Bridge Feishu to ACP-compatible agents with automatic session management for non-developers.
Project description
ACP Bridge
ACP Bridge is a bridge service that connects Feishu (Lark) group chats to ACP-compatible AI agents (such as Kiro CLI).
Core features:
- Auto session creation — @mention the bot in group chats, or just send a message in DM
- Message threading via reply chains — reply to any message in a thread to continue the conversation
- LRU + TTL session management — automatic eviction of idle sessions to control resource usage
- Message buffering — messages sent while the agent is busy are queued and delivered in order
- Evaluator quality gate — optional independent agent review of responses before sending to users
Installation
Prerequisites
Feishu App Setup
-
Go to the Feishu Open Platform and create a new app.
-
Under Credentials, copy the App ID and App Secret.
-
Under Event Subscriptions, enable WebSocket (long connection) mode, then add the
im.message.receive_v1event. -
Under Permissions, enable:
im:message— send and update messagesim:message:send_as_bot— send messages as botim:message.group_at_msg:readonly— receive group chat messages with @mentionim:message.group_msg— receive all group messages (required for reply-chain routing)im:message.p2p_msg:readonly— receive P2P messagesim:resource— download file resources from messagesim:chat:readonly— read chat infocontact:contact.base:readonly— get user name (optional, for identifying sender in prompts)contact:user.email:readonly— get user email (optional, for matching accounts in external systems)
For contact permissions to work, ensure the Data Permission (数据权限) contact scope includes all bot users.
Steps
-
Install dependencies:
uv sync -
Generate a config file:
uv run python -m acp_bridge.main init
-
Edit
bridge.tomlwith your Feishu app credentials:[feishu] app_id = "your_app_id" app_secret = "your_app_secret" [bridge] default_workspace = "~" # Agent working directory (cwd) attachment_dir = "tmp/attachments" # Where user attachments from Feishu are saved output_dir = "tmp/output" # Where agent output files (images, scripts) are saved auto_approve = false # Auto-approve all tool permission requests max_sessions = 10 # Max concurrent agent sessions (LRU eviction) session_ttl_minutes = 720 # Idle session timeout show_thinking = false # Forward agent thinking/reasoning to user show_intermediate = false # Forward intermediate tool output to user [agent] name = "kiro" # Agent identifier description = "Kiro CLI - https://kiro.dev/cli/" # command = "kiro-cli" # Command to spawn the agent process (default: kiro-cli) args = ["acp"] # Command arguments auto_approve = true # Auto-approve at agent level [[evaluator]] # Optional: quality gate for agent responses name = "my-evaluator" # Evaluator identifier trigger_pattern = "Conclusion|Result|Finding" # Regex to match agent response, triggers evaluation # command = "kiro-cli" # Inherits from [agent].command if empty args = ["acp", "--agent", "my-evaluator"] # Spawns a separate agent process # workspace = "" # Inherits from bridge.default_workspace if empty # auto_approve = true # Inherits from [agent].auto_approve if not set # Prepended to agent text sent to evaluator prompt = """Please evaluate the following report. End the final text response with a standalone line: RESULT: PASS or RESULT: FAIL.""" pass_pattern = "(?mi)^\\s*RESULT\\s*:\\s*PASS\\s*$" # Regex to match evaluator PASS verdict max_retries = 2 # Retry count on FAIL before sending with warning retry_prompt = """The evaluator found issues with your previous response Please revise and output the complete response again from the beginning (do not only output the changed parts): {feedback}"""
-
Start the service:
uv run python -m acp_bridge.main run
Tip: It's recommended to run in tmux so it persists in the background:
tmux new -s acp-bridge "uv run python -m acp_bridge.main run"
Image & File Support
The bridge automatically detects markdown links in agent responses and uploads them to Feishu:
— uploaded as image messages[description](path)— uploaded as file messages
For security, only files under output_dir and attachment_dir (relative to default_workspace) are allowed to be uploaded. Files outside these directories are blocked with a warning.
To enable this, configure your ACP client's system prompt to instruct the agent to save images to the output_dir and reference them in markdown format. Each client has its own system prompt mechanism:
- Kiro CLI:
.kiro/agents/<name>.json—promptfield - Claude Code:
CLAUDE.md - Cursor:
.cursorrules
Example
Given bridge.toml:
[bridge]
default_workspace = "~/code/ops-copilot"
attachment_dir = "bridge/tmp/attachments"
output_dir = "bridge/tmp/output"
For Kiro CLI, add to .kiro/agents/cli.json:
{
"prompt": "When you need to create temp files (scripts, debug output, test data, etc.), always save them under bridge/tmp/output/.\nWhen you need to visualize data (trends, comparisons, etc.), always use matplotlib to save charts to bridge/tmp/output/ and reference them as .\nWhen you need to share generated files (scripts, configs, logs, etc.), save them under bridge/tmp/output/ and reference them as [description](bridge/tmp/output/xxx.sh).\nNever copy external files into the output directory to send them. Only send files you generated yourself."
}
The agent generates a chart → saves to bridge/tmp/output/trend.png → responds with  → bridge uploads the image to Feishu and replaces the markdown with [pic1] in the text message.
Similarly, [deploy script](bridge/tmp/output/deploy.sh) → bridge uploads the file and replaces the markdown with [file1].
Evaluator
The bridge supports an optional evaluator quality gate. When configured, agent responses are reviewed by a separate evaluator agent before being sent to the user.
Flow:
- Agent completes a response
- Bridge checks
trigger_patternagainst the response text - If matched, spawns (or reuses) an evaluator agent session and sends the response for review
- Evaluator responds with a verdict — if it matches
pass_pattern, the response is sent to the user - If FAIL and retries remain, the evaluator's feedback is sent back to the original agent for revision
- If the final allowed evaluation still FAILs, the bridge sends that same evaluated response with a warning plus the final evaluator feedback
Evaluator sessions are persistent per main session, so retries share context. The evaluator agent is a separate process with its own system prompt and tools — it can use read-only tools to spot-check evidence in the report.
The bridge applies pass_pattern to the evaluator's final text response. Keep the evaluator prompt aligned with that pattern; for example, the config above expects a standalone RESULT: PASS line. Verdicts written only in tool calls, task context, plans, or other intermediate state are not matched.
Notification routing: the bridge distinguishes main agent sessions from evaluator sessions via SessionManager.find_by_session_id() — main sessions are tracked in the session manager, everything else is routed to the evaluator notification handler.
Commands
| Command | Description |
|---|---|
#mode |
Show available modes and current mode |
#mode <value> |
Switch to a different mode |
#model |
Show available models and current model |
#model <value> |
Switch to a different model |
#cancel |
Cancel ongoing agent operation |
#end |
End current agent session |
#read <file_path> |
Read local file content |
#diff [args] |
Show git diff |
#session |
Show current agent session info |
#sessions |
Show all active sessions |
#help |
Show help message |
License
MIT License. See LICENSE for details.
This project is derived from Juan by DiscreteTom.
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