unified-agent-interface 0.1.1

A unified interface for integrating various AI agents with a common protocol.

Unified Agent Interface (UAI)

UAI is a FastAPI service and CLI that exposes a simple, unified API to run different agent frameworks behind a consistent interface. It loads your agent from a kosmos.toml file and executes it either inline (for dev/tests) or via a Procrastinate worker.

Quickstart

• Install: pip install -e .
• Run API: uai serve --host 0.0.0.0 --port 8000
• Open docs: visit http://localhost:8000/docs

CLI Overview

• uai serve: starts the FastAPI server.
• uai run create --input '<json or string>': creates a run for the configured agent.
• uai run status <task_id>: fetches current run status.
• uai run input <task_id> --text '<reply>': provides human input to a waiting run.
• uai run logs <task_id> --message '<msg>' [--level INFO]: appends a log entry.
• uai run cancel <task_id> / uai run stop <task_id>: cancels/stops a run (deletes it from in-memory storage).
• uai worker install|check|start: installs schema, checks DB, and starts the worker.
• uai run watch <task_id>: watches status; when waiting_input, prompts for input and resumes automatically.

Project Structure

• src/unified_agent_interface/app.py: FastAPI app factory.
• src/unified_agent_interface/api/: Routers for /run and /chat.
• src/unified_agent_interface/models/: Pydantic models and schemas.
• src/unified_agent_interface/components/: In-memory storage and run agent shim.
• src/unified_agent_interface/frameworks/: Runtime adapters (crewai, langchain, callable).
• src/unified_agent_interface/queue.py: Procrastinate integration and job dispatch.
• examples/: Callable sample and CrewAI examples (with and without human input).

Changelog

• See CHANGELOG.md for release notes.

Agent Configuration

• Location search order: KOSMOS_TOML env var, ./kosmos.toml, ./examples/kosmos.toml.

• Example config:

  [agent]
  runtime = "callable"  # or "crewai", "langchain", or "custom"
  entrypoint = "examples.simple_entrypoint:run"
  # Optional: for custom adapters
  # adapter = "path.to.module:AdapterClassOrInstance"
  

• Entrypoint format: module:attr (e.g., examples.crewai_user_input.main:crew). UAI resolves imports relative to the kosmos.toml directory and also supports package-style modules.

• Custom adapters: set agent.adapter to a module:attr that resolves to either an instance or a zero-arg class. The adapter must explicitly inherit unified_agent_interface.frameworks.base.RuntimeAdapter. If runtime is unknown or set to custom, UAI will load this adapter. If both a known runtime and an adapter are specified, the adapter takes precedence.

Runtimes (Adapters)

• crewai: Imports a Crew and calls crew.kickoff(inputs=...).
  • Inputs: pass JSON via --input '{"topic":"..."}'. Dicts are used as-is; string inputs map to { "input": "..." }.
  • Human input: When the Crew calls input(), UAI sets status to waiting_input and populates input_prompt. Provide input with uai run input and the run resumes.
• langchain: Imports a LangChain Runnable/LLMChain and calls invoke(inputs) (or run(...) fallback).
  • Inputs: dicts are passed as-is. String inputs map to { "text": "..." }.
  • Output: tries message.content, then common dict keys (text, output_text, output, result), else str(result).
• callable: Imports a Python callable.
  • If --input is a JSON object, UAI tries fn(**obj), falling back to fn(obj); otherwise it calls fn({"input": "...", "params": {}}).
• custom: Provide adapter = "module:attr" in kosmos.toml; UAI imports and uses it for both run and chat if implemented.

Examples

• Callable: examples/simple_entrypoint.py with examples/kosmos_callable.toml.
• CrewAI (basic): examples/crewai/main.py with examples/crewai/kosmos.toml.
• CrewAI (human input): examples/crewai_user_input/main.py with examples/crewai_user_input/kosmos.toml.
• LangChain (LLMChain): examples/langchain/app.py with examples/langchain/kosmos.toml.
• LangChain with instrumentation (logs around LLM calls): set KOSMOS_TOML=examples/langchain/kosmos_patched.toml to use app:run_patched which patches LLMChain.invoke and ChatOpenAI.invoke via instrumentation.patch_many.
• CrewAI with custom logs/input detection (callbacks): examples/crewai_custom_log_input_detection/app.py with examples/crewai_custom_log_input_detection/kosmos.toml.

Run API

• POST /run/ (body: { "input": <any>, "params": <object?> }): creates a run. input may be a string or JSON object/array.
• GET /run/{id}: returns status with fields: status, result_text, logs, artifacts, input_prompt, input_buffer.
• POST /run/{id}/input (body: { "input": "..." }): appends to input_buffer and resumes a waiting run.
• POST /run/{id}/logs (body: { level, message }): appends a log.
• POST /run/{id}/artifacts (body: { id?, type?, name?, uri?, metadata? }): adds an artifact to the run (server generates id if missing).
• POST /run/{id}/complete (internal): worker callback to finalize a run.

