seahaven-openswe` (App `[bot]` accounts aren't user-mentionable), but the text still fires a run.
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**Engineering conventions & attribution (Sea Haven fork):** the main agent's system prompt is tuned to the Sea Haven engineering handbook — branch names are `feature|bug|hotfix/<kebab-desc>` (optional resolvable `<KEY>-` prefix), PR bodies use `## Summary / Validation / Tests / Notes`, and commit messages follow the handbook format (≤50-char imperative subject, *why* over *what*). The **PR title rule is repo-aware**: when the target repo enforces a conventional-commit title (an `amannn/action-semantic-pull-request` workflow, a `commitlint` config, or a documented requirement in `AGENTS.md` / `CONTRIBUTING.md`), the agent emits a conforming `type(scope): …` title that reads the action's allowed types/scopes — this lets it pass gates like this repo's own `PR Title Lint` and upstream `langchain-ai/open-swe` without manual retitling; otherwise it falls back to the Sea Haven imperative style with no `type:` prefix. PRs that resolve a GitHub issue **auto-link it** in the body (`Closes #<n>` for full fixes, `Refs #<n>`/`Part of #<n>` for partial work, `Closes owner/repo#<n>` cross-repo); because the Sea Haven flow targets `dev` rather than the default branch, the issue closes when `dev` is promoted, not at dev-merge. **No agent/AI attribution is added to any artifact** — no `Co-authored-by` bot trailer, no `Made by [Open SWE]` footer, no "generated by an agent" notes. Commits are currently authored as the **triggering user** (the upstream behavior, which keeps Vercel preview deploys resolvable); flipping authorship to the bot account is tracked separately in issue #11 pending the Vercel-resolvability decision.
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### 7. Validation — Prompt-Driven
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The agent is instructed to run linters, formatters, and tests before committing, and is responsible end-to-end for committing, pushing, opening/updating the draft PR, and replying in the source channel.
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This is an area where you can extend Open SWE for your org: add deterministic CI checks, visual verification, or review gates as additional middleware. See the [Customization Guide](CUSTOMIZATION.md#6-middleware) for how.
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---
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## Comparison
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| Decision | Open SWE | Stripe (Minions) | Ramp (Inspect) | Coinbase (Cloudbot) |
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|---|---|---|---|---|
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| **Harness** | Composed (Deep Agents/LangGraph) | Forked (Goose) | Composed (OpenCode) | Built from scratch |
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| **Sandbox** | Pluggable (Modal, Daytona, Runloop, etc.) | AWS EC2 devboxes (pre-warmed) | Modal containers (pre-warmed) | In-house |
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| **Tools** | ~15, curated | ~500, curated per-agent | OpenCode SDK + extensions | MCPs + custom Skills |
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| **Context** | AGENTS.md + issue/thread | Rule files + pre-hydration | OpenCode built-in | Linear-first + MCPs |
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| **Orchestration** | Subagents + middleware | Blueprints (deterministic + agentic) | Sessions + child sessions | Three modes |
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| **Invocation** | Slack, Linear, GitHub | Slack + embedded buttons | Slack + web + Chrome extension | Slack-native |
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| **Validation** | Prompt-driven | 3-layer (local + CI + 1 retry) | Visual DOM verification | Agent councils + auto-merge |
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---
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## Features
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- **Trigger from Linear, Slack, or GitHub** — mention `@openswe` in a comment to kick off a task
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- **Instant acknowledgement** — acknowledges the moment it picks up your message
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- **Message it while it's running** — send follow-up messages mid-task and it'll pick them up before its next step
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- **Run multiple tasks in parallel** — each task runs in its own isolated cloud sandbox
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- **GitHub OAuth built-in** — authenticates with your GitHub account automatically
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- **Opens PRs automatically** — commits changes and opens a draft PR when done, linked back to your ticket
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- **Subagent support** — the agent can spawn child agents for parallel subtasks
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- **Web dashboard** — a companion app (in `ui/`) for GitHub login, per-user model/profile settings, team defaults, enabled-repo and review-style management, user mappings, and an Agents chat UI
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---
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## Getting Started
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- **[Installation Guide](INSTALLATION.md)** — local dev (backend + dashboard), GitHub App creation, LangSmith, Linear/Slack/GitHub triggers, and production deployment
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- **[Customization Guide](CUSTOMIZATION.md)** — swap the sandbox, model, tools, triggers, system prompt, and middleware for your org
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## Deployment (Sea Haven fork)
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This fork runs on a **managed deployment**: the backend (all three graphs + the
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FastAPI webapp) runs on
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[LangGraph Cloud / Platform](https://langchain-ai.github.io/langgraph/cloud/),
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and the `ui/` dashboard deploys to [Vercel](https://vercel.com/). Configuration
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and secrets live in the LangGraph deployment config and Vercel environment
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variables. Promotion from `dev` to `prod` (`main`) is handled by
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[`.github/workflows/promote-to-main.yml`](.github/workflows/promote-to-main.yml).
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See **[INSTALLATION.md § 10 "Production deployment"](INSTALLATION.md#10-production-deployment)**
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for the full backend + dashboard setup.
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> The earlier self-hosted AWS stack (CDK under `infra/`, an ARM64 EC2 box + nginx
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> behind the shared ALB, and the `cd-infra` / `build-artifacts` release pipelines)
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> was **decommissioned** in favor of the managed deployment above.
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## License
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MIT
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