Chat API

• POST /chat/: creates a chat session and returns { session_id }.
• POST /chat/{session_id}: sends a user message; responds after generating the assistant reply with { state, artifacts, messages }.
• GET /chat/{session_id}/messages: lists messages in the session.
• DELETE /chat/{session_id}: deletes the session.
• POST /chat/{session_id}/artifacts (body: { id?, type?, name?, uri?, metadata? }): adds an artifact to the session (server generates id if missing).

Background Jobs (Procrastinate)

• Default local DB: If no PROCRASTINATE_DSN/DATABASE_URL is set, UAI connects to localhost:5432 with user=postgres, password=password, dbname=postgres.
• Start a local Postgres (optional): docker run --name pg-procrastinate --detach --rm -p 5432:5432 -e POSTGRES_PASSWORD=password postgres
• Worker commands:
  • uai worker install: installs Procrastinate schema (idempotent).
  • uai worker check: verifies DB connectivity.
  • uai worker start: auto-installs schema, checks DB, then starts the worker.
• Inline mode (no DB): UAI_PROCRASTINATE_INLINE=1 executes runs in-process (used in tests).

Environment Variables

• KOSMOS_TOML: Path to kosmos.toml to load agent config.
• UAI_BASE_URL: Base URL for server (used by worker callbacks). Defaults to http://localhost:8000.
• UAI_PROCRASTINATE_INLINE: Set to 1 to run jobs inline without Postgres.
• PROCRASTINATE_DSN/DATABASE_URL: Postgres connection for the worker. If unset, UAI uses local defaults.
• PROCRASTINATE_HOST/PORT/USER/PASSWORD/DB: Overrides for local default connection.

Troubleshooting

• Import errors: Ensure KOSMOS_TOML points to the right example and that the entrypoint’s dependencies (e.g., crewai, langchain_community) are installed in both server and worker environments.
• PoolTimeout on worker start: Check Docker Postgres is running and accessible; use uai worker check. Add required SSL options to your DSN if using a cloud provider (e.g., ?sslmode=require).
• Human input not progressing: Confirm status shows waiting_input with a non-empty input_prompt, then send uai run input <task_id> --text '...'.

Notes

• Storage is in-memory for now; swap with a persistent backend (Postgres/Redis) for multi-process reliability. The worker currently finalizes runs via a callback to POST /run/{id}/complete.
• LangChain chat requires sessions: stateless POST /chat/next is not supported and returns 400. UAI maintains a separate chain instance per session to isolate memory.

Developer Utilities

• Helper functions for user adapters/agents in unified_agent_interface.frameworks.utils:
  • post_wait(task_id, prompt): mark run as waiting for input with a prompt.
  • get_status(task_id): get run status JSON.
  • poll_for_next_input(task_id, baseline_index, timeout_seconds=300): poll until new input arrives; returns (value, new_index).
  • request_human_input(task_id, prompt="...", baseline_index=None): convenience wrapper that posts wait and polls; returns (value, new_index).
  • post_log(task_id, level, message): append a log entry to a run.
  • add_run_artifact(task_id, artifact_dict): add an artifact to a run.
  • add_chat_artifact(session_id, artifact_dict): add an artifact to a chat session.
• Instrumentation utilities in unified_agent_interface.utils:
  • patch_log(target, label=None, capture_return=False): persistently patch a function or method (callable or "module:attr") to auto-log calls using post_log.
  • unpatch_log(target): restore a target patched via patch_log.
• patch_function(target, label=None, capture_return=False): temporary/context-managed patch.
• patch_many(*targets, label=None, capture_return=False): patch multiple targets within one context.

Runtime Context

• UAI tracks the current run (task_id) and chat session (session_id) during execution so helpers can be called without IDs:
  • unified_agent_interface.runtime.task_context(task_id) and .session_context(session_id) are used internally; helpers fall back to these when task_id/session_id is omitted.
  • E.g., post_log(None, "INFO", "message") and request_human_input(None, "prompt") will route to the current run.

These utilities let custom adapters define their own session management and input-waiting behavior.

• Example (LangChain):
  • from langchain_openai import ChatOpenAI
  • from unified_agent_interface.utils import patch_log
  • patch_log(ChatOpenAI.invoke, capture_return=True)
  • Calls to ChatOpenAI.invoke will now be logged to the current run.

Artifacts Tracking

• Auto-collect file artifacts created during a run or chat turn.
• Enable via config or env:
  • kosmos.toml: [agent.artifacts] tracking = "auto" (optional: base_dir = "...")
  • env: UAI_ARTIFACTS=auto (overrides config)
  • filters: UAI_ARTIFACTS_INCLUDE="**/*.md,**/*.png", UAI_ARTIFACTS_EXCLUDE="**/.git/**", UAI_ARTIFACTS_BASEDIR=/path/to/repo
• How it works:
  • UAI registers a Python audit hook and uses contextvars to attribute file creations to the current run/session.
  • When enabled, opening files with create/append modes (e.g., w, x, a) or os.O_CREAT is recorded as artifacts.
  • Artifacts are posted immediately to /run/{id}/artifacts or /chat/{session}/artifacts and deduplicated per context.
• Notes:
  • Off by default; opt-in via config/env.
  • You can still add artifacts explicitly with add_run_artifact / add_chat_artifact.
  • For best results, write outputs inside a known base directory and use include/exclude globs.